Example 1:
data: Year|2018|x|line_chart Share_of_urban_population_in_total_population|98|y|line_chart Year|2017|x|line_chart Share_of_urban_population_in_total_population|97.96|y|line_chart Year|2016|x|line_chart Share_of_urban_population_in_total_population|97.92|y|line_chart Year|2015|x|line_chart Share_of_urban_population_in_total_population|97.88|y|line_chart Year|2014|x|line_chart Share_of_urban_population_in_total_population|97.83|y|line_chart Year|2013|x|line_chart Share_of_urban_population_in_total_population|97.79|y|line_chart Year|2012|x|line_chart Share_of_urban_population_in_total_population|97.74|y|line_chart Year|2011|x|line_chart Share_of_urban_population_in_total_population|97.7|y|line_chart Year|2010|x|line_chart Share_of_urban_population_in_total_population|97.65|y|line_chart Year|2009|x|line_chart Share_of_urban_population_in_total_population|97.6|y|line_chart Year|2008|x|line_chart Share_of_urban_population_in_total_population|97.56|y|line_chart 
title: Urbanization in Belgium 2018

gold: This statistic shows the degree of urbanization in Belgium from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 98 percent of Belgium 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] templateTitle[2] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Belgium 2018 from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 98 percent of Belgium 2018 's total population lived in urban areas and cities .


Example 2:
data: Brand|Dryvit|x|bar_chart Share_of_respondents|26.9|y|bar_chart Brand|STO|x|bar_chart Share_of_respondents|11.5|y|bar_chart Brand|Omega_Products|x|bar_chart Share_of_respondents|6.7|y|bar_chart Brand|Senergy|x|bar_chart Share_of_respondents|5.8|y|bar_chart Brand|Simplex_(Finestone)|x|bar_chart Share_of_respondents|5.8|y|bar_chart Brand|Parex/La_Habra|x|bar_chart Share_of_respondents|4.8|y|bar_chart Brand|TEC|x|bar_chart Share_of_respondents|3.8|y|bar_chart Brand|None_of_these|x|bar_chart Share_of_respondents|34.6|y|bar_chart 
title: Most used EIFS and STUCCO brands in the U.S. 2018

gold: This statistic depicts the most used EIFS and STUCCO siding brands by U.S. construction firms in 2018 . The survey revealed that 26.9 percent of the respondents used Dryvit brand the most .
gold_template: This statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] and templateTitle[3] siding templateTitle[4] by templateTitle[5] construction firms in templateTitle[6] . The survey revealed that templateYValue[0] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] the templateTitle[0] .

generated_template: This statistic shows the templateYLabel[0] of results of a survey templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[2] templateTitle[3] in templateTitle[4] , sorted templateTitle[5] templateXLabel[0] templateXLabel[1] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated they used templateXValue[0] and templateXValue[1] .
generated: This statistic shows the Share of results of a survey respondents yLabelErr yLabelErr EIFS STUCCO in brands , sorted U.S. Brand xLabelErr . During the survey period , 34.6 percent of respondents stated they used Dryvit and STO .


Example 3:
data: Year|2018|x|line_chart Real_GDP_in_billion_U.S._dollars|712.1|y|line_chart Year|2017|x|line_chart Real_GDP_in_billion_U.S._dollars|697.18|y|line_chart Year|2016|x|line_chart Real_GDP_in_billion_U.S._dollars|685.46|y|line_chart Year|2015|x|line_chart Real_GDP_in_billion_U.S._dollars|677.87|y|line_chart Year|2014|x|line_chart Real_GDP_in_billion_U.S._dollars|664.79|y|line_chart Year|2013|x|line_chart Real_GDP_in_billion_U.S._dollars|651.32|y|line_chart Year|2012|x|line_chart Real_GDP_in_billion_U.S._dollars|641.32|y|line_chart Year|2011|x|line_chart Real_GDP_in_billion_U.S._dollars|631.37|y|line_chart Year|2010|x|line_chart Real_GDP_in_billion_U.S._dollars|622.47|y|line_chart Year|2009|x|line_chart Real_GDP_in_billion_U.S._dollars|605.5|y|line_chart Year|2008|x|line_chart Real_GDP_in_billion_U.S._dollars|623.82|y|line_chart Year|2007|x|line_chart Real_GDP_in_billion_U.S._dollars|612.02|y|line_chart Year|2006|x|line_chart Real_GDP_in_billion_U.S._dollars|592.03|y|line_chart Year|2005|x|line_chart Real_GDP_in_billion_U.S._dollars|588.05|y|line_chart Year|2004|x|line_chart Real_GDP_in_billion_U.S._dollars|578.03|y|line_chart Year|2003|x|line_chart Real_GDP_in_billion_U.S._dollars|562.43|y|line_chart Year|2002|x|line_chart Real_GDP_in_billion_U.S._dollars|550.25|y|line_chart Year|2001|x|line_chart Real_GDP_in_billion_U.S._dollars|544.79|y|line_chart Year|2000|x|line_chart Real_GDP_in_billion_U.S._dollars|537.22|y|line_chart 
title: Pennsylvania - real GDP 2000 - 2018

gold: This statistic shows the development of Pennsylvania 's real GDP from 2000 to 2018 . In 2018 , the real GDP of Pennsylvania was 712.1 billion U.S. dollars .
gold_template: This statistic shows the development of templateTitle[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[0] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitle[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[0] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The annual templateYLabel[0] templateYLabel[1] growth of the templateTitle[2] templateXLabel[0] .
generated: This statistic shows the development of Pennsylvania 's Real GDP from 2000 to 2018 . In 2018 , the Real GDP of Pennsylvania was about 712.1 billion U.S. dollars . The annual Real GDP growth of the GDP Year .


Example 4:
data: Brand|PlayStation|x|bar_chart Brand_fans_in_millions|15.63|y|bar_chart Brand|Xbox|x|bar_chart Brand_fans_in_millions|12.87|y|bar_chart Brand|Nintendo_of_America|x|bar_chart Brand_fans_in_millions|9.44|y|bar_chart Brand|Rockstar_Games|x|bar_chart Brand_fans_in_millions|9.31|y|bar_chart Brand|Ubisoft|x|bar_chart Brand_fans_in_millions|7.11|y|bar_chart Brand|EA_SPORTS_FIFA|x|bar_chart Brand_fans_in_millions|6.4|y|bar_chart Brand|Sonic_the_Hedgehog|x|bar_chart Brand_fans_in_millions|5.76|y|bar_chart Brand|Fortnite|x|bar_chart Brand_fans_in_millions|5.64|y|bar_chart Brand|Electronic_Arts|x|bar_chart Brand_fans_in_millions|5.19|y|bar_chart Brand|League_of_Legends|x|bar_chart Brand_fans_in_millions|4.36|y|bar_chart 
title: Leading video gaming brands on Twitter 2018 , by followers

gold: This statistic gives information on the most popular gaming brands on Twitter , ranked by number of followers on the social network . As of September 2018 , Sony 's PlayStation was ranked first with 15.63 million Twitter followers . Ubisoft was ranked fifth with 7.1 million followers .
gold_template: This statistic gives information on the most popular templateTitle[2] templateTitle[3] on templateTitle[4] , ranked templateTitle[6] number of templateTitle[7] on the social network . As of September templateTitle[5] , Sony 's templateXValue[0] was ranked first with templateYValue[max] million templateTitle[4] templateTitle[7] . templateXValue[4] was ranked fifth with templateYValue[4] million templateTitle[7] .

generated_template: This statistic represents the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] in templateTitle[4] , ranked templateTitle[5] templateXLabel[0] . In that year , templateXValue[0] was ranked first with more than templateYValue[max] million templateTitle[2] templateTitle[3] .
generated: This statistic represents the Leading video of gaming brands in Twitter , ranked 2018 Brand . In that year , PlayStation was ranked first with more than 15.63 million gaming brands .


Example 5:
data: Year|2018|x|line_chart Employees_in_thousands|135|y|line_chart Year|2017|x|line_chart Employees_in_thousands|140|y|line_chart Year|2016|x|line_chart Employees_in_thousands|142|y|line_chart Year|2015|x|line_chart Employees_in_thousands|142|y|line_chart Year|2014|x|line_chart Employees_in_thousands|144|y|line_chart Year|2013|x|line_chart Employees_in_thousands|148|y|line_chart Year|2012|x|line_chart Employees_in_thousands|154|y|line_chart Year|2011|x|line_chart Employees_in_thousands|159|y|line_chart Year|2010|x|line_chart Employees_in_thousands|160|y|line_chart 
title: Number of people employed in the defense industry in the UK 2010 - 2018

gold: There were approximately 135,000 people directly employed by the defense industry in the United Kingdom in 2018 . This was a decrease of 31 percent since the beginning of the reporting period in 2010 . According to ADS - the UK trade organization representing the aerospace/space , defense and security sectors , the country is currently the second largest exporter of defense equipment and services in the world .
gold_template: There were approximately templateYValue[min] templateTitle[1] directly templateTitle[2] by the templateTitle[3] templateTitle[4] in the United Kingdom in templateXValue[max] . This was a decrease of 31 percent since the beginning of the reporting period in templateXValue[min] . According to ADS - the templateTitle[5] trade organization representing the aerospace/space , templateTitle[3] and security sectors , the country is currently the second largest exporter of templateTitle[3] equipment and services in the world .

generated_template: This statistic shows the number of people in thousands on a zero-hour contract in the United Kingdom from templateXValue[min] to templateXValue[max] . In templateXValue[min] , there were templateYValue[19] thousand people on zero-hour templateTitle[4] , templateTitle[1] this number increasing to approximately templateYValue[0] thousand by templateXValue[max] .
generated: This statistic shows the number of people in thousands on a zero-hour contract in the United Kingdom from 2010 to 2018 . In 2010 , there were yValErr thousand people on zero-hour industry , people this number increasing to approximately 135 thousand by 2018 .


Example 6:
data: Quarter|Q1_'15|x|bar_chart Cost_per_square_meter_in_euros|538|y|bar_chart Quarter|Q2_'15|x|bar_chart Cost_per_square_meter_in_euros|552|y|bar_chart Quarter|Q3_'15|x|bar_chart Cost_per_square_meter_in_euros|592|y|bar_chart Quarter|Q4_'15|x|bar_chart Cost_per_square_meter_in_euros|619|y|bar_chart Quarter|Q1_'16|x|bar_chart Cost_per_square_meter_in_euros|619|y|bar_chart Quarter|Q2_'16|x|bar_chart Cost_per_square_meter_in_euros|619|y|bar_chart Quarter|Q3_'16|x|bar_chart Cost_per_square_meter_in_euros|646|y|bar_chart Quarter|Q4_'16|x|bar_chart Cost_per_square_meter_in_euros|646|y|bar_chart Quarter|Q1_'17|x|bar_chart Cost_per_square_meter_in_euros|646|y|bar_chart Quarter|Q2_'17|x|bar_chart Cost_per_square_meter_in_euros|646|y|bar_chart Quarter|Q3_'17|x|bar_chart Cost_per_square_meter_in_euros|673|y|bar_chart Quarter|Q4_'17|x|bar_chart Cost_per_square_meter_in_euros|673|y|bar_chart Quarter|Q1_'18_|x|bar_chart Cost_per_square_meter_in_euros|-|y|bar_chart Quarter|Q2_'18|x|bar_chart Cost_per_square_meter_in_euros|673|y|bar_chart Quarter|Q3_'18|x|bar_chart Cost_per_square_meter_in_euros|673|y|bar_chart Quarter|Q4_'18|x|bar_chart Cost_per_square_meter_in_euros|-|y|bar_chart Quarter|Q1_'19|x|bar_chart Cost_per_square_meter_in_euros|673|y|bar_chart Quarter|Q2_'19|x|bar_chart Cost_per_square_meter_in_euros|673|y|bar_chart Quarter|Q3_'19|x|bar_chart Cost_per_square_meter_in_euros|673|y|bar_chart 
title: Prime office rental prices in Dublin Q1 2015-Q3 2019

gold: The statistic displays the rental costs per square meter of prime office spaces in Dublin , Ireland , from the first quarter of 2015 to the third quarter of 2019 . It can be seen that the price of prime office properties in Dublin grew over time , reaching 673 euros per square meter per year as of the 3rd quarter of 2017 and remaining constant up until the third quarter 2019 .
gold_template: The statistic displays the templateTitle[2] costs templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] spaces in templateTitle[4] , Ireland , from the first templateXLabel[0] of 2015 to the third templateXLabel[0] of templateTitle[7] . It can be seen that the price of templateTitle[0] templateTitle[1] properties in templateTitle[4] grew over time , reaching templateYValue[last] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year as of the 3rd templateXLabel[0] of 2017 and remaining constant up until the third templateXLabel[0] templateTitle[7] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] templateYLabel[2] templateYLabel[3] in the first templateXLabel[0] of templateTitle[7] to the third templateXLabel[0] of templateXValue[0] . In the fourth templateXLabel[0] of templateTitle[6] , the templateTitle[0] templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[5] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Cost per square meter of Prime office square meter in the first Quarter of 2019 to the third Quarter of Q1_'15 . In the fourth Quarter of 2015-Q3 , the Prime office per square meter of Q1 amounted to 538 meter euros yLabelErr .


Example 7:
data: Year|2018|x|line_chart Revenue_in_million_U.S._dollars|251|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|252|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|253|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|239|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|245|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|198|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|206|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|179|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|175|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|171|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|174|y|line_chart Year|2007|x|line_chart Revenue_in_million_U.S._dollars|166|y|line_chart Year|2006|x|line_chart Revenue_in_million_U.S._dollars|158|y|line_chart Year|2005|x|line_chart Revenue_in_million_U.S._dollars|156|y|line_chart Year|2004|x|line_chart Revenue_in_million_U.S._dollars|148|y|line_chart Year|2003|x|line_chart Revenue_in_million_U.S._dollars|129|y|line_chart Year|2002|x|line_chart Revenue_in_million_U.S._dollars|129|y|line_chart Year|2001|x|line_chart Revenue_in_million_U.S._dollars|133|y|line_chart 
title: Baltimore Orioles revenue 2001 - 2018

gold: The statistic depicts the revenue of the Baltimore Orioles from 2001 to 2018 . In 2018 , the revenue of the Major League Baseball franchise amounted to 251 million U.S. dollars.The Baltimore Orioles are owned by Peter Angelos , who bought the franchise for 173 million U.S. dollars in 1993 .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] dollars.The templateTitle[0] templateTitle[1] are owned by Peter Angelos , who bought the franchise for 173 templateYLabel[1] templateYLabel[2] templateYLabel[3] in 1993 .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitle[0] templateTitle[1] are owned by Robert Castellini , who bought the franchise for 270 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[12] .
generated: The statistic depicts the Revenue of the Baltimore Orioles from 2001 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 253 million U.S. dollars.The Baltimore Orioles are owned by Robert Castellini , who bought the franchise for 270 million U.S. dollars in 2006 .


Example 8:
data: Year|2018|x|line_chart Percentage_of_population|11|y|line_chart Year|2017|x|line_chart Percentage_of_population|11.3|y|line_chart Year|2016|x|line_chart Percentage_of_population|11.8|y|line_chart Year|2015|x|line_chart Percentage_of_population|12.1|y|line_chart Year|2014|x|line_chart Percentage_of_population|13.2|y|line_chart Year|2013|x|line_chart Percentage_of_population|13.5|y|line_chart Year|2012|x|line_chart Percentage_of_population|13.2|y|line_chart Year|2011|x|line_chart Percentage_of_population|13.1|y|line_chart Year|2010|x|line_chart Percentage_of_population|13.2|y|line_chart Year|2009|x|line_chart Percentage_of_population|12.4|y|line_chart Year|2008|x|line_chart Percentage_of_population|10.4|y|line_chart Year|2007|x|line_chart Percentage_of_population|10.8|y|line_chart Year|2006|x|line_chart Percentage_of_population|11|y|line_chart Year|2005|x|line_chart Percentage_of_population|10.2|y|line_chart Year|2004|x|line_chart Percentage_of_population|10.7|y|line_chart Year|2003|x|line_chart Percentage_of_population|10.5|y|line_chart Year|2002|x|line_chart Percentage_of_population|9.7|y|line_chart Year|2001|x|line_chart Percentage_of_population|9.8|y|line_chart Year|2000|x|line_chart Percentage_of_population|8.9|y|line_chart 
title: Wisconsin - poverty rate from 2000 to 2018

gold: This graph shows the poverty rate in Wisconsin from 2000 to 2018 . In 2018 , 11 percent of Wisconsin 's population lived below the poverty line .
gold_template: This graph shows the templateTitle[1] templateTitle[2] in templateTitle[0] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitle[0] 's templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitle[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the poverty rate in Wisconsin from 2000 to 2018 . In 2018 , 11 percent of Wisconsin 's population lived below the poverty line .


Example 9:
data: Region|North_America|x|bar_chart Number_of_UHNW_individuals|84054|y|bar_chart Region|Europe|x|bar_chart Number_of_UHNW_individuals|33551|y|bar_chart Region|Asia-Pacific|x|bar_chart Number_of_UHNW_individuals|22657|y|bar_chart Region|China|x|bar_chart Number_of_UHNW_individuals|18132|y|bar_chart Region|Latin_America|x|bar_chart Number_of_UHNW_individuals|4460|y|bar_chart Region|India|x|bar_chart Number_of_UHNW_individuals|4376|y|bar_chart Region|Africa|x|bar_chart Number_of_UHNW_individuals|804|y|bar_chart 
title: Ultra high net worth individuals - distribution by region 2019

gold: This statistic shows the regional distribution of ultra high net worth individuals in 2019 . About 84,054 individuals with net assets of at least 50 million U.S. dollars were residing in North America in 2019 . That is about 50 percent of the total number of UHNW individuals worldwide .
gold_template: This statistic shows the regional templateTitle[5] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] in templateTitle[8] . About templateYValue[max] templateYLabel[2] with templateTitle[2] assets of at least 50 million U.S. dollars were residing in templateXValue[0] templateXValue[0] in templateTitle[8] . That is about 50 percent of the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] worldwide .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of U.S. dollars in the United States in templateTitle[4] templateTitle[5] ( templateTitle[6] ) from January templateTitle[7] to templateTitle[9] . During that period , templateYValue[max] percent of all templateTitle[2] templateYLabel[1] were recorded in templateXValue[0] .
generated: This statistic shows the Number of UHNW of U.S. dollars in the United States in individuals distribution ( by ) from January region to titleErr . During that period , 84054 percent of all net UHNW were recorded in North_America .


Example 10:
data: Year|2014/15|x|line_chart Average_ticket_price_in_U.S._dollars|78.8|y|line_chart Year|2013/14|x|line_chart Average_ticket_price_in_U.S._dollars|72.95|y|line_chart Year|2012/13|x|line_chart Average_ticket_price_in_U.S._dollars|62.88|y|line_chart Year|2011/12|x|line_chart Average_ticket_price_in_U.S._dollars|55.72|y|line_chart Year|2010/11|x|line_chart Average_ticket_price_in_U.S._dollars|55.39|y|line_chart Year|2009/10|x|line_chart Average_ticket_price_in_U.S._dollars|55.39|y|line_chart Year|2008/09|x|line_chart Average_ticket_price_in_U.S._dollars|46.8|y|line_chart Year|2007/08|x|line_chart Average_ticket_price_in_U.S._dollars|52.22|y|line_chart Year|2006/07|x|line_chart Average_ticket_price_in_U.S._dollars|34.88|y|line_chart Year|2005/06|x|line_chart Average_ticket_price_in_U.S._dollars|34.88|y|line_chart 
title: Average ticket price Chicago Blackhawks ( NHL ) 2014/15

gold: This graph depicts the average ticket price of Chicago Blackhawks games within the National Hockey League from 2005/06 to 2014/15 In the 2005/06 season , the average ticket price was 34.88 U.S. dollars . The Blackhawks play their home games at the United Center .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] games within the National Hockey League from templateXValue[last] to templateXValue[0] In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] . The templateTitle[4] play their home games at the United Center .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitle[3] templateTitle[4] games in the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Chicago Blackhawks games in the National Basketball Association from 2005/06 to 2014/15 . In the 2005/06 season , the Average ticket price was 34.88 U.S. dollars .


Example 11:
data: Year|2018|x|line_chart Total_assets_in_million_euros|65598|y|line_chart Year|2017|x|line_chart Total_assets_in_million_euros|63680|y|line_chart Year|2016|x|line_chart Total_assets_in_million_euros|61090|y|line_chart Year|2015|x|line_chart Total_assets_in_million_euros|56763|y|line_chart Year|2014|x|line_chart Total_assets_in_million_euros|50769|y|line_chart Year|2013|x|line_chart Total_assets_in_million_euros|45156|y|line_chart Year|2012|x|line_chart Total_assets_in_million_euros|40401|y|line_chart Year|2011|x|line_chart Total_assets_in_million_euros|37019|y|line_chart Year|2010|x|line_chart Total_assets_in_million_euros|30772|y|line_chart Year|2009|x|line_chart Total_assets_in_million_euros|26550|y|line_chart Year|2008|x|line_chart Total_assets_in_million_euros|26056|y|line_chart Year|2007|x|line_chart Total_assets_in_million_euros|22578|y|line_chart Year|2006|x|line_chart Total_assets_in_million_euros|18910|y|line_chart Year|2005|x|line_chart Total_assets_in_million_euros|16112|y|line_chart Year|2004|x|line_chart Total_assets_in_million_euros|14904|y|line_chart Year|2003|x|line_chart Total_assets_in_million_euros|14063|y|line_chart Year|2002|x|line_chart Total_assets_in_million_euros|12650|y|line_chart 
title: Audi - total assets 2002 - 2018

gold: This statistic shows Audi 's total assets from the fiscal year of 2002 to the fiscal year of 2018 . In the fiscal year of 2018 , Audi held total assets of around 65.6 billion euros ( or about 74.43 billion US dollars ) .
gold_template: This statistic shows templateTitle[0] 's templateYLabel[0] templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , templateTitle[0] held templateYLabel[0] templateYLabel[1] of around templateYValue[max] billion templateYLabel[3] ( or about 74.43 billion US dollars ) .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] reached approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Total assets of assets 2002 2018 worldwide from 2002 to 2018 . In 2018 , the Audi total assets 2002 2018 reached approximately 65598 assets million euros .


Example 12:
data: Year|28_Nov_2010|x|line_chart Percentage_growth_(year-on-year)|2.5|y|line_chart Year|27_Nov_2011|x|line_chart Percentage_growth_(year-on-year)|4.2|y|line_chart Year|25_Nov_2012|x|line_chart Percentage_growth_(year-on-year)|3.2|y|line_chart Year|10_Nov_2013|x|line_chart Percentage_growth_(year-on-year)|3.2|y|line_chart Year|09_Nov_2014|x|line_chart Percentage_growth_(year-on-year)|-0.2|y|line_chart Year|09_Nov_2015|x|line_chart Percentage_growth_(year-on-year)|0.5|y|line_chart Year|06_Nov_2016|x|line_chart Percentage_growth_(year-on-year)|0.8|y|line_chart Year|05_Nov_2017|x|line_chart Percentage_growth_(year-on-year)|3.8|y|line_chart 
title: Grocery market growth year-on-year in Great Britain 2010 - 2017

gold: In the three months leading up to November 5 , 2017 , the grocery market in Great Britain grew by 3.8 percent compared to the same period a year earlier . Compared to the growth during the last three years this constitutes a sizeable increase . The entire grocery market amounted to 185.2 billion British pounds in 2017 .
gold_template: In the templateYValue[0] months leading up to November 5 , templateXValue[last] , the templateTitle[0] templateTitle[1] in templateTitle[4] templateTitle[5] grew by templateYValue[7] percent compared to the same period a templateXLabel[0] earlier . Compared to the templateYLabel[1] during the last templateYValue[0] years this constitutes a sizeable increase . The entire templateTitle[0] templateTitle[1] amounted to 185.2 billion British pounds in templateXValue[last] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[max] percent of the total population accessed the highest in this region from templateYValue[1] percent in the United States .
generated: This statistic shows the Percentage growth (year-on-year) in year-on-year Great in the United States from 28_Nov_2010 to 28_Nov_2010 . In 28_Nov_2010 , about 4.2 percent of the total population accessed the highest in this region from 4.2 percent in the United States .


Example 13:
data: Year|18/19|x|line_chart Revenue_in_million_U.S._dollars|304|y|line_chart Year|17/18|x|line_chart Revenue_in_million_U.S._dollars|290|y|line_chart Year|16/17|x|line_chart Revenue_in_million_U.S._dollars|273|y|line_chart Year|15/16|x|line_chart Revenue_in_million_U.S._dollars|223|y|line_chart Year|14/15|x|line_chart Revenue_in_million_U.S._dollars|220|y|line_chart Year|13/14|x|line_chart Revenue_in_million_U.S._dollars|212|y|line_chart Year|12/13|x|line_chart Revenue_in_million_U.S._dollars|190|y|line_chart Year|11/12|x|line_chart Revenue_in_million_U.S._dollars|84|y|line_chart Year|10/11|x|line_chart Revenue_in_million_U.S._dollars|89|y|line_chart Year|09/10|x|line_chart Revenue_in_million_U.S._dollars|89|y|line_chart Year|08/09|x|line_chart Revenue_in_million_U.S._dollars|92|y|line_chart Year|07/08|x|line_chart Revenue_in_million_U.S._dollars|98|y|line_chart Year|06/07|x|line_chart Revenue_in_million_U.S._dollars|102|y|line_chart Year|05/06|x|line_chart Revenue_in_million_U.S._dollars|93|y|line_chart Year|04/05|x|line_chart Revenue_in_million_U.S._dollars|87|y|line_chart Year|03/04|x|line_chart Revenue_in_million_U.S._dollars|93|y|line_chart Year|02/03|x|line_chart Revenue_in_million_U.S._dollars|94|y|line_chart Year|01/02|x|line_chart Revenue_in_million_U.S._dollars|91|y|line_chart 
title: Brooklyn Nets ' revenue 2001 - 2019

gold: The statistic shows the revenue of the New Jersey Nets franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise is 304 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the New Jersey templateTitle[1] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise is templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Brooklyn Nets franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 304 million U.S. dollars .


Example 14:
data: Response|Yes_more_than_once|x|bar_chart Share_of_respondents|14|y|bar_chart Response|Yes_but_only_once|x|bar_chart Share_of_respondents|22|y|bar_chart Response|No_never|x|bar_chart Share_of_respondents|51|y|bar_chart Response|Don't_know_/_can't_recall|x|bar_chart Share_of_respondents|12|y|bar_chart 
title: U.S. consumers who have personally experienced hacking 2018

gold: This statistic presents the share of internet users in the United States who have ever had any of their online accounts hacked . During the October 2018 survey , 14 percent of respondents stated that their online accounts had been hacked more than once .
gold_template: This statistic presents the templateYLabel[0] of internet users in the templateTitle[0] templateTitle[2] templateTitle[3] ever had any of their online accounts hacked . During the October templateTitle[7] survey , templateYValue[0] percent of templateYLabel[1] stated that their online accounts had been hacked templateXValue[0] templateXValue[0] templateXValue[0] .

generated_template: This statistic represents the results of a survey among female templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States in templateTitle[7] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated that they went to templateXValue[0] templateXValue[0] a templateXValue[0] .
generated: This statistic represents the results of a survey among female consumers who have personally experienced in the United States in 2018 . During the survey period , 51 percent of respondents stated that they went to Yes_more_than_once a Yes_more_than_once .


Example 15:
data: Country|Canada|x|bar_chart Export_value_in_billion_U.S._dollars|298.7|y|bar_chart Country|Mexico|x|bar_chart Export_value_in_billion_U.S._dollars|265.0|y|bar_chart Country|China|x|bar_chart Export_value_in_billion_U.S._dollars|120.3|y|bar_chart Country|Japan|x|bar_chart Export_value_in_billion_U.S._dollars|75.0|y|bar_chart Country|United_Kingdom|x|bar_chart Export_value_in_billion_U.S._dollars|66.2|y|bar_chart Country|Germany|x|bar_chart Export_value_in_billion_U.S._dollars|57.7|y|bar_chart Country|Korea_South|x|bar_chart Export_value_in_billion_U.S._dollars|56.3|y|bar_chart Country|Netherlands|x|bar_chart Export_value_in_billion_U.S._dollars|49.4|y|bar_chart Country|Brazil|x|bar_chart Export_value_in_billion_U.S._dollars|39.5|y|bar_chart Country|Hong_Kong|x|bar_chart Export_value_in_billion_U.S._dollars|37.5|y|bar_chart Country|France|x|bar_chart Export_value_in_billion_U.S._dollars|36.3|y|bar_chart Country|Singapore|x|bar_chart Export_value_in_billion_U.S._dollars|33.1|y|bar_chart Country|India|x|bar_chart Export_value_in_billion_U.S._dollars|33.1|y|bar_chart Country|Belgium|x|bar_chart Export_value_in_billion_U.S._dollars|31.4|y|bar_chart Country|Taiwan|x|bar_chart Export_value_in_billion_U.S._dollars|30.2|y|bar_chart 
title: U.S. exports - top trading partners 2018

gold: This graph shows the largest trading partners for the United States in 2018 , by value of exported trade goods . In 2018 , the United States exported trade goods worth about 39.5 billion U.S. dollars to Brazil .
gold_template: This graph shows the largest templateTitle[3] templateTitle[4] for the templateXValue[4] States in templateTitle[5] , by templateYLabel[1] of exported trade goods . In templateTitle[5] , the templateXValue[4] States exported trade goods worth about templateYValue[8] templateYLabel[2] templateYLabel[3] templateYLabel[4] to templateXValue[8] .

generated_template: In templateTitle[4] , there were over templateYValue[max] million templateYLabel[1] templateYLabel[2] 100,000 people in the templateXValue[0] templateXValue[0] , a higher than the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] with the UK 's most expensive templateXLabel[0] in which types of the world 's most expensive in the world , with only templateYValue[1] million people living in templateXValue[1] .
generated: In partners , there were over 298.7 million value billion 100,000 people in the Canada , a higher than the previous Country . The Export value with the UK 's most expensive Country in which types of the world 's most expensive in the world , with only 265.0 million people living in Mexico .


Example 16:
data: Year|2018|x|line_chart Percentage_of_population|12|y|line_chart Year|2017|x|line_chart Percentage_of_population|11.9|y|line_chart Year|2016|x|line_chart Percentage_of_population|12.1|y|line_chart Year|2015|x|line_chart Percentage_of_population|13|y|line_chart Year|2014|x|line_chart Percentage_of_population|13.6|y|line_chart Year|2013|x|line_chart Percentage_of_population|14|y|line_chart Year|2012|x|line_chart Percentage_of_population|14|y|line_chart Year|2011|x|line_chart Percentage_of_population|13.8|y|line_chart Year|2010|x|line_chart Percentage_of_population|13.6|y|line_chart Year|2009|x|line_chart Percentage_of_population|13.4|y|line_chart Year|2008|x|line_chart Percentage_of_population|11.3|y|line_chart Year|2007|x|line_chart Percentage_of_population|11.2|y|line_chart Year|2006|x|line_chart Percentage_of_population|12.4|y|line_chart Year|2005|x|line_chart Percentage_of_population|11.7|y|line_chart Year|2004|x|line_chart Percentage_of_population|10.5|y|line_chart Year|2003|x|line_chart Percentage_of_population|10.8|y|line_chart Year|2002|x|line_chart Percentage_of_population|12.1|y|line_chart Year|2001|x|line_chart Percentage_of_population|11.3|y|line_chart Year|2000|x|line_chart Percentage_of_population|9.5|y|line_chart 
title: Kansas - poverty rate 2000 - 2018

gold: This statistic shows the poverty rate in Kansas from 2000 to 2018 . In 2018 , about 12 percent of Kansas ' population lived below the poverty line .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateTitle[0] ' templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitle[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the poverty rate in Kansas from 2000 to 2018 . In 2018 , 12 percent of Kansas 's population lived below the poverty line .


Example 17:
data: Drought|United_States_June_2012|x|bar_chart Economic_loss_in_billion_U.S._dollars|20.0|y|bar_chart Drought|China_P_Rep_January_1994|x|bar_chart Economic_loss_in_billion_U.S._dollars|13.8|y|bar_chart Drought|United_States_January_2011|x|bar_chart Economic_loss_in_billion_U.S._dollars|8.0|y|bar_chart Drought|Australia_1981|x|bar_chart Economic_loss_in_billion_U.S._dollars|6.0|y|bar_chart Drought|Brazil_January_2014|x|bar_chart Economic_loss_in_billion_U.S._dollars|5.0|y|bar_chart Drought|Spain_September_1990|x|bar_chart Economic_loss_in_billion_U.S._dollars|4.5|y|bar_chart Drought|China_P_Rep_October_2009|x|bar_chart Economic_loss_in_billion_U.S._dollars|3.6|y|bar_chart Drought|United_States_July_2002|x|bar_chart Economic_loss_in_billion_U.S._dollars|3.3|y|bar_chart Drought|Iran_Islam_Rep_April_1999|x|bar_chart Economic_loss_in_billion_U.S._dollars|3.3|y|bar_chart Drought|Spain_April_1999|x|bar_chart Economic_loss_in_billion_U.S._dollars|3.2|y|bar_chart 
title: Economic loss due to major droughts worldwide up to 2016

gold: This statistic shows the economic harm as a result of droughts in certain countries in the period from 1900 to 2016* . The drought in China in 1994 caused an economic loss of almost 13.8 billion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] harm as a result of templateTitle[4] in certain countries in the period from 1900 to 2016* . The templateXLabel[0] in templateXValue[1] in templateXValue[1] caused an templateYLabel[0] templateYLabel[1] of almost templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] people in the United States in templateTitle[6] , templateTitle[4] templateXLabel[0] . In templateTitle[4] , templateYValue[max] percent of the households in the United States lived in templateXValue[0] templateXValue[0] .
generated: The statistic shows the Economic of loss people in the United States in up , droughts Drought . In droughts , 20.0 percent of the households in the United States lived in United_States_June_2012 .


Example 18:
data: Year|2000|x|line_chart Percentage_of_employees|24.57|y|line_chart Year|2001|x|line_chart Percentage_of_employees|23.75|y|line_chart Year|2002|x|line_chart Percentage_of_employees|23.51|y|line_chart Year|2003|x|line_chart Percentage_of_employees|23.02|y|line_chart Year|2004|x|line_chart Percentage_of_employees|22.17|y|line_chart Year|2005|x|line_chart Percentage_of_employees|21.68|y|line_chart Year|2006|x|line_chart Percentage_of_employees|20.72|y|line_chart Year|2007|x|line_chart Percentage_of_employees|19.89|y|line_chart Year|2008|x|line_chart Percentage_of_employees|19.13|y|line_chart Year|2009|x|line_chart Percentage_of_employees|18.93|y|line_chart Year|2010|x|line_chart Percentage_of_employees|18.56|y|line_chart Year|2011|x|line_chart Percentage_of_employees|18.49|y|line_chart Year|2012|x|line_chart Percentage_of_employees|18.3|y|line_chart Year|2013|x|line_chart Percentage_of_employees|18.13|y|line_chart 
title: Trade union density in Germany from 2000 to 2013

gold: This statistic shows the percentage of employees that were members of a trade union ( trade union density ) in Germany from 2000 to 2013 . Over this 14 year period , the percentage fell by a total of 6.44 percentage points .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] that were members of a templateTitle[0] templateTitle[1] ( templateTitle[0] templateTitle[1] templateTitle[2] ) in templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . Over this 14 templateXLabel[0] period , the templateYLabel[0] fell by a total of 6.44 templateYLabel[0] points .

generated_template: The templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitle[3] templateTitle[4] in templateXValue[max] was templateYValue[0] percent , an increase of 0.01 percent when compared with templateXValue[1] . In templateXValue[min] , the share of templateYLabel[1] who were members of a templateTitle[0] templateTitle[1] was templateYValue[max] percent , nine percent more than templateXValue[max] . Uniting the unions In 2016/17 one the biggest templateTitle[0] Unions in the templateTitle[3] templateTitle[4] was Unite , with a membership of over 1.28 million people .
generated: The Trade union density of the Germany from in 2013 was 24.57 percent , an increase of 0.01 percent when compared with 2001 . In 2000 , the share of employees who were members of a Trade union was 24.57 percent , nine percent more than 2013 . Uniting the unions In 2016/17 one the biggest Trade Unions in the Germany from was Unite , with a membership of over 1.28 million people .


Example 19:
data: Year|2018|x|line_chart Share_of_urban_population_in_total_population|85.38|y|line_chart Year|2017|x|line_chart Share_of_urban_population_in_total_population|85.33|y|line_chart Year|2016|x|line_chart Share_of_urban_population_in_total_population|85.28|y|line_chart Year|2015|x|line_chart Share_of_urban_population_in_total_population|85.23|y|line_chart Year|2014|x|line_chart Share_of_urban_population_in_total_population|85.18|y|line_chart Year|2013|x|line_chart Share_of_urban_population_in_total_population|85.13|y|line_chart Year|2012|x|line_chart Share_of_urban_population_in_total_population|84.84|y|line_chart Year|2011|x|line_chart Share_of_urban_population_in_total_population|84.31|y|line_chart Year|2010|x|line_chart Share_of_urban_population_in_total_population|83.77|y|line_chart Year|2009|x|line_chart Share_of_urban_population_in_total_population|83.43|y|line_chart Year|2008|x|line_chart Share_of_urban_population_in_total_population|83.3|y|line_chart 
title: Urbanization in Finland 2018

gold: This statistic shows the degree of urbanization in Finland from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 85.38 percent of Finland 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] templateTitle[2] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Finland 2018 from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 85.38 percent of Finland 2018 's total population lived in urban areas and cities .


Example 20:
data: Quarter|Q1_'19|x|bar_chart Number_of_monthly_active_users_in_millions|330.0|y|bar_chart Quarter|Q4_'18|x|bar_chart Number_of_monthly_active_users_in_millions|321.0|y|bar_chart Quarter|Q3_'18|x|bar_chart Number_of_monthly_active_users_in_millions|326.0|y|bar_chart Quarter|Q2_'18|x|bar_chart Number_of_monthly_active_users_in_millions|335.0|y|bar_chart Quarter|Q1_'18|x|bar_chart Number_of_monthly_active_users_in_millions|336.0|y|bar_chart Quarter|Q4_'17|x|bar_chart Number_of_monthly_active_users_in_millions|330.0|y|bar_chart Quarter|Q3_'17|x|bar_chart Number_of_monthly_active_users_in_millions|330.0|y|bar_chart Quarter|Q2_'17|x|bar_chart Number_of_monthly_active_users_in_millions|326.0|y|bar_chart Quarter|Q1_'17|x|bar_chart Number_of_monthly_active_users_in_millions|327.0|y|bar_chart Quarter|Q4_'16|x|bar_chart Number_of_monthly_active_users_in_millions|318.0|y|bar_chart Quarter|Q3_'16|x|bar_chart Number_of_monthly_active_users_in_millions|317.0|y|bar_chart Quarter|Q2_'16|x|bar_chart Number_of_monthly_active_users_in_millions|313.0|y|bar_chart Quarter|Q1_'16|x|bar_chart Number_of_monthly_active_users_in_millions|310.0|y|bar_chart Quarter|Q4_'15|x|bar_chart Number_of_monthly_active_users_in_millions|305.0|y|bar_chart Quarter|Q3_'15|x|bar_chart Number_of_monthly_active_users_in_millions|307.0|y|bar_chart Quarter|Q2_'15|x|bar_chart Number_of_monthly_active_users_in_millions|304.0|y|bar_chart Quarter|Q1_'15|x|bar_chart Number_of_monthly_active_users_in_millions|302.0|y|bar_chart Quarter|Q4_'14|x|bar_chart Number_of_monthly_active_users_in_millions|288.0|y|bar_chart Quarter|Q3_'14|x|bar_chart Number_of_monthly_active_users_in_millions|284.0|y|bar_chart Quarter|Q2_'14|x|bar_chart Number_of_monthly_active_users_in_millions|271.0|y|bar_chart Quarter|Q1_'14|x|bar_chart Number_of_monthly_active_users_in_millions|255.0|y|bar_chart Quarter|Q4_'13|x|bar_chart Number_of_monthly_active_users_in_millions|241.0|y|bar_chart Quarter|Q3_'13|x|bar_chart Number_of_monthly_active_users_in_millions|231.7|y|bar_chart Quarter|Q2_'13|x|bar_chart Number_of_monthly_active_users_in_millions|218.0|y|bar_chart Quarter|Q1_'13|x|bar_chart Number_of_monthly_active_users_in_millions|204.0|y|bar_chart Quarter|Q4_'12|x|bar_chart Number_of_monthly_active_users_in_millions|185.0|y|bar_chart Quarter|Q3_'12|x|bar_chart Number_of_monthly_active_users_in_millions|167.0|y|bar_chart Quarter|Q2_'12|x|bar_chart Number_of_monthly_active_users_in_millions|151.0|y|bar_chart Quarter|Q1_'12|x|bar_chart Number_of_monthly_active_users_in_millions|138.0|y|bar_chart Quarter|Q4_'11|x|bar_chart Number_of_monthly_active_users_in_millions|117.0|y|bar_chart Quarter|Q3_'11|x|bar_chart Number_of_monthly_active_users_in_millions|101.0|y|bar_chart Quarter|Q2_'11|x|bar_chart Number_of_monthly_active_users_in_millions|85.0|y|bar_chart Quarter|Q1_'11|x|bar_chart Number_of_monthly_active_users_in_millions|68.0|y|bar_chart Quarter|Q4_'10|x|bar_chart Number_of_monthly_active_users_in_millions|54.0|y|bar_chart Quarter|Q3_'10|x|bar_chart Number_of_monthly_active_users_in_millions|49.0|y|bar_chart Quarter|Q2_'10|x|bar_chart Number_of_monthly_active_users_in_millions|40.0|y|bar_chart Quarter|Q1_'10|x|bar_chart Number_of_monthly_active_users_in_millions|30.0|y|bar_chart 
title: Twitter : number of monthly active users 2010 - 2019

gold: How many people use Twitter ? As of the first quarter of 2019 , Twitter averaged 330 million monthly active users , a decline from its all-time high of 336 MAU in the first quarter of 2018 . As of the first quarter of 2019 , the company switched its user reporting metric to monetizable daily active users ( mDAU ) . Twitter Twitter is a social networking and microblogging service , enabling registered users to read and post short messages called tweets .
gold_template: How many people use templateTitle[0] ? As of the first templateXLabel[0] of templateTitle[6] , templateTitle[0] averaged templateYValue[0] million templateYLabel[1] templateYLabel[2] templateYLabel[3] , a decline from its all-time high of templateYValue[max] MAU in the first templateXLabel[0] of 2018 . As of the first templateXLabel[0] of templateTitle[6] , the company switched its user reporting metric to monetizable daily templateYLabel[2] templateYLabel[3] ( mDAU ) . templateTitle[0] is a social networking and microblogging service , enabling registered templateYLabel[3] to read and post short messages called tweets .

generated_template: This statistic shows a timeline with the amount of templateYLabel[1] templateYLabel[2] templateTitle[4] templateTitle[0] templateYLabel[3] as of the first templateXLabel[0] of templateTitle[7] . Excluding the United States , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[4] templateTitle[0] templateYLabel[3] amounted to templateYValue[0] million as of the most recent templateXLabel[0] . In total , templateTitle[0] had 330 million global templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows a timeline with the amount of monthly active users Twitter users as of the first Quarter of titleErr . Excluding the United States , the Number of monthly active users Twitter users amounted to 330.0 million as of the most recent Quarter . In total , Twitter had 330 million global monthly active users .


Example 21:
data: Country|Canada|x|bar_chart Percent_with_a_university_degree|48|y|bar_chart Country|New_Zealand|x|bar_chart Percent_with_a_university_degree|41|y|bar_chart Country|Japan|x|bar_chart Percent_with_a_university_degree|41|y|bar_chart Country|United_States|x|bar_chart Percent_with_a_university_degree|40|y|bar_chart Country|Korea_Republic_of|x|bar_chart Percent_with_a_university_degree|35|y|bar_chart Country|Norway|x|bar_chart Percent_with_a_university_degree|34|y|bar_chart Country|Australia|x|bar_chart Percent_with_a_university_degree|34|y|bar_chart Country|Ireland|x|bar_chart Percent_with_a_university_degree|32|y|bar_chart Country|United_Kingdom|x|bar_chart Percent_with_a_university_degree|32|y|bar_chart Country|Denmark|x|bar_chart Percent_with_a_university_degree|32|y|bar_chart Country|Sweden|x|bar_chart Percent_with_a_university_degree|31|y|bar_chart Country|Netherlands|x|bar_chart Percent_with_a_university_degree|31|y|bar_chart Country|Spain|x|bar_chart Percent_with_a_university_degree|29|y|bar_chart Country|France|x|bar_chart Percent_with_a_university_degree|27|y|bar_chart Country|Germany|x|bar_chart Percent_with_a_university_degree|24|y|bar_chart Country|Austria|x|bar_chart Percent_with_a_university_degree|18|y|bar_chart Country|Mexico|x|bar_chart Percent_with_a_university_degree|15|y|bar_chart Country|Portugal|x|bar_chart Percent_with_a_university_degree|14|y|bar_chart Country|Italy|x|bar_chart Percent_with_a_university_degree|13|y|bar_chart 
title: Percentage of population with a university degree , by country 2007

gold: In 2007 , Canada had the highest share of adults with a university degree , at 48 percent of those between the ages of 25 and 64 . Italy had the least amount of people with a university degree , at 13 percent of people between the ages of 25 and 64 . University around the world Deciding which university to attend can be a difficult decision for some and in today 's world , people are not left wanting for choice .
gold_template: In templateTitle[7] , templateXValue[0] had the highest share of adults templateYLabel[1] a templateYLabel[2] templateYLabel[3] , at templateYValue[max] templateYLabel[0] of those between the ages of 25 and 64 . templateXValue[last] had the least amount of people templateYLabel[1] a templateYLabel[2] templateYLabel[3] , at templateYValue[min] templateYLabel[0] of people between the ages of 25 and 64 . templateYLabel[2] around the world Deciding which templateYLabel[2] to attend can be a difficult decision for some and in today 's world , people are not left wanting for choice .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] as of templateTitle[6] . During the survey , templateYValue[max] percent of templateYLabel[1] had between templateYValue[2] and templateYValue[2] percent of the templateTitle[2] templateTitle[3] templateTitle[4] as a monthly basis .
generated: This statistic shows the Percent university of university degree by in country as of 2007 . During the survey , 48 percent of university had between 41 and 41 percent of the university degree by as a monthly basis .


Example 22:
data: Year|2100|x|line_chart Median_age|41.9|y|line_chart Year|2095|x|line_chart Median_age|41.3|y|line_chart Year|2090|x|line_chart Median_age|40.8|y|line_chart Year|2085|x|line_chart Median_age|40.3|y|line_chart Year|2080|x|line_chart Median_age|39.7|y|line_chart Year|2075|x|line_chart Median_age|39.2|y|line_chart Year|2070|x|line_chart Median_age|38.7|y|line_chart Year|2065|x|line_chart Median_age|38.2|y|line_chart Year|2060|x|line_chart Median_age|37.6|y|line_chart Year|2055|x|line_chart Median_age|36.9|y|line_chart Year|2050|x|line_chart Median_age|36.2|y|line_chart Year|2045|x|line_chart Median_age|35.4|y|line_chart Year|2040|x|line_chart Median_age|34.6|y|line_chart Year|2035|x|line_chart Median_age|33.9|y|line_chart Year|2030|x|line_chart Median_age|33.0|y|line_chart Year|2025|x|line_chart Median_age|32.0|y|line_chart Year|2020|x|line_chart Median_age|30.9|y|line_chart Year|2015|x|line_chart Median_age|29.6|y|line_chart Year|2010|x|line_chart Median_age|28.5|y|line_chart Year|2005|x|line_chart Median_age|27.4|y|line_chart Year|2000|x|line_chart Median_age|26.3|y|line_chart Year|1995|x|line_chart Median_age|25.1|y|line_chart Year|1990|x|line_chart Median_age|24.0|y|line_chart Year|1985|x|line_chart Median_age|23.3|y|line_chart Year|1980|x|line_chart Median_age|22.6|y|line_chart Year|1975|x|line_chart Median_age|21.9|y|line_chart Year|1970|x|line_chart Median_age|21.5|y|line_chart Year|1965|x|line_chart Median_age|22.0|y|line_chart Year|1960|x|line_chart Median_age|22.6|y|line_chart Year|1955|x|line_chart Median_age|23.1|y|line_chart Year|1950|x|line_chart Median_age|23.6|y|line_chart 
title: Projected global median age 1950 - 2100

gold: This statistic shows the median age of the world population from 1950 to 2100 . By 2100 , the global median age is projected to be 41.9 years of age .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the world population from templateXValue[min] to templateXValue[max] . By templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] is templateTitle[0] to be templateYValue[max] years of templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the United States templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were an estimated templateYValue[0] people living in the United States .
generated: This statistic shows the Median age of the United States age 1950 2100 titleErr 1950 to 2100 . In 2100 , there were an estimated 41.9 people living in the United States .


Example 23:
data: Year|2010|x|line_chart Million_transmissions|25.03|y|line_chart Year|2011|x|line_chart Million_transmissions|25.36|y|line_chart Year|2012|x|line_chart Million_transmissions|26.72|y|line_chart Year|2013|x|line_chart Million_transmissions|27.82|y|line_chart Year|2014|x|line_chart Million_transmissions|28.46|y|line_chart Year|2015|x|line_chart Million_transmissions|28.65|y|line_chart 
title: Automobile production - automatic transmission forecast 2010 - 2015

gold: The timeline shows an automatic transmission forecast for automobile production worldwide from 2010 to 2015 . In 2010 , about 25.03 million cars and light vehicles produced worldwide were equipped with an automatic transmission . It is forecast that this number will increase to about 28.65 million automobiles produced worldwide in 2015 .
gold_template: The timeline shows an templateTitle[2] templateTitle[3] templateTitle[4] for templateTitle[0] templateTitle[1] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[min] , about templateYValue[min] templateYLabel[0] cars and light vehicles produced worldwide were equipped with an templateTitle[2] templateTitle[3] . It is templateTitle[4] that this number will increase to about templateYValue[max] templateYLabel[0] automobiles produced worldwide in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] ( UK ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[0] million people living in templateTitle[2] templateTitle[3] templateTitle[4] , up from templateYValue[1] percent in the previous templateXLabel[0] .
generated: This statistic shows the Million transmissions of automatic transmission forecast ( UK ) from 2010 to 2015 . In 2015 , there were approximately 25.03 million people living in automatic transmission forecast , up from 25.36 percent in the previous Year .


Example 24:
data: Year|2019|x|line_chart EBIT_(adjusted;_in_million_U.S.dollars)|8393|y|line_chart Year|2018|x|line_chart EBIT_(adjusted;_in_million_U.S.dollars)|11783|y|line_chart Year|2017|x|line_chart EBIT_(adjusted;_in_million_U.S.dollars)|12844|y|line_chart Year|2016|x|line_chart EBIT_(adjusted;_in_million_U.S.dollars)|12848|y|line_chart Year|2015|x|line_chart EBIT_(adjusted;_in_million_U.S.dollars)|11449|y|line_chart 
title: General Motors - adjusted EBIT 2015 - 2019

gold: In 2019 , General Motors produced earnings before interests and taxes of around 8.4 billion U.S. dollars , down from almost 11.8 billion U.S. dollars in 2018 . GM 's earnings were affected by falling vehicle sales , particularly in China .
gold_template: In templateXValue[max] , templateTitle[0] templateTitle[1] produced earnings before interests and taxes of around templateYValue[min] billion U.S. dollars , down from almost templateYValue[1] billion U.S. dollars in templateXValue[1] . GM 's earnings were affected by falling vehicle sales , particularly in China .

generated_template: U.S. fashion company templateYValue[max] people in templateXValue[1] , which reached its highest level of templateTitle[2] at the highest level of templateYLabel[1] in templateXValue[max] fiscal templateXLabel[0] . The source templateYLabel[1] recorded in templateTitle[3] templateTitle[4] templateTitle[5] was just templateYValue[min] percent in templateYLabel[1] compared to the previous templateXLabel[0] . However , the value of templateYLabel[1] represents U.S. dollars in templateXValue[1] , which rose rose to 100 percent in recent years in templateXValue[1] .
generated: U.S. fashion company 12848 people in 2018 , which reached its highest level of adjusted at the highest level of (adjusted; in 2019 fiscal Year . The source (adjusted; recorded in EBIT 2015 2019 was just 8393 percent in (adjusted; compared to the previous Year . However , the value of (adjusted; represents U.S. dollars in 2018 , which rose to 100 percent in recent years in 2018 .


Example 25:
data: Year|2028|x|line_chart Value_in_billion_euros|411.0|y|line_chart Year|2018|x|line_chart Value_in_billion_euros|358.3|y|line_chart Year|2017|x|line_chart Value_in_billion_euros|348.1|y|line_chart Year|2016|x|line_chart Value_in_billion_euros|342.2|y|line_chart Year|2015|x|line_chart Value_in_billion_euros|336.2|y|line_chart Year|2014|x|line_chart Value_in_billion_euros|325.7|y|line_chart Year|2013|x|line_chart Value_in_billion_euros|315.9|y|line_chart Year|2012|x|line_chart Value_in_billion_euros|318.0|y|line_chart 
title: Travel and tourism 's total contribution to GDP in Germany 2012 - 2028

gold: This statistic presents the total contribution of travel and tourism to GDP in Germany from 2012 to 2018 , with a forecast for 2028 . Over this period , the contribution of the travel and tourism industry to GDP in Germany has increased , reaching around 348.1 billion euros in 2017 .
gold_template: This statistic presents the templateTitle[3] templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitle[5] in templateTitle[6] from templateXValue[min] to templateXValue[1] , with a forecast for templateXValue[max] . Over this period , the templateTitle[4] of the templateTitle[0] and templateTitle[1] industry to templateTitle[5] in templateTitle[6] has increased , reaching around templateYValue[2] templateYLabel[1] templateYLabel[2] in templateXValue[2] .

generated_template: The templateTitle[0] and templateTitle[1] industry contributed to the French Gross Domestic Product ( templateTitle[5] ) in templateTitle[3] with approximately templateYValue[2] templateYLabel[1] templateYLabel[2] in templateXValue[2] . The source predicted that this templateTitle[4] will increase in the coming ten years , reaching an expected templateTitle[3] of around templateYValue[max] templateYLabel[1] templateYLabel[2] in templateXValue[max] . In templateXValue[2] , templateTitle[6] welcomed almost 87 million international tourists , making it the most visited country in the world in that templateXLabel[0] .
generated: The Travel and tourism industry contributed to the French Gross Domestic Product ( GDP ) in total with approximately 348.1 billion euros in 2017 . The source predicted that this contribution will increase in the coming ten years , reaching an expected total of around 411.0 billion euros in 2028 . In 2017 , Germany welcomed almost 87 million international tourists , making it the most visited country in the world in that Year .


Example 26:
data: Year|2017|x|line_chart Percentage_of_individuals_using_internet|74.29|y|line_chart Year|2016|x|line_chart Percentage_of_individuals_using_internet|70.97|y|line_chart Year|2015|x|line_chart Percentage_of_individuals_using_internet|68.04|y|line_chart Year|2014|x|line_chart Percentage_of_individuals_using_internet|64.7|y|line_chart Year|2013|x|line_chart Percentage_of_individuals_using_internet|59.9|y|line_chart Year|2012|x|line_chart Percentage_of_individuals_using_internet|55.8|y|line_chart Year|2011|x|line_chart Percentage_of_individuals_using_internet|51|y|line_chart Year|2010|x|line_chart Percentage_of_individuals_using_internet|45|y|line_chart Year|2009|x|line_chart Percentage_of_individuals_using_internet|34|y|line_chart Year|2008|x|line_chart Percentage_of_individuals_using_internet|28.11|y|line_chart Year|2007|x|line_chart Percentage_of_individuals_using_internet|25.95|y|line_chart Year|2006|x|line_chart Percentage_of_individuals_using_internet|20.93|y|line_chart Year|2005|x|line_chart Percentage_of_individuals_using_internet|17.72|y|line_chart Year|2004|x|line_chart Percentage_of_individuals_using_internet|16.04|y|line_chart Year|2003|x|line_chart Percentage_of_individuals_using_internet|11.91|y|line_chart Year|2002|x|line_chart Percentage_of_individuals_using_internet|10.88|y|line_chart Year|2001|x|line_chart Percentage_of_individuals_using_internet|9.78|y|line_chart Year|2000|x|line_chart Percentage_of_individuals_using_internet|7.04|y|line_chart 
title: Argentina : internet penetration 2000 - 2017

gold: This statistic gives information on the internet penetration in Argentina from 2000 to 2017 . In 2017 , about 74.3 percent of the Argentinean population accessed the internet , a considerable increase when compared to only seven percent in 2000 .
gold_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[max] percent of the Argentinean population accessed the templateYLabel[3] , a considerable increase when compared to only templateYValue[min] percent in templateXValue[min] .

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the Chilean population accessed the templateYLabel[3] , up from templateYValue[16] percent in templateXValue[16] .
generated: This statistic gives information on the internet penetration in Argentina from 2000 to 2017 . In 2017 , 74.29 percent of the Chilean population accessed the internet , up from 9.78 percent in 2001 .


Example 27:
data: Year|2019|x|line_chart RPMs_in_billions|131.35|y|line_chart Year|2018|x|line_chart RPMs_in_billions|133.32|y|line_chart Year|2017|x|line_chart RPMs_in_billions|129.04|y|line_chart Year|2016|x|line_chart RPMs_in_billions|124.8|y|line_chart Year|2015|x|line_chart RPMs_in_billions|117.5|y|line_chart Year|2014|x|line_chart RPMs_in_billions|108.04|y|line_chart Year|2013|x|line_chart RPMs_in_billions|104.35|y|line_chart Year|2012|x|line_chart RPMs_in_billions|102.87|y|line_chart Year|2011|x|line_chart RPMs_in_billions|97.58|y|line_chart Year|2010|x|line_chart RPMs_in_billions|78.05|y|line_chart 
title: Revenue passenger miles ( RPMs ) of Southwest Airlines 2010 - 2019

gold: In the fiscal year of 2019 , Southwest Airlines transported passengers on a total of over 131.3 billion miles . The leading low-cost carrier had a capacity of 157.2 billion available seat miles in that same year , and as such was efficient in using its fleet to transport paying customers . Flying with Southwest Southwest Airlines ' main hub , Las Vegas McCarran International Airport , saw a traffic of 17.5 million Southwest passengers in 2018 .
gold_template: In the fiscal templateXLabel[0] of templateXValue[max] , templateTitle[4] templateTitle[5] transported passengers on a total of over 131.3 billion templateTitle[2] . The leading low-cost carrier had a capacity of 157.2 billion available seat templateTitle[2] in that same templateXLabel[0] , and as such was efficient in using its fleet to transport paying customers . Flying with templateTitle[4] templateTitle[5] ' main hub , Las Vegas McCarran International Airport , saw a traffic of 17.5 million templateTitle[4] passengers in templateXValue[1] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people living in the templateTitle[3] templateTitle[4] and templateTitle[5] in the United States .
generated: This statistic shows the RPMs billions yLabelErr of RPMs Southwest Airlines in the United States from 2010 to 2019 . In 2019 , there were 131.35 people living in the RPMs Southwest and Airlines in the United States .


Example 28:
data: Year|2019|x|line_chart Share_of_respondents|82|y|line_chart Year|2018|x|line_chart Share_of_respondents|78|y|line_chart Year|2017|x|line_chart Share_of_respondents|77|y|line_chart Year|2016|x|line_chart Share_of_respondents|77|y|line_chart Year|2015|x|line_chart Share_of_respondents|76|y|line_chart Year|2014|x|line_chart Share_of_respondents|74|y|line_chart Year|2013|x|line_chart Share_of_respondents|72|y|line_chart Year|2012|x|line_chart Share_of_respondents|67|y|line_chart Year|2011|x|line_chart Share_of_respondents|66|y|line_chart Year|2010|x|line_chart Share_of_respondents|62|y|line_chart Year|2009|x|line_chart Share_of_respondents|61|y|line_chart Year|2008|x|line_chart Share_of_respondents|53|y|line_chart 
title: Online purchasing penetration in Great Britain 2008 - 2019

gold: This statistic displays a timeline of online purchasing take up in Great Britain from 2008 to 2019 . In 2019 , 82 percent of polled Britons made purchases online . This is greater penetration than in 2008 , when 53 percent of individuals made purchases online .
gold_template: This statistic displays a timeline of templateTitle[0] templateTitle[1] take up in templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of polled Britons made purchases templateTitle[0] . This is greater templateTitle[2] than in templateXValue[min] , when templateYValue[min] percent of individuals made purchases templateTitle[0] .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitle[3] templateTitle[4] who were using templateTitle[0] as of February templateTitle[5] , sorted templateTitle[6] templateTitle[7] as of January templateTitle[8] . During the survey period it was found that templateYValue[0] percent of templateYLabel[1] said that they had to templateXValue[1] .
generated: This statistic shows the Share of adults in the Great Britain who were using Online as of February 2008 , sorted 2019 titleErr as of January titleErr . During the survey period it was found that 82 percent of respondents said that they had to 2018 .


Example 29:
data: Year|2018|x|line_chart Number_of_university_applicants|636960|y|line_chart Year|2017|x|line_chart Number_of_university_applicants|649700|y|line_chart Year|2016|x|line_chart Number_of_university_applicants|674890|y|line_chart Year|2015|x|line_chart Number_of_university_applicants|673040|y|line_chart Year|2014|x|line_chart Number_of_university_applicants|659030|y|line_chart Year|2013|x|line_chart Number_of_university_applicants|635910|y|line_chart Year|2012|x|line_chart Number_of_university_applicants|616700|y|line_chart Year|2011|x|line_chart Number_of_university_applicants|668150|y|line_chart Year|2010|x|line_chart Number_of_university_applicants|658560|y|line_chart 
title: University applicants in the United Kingdom ( UK ) 2010 - 2018

gold: This statistic shows the number of applicants to universities in the United Kingdom ( UK ) from 2010 to 2018 . The number of applicants peaked in 2016 . The lower figures in 2012 and 2013 may be connected to the rise of the tuition fee limit in 2012 to 9,000 British pounds per year .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[2] to universities in the templateTitle[2] templateTitle[3] ( templateTitle[4] ) from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[2] peaked in templateXValue[2] . The lower figures in templateXValue[6] and templateXValue[5] may be connected to the rise of the tuition fee limit in templateXValue[6] to 9,000 British pounds per templateXLabel[0] .

generated_template: The graph shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the templateYLabel[1] were recorded at the templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The graph shows the Number university applicants of Kingdom UK worldwide from 2010 to 2018 . In 2018 , 636960 percent of the university were recorded at the Kingdom UK 2010 .


Example 30:
data: Quarter|Q1_2012|x|bar_chart Viewers_in_thousands|55196|y|bar_chart Quarter|Q2_2012|x|bar_chart Viewers_in_thousands|55302|y|bar_chart Quarter|Q3_2012|x|bar_chart Viewers_in_thousands|54443|y|bar_chart Quarter|Q4_2012|x|bar_chart Viewers_in_thousands|55327|y|bar_chart Quarter|Q1_2013|x|bar_chart Viewers_in_thousands|55277|y|bar_chart Quarter|Q2_2013|x|bar_chart Viewers_in_thousands|55014|y|bar_chart Quarter|Q3_2013|x|bar_chart Viewers_in_thousands|54013|y|bar_chart Quarter|Q4_2013|x|bar_chart Viewers_in_thousands|55559|y|bar_chart Quarter|Q1_2014|x|bar_chart Viewers_in_thousands|55443|y|bar_chart Quarter|Q2_2014|x|bar_chart Viewers_in_thousands|55628|y|bar_chart Quarter|Q3_2014|x|bar_chart Viewers_in_thousands|54636|y|bar_chart Quarter|Q4_2014|x|bar_chart Viewers_in_thousands|55195|y|bar_chart Quarter|Q1_2015|x|bar_chart Viewers_in_thousands|53997|y|bar_chart Quarter|Q2_2015|x|bar_chart Viewers_in_thousands|53716|y|bar_chart Quarter|Q3_2015|x|bar_chart Viewers_in_thousands|53457|y|bar_chart Quarter|Q4_2015|x|bar_chart Viewers_in_thousands|54227|y|bar_chart Quarter|Q1_2016|x|bar_chart Viewers_in_thousands|53928|y|bar_chart Quarter|Q2_2016|x|bar_chart Viewers_in_thousands|54201|y|bar_chart Quarter|Q3_2016|x|bar_chart Viewers_in_thousands|52843|y|bar_chart Quarter|Q4_2016|x|bar_chart Viewers_in_thousands|54150|y|bar_chart Quarter|Q1_2017|x|bar_chart Viewers_in_thousands|53038|y|bar_chart Quarter|Q2_2017|x|bar_chart Viewers_in_thousands|51936|y|bar_chart Quarter|Q3_2017|x|bar_chart Viewers_in_thousands|51546|y|bar_chart Quarter|Q4_2017|x|bar_chart Viewers_in_thousands|53303|y|bar_chart Quarter|Q1_2018|x|bar_chart Viewers_in_thousands|52906|y|bar_chart Quarter|Q2_2018|x|bar_chart Viewers_in_thousands|51628|y|bar_chart Quarter|Q3_2018|x|bar_chart Viewers_in_thousands|47762|y|bar_chart Quarter|Q4_2018|x|bar_chart Viewers_in_thousands|48375|y|bar_chart Quarter|Q1_2019|x|bar_chart Viewers_in_thousands|48236|y|bar_chart Quarter|Q2_2019|x|bar_chart Viewers_in_thousands|46376|y|bar_chart Quarter|Q3_2019|x|bar_chart Viewers_in_thousands|45308|y|bar_chart 
title: ITV viewers reached quarterly in the UK Q1 2012-Q3 2019

gold: This statistic shows the quarterly reach of the ITV television channel in the United Kingdom ( UK ) from the first quarter of 2012 to the third quarter of 2019 . In the quarter ending September 2014 , ITV reached 54.64 million viewers . The number of viewers fell to roughly 45.3 million in the last period in consideration .
gold_template: This statistic shows the templateTitle[3] reach of the templateTitle[0] television channel in the United Kingdom ( templateTitle[4] ) from the first templateXLabel[0] of templateXValue[0] to the third templateXLabel[0] of templateXValue[28] . In the templateXLabel[0] ending September templateXValue[8] , templateTitle[0] templateTitle[2] templateYValue[10] million templateYLabel[0] . The number of templateYLabel[0] fell to roughly templateYValue[min] million in the last period in consideration .

generated_template: During the second templateXLabel[0] of templateXValue[0] , housing templateYLabel[1] templateTitle[5] templateTitle[5] in the United States increased by templateYValue[0] percent in templateYLabel[2] between the first templateXLabel[0] of templateXValue[0] to templateYValue[max] percent recorded in the first templateXLabel[0] of templateXValue[0] . The least one of templateTitle[4] templateTitle[5] templateTitle[5] templateYLabel[1] templateYLabel[2] in the first time , according to many birth in templateYLabel[1] financial year . A case the first templateXLabel[0] of history , 4 , 30 percent in the United States between the first templateXLabel[0] of templateTitle[4] templateTitle[5] earn an annual templateYLabel[1] templateYLabel[2] history with many percent of natural templateYLabel[0] templateYLabel[1] at templateYValue[0] percent since 4 has seen in case the first templateXLabel[0] of the first templateXLabel[0] of of templateXValue[0] and at the first templateXLabel[0] of templateXValue[24] in the gross domestic product at the first templateXLabel[0] of many years from many levels in templateYLabel[2] euros in templateYLabel[1] areas of the templateTitle[3] , continuing a templateYLabel[1] at the first templateXLabel[0] of templateXValue[0] , which offer between 4 , making it had increased again in the first templateXLabel[0] of templateXValue[0] of templateXValue[0] , which offer between the first templateXLabel[0] of templateXValue[0] , which offer between the third templateXLabel[0] of the first templateXLabel[0] of templateTitle[4] templateTitle[5] back in the first templateXLabel[0] of templateXValue[0] , which was seen the first templateXLabel[0] of the first templateXLabel[0] of templateXValue[0] , marking an annual time of templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] , which reported templateXLabel[0] of templateXValue[0] templateXValue[0] templateXValue[0] , exported by the first templateXLabel[0] of the first templateXLabel[0] of the United States between the third templateXLabel[0] of the first templateXLabel[0] of women 's history with the first templateXLabel[0] of the first templateXLabel[0] of templateXValue[24] to 100 people recorded the first templateXLabel[0] of templateXValue[0] and templateYValue[0] percent of templateXValue[0] templateXValue[0] at the first templateXLabel[0] of 2016 and templateYValue[0] percent since the first templateXLabel[0] of households in the first templateXLabel[0] of templateXValue[0] , which offer between 4 , which was seen in the first templateXLabel[0] of 2018 , according to 100 times in the first templateXLabel[0] of templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] , making it slightly increased again in the first templateXLabel[0] of templateXValue[0] , the first templateXLabel[0] of the first templateXLabel[0] of the third templateXLabel[0] of templateXValue[0] templateXValue[0] , which offer history with the company in the first templateXLabel[0] of templateXValue[0] . The first templateXLabel[0] of templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] , marking an increase of the first templateXLabel[0] of the third templateXLabel[0] of templateXValue[0] , according to 100 percent of the first templateXLabel[0] of the first templateXLabel[0] of templateXValue[0] templateXValue[0] templateXValue[0] , compared to the first templateXLabel[0] of templateXValue[0] templateXValue[0] templateXValue[0] , compared to templateTitle[6] the first templateXLabel[0] of templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] . However , according to 100 percent of the first templateXLabel[0] of templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] , which offer templateYValue[0] percent of the first templateXLabel[0] of templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] , including the first templateXLabel[0] of templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] , making it had risen dramatically in the least templateYValue[max] percent since the first templateXLabel[0] of women 's third templateXLabel[0] of templateYLabel[1] between the first templateXLabel[0] of templateXValue[0] templateXValue[0] .
generated: During the second Quarter of Q1_2012 , housing thousands Q1 in the United States increased by 55196 percent in yLabelErr between the first Quarter of Q1_2012 to 55628 percent recorded in the first Quarter of Q1_2012 . The least one of UK Q1 thousands yLabelErr in the first time , according to many birth in thousands financial year . A case the first Quarter of history , 4 , 30 percent in the United States between the first Quarter of UK Q1 earn an annual thousands yLabelErr history with many percent of natural Viewers thousands at 55196 percent since 4 has seen in case the first Quarter of the first Quarter of Q1_2012 and at the first Quarter of Q1_2018 in the gross domestic product at the first Quarter of many years from many levels in yLabelErr euros in thousands areas of the quarterly , continuing a thousands at the first Quarter of Q1_2012 , which offer between 4 , making it had increased again in the first Quarter of Q1_2012 of Q1_2012 , which offer between the first Quarter of Q1_2012 , which offer between the third Quarter of the first Quarter of UK Q1 back in the first Quarter of Q1_2012 , which was seen the first Quarter of the first Quarter of Q1_2012 , marking an annual time of Q1_2012 Q1_2012 , which reported Quarter of Q1_2012 Q1_2012 , exported by the first Quarter of the first Quarter of the United States between the third Quarter of the first Quarter of women 's history with the first Quarter of the first Quarter of Q1_2018 to 100 people recorded the first Quarter of Q1_2012 and 55196 percent of Q1_2012 at the first Quarter of 2016 and 55196 percent since the first Quarter of households in the first Quarter of Q1_2012 , which offer between 4 , which was seen in the first Quarter of 2018 , according to 100 times in the first Quarter of Q1_2012 Q1_2012 , making it slightly increased again in the first Quarter of Q1_2012 , the first Quarter of the first Quarter of the third Quarter of Q1_2012 , which offer history with the company in the first Quarter of Q1_2012 . The first Quarter of Q1_2012 Q1_2012 Q1_2012 Q1_2012 , marking an increase of the first Quarter of the third Quarter of Q1_2012 , according to 100 percent of the first Quarter of the first Quarter of Q1_2012 Q1_2012 , compared to the first Quarter of Q1_2012 Q1_2012 , compared to 2012-Q3 the first Quarter of Q1_2012 Q1_2012 Q1_2012 . However , according to 100 percent of the first Quarter of Q1_2012 Q1_2012 , which offer 55196 percent of the first Quarter of Q1_2012 Q1_2012 Q1_2012 , including the first Quarter of Q1_2012 Q1_2012 Q1_2012 , making it had risen dramatically in the least 55628 percent since the first Quarter of women 's third Quarter of thousands between the first Quarter of Q1_2012 .


Example 31:
data: Month|Valeri_"Vako"_Qazaishvili|x|bar_chart Payroll_in_thousand_U.S._dollars|1604.04|y|bar_chart Month|Chris_Wondolowski|x|bar_chart Payroll_in_thousand_U.S._dollars|800.0|y|bar_chart Month|Florian_Jungwirth|x|bar_chart Payroll_in_thousand_U.S._dollars|616.68|y|bar_chart Month|Guram_Kashia|x|bar_chart Payroll_in_thousand_U.S._dollars|590.0|y|bar_chart Month|Cristian_Espinoza|x|bar_chart Payroll_in_thousand_U.S._dollars|550.0|y|bar_chart Month|Danny_Hoesen|x|bar_chart Payroll_in_thousand_U.S._dollars|549.67|y|bar_chart Month|Anibal_Godoy|x|bar_chart Payroll_in_thousand_U.S._dollars|498.13|y|bar_chart Month|Magnus_Eriksson|x|bar_chart Payroll_in_thousand_U.S._dollars|450.0|y|bar_chart Month|Marcos_Lopez_Lanfranco|x|bar_chart Payroll_in_thousand_U.S._dollars|387.75|y|bar_chart Month|Harold_Cummings|x|bar_chart Payroll_in_thousand_U.S._dollars|320.67|y|bar_chart Month|Judson|x|bar_chart Payroll_in_thousand_U.S._dollars|305.0|y|bar_chart Month|Francois_Affolter|x|bar_chart Payroll_in_thousand_U.S._dollars|273.0|y|bar_chart Month|Shea_Salinas|x|bar_chart Payroll_in_thousand_U.S._dollars|250.0|y|bar_chart Month|Andrew_Tarbell|x|bar_chart Payroll_in_thousand_U.S._dollars|235.0|y|bar_chart Month|Nick_Lima|x|bar_chart Payroll_in_thousand_U.S._dollars|218.44|y|bar_chart Month|Daniel_Vega|x|bar_chart Payroll_in_thousand_U.S._dollars|210.0|y|bar_chart Month|Jackson_Yueill|x|bar_chart Payroll_in_thousand_U.S._dollars|190.0|y|bar_chart Month|Tommy_Thompson|x|bar_chart Payroll_in_thousand_U.S._dollars|175.0|y|bar_chart Month|JT_Marcinkowski|x|bar_chart Payroll_in_thousand_U.S._dollars|147.0|y|bar_chart Month|Eric_Calvillo|x|bar_chart Payroll_in_thousand_U.S._dollars|135.0|y|bar_chart Month|Siad_Haji|x|bar_chart Payroll_in_thousand_U.S._dollars|104.0|y|bar_chart Month|Gilbert_Fuentes|x|bar_chart Payroll_in_thousand_U.S._dollars|92.0|y|bar_chart Month|Matt_Bersano|x|bar_chart Payroll_in_thousand_U.S._dollars|71.67|y|bar_chart Month|Luis_Felipe|x|bar_chart Payroll_in_thousand_U.S._dollars|71.63|y|bar_chart Month|Jimmy_Ockford|x|bar_chart Payroll_in_thousand_U.S._dollars|70.88|y|bar_chart Month|Cade_Cowell|x|bar_chart Payroll_in_thousand_U.S._dollars|67.23|y|bar_chart Month|Jacob_Akanyirige|x|bar_chart Payroll_in_thousand_U.S._dollars|64.23|y|bar_chart Month|Kevin_Partida|x|bar_chart Payroll_in_thousand_U.S._dollars|57.23|y|bar_chart Month|Paul_Marie|x|bar_chart Payroll_in_thousand_U.S._dollars|57.23|y|bar_chart 
title: Player expenses ( payroll ) of San Jose Earthquakes 2019

gold: The statistic shows the player expenses ( payroll ) of the San Jose Earthquakes club of Major League Soccer by player in 2019 . Valeri `` Vako '' Qazaishvili received a salary of 1.6 million U.S. dollars .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitle[3] templateTitle[4] templateTitle[5] club of Major League Soccer by templateTitle[0] in templateTitle[6] . templateXValue[0] `` Vako '' templateXValue[0] received a salary of templateYValue[max] million templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitle[3] templateTitle[4] club of Major League Soccer by templateTitle[0] in templateTitle[5] . templateXValue[0] templateXValue[0] received a salary of templateYValue[max] million templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Player expenses ( Payroll ) of the San Jose club of Major League Soccer by Player in Earthquakes . Valeri_"Vako"_Qazaishvili received a salary of 1604.04 million U.S. dollars .


Example 32:
data: Year|2018|x|line_chart Revenue_in_billion_U.S._dollars|64.66|y|line_chart Year|2017|x|line_chart Revenue_in_billion_U.S._dollars|63.53|y|line_chart Year|2016|x|line_chart Revenue_in_billion_U.S._dollars|62.8|y|line_chart Year|2015|x|line_chart Revenue_in_billion_U.S._dollars|63.06|y|line_chart Year|2014|x|line_chart Revenue_in_billion_U.S._dollars|66.68|y|line_chart Year|2013|x|line_chart Revenue_in_billion_U.S._dollars|66.42|y|line_chart Year|2012|x|line_chart Revenue_in_billion_U.S._dollars|65.49|y|line_chart Year|2011|x|line_chart Revenue_in_billion_U.S._dollars|66.5|y|line_chart Year|2010|x|line_chart Revenue_in_billion_U.S._dollars|57.84|y|line_chart Year|2009|x|line_chart Revenue_in_billion_U.S._dollars|43.23|y|line_chart Year|2008|x|line_chart Revenue_in_billion_U.S._dollars|43.25|y|line_chart Year|2007|x|line_chart Revenue_in_billion_U.S._dollars|39.47|y|line_chart 
title: PepsiCo 's net revenue worldwide 2007 - 2018

gold: This timeline depicts PepsiCo 's net revenue worldwide from 2007 to 2018 . In 2018 , PepsiCo 's net revenue worldwide amounted to about 64.66 billion U.S. dollars . PepsiCoPepsiCo is an American food and beverage corporation , based in Purchase , NY .
gold_template: This timeline depicts templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[4] amounted to about templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . PepsiCoPepsiCo is an American food and beverage corporation , based in Purchase , NY .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of the Chinese search engine templateTitle[0] templateTitle[1] templateTitle[2] each templateXLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company generated a total of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] . Additional information Founded by the templateTitle[0] templateYLabel[0] consists of the templateTitle[0] Corporation is the largest construction and engineering company in the United States and the twelfth largest construction contractor worldwide .
generated: This statistic shows the PepsiCo Revenue of the Chinese search engine PepsiCo 's net each Year from 2007 to 2018 . In 2018 , the company generated a total of 66.68 billion U.S. dollars in Revenue . Additional information Founded by the PepsiCo Revenue consists of the PepsiCo Corporation is the largest construction and engineering company in the United States and the twelfth largest construction contractor worldwide .


Example 33:
data: Month|Mark_McGwire|x|bar_chart Number_of_home_runs|363|y|bar_chart Month|Jimmie_Foxx|x|bar_chart Number_of_home_runs|302|y|bar_chart Month|Reggie_Jackson|x|bar_chart Number_of_home_runs|269|y|bar_chart Month|Jose_Canseco|x|bar_chart Number_of_home_runs|254|y|bar_chart Month|Bob_Johnson|x|bar_chart Number_of_home_runs|252|y|bar_chart Month|Eric_Chavez|x|bar_chart Number_of_home_runs|230|y|bar_chart Month|Al_Simmons|x|bar_chart Number_of_home_runs|209|y|bar_chart Month|Jason_Giambi|x|bar_chart Number_of_home_runs|198|y|bar_chart Month|Sal_Bando|x|bar_chart Number_of_home_runs|192|y|bar_chart Month|Gus_Zernial|x|bar_chart Number_of_home_runs|191|y|bar_chart 
title: Oakland Athletics all-time home run leaders 2019

gold: This statistic shows the Oakland Athletics all-time home run leaders as of October 15 , 2019 . Mark McGwire has hit the most home runs in Oakland Athletics franchise history with 363 home runs .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[4] templateTitle[5] as of October 15 , templateTitle[6] . templateXValue[0] templateXValue[0] has hit the most templateYLabel[1] templateYLabel[2] in templateTitle[0] templateTitle[1] franchise history with templateYValue[max] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] worldwide as of January templateTitle[5] , templateTitle[6] . templateXValue[0] templateXValue[0] has hit the most templateYLabel[1] templateYLabel[2] in templateTitle[0] templateTitle[1] franchise history with templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Number of Athletics home runs worldwide as of January leaders , 2019 . Mark_McGwire has hit the most home runs in Oakland Athletics franchise history with 363 home runs .


Example 34:
data: Month|Apr_'15|x|bar_chart Number_of_monthly_video_viewers_in_millions|1000|y|bar_chart Month|Oct_'15|x|bar_chart Number_of_monthly_video_viewers_in_millions|5000|y|bar_chart Month|May_'16|x|bar_chart Number_of_monthly_video_viewers_in_millions|7000|y|bar_chart 
title: BuzzFeed : monthly content views 2015 - 2016

gold: This statistic presents the number of monthly BuzzFeed video viewers in 2013 and 2014 and as of May 2016 . In the most recent reported period , 7 billion viewers accessed content on the social sharing platform per month .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateTitle[0] templateYLabel[2] templateYLabel[3] in 2013 and 2014 and as of templateXValue[last] templateTitle[5] . In the most recent reported period , templateYValue[max] billion templateYLabel[3] accessed templateTitle[2] on the social sharing platform per templateXLabel[0] .

generated_template: templateXValue[0] templateXValue[0] United States , more than templateYValue[max] million people templateTitle[3] templateTitle[2] in the templateTitle[3] templateTitle[4] ( templateTitle[5] ) in templateTitle[6] . The United States is defined as an increase from the previous year . The United States has been adjusted for the last decades , or more than 40 percent of templateTitle[5] .
generated: Apr_'15 United States , more than 7000 million people views content in the views 2015 ( 2016 ) in titleErr . The United States is defined as an increase from the previous year . The United States has been adjusted for the last decades , or more than 40 percent of 2016 .


Example 35:
data: Year|2000|x|line_chart Number_of_robberies|30582|y|line_chart Year|2001|x|line_chart Number_of_robberies|30756|y|line_chart Year|2002|x|line_chart Number_of_robberies|30036|y|line_chart Year|2003|x|line_chart Number_of_robberies|32084|y|line_chart Year|2004|x|line_chart Number_of_robberies|30990|y|line_chart Year|2005|x|line_chart Number_of_robberies|32437|y|line_chart Year|2006|x|line_chart Number_of_robberies|34641|y|line_chart Year|2007|x|line_chart Number_of_robberies|34182|y|line_chart Year|2008|x|line_chart Number_of_robberies|32372|y|line_chart Year|2009|x|line_chart Number_of_robberies|32463|y|line_chart Year|2010|x|line_chart Number_of_robberies|30478|y|line_chart Year|2011|x|line_chart Number_of_robberies|29790|y|line_chart Year|2012|x|line_chart Number_of_robberies|27748|y|line_chart Year|2013|x|line_chart Number_of_robberies|23249|y|line_chart Year|2014|x|line_chart Number_of_robberies|20932|y|line_chart Year|2015|x|line_chart Number_of_robberies|22149|y|line_chart Year|2016|x|line_chart Number_of_robberies|21958|y|line_chart Year|2017|x|line_chart Number_of_robberies|22831|y|line_chart Year|2018|x|line_chart Number_of_robberies|22450|y|line_chart 
title: Canada : number of robberies 2000 - 2018

gold: This statistic shows the number of robberies in Canada from 2000 to 2018 . There were 22,450 robberies in Canada in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . There were templateYValue[18] templateYLabel[1] in templateTitle[0] in templateXValue[max] .

generated_template: The templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] in the United States has decreased since templateXValue[min] , around templateYValue[0] templateYLabel[3] in templateXValue[max] . From templateXValue[min] to templateXValue[1] , the share of alcoholic beverages in the United States has been higher than it in templateXValue[1] , and in templateXValue[min] . The highest templateYLabel[0] templateYLabel[1] templateYLabel[2] decreased in the templateXLabel[0] .
generated: The Number robberies yLabelErr of 2000 2018 in the United States has decreased since 2000 , around 30582 yLabelErr in 2018 . From 2000 to 2001 , the share of alcoholic beverages in the United States has been higher than it in 2001 , and in 2000 . The highest Number robberies yLabelErr decreased in the Year .


Example 36:
data: Year|2018|x|line_chart Per_capita_consumption_in_pounds|23.95|y|line_chart Year|2017|x|line_chart Per_capita_consumption_in_pounds|23.82|y|line_chart Year|2016|x|line_chart Per_capita_consumption_in_pounds|24.07|y|line_chart Year|2015|x|line_chart Per_capita_consumption_in_pounds|22.75|y|line_chart Year|2014|x|line_chart Per_capita_consumption_in_pounds|23.28|y|line_chart Year|2013|x|line_chart Per_capita_consumption_in_pounds|23.97|y|line_chart Year|2012|x|line_chart Per_capita_consumption_in_pounds|23.54|y|line_chart Year|2011|x|line_chart Per_capita_consumption_in_pounds|22.81|y|line_chart Year|2010|x|line_chart Per_capita_consumption_in_pounds|21.59|y|line_chart Year|2009|x|line_chart Per_capita_consumption_in_pounds|20.69|y|line_chart Year|2008|x|line_chart Per_capita_consumption_in_pounds|20.62|y|line_chart Year|2007|x|line_chart Per_capita_consumption_in_pounds|17.94|y|line_chart Year|2006|x|line_chart Per_capita_consumption_in_pounds|21.64|y|line_chart Year|2005|x|line_chart Per_capita_consumption_in_pounds|21.63|y|line_chart Year|2004|x|line_chart Per_capita_consumption_in_pounds|22.7|y|line_chart Year|2003|x|line_chart Per_capita_consumption_in_pounds|23.82|y|line_chart Year|2002|x|line_chart Per_capita_consumption_in_pounds|23.37|y|line_chart Year|2001|x|line_chart Per_capita_consumption_in_pounds|23.93|y|line_chart Year|2000|x|line_chart Per_capita_consumption_in_pounds|23.54|y|line_chart 
title: U.S. per capita consumption of fresh citrus fruit 2000 - 2018

gold: This statistic presents the per capita consumption of fresh citrus fruit in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh citrus fruit amounted to about 23.95 pounds in 2018 .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] templateTitle[6] amounted to about templateYValue[0] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of fresh citrus in the United States from 2000 to 2018 . According to the report , the U.S. Per capita consumption of fresh citrus amounted to approximately 23.95 pounds in 2018 .


Example 37:
data: Year|2002|x|line_chart Revenue_in_billion_U.S._dollars|16.43|y|line_chart Year|2007|x|line_chart Revenue_in_billion_U.S._dollars|21.49|y|line_chart Year|2012|x|line_chart Revenue_in_billion_U.S._dollars|23.63|y|line_chart Year|2017|x|line_chart Revenue_in_billion_U.S._dollars|28.63|y|line_chart 
title: Car rental industry in the U.S. - revenue 2002 - 2017

gold: This statistic represents the U.S. rental car industry 's total revenue between 2002 and 2017 . While the industry 's revenue grew to around 28.6 billion U.S. dollars in 2017 , about 1.59 million vehicles were purchased in 2017 by rental companies in the United States .
gold_template: This statistic represents the templateYLabel[2] templateTitle[1] templateTitle[0] templateTitle[2] 's total templateYLabel[0] between templateXValue[min] and templateXValue[max] . While the templateTitle[2] 's templateYLabel[0] grew to around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] , about 1.59 million vehicles were purchased in templateXValue[max] by templateTitle[1] companies in the templateTitle[3] .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] in the United States templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows Car rental Revenue in the United States revenue 2002 to 2017 . In 2017 , Car rental industry amounted to 28.63 billion U.S. dollars .


Example 38:
data: Financial_Quarter/Year|Q3_'15|x|bar_chart Net_income_in_million_U.S._dollars|3979.0|y|bar_chart Financial_Quarter/Year|Q2_'15|x|bar_chart Net_income_in_million_U.S._dollars|3931.0|y|bar_chart Financial_Quarter/Year|Q1_'15|x|bar_chart Net_income_in_million_U.S._dollars|3515.0|y|bar_chart Financial_Quarter/Year|Q4_'14|x|bar_chart Net_income_in_million_U.S._dollars|4675.0|y|bar_chart Financial_Quarter/Year|Q3_'14|x|bar_chart Net_income_in_million_U.S._dollars|2739.0|y|bar_chart Financial_Quarter/Year|Q2_'14|x|bar_chart Net_income_in_million_U.S._dollars|3351.0|y|bar_chart Financial_Quarter/Year|Q1_'14|x|bar_chart Net_income_in_million_U.S._dollars|3371.0|y|bar_chart Financial_Quarter/Year|Q4_'13|x|bar_chart Net_income_in_million_U.S._dollars|3324.0|y|bar_chart Financial_Quarter/Year|Q3_'13|x|bar_chart Net_income_in_million_U.S._dollars|2921.0|y|bar_chart Financial_Quarter/Year|Q2_'13|x|bar_chart Net_income_in_million_U.S._dollars|3376.0|y|bar_chart Financial_Quarter/Year|Q1_'13|x|bar_chart Net_income_in_million_U.S._dollars|2970.0|y|bar_chart Financial_Quarter/Year|Q4_'12|x|bar_chart Net_income_in_million_U.S._dollars|2886.0|y|bar_chart Financial_Quarter/Year|Q3_'12|x|bar_chart Net_income_in_million_U.S._dollars|2176.0|y|bar_chart Financial_Quarter/Year|Q2_'12|x|bar_chart Net_income_in_million_U.S._dollars|2785.0|y|bar_chart Financial_Quarter/Year|Q1_'12|x|bar_chart Net_income_in_million_U.S._dollars|2890.0|y|bar_chart Financial_Quarter/Year|Q4_'11|x|bar_chart Net_income_in_million_U.S._dollars|2705.0|y|bar_chart Financial_Quarter/Year|Q3_'11|x|bar_chart Net_income_in_million_U.S._dollars|2729.0|y|bar_chart Financial_Quarter/Year|Q2_'11|x|bar_chart Net_income_in_million_U.S._dollars|2505.0|y|bar_chart Financial_Quarter/Year|Q1_'11|x|bar_chart Net_income_in_million_U.S._dollars|1798.0|y|bar_chart Financial_Quarter/Year|Q4_'10|x|bar_chart Net_income_in_million_U.S._dollars|2543.0|y|bar_chart Financial_Quarter/Year|Q3_'10|x|bar_chart Net_income_in_million_U.S._dollars|2167.0|y|bar_chart Financial_Quarter/Year|Q2_'10|x|bar_chart Net_income_in_million_U.S._dollars|1840.0|y|bar_chart Financial_Quarter/Year|Q1_'10|x|bar_chart Net_income_in_million_U.S._dollars|1955.0|y|bar_chart Financial_Quarter/Year|Q4_'09|x|bar_chart Net_income_in_million_U.S._dollars|1974.0|y|bar_chart Financial_Quarter/Year|Q3_'09|x|bar_chart Net_income_in_million_U.S._dollars|1439.0|y|bar_chart Financial_Quarter/Year|Q2_'09|x|bar_chart Net_income_in_million_U.S._dollars|1485.0|y|bar_chart Financial_Quarter/Year|Q1_'09|x|bar_chart Net_income_in_million_U.S._dollars|1423.0|y|bar_chart Financial_Quarter/Year|Q4_'08|x|bar_chart Net_income_in_million_U.S._dollars|382.4|y|bar_chart Financial_Quarter/Year|Q3_'08|x|bar_chart Net_income_in_million_U.S._dollars|1289.9|y|bar_chart Financial_Quarter/Year|Q2_'08|x|bar_chart Net_income_in_million_U.S._dollars|1247.5|y|bar_chart Financial_Quarter/Year|Q1_'08|x|bar_chart Net_income_in_million_U.S._dollars|1307.1|y|bar_chart Financial_Quarter/Year|Q4_'07|x|bar_chart Net_income_in_million_U.S._dollars|1206.4|y|bar_chart Financial_Quarter/Year|Q3_'07|x|bar_chart Net_income_in_million_U.S._dollars|1070.0|y|bar_chart Financial_Quarter/Year|Q2_'07|x|bar_chart Net_income_in_million_U.S._dollars|925.1|y|bar_chart Financial_Quarter/Year|Q1_'07|x|bar_chart Net_income_in_million_U.S._dollars|1002.2|y|bar_chart Financial_Quarter/Year|Q4_'06|x|bar_chart Net_income_in_million_U.S._dollars|1030.72|y|bar_chart Financial_Quarter/Year|Q3_'06|x|bar_chart Net_income_in_million_U.S._dollars|733.36|y|bar_chart Financial_Quarter/Year|Q2_'06|x|bar_chart Net_income_in_million_U.S._dollars|721.08|y|bar_chart Financial_Quarter/Year|Q1_'06|x|bar_chart Net_income_in_million_U.S._dollars|592.29|y|bar_chart Financial_Quarter/Year|Q4_'05|x|bar_chart Net_income_in_million_U.S._dollars|372.21|y|bar_chart Financial_Quarter/Year|Q3_'05|x|bar_chart Net_income_in_million_U.S._dollars|381.18|y|bar_chart Financial_Quarter/Year|Q2_'05|x|bar_chart Net_income_in_million_U.S._dollars|342.81|y|bar_chart Financial_Quarter/Year|Q1_'05|x|bar_chart Net_income_in_million_U.S._dollars|369.19|y|bar_chart Financial_Quarter/Year|Q4_'04|x|bar_chart Net_income_in_million_U.S._dollars|204.1|y|bar_chart Financial_Quarter/Year|Q3_'04|x|bar_chart Net_income_in_million_U.S._dollars|51.98|y|bar_chart Financial_Quarter/Year|Q2_'04|x|bar_chart Net_income_in_million_U.S._dollars|79.06|y|bar_chart Financial_Quarter/Year|Q1_'04|x|bar_chart Net_income_in_million_U.S._dollars|63.97|y|bar_chart Financial_Quarter/Year|Q4_'03|x|bar_chart Net_income_in_million_U.S._dollars|27.25|y|bar_chart Financial_Quarter/Year|Q3_'03|x|bar_chart Net_income_in_million_U.S._dollars|20.43|y|bar_chart Financial_Quarter/Year|Q2_'03|x|bar_chart Net_income_in_million_U.S._dollars|32.17|y|bar_chart Financial_Quarter/Year|Q1_'03|x|bar_chart Net_income_in_million_U.S._dollars|25.8|y|bar_chart 
title: Google : quarterly net income 2003 - 2015

gold: This timeline shows Google 's quarterly net income from 2003 to 2015 . In the third quarter of 2015 , the California-based web company earned 3.98 billion US dollars , an increase from the 3.93 billion in the previous quarter . Due to restructuring under new parent company Alphabet , Google net income is not longer reported separately .
gold_template: This timeline shows templateTitle[0] 's templateTitle[1] templateYLabel[0] templateYLabel[1] from templateTitle[4] to templateTitle[5] . In the third quarter of templateTitle[5] , the California-based web company earned templateYValue[0] billion US templateYLabel[4] , an increase from the templateYValue[1] billion in the previous quarter . Due to restructuring under new parent company Alphabet , templateTitle[0] templateYLabel[0] templateYLabel[1] is not longer reported separately .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitle[7] , templateTitle[8] templateXLabel[0] . In that year , the templateXValue[1] templateXValue[1] was about templateYValue[1] billion templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Google quarterly of net income 2003 2015 titleErr Net income million U.S. dollars in titleErr , titleErr Financial . In that year , the Q2_'15 was about 3931.0 billion U.S. dollars .


Example 39:
data: Month|Aug_19|x|bar_chart Index_value|212.06|y|bar_chart Month|Jul_19|x|bar_chart Index_value|211.63|y|bar_chart Month|Jun_19|x|bar_chart Index_value|210.87|y|bar_chart Month|May_19|x|bar_chart Index_value|209.65|y|bar_chart Month|Apr_19|x|bar_chart Index_value|207.96|y|bar_chart Month|Mar_19|x|bar_chart Index_value|206.06|y|bar_chart Month|Feb_19|x|bar_chart Index_value|204.72|y|bar_chart Month|Jan_19|x|bar_chart Index_value|204.46|y|bar_chart Month|Dec_18|x|bar_chart Index_value|204.94|y|bar_chart Month|Nov_18|x|bar_chart Index_value|205.34|y|bar_chart Month|Oct_18|x|bar_chart Index_value|205.59|y|bar_chart Month|Sep_18|x|bar_chart Index_value|205.6|y|bar_chart Month|Aug_18|x|bar_chart Index_value|205.55|y|bar_chart Month|Jul_18|x|bar_chart Index_value|205.18|y|bar_chart Month|Jun_18|x|bar_chart Index_value|204.27|y|bar_chart Month|May_18|x|bar_chart Index_value|202.66|y|bar_chart Month|Apr_18|x|bar_chart Index_value|200.8|y|bar_chart Month|Mar_18|x|bar_chart Index_value|198.76|y|bar_chart Month|Feb_18|x|bar_chart Index_value|197.08|y|bar_chart Month|Jan_18|x|bar_chart Index_value|196.29|y|bar_chart Month|Dec_17|x|bar_chart Index_value|196.02|y|bar_chart Month|Nov_17|x|bar_chart Index_value|195.61|y|bar_chart Month|Oct_17|x|bar_chart Index_value|195.24|y|bar_chart Month|Sep_17|x|bar_chart Index_value|194.97|y|bar_chart Month|Aug_17|x|bar_chart Index_value|194.5|y|bar_chart Month|Jul_17|x|bar_chart Index_value|193.66|y|bar_chart Month|Jun_17|x|bar_chart Index_value|192.4|y|bar_chart Month|May_17|x|bar_chart Index_value|190.65|y|bar_chart Month|Apr_17|x|bar_chart Index_value|188.65|y|bar_chart Month|Mar_17|x|bar_chart Index_value|186.64|y|bar_chart Month|Feb_17|x|bar_chart Index_value|185.14|y|bar_chart 
title: U.S. housing : Case Shiller National Home Price Index 2017 - 2019

gold: This statistic presents the values of S & P Case Shiller U.S. National Home Price Index from February 2017 to August 2019 . The index value was equal to 100 as of January 2000 , so if the index value is equal to 130 in a given month , for example , it means that the house prices increased by 30 percent since 2000 . The value of the S & P Case Shiller National Home Price Index amounted to 212.06 in August 2019 .
gold_template: This statistic presents the values of S & P templateTitle[2] templateTitle[3] templateTitle[0] templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] from February templateTitle[8] to August templateTitle[9] . The templateYLabel[0] templateYLabel[1] was equal to 100 as of January 2000 , so if the templateYLabel[0] templateYLabel[1] is equal to 130 in a given templateXLabel[0] , for example , it means that the house prices increased by 30 percent since 2000 . The templateYLabel[1] of the S & P templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] amounted to templateYValue[max] in August templateTitle[9] .

generated_template: The statistic shows the monthly templateTitle[3] templateTitle[4] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] in templateTitle[6] between templateTitle[7] and December templateTitle[9] . As of December templateTitle[7] , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] templateXLabel[0] . templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] , including Goldman Sachs , IBM and Walt Disney . templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[5] templateTitle[6] in templateTitle[7] with a monthly basis .
generated: The statistic shows the monthly Shiller National of the housing Case Shiller National in Home in Price between Index and December 2019 . As of December Index , U.S. housing Case Shiller National amounted to 212.06 value yLabelErr in the Home Month . Case Shiller National Index value yLabelErr , including Goldman Sachs , IBM and Walt Disney . Index value yLabelErr Home Price in Index with a monthly basis .


Example 40:
data: Year|2019|x|line_chart Number_of_employees|101958|y|line_chart Year|2018|x|line_chart Number_of_employees|66421|y|line_chart Year|2017|x|line_chart Number_of_employees|50092|y|line_chart Year|2016|x|line_chart Number_of_employees|36450|y|line_chart Year|2015|x|line_chart Number_of_employees|34985|y|line_chart Year|2014|x|line_chart Number_of_employees|22072|y|line_chart Year|2013|x|line_chart Number_of_employees|20674|y|line_chart Year|2012|x|line_chart Number_of_employees|21930|y|line_chart 
title: Alibaba : number of employees 2012 - 2019

gold: Alibaba Group Holding , China 's biggest e-commerce company , had nearly 102 thousand full-time employees by the end of March 2019 . While not the biggest employer in the country , Alibaba is the most valuable listed internet company in China and among the largest ones in terms of revenue . What is Alibaba Group ? The company was founded in Hangzhou , China in 1999 by a group of people led by a former English teacher Jack Ma .
gold_template: templateTitle[0] Group Holding , China 's biggest e-commerce company , had nearly templateYValue[max] thousand full-time templateYLabel[1] by the end of March templateXValue[max] . While not the biggest employer in the country , templateTitle[0] is the most valuable listed internet company in China and among the largest ones in terms of revenue . What is templateTitle[0] Group ? The company was founded in Hangzhou , China in 1999 by a group of people led by a former English teacher Jack Ma .

generated_template: templateTitle[0] templateTitle[1] templateTitle[2] had almost templateYValue[0] templateYLabel[1] in the United States in templateXValue[max] , the highest level of templateTitle[3] group between templateXValue[min] and templateXValue[1] , recorded globally . Between templateXValue[min] and templateXValue[1] , the company templateYLabel[0] of templateYLabel[1] . The company recorded recorded in the U.S. dollars .
generated: Alibaba number employees had almost 101958 employees in the United States in 2019 , the highest level of 2012 group between 2012 and 2018 , recorded globally . Between 2012 and 2018 , the company Number of employees . The company recorded in the U.S. dollars .


Example 41:
data: Year|2022|x|line_chart Number_of_users_in_millions|19.27|y|line_chart Year|2021|x|line_chart Number_of_users_in_millions|18.94|y|line_chart Year|2020|x|line_chart Number_of_users_in_millions|18.58|y|line_chart Year|2019|x|line_chart Number_of_users_in_millions|18.17|y|line_chart Year|2018|x|line_chart Number_of_users_in_millions|17.7|y|line_chart Year|2017|x|line_chart Number_of_users_in_millions|17.19|y|line_chart Year|2016|x|line_chart Number_of_users_in_millions|16.62|y|line_chart Year|2015|x|line_chart Number_of_users_in_millions|15.99|y|line_chart 
title: Number of social network users Australia 2015 - 2022

gold: This statistic shows the number of social network users in Australia from 2015 to 2022 . In 2022 , it is estimated that there will be around 19.27 million social network users in Australia , up from around 17.19 million in 2017 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around templateYValue[max] million templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitle[4] , up from around templateYValue[5] million in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , approximately templateYValue[5] million people were using templateTitle[2] in templateTitle[0] . This figure is projected to grow to templateYValue[max] million in templateXValue[max] .
generated: This statistic shows the Number of network users in Number from 2015 to 2022 . In 2017 , approximately 17.19 million people were using network in Number . This figure is projected to grow to 19.27 million in 2022 .


Example 42:
data: Year|2019|x|line_chart Unemployment_rate|4.87|y|line_chart Year|2018|x|line_chart Unemployment_rate|4.48|y|line_chart Year|2017|x|line_chart Unemployment_rate|4.21|y|line_chart Year|2016|x|line_chart Unemployment_rate|4.34|y|line_chart Year|2015|x|line_chart Unemployment_rate|4.4|y|line_chart Year|2014|x|line_chart Unemployment_rate|4.52|y|line_chart Year|2013|x|line_chart Unemployment_rate|5.28|y|line_chart Year|2012|x|line_chart Unemployment_rate|5.21|y|line_chart Year|2011|x|line_chart Unemployment_rate|6.38|y|line_chart Year|2010|x|line_chart Unemployment_rate|7.83|y|line_chart Year|2009|x|line_chart Unemployment_rate|8.16|y|line_chart Year|2008|x|line_chart Unemployment_rate|6.2|y|line_chart Year|2007|x|line_chart Unemployment_rate|4.89|y|line_chart Year|2006|x|line_chart Unemployment_rate|5.31|y|line_chart Year|2005|x|line_chart Unemployment_rate|5.37|y|line_chart Year|2004|x|line_chart Unemployment_rate|6.41|y|line_chart Year|2003|x|line_chart Unemployment_rate|7.6|y|line_chart Year|2002|x|line_chart Unemployment_rate|7.61|y|line_chart Year|2001|x|line_chart Unemployment_rate|7.44|y|line_chart Year|2000|x|line_chart Unemployment_rate|7.47|y|line_chart Year|1999|x|line_chart Unemployment_rate|7.35|y|line_chart 
title: Unemployment rate in Nicaragua 2019

gold: This statistic shows the unemployment rate in Nicaragua from 1999 to 2019 . In 2019 , the unemployment rate in Nicaragua was at approximately 4.87 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent . templateYLabel[0] in templateTitle[2] 's population The Republic of templateTitle[2] is a sovereign state archipelago in Southeast Asia , composed of more than 17,000 islands .
generated: This statistic shows the Unemployment rate in Nicaragua from 1999 to 2019 . In 2019 , the Unemployment rate in Nicaragua was at approximately 4.87 percent . Unemployment in Nicaragua 's population The Republic of Nicaragua is a sovereign state archipelago in Southeast Asia , composed of more than 17,000 islands .


Example 43:
data: Year|2016|x|line_chart Number_of_internet_users_in_millions|55.86|y|line_chart Year|2015|x|line_chart Number_of_internet_users_in_millions|55.1|y|line_chart Year|2014|x|line_chart Number_of_internet_users_in_millions|53.7|y|line_chart Year|2013|x|line_chart Number_of_internet_users_in_millions|52.3|y|line_chart Year|2012|x|line_chart Number_of_internet_users_in_millions|51.76|y|line_chart Year|2011|x|line_chart Number_of_internet_users_in_millions|48.23|y|line_chart Year|2010|x|line_chart Number_of_internet_users_in_millions|48.65|y|line_chart Year|2009|x|line_chart Number_of_internet_users_in_millions|44.84|y|line_chart Year|2008|x|line_chart Number_of_internet_users_in_millions|44.04|y|line_chart Year|2007|x|line_chart Number_of_internet_users_in_millions|40.95|y|line_chart Year|2006|x|line_chart Number_of_internet_users_in_millions|28.87|y|line_chart Year|2005|x|line_chart Number_of_internet_users_in_millions|26.25|y|line_chart Year|2004|x|line_chart Number_of_internet_users_in_millions|23.82|y|line_chart Year|2003|x|line_chart Number_of_internet_users_in_millions|21.85|y|line_chart Year|2002|x|line_chart Number_of_internet_users_in_millions|18.13|y|line_chart 
title: France : number of internet users 2000 - 2016

gold: This statistic shows the number of people in France who access the Internet at home from 2000 to 2016 . It shows that in 2015 , over 55 million people in France had access to the internet , up from 53.7 million in the previous year .
gold_template: This statistic shows the templateYLabel[0] of people in templateTitle[0] who access the templateYLabel[1] at home from templateTitle[4] to templateXValue[max] . It shows that in templateXValue[1] , over templateYValue[1] million people in templateTitle[0] had access to the templateYLabel[1] , up from templateYValue[2] million in the previous templateXLabel[0] .

generated_template: This statistic shows the templateTitle[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] had an estimated templateYValue[max] million templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the France of internet users in the users from 2002 to 2016 . In 2016 , France had an estimated 55.86 million internet users .


Example 44:
data: State|Minneapolis|x|bar_chart Spending_per_resident_U.S._dollars|346.97|y|bar_chart State|Seattle|x|bar_chart Spending_per_resident_U.S._dollars|268.42|y|bar_chart State|San_Francisco|x|bar_chart Spending_per_resident_U.S._dollars|259.59|y|bar_chart State|Portland|x|bar_chart Spending_per_resident_U.S._dollars|233.99|y|bar_chart State|Arlington|x|bar_chart Spending_per_resident_U.S._dollars|232.59|y|bar_chart State|Irvine|x|bar_chart Spending_per_resident_U.S._dollars|206.12|y|bar_chart State|Washington_D.C.|x|bar_chart Spending_per_resident_U.S._dollars|203.21|y|bar_chart State|St._Paul|x|bar_chart Spending_per_resident_U.S._dollars|201.96|y|bar_chart State|Plano|x|bar_chart Spending_per_resident_U.S._dollars|201.02|y|bar_chart State|Boise|x|bar_chart Spending_per_resident_U.S._dollars|182.04|y|bar_chart State|New_York|x|bar_chart Spending_per_resident_U.S._dollars|176.0|y|bar_chart State|Long_Beach|x|bar_chart Spending_per_resident_U.S._dollars|171.14|y|bar_chart State|Chicago|x|bar_chart Spending_per_resident_U.S._dollars|162.87|y|bar_chart State|Cincinnati|x|bar_chart Spending_per_resident_U.S._dollars|153.37|y|bar_chart State|Aurora|x|bar_chart Spending_per_resident_U.S._dollars|146.33|y|bar_chart 
title: Cities with the highest spending on parks and recreation in the U.S. 2018

gold: This statistic shows the cities with the highest spending per resident on parks and recreation in the United States in 2018 . Seattle , Washington , spent around 268.42 U.S. dollars per resident on parks and recreation in 2018 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[4] and templateTitle[5] in the templateTitle[6] in templateTitle[7] . templateXValue[1] , templateXValue[6] , spent around templateYValue[1] templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] on templateTitle[4] and templateTitle[5] in templateTitle[7] .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] templateYLabel[2] of the United States in templateTitle[6] one templateTitle[3] templateTitle[4] templateXLabel[0] . During the survey , templateYValue[max] percent of the templateYLabel[1] throughout the templateXLabel[0] .
generated: This statistic shows the Spending highest per resident of the United States in 2018 one parks recreation State . During the survey , 346.97 percent of the per throughout the State .


Example 45:
data: Year|'19|x|line_chart Employment_rate|66.6|y|line_chart Year|'18|x|line_chart Employment_rate|66.3|y|line_chart Year|'17|x|line_chart Employment_rate|66|y|line_chart Year|'16|x|line_chart Employment_rate|65.8|y|line_chart Year|'15|x|line_chart Employment_rate|65.3|y|line_chart Year|'14|x|line_chart Employment_rate|64.9|y|line_chart Year|'13|x|line_chart Employment_rate|64.4|y|line_chart Year|'12|x|line_chart Employment_rate|64.4|y|line_chart Year|'11|x|line_chart Employment_rate|63.9|y|line_chart Year|'10|x|line_chart Employment_rate|63.7|y|line_chart Year|'09|x|line_chart Employment_rate|64.5|y|line_chart Year|'08|x|line_chart Employment_rate|68.5|y|line_chart Year|'07|x|line_chart Employment_rate|69.8|y|line_chart Year|'06|x|line_chart Employment_rate|70.1|y|line_chart Year|'05|x|line_chart Employment_rate|69.6|y|line_chart Year|'04|x|line_chart Employment_rate|69.2|y|line_chart Year|'03|x|line_chart Employment_rate|68.9|y|line_chart Year|'02|x|line_chart Employment_rate|69.7|y|line_chart Year|'01|x|line_chart Employment_rate|70.9|y|line_chart Year|'00|x|line_chart Employment_rate|71.9|y|line_chart Year|'99|x|line_chart Employment_rate|71.6|y|line_chart Year|'98|x|line_chart Employment_rate|71.6|y|line_chart Year|'97|x|line_chart Employment_rate|71.3|y|line_chart Year|'96|x|line_chart Employment_rate|70.9|y|line_chart Year|'95|x|line_chart Employment_rate|70.8|y|line_chart Year|'94|x|line_chart Employment_rate|70.4|y|line_chart Year|'93|x|line_chart Employment_rate|70|y|line_chart Year|'92|x|line_chart Employment_rate|69.8|y|line_chart Year|'91|x|line_chart Employment_rate|70.4|y|line_chart Year|'90|x|line_chart Employment_rate|72|y|line_chart 
title: Employment rate of men in the U.S. 1990 - 2019

gold: This statistic shows the employment rate of men in the United States from 1990 to 2019 . In 2019 , this rate was 66.6 percent . The monthly figure of full-time employees in the U.S. can be accessed here .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] in the templateTitle[3] from templateTitle[4] to templateTitle[5] . In templateTitle[5] , this templateYLabel[1] was templateYValue[0] percent . The monthly figure of full-time employees in the templateTitle[3] can be accessed here .

generated_template: Since templateTitle[4] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] in the United States has stayed more or less steady . In templateTitle[4] , the female templateYLabel[0] templateYLabel[1] was templateYValue[29] percent , and in 2018 , the templateYLabel[0] templateYLabel[1] was at templateYValue[0] percent . However , it reached a peak in 2000 at templateYValue[max] percent .
generated: Since 1990 , the Employment rate of men in the United States has stayed more or less steady . In 1990 , the female Employment rate was 72 percent , and in 2018 , the Employment rate was at 66.6 percent . However , it reached a peak in 2000 at 72 percent .


Example 46:
data: Type,_Year,_Location|Heat_wave_1980_Kansas_City_Missouri_St_Loius|x|bar_chart Number_of_fatalities|1260|y|bar_chart Type,_Year,_Location|Heat_wave_1936_Illinois|x|bar_chart Number_of_fatalities|1193|y|bar_chart Type,_Year,_Location|Heat_wave_1995_Missouri_Oklahoma_Illinois|x|bar_chart Number_of_fatalities|670|y|bar_chart Type,_Year,_Location|Heat_wave_1999_Illinois_Missouri_Wisconsin|x|bar_chart Number_of_fatalities|257|y|bar_chart Type,_Year,_Location|Heat_wave_1983|x|bar_chart Number_of_fatalities|188|y|bar_chart Type,_Year,_Location|Heat_wave_1966_St_Louis_Missouri|x|bar_chart Number_of_fatalities|182|y|bar_chart Type,_Year,_Location|Heat_wave_2006_California|x|bar_chart Number_of_fatalities|164|y|bar_chart Type,_Year,_Location|Cold_wave_1963|x|bar_chart Number_of_fatalities|150|y|bar_chart Type,_Year,_Location|Heat_wave_1998_Arizona_Florida_Colorado|x|bar_chart Number_of_fatalities|130|y|bar_chart Type,_Year,_Location|Heat_wave_2012_Washington_DC_Iowa_Virginia_North_Carolina|x|bar_chart Number_of_fatalities|107|y|bar_chart 
title: Fatality numbers from heat waves and cold waves in the U.S.from 1900 - 2016

gold: This statistic shows a list of heat waves and cold waves experienced in the United States from 1900 to 2016 , by the number of fatalities that occurred as a result . In the deadliest event on record , around 1,260 people lost their lives as a result of a heat wave in 1980 in Missouri and Tennessee .
gold_template: This statistic shows a list of templateXValue[0] templateTitle[4] and templateXValue[7] templateTitle[4] experienced in the United States templateTitle[2] templateTitle[8] to templateTitle[9] , by the templateYLabel[0] of templateYLabel[1] that occurred as a result . In the deadliest event on record , around templateYValue[max] people lost their lives as a result of a templateXValue[0] templateXValue[0] in templateXValue[0] in templateXValue[0] and Tennessee .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] due to templateTitle[3] in templateTitle[4] , sorted templateTitle[5] templateXLabel[0] . In templateTitle[4] , there were a total of templateYValue[0] people living in templateXValue[0] .
generated: The statistic shows the Number of fatalities due to heat in waves , sorted cold Type, . In waves , there were a total of 1260 people living in Heat_wave_1980_Kansas_City_Missouri_St_Loius .


Example 47:
data: Month|Dec_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82648|y|bar_chart Month|Nov_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82820|y|bar_chart Month|Oct_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82980|y|bar_chart Month|Sep_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82933|y|bar_chart Month|Aug_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82920|y|bar_chart Month|Jul_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82806|y|bar_chart Month|Jun_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82764|y|bar_chart Month|May_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82649|y|bar_chart Month|Apr_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82546|y|bar_chart Month|Mar_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82536|y|bar_chart Month|Feb_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82471|y|bar_chart Month|Jan_'19|x|bar_chart Amounts_outstanding_in_million_GBP|82318|y|bar_chart Month|Dec_'18|x|bar_chart Amounts_outstanding_in_million_GBP|82117|y|bar_chart Month|Nov_'18|x|bar_chart Amounts_outstanding_in_million_GBP|81945|y|bar_chart Month|Oct_'18|x|bar_chart Amounts_outstanding_in_million_GBP|81817|y|bar_chart Month|Sep_'18|x|bar_chart Amounts_outstanding_in_million_GBP|81812|y|bar_chart Month|Aug_'18|x|bar_chart Amounts_outstanding_in_million_GBP|81704|y|bar_chart Month|Jul_'18|x|bar_chart Amounts_outstanding_in_million_GBP|81698|y|bar_chart Month|Jun_'18|x|bar_chart Amounts_outstanding_in_million_GBP|81637|y|bar_chart Month|May_'18|x|bar_chart Amounts_outstanding_in_million_GBP|81561|y|bar_chart Month|Apr_'18|x|bar_chart Amounts_outstanding_in_million_GBP|81503|y|bar_chart Month|Mar_'18|x|bar_chart Amounts_outstanding_in_million_GBP|81345|y|bar_chart Month|Feb_'18|x|bar_chart Amounts_outstanding_in_million_GBP|81610|y|bar_chart Month|Jan_'18|x|bar_chart Amounts_outstanding_in_million_GBP|81804|y|bar_chart Month|Dec_'17|x|bar_chart Amounts_outstanding_in_million_GBP|81938|y|bar_chart Month|Nov_'17|x|bar_chart Amounts_outstanding_in_million_GBP|82069|y|bar_chart Month|Oct_'17|x|bar_chart Amounts_outstanding_in_million_GBP|82139|y|bar_chart Month|Sep_'17|x|bar_chart Amounts_outstanding_in_million_GBP|82114|y|bar_chart Month|Aug_'17|x|bar_chart Amounts_outstanding_in_million_GBP|82088|y|bar_chart Month|Jul_'17|x|bar_chart Amounts_outstanding_in_million_GBP|82132|y|bar_chart Month|Jun_'17|x|bar_chart Amounts_outstanding_in_million_GBP|82156|y|bar_chart Month|May_'17|x|bar_chart Amounts_outstanding_in_million_GBP|82170|y|bar_chart Month|Apr_'17|x|bar_chart Amounts_outstanding_in_million_GBP|82037|y|bar_chart Month|Mar_'17|x|bar_chart Amounts_outstanding_in_million_GBP|81627|y|bar_chart Month|Feb_'17|x|bar_chart Amounts_outstanding_in_million_GBP|81526|y|bar_chart Month|Jan_'17|x|bar_chart Amounts_outstanding_in_million_GBP|81210|y|bar_chart 
title: Amounts outstanding of notes and coin in circulation in the UK 2017 - 2019

gold: As of December 2019 , the value of outstanding notes and coins in circulation in the United Kingdom reached approximately 82.65 billion British pounds . This was an increase of over 1.4 billion British pounds as compared to January 2017 . When broken down by denomination , the twenty-pound note accounted for the highest share of notes in circulation .
gold_template: As of December templateTitle[7] , the value of templateYLabel[1] templateTitle[2] and coins in templateTitle[4] in the United Kingdom reached approximately templateYValue[0] billion British pounds . This was an increase of over 1.4 billion British pounds as compared to January templateTitle[6] . When broken down by denomination , the twenty-pound note accounted for the highest share of templateTitle[2] in templateTitle[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from January templateTitle[7] to templateTitle[8] . templateYLabel[1] templateYValue[0] percent of the templateTitle[3] templateTitle[4] templateTitle[5] .
generated: This statistic shows the Amounts outstanding notes of coin circulation UK in the United States from January 2019 to titleErr . outstanding 82648 percent of the coin circulation UK .


Example 48:
data: Payment_Method|Android_Pay|x|bar_chart Share_of_respondents|18|y|bar_chart Payment_Method|Retailer_mobile_app|x|bar_chart Share_of_respondents|12|y|bar_chart Payment_Method|Apple_Pay|x|bar_chart Share_of_respondents|11|y|bar_chart Payment_Method|Samsung_Pay|x|bar_chart Share_of_respondents|3|y|bar_chart Payment_Method|MasterPass|x|bar_chart Share_of_respondents|2|y|bar_chart Payment_Method|CurrenC|x|bar_chart Share_of_respondents|1|y|bar_chart Payment_Method|Windows_Phone_Wallet|x|bar_chart Share_of_respondents|1|y|bar_chart 
title: U.S. mobile payment app usage 2016

gold: This statistic presents the most commonly used mobile payment apps according to a survey of internet users carried out in February 2016 in the United States . During the survey period , 11 percent of respondents reported having used Apple Pay in the previous year .
gold_template: This statistic presents the most commonly used templateXValue[1] templateXLabel[0] apps according to a survey of internet users carried out in February templateTitle[5] in the templateTitle[0] . During the survey period , templateYValue[2] percent of templateYLabel[1] reported having used templateXValue[2] templateXValue[0] in the previous year .

generated_template: This statistic presents the results of a survey conducted in the United States in templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated they used templateXValue[0] templateXValue[0] templateXValue[0] or templateXValue[1] .
generated: This statistic presents the results of a survey conducted in the United States in 2016 titleErr titleErr titleErr . During the survey period , 18 percent of respondents stated they used Android_Pay Android_Pay or Retailer_mobile_app .


Example 49:
data: Year|18/19|x|line_chart Revenue_in_million_U.S._dollars|258|y|line_chart Year|17/18|x|line_chart Revenue_in_million_U.S._dollars|243|y|line_chart Year|16/17|x|line_chart Revenue_in_million_U.S._dollars|221|y|line_chart Year|15/16|x|line_chart Revenue_in_million_U.S._dollars|164|y|line_chart Year|14/15|x|line_chart Revenue_in_million_U.S._dollars|146|y|line_chart Year|13/14|x|line_chart Revenue_in_million_U.S._dollars|142|y|line_chart Year|12/13|x|line_chart Revenue_in_million_U.S._dollars|131|y|line_chart Year|11/12|x|line_chart Revenue_in_million_U.S._dollars|111|y|line_chart Year|10/11|x|line_chart Revenue_in_million_U.S._dollars|120|y|line_chart Year|09/10|x|line_chart Revenue_in_million_U.S._dollars|121|y|line_chart Year|08/09|x|line_chart Revenue_in_million_U.S._dollars|118|y|line_chart Year|07/08|x|line_chart Revenue_in_million_U.S._dollars|119|y|line_chart Year|06/07|x|line_chart Revenue_in_million_U.S._dollars|114|y|line_chart Year|05/06|x|line_chart Revenue_in_million_U.S._dollars|96|y|line_chart Year|04/05|x|line_chart Revenue_in_million_U.S._dollars|91|y|line_chart Year|03/04|x|line_chart Revenue_in_million_U.S._dollars|88|y|line_chart Year|02/03|x|line_chart Revenue_in_million_U.S._dollars|85|y|line_chart Year|01/02|x|line_chart Revenue_in_million_U.S._dollars|87|y|line_chart 
title: Utah Jazz 's revenue 2001 - 2019

gold: The statistic shows the revenue of the Utah Jazz franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 258 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Utah Jazz franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 258 million U.S. dollars .


Example 50:
data: Quarter|Q1_2012|x|bar_chart Viewers_in_thousands|10610|y|bar_chart Quarter|Q2_2012|x|bar_chart Viewers_in_thousands|9638|y|bar_chart Quarter|Q3_2012|x|bar_chart Viewers_in_thousands|10353|y|bar_chart Quarter|Q4_2012|x|bar_chart Viewers_in_thousands|9702|y|bar_chart Quarter|Q1_2013|x|bar_chart Viewers_in_thousands|9677|y|bar_chart Quarter|Q2_2013|x|bar_chart Viewers_in_thousands|10047|y|bar_chart Quarter|Q3_2013|x|bar_chart Viewers_in_thousands|10609|y|bar_chart Quarter|Q4_2013|x|bar_chart Viewers_in_thousands|11487|y|bar_chart Quarter|Q1_2014|x|bar_chart Viewers_in_thousands|9967|y|bar_chart Quarter|Q2_2014|x|bar_chart Viewers_in_thousands|10947|y|bar_chart Quarter|Q3_2014|x|bar_chart Viewers_in_thousands|9674|y|bar_chart Quarter|Q4_2014|x|bar_chart Viewers_in_thousands|9917|y|bar_chart Quarter|Q1_2015|x|bar_chart Viewers_in_thousands|10647|y|bar_chart Quarter|Q2_2015|x|bar_chart Viewers_in_thousands|9856|y|bar_chart Quarter|Q3_2015|x|bar_chart Viewers_in_thousands|9509|y|bar_chart Quarter|Q4_2015|x|bar_chart Viewers_in_thousands|8351|y|bar_chart Quarter|Q1_2016|x|bar_chart Viewers_in_thousands|10631|y|bar_chart Quarter|Q2_2016|x|bar_chart Viewers_in_thousands|10279|y|bar_chart Quarter|Q3_2016|x|bar_chart Viewers_in_thousands|10201|y|bar_chart Quarter|Q4_2016|x|bar_chart Viewers_in_thousands|9717|y|bar_chart Quarter|Q1_2017|x|bar_chart Viewers_in_thousands|8838|y|bar_chart Quarter|Q2_2017|x|bar_chart Viewers_in_thousands|8761|y|bar_chart Quarter|Q3_2017|x|bar_chart Viewers_in_thousands|8738|y|bar_chart Quarter|Q4_2017|x|bar_chart Viewers_in_thousands|8188|y|bar_chart Quarter|Q1_2018|x|bar_chart Viewers_in_thousands|8011|y|bar_chart Quarter|Q2_2018|x|bar_chart Viewers_in_thousands|7629|y|bar_chart Quarter|Q3_2018|x|bar_chart Viewers_in_thousands|8344|y|bar_chart Quarter|Q4_2018|x|bar_chart Viewers_in_thousands|6833|y|bar_chart Quarter|Q1_2019|x|bar_chart Viewers_in_thousands|6482|y|bar_chart Quarter|Q2_2019|x|bar_chart Viewers_in_thousands|5960|y|bar_chart Quarter|Q3_2019|x|bar_chart Viewers_in_thousands|6890|y|bar_chart 
title: MTV viewers reached quarterly in the United Kingdom ( UK ) Q1 2012-Q3 2019

gold: This statistic shows the quarterly reach of MTV in the United Kingdom ( UK ) from the first quarter of 2012 to the third quarter of 2019 . In the first quarter of 2016 , MTV reached 10.63 million viewers . In the second quarter of 2019 , MTV had its lowest reach during the period in consideration at nearly six million viewers .
gold_template: This statistic shows the templateTitle[3] reach of templateTitle[0] in the templateTitle[4] templateTitle[5] ( templateTitle[6] ) from the first templateXLabel[0] of templateXValue[0] to the third templateXLabel[0] of templateXValue[28] . In the first templateXLabel[0] of templateXValue[16] , templateTitle[0] templateTitle[2] templateYValue[16] million templateYLabel[0] . In the second templateXLabel[0] of templateXValue[28] , templateTitle[0] had its lowest reach during the period in consideration at nearly templateYValue[28] million templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitle[3] of templateTitle[4] templateTitle[5] templateTitle[6] at an increase of templateYValue[max] percent in the third templateXLabel[0] of templateXValue[0] , according to templateYValue[max] percent recorded in the United States between the first templateXLabel[0] of templateXValue[0] templateXValue[0] . The first templateXLabel[0] of templateXValue[0] , California templateYLabel[1] was calculated by the number of templateXValue[0] templateXValue[0] .
generated: This statistic shows the MTV viewers Viewers thousands quarterly of United Kingdom UK at an increase of 11487 percent in the third Quarter of Q1_2012 , according to 11487 percent recorded in the United States between the first Quarter of Q1_2012 . The first Quarter of Q1_2012 , California thousands was calculated by the number of Q1_2012 .


Example 51:
data: Frequency|Every_week|x|bar_chart Share_of_respondents|23|y|bar_chart Frequency|Almost_every_week|x|bar_chart Share_of_respondents|10|y|bar_chart Frequency|About_once_a_month|x|bar_chart Share_of_respondents|12|y|bar_chart Frequency|Seldom|x|bar_chart Share_of_respondents|24|y|bar_chart Frequency|Never|x|bar_chart Share_of_respondents|29|y|bar_chart Frequency|No_opinion|x|bar_chart Share_of_respondents|3|y|bar_chart 
title: Church attendance of Americans 2019

gold: According to a 2019 survey , 29 percent of Americans never attend church or synagogue , compared to 23 percent of Americans who attend every week . Religiosity in the United States Despite only about a fifth of Americans attending church or synagogue on a weekly basis , almost 40 percent consider themselves to be very religious . Additionally , states in the Deep South such as Mississippi , Alabama , and Louisiana had the most residents identifying as very religious .
gold_template: According to a templateTitle[3] survey , templateYValue[max] percent of templateTitle[2] templateXValue[4] attend templateTitle[0] or synagogue , compared to templateYValue[0] percent of templateTitle[2] who attend templateXValue[0] templateXValue[0] . Religiosity in the United States Despite only templateXValue[2] a fifth of templateTitle[2] attending templateTitle[0] or synagogue on a weekly basis , templateXValue[1] 40 percent consider themselves to be very religious . Additionally , states in the Deep South such as Mississippi , Alabama , and Louisiana had the most residents identifying as very religious .

generated_template: This statistic provides information on the results of a survey conducted in the United States as of February templateTitle[5] , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During the survey period of time , templateYValue[max] percent of templateYLabel[1] stated they used templateXValue[0] or templateXValue[0] .
generated: This statistic provides information on the results of a survey conducted in the United States as of February titleErr , sorted titleErr titleErr titleErr . During the survey period of time , 29 percent of respondents stated they used Every_week or Every_week .


Example 52:
data: Year|2018|x|line_chart Number_of_hospitals|174|y|line_chart Year|2017|x|line_chart Number_of_hospitals|175|y|line_chart Year|2016|x|line_chart Number_of_hospitals|177|y|line_chart Year|2015|x|line_chart Number_of_hospitals|178|y|line_chart Year|2014|x|line_chart Number_of_hospitals|187|y|line_chart Year|2013|x|line_chart Number_of_hospitals|191|y|line_chart Year|2012|x|line_chart Number_of_hospitals|192|y|line_chart Year|2011|x|line_chart Number_of_hospitals|195|y|line_chart Year|2010|x|line_chart Number_of_hospitals|198|y|line_chart Year|2009|x|line_chart Number_of_hospitals|203|y|line_chart Year|2008|x|line_chart Number_of_hospitals|209|y|line_chart Year|2007|x|line_chart Number_of_hospitals|210|y|line_chart Year|2006|x|line_chart Number_of_hospitals|215|y|line_chart Year|2005|x|line_chart Number_of_hospitals|216|y|line_chart Year|2004|x|line_chart Number_of_hospitals|214|y|line_chart Year|2003|x|line_chart Number_of_hospitals|218|y|line_chart Year|2002|x|line_chart Number_of_hospitals|219|y|line_chart Year|2001|x|line_chart Number_of_hospitals|225|y|line_chart Year|2000|x|line_chart Number_of_hospitals|228|y|line_chart 
title: Hospitals in Belgium 2000 - 2018

gold: Since 2000 , the number of hospitals in Belgium has declined nearly year-on-year . There were 228 hospitals in Belgium in the year 2000 and by 2018 this figure had fallen to 174 . This is a drop of over 23 percent in the provided time period .
gold_template: Since templateXValue[min] , the templateYLabel[0] of templateYLabel[1] in templateTitle[1] has declined nearly year-on-year . There were templateYValue[max] templateYLabel[1] in templateTitle[1] in the templateXLabel[0] templateXValue[min] and by templateXValue[max] this figure had fallen to templateYValue[min] . This is a drop of over 23 percent in the provided time period .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] . The templateYLabel[0] of hospital beds has been dropping also , from 571,600 in templateXValue[min] to 497,200 recorded in templateXValue[max] .
generated: The statistic shows the Number of hospitals in 2000 2018 titleErr from 2000 to 2018 . In 2018 , there were 228 hospitals in 2000 2018 titleErr . The Number of hospital beds has been dropping also , from 571,600 in 2000 to 497,200 recorded in 2018 .


Example 53:
data: Quarter|Q4_'16|x|bar_chart Number_of_mobile-only_users_in_millions|1149|y|bar_chart Quarter|Q3_'16|x|bar_chart Number_of_mobile-only_users_in_millions|1055|y|bar_chart Quarter|Q2_'16|x|bar_chart Number_of_mobile-only_users_in_millions|967|y|bar_chart Quarter|Q1_'16|x|bar_chart Number_of_mobile-only_users_in_millions|894|y|bar_chart Quarter|Q4_'15|x|bar_chart Number_of_mobile-only_users_in_millions|823|y|bar_chart Quarter|Q3_'15|x|bar_chart Number_of_mobile-only_users_in_millions|727|y|bar_chart Quarter|Q2_'15|x|bar_chart Number_of_mobile-only_users_in_millions|655|y|bar_chart Quarter|Q1_'15|x|bar_chart Number_of_mobile-only_users_in_millions|581|y|bar_chart Quarter|Q4_'14|x|bar_chart Number_of_mobile-only_users_in_millions|526|y|bar_chart Quarter|Q3_'14|x|bar_chart Number_of_mobile-only_users_in_millions|456|y|bar_chart Quarter|Q2_'14|x|bar_chart Number_of_mobile-only_users_in_millions|399|y|bar_chart Quarter|Q1_'14|x|bar_chart Number_of_mobile-only_users_in_millions|341|y|bar_chart Quarter|Q4_'13|x|bar_chart Number_of_mobile-only_users_in_millions|296|y|bar_chart Quarter|Q3_'13|x|bar_chart Number_of_mobile-only_users_in_millions|254|y|bar_chart Quarter|Q2_'13|x|bar_chart Number_of_mobile-only_users_in_millions|219|y|bar_chart Quarter|Q1_'13|x|bar_chart Number_of_mobile-only_users_in_millions|189|y|bar_chart Quarter|Q4_'12|x|bar_chart Number_of_mobile-only_users_in_millions|157|y|bar_chart Quarter|Q3_'12|x|bar_chart Number_of_mobile-only_users_in_millions|126|y|bar_chart Quarter|Q2_'12|x|bar_chart Number_of_mobile-only_users_in_millions|102|y|bar_chart Quarter|Q1_'12|x|bar_chart Number_of_mobile-only_users_in_millions|83|y|bar_chart Quarter|Q4_'11|x|bar_chart Number_of_mobile-only_users_in_millions|58|y|bar_chart 
title: Number of mobile-only monthly active Facebook users 2011 - 2016

gold: The timeline shows the number of mobile-only monthly active Facebook users . As of the fourth quarter of 2016 , 1,149 million Facebook users accessed the social media site exclusively via mobile device every month , up from 823 million users in the corresponding quarter of the previous year .
gold_template: The timeline shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[2] . As of the fourth templateXLabel[0] of templateTitle[7] , templateYValue[max] million templateTitle[4] templateYLabel[2] accessed the social media site exclusively via mobile device every month , up from templateYValue[4] million templateYLabel[2] in the corresponding templateXLabel[0] of the previous year .

generated_template: In the fourth templateXLabel[0] of templateTitle[6] , there were templateYValue[max] million templateYLabel[1] in the United States , up from templateYValue[1] million in the preceding templateXLabel[0] . The company 's templateYLabel[0] templateYLabel[1] compared to the previous templateXLabel[0] . Despite the number of templateYLabel[1] templateYLabel[2] in the United States has become one of the most expensive in the world , accounting for the United States .
generated: In the fourth Quarter of 2011 , there were 1149 million mobile-only in the United States , up from 1055 million in the preceding Quarter . The company 's Number mobile-only compared to the previous Quarter . Despite the number of mobile-only users in the United States has become one of the most expensive in the world , accounting for the United States .


Example 54:
data: Response|Slot_machines|x|bar_chart Share_of_respondents|48|y|bar_chart Response|Black_Jack|x|bar_chart Share_of_respondents|16|y|bar_chart Response|Poker|x|bar_chart Share_of_respondents|6|y|bar_chart Response|Roulette|x|bar_chart Share_of_respondents|5|y|bar_chart Response|Video_poker|x|bar_chart Share_of_respondents|4|y|bar_chart Response|Craps|x|bar_chart Share_of_respondents|4|y|bar_chart Response|Sports_book|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Baccarat|x|bar_chart Share_of_respondents|1|y|bar_chart Response|No_favorite|x|bar_chart Share_of_respondents|14|y|bar_chart 
title: Most popular games with casino visitors in the U.S. in 2014

gold: This statistic shows the most popular games with casino visitors in the United States as of May 2014 . During the survey , six percent of respondents said that poker was their favorite game to play at casinos .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of May templateTitle[7] . During the survey , templateYValue[2] percent of templateYLabel[1] said that templateXValue[2] was their templateXValue[last] game to play at casinos .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[10] on templateXValue[0] templateTitle[2] templateTitle[3] in templateTitle[4] templateTitle[5] . templateYValue[max] percent of templateYLabel[1] said that they planned to visit a templateXValue[1] of templateXValue[0] recommendations.Eyewear in the United States .
generated: This statistic shows the results of a survey conducted in the United States in titleErr on Slot_machines games casino in visitors U.S. . 48 percent of respondents said that they planned to visit a Black_Jack of Slot_machines recommendations.Eyewear in the United States .


Example 55:
data: Year|2018|x|line_chart Number_of_employees|76032|y|line_chart Year|2017|x|line_chart Number_of_employees|41900|y|line_chart Year|2016|x|line_chart Number_of_employees|50900|y|line_chart Year|2015|x|line_chart Number_of_employees|49500|y|line_chart Year|2014|x|line_chart Number_of_employees|30600|y|line_chart Year|2013|x|line_chart Number_of_employees|30000|y|line_chart Year|2012|x|line_chart Number_of_employees|29600|y|line_chart Year|2011|x|line_chart Number_of_employees|29400|y|line_chart 
title: Becton , Dickinson , and Company 's employees from 2011 - 2018

gold: The statistic shows the number of employees of Becton , Dickinson , and Company for the fiscal years 2011 to 2018 . The number of employees at Becton , Dickinson , and Company reached a high in 2016 with 50,900 employed at the company that year .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitle[0] , templateTitle[1] , and templateTitle[2] for the fiscal years templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] at templateTitle[0] , templateTitle[1] , and templateTitle[2] reached a high in templateXValue[2] with templateYValue[2] employed at the templateTitle[2] that templateXLabel[0] .

generated_template: templateTitle[0] templateTitle[1] templateTitle[2] had almost templateYValue[max] templateYLabel[1] U.S. dollars in templateXValue[1] , the United States in templateXValue[max] , a slight increase compared to the previous templateXLabel[0] . The company employed templateYValue[0] people in templateXValue[max] . templateTitle[1] templateTitle[0] templateTitle[1] was founded in 1969 and is a large increase in recent years .
generated: Becton Dickinson Company had almost 76032 employees U.S. dollars in 2017 , the United States in 2018 , a slight increase compared to the previous Year . The company employed 76032 people in 2018 . Dickinson Becton Dickinson was founded in 1969 and is a large increase in recent years .


Example 56:
data: Year|2018|x|line_chart Number_of_live_births|55120|y|line_chart Year|2017|x|line_chart Number_of_live_births|56633|y|line_chart Year|2016|x|line_chart Number_of_live_births|58890|y|line_chart Year|2015|x|line_chart Number_of_live_births|59058|y|line_chart Year|2014|x|line_chart Number_of_live_births|59084|y|line_chart Year|2013|x|line_chart Number_of_live_births|58995|y|line_chart Year|2012|x|line_chart Number_of_live_births|60255|y|line_chart Year|2011|x|line_chart Number_of_live_births|60220|y|line_chart Year|2010|x|line_chart Number_of_live_births|61442|y|line_chart Year|2009|x|line_chart Number_of_live_births|61807|y|line_chart Year|2008|x|line_chart Number_of_live_births|60497|y|line_chart 
title: Number of births in Norway 2008 - 2018

gold: This statistic shows the number of live births in Norway from 2008 to 2018 . The number of births has declined during the period , from the peak of roughly 61.8 thousand in 2009 to around 55.1 thousand in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitle[2] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[2] has declined during the period , from the peak of roughly templateYValue[max] thousand in templateXValue[9] to around templateYValue[min] thousand in templateXValue[max] .

generated_template: There were templateYValue[0] templateYLabel[1] templateYLabel[2] in the templateTitle[2] in templateXValue[max] , a slight decrease from the templateXLabel[0] before . templateTitle[2] is the highest with templateYValue[max] templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the past six years , the share of templateYLabel[1] templateYLabel[2] in templateXValue[6] , the level of templateYLabel[1] templateYLabel[2] decreased since templateXValue[6] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateYLabel[1] templateYLabel[2] in templateTitle[2] was templateYValue[min] percent in templateXValue[max] compared to templateYValue[0] percent , the highest level .
generated: There were 55120 live births in the Norway in 2018 , a slight decrease from the Year before . Norway is the highest with 61807 Number of live births in the past six years , the share of live births in 2012 , the level of live births decreased since 2012 , the Number of live births in live births in Norway was 55120 percent in 2018 compared to 55120 percent , the highest level .


Example 57:
data: Fiscal_year|2019|x|bar_chart Expenditure_in_million_U.S._dollars|1842|y|bar_chart Fiscal_year|2018|x|bar_chart Expenditure_in_million_U.S._dollars|1667|y|bar_chart Fiscal_year|2017|x|bar_chart Expenditure_in_million_U.S._dollars|1486|y|bar_chart Fiscal_year|2016|x|bar_chart Expenditure_in_million_U.S._dollars|1714|y|bar_chart Fiscal_year|2015|x|bar_chart Expenditure_in_million_U.S._dollars|2338|y|bar_chart Fiscal_year|2014|x|bar_chart Expenditure_in_million_U.S._dollars|2197|y|bar_chart Fiscal_year|2013|x|bar_chart Expenditure_in_million_U.S._dollars|1956|y|bar_chart 
title: HPE : research and development spending 2013 - 2019

gold: This statistic shows Hewlett Packard Enterprise 's ( previously a part of Hewlett-Packard Company ) expenditure on research and development for each fiscal year from 2013 to 2019 . In 2019 , HPE 's R & D expenditure came to 1.84 billion U.S. dollars . This represented a small portion of HPE 's net revenue , which reached 29.1 billion U.S. dollars .
gold_template: This statistic shows Hewlett Packard Enterprise 's ( previously a part of Hewlett-Packard Company ) templateYLabel[0] on templateTitle[1] and templateTitle[2] for each templateXLabel[0] templateXLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] 's R & D templateYLabel[0] came to templateYValue[0] billion templateYLabel[2] templateYLabel[3] . This represented a small portion of templateTitle[0] 's net revenue , which reached 29.1 billion templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was about templateYValue[1] billion templateYLabel[2] templateYLabel[3] . templateTitle[0] Company 's research and development development .
generated: The statistic depicts the Expenditure of research development spending 2013 worldwide from 2013 to 2019 . In 2018 , HPE research development spending 2013 was about 1667 billion U.S. dollars . HPE Company 's research and development .


Example 58:
data: Year|2024|x|line_chart GDP_per_capita_in_U.S._dollars|8096.64|y|line_chart Year|2023|x|line_chart GDP_per_capita_in_U.S._dollars|7729.02|y|line_chart Year|2022|x|line_chart GDP_per_capita_in_U.S._dollars|7381.73|y|line_chart Year|2021|x|line_chart GDP_per_capita_in_U.S._dollars|7053.53|y|line_chart Year|2020|x|line_chart GDP_per_capita_in_U.S._dollars|6744.01|y|line_chart Year|2019|x|line_chart GDP_per_capita_in_U.S._dollars|6508.13|y|line_chart Year|2018|x|line_chart GDP_per_capita_in_U.S._dollars|6641.51|y|line_chart Year|2017|x|line_chart GDP_per_capita_in_U.S._dollars|6325.49|y|line_chart Year|2016|x|line_chart GDP_per_capita_in_U.S._dollars|5799.62|y|line_chart Year|2015|x|line_chart GDP_per_capita_in_U.S._dollars|6088.68|y|line_chart Year|2014|x|line_chart GDP_per_capita_in_U.S._dollars|7998.84|y|line_chart Year|2013|x|line_chart GDP_per_capita_in_U.S._dollars|8103.49|y|line_chart Year|2012|x|line_chart GDP_per_capita_in_U.S._dollars|7950.38|y|line_chart Year|2011|x|line_chart GDP_per_capita_in_U.S._dollars|7264.11|y|line_chart Year|2010|x|line_chart GDP_per_capita_in_U.S._dollars|6285.2|y|line_chart Year|2009|x|line_chart GDP_per_capita_in_U.S._dollars|5170.53|y|line_chart Year|2008|x|line_chart GDP_per_capita_in_U.S._dollars|5444.82|y|line_chart Year|2007|x|line_chart GDP_per_capita_in_U.S._dollars|4684.01|y|line_chart Year|2006|x|line_chart GDP_per_capita_in_U.S._dollars|3717.55|y|line_chart Year|2005|x|line_chart GDP_per_capita_in_U.S._dollars|3384.61|y|line_chart Year|2004|x|line_chart GDP_per_capita_in_U.S._dollars|2737.58|y|line_chart Year|2003|x|line_chart GDP_per_capita_in_U.S._dollars|2240.21|y|line_chart Year|2002|x|line_chart GDP_per_capita_in_U.S._dollars|2347.51|y|line_chart Year|2001|x|line_chart GDP_per_capita_in_U.S._dollars|2383.31|y|line_chart Year|2000|x|line_chart GDP_per_capita_in_U.S._dollars|2455.11|y|line_chart Year|1999|x|line_chart GDP_per_capita_in_U.S._dollars|2588.92|y|line_chart Year|1998|x|line_chart GDP_per_capita_in_U.S._dollars|2992.93|y|line_chart Year|1997|x|line_chart GDP_per_capita_in_U.S._dollars|3291.58|y|line_chart Year|1996|x|line_chart GDP_per_capita_in_U.S._dollars|3037.63|y|line_chart Year|1995|x|line_chart GDP_per_capita_in_U.S._dollars|2939.1|y|line_chart Year|1994|x|line_chart GDP_per_capita_in_U.S._dollars|2640.39|y|line_chart Year|1993|x|line_chart GDP_per_capita_in_U.S._dollars|2139.18|y|line_chart Year|1992|x|line_chart GDP_per_capita_in_U.S._dollars|1923.58|y|line_chart Year|1991|x|line_chart GDP_per_capita_in_U.S._dollars|1658.07|y|line_chart Year|1990|x|line_chart GDP_per_capita_in_U.S._dollars|1637.5|y|line_chart Year|1989|x|line_chart GDP_per_capita_in_U.S._dollars|1640.39|y|line_chart Year|1988|x|line_chart GDP_per_capita_in_U.S._dollars|1660.55|y|line_chart Year|1987|x|line_chart GDP_per_capita_in_U.S._dollars|1572.58|y|line_chart Year|1986|x|line_chart GDP_per_capita_in_U.S._dollars|1542.36|y|line_chart Year|1985|x|line_chart GDP_per_capita_in_U.S._dollars|1572.19|y|line_chart Year|1984|x|line_chart GDP_per_capita_in_U.S._dollars|1711.86|y|line_chart 
title: Gross domestic product ( GDP ) per capita in Colombia 2024

gold: The statistic shows gross domestic product ( GDP ) per capita in Colombia from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services templateYLabel[2] in a country in a templateXLabel[0] . templateYLabel[0] templateYLabel[1] templateYLabel[2] of a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth growth .
generated: The statistic shows Gross domestic product ( GDP ) per capita in Colombia 2024 from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services capita in a country in a Year . GDP per capita of a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .


Example 59:
data: Year|2024|x|line_chart GDP_growth_compared_to_previous_year|4.53|y|line_chart Year|2023|x|line_chart GDP_growth_compared_to_previous_year|4.42|y|line_chart Year|2022|x|line_chart GDP_growth_compared_to_previous_year|4.3|y|line_chart Year|2021|x|line_chart GDP_growth_compared_to_previous_year|4.13|y|line_chart Year|2020|x|line_chart GDP_growth_compared_to_previous_year|3.69|y|line_chart Year|2019|x|line_chart GDP_growth_compared_to_previous_year|2.66|y|line_chart Year|2018|x|line_chart GDP_growth_compared_to_previous_year|2.96|y|line_chart Year|2017|x|line_chart GDP_growth_compared_to_previous_year|4.2|y|line_chart Year|2016|x|line_chart GDP_growth_compared_to_previous_year|1.06|y|line_chart Year|2015|x|line_chart GDP_growth_compared_to_previous_year|4.55|y|line_chart Year|2014|x|line_chart GDP_growth_compared_to_previous_year|2.67|y|line_chart 
title: Gross domestic product ( GDP ) growth rate in Morocco 2024*

gold: The statistic shows the growth in real GDP in Morocco from 2014 to 2018 , with projections up until 2024 . In 2018 , Morocco 's real gross domestic product increased by around 2.96 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Morocco from 2014 to 2018 , with projections up until 2024 . In 2018 , Morocco 's real Gross domestic product grew by around 2.96 percent compared to the previous Year .


Example 60:
data: Year|2020|x|line_chart Net_profit_in_billion_U.S._dollars|29.3|y|line_chart Year|2019|x|line_chart Net_profit_in_billion_U.S._dollars|25.9|y|line_chart Year|2018|x|line_chart Net_profit_in_billion_U.S._dollars|27.3|y|line_chart Year|2017|x|line_chart Net_profit_in_billion_U.S._dollars|37.6|y|line_chart Year|2016|x|line_chart Net_profit_in_billion_U.S._dollars|34.2|y|line_chart Year|2015|x|line_chart Net_profit_in_billion_U.S._dollars|36.0|y|line_chart Year|2014|x|line_chart Net_profit_in_billion_U.S._dollars|13.8|y|line_chart Year|2013|x|line_chart Net_profit_in_billion_U.S._dollars|10.7|y|line_chart Year|2012|x|line_chart Net_profit_in_billion_U.S._dollars|9.2|y|line_chart Year|2011|x|line_chart Net_profit_in_billion_U.S._dollars|8.3|y|line_chart Year|2010|x|line_chart Net_profit_in_billion_U.S._dollars|17.3|y|line_chart Year|2009|x|line_chart Net_profit_in_billion_U.S._dollars|-4.6|y|line_chart Year|2008|x|line_chart Net_profit_in_billion_U.S._dollars|-26.1|y|line_chart Year|2007|x|line_chart Net_profit_in_billion_U.S._dollars|14.7|y|line_chart Year|2006|x|line_chart Net_profit_in_billion_U.S._dollars|5.0|y|line_chart 
title: Net profit of airlines worldwide 2006 - 2020

gold: This statistic shows the net profit of commercial airlines worldwide from 2006 to 2019 . In 2019 , the net profit of commercial airlines is projected to reach around 28 billion U.S. dollars . Airline industry In 2018 , the U.S. airline industry generated total operating revenue of almost 240 billion U.S. dollars , making the United States one of the largest markets for the airline industry worldwide .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of commercial templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[1] . In templateXValue[1] , the templateYLabel[0] templateYLabel[1] of commercial templateTitle[2] is projected to reach around 28 templateYLabel[2] templateYLabel[3] templateYLabel[4] . Airline industry In templateXValue[2] , the templateYLabel[3] airline industry generated total operating revenue of almost 240 templateYLabel[2] templateYLabel[3] templateYLabel[4] , making the United States one of the largest markets for the airline industry templateTitle[3] .

generated_template: templateTitle[4] templateTitle[5] in the United States – additional information The number of people employed in the United States from the fiscal templateXLabel[0] of templateXValue[max] . In the most recently reported period , the world 's templateTitle[1] templateTitle[2] templateTitle[3] with an estimated templateYValue[0] people lived in the previous templateXLabel[0] .
generated: 2006 2020 in the United States – additional information The number of people employed in the United States from the fiscal Year of 2020 . In the most recently reported period , the world 's profit airlines worldwide with an estimated 29.3 people lived in the previous Year .


Example 61:
data: Year|2018|x|line_chart Capacity_in_megawatts|2400|y|line_chart Year|2017|x|line_chart Capacity_in_megawatts|2227|y|line_chart Year|2016|x|line_chart Capacity_in_megawatts|2583|y|line_chart Year|2015|x|line_chart Capacity_in_megawatts|2099|y|line_chart Year|2014|x|line_chart Capacity_in_megawatts|1231|y|line_chart Year|2013|x|line_chart Capacity_in_megawatts|792|y|line_chart Year|2012|x|line_chart Capacity_in_megawatts|494|y|line_chart Year|2011|x|line_chart Capacity_in_megawatts|304|y|line_chart Year|2010|x|line_chart Capacity_in_megawatts|246|y|line_chart Year|2009|x|line_chart Capacity_in_megawatts|164|y|line_chart Year|2008|x|line_chart Capacity_in_megawatts|82|y|line_chart Year|2007|x|line_chart Capacity_in_megawatts|58|y|line_chart Year|2006|x|line_chart Capacity_in_megawatts|38|y|line_chart Year|2005|x|line_chart Capacity_in_megawatts|27|y|line_chart 
title: U.S. residential sector annual solar PV capacity installations 2018

gold: This statistic represents solar photovoltaic capacity installations in the residential sector in the United States between 2005 and 2018 . In 2018 , residential sector PV installations reached a capacity of 2.4 gigawatts .
gold_template: This statistic represents templateTitle[4] photovoltaic templateYLabel[0] templateTitle[7] in the templateTitle[1] templateTitle[2] in the templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[1] templateTitle[2] templateTitle[5] templateTitle[7] reached a templateYLabel[0] of 2.4 gigawatts .

generated_template: The timeline shows the templateTitle[3] of the templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[0] amounted to about templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The timeline shows the annual of the U.S. residential from 2005 to 2018 . In 2018 , the U.S. residential sector in U.S. amounted to about 2400 megawatts yLabelErr yLabelErr yLabelErr .


Example 62:
data: Year|2019|x|line_chart Imports_in_million_bushels|28|y|line_chart Year|2018|x|line_chart Imports_in_million_bushels|36|y|line_chart Year|2017|x|line_chart Imports_in_million_bushels|57|y|line_chart Year|2016|x|line_chart Imports_in_million_bushels|57|y|line_chart Year|2015|x|line_chart Imports_in_million_bushels|68|y|line_chart Year|2014|x|line_chart Imports_in_million_bushels|32|y|line_chart Year|2013|x|line_chart Imports_in_million_bushels|36|y|line_chart Year|2012|x|line_chart Imports_in_million_bushels|160|y|line_chart Year|2011|x|line_chart Imports_in_million_bushels|29|y|line_chart Year|2010|x|line_chart Imports_in_million_bushels|28|y|line_chart Year|2009|x|line_chart Imports_in_million_bushels|8|y|line_chart Year|2008|x|line_chart Imports_in_million_bushels|14|y|line_chart Year|2007|x|line_chart Imports_in_million_bushels|20|y|line_chart Year|2006|x|line_chart Imports_in_million_bushels|12|y|line_chart Year|2005|x|line_chart Imports_in_million_bushels|9|y|line_chart Year|2004|x|line_chart Imports_in_million_bushels|11|y|line_chart Year|2003|x|line_chart Imports_in_million_bushels|14|y|line_chart Year|2002|x|line_chart Imports_in_million_bushels|14|y|line_chart Year|2001|x|line_chart Imports_in_million_bushels|10|y|line_chart 
title: American imports of corn 2001 - 2019

gold: This statistic shows imports of corn in the United States from 2001 to 2019 . According to the report , U.S. corn imports amounted to approximately 57 million bushels in 2016 , down from 68 million bushels the previous year .
gold_template: This statistic shows templateYLabel[0] of templateTitle[2] in the United States from templateXValue[min] to templateXValue[max] . According to the report , U.S. templateTitle[2] templateYLabel[0] amounted to approximately templateYValue[2] templateYLabel[1] templateYLabel[2] in templateXValue[3] , down from templateYValue[4] templateYLabel[1] templateYLabel[2] the previous templateXLabel[0] .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the templateYLabel[1] were committed in templateTitle[4] templateTitle[5] in the United States .
generated: This statistic presents the Imports million of corn 2001 in 2019 titleErr in the United States from 2001 to 2019 . In 2019 , 160 percent of the million were committed in 2019 titleErr in the United States .


Example 63:
data: Response|No|x|bar_chart Share_of_respondents|41.9|y|bar_chart Response|Yes|x|bar_chart Share_of_respondents|58.1|y|bar_chart 
title: Share of Americans who have had a one-night-stand , as of 2012

gold: This statistic shows the results of a survey in the United States in 2012 on one-night-stands . 58.1 percent of respondents in the United States stated they have had a one-night-stand before .
gold_template: This statistic shows the results of a survey in the United States in templateTitle[6] on one-night-stands . templateYValue[max] percent of templateYLabel[1] in the United States stated they templateTitle[3] templateTitle[4] a templateTitle[5] before .

generated_template: templateXValue[1] case of templateTitle[3] ( COVID-19 ) was confirmed in templateTitle[5] as of February 27 , templateTitle[6] . Nevertheless , more than 60 percent of the society believes that templateTitle[5] is not prepared templateTitle[2] a possible virus templateTitle[4] .
generated: Yes case of have ( COVID-19 ) was confirmed in one-night-stand as of February 27 , 2012 . Nevertheless , more than 60 percent of the society believes that one-night-stand is not prepared who a possible virus had .


Example 64:
data: Response|groupon.com|x|bar_chart Share_of_respondents|75|y|bar_chart Response|coupons.com|x|bar_chart Share_of_respondents|64|y|bar_chart Response|retailmenot.com|x|bar_chart Share_of_respondents|62|y|bar_chart Response|livingsocial.com|x|bar_chart Share_of_respondents|48|y|bar_chart Response|redplum.com|x|bar_chart Share_of_respondents|32|y|bar_chart Response|mycoupons.com|x|bar_chart Share_of_respondents|31|y|bar_chart Response|couponcabin.com|x|bar_chart Share_of_respondents|25|y|bar_chart Response|swagbugs.com|x|bar_chart Share_of_respondents|23|y|bar_chart Response|savingstar.com|x|bar_chart Share_of_respondents|22|y|bar_chart Response|slickdeals.net|x|bar_chart Share_of_respondents|19|y|bar_chart Response|fatwallet.com|x|bar_chart Share_of_respondents|18|y|bar_chart Response|us.toluna.com|x|bar_chart Share_of_respondents|14|y|bar_chart Response|travelzoo.com|x|bar_chart Share_of_respondents|14|y|bar_chart Response|shopathome.com|x|bar_chart Share_of_respondents|13|y|bar_chart Response|woot.com|x|bar_chart Share_of_respondents|12|y|bar_chart Response|hip2save.com|x|bar_chart Share_of_respondents|11|y|bar_chart Response|couponmountain.com|x|bar_chart Share_of_respondents|9|y|bar_chart Response|freeshipping.org|x|bar_chart Share_of_respondents|6|y|bar_chart Response|couponchief.com|x|bar_chart Share_of_respondents|5|y|bar_chart Response|yipit.com|x|bar_chart Share_of_respondents|4|y|bar_chart Response|shesaved.com|x|bar_chart Share_of_respondents|3|y|bar_chart Response|passionforsavings.com|x|bar_chart Share_of_respondents|3|y|bar_chart Response|fyvor.com|x|bar_chart Share_of_respondents|2|y|bar_chart Response|other|x|bar_chart Share_of_respondents|2|y|bar_chart 
title: Leading coupon website awareness according to U.S. users 2016

gold: This statistic gives information on the most popular discount and promo code websites according to users in the United States . During the August 2016 survey , 75 percent of respondents stated that they were aware of groupon.com . Coupons.com was ranked second with 64 percent awareness rate and retailmenot.com was in third place with 62 percent of respondents stating that they knew the platform .
gold_template: This statistic gives information on the most popular discount and promo code websites templateTitle[4] to templateTitle[6] in the templateTitle[5] . During the August templateTitle[7] survey , templateYValue[max] percent of templateYLabel[1] stated that they were aware of templateXValue[0] . templateXValue[1] was ranked second with templateYValue[1] percent templateTitle[3] rate and templateXValue[2] was in third place with templateYValue[2] percent of templateYLabel[1] stating that they knew the platform .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[5] according to a survey conducted in early templateTitle[8] . During the survey , templateYValue[max] percent of templateYLabel[1] stated they could be both templateXValue[1] and templateXValue[2] .
generated: This statistic shows the results of a survey conducted in the United States in U.S. according to a survey conducted in early titleErr . During the survey , 75 percent of respondents stated they could be both coupons.com and retailmenot.com .


Example 65:
data: Year|2023|x|line_chart Number_of_users_in_millions|37.2|y|line_chart Year|2022|x|line_chart Number_of_users_in_millions|36.3|y|line_chart Year|2021|x|line_chart Number_of_users_in_millions|35.6|y|line_chart Year|2020|x|line_chart Number_of_users_in_millions|34.7|y|line_chart Year|2019|x|line_chart Number_of_users_in_millions|33.8|y|line_chart Year|2018|x|line_chart Number_of_users_in_millions|33.0|y|line_chart Year|2017|x|line_chart Number_of_users_in_millions|32.1|y|line_chart 
title: Number of social network users Thailand 2017 - 2023

gold: This statistic shows the number of social network users in Thailand from 2017 to 2023 . In 2023 , it is estimated that there will be around 32.2 million social network users in the country , up from 32.1 million in 2017 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around 32.2 million templateTitle[1] templateTitle[2] templateYLabel[1] in the country , up from templateYValue[min] million in templateXValue[min] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateYLabel[0] of templateTitle[2] templateYLabel[1] in templateTitle[0] is expected to reach templateYValue[1] million , up from templateYValue[min] million in templateXValue[min] .
generated: This statistic shows the Number of network users in Number from 2017 to 2023 . In 2018 , the Number of network users in Number is expected to reach 36.3 million , up from 32.1 million in 2017 .


Example 66:
data: Year|2019|x|line_chart Youth_unemployment_rate|15.94|y|line_chart Year|2018|x|line_chart Youth_unemployment_rate|16.09|y|line_chart Year|2017|x|line_chart Youth_unemployment_rate|16.12|y|line_chart Year|2016|x|line_chart Youth_unemployment_rate|16.39|y|line_chart Year|2015|x|line_chart Youth_unemployment_rate|16.49|y|line_chart Year|2014|x|line_chart Youth_unemployment_rate|16.92|y|line_chart Year|2013|x|line_chart Youth_unemployment_rate|17.05|y|line_chart Year|2012|x|line_chart Youth_unemployment_rate|17.03|y|line_chart Year|2011|x|line_chart Youth_unemployment_rate|21.45|y|line_chart Year|2010|x|line_chart Youth_unemployment_rate|26.83|y|line_chart Year|2009|x|line_chart Youth_unemployment_rate|21.44|y|line_chart Year|2008|x|line_chart Youth_unemployment_rate|16.02|y|line_chart Year|2007|x|line_chart Youth_unemployment_rate|19.3|y|line_chart Year|2006|x|line_chart Youth_unemployment_rate|23.17|y|line_chart Year|2005|x|line_chart Youth_unemployment_rate|28.26|y|line_chart Year|2004|x|line_chart Youth_unemployment_rate|26.75|y|line_chart Year|2003|x|line_chart Youth_unemployment_rate|25.55|y|line_chart Year|2002|x|line_chart Youth_unemployment_rate|24.6|y|line_chart Year|2001|x|line_chart Youth_unemployment_rate|23.08|y|line_chart Year|2000|x|line_chart Youth_unemployment_rate|21.34|y|line_chart Year|1999|x|line_chart Youth_unemployment_rate|20.88|y|line_chart 
title: Youth unemployment rate in Zambia in 2019

gold: The statistic shows the youth unemployment rate in Zambia from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Zambia was at 15.94 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] was at templateYValue[min] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Zambia from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Zambia was at 15.94 percent .


Example 67:
data: Year|2019|x|line_chart Unit_shipments_in_millions|400.0|y|line_chart Year|2018|x|line_chart Unit_shipments_in_millions|380.7|y|line_chart Year|2017|x|line_chart Unit_shipments_in_millions|363.0|y|line_chart Year|2016|x|line_chart Unit_shipments_in_millions|349.0|y|line_chart Year|2015|x|line_chart Unit_shipments_in_millions|331.3|y|line_chart Year|2014|x|line_chart Unit_shipments_in_millions|309.5|y|line_chart Year|2013|x|line_chart Unit_shipments_in_millions|286.0|y|line_chart 
title: Worldwide shipments of headphones 2013 - 2019

gold: The statistic shows global headphone unit shipments from 2013 to 2019 . In 2018 , 380.7 million headphones were sold worldwide .
gold_template: The statistic shows global headphone templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateYValue[1] million templateTitle[2] were sold templateTitle[0] .

generated_template: The statistic shows dishwasher templateYLabel[0] templateYLabel[1] in the United States templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] million units of the United States were shipped templateTitle[6] . templateTitle[2] templateYLabel[1] in the United States are expected to increase to templateYValue[max] million units .
generated: The statistic shows dishwasher Unit shipments in the United States 2019 from 2013 to 2019 . In 2017 , 363.0 million units of the United States were shipped titleErr . headphones shipments in the United States are expected to increase to 400.0 million units .


Example 68:
data: Country|Canada|x|bar_chart Change_in_heating_oil_price|7|y|bar_chart Country|Italy|x|bar_chart Change_in_heating_oil_price|5.9|y|bar_chart Country|Spain|x|bar_chart Change_in_heating_oil_price|5.9|y|bar_chart Country|France|x|bar_chart Change_in_heating_oil_price|4|y|bar_chart Country|United_Kingdom|x|bar_chart Change_in_heating_oil_price|0.8|y|bar_chart Country|Japan|x|bar_chart Change_in_heating_oil_price|-0.1|y|bar_chart Country|Germany|x|bar_chart Change_in_heating_oil_price|-4.4|y|bar_chart Country|United_States|x|bar_chart Change_in_heating_oil_price|-|y|bar_chart 
title: Change of domestic heating oil price in selected countries 2018 - 2019

gold: This statistic shows the change in the price of domestic heating oil per liter in selected countries for the period between December 2018 and December 2019 . In December 2019 , the domestic heating oil price in Germany was some -4.4 percent lower than in December 2018 . Domestic heating oil The price of domestic heating oil per liter has decreased in many countries .
gold_template: This statistic shows the templateYLabel[0] in the templateYLabel[3] of templateTitle[1] templateYLabel[1] templateYLabel[2] per liter in templateTitle[5] templateTitle[6] for the period between December templateTitle[7] and December templateTitle[8] . In December templateTitle[8] , the templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[6] was some templateYValue[6] percent lower than in December templateTitle[7] . templateTitle[1] templateYLabel[1] templateYLabel[2] The templateYLabel[3] of templateTitle[1] templateYLabel[1] templateYLabel[2] per liter has decreased in many templateTitle[6] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] in the templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] . In that year , about templateYValue[max] percent of the templateYLabel[1] were living in templateXValue[0] templateXValue[0] .
generated: This statistic shows the Change heating of oil in the oil price selected in countries . In that year , about 7 percent of the heating were living in Canada .


Example 69:
data: Country|Germany|x|bar_chart Number_of_dogs_in_thousands|9400|y|bar_chart Country|United_Kingdom|x|bar_chart Number_of_dogs_in_thousands|9000|y|bar_chart Country|Poland|x|bar_chart Number_of_dogs_in_thousands|7600|y|bar_chart Country|Italy|x|bar_chart Number_of_dogs_in_thousands|7002|y|bar_chart Country|France|x|bar_chart Number_of_dogs_in_thousands|6950|y|bar_chart Country|Spain|x|bar_chart Number_of_dogs_in_thousands|6270|y|bar_chart Country|Romania|x|bar_chart Number_of_dogs_in_thousands|4000|y|bar_chart Country|Portugal|x|bar_chart Number_of_dogs_in_thousands|2100|y|bar_chart Country|Czechia|x|bar_chart Number_of_dogs_in_thousands|2000|y|bar_chart Country|Netherlands|x|bar_chart Number_of_dogs_in_thousands|1520|y|bar_chart Country|Belgium|x|bar_chart Number_of_dogs_in_thousands|1315|y|bar_chart Country|Hungary|x|bar_chart Number_of_dogs_in_thousands|1180|y|bar_chart Country|Slovakia|x|bar_chart Number_of_dogs_in_thousands|900|y|bar_chart Country|Sweden|x|bar_chart Number_of_dogs_in_thousands|880|y|bar_chart Country|Austria|x|bar_chart Number_of_dogs_in_thousands|827|y|bar_chart Country|Finland|x|bar_chart Number_of_dogs_in_thousands|810|y|bar_chart Country|Bulgaria|x|bar_chart Number_of_dogs_in_thousands|740|y|bar_chart Country|Greece|x|bar_chart Number_of_dogs_in_thousands|660|y|bar_chart Country|Denmark|x|bar_chart Number_of_dogs_in_thousands|595|y|bar_chart Country|Lithuania|x|bar_chart Number_of_dogs_in_thousands|550|y|bar_chart Country|Ireland|x|bar_chart Number_of_dogs_in_thousands|450|y|bar_chart Country|Slovenia|x|bar_chart Number_of_dogs_in_thousands|290|y|bar_chart Country|Latvia|x|bar_chart Number_of_dogs_in_thousands|260|y|bar_chart Country|Estonia|x|bar_chart Number_of_dogs_in_thousands|210|y|bar_chart 
title: Number of dogs in the European Union 2018 , by country

gold: This statistic presents the number of pet dogs in the European Union by country , as of 2018 . Germany ranked highest with a dog population of approximately 9.4 million in 2018 , followed by the United Kingdom ( UK ) with nine million . The number of dogs in Europe has seen a notable increase since 2010 , with the number of dogs significantly increasing by more than eleven million from 2010 to 2018 .
gold_template: This statistic presents the templateYLabel[0] of pet templateYLabel[1] in the templateTitle[2] templateTitle[3] templateTitle[5] templateXLabel[0] , as of templateTitle[4] . templateXValue[0] ranked highest with a dog population of approximately templateYValue[max] million in templateTitle[4] , followed templateTitle[5] the templateXValue[1] templateXValue[1] ( UK ) with templateYValue[max] million . The templateYLabel[0] of templateYLabel[1] in Europe has seen a notable increase since 2010 , with the templateYLabel[0] of templateYLabel[1] significantly increasing templateTitle[5] more than eleven million from 2010 to templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in selected European countries in templateTitle[4] templateTitle[5] . In this year , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYLabel[3] in the European Union , with templateYValue[max] . The templateXValue[1] had the second templateYLabel[1] rate of the lowest templateYLabel[0] templateYLabel[1] templateYLabel[2] , followed by templateXValue[1] , with templateYValue[1] percent of templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Number dogs thousands in selected European countries in 2018 by . In this year , Germany had the highest Number dogs thousands of yLabelErr in the European Union , with 9400 . The United_Kingdom had the second dogs rate of the lowest Number dogs thousands , followed by United_Kingdom , with 9000 percent of yLabelErr yLabelErr .


Example 70:
data: Age_group|0-4_years|x|bar_chart Percentage_of_people|9|y|bar_chart Age_group|5-13_years|x|bar_chart Percentage_of_people|24|y|bar_chart Age_group|14-18_years|x|bar_chart Percentage_of_people|13|y|bar_chart Age_group|19-44_years|x|bar_chart Percentage_of_people|34|y|bar_chart Age_group|Above_45_years|x|bar_chart Percentage_of_people|21|y|bar_chart 
title: People with hemophilia A in the U.S. 2018 , by age group

gold: This statistic displays the percentage of people in the United States diagnosed with Hemophilia A , sorted by age group , as of 2018 . In that year , nine percent of all Americans diagnosed with hemophilia A were between 0 and 4 years of age .
gold_template: This statistic displays the templateYLabel[0] of templateYLabel[1] in the templateTitle[3] diagnosed templateTitle[1] templateTitle[2] A , sorted templateTitle[5] templateXLabel[0] templateXLabel[1] , as of templateTitle[4] . In that year , templateYValue[min] percent of all Americans diagnosed templateTitle[1] templateTitle[2] A were between 0 and 4 templateXValue[0] of templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of people in the United States templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[7] , sorted templateTitle[8] templateXLabel[0] templateXLabel[1] . During the survey period , templateYValue[max] percent of templateYLabel[1] aged between templateXValue[0] templateXValue[0] to templateXValue[1] .
generated: This statistic shows the Percentage of people in the United States 2018 by age in titleErr , sorted titleErr Age group . During the survey period , 34 percent of people aged between 0-4_years to 5-13_years .


Example 71:
data: Year|2010_S1|x|line_chart Euro_cents_per_kilowatt-hour|9.7|y|line_chart Year|2010_S2|x|line_chart Euro_cents_per_kilowatt-hour|10.04|y|line_chart Year|2011_S1|x|line_chart Euro_cents_per_kilowatt-hour|9.73|y|line_chart Year|2011_S2|x|line_chart Euro_cents_per_kilowatt-hour|10.42|y|line_chart Year|2012_S1|x|line_chart Euro_cents_per_kilowatt-hour|10.96|y|line_chart Year|2012_S2|x|line_chart Euro_cents_per_kilowatt-hour|11.23|y|line_chart Year|2013_S1|x|line_chart Euro_cents_per_kilowatt-hour|13.51|y|line_chart Year|2013_S2|x|line_chart Euro_cents_per_kilowatt-hour|13.67|y|line_chart Year|2014_S1|x|line_chart Euro_cents_per_kilowatt-hour|13.07|y|line_chart Year|2014_S2|x|line_chart Euro_cents_per_kilowatt-hour|13.25|y|line_chart Year|2015_S1|x|line_chart Euro_cents_per_kilowatt-hour|13.02|y|line_chart Year|2015_S2|x|line_chart Euro_cents_per_kilowatt-hour|12.91|y|line_chart Year|2016_S1|x|line_chart Euro_cents_per_kilowatt-hour|12.08|y|line_chart Year|2016_S2|x|line_chart Euro_cents_per_kilowatt-hour|12.38|y|line_chart Year|2017_S1|x|line_chart Euro_cents_per_kilowatt-hour|12.07|y|line_chart 
title: Electricity prices for households in Estonia 2010 - 2017 , semi-annually

gold: This statistic shows the electricity prices for household end users in Estonia semi-annually from 2010 to 2017 . In the first half of 2017 , the average electricity price for households was 12.07 euro cents per kWh .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitle[4] templateTitle[7] from templateXValue[0] to templateXValue[last] . In the first half of templateXValue[last] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[14] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitle[4] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the first half of templateXValue[0] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: This statistic shows the Electricity prices for household end users in Estonia semi-annually from xValErr to 2010_S1 . In the first half of 2010_S1 , the average Electricity price for households was 9.7 Euro cents per kWh .


Example 72:
data: Year|2018|x|line_chart Death_rate_per_thousand_mid-year_population|9.1|y|line_chart Year|2017|x|line_chart Death_rate_per_thousand_mid-year_population|9.1|y|line_chart Year|2016|x|line_chart Death_rate_per_thousand_mid-year_population|9.2|y|line_chart Year|2015|x|line_chart Death_rate_per_thousand_mid-year_population|9.3|y|line_chart Year|2014|x|line_chart Death_rate_per_thousand_mid-year_population|9.2|y|line_chart Year|2013|x|line_chart Death_rate_per_thousand_mid-year_population|9.4|y|line_chart Year|2012|x|line_chart Death_rate_per_thousand_mid-year_population|9.7|y|line_chart Year|2011|x|line_chart Death_rate_per_thousand_mid-year_population|9.5|y|line_chart Year|2010|x|line_chart Death_rate_per_thousand_mid-year_population|9.6|y|line_chart Year|2009|x|line_chart Death_rate_per_thousand_mid-year_population|9.7|y|line_chart Year|2008|x|line_chart Death_rate_per_thousand_mid-year_population|9.9|y|line_chart 
title: Crude death rate in Sweden 2008 - 2018

gold: The crude death rate in Sweden was decreasing over the period and was down at the lowest point in 2017 and 2018 , when 9.1 deaths occurred per thousand mid-year population . Just over 92 thousand individuals in Sweden died during 2018 , a number that increased over the last few years , even though it was relatively stable during the period from 2008 to 2018 . Circulatory system diseases caused the most deaths Diseases in the circulatory system ( cardiovascular diseases ) were the most common cause of death in 2018 , which little more than 31 thousand individuals died from during that year .
gold_template: The templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitle[3] was decreasing over the period and was down at the lowest point in templateXValue[1] and templateXValue[max] , when templateYValue[min] deaths occurred templateYLabel[2] thousand templateYLabel[4] templateYLabel[5] . Just over 92 thousand individuals in templateTitle[3] died during templateXValue[max] , a number that increased over the last few years , even though it was relatively stable during the period from templateXValue[min] to templateXValue[max] . Circulatory system diseases caused the most deaths Diseases in the circulatory system ( cardiovascular diseases ) were the most common cause of templateYLabel[0] in templateXValue[max] , which little more than 31 thousand individuals died from during that templateXLabel[0] .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[0] at approximately templateYValue[0] people . This figure was a decrease from the previous templateXLabel[0] 's most analyzed from the previous templateXLabel[0] . The number of people held by about 0.6 percent compared to the previous templateXLabel[0] . Over the last decades , the number of people in this product basket defined as a number of people living in their level of people living in the United States .
generated: In 2018 , the Death rate of Crude at approximately 9.1 people . This figure was a decrease from the previous Year 's most analyzed from the previous Year . The number of people held by about 0.6 percent compared to the previous Year . Over the last decades , the number of people in this product basket defined as a number of people living in their level of people living in the United States .


Example 73:
data: Year|2020|x|line_chart Sales_in_million_US_dollars|52714.03|y|line_chart Year|2019|x|line_chart Sales_in_million_US_dollars|50821.69|y|line_chart Year|2018|x|line_chart Sales_in_million_US_dollars|49342.45|y|line_chart Year|2017|x|line_chart Sales_in_million_US_dollars|47921.14|y|line_chart Year|2016|x|line_chart Sales_in_million_US_dollars|46631.26|y|line_chart Year|2015|x|line_chart Sales_in_million_US_dollars|45524.45|y|line_chart Year|2014|x|line_chart Sales_in_million_US_dollars|47444.1|y|line_chart Year|2013|x|line_chart Sales_in_million_US_dollars|44802.19|y|line_chart Year|2012|x|line_chart Sales_in_million_US_dollars|45335.47|y|line_chart Year|2011|x|line_chart Sales_in_million_US_dollars|44899.26|y|line_chart Year|2010|x|line_chart Sales_in_million_US_dollars|40675.81|y|line_chart 
title: Edible grocery sales forecast for Tesco in the United Kingdom ( UK ) from 2010 to 2020

gold: This statistic shows the edible grocery sales forecast for Tesco in the United Kingdom ( UK ) from 2010 forecast to 2020 . In 2010 , Tesco made 40.68 billion US dollars in sales , which is forecast to increase to 52.71 billion US dollars by 2020 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] templateTitle[7] ( templateTitle[8] ) templateTitle[9] templateXValue[min] templateTitle[3] to templateXValue[max] . In templateXValue[min] , templateTitle[5] made templateYValue[min] billion templateYLabel[2] templateYLabel[3] in templateYLabel[0] , which is templateTitle[3] to increase to templateYValue[max] billion templateYLabel[2] templateYLabel[3] by templateXValue[max] .

generated_template: The graph shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in United States between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were templateYValue[0] percent of the templateYLabel[1] at an increase of templateYValue[0] percent .
generated: The graph shows the Sales million US of forecast for Tesco United Kingdom in United States between 2010 and 2020 . In 2020 , there were 52714.03 percent of the million at an increase of 52714.03 percent .


Example 74:
data: Year|2018|x|line_chart Number_of_visitors_in_millions|5.83|y|line_chart Year|2017|x|line_chart Number_of_visitors_in_millions|5.66|y|line_chart Year|2016|x|line_chart Number_of_visitors_in_millions|5.84|y|line_chart Year|2015|x|line_chart Number_of_visitors_in_millions|4.71|y|line_chart Year|2014|x|line_chart Number_of_visitors_in_millions|5.79|y|line_chart Year|2013|x|line_chart Number_of_visitors_in_millions|4.8|y|line_chart Year|2012|x|line_chart Number_of_visitors_in_millions|5.3|y|line_chart Year|2011|x|line_chart Number_of_visitors_in_millions|4.8|y|line_chart Year|2010|x|line_chart Number_of_visitors_in_millions|5.06|y|line_chart Year|2009|x|line_chart Number_of_visitors_in_millions|4.75|y|line_chart Year|2008|x|line_chart Number_of_visitors_in_millions|4.95|y|line_chart Year|2007|x|line_chart Number_of_visitors_in_millions|5.19|y|line_chart 
title: Number of visitors to the Tate Modern in London 2007 - 2018

gold: This statistic shows the number of visitors to the Tate Modern in London from 2007 to 2018 . Approximately 5.83 million people visited the Tate Modern art museum in London in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] to the templateTitle[2] templateTitle[3] in templateTitle[4] from templateXValue[min] to templateXValue[max] . Approximately templateYValue[0] million people visited the templateTitle[2] templateTitle[3] art museum in templateTitle[4] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[1] to the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] to the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] million in templateXValue[max] .
generated: This statistic shows the Number of recreational visitors to the Tate Modern London 2007 in the United States from 2007 to 2018 . The Number of visitors to the Tate Modern London 2007 amounted to approximately 5.83 million in 2018 .


Example 75:
data: Year|2018|x|line_chart Ticket_sales_in_million_U.S._dollars|104|y|line_chart Year|2017|x|line_chart Ticket_sales_in_million_U.S._dollars|103|y|line_chart Year|2016|x|line_chart Ticket_sales_in_million_U.S._dollars|103|y|line_chart Year|2015|x|line_chart Ticket_sales_in_million_U.S._dollars|99|y|line_chart Year|2014|x|line_chart Ticket_sales_in_million_U.S._dollars|100|y|line_chart Year|2013|x|line_chart Ticket_sales_in_million_U.S._dollars|95|y|line_chart Year|2012|x|line_chart Ticket_sales_in_million_U.S._dollars|94|y|line_chart Year|2011|x|line_chart Ticket_sales_in_million_U.S._dollars|97|y|line_chart Year|2010|x|line_chart Ticket_sales_in_million_U.S._dollars|93|y|line_chart 
title: NFL - New England Patriots revenue from ticket sales 2010 - 2018

gold: The statistic depicts the annual ticket sales of the New England Patriots in the years 2010 to 2018 . The New England Patriots , a franchise of the National Football League , generated 104 million U.S. dollars in revenue from gate receipts in 2018 .
gold_template: The statistic depicts the annual templateYLabel[0] templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] in the years templateXValue[min] to templateXValue[max] . The templateTitle[1] templateTitle[2] templateTitle[3] , a franchise of the National Football League , generated templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitle[4] templateTitle[5] gate receipts in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[0] thousand people living in the templateTitle[4] templateTitle[5] , up from templateYValue[1] percent in the previous templateXLabel[0] .
generated: The statistic shows the Ticket sales of revenue from in the United States from 2010 to 2018 . In 2018 , there were approximately 104 thousand people living in the revenue from , up from 103 percent in the previous Year .


Example 76:
data: Year|2017/18|x|line_chart Number_of_players|73374|y|line_chart Year|2016/17|x|line_chart Number_of_players|76387|y|line_chart Year|2015/16|x|line_chart Number_of_players|74150|y|line_chart Year|2014/15|x|line_chart Number_of_players|75871|y|line_chart Year|2013/14|x|line_chart Number_of_players|73682|y|line_chart Year|2012/13|x|line_chart Number_of_players|66636|y|line_chart Year|2011/12|x|line_chart Number_of_players|56626|y|line_chart Year|2010/11|x|line_chart Number_of_players|65251|y|line_chart 
title: Ice hockey players in Finland 2010/11 - 2017/18

gold: The statistics shows the number of registered ice hockey players in Finland from 2010/11 to 2017/18 . The number of registered ice hockey players in 2017/18 amounted to nearly 73.4 thousand . The highest player number was reported in the previous season ( 2016/17 ) with over 76 thousand players .
gold_template: The statistics shows the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitle[3] from templateXValue[last] to templateXValue[0] . The templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateXValue[0] amounted to nearly templateYValue[0] thousand . The highest player templateYLabel[0] was reported in the previous season ( templateXValue[1] ) with over templateYValue[max] thousand templateYLabel[1] .

generated_template: The statistics shows the templateYLabel[0] of templateYLabel[1] in the templateTitle[3] templateTitle[4] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , there were a total of templateYValue[0] people living in the templateTitle[3] templateTitle[4] and templateTitle[5] U.S. in the templateXValue[0] season .
generated: The statistics shows the Number of players in the Finland 2010/11 from 2010/11 to 2017/18 . In 2017/18 , there were a total of 73374 people living in the Finland 2010/11 and 2017/18 U.S. in the 2017/18 season .


Example 77:
data: Year|2020|x|line_chart Franchise_value_in_million_U.S._dollars|1625|y|line_chart Year|2019|x|line_chart Franchise_value_in_million_U.S._dollars|1500|y|line_chart Year|2018|x|line_chart Franchise_value_in_million_U.S._dollars|1280|y|line_chart Year|2017|x|line_chart Franchise_value_in_million_U.S._dollars|1100|y|line_chart Year|2016|x|line_chart Franchise_value_in_million_U.S._dollars|1000|y|line_chart Year|2015|x|line_chart Franchise_value_in_million_U.S._dollars|910|y|line_chart Year|2014|x|line_chart Franchise_value_in_million_U.S._dollars|565|y|line_chart Year|2013|x|line_chart Franchise_value_in_million_U.S._dollars|474|y|line_chart Year|2012|x|line_chart Franchise_value_in_million_U.S._dollars|395|y|line_chart Year|2011|x|line_chart Franchise_value_in_million_U.S._dollars|411|y|line_chart Year|2010|x|line_chart Franchise_value_in_million_U.S._dollars|429|y|line_chart Year|2009|x|line_chart Franchise_value_in_million_U.S._dollars|452|y|line_chart Year|2008|x|line_chart Franchise_value_in_million_U.S._dollars|449|y|line_chart Year|2007|x|line_chart Franchise_value_in_million_U.S._dollars|410|y|line_chart Year|2006|x|line_chart Franchise_value_in_million_U.S._dollars|395|y|line_chart Year|2005|x|line_chart Franchise_value_in_million_U.S._dollars|356|y|line_chart Year|2004|x|line_chart Franchise_value_in_million_U.S._dollars|282|y|line_chart Year|2003|x|line_chart Franchise_value_in_million_U.S._dollars|272|y|line_chart 
title: Franchise value of the Phoenix Suns ( NBA ) 2003 - 2020

gold: This graph depicts the value of the Phoenix Suns franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the franchise had an estimated value of 1.625 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of 1.625 billion templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the value of the Phoenix Suns NBA Franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the Franchise had an estimated value of 1625 billion U.S. dollars .


Example 78:
data: Year|2018|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|10.58|y|line_chart Year|2017|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|10.48|y|line_chart Year|2016|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|10.27|y|line_chart Year|2015|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|10.41|y|line_chart Year|2014|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|10.44|y|line_chart Year|2013|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|10.07|y|line_chart Year|2012|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|9.84|y|line_chart Year|2011|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|9.9|y|line_chart Year|2010|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|9.83|y|line_chart Year|2009|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|9.82|y|line_chart Year|2008|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|9.74|y|line_chart Year|2007|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|9.13|y|line_chart Year|2006|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|8.9|y|line_chart Year|2005|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|8.14|y|line_chart Year|2000|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|6.81|y|line_chart Year|1995|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|6.89|y|line_chart Year|1990|x|line_chart Average_electricity_price_in_U.S._cents_per_kilowatt_hour|6.57|y|line_chart 
title: U.S. average retail electricity prices 1990 - 2018

gold: This statistic reflects the average retail price of electricity in the United States between 1990 and 2018 . Here , the average retail price of electricity was at 10.58 cents per kilowatt hour in 2018 . A ranking of the largest electric utilities companies in the United States can be found here as well as a ranking of global electricity prices in select countries .
gold_template: This statistic reflects the templateYLabel[0] templateTitle[2] templateYLabel[2] of templateYLabel[1] in the templateTitle[0] between templateXValue[min] and templateXValue[max] . Here , the templateYLabel[0] templateTitle[2] templateYLabel[2] of templateYLabel[1] was at templateYValue[max] templateYLabel[4] templateYLabel[5] templateYLabel[6] templateYLabel[7] in templateXValue[max] . A ranking of the largest electric utilities companies in the templateTitle[0] can be found here as well as a ranking of global templateYLabel[1] templateTitle[4] in select countries .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] people died as a result of templateTitle[4] .
generated: This statistic shows the Average electricity of electricity prices 1990 in the United States from 1990 to 2018 . In 2018 , about 10.58 people died as a result of prices .


Example 79:
data: Year|2015|x|line_chart Penetration_in_percent|7|y|line_chart Year|2014|x|line_chart Penetration_in_percent|5|y|line_chart Year|2013|x|line_chart Penetration_in_percent|3|y|line_chart Year|2011|x|line_chart Penetration_in_percent|2|y|line_chart Year|2012|x|line_chart Penetration_in_percent|2|y|line_chart Year|2010|x|line_chart Penetration_in_percent|1|y|line_chart Year|2009|x|line_chart Penetration_in_percent|1|y|line_chart Year|2008|x|line_chart Penetration_in_percent|1|y|line_chart Year|2007|x|line_chart Penetration_in_percent|1|y|line_chart Year|2006|x|line_chart Penetration_in_percent|1|y|line_chart Year|2005|x|line_chart Penetration_in_percent|1|y|line_chart 
title: PC penetration in the Middle East and Africa 2005 - 2015

gold: The statistic depicts the consumer PC penetration per capita in the Middle East and Africa from 2005 to 2015 . In the Middle East and Africa , the consumer PC penetration per capita was 1 percent in 2006 . In 2015 , the PC penetration per capita is projected to reach 7 percent .
gold_template: The statistic depicts the consumer templateTitle[0] templateYLabel[0] per capita in the templateTitle[2] templateTitle[3] and templateTitle[4] from templateXValue[min] to templateXValue[max] . In the templateTitle[2] templateTitle[3] and templateTitle[4] , the consumer templateTitle[0] templateYLabel[0] per capita was templateYValue[min] templateYLabel[1] in templateXValue[9] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] per capita is projected to reach templateYValue[max] templateYLabel[1] .

generated_template: In templateXValue[max] , there were templateYValue[0] in the United States , a slight decrease from the templateXLabel[0] since templateXValue[min] , when there were located in the country . The templateYLabel[0] of templateYLabel[1] has increased in the last two countries have seen in the last decades , reaching templateYValue[2] times . However , California , there were located in California .
generated: In 2015 , there were 7 in the United States , a slight decrease from the Year since 2005 , when there were located in the country . The Penetration of percent has increased in the last two countries have seen in the last decades , reaching 3 times . However , California , there were located in California .


Example 80:
data: Year|2016|x|line_chart Annual_profit_in_million_U.S._dollars|101.88|y|line_chart Year|2015|x|line_chart Annual_profit_in_million_U.S._dollars|106.95|y|line_chart Year|2014|x|line_chart Annual_profit_in_million_U.S._dollars|109.48|y|line_chart Year|2013|x|line_chart Annual_profit_in_million_U.S._dollars|135.87|y|line_chart Year|2012|x|line_chart Annual_profit_in_million_U.S._dollars|126.19|y|line_chart Year|2011|x|line_chart Annual_profit_in_million_U.S._dollars|132.32|y|line_chart Year|2010|x|line_chart Annual_profit_in_million_U.S._dollars|135.19|y|line_chart Year|2009|x|line_chart Annual_profit_in_million_U.S._dollars|135.21|y|line_chart 
title: Mast-Jägermeister SE - annual profit 2009 - 2016

gold: The statistic shows the trend in profit at Mast-Jägermeister SE between 2009 and 2016 . In 2016 , Mast-Jägermeister SE generated a profit of around 101.88 million U.S. dollars . The spirits company is based in Wolfenbüttel , Germany .
gold_template: The statistic shows the trend in templateYLabel[1] at templateTitle[0] templateTitle[1] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] generated a templateYLabel[1] of around templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The spirits company is based in Wolfenbüttel , Germany .

generated_template: U.S. fashion fashion retailer templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] United States increased from templateYValue[max] percent in templateXValue[max] , an increase of templateYValue[0] percent compared with the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] - additional information The highest number of templateYLabel[1] templateYLabel[2] services , which rose suddenly in templateXValue[1] . was one of the most common activities in templateTitle[3] templateTitle[4] templateTitle[5] in the templateXLabel[0] .
generated: U.S. fashion retailer profit 2009 2016 in million United States increased from 135.87 percent in 2016 , an increase of 101.88 percent compared with the previous Year . The Annual profit - additional information The highest number of profit million services , which rose suddenly in 2015 . was one of the most common activities in profit 2009 2016 in the Year .


Example 81:
data: Year|2018|x|line_chart Revenue_in_billion_euros|84.7|y|line_chart Year|2017|x|line_chart Revenue_in_billion_euros|82.1|y|line_chart Year|2016|x|line_chart Revenue_in_billion_euros|84.62|y|line_chart Year|2015|x|line_chart Revenue_in_billion_euros|77.85|y|line_chart Year|2014|x|line_chart Revenue_in_billion_euros|69.78|y|line_chart Year|2013|x|line_chart Revenue_in_billion_euros|66.81|y|line_chart Year|2012|x|line_chart Revenue_in_billion_euros|62.98|y|line_chart Year|2011|x|line_chart Revenue_in_billion_euros|66.32|y|line_chart Year|2010|x|line_chart Revenue_in_billion_euros|56.54|y|line_chart Year|2009|x|line_chart Revenue_in_billion_euros|51.65|y|line_chart Year|2008|x|line_chart Revenue_in_billion_euros|53.11|y|line_chart Year|2007|x|line_chart Revenue_in_billion_euros|52.6|y|line_chart Year|2006|x|line_chart Revenue_in_billion_euros|55.95|y|line_chart Year|2005|x|line_chart Revenue_in_billion_euros|55.44|y|line_chart Year|2004|x|line_chart Revenue_in_billion_euros|52.22|y|line_chart Year|2003|x|line_chart Revenue_in_billion_euros|55.65|y|line_chart Year|2002|x|line_chart Revenue_in_billion_euros|54.03|y|line_chart Year|2001|x|line_chart Revenue_in_billion_euros|59.92|y|line_chart Year|2000|x|line_chart Revenue_in_billion_euros|59.0|y|line_chart 
title: Revenue from used cars in Germany from 2000 to 2018

gold: This statistic shows the revenue of the used car market in Germany between 2000 and 2018 . In 2018 , the revenue amounted to roughly 84.7 billion euros .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[2] car market in templateTitle[4] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateYLabel[0] amounted to roughly templateYValue[max] templateYLabel[1] templateYLabel[2] .

generated_template: In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] generated a total of templateYValue[max] templateYLabel[1] templateYLabel[2] in the fiscal templateXLabel[0] . templateTitle[1] templateTitle[2] is a multinational engineering and its value of over 2.1 billion templateYLabel[2] . When it comes to sustainable development , trade can be the best of servants , but the worst of masters .
generated: In 2018 , Revenue from used generated a total of 84.7 billion euros in the fiscal Year . from used is a multinational engineering and its value of over 2.1 billion euros . When it comes to sustainable development , trade can be the best of servants , but the worst of masters .


Example 82:
data: Month|Jun_15|x|bar_chart Number_of_deals|1222|y|bar_chart Month|May_15|x|bar_chart Number_of_deals|1030|y|bar_chart Month|Apr_15|x|bar_chart Number_of_deals|1107|y|bar_chart Month|Mar_15|x|bar_chart Number_of_deals|1166|y|bar_chart Month|Feb_15|x|bar_chart Number_of_deals|1097|y|bar_chart Month|Jan_15|x|bar_chart Number_of_deals|1076|y|bar_chart Month|Dec_14|x|bar_chart Number_of_deals|1235|y|bar_chart Month|Nov_14|x|bar_chart Number_of_deals|1152|y|bar_chart Month|Oct_14|x|bar_chart Number_of_deals|1167|y|bar_chart Month|Sep_14|x|bar_chart Number_of_deals|1222|y|bar_chart Month|Aug_14|x|bar_chart Number_of_deals|912|y|bar_chart Month|Jul_14|x|bar_chart Number_of_deals|1253|y|bar_chart Month|Jun_14|x|bar_chart Number_of_deals|1171|y|bar_chart Month|May_14|x|bar_chart Number_of_deals|1070|y|bar_chart Month|Apr_14|x|bar_chart Number_of_deals|1185|y|bar_chart Month|Mar_14|x|bar_chart Number_of_deals|1062|y|bar_chart 
title: Number of M & A deals in Europe 2014 - 2015

gold: The statistic presents the number of merger and acquisition transactions in Europe from March 2014 to June 2015 . There were 1,222 M & A deals in Europe in June 2015 . The number of merger and acquisition transactions in Europe remained fairly steady between March 2014 and June 2015 .
gold_template: The statistic presents the templateYLabel[0] of merger and acquisition transactions in templateTitle[4] from March templateTitle[5] to June templateTitle[6] . There were templateYValue[0] templateTitle[1] templateTitle[2] A templateYLabel[1] in templateTitle[4] in June templateTitle[6] . The templateYLabel[0] of merger and acquisition transactions in templateTitle[4] remained fairly steady between March templateTitle[5] and June templateTitle[6] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States as of September templateTitle[5] , templateTitle[6] templateXLabel[0] . In the last reported period , templateYValue[max] percent of the templateYLabel[1] were located in the United States .
generated: The statistic shows the Number of M & deals Europe in the United States as of September 2014 , 2015 Month . In the last reported period , 1253 percent of the deals were located in the United States .


Example 83:
data: Year|2017|x|line_chart Turnover_in_million_euros|177.0|y|line_chart Year|2016|x|line_chart Turnover_in_million_euros|194.9|y|line_chart Year|2015|x|line_chart Turnover_in_million_euros|143.4|y|line_chart Year|2014|x|line_chart Turnover_in_million_euros|121.6|y|line_chart Year|2013|x|line_chart Turnover_in_million_euros|96.8|y|line_chart Year|2012|x|line_chart Turnover_in_million_euros|104.7|y|line_chart Year|2011|x|line_chart Turnover_in_million_euros|96.4|y|line_chart 
title: Bulgari : turnover 2011 - 2017

gold: The turnover of the Italian high-end jewelry company Bulgari increased almost twofold from 2011 to 2017 . The peak was reached in 2016 , when Bulgari 's annual turnover amounted to approximately 195 million euros . In 2017 the turnover decreased to 177 million euros .
gold_template: The templateYLabel[0] of the Italian high-end jewelry company templateTitle[0] increased almost twofold from templateXValue[min] to templateXValue[max] . The peak was reached in templateXValue[1] , when templateTitle[0] 's annual templateYLabel[0] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] . In templateXValue[max] the templateYLabel[0] decreased to templateYValue[0] templateYLabel[1] templateYLabel[2] .

generated_template: In templateXValue[max] , the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] Group reported a templateYLabel[0] of almost templateYValue[min] templateYLabel[1] templateYLabel[2] . The first templateXLabel[0] templateXValue[max] , the highest templateYLabel[0] of templateTitle[2] Group reported a templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] compared to templateYValue[max] templateYLabel[1] templateYLabel[2] in templateXValue[min] . templateTitle[2] first templateXLabel[0] considered in this graph , the templateYLabel[0] of the templateTitle[3] amounted to approximately 25 billion templateYLabel[2] .
generated: In 2017 , the turnover 2011 2017 titleErr Group reported a Turnover of almost 96.4 million euros . The first Year 2017 , the highest Turnover of 2011 Group reported a turnover 2011 2017 titleErr compared to 194.9 million euros in 2011 . 2011 first Year considered in this graph , the Turnover of the 2017 amounted to approximately 25 billion euros .


Example 84:
data: Year|2015/16|x|line_chart Average_ticket_price_in_U.S._dollars|51.21|y|line_chart Year|2014/15|x|line_chart Average_ticket_price_in_U.S._dollars|32.7|y|line_chart Year|2013/14|x|line_chart Average_ticket_price_in_U.S._dollars|30.31|y|line_chart Year|2012/13|x|line_chart Average_ticket_price_in_U.S._dollars|32.24|y|line_chart Year|2011/12|x|line_chart Average_ticket_price_in_U.S._dollars|23.64|y|line_chart Year|2010/11|x|line_chart Average_ticket_price_in_U.S._dollars|24.52|y|line_chart Year|2009/10|x|line_chart Average_ticket_price_in_U.S._dollars|27.21|y|line_chart Year|2008/09|x|line_chart Average_ticket_price_in_U.S._dollars|29.14|y|line_chart Year|2007/08|x|line_chart Average_ticket_price_in_U.S._dollars|30.89|y|line_chart Year|2006/07|x|line_chart Average_ticket_price_in_U.S._dollars|46.83|y|line_chart 
title: Average ticket price Washington Wizards ( NBA ) games 2015/16

gold: This graph depicts the average ticket price for Washington Wizards games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 46.83 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitle[3] templateTitle[4] templateTitle[6] of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[9] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitle[3] templateTitle[4] templateTitle[6] of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[8] templateYLabel[3] templateYLabel[4] . templateTitle[3] templateTitle[4] The templateTitle[3] templateTitle[4] are a professional basketball team of the National Basketball Association ( templateTitle[5] ) playing in the Eastern Conference of the league .
generated: This graph depicts the Average ticket price for Washington Wizards games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the Average ticket price was 30.89 U.S. dollars . Washington Wizards The Washington Wizards are a professional basketball team of the National Basketball Association ( NBA ) playing in the Eastern Conference of the league .


Example 85:
data: Country|Singapore|x|bar_chart Average_working_hours_per_week|44|y|bar_chart Country|India|x|bar_chart Average_working_hours_per_week|42|y|bar_chart Country|Brazil|x|bar_chart Average_working_hours_per_week|40|y|bar_chart Country|Mexico|x|bar_chart Average_working_hours_per_week|40|y|bar_chart Country|U.S.|x|bar_chart Average_working_hours_per_week|40|y|bar_chart Country|Worldwide|x|bar_chart Average_working_hours_per_week|40|y|bar_chart Country|Japan|x|bar_chart Average_working_hours_per_week|40|y|bar_chart Country|Argentina|x|bar_chart Average_working_hours_per_week|40|y|bar_chart Country|Spain|x|bar_chart Average_working_hours_per_week|40|y|bar_chart Country|Sweden|x|bar_chart Average_working_hours_per_week|40|y|bar_chart Country|Germany|x|bar_chart Average_working_hours_per_week|39|y|bar_chart Country|U.K.|x|bar_chart Average_working_hours_per_week|37|y|bar_chart Country|France|x|bar_chart Average_working_hours_per_week|37|y|bar_chart Country|Italy|x|bar_chart Average_working_hours_per_week|36|y|bar_chart Country|Netherlands|x|bar_chart Average_working_hours_per_week|36|y|bar_chart 
title: Employees ' average working hours per week worldwide 2011

gold: The statistic depicts how many hours employees work per week on average worldwide . Respondents from India work 42 hours per week on average .
gold_template: The statistic depicts how many templateYLabel[2] templateTitle[0] work templateYLabel[3] templateYLabel[4] on templateYLabel[0] templateXValue[5] . Respondents from templateXValue[1] work templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] on templateYLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitle[3] templateTitle[4] who were using templateTitle[0] as of February templateTitle[5] , sorted templateTitle[6] templateXLabel[0] . The survey period , templateYValue[max] percent of templateYLabel[1] were located in templateXValue[0] .
generated: This statistic shows the Average of adults in the working hours who were using Employees as of February per , sorted week Country . The survey period , 44 percent of working were located in Singapore .


Example 86:
data: Year|2018|x|line_chart Sales_in_million_units|6.4|y|line_chart Year|2017|x|line_chart Sales_in_million_units|7.27|y|line_chart Year|2016|x|line_chart Sales_in_million_units|6.79|y|line_chart Year|2015|x|line_chart Sales_in_million_units|8.73|y|line_chart Year|2014|x|line_chart Sales_in_million_units|12.24|y|line_chart Year|2013|x|line_chart Sales_in_million_units|13.95|y|line_chart Year|2012|x|line_chart Sales_in_million_units|13.53|y|line_chart Year|2011|x|line_chart Sales_in_million_units|3.61|y|line_chart 
title: Nintendo 3DS sales worldwide 2011 - 2018

gold: This statistic shows the number of Nintendo 3DS hardware units sold worldwide from 2011 to 2018 . In the 2018 financial year which ended in March 2018 , Nintendo sold 6.4 million 3DS consoles , down from 7.27 million units sold in the year prior . In terms of 3DS software , fiscal 2017 was the year of Pokemon , when more than 16 million copies of Pokemon X/Y for 3DS were sold .
gold_template: This statistic shows the number of templateTitle[0] templateTitle[1] hardware templateYLabel[2] sold templateTitle[3] from templateXValue[min] to templateXValue[max] . In the templateXValue[max] financial templateXLabel[0] which ended in March templateXValue[max] , templateTitle[0] sold templateYValue[0] templateYLabel[1] templateTitle[1] consoles , down from templateYValue[1] templateYLabel[1] templateYLabel[2] sold in the templateXLabel[0] prior . In terms of templateTitle[1] software , fiscal templateXValue[1] was the templateXLabel[0] of Pokemon , when more than 16 templateYLabel[1] copies of Pokemon X/Y for templateTitle[1] were sold .

generated_template: In templateXValue[max] , an estimated templateYValue[max] templateYLabel[1] smartwatches were sold in the United States . Between templateXValue[min] and templateXValue[max] annual templateTitle[0] templateYLabel[0] grew from just templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] to templateYValue[max] templateYLabel[1] by around templateYValue[2] templateYLabel[1] templateYLabel[4] in templateXValue[min] .
generated: In 2018 , an estimated 13.95 million smartwatches were sold in the United States . Between 2011 and 2018 annual Nintendo Sales grew from just 3.61 million units yLabelErr yLabelErr to 13.95 million by around 6.79 million yLabelErr in 2011 .


Example 87:
data: Year|2023|x|line_chart Number_of_mobile_phone_internet_users_in_millions|71.9|y|line_chart Year|2022|x|line_chart Number_of_mobile_phone_internet_users_in_millions|71.5|y|line_chart Year|2021|x|line_chart Number_of_mobile_phone_internet_users_in_millions|70.8|y|line_chart Year|2020|x|line_chart Number_of_mobile_phone_internet_users_in_millions|69.9|y|line_chart Year|2019|x|line_chart Number_of_mobile_phone_internet_users_in_millions|68.4|y|line_chart Year|2018|x|line_chart Number_of_mobile_phone_internet_users_in_millions|66.6|y|line_chart Year|2017|x|line_chart Number_of_mobile_phone_internet_users_in_millions|64.0|y|line_chart 
title: Japan : mobile internet users 2017 - 2023

gold: The statistic shows the number of mobile internet users in Japan from 2017 to 2023 . In 2018 , approximately 66.6 million people accessed the internet through mobile devices . In 2023 , this figure is projected to reach about 71.9 million mobile internet users .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , approximately templateYValue[5] million people accessed the templateYLabel[3] through templateYLabel[1] devices . In templateXValue[max] , this figure is projected to reach about templateYValue[max] million templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] million people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] million templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number of mobile internet users in Japan from 2017 to 2023 . In 2017 , 64.0 million people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 71.9 million mobile phone internet users .


Example 88:
data: Year|2018|x|line_chart Number_of_employees|10351|y|line_chart Year|2017|x|line_chart Number_of_employees|10615|y|line_chart Year|2016|x|line_chart Number_of_employees|10594|y|line_chart Year|2015|x|line_chart Number_of_employees|10582|y|line_chart Year|2014|x|line_chart Number_of_employees|12812|y|line_chart Year|2013|x|line_chart Number_of_employees|11838|y|line_chart Year|2012|x|line_chart Number_of_employees|5712|y|line_chart Year|2011|x|line_chart Number_of_employees|5343|y|line_chart Year|2010|x|line_chart Number_of_employees|5264|y|line_chart Year|2009|x|line_chart Number_of_employees|5432|y|line_chart Year|2008|x|line_chart Number_of_employees|5779|y|line_chart Year|2007|x|line_chart Number_of_employees|5764|y|line_chart Year|2006|x|line_chart Number_of_employees|5804|y|line_chart Year|2005|x|line_chart Number_of_employees|5395|y|line_chart 
title: Number of employees of Penguin Random House 2005 - 2018

gold: This statistic shows the number of employees of Penguin Random House from 2005 to 2018 . In 2018 , 10,351 people were employed by the company worldwide , down from 10,615 a year earlier .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] people were employed by the company worldwide , down from templateYValue[1] a templateXLabel[0] earlier .

generated_template: There were templateYValue[0] people in the United States in templateXValue[max] . The templateYLabel[0] of templateYLabel[1] employed in the templateTitle[3] group between templateXValue[min] and templateXValue[1] , with a highest templateYLabel[0] of templateYLabel[1] reaching a decline of templateYLabel[1] compared to templateXValue[1] , there were almost 220,000 templateYLabel[1] in the previous templateXLabel[0] . The highest templateTitle[3] was in templateXValue[1] , when there were almost 220,000 templateYLabel[1] between templateXValue[2] and templateXValue[6] .
generated: There were 10351 people in the United States in 2018 . The Number of employees employed in the Random group between 2005 and 2017 , with a highest Number of employees reaching a decline of employees compared to 2017 , there were almost 220,000 employees in the previous Year . The highest Random was in 2017 , when there were almost 220,000 employees between 2016 and 2012 .


Example 89:
data: Year|2019|x|line_chart Revenue_in_million_GBP|1251.84|y|line_chart Year|2018|x|line_chart Revenue_in_million_GBP|1277.12|y|line_chart Year|2017|x|line_chart Revenue_in_million_GBP|1277.88|y|line_chart Year|2016|x|line_chart Revenue_in_million_GBP|1246.56|y|line_chart Year|2015|x|line_chart Revenue_in_million_GBP|1240.38|y|line_chart Year|2014|x|line_chart Revenue_in_million_GBP|1057.68|y|line_chart Year|2013|x|line_chart Revenue_in_million_GBP|1082.1|y|line_chart Year|2012|x|line_chart Revenue_in_million_GBP|1099.1|y|line_chart Year|2011|x|line_chart Revenue_in_million_GBP|1038.0|y|line_chart Year|2010|x|line_chart Revenue_in_million_GBP|988.0|y|line_chart Year|2009|x|line_chart Revenue_in_million_GBP|944.0|y|line_chart Year|2008|x|line_chart Revenue_in_million_GBP|854.4|y|line_chart Year|2007|x|line_chart Revenue_in_million_GBP|821.0|y|line_chart Year|2006|x|line_chart Revenue_in_million_GBP|762.1|y|line_chart Year|2005|x|line_chart Revenue_in_million_GBP|776.3|y|line_chart Year|2004|x|line_chart Revenue_in_million_GBP|769.6|y|line_chart Year|2003|x|line_chart Revenue_in_million_GBP|742.0|y|line_chart Year|2002|x|line_chart Revenue_in_million_GBP|755.0|y|line_chart Year|2001|x|line_chart Revenue_in_million_GBP|645.0|y|line_chart Year|2000|x|line_chart Revenue_in_million_GBP|627.5|y|line_chart 
title: Cinema box office revenue in the United Kingdom ( UK ) 2000 - 2019

gold: This statistic shows a timeline of cinema box office revenue generated in the United Kingdom ( UK ) from 2000 to 2019 . In 2010 , box office revenue was 988 million British pounds . In 2019 , box office revenue exceeded 1.25 billion British pounds .
gold_template: This statistic shows a timeline of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] generated in the templateTitle[4] templateTitle[5] ( templateTitle[6] ) from templateXValue[min] to templateXValue[max] . In templateXValue[9] , templateTitle[1] templateTitle[2] templateYLabel[0] was templateYValue[9] templateYLabel[1] British pounds . In templateXValue[max] , templateTitle[1] templateTitle[2] templateYLabel[0] exceeded templateYValue[0] billion British pounds .

generated_template: templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] rose in templateXValue[1] to around templateYValue[0] percent in the United Kingdom ( templateTitle[5] ) from templateXValue[min] to templateXValue[max] . The source templateTitle[0] templateTitle[1] templateTitle[2] for the templateXLabel[0] templateXValue[9] , doubling what it made in fashion retailer with annual growth rate between templateXValue[min] and templateXValue[max] , . templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] peaked at templateYValue[max] percent .
generated: Cinema box office revenue rose in 2018 to around 1251.84 percent in the United Kingdom ( Kingdom ) from 2000 to 2019 . The source Cinema box office for the Year 2010 , doubling what it made in fashion retailer with annual growth rate between 2000 and 2019 , . Cinema box office Revenue peaked at 1277.88 percent .


Example 90:
data: Year|2017|x|line_chart Consumer_price_index|107.83|y|line_chart Year|2016|x|line_chart Consumer_price_index|105.75|y|line_chart Year|2015|x|line_chart Consumer_price_index|125.75|y|line_chart Year|2014|x|line_chart Consumer_price_index|120.84|y|line_chart Year|2013|x|line_chart Consumer_price_index|118.07|y|line_chart Year|2012|x|line_chart Consumer_price_index|116.78|y|line_chart 
title: Average CPI in the UAE 2012 - 2017

gold: The statistic shows the average consumer price index ( CPI ) in the United Arab Emirates from 2012 to 2017 . The overall average for the 2017 consumer price index in the country was 107.83 , implying a 7.83 increase in the price level .
gold_template: The statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[1] ) in the United Arab Emirates from templateXValue[min] to templateXValue[max] . The overall templateTitle[0] for the templateXValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the country was templateYValue[0] , implying a 7.83 increase in the templateYLabel[1] level .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] reported templateTitle[2] templateYLabel[1] U.S. dollars in the United States .
generated: This statistic shows the Consumer price index of 2012 2017 titleErr in the United States from 2012 to 2017 . In 2017 , there were 107.83 reported UAE price U.S. dollars in the United States .


Example 91:
data: Quarter|Q4_'19|x|bar_chart Net_income_in_million_U.S._dollars|3268|y|bar_chart Quarter|Q4_'18|x|bar_chart Net_income_in_million_U.S._dollars|3000|y|bar_chart Quarter|Q4_'17|x|bar_chart Net_income_in_million_U.S._dollars|1900|y|bar_chart Quarter|Q4_'16|x|bar_chart Net_income_in_million_U.S._dollars|749|y|bar_chart Quarter|Q4_'15|x|bar_chart Net_income_in_million_U.S._dollars|482|y|bar_chart Quarter|Q4_'14|x|bar_chart Net_income_in_million_U.S._dollars|214|y|bar_chart Quarter|Q4_'13|x|bar_chart Net_income_in_million_U.S._dollars|239|y|bar_chart Quarter|Q4_'12|x|bar_chart Net_income_in_million_U.S._dollars|97|y|bar_chart Quarter|Q4_'11|x|bar_chart Net_income_in_million_U.S._dollars|177|y|bar_chart Quarter|Q4_'10|x|bar_chart Net_income_in_million_U.S._dollars|416|y|bar_chart Quarter|Q4_'09|x|bar_chart Net_income_in_million_U.S._dollars|384|y|bar_chart 
title: Amazon 's Q4 income including seasonal sales 2009 - 2019

gold: This statistic shows Amazon.com 's net income in the fourth quarters from 2009 to 2019 , a comparison of the company 's quarterly income including the yearly Christmas sales . In 2019 , Amazon 's net income of that quarter amounted to 3.27 billion U.S. dollars .
gold_template: This statistic shows Amazon.com templateTitle[1] templateYLabel[0] templateYLabel[1] in the fourth quarters from templateTitle[7] to templateTitle[8] , a comparison of the company templateTitle[1] quarterly templateYLabel[1] templateTitle[4] the yearly Christmas templateTitle[6] . In templateTitle[8] , templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of that templateXLabel[0] amounted to templateYValue[max] billion templateYLabel[3] templateYLabel[4] .

generated_template: This statistic gives information on templateTitle[0] 's templateYLabel[0] revenue from the second templateXLabel[0] of templateTitle[3] to the fourth templateXLabel[0] of templateTitle[4] . During the last reported period , templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] billion templateYLabel[3] templateYLabel[4] .
generated: This statistic gives information on Amazon 's Net revenue from the second Quarter of income to the fourth Quarter of including . During the last reported period , Amazon 's Net income amounted to 3268 billion U.S. dollars .


Example 92:
data: Response|Less_than_once_per_week|x|bar_chart Share_of_respondents|44|y|bar_chart Response|One_to_three_times_per_week|x|bar_chart Share_of_respondents|27|y|bar_chart Response|Four_to_six_times_per_week|x|bar_chart Share_of_respondents|5|y|bar_chart Response|Seven_to_nine_times_per_week|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Ten_times_or_more_per_week|x|bar_chart Share_of_respondents|1|y|bar_chart Response|I_don't_eat_at_fast_food_restaurants|x|bar_chart Share_of_respondents|24|y|bar_chart 
title: Fast food restaurant visit frequency in the United Kingdom ( UK ) 2015

gold: This statistic shows the frequency of fast food ( any quick service ) restaurant visits in any given fast food restaurant in 2015 . In 2015 , 44 percent of respondents visited fast food restaurants less than once per week .
gold_template: This statistic shows the templateTitle[4] of templateXValue[last] templateXValue[last] ( any quick service ) templateTitle[2] visits in any given templateXValue[last] templateXValue[last] templateTitle[2] in templateTitle[8] . In templateTitle[8] , templateYValue[max] percent of templateYLabel[1] visited templateXValue[last] templateXValue[last] templateXValue[last] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[5] on fashion fashion industry in templateTitle[7] . During the survey , templateYValue[max] percent of templateYLabel[1] stated they purchased goods of templateXValue[0] templateXValue[0] templateXValue[0] or templateXValue[1] .
generated: This statistic shows the results of a survey conducted in the United States in United on fashion industry in UK . During the survey , 44 percent of respondents stated they purchased goods of Less_than_once_per_week Less_than_once_per_week or One_to_three_times_per_week .


Example 93:
data: Year|2017|x|line_chart Million_metric_tons|13.02|y|line_chart Year|2016|x|line_chart Million_metric_tons|13.05|y|line_chart Year|2015|x|line_chart Million_metric_tons|12.08|y|line_chart Year|2014|x|line_chart Million_metric_tons|12.67|y|line_chart Year|2013|x|line_chart Million_metric_tons|12.36|y|line_chart Year|2012|x|line_chart Million_metric_tons|12.01|y|line_chart Year|2011|x|line_chart Million_metric_tons|11.3|y|line_chart Year|2010|x|line_chart Million_metric_tons|11.19|y|line_chart Year|2009|x|line_chart Million_metric_tons|10.73|y|line_chart Year|2008|x|line_chart Million_metric_tons|10.02|y|line_chart Year|2007|x|line_chart Million_metric_tons|9.53|y|line_chart Year|2006|x|line_chart Million_metric_tons|8.91|y|line_chart Year|2005|x|line_chart Million_metric_tons|8.03|y|line_chart Year|2004|x|line_chart Million_metric_tons|8.67|y|line_chart Year|2003|x|line_chart Million_metric_tons|8.16|y|line_chart Year|2002|x|line_chart Million_metric_tons|8.42|y|line_chart Year|2001|x|line_chart Million_metric_tons|8.55|y|line_chart Year|2000|x|line_chart Million_metric_tons|7.25|y|line_chart 
title: Global papaya production 2000 - 2017

gold: This statistic depicts the production of papaya worldwide from 2000 to 2017 . According to the report , global papaya production amounted to approximately 12.67 million metric tons in 2014 .
gold_template: This statistic depicts the templateTitle[2] of templateTitle[1] worldwide from templateXValue[min] to templateXValue[max] . According to the report , templateTitle[0] templateTitle[1] templateTitle[2] amounted to approximately templateYValue[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[3] .

generated_template: This statistic depicts templateTitle[1] templateTitle[2] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] amounted to about templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] .
generated: This statistic depicts papaya production worldwide from 2000 to 2017 . In 2017 , Global papaya production amounted to about 13.05 Million metric tons .


Example 94:
data: Year|2018|x|line_chart Revenues_in_million_euros|267.63|y|line_chart Year|2017|x|line_chart Revenues_in_million_euros|214.28|y|line_chart Year|2016|x|line_chart Revenues_in_million_euros|162.31|y|line_chart Year|2015|x|line_chart Revenues_in_million_euros|181.03|y|line_chart Year|2014|x|line_chart Revenues_in_million_euros|184.24|y|line_chart Year|2013|x|line_chart Revenues_in_million_euros|182.72|y|line_chart Year|2012|x|line_chart Revenues_in_million_euros|177.24|y|line_chart Year|2011|x|line_chart Revenues_in_million_euros|157.41|y|line_chart 
title: Revenues of Italian company Guccio Gucci in 2011 - 2018

gold: In 2018 , the Italian fashion company Guccio Gucci S.p.A. , well known worldwide for designing and producing high-end clothing , footwear and accessories for women , men and kids , reported total revenues worth approximately 267 million euros . The figure represented an increase of over 50 million euros compared to the previous year , when the company registered a revenue of 214 million euros . An Italian success story Founded in 1921 in Florence as a leather goods manufacturer , Gucci has grown to become one of the leading players in the global luxury goods market and ranked third on the list of the most valuable luxury brands worldwide in 2018 .
gold_template: In templateXValue[max] , the templateTitle[1] fashion templateTitle[2] templateTitle[3] templateTitle[4] S.p.A. , well known worldwide for designing and producing high-end clothing , footwear and accessories for women , men and kids , reported total templateYLabel[0] worth approximately 267 templateYLabel[1] templateYLabel[2] . The figure represented an increase of over 50 templateYLabel[1] templateYLabel[2] compared to the previous templateXLabel[0] , when the templateTitle[2] registered a revenue of templateYValue[1] templateYLabel[1] templateYLabel[2] . An templateTitle[1] success story Founded in 1921 in Florence as a leather goods manufacturer , templateTitle[4] has grown to become one of the leading players in the global luxury goods market and ranked third on the list of the most valuable luxury brands worldwide in templateXValue[max] .

generated_template: There were templateYValue[0] templateYLabel[1] templateYLabel[2] recorded in templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] a slight decrease from templateXValue[min] to templateXValue[max] . Between templateXValue[min] and templateXValue[max] the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitle[3] templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] percent in templateYLabel[0] compared to the previous templateXLabel[0] . templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] The templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateXValue[1] , a templateYValue[1] percent of the templateXLabel[0] .
generated: There were 267.63 million euros recorded in company Guccio Gucci 2011 a slight decrease from 2011 to 2018 . Between 2011 and 2018 the Revenues of million euros in Guccio Gucci 2011 amounted to approximately 267.63 percent in Revenues compared to the previous Year . company Guccio Gucci 2011 The Revenues of million euros in 2017 , a 214.28 percent of the Year .


Example 95:
data: Year|2020|x|line_chart Franchise_value_in_million_U.S._dollars|2475|y|line_chart Year|2019|x|line_chart Franchise_value_in_million_U.S._dollars|2300|y|line_chart Year|2018|x|line_chart Franchise_value_in_million_U.S._dollars|2200|y|line_chart Year|2017|x|line_chart Franchise_value_in_million_U.S._dollars|1650|y|line_chart Year|2016|x|line_chart Franchise_value_in_million_U.S._dollars|1500|y|line_chart Year|2015|x|line_chart Franchise_value_in_million_U.S._dollars|1250|y|line_chart Year|2014|x|line_chart Franchise_value_in_million_U.S._dollars|775|y|line_chart Year|2013|x|line_chart Franchise_value_in_million_U.S._dollars|568|y|line_chart Year|2012|x|line_chart Franchise_value_in_million_U.S._dollars|453|y|line_chart Year|2011|x|line_chart Franchise_value_in_million_U.S._dollars|443|y|line_chart Year|2010|x|line_chart Franchise_value_in_million_U.S._dollars|470|y|line_chart Year|2009|x|line_chart Franchise_value_in_million_U.S._dollars|469|y|line_chart Year|2008|x|line_chart Franchise_value_in_million_U.S._dollars|462|y|line_chart Year|2007|x|line_chart Franchise_value_in_million_U.S._dollars|439|y|line_chart Year|2006|x|line_chart Franchise_value_in_million_U.S._dollars|422|y|line_chart Year|2005|x|line_chart Franchise_value_in_million_U.S._dollars|369|y|line_chart Year|2004|x|line_chart Franchise_value_in_million_U.S._dollars|278|y|line_chart Year|2003|x|line_chart Franchise_value_in_million_U.S._dollars|255|y|line_chart 
title: Franchise value of the Houston Rockets ( NBA ) 2003 - 2020

gold: This graph depicts the franchise value of the Houston Rockets of the National Basketball Association from 2003 to 2020 . In 2020 , the Houston Rockets franchise had an estimated value of 2.475 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateTitle[3] templateYLabel[0] had an estimated templateYLabel[1] of 2.475 billion templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] . templateTitle[2] templateTitle[3] templateTitle[4] The templateTitle[2] templateTitle[3] templateTitle[4] are a professional basketball templateYLabel[0] of the National Basketball Association ( templateTitle[5] ) that is part of the league 's Western Conference .
generated: This graph depicts the value of the Houston Rockets NBA Franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the Franchise had an estimated value of 2475 billion U.S. dollars . Houston Rockets NBA The Houston Rockets NBA are a professional basketball Franchise of the National Basketball Association ( 2003 ) that is part of the league 's Western Conference .


Example 96:
data: Country|United_States|x|bar_chart Audience_size_in_millions|101.25|y|bar_chart Country|India|x|bar_chart Audience_size_in_millions|22.95|y|bar_chart Country|France|x|bar_chart Audience_size_in_millions|21.25|y|bar_chart Country|United_Kingdom|x|bar_chart Audience_size_in_millions|18.7|y|bar_chart Country|Saudi_Arabia|x|bar_chart Audience_size_in_millions|16.1|y|bar_chart Country|Mexico|x|bar_chart Audience_size_in_millions|14.8|y|bar_chart Country|Brazil|x|bar_chart Audience_size_in_millions|13.95|y|bar_chart Country|Germany|x|bar_chart Audience_size_in_millions|12.15|y|bar_chart Country|Canada|x|bar_chart Audience_size_in_millions|8.15|y|bar_chart Country|Russia|x|bar_chart Audience_size_in_millions|7.75|y|bar_chart 
title: Countries with the most Snapchat users 2020

gold: As of January 2020 , the United States had the biggest Snapchat user base in the world , with an audience of 101.25 million users . India ranked in second place with a Snapchat audience base of 22.95 million users . The photo-sharing platform is projected to surpass 365 million global users in 2023 .
gold_template: As of January templateTitle[5] , the templateXValue[0] templateXValue[0] had the biggest templateTitle[3] user base in the world , templateTitle[1] an templateYLabel[0] of templateYValue[max] million templateTitle[4] . templateXValue[1] ranked in second place templateTitle[1] a templateTitle[3] templateYLabel[0] base of templateYValue[1] million templateTitle[4] . The photo-sharing platform is projected to surpass 365 million global templateTitle[4] in 2023 .

generated_template: This statistic presents a ranking of the templateTitle[0] templateTitle[1] the largest templateTitle[3] audiences worldwide as of January templateTitle[5] . During the measured period , the templateXValue[0] templateXValue[0] were ranked first templateTitle[1] an templateYLabel[0] of templateYValue[max] percent .
generated: This statistic presents a ranking of the Countries most the largest users audiences worldwide as of January titleErr . During the measured period , the United_States were ranked first most an Audience of 101.25 percent .


Example 97:
data: Year|17/18|x|line_chart Revenue_in_million_U.S._dollars|223|y|line_chart Year|16/17|x|line_chart Revenue_in_million_U.S._dollars|204|y|line_chart Year|15/16|x|line_chart Revenue_in_million_U.S._dollars|154|y|line_chart Year|14/15|x|line_chart Revenue_in_million_U.S._dollars|146|y|line_chart Year|13/14|x|line_chart Revenue_in_million_U.S._dollars|128|y|line_chart Year|12/13|x|line_chart Revenue_in_million_U.S._dollars|116|y|line_chart Year|11/12|x|line_chart Revenue_in_million_U.S._dollars|96|y|line_chart Year|10/11|x|line_chart Revenue_in_million_U.S._dollars|97|y|line_chart Year|09/10|x|line_chart Revenue_in_million_U.S._dollars|95|y|line_chart Year|08/09|x|line_chart Revenue_in_million_U.S._dollars|96|y|line_chart Year|07/08|x|line_chart Revenue_in_million_U.S._dollars|100|y|line_chart Year|06/07|x|line_chart Revenue_in_million_U.S._dollars|103|y|line_chart Year|05/06|x|line_chart Revenue_in_million_U.S._dollars|103|y|line_chart Year|04/05|x|line_chart Revenue_in_million_U.S._dollars|101|y|line_chart Year|03/04|x|line_chart Revenue_in_million_U.S._dollars|97|y|line_chart Year|02/03|x|line_chart Revenue_in_million_U.S._dollars|85|y|line_chart Year|01/02|x|line_chart Revenue_in_million_U.S._dollars|85|y|line_chart 
title: Minnesota Timberwolves ' revenue 2001 - 2018

gold: The statistic shows the revenue of the Minnesota Timberwolves franchise from the 2001/02 season to the 2017/18 season . In 2017/18 , the estimated revenue of the National Basketball Association franchise amounted to 223 million U.S. dollars . Minnesota Timberwolves - additional information The Minnesota Timberwolves are a professional basketball team of the National Basketball Association ( NBA ) , joining the league as an expansion team in 1989 .
gold_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the 2001/02 season to the 2017/18 season . In 2017/18 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitle[0] templateTitle[1] - additional information The templateTitle[0] templateTitle[1] are a professional basketball team of the National Basketball Association ( NBA ) , joining the league as an expansion team in 1989 .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Minnesota Timberwolves franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 223 million U.S. dollars .


Example 98:
data: Year|1999|x|line_chart Market_cap_in_billion_U.S._dollars|1.16|y|line_chart Year|2013|x|line_chart Market_cap_in_billion_U.S._dollars|2.54|y|line_chart 
title: Market capitalization of the NASDAQ exchange in 1999 and 2013

gold: This statistic presents the average market capitalization of component listings of the NASDAQ in 1999 and 2013 . It was reported that the average market capitalization of companies listed on the exchange rose from 1.16 billion US dollars in 1999 to 2.54 billion US dollars in 2013 .
gold_template: This statistic presents the average templateYLabel[0] templateTitle[1] of component listings of the templateTitle[2] in templateXValue[min] and templateXValue[max] . It was reported that the average templateYLabel[0] templateTitle[1] of companies listed on the templateTitle[3] rose from templateYValue[min] templateYLabel[2] US templateYLabel[4] in templateXValue[min] to templateYValue[max] templateYLabel[2] US templateYLabel[4] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[max] million templateYLabel[1] in the United States .
generated: This statistic shows the Market cap in NASDAQ exchange 1999 2013 titleErr titleErr from 1999 to 2013 . In 2013 , there were approximately 2.54 million cap in the United States .


Example 99:
data: Year|2019|x|line_chart Unemployment_rate|17.71|y|line_chart Year|2018|x|line_chart Unemployment_rate|17.71|y|line_chart Year|2017|x|line_chart Unemployment_rate|17.83|y|line_chart Year|2016|x|line_chart Unemployment_rate|17.62|y|line_chart Year|2015|x|line_chart Unemployment_rate|18.26|y|line_chart Year|2014|x|line_chart Unemployment_rate|17.5|y|line_chart Year|2013|x|line_chart Unemployment_rate|16.18|y|line_chart Year|2012|x|line_chart Unemployment_rate|17.31|y|line_chart Year|2011|x|line_chart Unemployment_rate|18.44|y|line_chart Year|2010|x|line_chart Unemployment_rate|19.01|y|line_chart Year|2009|x|line_chart Unemployment_rate|18.74|y|line_chart Year|2008|x|line_chart Unemployment_rate|16.37|y|line_chart Year|2007|x|line_chart Unemployment_rate|9.81|y|line_chart Year|2006|x|line_chart Unemployment_rate|10.28|y|line_chart Year|2005|x|line_chart Unemployment_rate|10.8|y|line_chart Year|2004|x|line_chart Unemployment_rate|11.22|y|line_chart Year|2003|x|line_chart Unemployment_rate|11.23|y|line_chart Year|2002|x|line_chart Unemployment_rate|11.52|y|line_chart Year|2001|x|line_chart Unemployment_rate|11.44|y|line_chart Year|2000|x|line_chart Unemployment_rate|11.25|y|line_chart Year|1999|x|line_chart Unemployment_rate|11.2|y|line_chart 
title: Unemployment rate in Armenia 2019

gold: This statistic shows the unemployment rate in Armenia from 1999 to 2019 . In 2019 , the unemployment rate in Armenia was estimated to be 17.71 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was estimated to be templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Armenia from 1999 to 2019 . In 2019 , the Unemployment rate in Armenia was at approximately 17.71 percent .


Example 100:
data: Year|2050|x|line_chart Median_age_in_years|39.4|y|line_chart Year|2045|x|line_chart Median_age_in_years|38.0|y|line_chart Year|2040|x|line_chart Median_age_in_years|36.8|y|line_chart Year|2035|x|line_chart Median_age_in_years|35.6|y|line_chart Year|2030|x|line_chart Median_age_in_years|34.2|y|line_chart Year|2025|x|line_chart Median_age_in_years|32.6|y|line_chart Year|2020|x|line_chart Median_age_in_years|31.0|y|line_chart Year|2015|x|line_chart Median_age_in_years|27.5|y|line_chart Year|2010|x|line_chart Median_age_in_years|25.5|y|line_chart Year|2005|x|line_chart Median_age_in_years|24.1|y|line_chart Year|2000|x|line_chart Median_age_in_years|22.7|y|line_chart Year|1995|x|line_chart Median_age_in_years|21.2|y|line_chart Year|1990|x|line_chart Median_age_in_years|20.1|y|line_chart Year|1985|x|line_chart Median_age_in_years|19.2|y|line_chart Year|1980|x|line_chart Median_age_in_years|18.5|y|line_chart Year|1975|x|line_chart Median_age_in_years|17.9|y|line_chart Year|1970|x|line_chart Median_age_in_years|17.6|y|line_chart Year|1965|x|line_chart Median_age_in_years|17.7|y|line_chart Year|1960|x|line_chart Median_age_in_years|18.2|y|line_chart Year|1955|x|line_chart Median_age_in_years|18.7|y|line_chart Year|1950|x|line_chart Median_age_in_years|19.2|y|line_chart 
title: Median age of the population in Peru 2015

gold: This statistic shows the median age of the population in Peru from 1950 to 2050*.The median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population . In 2015 , the median age of the population of Peru was 27.5 years .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitle[3] from templateXValue[min] to 2050*.The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of the templateTitle[2] of templateTitle[3] was templateYValue[7] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .
generated: This statistic shows the Median age of the population in Peru from 1950 to 2050 . The Median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population .


Example 101:
data: Month|Personal_freedom|x|bar_chart Share_of_respondents|66|y|bar_chart Month|Religious_freedom|x|bar_chart Share_of_respondents|56|y|bar_chart Month|Equality|x|bar_chart Share_of_respondents|55|y|bar_chart Month|Security|x|bar_chart Share_of_respondents|54|y|bar_chart Month|The_pursuit_of_happiness|x|bar_chart Share_of_respondents|53|y|bar_chart Month|Economic_freedom|x|bar_chart Share_of_respondents|51|y|bar_chart Month|Freedom_of_justice|x|bar_chart Share_of_respondents|48|y|bar_chart Month|Political_freedom|x|bar_chart Share_of_respondents|45|y|bar_chart Month|Common_good|x|bar_chart Share_of_respondents|44|y|bar_chart Month|Diversity|x|bar_chart Share_of_respondents|42|y|bar_chart Month|Freedom_of_the_press|x|bar_chart Share_of_respondents|40|y|bar_chart Month|Progress_and_change|x|bar_chart Share_of_respondents|39|y|bar_chart Month|Patriotism|x|bar_chart Share_of_respondents|37|y|bar_chart Month|Scientific_progress|x|bar_chart Share_of_respondents|36|y|bar_chart Month|Separation_of_powers|x|bar_chart Share_of_respondents|36|y|bar_chart Month|Individualism|x|bar_chart Share_of_respondents|34|y|bar_chart Month|Action_and_achievement|x|bar_chart Share_of_respondents|34|y|bar_chart Month|Competition|x|bar_chart Share_of_respondents|30|y|bar_chart Month|Capitalism|x|bar_chart Share_of_respondents|28|y|bar_chart Month|Solidarity|x|bar_chart Share_of_respondents|21|y|bar_chart Month|Volunteerism|x|bar_chart Share_of_respondents|19|y|bar_chart Month|None_of_the_above|x|bar_chart Share_of_respondents|4|y|bar_chart 
title: Americans on the concept of the American Dream in 2017

gold: This statistic shows the results of a 2017 survey among Americans on the concepts essential for the American Dream . During the survey , 34 percent of respondents stated that action and achievement is essential for the American Dream .
gold_template: This statistic shows the results of a templateTitle[4] survey among templateTitle[0] on the concepts essential for the templateTitle[2] templateTitle[3] . During the survey , templateYValue[15] percent of templateYLabel[1] stated that templateXValue[16] and templateXValue[16] is essential for the templateTitle[2] templateTitle[3] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[2] templateTitle[4] templateTitle[5] in the United States as of July templateTitle[6] . During the survey period it was found that templateYValue[0] percent of templateXValue[0] templateXValue[0] to templateXValue[0] templateXValue[0] .
generated: This statistic shows the Share of Americans concept American Dream American 2017 titleErr in the United States as of July titleErr . During the survey period it was found that 66 percent of Personal_freedom to Personal_freedom .


Example 102:
data: Year|2013|x|line_chart Attendance_in_millions|73.54|y|line_chart Year|2012|x|line_chart Attendance_in_millions|74.94|y|line_chart Year|2011|x|line_chart Attendance_in_millions|77.33|y|line_chart Year|2010|x|line_chart Attendance_in_millions|74.98|y|line_chart Year|2009|x|line_chart Attendance_in_millions|77.49|y|line_chart Year|2008|x|line_chart Attendance_in_millions|76.68|y|line_chart Year|2007|x|line_chart Attendance_in_millions|80.13|y|line_chart Year|2006|x|line_chart Attendance_in_millions|82.28|y|line_chart Year|2005|x|line_chart Attendance_in_millions|84.48|y|line_chart Year|2004|x|line_chart Attendance_in_millions|85.6|y|line_chart Year|2003|x|line_chart Attendance_in_millions|86.38|y|line_chart 
title: Attendance at performing arts events in the U.S. 2003 - 2013

gold: The statistic above shows the attendance at performing arts events in the United States from 2003 to 2013 . In year 2013 , approximately 73.54 million people attended a performing arts event at least once . Performing arts – additional information Performing arts are any type of art form in which a performer physically conveys an artistic piece ; this includes dance , opera , symphony and theatre performances .
gold_template: The statistic above shows the templateYLabel[0] at templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXLabel[0] templateXValue[max] , approximately templateYValue[min] million people attended a templateTitle[1] templateTitle[2] event at least once . templateTitle[1] templateTitle[2] – additional information templateTitle[1] templateTitle[2] are any type of art form in which a performer physically conveys an artistic piece ; this includes dance , opera , symphony and theatre performances .

generated_template: This statistic depicts the templateTitle[0] templateYLabel[0] at templateTitle[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] at templateYValue[max] million templateTitle[5] templateTitle[2] in the United States . The highest rate was founded in 1997 .
generated: This statistic depicts the Attendance Attendance at arts in the United States from 2003 to 2013 . In 2013 , Attendance performing at 86.38 million 2003 arts in the United States . The highest rate was founded in 1997 .


Example 103:
data: Quarter|Q1_2019|x|bar_chart Average_revenue_per_user_in_GBP_per_month|37.9|y|bar_chart Quarter|Q4_2018|x|bar_chart Average_revenue_per_user_in_GBP_per_month|38.8|y|bar_chart Quarter|Q3_2018|x|bar_chart Average_revenue_per_user_in_GBP_per_month|39.6|y|bar_chart Quarter|Q2_2018|x|bar_chart Average_revenue_per_user_in_GBP_per_month|38.3|y|bar_chart Quarter|Q1_2018|x|bar_chart Average_revenue_per_user_in_GBP_per_month|37.9|y|bar_chart Quarter|Q4_2017|x|bar_chart Average_revenue_per_user_in_GBP_per_month|38.9|y|bar_chart Quarter|Q3_2017|x|bar_chart Average_revenue_per_user_in_GBP_per_month|37.7|y|bar_chart Quarter|Q2_2017|x|bar_chart Average_revenue_per_user_in_GBP_per_month|37.8|y|bar_chart Quarter|Q1_2017|x|bar_chart Average_revenue_per_user_in_GBP_per_month|37.6|y|bar_chart Quarter|Q4_2016|x|bar_chart Average_revenue_per_user_in_GBP_per_month|36.7|y|bar_chart Quarter|Q3_2016|x|bar_chart Average_revenue_per_user_in_GBP_per_month|37.3|y|bar_chart Quarter|Q2_2016|x|bar_chart Average_revenue_per_user_in_GBP_per_month|37.4|y|bar_chart Quarter|Q1_2016|x|bar_chart Average_revenue_per_user_in_GBP_per_month|35.0|y|bar_chart Quarter|Q4_2015|x|bar_chart Average_revenue_per_user_in_GBP_per_month|37.1|y|bar_chart Quarter|Q3_2015|x|bar_chart Average_revenue_per_user_in_GBP_per_month|36.6|y|bar_chart Quarter|Q2_2015|x|bar_chart Average_revenue_per_user_in_GBP_per_month|35.6|y|bar_chart Quarter|Q1_2015|x|bar_chart Average_revenue_per_user_in_GBP_per_month|34.9|y|bar_chart Quarter|Q4_2014|x|bar_chart Average_revenue_per_user_in_GBP_per_month|34.6|y|bar_chart Quarter|Q3_2014|x|bar_chart Average_revenue_per_user_in_GBP_per_month|34.2|y|bar_chart Quarter|Q2_2014|x|bar_chart Average_revenue_per_user_in_GBP_per_month|33.6|y|bar_chart Quarter|Q1_2014|x|bar_chart Average_revenue_per_user_in_GBP_per_month|33.2|y|bar_chart 
title: British Telecommunications ( BT ) : consumer ARPU Q1 2014/15-Q1 2019/20

gold: This statistic shows the Consumer ARPU of British Telecommunications ( BT Consumer ) from the first quarter of 2014/15 to the first quarter of 2019/20 . In the first quarter of 2019/20 ending June 30 , the ARPU was 37.9 British pounds per month .
gold_template: This statistic shows the templateTitle[3] templateTitle[4] of templateTitle[0] templateTitle[1] ( templateTitle[2] templateTitle[3] ) from the first templateXLabel[0] of 2014/15 to the first templateXLabel[0] of templateTitle[7] . In the first templateXLabel[0] of templateTitle[7] ending June 30 , the templateTitle[4] was templateYValue[0] templateTitle[0] pounds templateYLabel[2] templateYLabel[6] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in the United States from templateXValue[last] to templateXValue[0] . In the first templateXLabel[0] of templateXValue[0] , there were templateYValue[0] people living in the United States .
generated: This statistic shows the Average revenue of ARPU Q1 in the United States from Q1_2014 to Q1_2019 . In the first Quarter of Q1_2019 , there were 37.9 people living in the United States .


Example 104:
data: Year|2017|x|line_chart Net_income_in_million_U.S._dollars|112.3|y|line_chart Year|2016|x|line_chart Net_income_in_million_U.S._dollars|170.5|y|line_chart Year|2015|x|line_chart Net_income_in_million_U.S._dollars|153.2|y|line_chart Year|2014|x|line_chart Net_income_in_million_U.S._dollars|105.2|y|line_chart Year|2013|x|line_chart Net_income_in_million_U.S._dollars|157.6|y|line_chart Year|2012|x|line_chart Net_income_in_million_U.S._dollars|142.2|y|line_chart Year|2011|x|line_chart Net_income_in_million_U.S._dollars|40.1|y|line_chart Year|2010|x|line_chart Net_income_in_million_U.S._dollars|77.6|y|line_chart Year|2009|x|line_chart Net_income_in_million_U.S._dollars|95.5|y|line_chart Year|2008|x|line_chart Net_income_in_million_U.S._dollars|112.2|y|line_chart Year|2007|x|line_chart Net_income_in_million_U.S._dollars|360.4|y|line_chart Year|2006|x|line_chart Net_income_in_million_U.S._dollars|104.3|y|line_chart 
title: Regal Entertainment Group 's net income 2006 - 2017

gold: This statistic contains income data for the Regal Entertainment Group from 2006 to 2017 . In 2017 , REG reported a net profit of 112.3 million U.S dollars .
gold_template: This statistic contains templateYLabel[1] data for the templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , REG reported a templateYLabel[0] profit of templateYValue[0] templateYLabel[2] U.S templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] had a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . This represented a 70 percent decrease over templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[1] .
generated: This statistic shows the Net income of Group 's net income 2006 from 2006 to 2017 . In 2017 , Regal Entertainment Group 's net had a Net income of 360.4 million U.S. dollars . This represented a 70 percent decrease over 170.5 million U.S. dollars in 2016 .


Example 105:
data: Year|2024|x|line_chart GDP_growth_compared_to_previous_year|3.76|y|line_chart Year|2023|x|line_chart GDP_growth_compared_to_previous_year|3.9|y|line_chart Year|2022|x|line_chart GDP_growth_compared_to_previous_year|3.99|y|line_chart Year|2021|x|line_chart GDP_growth_compared_to_previous_year|4|y|line_chart Year|2020|x|line_chart GDP_growth_compared_to_previous_year|3.62|y|line_chart Year|2019|x|line_chart GDP_growth_compared_to_previous_year|2.6|y|line_chart Year|2018|x|line_chart GDP_growth_compared_to_previous_year|3.99|y|line_chart Year|2017|x|line_chart GDP_growth_compared_to_previous_year|2.47|y|line_chart Year|2016|x|line_chart GDP_growth_compared_to_previous_year|4.05|y|line_chart Year|2015|x|line_chart GDP_growth_compared_to_previous_year|3.27|y|line_chart Year|2014|x|line_chart GDP_growth_compared_to_previous_year|2.39|y|line_chart 
title: Gross domestic product ( GDP ) growth rate in Peru 2024

gold: The statistic shows the growth in real GDP in Peru from 2014 to 2018 , with projections up until 2024 . In 2018 , Peru 's real gross domestic product grew by around 3.99 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[2] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Peru from 2014 to 2018 , with projections up until 2024 . In 2018 , Peru 's real Gross domestic product increased by around 3.99 percent compared to the previous Year .


Example 106:
data: Brand|Liftmaster|x|bar_chart Share_of_respondents|50|y|bar_chart Brand|Chamberlain|x|bar_chart Share_of_respondents|14.9|y|bar_chart Brand|Overhead_Door|x|bar_chart Share_of_respondents|9.6|y|bar_chart Brand|Genie|x|bar_chart Share_of_respondents|7.9|y|bar_chart Brand|Craftsmen|x|bar_chart Share_of_respondents|5.3|y|bar_chart Brand|Wayne-Dalton|x|bar_chart Share_of_respondents|3.5|y|bar_chart Brand|Raynor|x|bar_chart Share_of_respondents|3.5|y|bar_chart Brand|Linear|x|bar_chart Share_of_respondents|0.9|y|bar_chart Brand|Access_Master|x|bar_chart Share_of_respondents|0.9|y|bar_chart Brand|Marantec|x|bar_chart Share_of_respondents|0.9|y|bar_chart Brand|None_of_these|x|bar_chart Share_of_respondents|2.6|y|bar_chart 
title: Most used garage door openers in the U.S. 2018

gold: This statistic depicts garage door openers brands used the most by U.S. construction firms in 2018 . The survey revealed that 50 percent of the respondents used LiftMaster garage door openers brand the most .
gold_template: This statistic depicts templateTitle[2] templateXValue[2] templateTitle[4] brands templateTitle[1] the templateTitle[0] by templateTitle[5] construction firms in templateTitle[6] . The survey revealed that templateYValue[max] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateTitle[2] templateXValue[2] templateTitle[4] templateXLabel[0] the templateTitle[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] construction construction firms in templateTitle[5] . The survey revealed that templateYValue[max] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateTitle[2] the templateTitle[0] .
generated: The statistic shows the Most used garage door openers construction firms in U.S. . The survey revealed that 50 percent of the respondents used Liftmaster Brand garage the Most .


Example 107:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|16.98|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|22.21|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|26.87|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|32.26|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|51|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|54.44|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|34.28|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|25.68|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|10.62|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|10.04|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|9.78|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|10.46|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|6.27|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|8.59|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|8.83|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|10.9|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|9.64|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|4.42|y|line_chart 
title: Inflation rate in Argentina 2024*

gold: Inflation in Argentina was 34 percent in 2018 , expected to rise to nearly 44 percent the following year before declining to 6 percent by 2024 . A major factor in this is the legacy of poor fiscal discipline left by the economic depression from 1998 to 2002 . What causes inflation ? Inflation is a rise in price levels for all goods .
gold_template: templateYLabel[0] in templateTitle[2] was templateYValue[6] percent in templateXValue[6] , expected to rise to nearly 44 percent the following templateXLabel[0] before declining to templateYValue[12] percent by templateXValue[max] . A major factor in this is the legacy of poor fiscal discipline left by the economic depression from 1998 to 2002 . What causes templateYLabel[0] ? templateYLabel[0] is a rise in price levels for all goods .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the Inflation rate in Argentina from 2004 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Argentina amounted to about 34.28 percent compared to the previous Year .


Example 108:
data: Response|Significantly_decline|x|bar_chart Share_of_respondents|1|y|bar_chart Response|Slightly_decline|x|bar_chart Share_of_respondents|11|y|bar_chart Response|No_change|x|bar_chart Share_of_respondents|14|y|bar_chart Response|Slightly_grow|x|bar_chart Share_of_respondents|56|y|bar_chart Response|Significantly_grow|x|bar_chart Share_of_respondents|18|y|bar_chart 
title: Tourism industry growth expectations according to travel experts 2018

gold: This statistic shows the results of a survey asking travel industry representatives and stakeholders at the London WTM exhibition ( UK ) to what extent do they expect the tourism industry to grow or decline in 2018 . Of respondents , 56 percent believed that the tourism industry will grow slightly in 2018 .
gold_template: This statistic shows the results of a survey asking templateTitle[5] templateTitle[1] representatives and stakeholders at the London WTM exhibition ( UK ) to what extent do they expect the templateTitle[0] templateTitle[1] to templateXValue[3] or templateXValue[0] in templateTitle[7] . Of templateYLabel[1] , templateYValue[max] percent believed that the templateTitle[0] templateTitle[1] will templateXValue[3] templateXValue[1] in templateTitle[7] .

generated_template: This statistic shows the results of a survey among female templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States in templateTitle[8] . During the survey , templateYValue[max] percent of templateYLabel[1] stated they purchased goods and templateXValue[0] templateXValue[0] templateXValue[0] a templateXValue[0] basis .
generated: This statistic shows the results of a survey among female industry growth expectations according travel in the United States in titleErr . During the survey , 56 percent of respondents stated they purchased goods and Significantly_decline Significantly_decline a Significantly_decline basis .


Example 109:
data: Response|Crime|x|bar_chart Share_of_respondents|38.2|y|bar_chart Response|Unemployment|x|bar_chart Share_of_respondents|8.8|y|bar_chart Response|Corruption|x|bar_chart Share_of_respondents|6.2|y|bar_chart Response|Health|x|bar_chart Share_of_respondents|6|y|bar_chart Response|Education|x|bar_chart Share_of_respondents|5.8|y|bar_chart Response|Low_salaries|x|bar_chart Share_of_respondents|4.4|y|bar_chart Response|Violation_of_human_rights|x|bar_chart Share_of_respondents|3.9|y|bar_chart Response|Immigrants|x|bar_chart Share_of_respondents|3.2|y|bar_chart Response|Income_distribution|x|bar_chart Share_of_respondents|2.8|y|bar_chart Response|Lack_of_care_for_the_elderly|x|bar_chart Share_of_respondents|2.8|y|bar_chart Response|Political_situation|x|bar_chart Share_of_respondents|2.7|y|bar_chart Response|The_economy|x|bar_chart Share_of_respondents|2.3|y|bar_chart Response|Poverty|x|bar_chart Share_of_respondents|1.8|y|bar_chart Response|Drug_consumption|x|bar_chart Share_of_respondents|1.6|y|bar_chart 
title: Chile : public perception on the country 's main problems in 2018

gold: During a survey conducted in 2018 , over 38 percent of respondents in Chile mentioned criminality as one of the most important problems affecting the South American country . Furthermore , 3.2 percent of the people interviewed quoted immigrants as one of the main problems in their country , leaving behind income distribution , the political situation and the economy.Up to 45 percent of the people surveyed in Chile claim that most religious leaders are involved in acts of corruption .
gold_template: During a survey conducted in templateTitle[7] , over templateYValue[max] percent of templateYLabel[1] in templateTitle[0] mentioned criminality as one of the most important templateTitle[6] affecting the South American templateTitle[3] . Furthermore , templateYValue[7] percent of the people interviewed quoted templateXValue[7] as one of the templateTitle[5] templateTitle[6] in their templateTitle[3] , leaving behind templateXValue[8] templateXValue[8] , the templateXValue[10] templateXValue[10] and the economy.Up to 45 percent of the people surveyed in templateTitle[0] claim that most religious leaders are involved in acts of templateXValue[2] .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[5] according to a survey conducted in templateTitle[7] . templateYValue[max] percent of templateYLabel[1] said that they planned to visit a templateXValue[1] via templateXValue[0] recommendations.Eyewear in the United States .
generated: This statistic shows the results of a survey conducted in the United States in main according to a survey conducted in 2018 . 38.2 percent of respondents said that they planned to visit a Unemployment via Crime recommendations.Eyewear in the United States .


Example 110:
data: Year|2019|x|line_chart Home_attendance|177755|y|line_chart Year|2018|x|line_chart Home_attendance|262145|y|line_chart Year|2017|x|line_chart Home_attendance|202687|y|line_chart Year|2016|x|line_chart Home_attendance|456197|y|line_chart Year|2015|x|line_chart Home_attendance|534180|y|line_chart Year|2014|x|line_chart Home_attendance|523457|y|line_chart Year|2013|x|line_chart Home_attendance|513641|y|line_chart Year|2012|x|line_chart Home_attendance|479716|y|line_chart Year|2011|x|line_chart Home_attendance|523143|y|line_chart Year|2010|x|line_chart Home_attendance|524240|y|line_chart Year|2009|x|line_chart Home_attendance|540344|y|line_chart Year|2008|x|line_chart Home_attendance|545104|y|line_chart Year|2007|x|line_chart Home_attendance|524016|y|line_chart Year|2006|x|line_chart Home_attendance|531024|y|line_chart 
title: Regular season home attendance of the Los Angeles Chargers 2006 - 2019

gold: This graph depicts the total regular season home attendance of the Los Angeles Chargers franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 177,755 .
gold_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[min] .

generated_template: This graph depicts the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] .
generated: This graph depicts the Regular season Home attendance of the Los Angeles franchise of the National Football League from 2006 to 2019 . In 2019 , the Regular season home Home attendance of the franchise was 177755 .


Example 111:
data: Year|2019|x|line_chart Index_points|5978.06|y|line_chart Year|2018|x|line_chart Index_points|4730.69|y|line_chart Year|2017|x|line_chart Index_points|5312.56|y|line_chart Year|2016|x|line_chart Index_points|4862.31|y|line_chart Year|2015|x|line_chart Index_points|4637.06|y|line_chart Year|2014|x|line_chart Index_points|4272.75|y|line_chart Year|2013|x|line_chart Index_points|4295.95|y|line_chart Year|2012|x|line_chart Index_points|3641.07|y|line_chart Year|2011|x|line_chart Index_points|3159.81|y|line_chart Year|2010|x|line_chart Index_points|3804.78|y|line_chart Year|2009|x|line_chart Index_points|3936.33|y|line_chart Year|2008|x|line_chart Index_points|3217.97|y|line_chart Year|2007|x|line_chart Index_points|5614.08|y|line_chart Year|2006|x|line_chart Index_points|5541.76|y|line_chart Year|2005|x|line_chart Index_points|4715.23|y|line_chart Year|2004|x|line_chart Index_points|3821.16|y|line_chart Year|2003|x|line_chart Index_points|3557.9|y|line_chart Year|2002|x|line_chart Index_points|3063.91|y|line_chart Year|2001|x|line_chart Index_points|4624.58|y|line_chart Year|2000|x|line_chart Index_points|5926.42|y|line_chart Year|1999|x|line_chart Index_points|5958.32|y|line_chart Year|1998|x|line_chart Index_points|3942.66|y|line_chart Year|1997|x|line_chart Index_points|2998.9|y|line_chart Year|1996|x|line_chart Index_points|2315.7|y|line_chart Year|1995|x|line_chart Index_points|1872.0|y|line_chart 
title: Annual CAC 40 performance 1995 - 2019

gold: The statistic presents the annual development of the CAC 40 index from 1995 to 2019 . CAC 40 is the leading indicator of the French stock market . The year-end value of the CAC 40 index amounted to 1,872 in 1995 and reached 5,978.05 in 2019 .
gold_template: The statistic presents the templateTitle[0] development of the templateTitle[1] templateTitle[2] templateYLabel[0] from templateXValue[min] to templateXValue[max] . templateTitle[1] templateTitle[2] is the leading indicator of the French stock market . The year-end value of the templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[min] in templateXValue[min] and reached 5,978.05 in templateXValue[max] .

generated_template: The statistic shows the templateTitle[0] development of the templateTitle[1] templateTitle[2] templateYLabel[0] worldwide between templateXValue[min] and templateXValue[max] . According to the source , templateXValue[1] the source around templateYValue[1] templateYLabel[1] templateYLabel[2] of all templateYLabel[2] in the United States . Between templateXValue[min] and templateXValue[9] , templateXValue[1] marked an annual growth of templateYValue[0] percent of templateYLabel[1] .
generated: The statistic shows the Annual development of the CAC 40 Index worldwide between 1995 and 2019 . According to the source , 2018 the source around 4730.69 points yLabelErr of all yLabelErr in the United States . Between 1995 and 2010 , 2018 marked an annual growth of 5978.06 percent of points .


Example 112:
data: Console|Xbox_One_S|x|bar_chart Installed_base_in_millions|9|y|bar_chart Console|Xbox_One|x|bar_chart Installed_base_in_millions|12|y|bar_chart Console|Xbox_360|x|bar_chart Installed_base_in_millions|21|y|bar_chart Console|Xbox|x|bar_chart Installed_base_in_millions|5|y|bar_chart Console|PlayStation_4|x|bar_chart Installed_base_in_millions|10|y|bar_chart Console|PlayStation_3|x|bar_chart Installed_base_in_millions|13|y|bar_chart Console|PlayStation_2|x|bar_chart Installed_base_in_millions|14|y|bar_chart Console|Wii_U|x|bar_chart Installed_base_in_millions|6|y|bar_chart Console|Wii|x|bar_chart Installed_base_in_millions|16|y|bar_chart 
title: Video game systems : U.S. installed base in 2017 , by platform

gold: The statistic depicts the installed base of video game systems in the United States in 2017 , by platform . The installed base of Microsoft 's Xbox 360 was 21 million .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] in templateTitle[6] , templateTitle[7] templateTitle[8] . The templateYLabel[0] templateYLabel[1] of Microsoft 's templateXValue[0] templateXValue[2] was templateYValue[max] million .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] . According to the report , templateYValue[max] percent of templateYLabel[1] aged between templateXValue[0] and templateXValue[0] templateXValue[0] templateXValue[0] in the United States .
generated: This statistic shows the results of a survey conducted in the United States in installed , base Console . According to the report , 21 percent of base aged between Xbox_One_S and Xbox_One_S Xbox_One_S in the United States .


Example 113:
data: Year|2018|x|line_chart Capital_expenditure_in_million_U.S._dollars|33200|y|line_chart Year|2017|x|line_chart Capital_expenditure_in_million_U.S._dollars|31900|y|line_chart Year|2016|x|line_chart Capital_expenditure_in_million_U.S._dollars|31100|y|line_chart Year|2015|x|line_chart Capital_expenditure_in_million_U.S._dollars|31000|y|line_chart Year|2014|x|line_chart Capital_expenditure_in_million_U.S._dollars|26700|y|line_chart Year|2013|x|line_chart Capital_expenditure_in_million_U.S._dollars|28900|y|line_chart Year|2012|x|line_chart Capital_expenditure_in_million_U.S._dollars|28800|y|line_chart Year|2011|x|line_chart Capital_expenditure_in_million_U.S._dollars|25758|y|line_chart Year|2010|x|line_chart Capital_expenditure_in_million_U.S._dollars|20339|y|line_chart Year|2009|x|line_chart Capital_expenditure_in_million_U.S._dollars|23625|y|line_chart Year|2008|x|line_chart Capital_expenditure_in_million_U.S._dollars|23839|y|line_chart Year|2007|x|line_chart Capital_expenditure_in_million_U.S._dollars|27412|y|line_chart Year|2006|x|line_chart Capital_expenditure_in_million_U.S._dollars|18151|y|line_chart Year|2005|x|line_chart Capital_expenditure_in_million_U.S._dollars|18056|y|line_chart Year|2004|x|line_chart Capital_expenditure_in_million_U.S._dollars|18857|y|line_chart 
title: Capital expenditure of the U.S. chemical industry 2004 - 2018

gold: This statistic shows the capital expenditure level for the United States chemical industry from 2004 to 2018 . In 2018 , the U.S. chemical industry capital expenditure reached approximately 33.2 billion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] level for the templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[3] templateTitle[3] templateTitle[4] templateYLabel[0] templateYLabel[1] reached approximately templateYValue[max] billion templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] of the templateTitle[3] templateTitle[4] templateTitle[5] Plc from templateXValue[min] to templateXValue[max] , in templateYLabel[2] templateYLabel[3] . In templateXValue[max] , the templateYLabel[1] templateYLabel[2] of templateTitle[5] was approximately templateYValue[0] percent .
generated: This statistic shows the Capital expenditure of million of the chemical industry 2004 Plc from 2004 to 2018 , in million U.S. . In 2018 , the expenditure million of 2004 was approximately 33200 percent .


Example 114:
data: Month|September_2006|x|bar_chart Number_of_players_(in_millions)|17.8|y|bar_chart Month|January_2007|x|bar_chart Number_of_players_(in_millions)|15.2|y|bar_chart Month|Spring_2008|x|bar_chart Number_of_players_(in_millions)|20.0|y|bar_chart Month|Spring_2009|x|bar_chart Number_of_players_(in_millions)|20.8|y|bar_chart Month|Spring_2010|x|bar_chart Number_of_players_(in_millions)|22.2|y|bar_chart 
title: Number of poker players who play for money

gold: This statistic shows the number of poker players worldwide who play for money , from 2006 to 2010 ( in millions ) . In Spring 2008 , 20 million people played poker online for money .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] worldwide templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] , from templateXValue[0] to templateXValue[last] ( in millions ) . In templateXValue[2] templateXValue[2] , templateYValue[2] million people played templateTitle[1] online templateTitle[5] templateTitle[6] .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[1] templateYLabel[1] in templateTitle[4] in the United States in templateTitle[5] . In templateTitle[7] , about templateYValue[max] percent of the templateYLabel[1] were reported in the United States .
generated: This statistic shows the Number poker players in play in the United States in for . In titleErr , about 22.2 percent of the players were reported in the United States .


Example 115:
data: Year|2009|x|line_chart Average_price_in_U.S._dollars|33.95|y|line_chart Year|2010|x|line_chart Average_price_in_U.S._dollars|37.03|y|line_chart Year|2011|x|line_chart Average_price_in_U.S._dollars|36.63|y|line_chart Year|2012|x|line_chart Average_price_in_U.S._dollars|39.16|y|line_chart Year|2013|x|line_chart Average_price_in_U.S._dollars|34.02|y|line_chart Year|2014|x|line_chart Average_price_in_U.S._dollars|34.01|y|line_chart 
title: Average price for a full set of gel toenails in nail salons in the U.S. 2009 - 2014

gold: This statistic shows the average price for a full set of gel toenails in nail salons in the United States from 2009 to 2014 . Nail salons charged an average price of 34.01 U.S. dollars for a full set of gel toenails in 2014 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] a templateTitle[3] templateTitle[4] of templateTitle[5] templateTitle[6] in templateTitle[7] templateTitle[8] in the templateTitle[9] from templateXValue[min] to templateXValue[max] . templateTitle[7] templateTitle[8] charged an templateYLabel[0] templateYLabel[1] of templateYValue[5] templateYLabel[2] templateYLabel[3] templateTitle[2] a templateTitle[3] templateTitle[4] of templateTitle[5] templateTitle[6] in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] templateTitle[4] at templateYValue[0] templateYLabel[2] templateYLabel[3] in templateTitle[5] , up from templateYValue[1] templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] .
generated: The statistic shows the Average price of for full set in gel from 2009 to 2014 . In 2014 , full set at 33.95 U.S. dollars in gel , up from 37.03 U.S. dollars in the previous Year .


Example 116:
data: Platform|WhatsApp|x|bar_chart Monthly_active_users_in_millions|1600|y|bar_chart Platform|Facebook_Messenger|x|bar_chart Monthly_active_users_in_millions|1300|y|bar_chart Platform|WeChat|x|bar_chart Monthly_active_users_in_millions|1133|y|bar_chart Platform|QQ_Mobile|x|bar_chart Monthly_active_users_in_millions|808|y|bar_chart Platform|Snapchat|x|bar_chart Monthly_active_users_in_millions|314|y|bar_chart Platform|Telegram|x|bar_chart Monthly_active_users_in_millions|200|y|bar_chart 
title: Most popular global mobile messaging apps 2019

gold: As of October 2019 , 1.6 billion users were accessing the WhatsApp messenger on a monthly basis . The app 's reach is usage penetration is particularly strong in markets outside of the United States and it is one of the most popular mobile social apps worldwide . In February 2014 , social network Facebook acquired the mobile app for 19 billion U.S. dollars .
gold_template: As of October templateTitle[6] , templateYValue[max] billion templateYLabel[2] were accessing the templateXValue[0] templateXValue[1] on a templateYLabel[0] basis . The app 's reach is usage penetration is particularly strong in markets outside of the United States and it is one of the templateTitle[0] templateTitle[1] templateXValue[3] social templateTitle[5] worldwide . In February 2014 , social network templateXValue[1] acquired the templateXValue[3] app for 19 billion U.S. dollars .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States as of July templateTitle[5] , templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateXValue[0] was ranked first with a templateYLabel[0] of templateYValue[max] million templateYLabel[2] .
generated: This statistic gives information on the Most popular global mobile messaging in the United States as of July apps , 2019 titleErr titleErr . During that period of time , WhatsApp was ranked first with a Monthly of 1600 million users .


Example 117:
data: Month|Nov_'19|x|bar_chart Inflation_rate|1.5|y|bar_chart Month|Oct_'19|x|bar_chart Inflation_rate|1.5|y|bar_chart Month|Sep_'19|x|bar_chart Inflation_rate|1.7|y|bar_chart Month|Aug_'19|x|bar_chart Inflation_rate|1.7|y|bar_chart Month|Jul_'19|x|bar_chart Inflation_rate|2.1|y|bar_chart Month|Jun_'19|x|bar_chart Inflation_rate|2|y|bar_chart Month|May_'19|x|bar_chart Inflation_rate|2|y|bar_chart Month|Apr_'19|x|bar_chart Inflation_rate|2.1|y|bar_chart Month|Mar_'19|x|bar_chart Inflation_rate|1.9|y|bar_chart Month|Feb_'19|x|bar_chart Inflation_rate|1.9|y|bar_chart Month|Jan_'19|x|bar_chart Inflation_rate|1.8|y|bar_chart Month|Dec_'18|x|bar_chart Inflation_rate|2.1|y|bar_chart Month|Nov_'18|x|bar_chart Inflation_rate|2.3|y|bar_chart Month|Oct_'18|x|bar_chart Inflation_rate|2.4|y|bar_chart Month|Sep_'18|x|bar_chart Inflation_rate|2.4|y|bar_chart Month|Aug_'18|x|bar_chart Inflation_rate|2.7|y|bar_chart Month|Jul_'18|x|bar_chart Inflation_rate|2.5|y|bar_chart Month|Jun_'18|x|bar_chart Inflation_rate|2.4|y|bar_chart Month|May_'18|x|bar_chart Inflation_rate|2.4|y|bar_chart Month|Apr_'18|x|bar_chart Inflation_rate|2.4|y|bar_chart Month|Mar_'18|x|bar_chart Inflation_rate|2.5|y|bar_chart Month|Feb_'18|x|bar_chart Inflation_rate|2.7|y|bar_chart Month|Jan_'18|x|bar_chart Inflation_rate|3|y|bar_chart Month|Dec_'17|x|bar_chart Inflation_rate|3|y|bar_chart Month|Nov_'17|x|bar_chart Inflation_rate|3.1|y|bar_chart 
title: Inflation rate ( CPI ) in the United Kingdom ( UK ) 2017 - 2019

gold: The Consumer Price Index ( CPI ) rate of the United Kingdom in November 2019 was 1.5 percent , which together with the previous month , was the lowest rate recorded in this two year period . Between November 2017 and November 2019 the CPI rate was at it 's highest in November of 2017 , when an inflation rate of 3.1 percent was recorded .
gold_template: The Consumer Price Index ( templateTitle[2] ) templateYLabel[1] of the templateTitle[3] templateTitle[4] in November templateTitle[7] was templateYValue[min] percent , which together with the previous templateXLabel[0] , was the lowest templateYLabel[1] recorded in this templateYValue[min] year period . Between November templateTitle[6] and November templateTitle[7] the templateTitle[2] templateYLabel[1] was at it 's highest in November of templateTitle[6] , when an templateYLabel[0] templateYLabel[1] of templateYValue[max] percent was recorded .

generated_template: Between January templateTitle[8] and January 2020 , templateTitle[3] prices in the templateTitle[4] templateTitle[5] increased by templateYValue[0] percent . A period of continuous deflation between March templateTitle[7] and January 2017 preceded a return to a sustained rise of the cost of templateTitle[3] from February 2017 onwards . templateYLabel[0] templateYLabel[1] and consumer price index templateYLabel[0] is commonly measured via the consumer price index , which illustrates changes to prices paid by consumers templateTitle[2] a representative basket of goods and services .
generated: Between January titleErr and January 2020 , United prices in the Kingdom UK increased by 1.5 percent . A period of continuous deflation between March 2019 and January 2017 preceded a return to a sustained rise of the cost of United from February 2017 onwards . Inflation rate and consumer price index Inflation is commonly measured via the consumer price index , which illustrates changes to prices paid by consumers CPI a representative basket of goods and services .


Example 118:
data: Year|2017|x|line_chart Spending_in_billion_U.S._dollars|5.75|y|line_chart Year|2016|x|line_chart Spending_in_billion_U.S._dollars|5.58|y|line_chart Year|2015|x|line_chart Spending_in_billion_U.S._dollars|5.43|y|line_chart Year|2014|x|line_chart Spending_in_billion_U.S._dollars|5.26|y|line_chart Year|2013|x|line_chart Spending_in_billion_U.S._dollars|5.12|y|line_chart Year|2012|x|line_chart Spending_in_billion_U.S._dollars|4.97|y|line_chart Year|2011|x|line_chart Spending_in_billion_U.S._dollars|4.83|y|line_chart 
title: Global spending on motorsports sponsorships 2011 - 2017

gold: This statistic shows the worldwide spending for motorsports sponsorship from 2011 to 2017 . In the year 2013 , the global motorsports sponsorship spending amounted to 5.12 billion U.S. dollars .
gold_template: This statistic shows the worldwide templateYLabel[0] for templateTitle[2] sponsorship from templateXValue[min] to templateXValue[max] . In the templateXLabel[0] templateXValue[4] , the templateTitle[0] templateTitle[2] sponsorship templateYLabel[0] amounted to templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic depicts the total templateTitle[0] templateYLabel[0] in the global templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . The global templateTitle[2] templateTitle[3] templateYLabel[0] amounted to about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic depicts the total Global Spending in the global motorsports sponsorships from 2011 to 2017 . The global motorsports sponsorships Spending amounted to about 5.75 billion U.S. dollars .


Example 119:
data: Year|2018|x|line_chart Number_of_participants_in_millions|5.23|y|line_chart Year|2017|x|line_chart Number_of_participants_in_millions|5.4|y|line_chart Year|2016|x|line_chart Number_of_participants_in_millions|5.12|y|line_chart Year|2015|x|line_chart Number_of_participants_in_millions|4.81|y|line_chart Year|2014|x|line_chart Number_of_participants_in_millions|4.53|y|line_chart Year|2013|x|line_chart Number_of_participants_in_millions|4.8|y|line_chart Year|2012|x|line_chart Number_of_participants_in_millions|4.9|y|line_chart Year|2011|x|line_chart Number_of_participants_in_millions|4.34|y|line_chart Year|2010|x|line_chart Number_of_participants_in_millions|4.93|y|line_chart Year|2009|x|line_chart Number_of_participants_in_millions|4.91|y|line_chart Year|2008|x|line_chart Number_of_participants_in_millions|4.74|y|line_chart Year|2007|x|line_chart Number_of_participants_in_millions|4.24|y|line_chart Year|2006|x|line_chart Number_of_participants_in_millions|4.7|y|line_chart 
title: Participants in indoor soccer in the U.S. from 2006 to 2018

gold: This statistic shows the number of participants in indoor soccer in the United States from 2006 to 2018 . In 2018 , the number of participants ( aged six years and older ) in indoor soccer amounted to approximately 5.23 million . All in all , the number of soccer players in the United States seems to be on the rise .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[0] million . All in all , the templateYLabel[0] of templateTitle[2] players in the templateTitle[3] seems to be on the rise .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the United States templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[0] million .
generated: This statistic shows the Number of participants in indoor in the United States U.S. 2006 to 2018 . In 2018 , the Number of participants ( aged six years and older ) in indoor amounted to approximately 5.23 million .


Example 120:
data: Year|2018|x|line_chart Per_capita_consumption_in_pounds|8.0|y|line_chart Year|2017|x|line_chart Per_capita_consumption_in_pounds|7.4|y|line_chart Year|2016|x|line_chart Per_capita_consumption_in_pounds|8.1|y|line_chart Year|2015|x|line_chart Per_capita_consumption_in_pounds|7.5|y|line_chart Year|2014|x|line_chart Per_capita_consumption_in_pounds|7.4|y|line_chart Year|2013|x|line_chart Per_capita_consumption_in_pounds|7.3|y|line_chart Year|2012|x|line_chart Per_capita_consumption_in_pounds|7.1|y|line_chart Year|2011|x|line_chart Per_capita_consumption_in_pounds|6.4|y|line_chart Year|2010|x|line_chart Per_capita_consumption_in_pounds|6.7|y|line_chart Year|2009|x|line_chart Per_capita_consumption_in_pounds|6.8|y|line_chart Year|2008|x|line_chart Per_capita_consumption_in_pounds|6.4|y|line_chart Year|2007|x|line_chart Per_capita_consumption_in_pounds|6.4|y|line_chart Year|2006|x|line_chart Per_capita_consumption_in_pounds|6.1|y|line_chart Year|2005|x|line_chart Per_capita_consumption_in_pounds|6.2|y|line_chart Year|2004|x|line_chart Per_capita_consumption_in_pounds|6.4|y|line_chart Year|2003|x|line_chart Per_capita_consumption_in_pounds|6.2|y|line_chart Year|2002|x|line_chart Per_capita_consumption_in_pounds|6.6|y|line_chart Year|2001|x|line_chart Per_capita_consumption_in_pounds|6.3|y|line_chart Year|2000|x|line_chart Per_capita_consumption_in_pounds|6.4|y|line_chart 
title: U.S. per capita consumption of fresh cucumbers 2000 - 2018

gold: The timeline shows the per capita consumption of fresh cucumbers in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh cucumbers amounted to approximately eight pounds in 2018 .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of fresh cucumbers in the United States from 2000 to 2018 . According to the report , the U.S. Per capita consumption of fresh cucumbers amounted to approximately 8.0 pounds in 2018 .


Example 121:
data: Year|2019|x|line_chart Ad_expenditure_in_million_U.S._dollars|551.5|y|line_chart Year|2018|x|line_chart Ad_expenditure_in_million_U.S._dollars|524.3|y|line_chart Year|2017|x|line_chart Ad_expenditure_in_million_U.S._dollars|642.3|y|line_chart Year|2016|x|line_chart Ad_expenditure_in_million_U.S._dollars|634.9|y|line_chart Year|2015|x|line_chart Ad_expenditure_in_million_U.S._dollars|717.85|y|line_chart Year|2014|x|line_chart Ad_expenditure_in_million_U.S._dollars|733.2|y|line_chart Year|2013|x|line_chart Ad_expenditure_in_million_U.S._dollars|750.2|y|line_chart 
title: Global ad spending of Mattel from 2013 to 2019

gold: This statistic shows the advertising and promotion expenditure of Mattel worldwide from 2013 to 2019 . In 2019 , Mattel spent approximately 551.5 million U.S. dollars on advertising and promotion .
gold_template: This statistic shows the advertising and promotion templateYLabel[1] of templateTitle[3] worldwide templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] spent approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] on advertising and promotion .

generated_template: In templateXValue[max] , over templateYValue[0] percent of templateTitle[2] templateYLabel[2] accessed the web from the United States . The templateYLabel[0] templateYLabel[1] number of templateTitle[4] templateTitle[5] had between templateYValue[min] and templateYValue[0] percent in templateXValue[max] , compared to 100 people in templateXValue[1] . templateYLabel[0] templateYLabel[1] templateYLabel[2] for the templateTitle[3] templateTitle[4] templateTitle[5] – additional information , California , templateTitle[3] templateTitle[4] templateTitle[5] templateYLabel[0] templateYLabel[1] observed within the templateXLabel[0] .
generated: In 2019 , over 551.5 percent of spending million accessed the web from the United States . The Ad expenditure number of from 2013 had between 524.3 and 551.5 percent in 2019 , compared to 100 people in 2018 . Ad expenditure million for the Mattel from 2013 – additional information , California , Mattel from 2013 Ad expenditure observed within the Year .


Example 122:
data: Country|Mexico|x|bar_chart Illegal_immigrants_in_thousands|6580|y|bar_chart Country|El_Salvador|x|bar_chart Illegal_immigrants_in_thousands|750|y|bar_chart Country|Guatemala|x|bar_chart Illegal_immigrants_in_thousands|620|y|bar_chart Country|India|x|bar_chart Illegal_immigrants_in_thousands|470|y|bar_chart Country|Honduras|x|bar_chart Illegal_immigrants_in_thousands|440|y|bar_chart Country|Philippines|x|bar_chart Illegal_immigrants_in_thousands|370|y|bar_chart Country|China|x|bar_chart Illegal_immigrants_in_thousands|320|y|bar_chart Country|Korea|x|bar_chart Illegal_immigrants_in_thousands|230|y|bar_chart Country|Vietnam|x|bar_chart Illegal_immigrants_in_thousands|170|y|bar_chart Country|Ecuador|x|bar_chart Illegal_immigrants_in_thousands|150|y|bar_chart Country|Other|x|bar_chart Illegal_immigrants_in_thousands|1870|y|bar_chart 
title: Origin of illegal immigrants in the U.S. 2015

gold: The statistic shows the estimated number of illegal immigrants in the U.S. in 2015 , by country of origin . As of January 2015 , about 6.58 million illegal immigrants from Mexico were living in the United States .
gold_template: The statistic shows the estimated number of templateYLabel[0] templateYLabel[1] in the templateTitle[3] in templateTitle[4] , by templateXLabel[0] of templateTitle[0] . As of January templateTitle[4] , about templateYValue[max] million templateYLabel[0] templateYLabel[1] from templateXValue[0] were living in the templateTitle[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] . In that year , templateXValue[0] had the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] , with an templateYLabel[0] templateYLabel[1] of templateYValue[max] million templateTitle[4] templateTitle[5] in templateXValue[0] .
generated: The statistic shows the Illegal immigrants in immigrants U.S. 2015 titleErr in titleErr . In that year , Mexico had the Origin illegal immigrants U.S. 2015 titleErr , with an Illegal immigrants of 6580 million 2015 titleErr in Mexico .


Example 123:
data: Year|Less_than_35K_USD|x|line_chart Share_of_respondents|31.7|y|line_chart Year|35000_-_74999_USD|x|line_chart Share_of_respondents|41.6|y|line_chart Year|75000_USD_and_over|x|line_chart Share_of_respondents|55.7|y|line_chart Year|100000_USD_and_over|x|line_chart Share_of_respondents|58.5|y|line_chart Year|150000_USD_and_over|x|line_chart Share_of_respondents|63.3|y|line_chart 
title: U.S. Amazon Prime membership penetration 2018 , by income

gold: This statistic presents the percentage of online consumers in the United States who are Amazon Prime members . During the measured period in August 2018 , it was found that 55.7 percent of consumers with an income of 75,000 U.S. dollars and more had an Amazon Prime membership .
gold_template: This statistic presents the percentage of online consumers in the templateTitle[0] who are templateTitle[1] templateTitle[2] members . During the measured period in August templateTitle[5] , it was found that templateYValue[2] percent of consumers with an templateTitle[7] of templateXValue[2] templateTitle[0] dollars and more had an templateTitle[1] templateTitle[2] templateTitle[3] .

generated_template: In United States , templateYValue[max] percent of the population shop templateTitle[1] in the United States . According to the report , templateYValue[max] percent of templateYLabel[1] in the United States stated they used the templateTitle[7] templateTitle[8] to templateXValue[0] templateXValue[0] or less than 25 to 34 templateXValue[0] old when they did n't like templateTitle[3] in a survey .
generated: In United States , 63.3 percent of the population shop Amazon in the United States . According to the report , 63.3 percent of respondents in the United States stated they used the income titleErr to Less_than_35K_USD or less than 25 to 34 Less_than_35K_USD old when they did n't like membership in a survey .


Example 124:
data: Year|'18|x|line_chart Unemployment_rate|3.9|y|line_chart Year|'17|x|line_chart Unemployment_rate|4.3|y|line_chart Year|'16|x|line_chart Unemployment_rate|4.6|y|line_chart Year|'15|x|line_chart Unemployment_rate|4.4|y|line_chart Year|'14|x|line_chart Unemployment_rate|5.1|y|line_chart Year|'13|x|line_chart Unemployment_rate|6.3|y|line_chart Year|'12|x|line_chart Unemployment_rate|6.7|y|line_chart Year|'11|x|line_chart Unemployment_rate|7.8|y|line_chart Year|'10|x|line_chart Unemployment_rate|8.1|y|line_chart Year|'09|x|line_chart Unemployment_rate|7.6|y|line_chart Year|'08|x|line_chart Unemployment_rate|4.8|y|line_chart Year|'07|x|line_chart Unemployment_rate|4.3|y|line_chart Year|'06|x|line_chart Unemployment_rate|4.9|y|line_chart Year|'05|x|line_chart Unemployment_rate|5.4|y|line_chart Year|'04|x|line_chart Unemployment_rate|5.9|y|line_chart Year|'03|x|line_chart Unemployment_rate|6.7|y|line_chart Year|'02|x|line_chart Unemployment_rate|6.4|y|line_chart Year|'01|x|line_chart Unemployment_rate|5|y|line_chart Year|'00|x|line_chart Unemployment_rate|4.3|y|line_chart Year|'99|x|line_chart Unemployment_rate|4.7|y|line_chart Year|'98|x|line_chart Unemployment_rate|4.9|y|line_chart Year|'97|x|line_chart Unemployment_rate|5.3|y|line_chart Year|'96|x|line_chart Unemployment_rate|5.7|y|line_chart Year|'95|x|line_chart Unemployment_rate|6.1|y|line_chart Year|'94|x|line_chart Unemployment_rate|6.5|y|line_chart Year|'93|x|line_chart Unemployment_rate|7.2|y|line_chart Year|'92|x|line_chart Unemployment_rate|7.6|y|line_chart 
title: Texas - Unemployment rate 1992 - 2018

gold: This statistic displays the unemployment rate in Texas from 1992 to 2018 . In 2018 , unemployment in Texas was 3.9 percent .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateTitle[3] to templateTitle[4] . In templateTitle[4] , templateYLabel[0] in templateTitle[0] was templateYValue[min] percent .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateTitle[3] to templateTitle[4] . In templateTitle[4] , templateYLabel[0] in templateTitle[0] was templateYValue[0] percent .
generated: This statistic displays the Unemployment rate in Texas from 1992 to 2018 . In 2018 , Unemployment in Texas was 3.9 percent .


Example 125:
data: Year|2018|x|line_chart Number_of_arrests|10310960|y|line_chart Year|2017|x|line_chart Number_of_arrests|10554985|y|line_chart Year|2016|x|line_chart Number_of_arrests|10662252|y|line_chart Year|2015|x|line_chart Number_of_arrests|10797088|y|line_chart Year|2014|x|line_chart Number_of_arrests|11205833|y|line_chart Year|2013|x|line_chart Number_of_arrests|11302102|y|line_chart Year|2012|x|line_chart Number_of_arrests|12196959|y|line_chart Year|2011|x|line_chart Number_of_arrests|12408899|y|line_chart Year|2010|x|line_chart Number_of_arrests|13120947|y|line_chart Year|2009|x|line_chart Number_of_arrests|13687241|y|line_chart Year|2008|x|line_chart Number_of_arrests|14005615|y|line_chart Year|2007|x|line_chart Number_of_arrests|14209365|y|line_chart Year|2006|x|line_chart Number_of_arrests|14380370|y|line_chart Year|2005|x|line_chart Number_of_arrests|14094186|y|line_chart Year|2004|x|line_chart Number_of_arrests|13938071|y|line_chart Year|2003|x|line_chart Number_of_arrests|13639500|y|line_chart Year|2002|x|line_chart Number_of_arrests|13741400|y|line_chart Year|2001|x|line_chart Number_of_arrests|13699300|y|line_chart Year|2000|x|line_chart Number_of_arrests|13980300|y|line_chart Year|1999|x|line_chart Number_of_arrests|14031100|y|line_chart Year|1998|x|line_chart Number_of_arrests|14528300|y|line_chart Year|1997|x|line_chart Number_of_arrests|15284300|y|line_chart Year|1996|x|line_chart Number_of_arrests|15168100|y|line_chart Year|1995|x|line_chart Number_of_arrests|15119800|y|line_chart Year|1994|x|line_chart Number_of_arrests|14648700|y|line_chart Year|1993|x|line_chart Number_of_arrests|14036300|y|line_chart Year|1992|x|line_chart Number_of_arrests|14075100|y|line_chart Year|1991|x|line_chart Number_of_arrests|14211900|y|line_chart Year|1990|x|line_chart Number_of_arrests|14195100|y|line_chart 
title: USA - number of arrests for all offenses 1990 - 2018

gold: There were over 10.3 million arrests for all offenses in the United States in 2018 . This figure is a decrease from 1990 levels , when the number of arrests was over 14.1 million . Arrest rate in the U.S .
gold_template: There were over templateYValue[min] million templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] in the United States in templateXValue[max] . This figure is a decrease from templateXValue[min] levels , when the templateYLabel[0] of templateYLabel[1] was over templateYValue[13] million . Arrest rate in the U.S .

generated_template: U.S. fashion fashion retailer templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] United States between templateXValue[min] and templateXValue[max] , templateYValue[0] percent of the highest in templateTitle[3] United States in templateXValue[max] . the United States had the highest level of templateYLabel[1] represents since templateYValue[max] percent of templateYLabel[1] at least once in the templateXLabel[0] .
generated: U.S. fashion retailer for all offenses in yLabelErr United States between 1990 and 2018 , 10310960 percent of the highest in for United States in 2018 . the United States had the highest level of arrests represents since 15284300 percent of arrests at least once in the Year .


Example 126:
data: U.S._states|California|x|bar_chart Number_of_homicide_victims|232|y|bar_chart U.S._states|Texas|x|bar_chart Number_of_homicide_victims|221|y|bar_chart U.S._states|Georgia|x|bar_chart Number_of_homicide_victims|96|y|bar_chart U.S._states|Pennsylvania|x|bar_chart Number_of_homicide_victims|82|y|bar_chart U.S._states|Ohio|x|bar_chart Number_of_homicide_victims|78|y|bar_chart U.S._states|New_York|x|bar_chart Number_of_homicide_victims|74|y|bar_chart U.S._states|Virginia|x|bar_chart Number_of_homicide_victims|73|y|bar_chart U.S._states|Tennessee|x|bar_chart Number_of_homicide_victims|69|y|bar_chart U.S._states|Arizona|x|bar_chart Number_of_homicide_victims|68|y|bar_chart U.S._states|North_Carolina|x|bar_chart Number_of_homicide_victims|65|y|bar_chart U.S._states|Louisiana|x|bar_chart Number_of_homicide_victims|63|y|bar_chart U.S._states|Michigan|x|bar_chart Number_of_homicide_victims|58|y|bar_chart U.S._states|South_Carolina|x|bar_chart Number_of_homicide_victims|52|y|bar_chart U.S._states|Missouri|x|bar_chart Number_of_homicide_victims|51|y|bar_chart U.S._states|Illinois|x|bar_chart Number_of_homicide_victims|49|y|bar_chart U.S._states|Washington|x|bar_chart Number_of_homicide_victims|42|y|bar_chart U.S._states|New_Jersey|x|bar_chart Number_of_homicide_victims|41|y|bar_chart U.S._states|Kentucky|x|bar_chart Number_of_homicide_victims|40|y|bar_chart U.S._states|Maryland|x|bar_chart Number_of_homicide_victims|39|y|bar_chart U.S._states|Indiana|x|bar_chart Number_of_homicide_victims|38|y|bar_chart U.S._states|Arkansas|x|bar_chart Number_of_homicide_victims|34|y|bar_chart U.S._states|Colorado|x|bar_chart Number_of_homicide_victims|32|y|bar_chart U.S._states|Nevada|x|bar_chart Number_of_homicide_victims|30|y|bar_chart U.S._states|Wisconsin|x|bar_chart Number_of_homicide_victims|30|y|bar_chart U.S._states|Oklahoma|x|bar_chart Number_of_homicide_victims|28|y|bar_chart U.S._states|Massachusetts|x|bar_chart Number_of_homicide_victims|23|y|bar_chart U.S._states|Mississippi|x|bar_chart Number_of_homicide_victims|22|y|bar_chart U.S._states|Oregon|x|bar_chart Number_of_homicide_victims|21|y|bar_chart U.S._states|Minnesota|x|bar_chart Number_of_homicide_victims|21|y|bar_chart U.S._states|Kansas|x|bar_chart Number_of_homicide_victims|16|y|bar_chart U.S._states|West_Virginia|x|bar_chart Number_of_homicide_victims|15|y|bar_chart U.S._states|New_Mexico|x|bar_chart Number_of_homicide_victims|15|y|bar_chart U.S._states|Connecticut|x|bar_chart Number_of_homicide_victims|15|y|bar_chart U.S._states|Alaska|x|bar_chart Number_of_homicide_victims|14|y|bar_chart U.S._states|Idaho|x|bar_chart Number_of_homicide_victims|13|y|bar_chart U.S._states|Iowa|x|bar_chart Number_of_homicide_victims|12|y|bar_chart U.S._states|Utah|x|bar_chart Number_of_homicide_victims|12|y|bar_chart U.S._states|Montana|x|bar_chart Number_of_homicide_victims|10|y|bar_chart U.S._states|Delaware|x|bar_chart Number_of_homicide_victims|7|y|bar_chart U.S._states|Nebraska|x|bar_chart Number_of_homicide_victims|7|y|bar_chart U.S._states|Hawaii|x|bar_chart Number_of_homicide_victims|6|y|bar_chart U.S._states|Vermont|x|bar_chart Number_of_homicide_victims|6|y|bar_chart U.S._states|South_Dakota|x|bar_chart Number_of_homicide_victims|6|y|bar_chart U.S._states|Rhode_Island|x|bar_chart Number_of_homicide_victims|5|y|bar_chart U.S._states|Maine|x|bar_chart Number_of_homicide_victims|5|y|bar_chart U.S._states|North_Dakoda|x|bar_chart Number_of_homicide_victims|4|y|bar_chart U.S._states|New_Hampshire|x|bar_chart Number_of_homicide_victims|4|y|bar_chart U.S._states|Wyoming|x|bar_chart Number_of_homicide_victims|4|y|bar_chart 
title: Number of women murdered by men in the U.S. by state 2017

gold: This statistic shows the number of female homicide victims in single offender homicides per state in the United States in 2017 . In 2017 , there were 221 women killed by male single offenders in the state of Texas . Texas was the state with the highest number of women murdered by men in single offender homicides .
gold_template: This statistic shows the templateYLabel[0] of female templateYLabel[1] templateYLabel[2] in single offender homicides per templateTitle[7] in the templateTitle[5] in templateTitle[8] . In templateTitle[8] , there were templateYValue[1] templateTitle[1] killed templateTitle[3] male single offenders in the templateTitle[7] of templateXValue[1] . templateXValue[1] was the templateTitle[7] with the highest templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in single offender homicides .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the United States as of April templateTitle[7] . During the survey , templateYValue[3] percent of templateTitle[5] templateYLabel[1] stated they used templateXValue[0] templateXValue[0] or templateYValue[max] percent in the previous year .
generated: This statistic shows the Number women murdered by men U.S. by in the United States as of April state . During the survey , 82 percent of U.S. homicide stated they used California or 232 percent in the previous year .


Example 127:
data: Year|18-34|x|line_chart Share_of_population|75|y|line_chart Year|35-44|x|line_chart Share_of_population|68|y|line_chart Year|45-54|x|line_chart Share_of_population|63|y|line_chart Year|55-64|x|line_chart Share_of_population|47|y|line_chart Year|65+|x|line_chart Share_of_population|32|y|line_chart Year|Total|x|line_chart Share_of_population|59|y|line_chart 
title: Canada : Facebook user penetration 2015 , by age

gold: This statistic shows the responses on a study about social network usage in Canada as of January 2015 . During the reported period , Facebook reached 75 percent of Canadians aged 18 to 34 , while among Canadians aged 65 and older Facebook had a 32 percent penetration rate . In general the platform reached 59 percent of people in Canada .
gold_template: This statistic shows the responses on a study about social network usage in templateTitle[0] as of January templateTitle[4] . During the reported period , templateTitle[1] reached templateYValue[max] percent of Canadians aged 18 to 34 , while among Canadians aged 65 and older templateTitle[1] had a templateYValue[min] percent templateTitle[3] rate . In general the platform reached templateYValue[5] percent of people in templateTitle[0] .

generated_template: This statistic shows the templateYLabel[0] of internet users in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[0] , templateYValue[max] percent of the Indonesian templateYLabel[1] were using the templateTitle[1] . In 2016 , this figure is projected to grow to templateYValue[0] percent .
generated: This statistic shows the Share of internet users in the Canada from 18-34 to 18-34 . In 18-34 , 75 percent of the Indonesian population were using the Facebook . In 2016 , this figure is projected to grow to 75 percent .


Example 128:
data: Month|Assyrian|x|bar_chart Current_year_(as_of_January_25,_2020)|6770|y|bar_chart Month|Hebrew|x|bar_chart Current_year_(as_of_January_25,_2020)|5780|y|bar_chart Month|Chinese|x|bar_chart Current_year_(as_of_January_25,_2020)|4718|y|bar_chart Month|Julian|x|bar_chart Current_year_(as_of_January_25,_2020)|2773|y|bar_chart Month|Buddhist|x|bar_chart Current_year_(as_of_January_25,_2020)|2563|y|bar_chart Month|Gregorian|x|bar_chart Current_year_(as_of_January_25,_2020)|2020|y|bar_chart Month|Hindu|x|bar_chart Current_year_(as_of_January_25,_2020)|1941|y|bar_chart Month|Islamic|x|bar_chart Current_year_(as_of_January_25,_2020)|1441|y|bar_chart Month|Iranian|x|bar_chart Current_year_(as_of_January_25,_2020)|1398|y|bar_chart Month|French_Revolutionary|x|bar_chart Current_year_(as_of_January_25,_2020)|228|y|bar_chart 
title: Current year in various historical and world calendars 2020

gold: Today , the vast majority of the world uses what is known as the Gregorian calendar , Named after Pope Gregory XIII , who introduced it in 1582 . The Gregorian calendar replaced the Julian calendar , which had been the most used calendar in Europe until this point . The Gregorian calendar lasts for approximately 365.24 days ; this means that most years have 365 days , with one extra day being added every fourth year , unless the year is divisible by 100 but not 400 ( for example , the year 2000 was a leap year , whereas 2100 and 2200 will not be ) .
gold_template: Today , the vast majority of the templateTitle[4] uses what is known as the templateXValue[5] calendar , Named after Pope Gregory XIII , who introduced it in 1582 . The templateXValue[5] calendar replaced the templateXValue[3] calendar , which had been the most used calendar in Europe until this point . The templateXValue[5] calendar lasts for approximately 365.24 days ; this means that most years have 365 days , with one extra day being added every fourth templateYLabel[1] , unless the templateYLabel[1] is divisible by 100 but not 400 ( for example , the templateYLabel[1] 2000 was a leap templateYLabel[1] , whereas 2100 and 2200 will not be ) .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States in templateTitle[8] . In September templateXValue[1] , templateYValue[1] people were killed in the United States .
generated: This statistic shows the Current year of various historical world calendars in the United States in titleErr . In September Hebrew , 5780 people were killed in the United States .


Example 129:
data: Year|2010|x|line_chart Number_of_Coast_Guard_personnel|41327|y|line_chart Year|2009|x|line_chart Number_of_Coast_Guard_personnel|42426|y|line_chart Year|2008|x|line_chart Number_of_Coast_Guard_personnel|41362|y|line_chart Year|2007|x|line_chart Number_of_Coast_Guard_personnel|40650|y|line_chart Year|2006|x|line_chart Number_of_Coast_Guard_personnel|39980|y|line_chart Year|2005|x|line_chart Number_of_Coast_Guard_personnel|39361|y|line_chart Year|2000|x|line_chart Number_of_Coast_Guard_personnel|34804|y|line_chart Year|1995|x|line_chart Number_of_Coast_Guard_personnel|35582|y|line_chart 
title: Active duty U.S Coast Guard personnel numbers from 1995 to 2010

gold: This graph shows the total number of active duty U.S. Coast Guard personnel from 1995 to 2010 . In 2010 there were 41,327 active duty U.S. Coast Guard members .
gold_template: This graph shows the total templateYLabel[0] of templateTitle[0] templateTitle[1] U.S. templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[7] templateXValue[min] to templateXValue[max] . In templateXValue[max] there were templateYValue[0] templateTitle[0] templateTitle[1] U.S. templateYLabel[1] templateYLabel[2] members .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people in the United States .
generated: This statistic shows the Number Coast Guard of Coast Guard personnel in the United States from 1995 to 2010 . In 2010 , there were 41327 people in the United States .


Example 130:
data: Country|Europe|x|bar_chart Physicians_per_10,000_population|32.1|y|bar_chart Country|Americas|x|bar_chart Physicians_per_10,000_population|21.5|y|bar_chart Country|Western_Pacific|x|bar_chart Physicians_per_10,000_population|15.5|y|bar_chart Country|World|x|bar_chart Physicians_per_10,000_population|13.9|y|bar_chart Country|Eastern_Mediterranean|x|bar_chart Physicians_per_10,000_population|12.7|y|bar_chart Country|Southeast_Asia|x|bar_chart Physicians_per_10,000_population|5.9|y|bar_chart Country|Africa|x|bar_chart Physicians_per_10,000_population|2.7|y|bar_chart 
title: Physicians density worldwide by region 2013

gold: This statistic depicts the physicians density worldwide as of 2013 , by WHO region . As of that year , the average density in Southeast Asia stood at 5.9 physicians per 10,000 inhabitants . The world average was around 14 physicians per 10,000 inhabitants .
gold_template: This statistic depicts the templateYLabel[0] templateTitle[1] templateTitle[2] as of templateTitle[5] , templateTitle[3] WHO templateTitle[4] . As of that year , the average templateTitle[1] in templateXValue[5] templateXValue[5] stood at templateYValue[5] templateYLabel[0] templateYLabel[1] 10,000 inhabitants . The templateXValue[3] average was around templateYValue[3] templateYLabel[0] templateYLabel[1] 10,000 inhabitants .

generated_template: In templateXValue[max] , there were approximately templateYValue[max] million templateYLabel[1] living at the templateTitle[2] templateTitle[3] templateTitle[4] , up from templateYValue[1] million in the previous year . The templateYValue[2] million templateTitle[2] templateTitle[3] templateTitle[4] in the United States – additional information The two nations in templateTitle[6] , many of America had the highest templateYLabel[0] templateYLabel[1] at least one of the birth rate .
generated: In Europe , there were approximately 32.1 million per living at the worldwide by region , up from 21.5 million in the previous year . The 15.5 million worldwide by region in the United States – additional information The two nations in titleErr , many of America had the highest Physicians per at least one of the birth rate .


Example 131:
data: Year|2018|x|line_chart Number_of_active_customers_in_millions|15.16|y|line_chart Year|2017|x|line_chart Number_of_active_customers_in_millions|10.99|y|line_chart Year|2016|x|line_chart Number_of_active_customers_in_millions|8.25|y|line_chart Year|2015|x|line_chart Number_of_active_customers_in_millions|5.36|y|line_chart Year|2014|x|line_chart Number_of_active_customers_in_millions|3.22|y|line_chart Year|2013|x|line_chart Number_of_active_customers_in_millions|2.09|y|line_chart 
title: Wayfair active customers 2013 - 2018

gold: The timeline shows e-retailer Wayfair 's active customer base from 2013 to 2018 . During the last reported fiscal period , the online furniture retailer had 15.16 million active customers , up from 10.99 million customers in the previous year .
gold_template: The timeline shows e-retailer templateTitle[0] 's templateYLabel[1] customer base from templateXValue[min] to templateXValue[max] . During the last reported fiscal period , the online furniture retailer had templateYValue[max] million templateYLabel[1] templateYLabel[2] , up from templateYValue[1] million templateYLabel[2] in the previous templateXLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[3] was at around templateYValue[0] million .
generated: This statistic shows the Wayfair Number of active customers millions in the 2013 2018 from 2013 to 2018 . In 2018 , the Wayfair Number of active customers in the 2013 was at around 15.16 million .


Example 132:
data: Response|Friend|x|bar_chart Share_of_respondents|25.4|y|bar_chart Response|Colleague_from_work|x|bar_chart Share_of_respondents|22.6|y|bar_chart Response|Stranger_met_in_a_particular_context_(disco_gym_holidays_etc.)|x|bar_chart Share_of_respondents|17.5|y|bar_chart Response|Neighbor|x|bar_chart Share_of_respondents|10.7|y|bar_chart Response|Does_not_reply|x|bar_chart Share_of_respondents|9.3|y|bar_chart Response|Partner_of_a_friend_of_mine|x|bar_chart Share_of_respondents|5.3|y|bar_chart Response|Stranger_met_by_chance|x|bar_chart Share_of_respondents|4.4|y|bar_chart Response|Escort|x|bar_chart Share_of_respondents|3.5|y|bar_chart Response|Family_member|x|bar_chart Share_of_respondents|1.3|y|bar_chart 
title: Persons with whom Italians cheat on their partners 2017

gold: A survey conducted in 2017 reveals that the largest groups of Italian respondents cheated on their partner either with a friend or with a colleague . In particular , 25.4 percent of interviewees in Italy stated that they betrayed their partner with a friend , while 22.6 percent did so with a colleague . However , it was common to cheat with strangers as well .
gold_template: A survey conducted in templateTitle[7] reveals that the largest groups of Italian templateYLabel[1] cheated on templateTitle[5] templateXValue[5] either templateTitle[1] a templateXValue[0] or templateTitle[1] a templateXValue[1] . In templateXValue[2] , templateYValue[max] percent of interviewees in Italy stated that they betrayed templateTitle[5] templateXValue[5] templateTitle[1] a templateXValue[0] , while templateYValue[1] percent did so templateTitle[1] a templateXValue[1] . However , it was common to templateTitle[4] templateTitle[1] strangers as well .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[5] according to a survey conducted in templateTitle[7] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated they could be both templateXValue[0] and templateXValue[0] .
generated: This statistic shows the results of a survey conducted in the United States in partners according to a survey conducted in titleErr . During the survey period , 25.4 percent of respondents stated they could be both Friend and Friend .


Example 133:
data: Year|2002|x|line_chart Cars_in_service_in_thousands|1643|y|line_chart Year|2003|x|line_chart Cars_in_service_in_thousands|1617|y|line_chart Year|2004|x|line_chart Cars_in_service_in_thousands|1665|y|line_chart Year|2005|x|line_chart Cars_in_service_in_thousands|1714|y|line_chart Year|2006|x|line_chart Cars_in_service_in_thousands|1768|y|line_chart Year|2007|x|line_chart Cars_in_service_in_thousands|1861|y|line_chart Year|2008|x|line_chart Cars_in_service_in_thousands|1813|y|line_chart Year|2009|x|line_chart Cars_in_service_in_thousands|1637|y|line_chart Year|2010|x|line_chart Cars_in_service_in_thousands|1629|y|line_chart Year|2011|x|line_chart Cars_in_service_in_thousands|1761|y|line_chart Year|2012|x|line_chart Cars_in_service_in_thousands|1857|y|line_chart 
title: Car rental in the U.S. - total cars in service 2002 - 2012

gold: This statistic represents the US rental car industry 's total number of cars in service between 2002 and 2012 . In 2012 , there were about 1.86 million rental cars in service , while the industry 's revenue grew to around 23.6 billion US dollars .
gold_template: This statistic represents the US templateTitle[1] templateTitle[0] industry 's templateTitle[3] number of templateYLabel[0] in templateYLabel[1] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were about templateYValue[max] million templateTitle[1] templateYLabel[0] in templateYLabel[1] , while the industry 's revenue grew to around 23.6 billion US dollars .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people living in the United States .
generated: This statistic shows the Cars service of U.S. total cars service in the United States from 2002 to 2012 . In 2012 , there were 1643 people living in the United States .


Example 134:
data: Month|Moscow|x|bar_chart Residents_in_million|12.05|y|bar_chart Month|St._Petersburg|x|bar_chart Residents_in_million|5.19|y|bar_chart Month|Novosibirsk|x|bar_chart Residents_in_million|1.57|y|bar_chart Month|Jekaterinburg|x|bar_chart Residents_in_million|1.43|y|bar_chart Month|Nižnij_Novgorod|x|bar_chart Residents_in_million|1.27|y|bar_chart Month|Kazan'|x|bar_chart Residents_in_million|1.21|y|bar_chart Month|Čel'abinsk|x|bar_chart Residents_in_million|1.18|y|bar_chart Month|Samara|x|bar_chart Residents_in_million|1.17|y|bar_chart Month|Omsk|x|bar_chart Residents_in_million|1.17|y|bar_chart Month|Rostov-na-Donu|x|bar_chart Residents_in_million|1.11|y|bar_chart 
title: Largest cities in Russia 2015

gold: The statistic depicts the ten largest cities in Russia in 2015 . In 2015 , Moscow had around 12.1 million residents , making it the largest city in Russia . Population and workforce of Russia Russia is the largest country in the world ; with a total population of around 144 million people ( 263767 ) , it is the ninth most populous nation worldwide .
gold_template: The statistic depicts the ten templateTitle[0] templateTitle[1] in templateTitle[2] in templateTitle[3] . In templateTitle[3] , templateXValue[0] had around templateYValue[max] templateYLabel[1] templateYLabel[0] , making it the templateTitle[0] city in templateTitle[2] . Population and workforce of templateTitle[2] is the templateTitle[0] country in the world ; with a total population of around 144 templateYLabel[1] people ( 263767 ) , it is the ninth most populous nation worldwide .

generated_template: The statistic shows the ten templateTitle[0] templateTitle[1] in templateTitle[2] as of templateTitle[3] . In that year , templateYValue[max] templateYLabel[1] people lived in templateXValue[0] , making it the templateTitle[0] city in templateTitle[2] .
generated: The statistic shows the ten Largest cities in Russia as of 2015 . In that year , 12.05 million people lived in Moscow , making it the Largest city in Russia .


Example 135:
data: Year|2017|x|line_chart Number_of_arrivals_in_millions|129.4|y|line_chart Year|2016|x|line_chart Number_of_arrivals_in_millions|123.5|y|line_chart Year|2015|x|line_chart Number_of_arrivals_in_millions|114.4|y|line_chart Year|2014|x|line_chart Number_of_arrivals_in_millions|107.5|y|line_chart Year|2013|x|line_chart Number_of_arrivals_in_millions|101.7|y|line_chart Year|2012|x|line_chart Number_of_arrivals_in_millions|100.4|y|line_chart Year|2011|x|line_chart Number_of_arrivals_in_millions|103.1|y|line_chart Year|2010|x|line_chart Number_of_arrivals_in_millions|98.8|y|line_chart Year|2009|x|line_chart Number_of_arrivals_in_millions|93.7|y|line_chart Year|2008|x|line_chart Number_of_arrivals_in_millions|100.1|y|line_chart Year|2007|x|line_chart Number_of_arrivals_in_millions|101.8|y|line_chart Year|2006|x|line_chart Number_of_arrivals_in_millions|99.9|y|line_chart 
title: Number of arrivals in tourist accommodation Spain 2006 - 2017

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Spain from 2006 to 2017 . There were over 129 million arrivals at accommodation establishments in Spain in 2017 , a rise since the previous year .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitle[4] from templateXValue[min] to templateXValue[max] . There were over templateYValue[max] million templateYLabel[1] at templateTitle[3] establishments in templateTitle[4] in templateXValue[max] , a rise since the previous templateXLabel[0] .

generated_template: Around templateYValue[max] million templateYLabel[1] were recorded at templateTitle[3] establishments in templateTitle[4] in templateXValue[max] . Over the last five years templateYLabel[1] in templateTitle[2] templateTitle[3] have been increasing at a higher rate , with an extra ten million templateYLabel[1] in templateXValue[max] compared to templateXValue[5] . Tourism development in templateTitle[4] The tourism industry makes a significant contribution to templateTitle[4] 's economy .
generated: Around 129.4 million arrivals were recorded at accommodation establishments in Spain in 2017 . Over the last five years arrivals in tourist accommodation have been increasing at a higher rate , with an extra ten million arrivals in 2017 compared to 2012 . Tourism development in Spain The tourism industry makes a significant contribution to Spain 's economy .


Example 136:
data: Country|Germany|x|bar_chart Number_of_users|757000|y|bar_chart Country|United_Kingdom|x|bar_chart Number_of_users|163000|y|bar_chart Country|France|x|bar_chart Number_of_users|153000|y|bar_chart Country|Italy|x|bar_chart Number_of_users|130000|y|bar_chart Country|Switzerland|x|bar_chart Number_of_users|105000|y|bar_chart Country|Austria|x|bar_chart Number_of_users|75000|y|bar_chart Country|Netherlands|x|bar_chart Number_of_users|51000|y|bar_chart Country|Sweden|x|bar_chart Number_of_users|21000|y|bar_chart Country|Spain|x|bar_chart Number_of_users|20000|y|bar_chart Country|Belgium|x|bar_chart Number_of_users|16000|y|bar_chart Country|Norway|x|bar_chart Number_of_users|8500|y|bar_chart Country|Denmark|x|bar_chart Number_of_users|7800|y|bar_chart 
title: Car sharing users in Europe 2014 , by country

gold: This statistic displays the number of car sharing users in selected countries in Europe in 2014 . Germany had by far the highest number of people using car sharing companies in Europe and almost five times as many as France . In Italy there were an estimated 130,000 car share users .
gold_template: This statistic displays the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in selected countries in templateTitle[3] in templateTitle[4] . templateXValue[0] had templateTitle[5] far the highest templateYLabel[0] of people using templateTitle[0] templateTitle[1] companies in templateTitle[3] and almost five times as many as templateXValue[2] . In templateXValue[3] there were an estimated templateYValue[3] templateTitle[0] share templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] people in selected countries countries in templateTitle[8] . In templateTitle[6] , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] templateTitle[7] templateTitle[1] templateYLabel[2] market templateTitle[4] templateTitle[5] at templateYValue[max] percent of the previous year .
generated: This statistic shows the Number users yLabelErr of Europe 2014 by people in selected countries in titleErr . In country , Germany had the highest Number users titleErr sharing yLabelErr market 2014 by at 757000 percent of the previous year .


Example 137:
data: Country|European_Union|x|bar_chart Share_of_market|26|y|bar_chart Country|NAFTA|x|bar_chart Share_of_market|22|y|bar_chart Country|China|x|bar_chart Share_of_market|15|y|bar_chart Country|South_America|x|bar_chart Share_of_market|8|y|bar_chart Country|India|x|bar_chart Share_of_market|6|y|bar_chart Country|Commonwealth_of_Independent_States|x|bar_chart Share_of_market|6|y|bar_chart Country|Japan|x|bar_chart Share_of_market|4|y|bar_chart Country|Turkey|x|bar_chart Share_of_market|3|y|bar_chart Country|Rest_of_World|x|bar_chart Share_of_market|10|y|bar_chart 
title: Share of the global agricultural machinery market , by region 2015

gold: This statistic shows the share of the global farming machinery market in 2015 , by region . The European Union and NAFTA accounted for 48 percent of the agricultural machinery market in this year , though China in third place is one of the fastest growing markets .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] farming templateTitle[3] templateYLabel[1] in templateTitle[7] , templateTitle[5] templateTitle[6] . The templateXValue[0] templateXValue[0] and templateXValue[1] accounted for 48 percent of the templateTitle[2] templateTitle[3] templateYLabel[1] in this year , though templateXValue[2] in third place is one of the fastest growing markets .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateTitle[2] templateTitle[3] as of templateTitle[4] . templateYValue[2] percent of templateYLabel[1] stated templateXValue[2] was currently the templateTitle[0] population .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Share nation in agricultural machinery as of market . 15 percent of market stated China was currently the Share population .


Example 138:
data: Year|2019|x|line_chart Unemployment_rate|1.02|y|line_chart Year|2018|x|line_chart Unemployment_rate|1.03|y|line_chart Year|2017|x|line_chart Unemployment_rate|1|y|line_chart Year|2016|x|line_chart Unemployment_rate|1.06|y|line_chart Year|2015|x|line_chart Unemployment_rate|1.08|y|line_chart Year|2014|x|line_chart Unemployment_rate|1.11|y|line_chart Year|2013|x|line_chart Unemployment_rate|1.14|y|line_chart Year|2012|x|line_chart Unemployment_rate|1.11|y|line_chart Year|2011|x|line_chart Unemployment_rate|1.13|y|line_chart Year|2010|x|line_chart Unemployment_rate|1.15|y|line_chart Year|2009|x|line_chart Unemployment_rate|1.13|y|line_chart Year|2008|x|line_chart Unemployment_rate|0.96|y|line_chart Year|2007|x|line_chart Unemployment_rate|0.99|y|line_chart Year|2006|x|line_chart Unemployment_rate|1.09|y|line_chart Year|2005|x|line_chart Unemployment_rate|2.14|y|line_chart Year|2004|x|line_chart Unemployment_rate|3.49|y|line_chart Year|2003|x|line_chart Unemployment_rate|5.18|y|line_chart Year|2002|x|line_chart Unemployment_rate|4.74|y|line_chart Year|2001|x|line_chart Unemployment_rate|4.19|y|line_chart Year|2000|x|line_chart Unemployment_rate|3.8|y|line_chart Year|1999|x|line_chart Unemployment_rate|3.43|y|line_chart 
title: Unemployment rate in Tonga 2019

gold: This statistic shows the unemployment rate in Tonga from 1999 to 2019 . In 2019 , the unemployment rate in Tonga was at approximately 1.02 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Tonga from 1999 to 2019 . In 2019 , the Unemployment rate in Tonga was at approximately 1.02 percent .


Example 139:
data: Month|Carps_barbels_and_other_cyprinids|x|bar_chart Production_in_thousand_metric_tons|28345|y|bar_chart Month|Miscellaneous_freshwater_fishes|x|bar_chart Production_in_thousand_metric_tons|10433|y|bar_chart Month|Tilapias_and_other_cichlids|x|bar_chart Production_in_thousand_metric_tons|5881|y|bar_chart Month|Oysters|x|bar_chart Production_in_thousand_metric_tons|5711|y|bar_chart Month|Clams_cockles_arkshells|x|bar_chart Production_in_thousand_metric_tons|5658|y|bar_chart Month|Shrimps_prawns|x|bar_chart Production_in_thousand_metric_tons|5512|y|bar_chart Month|Salmons_trouts_smelts|x|bar_chart Production_in_thousand_metric_tons|3477|y|bar_chart Month|Freshwater_crustaceans|x|bar_chart Production_in_thousand_metric_tons|2526|y|bar_chart Month|Scallops_pectens|x|bar_chart Production_in_thousand_metric_tons|2185|y|bar_chart Month|Mussels|x|bar_chart Production_in_thousand_metric_tons|2164|y|bar_chart Month|Marine_fishes_not_identified|x|bar_chart Production_in_thousand_metric_tons|990|y|bar_chart 
title: Major species groups in global aquaculture production worldwide 2017

gold: This statistic shows the top 10 species groups for aquaculture production worldwide in 2017 . In that year , with over 3.5 million metric tons , salmons , trouts and smelts represented one of the world 's most produced species groups in aquaculture .
gold_template: This statistic shows the top 10 templateTitle[1] templateTitle[2] for templateTitle[4] templateYLabel[0] templateTitle[6] in templateTitle[7] . In that year , with over templateYValue[6] million templateYLabel[2] templateYLabel[3] , templateXValue[6] , templateXValue[6] and templateXValue[6] represented one of the world 's most produced templateTitle[1] templateTitle[2] in templateTitle[4] .

generated_template: This statistic shows the leading templateTitle[1] of templateTitle[2] templateYLabel[1] of templateTitle[4] templateTitle[5] in the United States in templateTitle[7] . templateYValue[0] thousand templateYLabel[2] templateYLabel[3] of templateTitle[2] were produced in the United States .
generated: This statistic shows the leading species of groups thousand of aquaculture production in the United States in 2017 . 28345 thousand metric tons of groups were produced in the United States .


Example 140:
data: Year|2013|x|line_chart Sales_in_billion_euros|25|y|line_chart Year|2018|x|line_chart Sales_in_billion_euros|41|y|line_chart 
title: Forecast : Online retail sales value in Germany 2013 - 2018

gold: This statistic shows the value of online retail sales in Germany in 2013 and with a forecast for 2018 . Online sales were measured at 25 billion euros in Germany in 2013 , but were expected to grow to 41 billion euros in 2018 .
gold_template: This statistic shows the templateTitle[4] of templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitle[5] in templateXValue[min] and with a templateTitle[0] for templateXValue[max] . templateTitle[1] templateYLabel[0] were measured at templateYValue[min] templateYLabel[1] templateYLabel[2] in templateTitle[5] in templateXValue[min] , but were expected to grow to templateYValue[max] templateYLabel[1] templateYLabel[2] in templateXValue[max] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateTitle[0] templateTitle[1] market had a size of templateYValue[1] templateYLabel[1] templateYLabel[2] .
generated: The statistic shows Forecast Online Sales sales value from 2013 to 2018 . In 2018 , the Forecast Online market had a size of 41 billion euros .


Example 141:
data: Year|2019|x|line_chart Number_of_shutdowns|3|y|line_chart Year|2018|x|line_chart Number_of_shutdowns|7|y|line_chart Year|2017|x|line_chart Number_of_shutdowns|5|y|line_chart Year|2016|x|line_chart Number_of_shutdowns|4|y|line_chart Year|2015|x|line_chart Number_of_shutdowns|7|y|line_chart Year|2014|x|line_chart Number_of_shutdowns|1|y|line_chart Year|2013|x|line_chart Number_of_shutdowns|6|y|line_chart Year|2012|x|line_chart Number_of_shutdowns|3|y|line_chart Year|2011|x|line_chart Number_of_shutdowns|13|y|line_chart Year|2010|x|line_chart Number_of_shutdowns|1|y|line_chart Year|2009|x|line_chart Number_of_shutdowns|3|y|line_chart Year|2008|x|line_chart Number_of_shutdowns|2|y|line_chart Year|2007|x|line_chart Number_of_shutdowns|1|y|line_chart Year|2006|x|line_chart Number_of_shutdowns|8|y|line_chart Year|2005|x|line_chart Number_of_shutdowns|2|y|line_chart 
title: Nuclear power plants : permanent shutdowns 2005 - 2019

gold: This statistic represents the global number of permanent nuclear reactor shutdowns between 2005 and August 2019 . Following the nuclear accident in Japan in March 2011 , four reactors at Fukushima Daiichi nuclear power plant were shut down permanently . In total , there were seven permanent shutdowns in 2018 .
gold_template: This statistic represents the global templateYLabel[0] of templateTitle[3] templateTitle[0] reactor templateYLabel[1] between templateXValue[min] and August templateXValue[max] . Following the templateTitle[0] accident in Japan in March templateXValue[8] , templateYValue[3] reactors at Fukushima Daiichi templateTitle[0] templateTitle[1] plant were shut down permanently . In total , there were templateYValue[1] templateTitle[3] templateYLabel[1] in templateXValue[1] .

generated_template: In templateXValue[max] , templateYValue[max] people died as a result of templateTitle[3] templateTitle[2] at least once figure has been since the previous templateXLabel[0] . The templateYLabel[0] of templateYLabel[1] came in the past two years , the templateYValue[0] , up from templateYValue[1] percent in the previous templateXLabel[0] . However , the majority of templateYLabel[1] Just like it 's population , which has gradually risen dramatically in the world .
generated: In 2019 , 13 people died as a result of permanent plants at least once figure has been since the previous Year . The Number of shutdowns came in the past two years , the 3 , up from 7 percent in the previous Year . However , the majority of shutdowns Just like it 's population , which has gradually risen dramatically in the world .


Example 142:
data: Year|2015|x|line_chart Share_of_respondents|59|y|line_chart Year|2016|x|line_chart Share_of_respondents|51|y|line_chart Year|2017|x|line_chart Share_of_respondents|35|y|line_chart Year|2018|x|line_chart Share_of_respondents|37|y|line_chart Year|2019|x|line_chart Share_of_respondents|36|y|line_chart 
title: Share of U.S. consumers shopping on Black Friday 2015 - 2019

gold: Some 36 percent of survey respondents stated that they planned to do most of their holiday shopping on Black Friday in the United States in 2019 . This is a decrease of 23 percent since 2015 , when some 59 percent of respondents were planning to do the majority of their shopping on Black Friday .
gold_template: Some templateYValue[4] percent of survey templateYLabel[1] stated that they planned to do most of their holiday templateTitle[3] on templateTitle[4] templateTitle[5] in the templateTitle[1] in templateXValue[max] . This is a decrease of 23 percent since templateXValue[min] , when some templateYValue[max] percent of templateYLabel[1] were planning to do the majority of their templateTitle[3] on templateTitle[4] templateTitle[5] .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . During the survey period it was found that templateYValue[max] percent of the templateYLabel[1] visited a templateXValue[0] templateXValue[0] , up from templateYValue[1] percent in templateXValue[min] to previous templateXLabel[0] .
generated: This statistic presents the Share U.S. consumers of shopping Black from 2015 to 2019 . During the survey period it was found that 59 percent of the respondents visited a 2015 , up from 51 percent in 2015 to previous Year .


Example 143:
data: Year|2018|x|line_chart Production_in_units|162687|y|line_chart Year|2017|x|line_chart Production_in_units|185682|y|line_chart Year|2016|x|line_chart Production_in_units|145555|y|line_chart Year|2015|x|line_chart Production_in_units|151004|y|line_chart Year|2014|x|line_chart Production_in_units|133615|y|line_chart Year|2013|x|line_chart Production_in_units|110127|y|line_chart Year|2012|x|line_chart Production_in_units|113811|y|line_chart Year|2011|x|line_chart Production_in_units|110360|y|line_chart Year|2010|x|line_chart Production_in_units|99236|y|line_chart 
title: BMW Group - motorcycle production 2010 - 2018

gold: This statistic represents the number of motorcycles produced by BMW Group from the fiscal year of 2010 to the fiscal year of 2018 . BMW Group produced around 162,700 motorcycles in the fiscal year of 2018 .
gold_template: This statistic represents the number of motorcycles produced by templateTitle[0] templateTitle[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . templateTitle[0] templateTitle[1] produced around 162,700 motorcycles in the fiscal templateXLabel[0] of templateXValue[max] .

generated_template: The graph shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the templateYLabel[1] were recorded at the templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The graph shows the Production units yLabelErr of production 2010 worldwide from 2010 to 2018 . In 2018 , 162687 percent of the units were recorded at the production 2010 2018 .


Example 144:
data: Month|Sep_'17|x|bar_chart Number_of_monthly_active_users_in_millions|1300|y|bar_chart Month|Apr_'17|x|bar_chart Number_of_monthly_active_users_in_millions|1200|y|bar_chart Month|Jul_'16|x|bar_chart Number_of_monthly_active_users_in_millions|1000|y|bar_chart Month|Apr_'16|x|bar_chart Number_of_monthly_active_users_in_millions|900|y|bar_chart Month|Dec_'15|x|bar_chart Number_of_monthly_active_users_in_millions|800|y|bar_chart Month|Jun_'15|x|bar_chart Number_of_monthly_active_users_in_millions|700|y|bar_chart Month|Mar_'15|x|bar_chart Number_of_monthly_active_users_in_millions|600|y|bar_chart Month|Nov_'14|x|bar_chart Number_of_monthly_active_users_in_millions|500|y|bar_chart Month|Apr_'14|x|bar_chart Number_of_monthly_active_users_in_millions|200|y|bar_chart 
title: Facebook Messenger : number of monthly active users 2014 - 2017

gold: This statistic presents the number of monthly active Facebook Messenger chat app users from April 2014 to September 2017 . As of the last reported period , the mobile messenger had 1.3 billion monthly active users worldwide , ranking second among mobile chat apps worldwide . Facebook messenger users – additional information Mobile messenger apps are on the rise , with optimistic projections for this market in the coming years .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[0] templateTitle[1] chat app templateYLabel[3] from April templateTitle[6] to September templateTitle[7] . As of the last reported period , the mobile templateTitle[1] had templateYValue[max] billion templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide , ranking second among mobile chat apps worldwide . templateTitle[0] templateTitle[1] templateYLabel[3] – additional information Mobile templateTitle[1] apps are on the rise , with optimistic projections for this market in the coming years .

generated_template: This statistic shows a timeline with the amount of templateYLabel[1] templateYLabel[2] templateTitle[3] templateYLabel[3] worldwide as of December templateTitle[6] . As of that templateXLabel[0] , the mobile messaging app announced more than templateYValue[max] billion templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from over templateYValue[1] billion MAU in February 2016 . The service is one of the most popular mobile apps worldwide .
generated: This statistic shows a timeline with the amount of monthly active monthly users worldwide as of December 2014 . As of that Month , the mobile messaging app announced more than 1300 billion monthly active users , up from over 1200 billion MAU in February 2016 . The service is one of the most popular mobile apps worldwide .


Example 145:
data: Year|2023|x|line_chart Share_of_population|52|y|line_chart Year|2022|x|line_chart Share_of_population|52|y|line_chart Year|2021|x|line_chart Share_of_population|51|y|line_chart Year|2020|x|line_chart Share_of_population|50|y|line_chart Year|2019|x|line_chart Share_of_population|49|y|line_chart Year|2018|x|line_chart Share_of_population|48|y|line_chart Year|2017|x|line_chart Share_of_population|46|y|line_chart 
title: Vietnam social media user penetration 2017 - 2023

gold: This statistic presents the social networking reach in Vietnam from 2017 to 2023 . In 2017 , 46 percent of the population in the country accessed social media . In 2019 , this share is projected to reach 49 percent .
gold_template: This statistic presents the templateTitle[1] networking reach in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[1] in the country accessed templateTitle[1] templateTitle[2] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] percent .

generated_template: This statistic presents the templateTitle[0] networking reach in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[1] in the country accessed templateTitle[0] templateTitle[1] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] percent .
generated: This statistic presents the Vietnam networking reach in penetration from 2017 to 2023 . In 2017 , 46 percent of the population in the country accessed Vietnam social . In 2019 , this Share is projected to reach 49 percent .


Example 146:
data: Year|2013|x|line_chart Revenue_(in_million_U.S._dollars)|780.4|y|line_chart Year|2012|x|line_chart Revenue_(in_million_U.S._dollars)|827.2|y|line_chart Year|2011|x|line_chart Revenue_(in_million_U.S._dollars)|834.9|y|line_chart Year|2010|x|line_chart Revenue_(in_million_U.S._dollars)|772.8|y|line_chart Year|2009|x|line_chart Revenue_(in_million_U.S._dollars)|716.3|y|line_chart Year|2008|x|line_chart Revenue_(in_million_U.S._dollars)|775.5|y|line_chart Year|2007|x|line_chart Revenue_(in_million_U.S._dollars)|724.7|y|line_chart Year|2006|x|line_chart Revenue_(in_million_U.S._dollars)|639.0|y|line_chart 
title: Sporting goods industry : Revenue of Easton Bell Sports 2013

gold: This graph depicts the revenue of the sporting goods company Easton-Bell Sports from 2006 to 2013 . In 2006 , Easton-Bell Sports generated revenues of 639 million U.S. dollars . Easton-Bell Sports makes sports equipment and clothing under the brands Bell Sports , Blackburn , Easton , Giro , and Riddell .
gold_template: This graph depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] company Easton-Bell templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , Easton-Bell templateTitle[6] generated revenues of templateYValue[min] templateYLabel[2] templateYLabel[3] dollars . Easton-Bell templateTitle[6] makes templateTitle[6] equipment and clothing under the brands templateTitle[5] templateTitle[6] , Blackburn , templateTitle[4] , Giro , and Riddell .

generated_template: This statistic depicts the annual National Hockey League templateYLabel[0] of the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[max] percent .
generated: This statistic depicts the annual National Hockey League Revenue of the United States from 2006 to 2013 . In 2013 , the Revenue (in million U.S. of the goods industry Revenue Easton was 834.9 percent .


Example 147:
data: Automaker|Nissan|x|bar_chart Number_of_units_sold|174706|y|bar_chart Automaker|General_Motors|x|bar_chart Number_of_units_sold|133823|y|bar_chart Automaker|Volkswagen|x|bar_chart Number_of_units_sold|117045|y|bar_chart Automaker|Toyota|x|bar_chart Number_of_units_sold|67670|y|bar_chart Automaker|KIA|x|bar_chart Number_of_units_sold|62762|y|bar_chart Automaker|Honda|x|bar_chart Number_of_units_sold|48937|y|bar_chart Automaker|FCA_Mexico|x|bar_chart Number_of_units_sold|41413|y|bar_chart Automaker|Ford|x|bar_chart Number_of_units_sold|39731|y|bar_chart Automaker|Mazda|x|bar_chart Number_of_units_sold|39040|y|bar_chart Automaker|Hyundai|x|bar_chart Number_of_units_sold|29018|y|bar_chart Automaker|Baic|x|bar_chart Number_of_units_sold|21147|y|bar_chart Automaker|Renault|x|bar_chart Number_of_units_sold|20308|y|bar_chart Automaker|Suzuki|x|bar_chart Number_of_units_sold|19880|y|bar_chart Automaker|BMW_Group|x|bar_chart Number_of_units_sold|15859|y|bar_chart Automaker|Mercedes_Benz|x|bar_chart Number_of_units_sold|13627|y|bar_chart Automaker|Mitsubishi|x|bar_chart Number_of_units_sold|11134|y|bar_chart Automaker|Peugeot|x|bar_chart Number_of_units_sold|7097|y|bar_chart Automaker|Volvo|x|bar_chart Number_of_units_sold|1334|y|bar_chart Automaker|ISUZU|x|bar_chart Number_of_units_sold|1224|y|bar_chart Automaker|Acura|x|bar_chart Number_of_units_sold|1224|y|bar_chart Automaker|Lincoln|x|bar_chart Number_of_units_sold|1082|y|bar_chart Automaker|Infiniti|x|bar_chart Number_of_units_sold|951|y|bar_chart Automaker|Land_Rover|x|bar_chart Number_of_units_sold|908|y|bar_chart Automaker|Subaru|x|bar_chart Number_of_units_sold|790|y|bar_chart Automaker|Jaguar|x|bar_chart Number_of_units_sold|207|y|bar_chart Automaker|Smart|x|bar_chart Number_of_units_sold|30|y|bar_chart 
title: Mexico : Light vehicle sales by manufacturer 2019

gold: The Japanese multinational Nissan was the automobile manufacturer with the highest number of light vehicles sold in Mexico , with more than 174 thousand units sold as of August 2019 . The American multinational General Motors ranked second with almost 134 thousand light vehicle units sold in 2019 .
gold_template: The Japanese multinational templateXValue[0] was the automobile templateTitle[5] with the highest templateYLabel[0] of templateTitle[1] vehicles templateYLabel[2] in templateXValue[6] , with more than 174 thousand templateYLabel[1] templateYLabel[2] as of August templateTitle[6] . The American multinational templateXValue[1] templateXValue[1] ranked second with almost templateYValue[1] thousand templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] in templateTitle[6] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYLabel[4] templateYLabel[5] and represents represents represents represents represents the United States in templateTitle[7] . During the survey period , the templateXValue[0] templateXValue[1] was templateYValue[0] percent of templateTitle[1] people in templateTitle[5] .
generated: This statistic shows the Number units sold of yLabelErr yLabelErr and represents represents represents the United States in titleErr . During the survey period , the Nissan General_Motors was 174706 percent of Light people in manufacturer .


Example 148:
data: Year|2023|x|line_chart Number_of_users_in_millions|73.0|y|line_chart Year|2022|x|line_chart Number_of_users_in_millions|71.1|y|line_chart Year|2021|x|line_chart Number_of_users_in_millions|69.1|y|line_chart Year|2020|x|line_chart Number_of_users_in_millions|66.7|y|line_chart Year|2019|x|line_chart Number_of_users_in_millions|64.1|y|line_chart Year|2018|x|line_chart Number_of_users_in_millions|61.7|y|line_chart Year|2017|x|line_chart Number_of_users_in_millions|58.8|y|line_chart 
title: Mexico : number of social network users 2017 - 2023

gold: This statistic shows the number of social network users in Mexico from 2017 to 2023 . In 2018 , there were around 62 million social network users in Mexico , up from 59 million in 2017 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there were around templateYValue[5] million templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitle[0] , up from templateYValue[min] million in templateXValue[min] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there were approximately templateYValue[5] million templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitle[0] , and this figure is projected to grow to templateYValue[max] million in templateXValue[max] .
generated: This statistic shows the Number of social network users in Mexico from 2017 to 2023 . In 2018 , there were approximately 61.7 million social network users in Mexico , and this figure is projected to grow to 73.0 million in 2023 .


Example 149:
data: Year|2018|x|line_chart Revenue_in_billion_U.S._dollars|8.91|y|line_chart Year|2017|x|line_chart Revenue_in_billion_U.S._dollars|8.55|y|line_chart Year|2016|x|line_chart Revenue_in_billion_U.S._dollars|7.6|y|line_chart Year|2015|x|line_chart Revenue_in_billion_U.S._dollars|8.3|y|line_chart Year|2014|x|line_chart Revenue_in_billion_U.S._dollars|9.6|y|line_chart Year|2013|x|line_chart Revenue_in_billion_U.S._dollars|8.8|y|line_chart Year|2012|x|line_chart Revenue_in_billion_U.S._dollars|7.8|y|line_chart Year|2011|x|line_chart Revenue_in_billion_U.S._dollars|9.3|y|line_chart Year|2010|x|line_chart Revenue_in_billion_U.S._dollars|10.0|y|line_chart Year|2009|x|line_chart Revenue_in_billion_U.S._dollars|9.8|y|line_chart Year|2008|x|line_chart Revenue_in_billion_U.S._dollars|7.8|y|line_chart Year|2007|x|line_chart Revenue_in_billion_U.S._dollars|6.6|y|line_chart 
title: Bombardier - transportation revenue 2007 - 2018

gold: The timeline shows Bombardier 's transportation revenues between the 2007 and 2018 fiscal years . Bombardier Inc. is a Canadian conglomerate , founded by Joseph-Armand Bombardier as L'Auto-Neige Bombardier Limitée in 1942 , at Valcourt in the Eastern Townships , Quebec . Over the years it has been a large manufacturer of regional aircraft , business jets , mass transportation equipment , recreational equipment and a financial services provider .
gold_template: The timeline shows templateTitle[0] 's templateTitle[1] revenues between the templateXValue[min] and templateXValue[max] fiscal years . templateTitle[0] Inc. is a Canadian conglomerate , founded by Joseph-Armand templateTitle[0] as L'Auto-Neige templateTitle[0] Limitée in 1942 , at Valcourt in the Eastern Townships , Quebec . Over the years it has been a large manufacturer of regional aircraft , business jets , mass templateTitle[1] equipment , recreational equipment and a financial services provider .

generated_template: The statistic shows the templateTitle[0] templateYLabel[0] of the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the Germany-based electricity provider generated around templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] .
generated: The statistic shows the Bombardier Revenue of the fiscal Year of 2007 to the fiscal Year of 2018 . In the fiscal Year of 2018 , the Germany-based electricity provider generated around 8.91 billion U.S. dollars in Revenue .


Example 150:
data: Year|2024|x|line_chart Market_share|24.4|y|line_chart Year|2023|x|line_chart Market_share|23.4|y|line_chart Year|2022|x|line_chart Market_share|22.4|y|line_chart Year|2021|x|line_chart Market_share|21.4|y|line_chart Year|2020|x|line_chart Market_share|20.4|y|line_chart Year|2019|x|line_chart Market_share|18.9|y|line_chart Year|2018|x|line_chart Market_share|17.4|y|line_chart Year|2017|x|line_chart Market_share|16.4|y|line_chart Year|2016|x|line_chart Market_share|13.6|y|line_chart Year|2015|x|line_chart Market_share|12.9|y|line_chart Year|2014|x|line_chart Market_share|14.2|y|line_chart Year|2013|x|line_chart Market_share|13.8|y|line_chart Year|2012|x|line_chart Market_share|12|y|line_chart 
title: Estée Lauder 's share of the makeup products market worldwide 2012 - 2024

gold: This statistic shows Estée Lauder 's share of the makeup products market worldwide from 2012 to 2024 . In 2018 , Estée Lauder 's share of the global makeup products market is estimated to be 17.4 percent .
gold_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateYLabel[0] templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] of the global templateTitle[4] templateTitle[5] templateYLabel[0] is estimated to be templateYValue[6] percent .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of the templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] ( templateTitle[7] ) between templateXValue[min] and templateXValue[max] . In templateXValue[7] , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] of the global automobile industry was around templateYValue[min] percent .
generated: This statistic shows the Estée Lauder of the 's share Market share of the makeup products market ( worldwide ) between 2012 and 2024 . In 2017 , Estée Lauder 's share Market share of the global automobile industry was around 12 percent .


Example 151:
data: Year|2022|x|line_chart Number_of_users_in_millions|28.16|y|line_chart Year|2021|x|line_chart Number_of_users_in_millions|27.66|y|line_chart Year|2020|x|line_chart Number_of_users_in_millions|27.1|y|line_chart Year|2019|x|line_chart Number_of_users_in_millions|26.32|y|line_chart Year|2018|x|line_chart Number_of_users_in_millions|25.53|y|line_chart Year|2017|x|line_chart Number_of_users_in_millions|24.77|y|line_chart Year|2016|x|line_chart Number_of_users_in_millions|23.99|y|line_chart Year|2015|x|line_chart Number_of_users_in_millions|23.07|y|line_chart 
title: South Korea : number of social network users 2015 - 2022

gold: This statistic shows the number of social network users in South Korea from 2015 to 2022 . In 2022 , it is estimated that there will be around 28.16 million social network users in South Korea , up from 24.77 million in 2017 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[3] templateTitle[4] templateYLabel[1] in templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around templateYValue[max] million templateTitle[3] templateTitle[4] templateYLabel[1] in templateTitle[0] templateTitle[1] , up from templateYValue[5] million in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there were approximately templateYValue[5] million templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitle[0] , and this figure is projected to grow to templateYValue[max] million in templateXValue[max] .
generated: This statistic shows the Number of number social users in South from 2015 to 2022 . In 2017 , there were approximately 24.77 million number social users in South , and this figure is projected to grow to 28.16 million in 2022 .


Example 152:
data: Year|2050|x|line_chart Number_of_people_in_thousands|590|y|line_chart Year|2030|x|line_chart Number_of_people_in_thousands|261|y|line_chart Year|2015|x|line_chart Number_of_people_in_thousands|123|y|line_chart 
title: Projected number of people with dementia in Malaysia 2015 - 2050

gold: This statistic displays the projected number of people with dementia in Malaysia in 2015 , 2030 and 2050 . According to the source , approximately 590 thousand people are projected to have dementia by 2050 in Malaysia .
gold_template: This statistic displays the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateTitle[3] templateTitle[4] in templateTitle[5] in templateXValue[min] , templateXValue[1] and templateXValue[max] . According to the source , approximately templateYValue[max] thousand templateYLabel[1] are templateTitle[0] to have templateTitle[4] by templateXValue[max] in templateTitle[5] .

generated_template: This statistic displays the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateTitle[3] templateTitle[4] in templateTitle[5] in templateXValue[min] , templateXValue[1] and templateXValue[max] . According to the source , approximately 240 thousand templateYLabel[1] are templateTitle[0] to have templateTitle[4] by templateXValue[max] in templateTitle[5] .
generated: This statistic displays the Projected Number of people dementia Malaysia in 2015 in 2015 , 2030 and 2050 . According to the source , approximately 240 thousand people are Projected to have Malaysia by 2050 in 2015 .


Example 153:
data: Year|2019|x|line_chart Unemployment_rate|0.15|y|line_chart Year|2018|x|line_chart Unemployment_rate|0.14|y|line_chart Year|2017|x|line_chart Unemployment_rate|0.14|y|line_chart Year|2016|x|line_chart Unemployment_rate|0.14|y|line_chart Year|2015|x|line_chart Unemployment_rate|0.16|y|line_chart Year|2014|x|line_chart Unemployment_rate|0.19|y|line_chart Year|2013|x|line_chart Unemployment_rate|0.27|y|line_chart Year|2012|x|line_chart Unemployment_rate|0.48|y|line_chart Year|2011|x|line_chart Unemployment_rate|0.56|y|line_chart Year|2010|x|line_chart Unemployment_rate|0.45|y|line_chart Year|2009|x|line_chart Unemployment_rate|0.31|y|line_chart Year|2008|x|line_chart Unemployment_rate|0.31|y|line_chart Year|2007|x|line_chart Unemployment_rate|0.52|y|line_chart Year|2006|x|line_chart Unemployment_rate|0.87|y|line_chart Year|2005|x|line_chart Unemployment_rate|1.21|y|line_chart Year|2004|x|line_chart Unemployment_rate|1.48|y|line_chart Year|2003|x|line_chart Unemployment_rate|1.53|y|line_chart Year|2002|x|line_chart Unemployment_rate|1.51|y|line_chart Year|2001|x|line_chart Unemployment_rate|1.46|y|line_chart Year|2000|x|line_chart Unemployment_rate|1.51|y|line_chart Year|1999|x|line_chart Unemployment_rate|1.57|y|line_chart 
title: Unemployment rate in Qatar 2019

gold: This statistic shows the unemployment rate in Qatar from 1999 to 2019 . In 2019 , 0.15 percent of the total labor force in Qatar was unemployed .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the total labor force in templateTitle[2] was unemployed .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate Unemployment rate in Qatar from 1999 to 2019 . In 2019 , the Unemployment rate in Qatar was at approximately 0.15 percent .


Example 154:
data: Month|Dec_'19|x|bar_chart Number_of_employees|4900|y|bar_chart Month|Dec_'18|x|bar_chart Number_of_employees|3920|y|bar_chart Month|Dec_'17|x|bar_chart Number_of_employees|3372|y|bar_chart Month|Dec_'16|x|bar_chart Number_of_employees|3583|y|bar_chart Month|Dec_'15|x|bar_chart Number_of_employees|3900|y|bar_chart Month|Dec_'14|x|bar_chart Number_of_employees|3638|y|bar_chart Month|Dec_'13|x|bar_chart Number_of_employees|2712|y|bar_chart Month|Jan_'11|x|bar_chart Number_of_employees|350|y|bar_chart Month|Jan_'10|x|bar_chart Number_of_employees|130|y|bar_chart Month|Jan_'09|x|bar_chart Number_of_employees|29|y|bar_chart Month|Jan_'08|x|bar_chart Number_of_employees|8|y|bar_chart 
title: Twitter : number of employees 2008 - 2019

gold: The statistic provides the number of employees of Twitter from 2008 to 2019 . At the end of the most recently reported year , the microblogging company employed 4,900 people , up from 3,920 people in the previous year . Twitter 's corporate demography In 2018 , the majority of Twitter 's employees were male with a share of 59.8 percent and of a white ethnicity with 42.3 percent .
gold_template: The statistic provides the templateYLabel[0] of templateYLabel[1] of templateTitle[0] from templateTitle[3] to templateTitle[4] . At the end of the most recently reported year , the microblogging company employed templateYValue[max] people , up from templateYValue[1] people in the previous year . templateTitle[0] 's corporate demography In 2018 , the majority of templateTitle[0] 's templateYLabel[1] were male with a share of 59.8 percent and of a white ethnicity with 42.3 percent .

generated_template: This statistic represents the templateYLabel[0] of templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] worldwide from 2011 to December templateTitle[7] . As of that year , there were about templateYValue[max] people in the United States . The average templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the last year .
generated: This statistic represents the Number of employees of employees 2008 2019 worldwide from 2011 to December titleErr . As of that year , there were about 4900 people in the United States . The average Number of employees yLabelErr in the last year .


Example 155:
data: Response|Extremely|x|bar_chart Share_of_respondents|47|y|bar_chart Response|Very|x|bar_chart Share_of_respondents|25|y|bar_chart Response|Moderately|x|bar_chart Share_of_respondents|16|y|bar_chart Response|Only_a_little|x|bar_chart Share_of_respondents|7|y|bar_chart Response|Not_at_all|x|bar_chart Share_of_respondents|3|y|bar_chart Response|No_opinion|x|bar_chart Share_of_respondents|1|y|bar_chart 
title: Americans ' level of pride to be an American 2018

gold: This statistic shows the results of a 2018 survey regarding patriotism in the United States . The respondents were asked how proud they are to be an American . In 2018 , some 47 percent of survey respondents stated they were extremely proud to be an American .
gold_template: This statistic shows the results of a templateTitle[5] survey regarding patriotism in the United States . The templateYLabel[1] were asked how proud they are to be an templateTitle[4] . In templateTitle[5] , some templateYValue[max] percent of survey templateYLabel[1] stated they were templateXValue[0] proud to be an templateTitle[4] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in the United States as of July to templateTitle[5] . During the survey , templateYValue[max] percent of templateYLabel[1] stated that they used templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Americans nation in the United States as of July to 2018 . During the survey , 47 percent of respondents stated that they used Extremely Extremely Extremely .


Example 156:
data: Year|2019|x|line_chart Unemployment_rate|4.82|y|line_chart Year|2018|x|line_chart Unemployment_rate|5.26|y|line_chart Year|2017|x|line_chart Unemployment_rate|6.16|y|line_chart Year|2016|x|line_chart Unemployment_rate|7.58|y|line_chart Year|2015|x|line_chart Unemployment_rate|9.14|y|line_chart Year|2014|x|line_chart Unemployment_rate|11.42|y|line_chart Year|2013|x|line_chart Unemployment_rate|12.94|y|line_chart Year|2012|x|line_chart Unemployment_rate|12.27|y|line_chart Year|2011|x|line_chart Unemployment_rate|11.26|y|line_chart Year|2010|x|line_chart Unemployment_rate|10.28|y|line_chart Year|2009|x|line_chart Unemployment_rate|6.82|y|line_chart Year|2008|x|line_chart Unemployment_rate|5.61|y|line_chart Year|2007|x|line_chart Unemployment_rate|6.88|y|line_chart Year|2006|x|line_chart Unemployment_rate|8.95|y|line_chart Year|2005|x|line_chart Unemployment_rate|10.08|y|line_chart Year|2004|x|line_chart Unemployment_rate|12.04|y|line_chart Year|2003|x|line_chart Unemployment_rate|13.73|y|line_chart Year|2002|x|line_chart Unemployment_rate|18.11|y|line_chart Year|2001|x|line_chart Unemployment_rate|19.92|y|line_chart Year|2000|x|line_chart Unemployment_rate|16.22|y|line_chart Year|1999|x|line_chart Unemployment_rate|14.1|y|line_chart 
title: Unemployment rate in Bulgaria 2019

gold: This statistic shows the unemployment rate in Bulgaria from 1999 to 2019 . In 2019 , the unemployment rate in Bulgaria was at approximately 4.82 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[min] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was estimated to be at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Bulgaria from 1999 to 2019 . In 2019 , the Unemployment rate in Bulgaria was estimated to be at approximately 4.82 percent .


Example 157:
data: Year|2018|x|line_chart Retail_value_sales_in_million_U.S._dollars|2984.2|y|line_chart Year|2017|x|line_chart Retail_value_sales_in_million_U.S._dollars|2997.6|y|line_chart Year|2016|x|line_chart Retail_value_sales_in_million_U.S._dollars|3011.1|y|line_chart Year|2015|x|line_chart Retail_value_sales_in_million_U.S._dollars|3033.8|y|line_chart Year|2014|x|line_chart Retail_value_sales_in_million_U.S._dollars|3063.4|y|line_chart Year|2013|x|line_chart Retail_value_sales_in_million_U.S._dollars|3030.9|y|line_chart Year|2012|x|line_chart Retail_value_sales_in_million_U.S._dollars|2941.6|y|line_chart Year|2011|x|line_chart Retail_value_sales_in_million_U.S._dollars|2856.4|y|line_chart Year|2010|x|line_chart Retail_value_sales_in_million_U.S._dollars|2773.4|y|line_chart Year|2009|x|line_chart Retail_value_sales_in_million_U.S._dollars|2633.5|y|line_chart 
title: Retail sales value of yogurt & sour milk products in the United Kingdom 2009 - 2018

gold: This statistic presents data on the retail sales value of yogurt and sour milk products in the United Kingdom ( UK ) from 2009 to 2018 . In 2013 , the retail sales value of yogurt and sour milk products amounted to approximately 3 billion U.S. dollars , according to historic retail figures . This is expected to decrease by 2018 .
gold_template: This statistic presents data on the templateYLabel[0] templateYLabel[2] templateYLabel[1] of templateTitle[3] and templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[8] templateTitle[9] ( UK ) from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateYLabel[0] templateYLabel[2] templateYLabel[1] of templateTitle[3] and templateTitle[5] templateTitle[6] templateTitle[7] amounted to approximately templateYValue[0] billion templateYLabel[4] templateYLabel[5] , according to historic templateYLabel[0] figures . This is expected to decrease by templateXValue[max] .

generated_template: More than templateYValue[0] templateYLabel[1] were recorded in templateTitle[3] templateTitle[4] templateTitle[5] in templateXValue[max] . templateTitle[1] citizens between the years and templateYValue[0] percent of the templateXLabel[0] 's history with an increase of templateYValue[max] percent in templateYLabel[1] compared with the previous templateXLabel[0] . The source templateYLabel[1] Japan , many birth rate , was one of the highest in templateXValue[1] .
generated: More than 2984.2 value were recorded in yogurt & sour in 2018 . sales citizens between the years and 2984.2 percent of the Year 's history with an increase of 3063.4 percent in value compared with the previous Year . The source value Japan , many birth rate , was one of the highest in 2017 .


Example 158:
data: Year|2018/19|x|line_chart Number_of_players|55431|y|line_chart Year|2017/18|x|line_chart Number_of_players|62701|y|line_chart Year|2016/17|x|line_chart Number_of_players|63901|y|line_chart Year|2015/16|x|line_chart Number_of_players|60408|y|line_chart Year|2014/15|x|line_chart Number_of_players|60089|y|line_chart Year|2013/14|x|line_chart Number_of_players|56839|y|line_chart Year|2012/13|x|line_chart Number_of_players|64214|y|line_chart Year|2011/12|x|line_chart Number_of_players|69921|y|line_chart Year|2010/11|x|line_chart Number_of_players|62003|y|line_chart 
title: Ice hockey players in Sweden 2010 - 2019

gold: The statistics depicts the number of registered ice hockey players in Sweden from 2010/11 to 2018/19 . In the 2018/19 season , there were a total of 55,431 registered ice hockey players in Sweden according to the International Ice Hockey Federation .
gold_template: The statistics depicts the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitle[3] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[min] registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitle[3] according to the International templateTitle[0] templateTitle[1] Federation .

generated_template: The graph depicts the templateTitle[3] templateTitle[4] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] templateTitle[4] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a templateYLabel[1] of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] templateTitle[4] according to the International templateTitle[0] templateTitle[1] Federation .
generated: The graph depicts the Sweden 2010 of Ice hockey players in the Sweden 2010 from 2010/11 to 2018/19 . In the 2018/19 season , there were a players of 69921 registered Ice hockey players in the Sweden 2010 according to the International Ice hockey Federation .


Example 159:
data: Year|2050|x|line_chart Median_age_in_years|33.0|y|line_chart Year|2045|x|line_chart Median_age_in_years|31.3|y|line_chart Year|2040|x|line_chart Median_age_in_years|29.6|y|line_chart Year|2035|x|line_chart Median_age_in_years|28.0|y|line_chart Year|2030|x|line_chart Median_age_in_years|26.3|y|line_chart Year|2025|x|line_chart Median_age_in_years|24.5|y|line_chart Year|2020|x|line_chart Median_age_in_years|22.9|y|line_chart Year|2015|x|line_chart Median_age_in_years|21.3|y|line_chart Year|2010|x|line_chart Median_age_in_years|19.7|y|line_chart Year|2005|x|line_chart Median_age_in_years|18.5|y|line_chart Year|2000|x|line_chart Median_age_in_years|17.7|y|line_chart Year|1995|x|line_chart Median_age_in_years|17.2|y|line_chart Year|1990|x|line_chart Median_age_in_years|16.9|y|line_chart Year|1985|x|line_chart Median_age_in_years|16.6|y|line_chart Year|1980|x|line_chart Median_age_in_years|16.8|y|line_chart Year|1975|x|line_chart Median_age_in_years|17.0|y|line_chart Year|1970|x|line_chart Median_age_in_years|16.9|y|line_chart Year|1965|x|line_chart Median_age_in_years|16.8|y|line_chart Year|1960|x|line_chart Median_age_in_years|17.2|y|line_chart Year|1955|x|line_chart Median_age_in_years|17.8|y|line_chart Year|1950|x|line_chart Median_age_in_years|18.4|y|line_chart 
title: Median age of the population in Guatemala 2015

gold: This statistic shows the median age of the population in Guatemala from 1950 to 2050 . The median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .
generated: This statistic shows the Median age of the population in Guatemala from 1950 to 2050 . The Median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population .


Example 160:
data: State|Florida|x|bar_chart Number_of_signups|1783304|y|bar_chart State|California|x|bar_chart Number_of_signups|1513883|y|bar_chart State|Texas|x|bar_chart Number_of_signups|1087240|y|bar_chart State|North_Carolina|x|bar_chart Number_of_signups|501271|y|bar_chart State|Georgia|x|bar_chart Number_of_signups|458437|y|bar_chart State|Pennsylvania|x|bar_chart Number_of_signups|365888|y|bar_chart State|Virginia|x|bar_chart Number_of_signups|328020|y|bar_chart State|Illinois|x|bar_chart Number_of_signups|312280|y|bar_chart State|Massachusetts|x|bar_chart Number_of_signups|300085|y|bar_chart State|Michigan|x|bar_chart Number_of_signups|274058|y|bar_chart State|New_York|x|bar_chart Number_of_signups|271873|y|bar_chart State|New_Jersey|x|bar_chart Number_of_signups|255246|y|bar_chart State|Washington|x|bar_chart Number_of_signups|222636|y|bar_chart State|Tennessee|x|bar_chart Number_of_signups|221533|y|bar_chart State|Missouri|x|bar_chart Number_of_signups|220461|y|bar_chart State|South_Carolina|x|bar_chart Number_of_signups|214956|y|bar_chart State|Ohio|x|bar_chart Number_of_signups|206871|y|bar_chart State|Wisconsin|x|bar_chart Number_of_signups|205118|y|bar_chart State|Utah|x|bar_chart Number_of_signups|194570|y|bar_chart State|Colorado|x|bar_chart Number_of_signups|169672|y|bar_chart State|Alabama|x|bar_chart Number_of_signups|166128|y|bar_chart State|Arizona|x|bar_chart Number_of_signups|160456|y|bar_chart State|Maryland|x|bar_chart Number_of_signups|156963|y|bar_chart State|Oklahoma|x|bar_chart Number_of_signups|150759|y|bar_chart State|Indiana|x|bar_chart Number_of_signups|148404|y|bar_chart State|Oregon|x|bar_chart Number_of_signups|148180|y|bar_chart State|Minnesota|x|bar_chart Number_of_signups|123731|y|bar_chart State|Connecticut|x|bar_chart Number_of_signups|111066|y|bar_chart State|Idaho|x|bar_chart Number_of_signups|103154|y|bar_chart State|Louisiana|x|bar_chart Number_of_signups|92948|y|bar_chart State|Kansas|x|bar_chart Number_of_signups|89993|y|bar_chart State|Mississippi|x|bar_chart Number_of_signups|88542|y|bar_chart State|Nebraska|x|bar_chart Number_of_signups|87416|y|bar_chart State|Kentucky|x|bar_chart Number_of_signups|84620|y|bar_chart State|Nevada|x|bar_chart Number_of_signups|83449|y|bar_chart State|Maine|x|bar_chart Number_of_signups|70987|y|bar_chart State|Arkansas|x|bar_chart Number_of_signups|67413|y|bar_chart State|Iowa|x|bar_chart Number_of_signups|49210|y|bar_chart State|Montana|x|bar_chart Number_of_signups|45374|y|bar_chart State|New_Mexico|x|bar_chart Number_of_signups|45001|y|bar_chart State|New_Hampshire|x|bar_chart Number_of_signups|44581|y|bar_chart State|Rhode_Island|x|bar_chart Number_of_signups|34533|y|bar_chart State|South_Dakota|x|bar_chart Number_of_signups|29069|y|bar_chart State|Vermont|x|bar_chart Number_of_signups|25223|y|bar_chart State|Wyoming|x|bar_chart Number_of_signups|24852|y|bar_chart State|West_Virginia|x|bar_chart Number_of_signups|22599|y|bar_chart State|Delaware|x|bar_chart Number_of_signups|22562|y|bar_chart State|North_Dakota|x|bar_chart Number_of_signups|21820|y|bar_chart State|District_of_Columbia|x|bar_chart Number_of_signups|20894|y|bar_chart State|Hawaii|x|bar_chart Number_of_signups|20193|y|bar_chart State|Alaska|x|bar_chart Number_of_signups|17805|y|bar_chart 
title: Number of sign-ups during 2019 Obamacare open enrollment , by U.S. state

gold: This statistic displays the number of Affordable Care Act ( Obamacare ) sign-ups during the 2019 open enrollment period as of February 2019 , by U.S. state . Until February 2019 , there were around 1.51 million sign-ups in California . Open enrollment allows U.S. citizens to enroll , switch plans , and get subsidies on various plans under the Affordable Care Act .
gold_template: This statistic displays the templateYLabel[0] of Affordable Care Act ( templateTitle[4] ) templateTitle[1] templateTitle[2] the templateTitle[3] templateTitle[5] templateTitle[6] period as of February templateTitle[3] , templateTitle[7] templateTitle[8] templateXLabel[0] . Until February templateTitle[3] , there were around templateYValue[1] million templateTitle[1] in templateXValue[1] . templateTitle[5] templateTitle[6] allows templateTitle[8] citizens to enroll , switch plans , and get subsidies on various plans under the Affordable Care Act .

generated_template: templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] in the United States in templateTitle[7] , templateYValue[max] percent of templateTitle[6] templateXLabel[0] . templateXValue[1] , about templateYValue[1] percent of the templateXLabel[0] 's templateYLabel[1] 100,000 people at templateXValue[1] templateXValue[1] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] for the templateTitle[2] for the United States are many templateTitle[2] number of people born in templateTitle[3] templateTitle[4] and templateXValue[1] .
generated: Florida had the highest Number signups yLabelErr of 2019 Obamacare in the United States in by , 1783304 percent of enrollment State . California , about 1513883 percent of the State 's signups 100,000 people at California . The Number signups yLabelErr for the during for the United States are many during number of people born in 2019 Obamacare and California .


Example 161:
data: Industry|Manufacturing|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|88.79|y|bar_chart Industry|Finance_insurance_real_estate_rental_and_leasing|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|76.55|y|bar_chart Industry|Professional_and_business_services|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|65.72|y|bar_chart Industry|Government_and_government_enterprises|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|47.6|y|bar_chart Industry|Educational_services_health_care_and_social_assistance|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|44.72|y|bar_chart Industry|Wholesale_trade|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|32.33|y|bar_chart Industry|Retail_trade|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|31.09|y|bar_chart Industry|Construction|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|16.71|y|bar_chart Industry|Arts_entertainment_recreation_accommodation_and_food_services|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|15.81|y|bar_chart Industry|Information|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|14.24|y|bar_chart Industry|Transportation_and_warehousing|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|11.63|y|bar_chart Industry|Other_services_(except_government_and_government_enterprises)|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|10.14|y|bar_chart Industry|Utilities|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|9.24|y|bar_chart Industry|Agriculture_forestry_fishing_and_hunting|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|4.23|y|bar_chart Industry|Mining_quarrying_and_oil_and_gas_extraction|x|bar_chart Real_value_added_in_billion_chained_(2012)_U.S._dollars|2.36|y|bar_chart 
title: Real GDP of Michigan 2018 , by industry

gold: This graph shows the real value added to the Gross Domestic Product ( GDP ) of Michigan in 2018 , by industry . In 2018 , the construction industry added 16.71 billion chained 2012 U.S. dollars of value to the total state GDP .
gold_template: This graph shows the templateXValue[1] templateYLabel[1] templateYLabel[2] to the Gross Domestic Product ( templateTitle[1] ) of templateTitle[2] in templateTitle[3] , templateTitle[4] templateXLabel[0] . In templateTitle[3] , the templateXValue[7] templateXLabel[0] templateYLabel[2] templateYValue[7] templateYLabel[3] templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the total state templateTitle[1] .

generated_template: This graph shows the templateXValue[0] templateYLabel[1] templateYLabel[2] to the Gross Domestic Product ( templateTitle[1] ) of templateTitle[2] in templateTitle[5] , templateTitle[3] templateXLabel[0] . In templateTitle[3] , the templateXValue[6] templateXLabel[0] templateYLabel[2] templateYValue[6] templateYLabel[3] templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the state templateTitle[1] .
generated: This graph shows the Manufacturing value added to the Gross Domestic Product ( GDP ) of Michigan in industry , 2018 Industry . In 2018 , the Retail_trade Industry added 31.09 billion chained 2012 U.S. dollars of value to the state GDP .


Example 162:
data: Response|Parents|x|bar_chart Share_of_respondents|38|y|bar_chart Response|Friends|x|bar_chart Share_of_respondents|22|y|bar_chart Response|The_media|x|bar_chart Share_of_respondents|9|y|bar_chart Response|Religious_leaders|x|bar_chart Share_of_respondents|6|y|bar_chart Response|Siblings|x|bar_chart Share_of_respondents|6|y|bar_chart Response|Teachers_and_educators|x|bar_chart Share_of_respondents|4|y|bar_chart Response|Someone_else|x|bar_chart Share_of_respondents|10|y|bar_chart 
title: Influence of friends and family on American teenagers ' decisions regarding sex in 2012

gold: This statistic shows the results of a 2012 survey among American teenagers aged 12 to 19 regarding the influence of their social environment on their decisions about sex . Some 38 percent of surveyed teenagers stated that parents most influenced their decisions about sex .
gold_template: This statistic shows the results of a templateTitle[9] survey among templateTitle[3] templateTitle[4] aged 12 to 19 templateTitle[7] the templateTitle[0] of their social environment on their templateTitle[6] about templateTitle[8] . Some templateYValue[max] percent of surveyed templateTitle[4] stated that templateXValue[0] most influenced their templateTitle[6] about templateTitle[8] .

generated_template: This statistic shows the results of a survey among female templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . According to the survey , templateYValue[max] percent of templateYLabel[1] cited templateXValue[1] templateXValue[1] or templateYValue[1] percent of templateYLabel[1] claimed that they used templateXValue[0] templateXValue[0] , while templateYValue[1] percent of templateYLabel[1] stated templateXValue[1] at templateXValue[1] .
generated: This statistic shows the results of a survey among female friends family American teenagers ' decisions in regarding . According to the survey , 38 percent of respondents cited Friends or 22 percent of respondents claimed that they used Parents , while 22 percent of respondents stated Friends at Friends .


Example 163:
data: Response|Software_application_development|x|bar_chart Share_of_respondents|64|y|bar_chart Response|Software_application_maintenance|x|bar_chart Share_of_respondents|51|y|bar_chart Response|Data_centers|x|bar_chart Share_of_respondents|40|y|bar_chart Response|IT_infrastructure|x|bar_chart Share_of_respondents|32|y|bar_chart Response|Service_desk_/_help_desk|x|bar_chart Share_of_respondents|32|y|bar_chart Response|Networks|x|bar_chart Share_of_respondents|29|y|bar_chart Response|Systems_integration|x|bar_chart Share_of_respondents|29|y|bar_chart Response|HR_BPO|x|bar_chart Share_of_respondents|12|y|bar_chart Response|IT_department|x|bar_chart Share_of_respondents|12|y|bar_chart Response|IT_BPO|x|bar_chart Share_of_respondents|12|y|bar_chart Response|KPO|x|bar_chart Share_of_respondents|6|y|bar_chart 
title: IT functions outsourced worldwide 2017

gold: The statistic reveals the IT functions most commonly outsourced by IT leaders worldwide , as of 2017 . In 2017 , software application development was outsourced by 64 percent of respondents .
gold_template: The statistic reveals the IT templateTitle[0] most commonly templateTitle[1] by IT leaders templateTitle[2] , as of templateTitle[3] . In templateTitle[3] , templateXValue[0] templateXValue[0] templateXValue[0] was templateTitle[1] by templateYValue[max] percent of templateYLabel[1] .

generated_template: This statistic represents the results of a survey among female templateTitle[1] high school students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , templateYValue[max] percent of female students in the United States were templateTitle[5] templateTitle[6] as of 2013 .
generated: This statistic represents the results of a survey among female outsourced high school students 2017 titleErr titleErr titleErr . According to the source , 64 percent of female students in the United States were titleErr titleErr as of 2013 .


Example 164:
data: Year|2010|x|line_chart Equipment_sales_in_million_U.S._dollars|191.76|y|line_chart Year|2011|x|line_chart Equipment_sales_in_million_U.S._dollars|210.38|y|line_chart 
title: Camping equipment sales in the U.S. - sleeping bags 2011

gold: The statistic shows the total sales of sleeping bags in the United States from 2010 to 2011 . In 2010 , sales of sleeping bags amounted to 191.76 million U.S. dollars .
gold_template: The statistic shows the total templateYLabel[1] of templateTitle[4] templateTitle[5] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYLabel[1] of templateTitle[4] templateTitle[5] amounted to templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The graph shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the templateYLabel[1] were recorded in templateYLabel[2] .
generated: The graph shows the Equipment sales million of U.S. sleeping in the United States from 2010 to 2011 . In 2011 , 210.38 percent of the sales were recorded in million .


Example 165:
data: Year|2018|x|line_chart Average_sales_in_thousand_U.S._dollars|3580|y|line_chart Year|2017|x|line_chart Average_sales_in_thousand_U.S._dollars|3542|y|line_chart Year|2016|x|line_chart Average_sales_in_thousand_U.S._dollars|3354|y|line_chart Year|2015|x|line_chart Average_sales_in_thousand_U.S._dollars|3430|y|line_chart 
title: Average sales per unit of Outback Steakhouse restaurants in the U.S. 2015 - 2018

gold: This statistic shows the average sales per unit of Outback Steakhouse restaurants in the United States from 2015 to 2018 . Average sales per Outback Steakhouse restaurant amounted to approximately 3.58 million U.S. dollars in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] of templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[7] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[4] templateTitle[5] restaurant amounted to approximately templateYValue[max] million templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were about templateYValue[0] people throughout the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Average sales of per unit Outback Steakhouse in the United States from 2015 to 2018 . In 2018 , there were about 3580 people throughout the per unit Outback Steakhouse in the United States .


Example 166:
data: Year|2019|x|line_chart Franchise_value_in_million_U.S._dollars|1085|y|line_chart Year|2018|x|line_chart Franchise_value_in_million_U.S._dollars|1050|y|line_chart Year|2017|x|line_chart Franchise_value_in_million_U.S._dollars|1000|y|line_chart Year|2016|x|line_chart Franchise_value_in_million_U.S._dollars|925|y|line_chart Year|2015|x|line_chart Franchise_value_in_million_U.S._dollars|925|y|line_chart Year|2014|x|line_chart Franchise_value_in_million_U.S._dollars|825|y|line_chart Year|2013|x|line_chart Franchise_value_in_million_U.S._dollars|625|y|line_chart Year|2012|x|line_chart Franchise_value_in_million_U.S._dollars|350|y|line_chart Year|2011|x|line_chart Franchise_value_in_million_U.S._dollars|306|y|line_chart Year|2010|x|line_chart Franchise_value_in_million_U.S._dollars|300|y|line_chart Year|2009|x|line_chart Franchise_value_in_million_U.S._dollars|258|y|line_chart Year|2008|x|line_chart Franchise_value_in_million_U.S._dollars|205|y|line_chart Year|2007|x|line_chart Franchise_value_in_million_U.S._dollars|179|y|line_chart Year|2006|x|line_chart Franchise_value_in_million_U.S._dollars|168|y|line_chart 
title: Franchise value of the Chicago Blackhawks 2006 - 2019

gold: This graph depicts the value of the Chicago Blackhawks franchise of the National Hockey League from 2006 to 2019 . In 2019 , the franchise had an estimated value of 1.09 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateYLabel[0] of the National Hockey League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[4] are owned by Stuart Sternberg , who bought the templateYLabel[0] for 200 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[15] .
generated: This graph depicts the value of the Chicago Blackhawks 2006 Franchise of Major League Baseball from 2006 to 2019 . In 2019 , the Franchise had an estimated value of 1085 billion U.S. dollars . The Chicago Blackhawks 2006 are owned by Stuart Sternberg , who bought the Franchise for 200 million U.S. dollars in xValErr .


Example 167:
data: Country|New_Zealand|x|bar_chart Index_score|87|y|bar_chart Country|Singapore|x|bar_chart Index_score|85|y|bar_chart Country|Australia|x|bar_chart Index_score|77|y|bar_chart Country|Hong_Kong|x|bar_chart Index_score|76|y|bar_chart Country|Japan|x|bar_chart Index_score|73|y|bar_chart Country|Bhutan|x|bar_chart Index_score|68|y|bar_chart Country|Taiwan|x|bar_chart Index_score|63|y|bar_chart Country|South_Korea|x|bar_chart Index_score|57|y|bar_chart Country|Malaysia|x|bar_chart Index_score|47|y|bar_chart Country|India|x|bar_chart Index_score|41|y|bar_chart Country|China|x|bar_chart Index_score|39|y|bar_chart Country|Sri_Lanka|x|bar_chart Index_score|38|y|bar_chart Country|Indonesia|x|bar_chart Index_score|38|y|bar_chart Country|Mongolia|x|bar_chart Index_score|37|y|bar_chart Country|Philippines|x|bar_chart Index_score|36|y|bar_chart Country|Thailand|x|bar_chart Index_score|36|y|bar_chart Country|Timor-Leste|x|bar_chart Index_score|35|y|bar_chart Country|Vietnam|x|bar_chart Index_score|33|y|bar_chart Country|Pakistan|x|bar_chart Index_score|33|y|bar_chart Country|Nepal|x|bar_chart Index_score|31|y|bar_chart Country|Myanmar|x|bar_chart Index_score|29|y|bar_chart Country|Laos|x|bar_chart Index_score|29|y|bar_chart Country|Papua_New_Guinea|x|bar_chart Index_score|28|y|bar_chart Country|Bangladesh|x|bar_chart Index_score|26|y|bar_chart Country|Cambodia|x|bar_chart Index_score|20|y|bar_chart Country|Afghanistan|x|bar_chart Index_score|16|y|bar_chart Country|North_Korea|x|bar_chart Index_score|14|y|bar_chart 
title: Corruption perception index APAC 2018 , by country

gold: This statistic shows the perceived corruption of the countries in the Asia Pacific region , as calculated by the Corruption Perception Index in 2018 . In that year , New Zealand was perceived as the least corrupt country with an index score of 87 out of 100 .
gold_template: This statistic shows the perceived templateTitle[0] of the countries in the Asia Pacific region , as calculated templateTitle[5] the templateTitle[0] templateTitle[1] templateYLabel[0] in templateTitle[4] . In that year , templateXValue[0] templateXValue[0] was perceived as the least corrupt templateXLabel[0] with an templateYLabel[0] templateYLabel[1] of templateYValue[max] out of 100 .

generated_template: templateXValue[0] had the templateTitle[2] templateTitle[3] of templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] templateYLabel[1] in the templateTitle[0] templateTitle[1] templateYLabel[0] , with templateYValue[max] , followed by templateXValue[1] . The templateTitle[0] templateTitle[1] templateYLabel[0] measures on-the-ground trade templateTitle[0] templateTitle[1] . The templateTitle[0] market in templateXValue[0] According to the first templateTitle[1] templateYLabel[0] , templateXValue[0] had the highest templateTitle[1] in terms of infrastructure , customs and templateTitle[0] competence .
generated: New_Zealand had the index APAC of 2018 by country Index score in the Corruption perception Index , with 87 , followed by Singapore . The Corruption perception Index measures on-the-ground trade Corruption perception . The Corruption market in New_Zealand According to the first perception Index , New_Zealand had the highest perception in terms of infrastructure , customs and Corruption competence .


Example 168:
data: Country|Western_Europe_(excluding_Germany)|x|bar_chart Sales_in_million_euros|8885|y|bar_chart Country|Eastern_Europe_(exluding_Russia)|x|bar_chart Sales_in_million_euros|5986|y|bar_chart Country|Germany|x|bar_chart Sales_in_million_euros|4075|y|bar_chart Country|Russia|x|bar_chart Sales_in_million_euros|2406|y|bar_chart Country|Asia|x|bar_chart Sales_in_million_euros|1097|y|bar_chart 
title: Metro Group 's sales worldwide 2018/2019 , by region

gold: This statistic shows the global sales of the Metro Group in 2018/2019 , by region . In that year , the sales of the Metro Group in Germany amounted to approximately 2.25 billion euros . The company discontinued its hypermarket sales and its operations in China .
gold_template: This statistic shows the global templateYLabel[0] of the templateTitle[0] templateTitle[1] in templateTitle[5] , templateTitle[6] templateTitle[7] . In that year , the templateYLabel[0] of the templateTitle[0] templateTitle[1] in templateXValue[2] amounted to approximately 2.25 billion templateYLabel[2] . The company discontinued its hypermarket templateYLabel[0] and its operations in China .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitle[5] in templateTitle[6] . templateXValue[0] ranked first templateXLabel[1] with templateYValue[max] templateYLabel[1] templateYLabel[2] . templateYValue[2] percent of the U.S. dollars per templateXLabel[0] in templateTitle[5] .
generated: This statistic shows the Metro Group of 's Sales of million euros in 2018/2019 in by . Western_Europe_(excluding_Germany) ranked first xLabelErr with 8885 million euros . 4075 percent of the U.S. dollars per Country in 2018/2019 .


Example 169:
data: Year|2024|x|line_chart GDP_per_capita_in_U.S._dollars|3346.39|y|line_chart Year|2023|x|line_chart GDP_per_capita_in_U.S._dollars|3074.98|y|line_chart Year|2022|x|line_chart GDP_per_capita_in_U.S._dollars|3830.0|y|line_chart Year|2021|x|line_chart GDP_per_capita_in_U.S._dollars|2602.02|y|line_chart Year|2020|x|line_chart GDP_per_capita_in_U.S._dollars|2400.45|y|line_chart Year|2019|x|line_chart GDP_per_capita_in_U.S._dollars|2222.01|y|line_chart Year|2018|x|line_chart GDP_per_capita_in_U.S._dollars|2032.86|y|line_chart Year|2017|x|line_chart GDP_per_capita_in_U.S._dollars|1971.79|y|line_chart Year|2016|x|line_chart GDP_per_capita_in_U.S._dollars|2180.27|y|line_chart Year|2015|x|line_chart GDP_per_capita_in_U.S._dollars|2726.34|y|line_chart Year|2014|x|line_chart GDP_per_capita_in_U.S._dollars|3222.68|y|line_chart Year|2013|x|line_chart GDP_per_capita_in_U.S._dollars|3042.05|y|line_chart Year|2012|x|line_chart GDP_per_capita_in_U.S._dollars|2797.86|y|line_chart Year|2011|x|line_chart GDP_per_capita_in_U.S._dollars|2582.57|y|line_chart Year|2010|x|line_chart GDP_per_capita_in_U.S._dollars|2365.01|y|line_chart Year|2009|x|line_chart GDP_per_capita_in_U.S._dollars|1958.58|y|line_chart Year|2008|x|line_chart GDP_per_capita_in_U.S._dollars|2234.36|y|line_chart Year|2007|x|line_chart GDP_per_capita_in_U.S._dollars|1822.79|y|line_chart Year|2006|x|line_chart GDP_per_capita_in_U.S._dollars|1591.33|y|line_chart Year|2005|x|line_chart GDP_per_capita_in_U.S._dollars|1245.07|y|line_chart Year|2004|x|line_chart GDP_per_capita_in_U.S._dollars|982.98|y|line_chart Year|2003|x|line_chart GDP_per_capita_in_U.S._dollars|797.64|y|line_chart Year|2002|x|line_chart GDP_per_capita_in_U.S._dollars|748.31|y|line_chart Year|2001|x|line_chart GDP_per_capita_in_U.S._dollars|598.29|y|line_chart Year|2000|x|line_chart GDP_per_capita_in_U.S._dollars|570.17|y|line_chart Year|1999|x|line_chart GDP_per_capita_in_U.S._dollars|496.5|y|line_chart Year|1998|x|line_chart GDP_per_capita_in_U.S._dollars|1861.07|y|line_chart Year|1997|x|line_chart GDP_per_capita_in_U.S._dollars|1713.38|y|line_chart Year|1996|x|line_chart GDP_per_capita_in_U.S._dollars|1618.29|y|line_chart Year|1995|x|line_chart GDP_per_capita_in_U.S._dollars|1273.27|y|line_chart Year|1994|x|line_chart GDP_per_capita_in_U.S._dollars|792.81|y|line_chart 
title: Gross domestic product ( GDP ) per capita in Nigeria 2024

gold: The statistic shows gross domestic product ( GDP ) per capita in Nigeria from 1994 to 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] from templateXValue[min] to templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services templateYLabel[2] in a country in a templateXLabel[0] . templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[7] stood at around templateYValue[0] percent .
generated: The statistic shows Gross domestic product ( GDP ) per capita in Nigeria 2024 from 1994 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services capita in a country in a Year . GDP per capita in 2024 stood at around 3346.39 percent .


Example 170:
data: Year|2024|x|line_chart GDP_in_billion_U.S._dollars|6546.54|y|line_chart Year|2023|x|line_chart GDP_in_billion_U.S._dollars|6234.32|y|line_chart Year|2022|x|line_chart GDP_in_billion_U.S._dollars|5922.28|y|line_chart Year|2021|x|line_chart GDP_in_billion_U.S._dollars|5634.42|y|line_chart Year|2020|x|line_chart GDP_in_billion_U.S._dollars|5352.4|y|line_chart Year|2019|x|line_chart GDP_in_billion_U.S._dollars|5188.25|y|line_chart Year|2018|x|line_chart GDP_in_billion_U.S._dollars|5249.66|y|line_chart Year|2017|x|line_chart GDP_in_billion_U.S._dollars|5459.78|y|line_chart Year|2016|x|line_chart GDP_in_billion_U.S._dollars|5057.11|y|line_chart Year|2015|x|line_chart GDP_in_billion_U.S._dollars|5271.94|y|line_chart Year|2014|x|line_chart GDP_in_billion_U.S._dollars|5988.65|y|line_chart 
title: Gross domestic product of Latin America and the Caribbean 2024

gold: This statistic shows gross domestic product ( GDP ) of Latin America and the Caribbean from 2014 to 2024 in billion U.S. dollars . In 2018 , Latin America and the Caribbean 's GDP amounted to about 5.25 trillion U.S. dollars .
gold_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of templateTitle[3] templateTitle[4] and the templateTitle[5] from templateXValue[min] to templateXValue[max] in templateYLabel[1] templateYLabel[2] templateYLabel[3] . In templateXValue[6] , templateTitle[3] templateTitle[4] and the templateTitle[5] 's templateYLabel[0] amounted to about templateYValue[6] trillion templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] amounted to approximately templateYValue[6] templateYLabel[1] templateYLabel[2] templateYLabel[3] . U.S. value of templateTitle[2] ( templateYLabel[0] ) templateTitle[2] templateTitle[3] , a worldwide templateYLabel[0] figures at approximately templateYValue[6] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[1] .
generated: This statistic shows the Gross domestic product ( GDP ) in Latin from 2014 to 2018 , with projections up until 2024 . In 2018 , the Gross domestic product of Latin America amounted to approximately 5249.66 billion U.S. dollars . U.S. value of product ( GDP ) product Latin , a worldwide GDP figures at approximately 5249.66 billion U.S. dollars in 2023 .


Example 171:
data: Country|Africa|x|bar_chart Number_of_hostages_taken|2651|y|bar_chart Country|South_Asia|x|bar_chart Number_of_hostages_taken|1748|y|bar_chart Country|Near_East|x|bar_chart Number_of_hostages_taken|1206|y|bar_chart Country|East_Asia_and_Pacific|x|bar_chart Number_of_hostages_taken|246|y|bar_chart Country|Western_Hemisphere|x|bar_chart Number_of_hostages_taken|190|y|bar_chart Country|Europe_and_Eurasia|x|bar_chart Number_of_hostages_taken|9|y|bar_chart 
title: Terrorism - number of hostages taken by region

gold: The statistic shows the number of hostages taken by terrorists in 2010 by region . 2.651 people were taken hostage by terrorists in Africa .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[4] terrorists in 2010 templateTitle[4] templateTitle[5] . 2.651 people were templateYLabel[2] hostage templateTitle[4] terrorists in templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States as of April templateTitle[6] . During the survey , templateYValue[max] percent of templateYLabel[1] cited templateXValue[1] had the highest rate of templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Number hostages of hostages taken by region in the United States as of April titleErr . During the survey , 2651 percent of hostages cited South_Asia had the highest rate of taken by region in the United States .


Example 172:
data: Brand_(top_influencer)|Valentino_(Demi_Lovato)|x|bar_chart Number_of_social_media_actions_per_post|1385467|y|bar_chart Brand_(top_influencer)|Tory_Burch_(Shay_Mitchell)|x|bar_chart Number_of_social_media_actions_per_post|134751|y|bar_chart Brand_(top_influencer)|Tod's_(Naomi_Campbell)|x|bar_chart Number_of_social_media_actions_per_post|40647|y|bar_chart Brand_(top_influencer)|Tiffany_&_Co._(Reese_Witherspoon)|x|bar_chart Number_of_social_media_actions_per_post|77643|y|bar_chart Brand_(top_influencer)|Salvatore_Ferragamo_(Nina_Dobrev)|x|bar_chart Number_of_social_media_actions_per_post|601316|y|bar_chart Brand_(top_influencer)|Saint_Laurent_(J_Balvin)|x|bar_chart Number_of_social_media_actions_per_post|181475|y|bar_chart Brand_(top_influencer)|Prada_(Chiara_Ferragni)|x|bar_chart Number_of_social_media_actions_per_post|116169|y|bar_chart Brand_(top_influencer)|Michael_Kors_(Blake_Lively)|x|bar_chart Number_of_social_media_actions_per_post|759670|y|bar_chart Brand_(top_influencer)|Louis_Vuitton_(Kevin_Ma)|x|bar_chart Number_of_social_media_actions_per_post|26689|y|bar_chart Brand_(top_influencer)|Hugo_Boss_(Mariano_Di_Vaio)|x|bar_chart Number_of_social_media_actions_per_post|91041|y|bar_chart Brand_(top_influencer)|Hermes_(Xenia_Tchoumi)|x|bar_chart Number_of_social_media_actions_per_post|27692|y|bar_chart Brand_(top_influencer)|Gucci_(Nina_Dobrev)|x|bar_chart Number_of_social_media_actions_per_post|458444|y|bar_chart Brand_(top_influencer)|Givenchy_(Nicki_Minaj)|x|bar_chart Number_of_social_media_actions_per_post|629753|y|bar_chart Brand_(top_influencer)|Fendi_(Gigi_Hadid)|x|bar_chart Number_of_social_media_actions_per_post|653272|y|bar_chart Brand_(top_influencer)|Dolce_&_Gabbana_(Cameron_Dallas)|x|bar_chart Number_of_social_media_actions_per_post|742342|y|bar_chart Brand_(top_influencer)|Dior_(Rihanna)|x|bar_chart Number_of_social_media_actions_per_post|629179|y|bar_chart Brand_(top_influencer)|Chanel_(Cara_Delevigne)|x|bar_chart Number_of_social_media_actions_per_post|662894|y|bar_chart Brand_(top_influencer)|Celine_(Kim_Kardashian)|x|bar_chart Number_of_social_media_actions_per_post|1182087|y|bar_chart Brand_(top_influencer)|Cartier_(Nikkie_Tutorials)|x|bar_chart Number_of_social_media_actions_per_post|290287|y|bar_chart Brand_(top_influencer)|Burberry_(Dove_Cameron)|x|bar_chart Number_of_social_media_actions_per_post|447287|y|bar_chart Brand_(top_influencer)|Bulgari_(Bella_Hadid)|x|bar_chart Number_of_social_media_actions_per_post|591423|y|bar_chart Brand_(top_influencer)|Bottega_Veneta_(Kris_Jenner)|x|bar_chart Number_of_social_media_actions_per_post|102457|y|bar_chart Brand_(top_influencer)|Balenciaga_(Nicki_Minaj)|x|bar_chart Number_of_social_media_actions_per_post|341862|y|bar_chart Brand_(top_influencer)|Average|x|bar_chart Number_of_social_media_actions_per_post|442341|y|bar_chart 
title: Luxury brand social media engagement generated by top influencers 2017

gold: This statistic presents the number of social media actions generated per post by top luxury brand influencer . During the first quarter of 2017 , an average social media post by Kim Kardashian generated 1.2 million social media actions . Kardashian is the top influencer for luxury brand Celine .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[5] templateYLabel[4] templateYLabel[5] templateTitle[6] templateTitle[7] templateTitle[0] templateXLabel[0] influencer . During the first quarter of templateTitle[9] , an templateXValue[last] templateYLabel[1] templateYLabel[2] templateYLabel[5] templateTitle[6] Kim Kardashian templateTitle[5] templateYValue[17] million templateYLabel[1] templateYLabel[2] templateYLabel[3] . Kardashian is the templateTitle[7] influencer for templateTitle[0] templateXLabel[0] templateXValue[17] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[5] templateYLabel[4] templateYLabel[5] templateTitle[6] at selected countries in templateTitle[7] . According to the report , templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYLabel[5] templateYLabel[5] templateTitle[5] templateXValue[0] in templateTitle[7] .
generated: This statistic shows the Number of social media actions generated per post by at selected countries in top . According to the report , 1385467 social media actions of post generated Valentino_(Demi_Lovato) in top .


Example 173:
data: Country|Latvia|x|bar_chart Inflation_rate_compared_to_previous_year|3.1|y|bar_chart Country|Slowakia|x|bar_chart Inflation_rate_compared_to_previous_year|2.7|y|bar_chart Country|Netherlands|x|bar_chart Inflation_rate_compared_to_previous_year|2.7|y|bar_chart Country|Estonia|x|bar_chart Inflation_rate_compared_to_previous_year|2.6|y|bar_chart Country|Lithuania|x|bar_chart Inflation_rate_compared_to_previous_year|2.4|y|bar_chart Country|Slovenia|x|bar_chart Inflation_rate_compared_to_previous_year|1.9|y|bar_chart Country|Malta|x|bar_chart Inflation_rate_compared_to_previous_year|1.8|y|bar_chart Country|Austria|x|bar_chart Inflation_rate_compared_to_previous_year|1.6|y|bar_chart Country|Luxembourg|x|bar_chart Inflation_rate_compared_to_previous_year|1.5|y|bar_chart Country|Germany|x|bar_chart Inflation_rate_compared_to_previous_year|1.5|y|bar_chart Country|France|x|bar_chart Inflation_rate_compared_to_previous_year|1.4|y|bar_chart Country|Belgium|x|bar_chart Inflation_rate_compared_to_previous_year|1.3|y|bar_chart Country|Euro_area|x|bar_chart Inflation_rate_compared_to_previous_year|1.3|y|bar_chart Country|Ireland|x|bar_chart Inflation_rate_compared_to_previous_year|1.1|y|bar_chart Country|Finland|x|bar_chart Inflation_rate_compared_to_previous_year|1.1|y|bar_chart Country|Italy|x|bar_chart Inflation_rate_compared_to_previous_year|0.8|y|bar_chart Country|Portugal|x|bar_chart Inflation_rate_compared_to_previous_year|0.7|y|bar_chart Country|Spain|x|bar_chart Inflation_rate_compared_to_previous_year|0.6|y|bar_chart Country|Cyprus|x|bar_chart Inflation_rate_compared_to_previous_year|0.3|y|bar_chart Country|Greece|x|bar_chart Inflation_rate_compared_to_previous_year|0.2|y|bar_chart 
title: Monthly inflation rate in Euro area countries June 2019

gold: The statistic shows the inflation rate in the Euro area countries in June 2019 . Inflation or currency devaluation ( drop in the value of money ) , is characterized by a steady rise in the prices of finished products ( consumer goods , capital goods ) . The consumer price index tracks price trends of private consumption expenditure , and shows an increase in the index 's current level of inflation .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateXValue[12] templateXValue[12] templateTitle[5] in templateTitle[6] templateTitle[7] . templateYLabel[0] or currency devaluation ( drop in the value of money ) , is characterized by a steady rise in the prices of finished products ( consumer goods , capital goods ) . The consumer price index tracks price trends of private consumption expenditure , and shows an increase in the index 's current level of templateYLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateXValue[14] templateTitle[4] in templateTitle[5] templateTitle[6] . templateYLabel[0] or currency devaluation ( drop in the value of money ) , is characterized by a steady rise in the prices of finished products ( consumer goods , capital goods ) . The consumer price index tracks price trends of private consumption expenditure , and shows an increase in the indexes current level of templateYLabel[0] .
generated: The statistic shows the Inflation rate in the Finland area in countries June . Inflation or currency devaluation ( drop in the value of money ) , is characterized by a steady rise in the prices of finished products ( consumer goods , capital goods ) . The consumer price index tracks price trends of private consumption expenditure , and shows an increase in the indexes current level of Inflation .


Example 174:
data: Year|1995|x|line_chart Price_in_U.S._dollars_per_pound|1.86|y|line_chart Year|1997|x|line_chart Price_in_U.S._dollars_per_pound|2.19|y|line_chart Year|1999|x|line_chart Price_in_U.S._dollars_per_pound|2.4|y|line_chart Year|2000|x|line_chart Price_in_U.S._dollars_per_pound|2.36|y|line_chart Year|2001|x|line_chart Price_in_U.S._dollars_per_pound|2.31|y|line_chart Year|2002|x|line_chart Price_in_U.S._dollars_per_pound|2.27|y|line_chart Year|2003|x|line_chart Price_in_U.S._dollars_per_pound|2.25|y|line_chart Year|2004|x|line_chart Price_in_U.S._dollars_per_pound|3.09|y|line_chart Year|2005|x|line_chart Price_in_U.S._dollars_per_pound|2.76|y|line_chart Year|2006|x|line_chart Price_in_U.S._dollars_per_pound|2.89|y|line_chart Year|2007|x|line_chart Price_in_U.S._dollars_per_pound|2.72|y|line_chart Year|2008|x|line_chart Price_in_U.S._dollars_per_pound|2.46|y|line_chart Year|2009|x|line_chart Price_in_U.S._dollars_per_pound|3.14|y|line_chart Year|2010|x|line_chart Price_in_U.S._dollars_per_pound|2.87|y|line_chart Year|2011|x|line_chart Price_in_U.S._dollars_per_pound|2.86|y|line_chart Year|2012|x|line_chart Price_in_U.S._dollars_per_pound|3.09|y|line_chart Year|2013|x|line_chart Price_in_U.S._dollars_per_pound|2.57|y|line_chart Year|2014|x|line_chart Price_in_U.S._dollars_per_pound|2.96|y|line_chart Year|2015|x|line_chart Price_in_U.S._dollars_per_pound|2.83|y|line_chart Year|2016|x|line_chart Price_in_U.S._dollars_per_pound|2.88|y|line_chart Year|2017|x|line_chart Price_in_U.S._dollars_per_pound|2.61|y|line_chart Year|2018|x|line_chart Price_in_U.S._dollars_per_pound|2.28|y|line_chart Year|2019|x|line_chart Price_in_U.S._dollars_per_pound|2.39|y|line_chart 
title: U.S. retail price of grapes 1995 - 2019

gold: The price of one pound of seedless grapes ( Thompson ) in the United States was 2.39 U.S. dollars in 2019 . U.S. seedless grape prices peaked in 2009 at 3.14 U.S. dollars per pound . Grape Production in the United States Grapes have the highest production volume of any fruit in the United States .
gold_template: The templateYLabel[0] of one templateYLabel[4] of seedless templateTitle[3] ( Thompson ) in the templateTitle[0] was templateYValue[22] templateYLabel[1] templateYLabel[2] in templateXValue[max] . templateYLabel[1] seedless grape prices peaked in templateXValue[12] at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Grape Production in the templateTitle[0] templateTitle[3] have the highest production volume of any fruit in the templateTitle[0] .

generated_template: The templateTitle[0] templateYLabel[0] of templateTitle[3] templateTitle[4] in the United States in templateXValue[max] was templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . This was a decrease from around templateYValue[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[2] . Global seafood market The global demand for seafood is on the rise .
generated: The U.S. Price of grapes 1995 in the United States in 2019 was 1.86 U.S. dollars per pound . This was a decrease from around 2.4 U.S. dollars per pound in 1999 . Global seafood market The global demand for seafood is on the rise .


Example 175:
data: Year|2017|x|line_chart Percentage_of_individuals_using_internet|43.83|y|line_chart Year|2016|x|line_chart Percentage_of_individuals_using_internet|39.7|y|line_chart Year|2015|x|line_chart Percentage_of_individuals_using_internet|35.56|y|line_chart Year|2014|x|line_chart Percentage_of_individuals_using_internet|34.6|y|line_chart Year|2013|x|line_chart Percentage_of_individuals_using_internet|36.99|y|line_chart Year|2012|x|line_chart Percentage_of_individuals_using_internet|35.34|y|line_chart Year|2011|x|line_chart Percentage_of_individuals_using_internet|30|y|line_chart Year|2010|x|line_chart Percentage_of_individuals_using_internet|22.4|y|line_chart Year|2009|x|line_chart Percentage_of_individuals_using_internet|16.8|y|line_chart Year|2008|x|line_chart Percentage_of_individuals_using_internet|12.5|y|line_chart Year|2007|x|line_chart Percentage_of_individuals_using_internet|10.5|y|line_chart Year|2006|x|line_chart Percentage_of_individuals_using_internet|6.2|y|line_chart Year|2005|x|line_chart Percentage_of_individuals_using_internet|5.23|y|line_chart Year|2004|x|line_chart Percentage_of_individuals_using_internet|4.44|y|line_chart Year|2003|x|line_chart Percentage_of_individuals_using_internet|3.51|y|line_chart Year|2002|x|line_chart Percentage_of_individuals_using_internet|3.12|y|line_chart Year|2001|x|line_chart Percentage_of_individuals_using_internet|2.12|y|line_chart Year|2000|x|line_chart Percentage_of_individuals_using_internet|1.44|y|line_chart 
title: Bolivia : internet penetration 2000 - 2017

gold: This statistic gives information on the internet penetration in Bolivia from 2000 to 2017 . In 2017 , 43.83 percent of the Bolivian population accessed the internet , up from 6.2 percent in 2006 .
gold_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the Bolivian population accessed the templateYLabel[3] , up from templateYValue[11] percent in templateXValue[11] .

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the Chilean population accessed the templateYLabel[3] , up from templateYValue[16] percent in templateXValue[16] . In templateXValue[1] , templateYValue[max] percent of the Chilean population acceded to templateYLabel[3] .
generated: This statistic gives information on the internet penetration in Bolivia from 2000 to 2017 . In 2017 , 43.83 percent of the Chilean population accessed the internet , up from 2.12 percent in 2001 . In 2016 , 43.83 percent of the Chilean population acceded to internet .


Example 176:
data: Year|2017|x|line_chart Life_expectancy_at_birth_in_years|78.83|y|line_chart Year|2016|x|line_chart Life_expectancy_at_birth_in_years|78.57|y|line_chart Year|2015|x|line_chart Life_expectancy_at_birth_in_years|78.29|y|line_chart Year|2014|x|line_chart Life_expectancy_at_birth_in_years|77.97|y|line_chart Year|2013|x|line_chart Life_expectancy_at_birth_in_years|77.64|y|line_chart Year|2012|x|line_chart Life_expectancy_at_birth_in_years|77.28|y|line_chart Year|2011|x|line_chart Life_expectancy_at_birth_in_years|76.93|y|line_chart Year|2010|x|line_chart Life_expectancy_at_birth_in_years|76.57|y|line_chart Year|2009|x|line_chart Life_expectancy_at_birth_in_years|76.21|y|line_chart Year|2008|x|line_chart Life_expectancy_at_birth_in_years|75.86|y|line_chart Year|2007|x|line_chart Life_expectancy_at_birth_in_years|75.51|y|line_chart Year|2006|x|line_chart Life_expectancy_at_birth_in_years|75.17|y|line_chart Year|2005|x|line_chart Life_expectancy_at_birth_in_years|74.85|y|line_chart Year|2004|x|line_chart Life_expectancy_at_birth_in_years|74.54|y|line_chart Year|2003|x|line_chart Life_expectancy_at_birth_in_years|74.24|y|line_chart Year|2002|x|line_chart Life_expectancy_at_birth_in_years|73.95|y|line_chart Year|2001|x|line_chart Life_expectancy_at_birth_in_years|73.67|y|line_chart Year|2000|x|line_chart Life_expectancy_at_birth_in_years|73.41|y|line_chart Year|1999|x|line_chart Life_expectancy_at_birth_in_years|73.14|y|line_chart Year|1998|x|line_chart Life_expectancy_at_birth_in_years|72.88|y|line_chart Year|1997|x|line_chart Life_expectancy_at_birth_in_years|72.62|y|line_chart Year|1996|x|line_chart Life_expectancy_at_birth_in_years|72.36|y|line_chart Year|1995|x|line_chart Life_expectancy_at_birth_in_years|72.1|y|line_chart Year|1994|x|line_chart Life_expectancy_at_birth_in_years|71.86|y|line_chart Year|1993|x|line_chart Life_expectancy_at_birth_in_years|71.63|y|line_chart Year|1992|x|line_chart Life_expectancy_at_birth_in_years|71.41|y|line_chart Year|1991|x|line_chart Life_expectancy_at_birth_in_years|71.2|y|line_chart Year|1990|x|line_chart Life_expectancy_at_birth_in_years|71.0|y|line_chart Year|1989|x|line_chart Life_expectancy_at_birth_in_years|70.82|y|line_chart Year|1988|x|line_chart Life_expectancy_at_birth_in_years|70.64|y|line_chart Year|1987|x|line_chart Life_expectancy_at_birth_in_years|70.45|y|line_chart Year|1986|x|line_chart Life_expectancy_at_birth_in_years|70.25|y|line_chart Year|1985|x|line_chart Life_expectancy_at_birth_in_years|70.02|y|line_chart Year|1984|x|line_chart Life_expectancy_at_birth_in_years|69.77|y|line_chart Year|1983|x|line_chart Life_expectancy_at_birth_in_years|69.47|y|line_chart Year|1982|x|line_chart Life_expectancy_at_birth_in_years|69.13|y|line_chart Year|1981|x|line_chart Life_expectancy_at_birth_in_years|68.74|y|line_chart Year|1980|x|line_chart Life_expectancy_at_birth_in_years|68.3|y|line_chart Year|1979|x|line_chart Life_expectancy_at_birth_in_years|67.8|y|line_chart Year|1978|x|line_chart Life_expectancy_at_birth_in_years|67.26|y|line_chart Year|1977|x|line_chart Life_expectancy_at_birth_in_years|66.66|y|line_chart Year|1976|x|line_chart Life_expectancy_at_birth_in_years|66.02|y|line_chart Year|1975|x|line_chart Life_expectancy_at_birth_in_years|65.32|y|line_chart Year|1974|x|line_chart Life_expectancy_at_birth_in_years|64.58|y|line_chart Year|1973|x|line_chart Life_expectancy_at_birth_in_years|63.8|y|line_chart Year|1972|x|line_chart Life_expectancy_at_birth_in_years|62.96|y|line_chart Year|1971|x|line_chart Life_expectancy_at_birth_in_years|62.02|y|line_chart Year|1970|x|line_chart Life_expectancy_at_birth_in_years|60.9|y|line_chart Year|1969|x|line_chart Life_expectancy_at_birth_in_years|59.48|y|line_chart Year|1968|x|line_chart Life_expectancy_at_birth_in_years|57.75|y|line_chart Year|1967|x|line_chart Life_expectancy_at_birth_in_years|55.73|y|line_chart Year|1966|x|line_chart Life_expectancy_at_birth_in_years|53.51|y|line_chart Year|1965|x|line_chart Life_expectancy_at_birth_in_years|51.26|y|line_chart Year|1964|x|line_chart Life_expectancy_at_birth_in_years|49.19|y|line_chart Year|1963|x|line_chart Life_expectancy_at_birth_in_years|47.49|y|line_chart Year|1962|x|line_chart Life_expectancy_at_birth_in_years|46.24|y|line_chart Year|1961|x|line_chart Life_expectancy_at_birth_in_years|45.5|y|line_chart Year|1960|x|line_chart Life_expectancy_at_birth_in_years|45.19|y|line_chart 
title: Female life expectancy in China 1960 - 2017

gold: The graph shows the life expectancy of women in China from 1960 until 2017 . In 2017 , the average life expectancy of women at birth in China was about 78.8 years .
gold_template: The graph shows the templateYLabel[0] templateYLabel[1] of women in templateTitle[3] from templateXValue[min] until templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of women at templateYLabel[2] in templateTitle[3] was about templateYValue[max] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitle[2] was about templateYValue[max] templateYLabel[3] . templateYLabel[0] templateYLabel[1] at templateYLabel[2] at templateYLabel[2] in templateTitle[2] The average templateYLabel[0] templateYLabel[1] at templateYLabel[2] among the U.S. dollars .
generated: The statistic shows the Life expectancy at birth in expectancy from 1960 to 2017 . In 2017 , the average Life expectancy at birth in expectancy was about 78.83 years . Life expectancy at birth at birth in expectancy The average Life expectancy at birth among the U.S. dollars .


Example 177:
data: Year|2017|x|line_chart Number_of_children_born_per_woman|1.6|y|line_chart Year|2016|x|line_chart Number_of_children_born_per_woman|1.6|y|line_chart Year|2015|x|line_chart Number_of_children_born_per_woman|1.57|y|line_chart Year|2014|x|line_chart Number_of_children_born_per_woman|1.56|y|line_chart Year|2013|x|line_chart Number_of_children_born_per_woman|1.56|y|line_chart Year|2012|x|line_chart Number_of_children_born_per_woman|1.55|y|line_chart Year|2011|x|line_chart Number_of_children_born_per_woman|1.54|y|line_chart Year|2010|x|line_chart Number_of_children_born_per_woman|1.54|y|line_chart Year|2009|x|line_chart Number_of_children_born_per_woman|1.54|y|line_chart Year|2008|x|line_chart Number_of_children_born_per_woman|1.53|y|line_chart Year|2007|x|line_chart Number_of_children_born_per_woman|1.53|y|line_chart 
title: Fertility rate in China 2007 - 2017

gold: The statistic shows the fertility rate in China from 2007 to 2017 . In 2017 , the fertility rate in China was 1.6 children per woman . Overall , the number of births in China reached about 17.23 million that year .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[2] was templateYValue[max] templateYLabel[1] templateYLabel[3] templateYLabel[4] . Overall , the templateYLabel[0] of births in templateTitle[2] reached about 17.23 million that templateXLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , templateTitle[2] templateTitle[3] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate in China 2007 from 2007 to 2017 . The Fertility rate is the average Number of children born by one woman while being of child-bearing age . In 2017 , China 2007 's Fertility rate amounted to 1.53 children born per woman .


Example 178:
data: Response|Family|x|bar_chart Share_of_respondents|51|y|bar_chart Response|Friends|x|bar_chart Share_of_respondents|14|y|bar_chart Response|Having_a_good_education|x|bar_chart Share_of_respondents|11|y|bar_chart Response|Living_a_healthy_active_lifestyle|x|bar_chart Share_of_respondents|8|y|bar_chart Response|Having_money|x|bar_chart Share_of_respondents|6|y|bar_chart Response|Doing_good_things_for_the_community|x|bar_chart Share_of_respondents|3|y|bar_chart Response|The_environment|x|bar_chart Share_of_respondents|1|y|bar_chart Response|None_of_the_above|x|bar_chart Share_of_respondents|5|y|bar_chart 
title: Most important things for young people ( 16 - 24 ) in Great Britain 2013

gold: This statistic shows the result of a survey question designed to find out what is most important to young people ( 16 - 24 ) in Great Britain , as of November 2013 . The majority of respondents said that their family is the most important thing to them .
gold_template: This statistic shows the result of a survey question designed to find out what is templateTitle[0] templateTitle[1] to templateTitle[4] templateTitle[5] ( templateTitle[6] - templateTitle[7] ) in templateTitle[8] templateTitle[9] , as of November templateTitle[10] . The majority of templateYLabel[1] said that their templateXValue[0] is the templateTitle[0] templateTitle[1] thing to them .

generated_template: This statistic shows the results of a survey among Americans in the United States in templateTitle[4] templateTitle[5] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated that they planned to templateXValue[0] templateXValue[0] or their templateTitle[2] templateTitle[3] templateTitle[4] templateXValue[0] .
generated: This statistic shows the results of a survey among Americans in the United States in young people . During the survey period , 51 percent of respondents stated that they planned to Family or their things for young Family .


Example 179:
data: Year|2019|x|line_chart R&D_expenditure_in_million_euros|61.7|y|line_chart Year|2018|x|line_chart R&D_expenditure_in_million_euros|54.0|y|line_chart Year|2017|x|line_chart R&D_expenditure_in_million_euros|53.4|y|line_chart Year|2016|x|line_chart R&D_expenditure_in_million_euros|52.0|y|line_chart Year|2015|x|line_chart R&D_expenditure_in_million_euros|56.7|y|line_chart Year|2014|x|line_chart R&D_expenditure_in_million_euros|46.2|y|line_chart Year|2013|x|line_chart R&D_expenditure_in_million_euros|47.9|y|line_chart 
title: Global R & D expenditure of Puma from 2013 to 2019

gold: Sportswear company Puma has increased its research and development expenditure since 2016 , with 61.7 million euros spent by the department in 2019 – an increase of 7.7 million euros on the previous year . What is the role of research and development ? As of December 2019 , Puma had 14,332 employees worldwide , of which 986 were employed in the field of research and development/product management . In a sportswear market that is constantly changing , Puma 's research and development team is responsible for providing insights into product design and new technologies .
gold_template: Sportswear company templateTitle[5] has increased its research and development templateYLabel[1] since templateXValue[3] , with templateYValue[max] templateYLabel[2] templateYLabel[3] spent by the department in templateXValue[max] – an increase of 7.7 templateYLabel[2] templateYLabel[3] on the previous templateXLabel[0] . What is the role of research and development ? As of December templateXValue[max] , templateTitle[5] had 14,332 employees worldwide , of which 986 were employed in the field of research and development/product management . In a sportswear market that is constantly changing , templateTitle[5] 's research and development team is responsible for providing insights into product design and new technologies .

generated_template: In templateXValue[max] , there were templateYValue[0] templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[5] templateYLabel[2] in the United States . This figure is an increase from the previous templateXLabel[0] 's lower with the low of templateYValue[max] million in templateXValue[3] . The global templateYLabel[1] penetration rate is an indicator of the population .
generated: In 2019 , there were 61.7 Global R & Puma million in the United States . This figure is an increase from the previous Year 's lower with the low of 61.7 million in 2016 . The global expenditure penetration rate is an indicator of the population .


Example 180:
data: Year|2024|x|line_chart GDP_growth_compared_to_previous_year|2.42|y|line_chart Year|2023|x|line_chart GDP_growth_compared_to_previous_year|2.35|y|line_chart Year|2022|x|line_chart GDP_growth_compared_to_previous_year|2.21|y|line_chart Year|2021|x|line_chart GDP_growth_compared_to_previous_year|2.15|y|line_chart Year|2020|x|line_chart GDP_growth_compared_to_previous_year|2.11|y|line_chart Year|2019|x|line_chart GDP_growth_compared_to_previous_year|2.7|y|line_chart Year|2018|x|line_chart GDP_growth_compared_to_previous_year|0.97|y|line_chart Year|2017|x|line_chart GDP_growth_compared_to_previous_year|0.15|y|line_chart Year|2016|x|line_chart GDP_growth_compared_to_previous_year|-3.06|y|line_chart Year|2015|x|line_chart GDP_growth_compared_to_previous_year|1.05|y|line_chart Year|2014|x|line_chart GDP_growth_compared_to_previous_year|2.8|y|line_chart 
title: Gross domestic product ( GDP ) growth rate in Azerbaijan 2024

gold: The statistic shows the growth in real GDP in Azerbaijan from 2014 to 2018 , with projections up until 2024 . In 2018 , Azerbaijan 's real gross domestic product increased by around 0.97 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Azerbaijan from 2014 to 2018 , with projections up until 2024 . In 2018 , Azerbaijan 's real Gross domestic product grew by around 0.97 percent compared to the previous Year .


Example 181:
data: Year|2024|x|line_chart Budget_balance_in_relation_to_GDP|-2.93|y|line_chart Year|2023|x|line_chart Budget_balance_in_relation_to_GDP|-2.72|y|line_chart Year|2022|x|line_chart Budget_balance_in_relation_to_GDP|-2.99|y|line_chart Year|2021|x|line_chart Budget_balance_in_relation_to_GDP|-3.13|y|line_chart Year|2020|x|line_chart Budget_balance_in_relation_to_GDP|-2.68|y|line_chart Year|2019|x|line_chart Budget_balance_in_relation_to_GDP|-3.97|y|line_chart Year|2018|x|line_chart Budget_balance_in_relation_to_GDP|-5.23|y|line_chart Year|2017|x|line_chart Budget_balance_in_relation_to_GDP|-6.7|y|line_chart Year|2016|x|line_chart Budget_balance_in_relation_to_GDP|-6.66|y|line_chart Year|2015|x|line_chart Budget_balance_in_relation_to_GDP|-6|y|line_chart Year|2014|x|line_chart Budget_balance_in_relation_to_GDP|-4.25|y|line_chart 
title: Argentina 's budget balance in relation to GDP 2024*

gold: The statistic shows Argentina 's budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 . A positive value indicates a budget surplus , a negative value indicates a deficit . In 2018 , Argentina 's deficit amounted to around 5.23 percent of GDP .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[6] , templateTitle[0] templateTitle[1] deficit amounted to around 5.23 percent of templateYLabel[3] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[6] , templateTitle[0] templateTitle[1] templateYLabel[0] surplus amounted to around templateYValue[max] percent of templateYLabel[3] .
generated: The statistic shows Argentina 's Budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 . A positive value indicates a Budget surplus , a negative value indicates a deficit . In 2018 , Argentina 's Budget surplus amounted to around -2.68 percent of GDP .


Example 182:
data: Country|European_Union_(28)|x|bar_chart Import_value_in_billion_U.S._dollars|77|y|bar_chart Country|United_States|x|bar_chart Import_value_in_billion_U.S._dollars|30|y|bar_chart Country|China|x|bar_chart Import_value_in_billion_U.S._dollars|18|y|bar_chart Country|Viet_Nam|x|bar_chart Import_value_in_billion_U.S._dollars|18|y|bar_chart Country|Bangladesh|x|bar_chart Import_value_in_billion_U.S._dollars|11|y|bar_chart Country|Japan|x|bar_chart Import_value_in_billion_U.S._dollars|9|y|bar_chart Country|Hong_Kong_China|x|bar_chart Import_value_in_billion_U.S._dollars|7|y|bar_chart Country|Indonesia|x|bar_chart Import_value_in_billion_U.S._dollars|7|y|bar_chart Country|Mexico_|x|bar_chart Import_value_in_billion_U.S._dollars|7|y|bar_chart Country|Turkey|x|bar_chart Import_value_in_billion_U.S._dollars|6|y|bar_chart 
title: Value of the leading 10 textile importers worldwide 2018 , by country

gold: The European Union ( EU28 ) was the leading importer of textiles worldwide , with textile imports valuing approximately 77 billion U.S. dollars in 2018 . That year , the United States and China were the next two largest importers of textiles , with imports of 30 billion U.S. dollars and 18 billion U.S. dollars respectively . What are textiles ? Textiles are the materials which are made from natural or synthetic fibers , thin threads or filaments .
gold_template: The templateXValue[0] templateXValue[0] ( EU28 ) was the templateTitle[1] importer of textiles templateTitle[5] , with templateTitle[3] imports valuing approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitle[6] . That year , the templateXValue[1] templateXValue[1] and templateXValue[2] were the next two largest templateTitle[4] of textiles , with imports of templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] and templateYValue[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] respectively . What are textiles ? Textiles are the materials which are made from natural or synthetic fibers , thin threads or filaments .

generated_template: This graph shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] , templateTitle[6] templateXLabel[0] . In that year , the templateXValue[0] templateXValue[0] had the largest templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of its templateYLabel[0] templateYLabel[1] .
generated: This graph shows the Value leading 10 textile importers in worldwide , 2018 Country . In that year , the European_Union_(28) had the largest leading 10 Import value of 77 billion U.S. dollars yLabelErr of its Import value .


Example 183:
data: Year|2019|x|line_chart Franchise_value_in_million_U.S._dollars|1900|y|line_chart Year|2018|x|line_chart Franchise_value_in_million_U.S._dollars|1800|y|line_chart Year|2017|x|line_chart Franchise_value_in_million_U.S._dollars|1750|y|line_chart Year|2016|x|line_chart Franchise_value_in_million_U.S._dollars|1340|y|line_chart Year|2015|x|line_chart Franchise_value_in_million_U.S._dollars|1300|y|line_chart Year|2014|x|line_chart Franchise_value_in_million_U.S._dollars|775|y|line_chart Year|2013|x|line_chart Franchise_value_in_million_U.S._dollars|718|y|line_chart Year|2012|x|line_chart Franchise_value_in_million_U.S._dollars|656|y|line_chart Year|2011|x|line_chart Franchise_value_in_million_U.S._dollars|554|y|line_chart Year|2010|x|line_chart Franchise_value_in_million_U.S._dollars|521|y|line_chart Year|2009|x|line_chart Franchise_value_in_million_U.S._dollars|509|y|line_chart Year|2008|x|line_chart Franchise_value_in_million_U.S._dollars|500|y|line_chart Year|2007|x|line_chart Franchise_value_in_million_U.S._dollars|431|y|line_chart Year|2006|x|line_chart Franchise_value_in_million_U.S._dollars|368|y|line_chart Year|2005|x|line_chart Franchise_value_in_million_U.S._dollars|294|y|line_chart Year|2004|x|line_chart Franchise_value_in_million_U.S._dollars|241|y|line_chart Year|2003|x|line_chart Franchise_value_in_million_U.S._dollars|225|y|line_chart Year|2002|x|line_chart Franchise_value_in_million_U.S._dollars|195|y|line_chart 
title: Franchise value of the Los Angeles Angels 2002 - 2019

gold: This graph depicts the value of the Los Angeles Angels franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 1.9 billion U.S. dollars . The Los Angeles Angels are owned by Arturo Moreno , who bought the franchise for 184 million U.S. dollars in 2003 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[4] are owned by Arturo Moreno , who bought the templateYLabel[0] for 184 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[16] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The current owner Robert Kraft bought the templateYLabel[0] in 1994 for 172 templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Franchise value of the Los Angeles Angels of the National Football League from 2002 to 2019 . In 2019 , the Franchise value of the Los Angeles Angels was 1900 billion U.S. dollars . The current owner Robert Kraft bought the Franchise in 1994 for 172 million U.S. dollars .


Example 184:
data: Year|2015/16|x|line_chart Average_ticket_price_in_U.S._dollars|31.42|y|line_chart Year|2014/15|x|line_chart Average_ticket_price_in_U.S._dollars|38.8|y|line_chart Year|2013/14|x|line_chart Average_ticket_price_in_U.S._dollars|40.1|y|line_chart Year|2012/13|x|line_chart Average_ticket_price_in_U.S._dollars|40.1|y|line_chart Year|2011/12|x|line_chart Average_ticket_price_in_U.S._dollars|41.26|y|line_chart Year|2010/11|x|line_chart Average_ticket_price_in_U.S._dollars|42.76|y|line_chart Year|2009/10|x|line_chart Average_ticket_price_in_U.S._dollars|47.5|y|line_chart Year|2008/09|x|line_chart Average_ticket_price_in_U.S._dollars|47.5|y|line_chart Year|2007/08|x|line_chart Average_ticket_price_in_U.S._dollars|47.5|y|line_chart Year|2006/07|x|line_chart Average_ticket_price_in_U.S._dollars|46.23|y|line_chart 
title: Average ticket price Detroit Pistons ( NBA ) games 2015/16

gold: This graph depicts the average ticket price for Detroit Pistons games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 46.23 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitle[3] templateTitle[4] templateTitle[6] of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[9] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitle[3] templateTitle[4] templateTitle[6] of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] . templateTitle[3] templateTitle[4] The templateTitle[3] templateTitle[4] are a franchise of the National Basketball Association ( templateTitle[5] ) which joined the league as the New Jersey templateTitle[4] in 1976 as part of the ABA-NBA merger .
generated: This graph depicts the Average ticket price for Detroit Pistons games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the Average ticket price was 31.42 U.S. dollars . Detroit Pistons The Detroit Pistons are a franchise of the National Basketball Association ( NBA ) which joined the league as the New Jersey Pistons in 1976 as part of the ABA-NBA merger .


Example 185:
data: Year|2013–2015|x|line_chart Average_age_in_years|14.64|y|line_chart Year|2012–2014|x|line_chart Average_age_in_years|14.56|y|line_chart Year|2011–2013|x|line_chart Average_age_in_years|14.47|y|line_chart Year|2010–2012|x|line_chart Average_age_in_years|14.4|y|line_chart Year|2009–2011|x|line_chart Average_age_in_years|14.35|y|line_chart Year|2008–2010|x|line_chart Average_age_in_years|14.29|y|line_chart Year|2007–2009|x|line_chart Average_age_in_years|14.22|y|line_chart Year|2006–2008|x|line_chart Average_age_in_years|14.14|y|line_chart Year|2005–2007|x|line_chart Average_age_in_years|14.06|y|line_chart Year|2004–2006|x|line_chart Average_age_in_years|13.97|y|line_chart Year|2003–2005|x|line_chart Average_age_in_years|13.9|y|line_chart Year|2002–2004|x|line_chart Average_age_in_years|13.85|y|line_chart Year|2001–2003|x|line_chart Average_age_in_years|13.82|y|line_chart Year|2000–2002|x|line_chart Average_age_in_years|13.81|y|line_chart Year|1999–2001|x|line_chart Average_age_in_years|13.81|y|line_chart Year|1998–2000|x|line_chart Average_age_in_years|13.88|y|line_chart Year|1997–1999|x|line_chart Average_age_in_years|13.91|y|line_chart Year|1996–1998|x|line_chart Average_age_in_years|13.94|y|line_chart Year|1995–1997|x|line_chart Average_age_in_years|13.85|y|line_chart Year|1994–1996|x|line_chart Average_age_in_years|13.8|y|line_chart Year|1993–1995|x|line_chart Average_age_in_years|13.76|y|line_chart Year|1992–1994|x|line_chart Average_age_in_years|13.76|y|line_chart Year|1991–1993|x|line_chart Average_age_in_years|13.65|y|line_chart 
title: Median age of first alcohol use among U.S. youth 1991 - 2015

gold: This statistic shows the average age youths aged 12 to 20 years first drank alcohol in the U.S. from 1991 to 2015 . In the period 2013 to 2015 , on average those aged 12 to 20 years first drank alcohol when they were 14.64 years old .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] youths aged 12 to 20 templateYLabel[2] templateTitle[2] drank templateTitle[3] in the templateTitle[6] from templateTitle[8] to templateTitle[9] . In the period 2013 to templateTitle[9] , on templateYLabel[0] those aged 12 to 20 templateYLabel[2] templateTitle[2] drank templateTitle[3] when they were templateYValue[max] templateYLabel[2] old .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] in templateTitle[3] from templateTitle[5] to templateTitle[6] . In templateXValue[0] , the templateYLabel[0] templateTitle[2] of templateTitle[3] was templateYValue[max] templateYLabel[2] .
generated: This statistic shows the Average age of first in alcohol from among to U.S. . In 2013–2015 , the Average first of alcohol was 14.64 years .


Example 186:
data: Year|2017|x|line_chart Number_of_companies|436|y|line_chart Year|2016|x|line_chart Number_of_companies|464|y|line_chart Year|2015|x|line_chart Number_of_companies|496|y|line_chart Year|2014|x|line_chart Number_of_companies|526|y|line_chart Year|2013|x|line_chart Number_of_companies|613|y|line_chart Year|2012|x|line_chart Number_of_companies|528|y|line_chart Year|2011|x|line_chart Number_of_companies|540|y|line_chart Year|2010|x|line_chart Number_of_companies|521|y|line_chart Year|2009|x|line_chart Number_of_companies|534|y|line_chart Year|2008|x|line_chart Number_of_companies|549|y|line_chart Year|2007|x|line_chart Number_of_companies|576|y|line_chart Year|2006|x|line_chart Number_of_companies|594|y|line_chart Year|2005|x|line_chart Number_of_companies|640|y|line_chart Year|2004|x|line_chart Number_of_companies|673|y|line_chart 
title: Companies on the insurance market in the United Kingdom ( UK ) 2004 - 2017

gold: As of 2017 , The United Kingdom ( UK ) had the largest insurance market in Europe , and fourth largest globally . Direct premiums written by UK companies , increased between 2016 to 2017 , with claims and benefits paid reaching nearly 290 billion euros . Leading insurance companies As of 2016 , Prudential Plc was the leading insurance company in the United Kingdom in terms of total assets and market value ( market capitalization ) .
gold_template: As of templateXValue[max] , The templateTitle[3] templateTitle[4] ( templateTitle[5] ) had the largest templateTitle[1] templateTitle[2] in Europe , and fourth largest globally . Direct premiums written by templateTitle[5] templateYLabel[1] , increased between templateXValue[1] to templateXValue[max] , with claims and benefits paid reaching nearly 290 billion euros . Leading templateTitle[1] templateYLabel[1] As of templateXValue[1] , Prudential Plc was the leading templateTitle[1] company in the templateTitle[3] templateTitle[4] in terms of total assets and templateTitle[2] value ( templateTitle[2] capitalization ) .

generated_template: In templateXValue[max] , the templateTitle[0] templateYLabel[0] of templateYLabel[1] in the templateTitle[3] templateTitle[4] has been increasing since templateXValue[min] , when there were approximately templateYValue[0] templateYLabel[1] templateYLabel[2] in the United States . templateTitle[2] templateTitle[3] templateTitle[4] have increased drastically over the past few years ; the measured period , recent years , but this figure has grown annually .
generated: In 2017 , the Companies Number of companies in the United Kingdom has been increasing since 2004 , when there were approximately 436 companies yLabelErr in the United States . market United Kingdom have increased drastically over the past few years ; the measured period , recent years , but this figure has grown annually .


Example 187:
data: Year|2019|x|line_chart Franchise_value_in_million_U.S._dollars|3800|y|line_chart Year|2018|x|line_chart Franchise_value_in_million_U.S._dollars|3200|y|line_chart Year|2017|x|line_chart Franchise_value_in_million_U.S._dollars|3000|y|line_chart Year|2016|x|line_chart Franchise_value_in_million_U.S._dollars|2900|y|line_chart Year|2015|x|line_chart Franchise_value_in_million_U.S._dollars|1450|y|line_chart Year|2014|x|line_chart Franchise_value_in_million_U.S._dollars|930|y|line_chart Year|2013|x|line_chart Franchise_value_in_million_U.S._dollars|875|y|line_chart Year|2012|x|line_chart Franchise_value_in_million_U.S._dollars|780|y|line_chart Year|2011|x|line_chart Franchise_value_in_million_U.S._dollars|775|y|line_chart Year|2010|x|line_chart Franchise_value_in_million_U.S._dollars|779|y|line_chart Year|2009|x|line_chart Franchise_value_in_million_U.S._dollars|913|y|line_chart Year|2008|x|line_chart Franchise_value_in_million_U.S._dollars|929|y|line_chart Year|2007|x|line_chart Franchise_value_in_million_U.S._dollars|908|y|line_chart Year|2006|x|line_chart Franchise_value_in_million_U.S._dollars|841|y|line_chart Year|2005|x|line_chart Franchise_value_in_million_U.S._dollars|757|y|line_chart Year|2004|x|line_chart Franchise_value_in_million_U.S._dollars|708|y|line_chart Year|2003|x|line_chart Franchise_value_in_million_U.S._dollars|602|y|line_chart Year|2002|x|line_chart Franchise_value_in_million_U.S._dollars|544|y|line_chart 
title: Franchise value of the Los Angeles Rams ( NFL ) 2002 - 2019

gold: This graph depicts the franchise value of the Los Angeles Rams of the National Football League from 2002 to 2019 . In 2019 , the franchise value amounted to around 3.8 billion U.S. dollars . The Los Angeles Rams are owned by Stan Kroenke , who bought the franchise for 750 million U.S. dollars in 2010 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] amounted to around templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[4] are owned by Stan Kroenke , who bought the templateYLabel[0] for 750 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[9] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to around templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] are owned by Jerry Jones who bought the templateYLabel[0] for 150 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1989 .
generated: This graph depicts the Franchise value of the Los Angeles of the National Football League from 2002 to 2019 . In 2019 , the Franchise value came to around 3800 billion U.S. dollars . The Los Angeles are owned by Jerry Jones who bought the Franchise for 150 million U.S. dollars in 1989 .


Example 188:
data: Year|2018|x|line_chart Value_added_as_a_percentage_of_GDP|11.4|y|line_chart Year|2017|x|line_chart Value_added_as_a_percentage_of_GDP|11.2|y|line_chart Year|2016|x|line_chart Value_added_as_a_percentage_of_GDP|11.1|y|line_chart Year|2015|x|line_chart Value_added_as_a_percentage_of_GDP|11.7|y|line_chart Year|2014|x|line_chart Value_added_as_a_percentage_of_GDP|11.7|y|line_chart Year|2013|x|line_chart Value_added_as_a_percentage_of_GDP|11.9|y|line_chart Year|2012|x|line_chart Value_added_as_a_percentage_of_GDP|11.9|y|line_chart Year|2011|x|line_chart Value_added_as_a_percentage_of_GDP|12|y|line_chart Year|2010|x|line_chart Value_added_as_a_percentage_of_GDP|12|y|line_chart Year|2009|x|line_chart Value_added_as_a_percentage_of_GDP|11.8|y|line_chart Year|2008|x|line_chart Value_added_as_a_percentage_of_GDP|12.2|y|line_chart Year|2007|x|line_chart Value_added_as_a_percentage_of_GDP|12.8|y|line_chart 
title: Value added of manufacturing as a percentage of GDP 2007 - 2018

gold: The statistic displays the value added of the U.S. manufacturing industry as a percentage of GDP between 2007 and 2018 . In 2018 , this sector accounted for 11.4 % of the nation 's Gross Domestic Product , making it one of the largest sectors of the U.S. economy .
gold_template: The statistic displays the templateYLabel[0] templateYLabel[1] of the U.S. templateTitle[2] industry as a templateYLabel[2] of templateYLabel[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , this sector accounted for templateYValue[0] % of the nation 's Gross Domestic Product , making it one of the largest sectors of the U.S. economy .

generated_template: templateTitle[2] templateTitle[3] templateTitle[4] in the United States – additional information In templateXValue[max] , there were templateYValue[0] templateYLabel[1] 100,000 people in the United Kingdom , up from templateYValue[1] million in the previous templateXLabel[0] . The number of people killed by 18 percent in the previous templateXLabel[0] .
generated: manufacturing percentage GDP in the United States – additional information In 2018 , there were 11.4 added 100,000 people in the United Kingdom , up from 11.2 million in the previous Year . The number of people killed by 18 percent in the previous Year .


Example 189:
data: Country|Belgium|x|bar_chart Share_of_respondents|85|y|bar_chart Country|Norway|x|bar_chart Share_of_respondents|85|y|bar_chart Country|South_Africa|x|bar_chart Share_of_respondents|85|y|bar_chart Country|Turkey|x|bar_chart Share_of_respondents|83|y|bar_chart Country|Germany|x|bar_chart Share_of_respondents|82|y|bar_chart Country|Great_Britain|x|bar_chart Share_of_respondents|82|y|bar_chart Country|Australia|x|bar_chart Share_of_respondents|82|y|bar_chart Country|Poland|x|bar_chart Share_of_respondents|80|y|bar_chart Country|Sweden|x|bar_chart Share_of_respondents|79|y|bar_chart Country|France|x|bar_chart Share_of_respondents|78|y|bar_chart Country|Canada|x|bar_chart Share_of_respondents|78|y|bar_chart Country|Argentina|x|bar_chart Share_of_respondents|78|y|bar_chart Country|Italy|x|bar_chart Share_of_respondents|76|y|bar_chart Country|Total|x|bar_chart Share_of_respondents|74|y|bar_chart Country|Spain|x|bar_chart Share_of_respondents|74|y|bar_chart Country|Brazil|x|bar_chart Share_of_respondents|73|y|bar_chart Country|South_Korea|x|bar_chart Share_of_respondents|71|y|bar_chart Country|India|x|bar_chart Share_of_respondents|70|y|bar_chart Country|Mexico|x|bar_chart Share_of_respondents|69|y|bar_chart Country|Saudi_Arabia|x|bar_chart Share_of_respondents|69|y|bar_chart Country|China|x|bar_chart Share_of_respondents|69|y|bar_chart Country|United_States|x|bar_chart Share_of_respondents|68|y|bar_chart Country|Hungary|x|bar_chart Share_of_respondents|67|y|bar_chart Country|Russia|x|bar_chart Share_of_respondents|57|y|bar_chart Country|Japan|x|bar_chart Share_of_respondents|56|y|bar_chart Country|Indonesia|x|bar_chart Share_of_respondents|40|y|bar_chart 
title: Search engines : regular usage penetration 2013 , by country

gold: This statistic displays the share of internet users in selected countries visiting search engines regularly as of August 2013 . Penetration was 85 percent in Belgium , Norway and South Africa .
gold_template: This statistic displays the templateYLabel[0] of internet users in selected countries visiting templateTitle[0] templateTitle[1] regularly as of August templateTitle[5] . templateTitle[4] was templateYValue[max] percent in templateXValue[0] , templateXValue[1] and templateXValue[2] templateXValue[2] .

generated_template: This statistic shows the global templateTitle[2] of templateTitle[0] templateTitle[1] in selected countries . The templateXLabel[0] with the highest rate of templateTitle[0] templateTitle[1] templateTitle[2] was templateXValue[1] , with templateYValue[max] percent of templateYLabel[1] reporting that they knew about templateTitle[0] templateTitle[1] . According to Ipsos , templateYValue[18] percent of global templateYLabel[1] were aware of templateTitle[0] templateTitle[1] .
generated: This statistic shows the global regular of Search engines in selected countries . The Country with the highest rate of Search engines regular was Norway , with 85 percent of respondents reporting that they knew about Search engines . According to Ipsos , 69 percent of global respondents were aware of Search engines .


Example 190:
data: Response|What_sites_he/she_can_access|x|bar_chart Share_of_respondents|79|y|bar_chart Response|What_online_accounts_he/she_can_have|x|bar_chart Share_of_respondents|77|y|bar_chart Response|What_he/she_can_post_online_for_others_to_see|x|bar_chart Share_of_respondents|75|y|bar_chart Response|The_time_of_day_he/she_can_use_technology|x|bar_chart Share_of_respondents|74|y|bar_chart Response|May_only_download_apps_with_age_ratings|x|bar_chart Share_of_respondents|67|y|bar_chart Response|Amount_of_time_he/she_can_use_technology_per_day_or_per_week|x|bar_chart Share_of_respondents|65|y|bar_chart Response|Must_check_devices_with_parents/leave_in_common_area_before_going_to_bed|x|bar_chart Share_of_respondents|60|y|bar_chart Response|When_in_home_he/she_can_use_or_be_online|x|bar_chart Share_of_respondents|59|y|bar_chart 
title: U.S. parental digital monitoring on teen online behavior 2015

gold: This statistic presents the most popular rules of parents in the United States to monitor their teen 's activities online . As of October 2015 , 77 percent of responding parents stated that they had rules about what online accounts their children were allowed to have .
gold_template: This statistic presents the most popular rules of parents in the templateTitle[0] to monitor their templateTitle[4] 's activities templateXValue[1] . As of October templateTitle[7] , templateYValue[1] percent of responding parents stated that they had rules about templateXValue[0] templateXValue[1] templateXValue[1] their children were allowed to templateXValue[1] .

generated_template: This statistic shows the results of a survey about the results of a survey conducted in templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . According to the survey , templateYValue[max] percent of templateYLabel[1] claimed that they templateXValue[6] templateXValue[6] for the United States .
generated: This statistic shows the results of a survey about the results of a survey conducted in teen online behavior in 2015 . According to the survey , 79 percent of respondents claimed that they Must_check_devices_with_parents/leave_in_common_area_before_going_to_bed for the United States .


Example 191:
data: December_value|2019|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|144.73|y|bar_chart December_value|2018|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|141.7|y|bar_chart December_value|2017|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|139.55|y|bar_chart December_value|2016|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|137.22|y|bar_chart December_value|2015|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|134.79|y|bar_chart December_value|2014|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|134.21|y|bar_chart December_value|2013|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|133.51|y|bar_chart December_value|2012|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|131.77|y|bar_chart December_value|2011|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|129.84|y|bar_chart December_value|2010|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|126.14|y|bar_chart December_value|2009|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|124.54|y|bar_chart December_value|2008|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|121.56|y|bar_chart December_value|2007|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|121.3|y|bar_chart December_value|2006|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|117.0|y|bar_chart December_value|2005|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|114.4|y|bar_chart December_value|2004|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|111.2|y|bar_chart December_value|2003|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|107.8|y|bar_chart December_value|2002|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|106.0|y|bar_chart December_value|2001|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|103.9|y|bar_chart December_value|2000|x|bar_chart Chained_Consumer_Price_Index_(1999=100)|102.6|y|bar_chart 
title: Chained consumer price index of all urban consumers 2000 - 2019

gold: This statistic shows the unadjusted chained Consumer Price Index in the United States from 2000 to 2019 , at year-end . In December 2019 , the chained consumer price index stood at 144.73 , reflecting a 44.73 percent increase from the base year of 1999 . The average wages garnered in select countries around the world based on purchasing power can be accessed here .
gold_template: This statistic shows the unadjusted templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the United States from templateXValue[min] to templateXValue[max] , at year-end . In templateXLabel[0] templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] stood at templateYValue[max] , reflecting a 44.73 percent increase from the base year of 1999 . The average wages garnered in select countries around the world based on purchasing power can be accessed here .

generated_template: In templateXValue[max] , there were templateYValue[0] thousand people employed in the United States . This was a decrease from the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] changes in the United States has risen dramatically in the past few years . The templateYLabel[0] templateYLabel[1] number of people employed by the templateYLabel[1] in the United States has seen in the templateXLabel[0] .
generated: In 2019 , there were 144.73 thousand people employed in the United States . This was a decrease from the previous December . The Chained Consumer Price changes in the United States has risen dramatically in the past few years . The Chained Consumer number of people employed by the Consumer in the United States has seen in the December .


Example 192:
data: Year|2018|x|line_chart Household_income_in_current_U.S._dollars|59785|y|line_chart Year|2017|x|line_chart Household_income_in_current_U.S._dollars|59295|y|line_chart Year|2016|x|line_chart Household_income_in_current_U.S._dollars|58146|y|line_chart Year|2015|x|line_chart Household_income_in_current_U.S._dollars|56473|y|line_chart Year|2014|x|line_chart Household_income_in_current_U.S._dollars|53875|y|line_chart Year|2013|x|line_chart Household_income_in_current_U.S._dollars|53027|y|line_chart Year|2012|x|line_chart Household_income_in_current_U.S._dollars|51926|y|line_chart Year|2011|x|line_chart Household_income_in_current_U.S._dollars|49047|y|line_chart Year|2010|x|line_chart Household_income_in_current_U.S._dollars|47266|y|line_chart Year|2009|x|line_chart Household_income_in_current_U.S._dollars|47475|y|line_chart Year|2008|x|line_chart Household_income_in_current_U.S._dollars|46490|y|line_chart Year|2007|x|line_chart Household_income_in_current_U.S._dollars|46053|y|line_chart Year|2006|x|line_chart Household_income_in_current_U.S._dollars|43307|y|line_chart Year|2005|x|line_chart Household_income_in_current_U.S._dollars|41422|y|line_chart Year|2004|x|line_chart Household_income_in_current_U.S._dollars|41397|y|line_chart Year|2003|x|line_chart Household_income_in_current_U.S._dollars|39271|y|line_chart Year|2002|x|line_chart Household_income_in_current_U.S._dollars|40149|y|line_chart Year|2001|x|line_chart Household_income_in_current_U.S._dollars|40860|y|line_chart Year|2000|x|line_chart Household_income_in_current_U.S._dollars|38609|y|line_chart Year|1999|x|line_chart Household_income_in_current_U.S._dollars|38688|y|line_chart Year|1998|x|line_chart Household_income_in_current_U.S._dollars|35783|y|line_chart Year|1997|x|line_chart Household_income_in_current_U.S._dollars|35075|y|line_chart Year|1996|x|line_chart Household_income_in_current_U.S._dollars|33072|y|line_chart Year|1995|x|line_chart Household_income_in_current_U.S._dollars|32039|y|line_chart Year|1994|x|line_chart Household_income_in_current_U.S._dollars|30755|y|line_chart Year|1993|x|line_chart Household_income_in_current_U.S._dollars|28727|y|line_chart Year|1992|x|line_chart Household_income_in_current_U.S._dollars|27953|y|line_chart Year|1991|x|line_chart Household_income_in_current_U.S._dollars|27733|y|line_chart Year|1990|x|line_chart Household_income_in_current_U.S._dollars|28228|y|line_chart 
title: Texas - Median household income 1990 - 2018

gold: This statistic shows the median household income in Texas from 1990 to 2018 . In 2018 , the median household income in Texas amounted to 59,785 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[3] was templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Texas Median Household income income from 1990 to 2018 . In 2018 , the Texas Median Household income in income was 59785 U.S. dollars .


Example 193:
data: Year|2018//19|x|line_chart Expenditure_in_million_GBP|2947|y|line_chart Year|2017/18|x|line_chart Expenditure_in_million_GBP|3048|y|line_chart Year|2016/17|x|line_chart Expenditure_in_million_GBP|3230|y|line_chart Year|2015/16|x|line_chart Expenditure_in_million_GBP|3268|y|line_chart Year|2014/15|x|line_chart Expenditure_in_million_GBP|3997|y|line_chart Year|2013/14|x|line_chart Expenditure_in_million_GBP|3472|y|line_chart 
title: Public expenditure on recreational and sporting services in the UK 2013 - 2019

gold: This statistic shows public sector expenditure on recreational and sporting services in the United Kingdom from 2013/14 to 2018/19 . In the year 2014/15 , the United Kingdom spent the most during this period with approximately 4 billion British pounds going toward recreational and sporting services . In 2017/18 , 2.9 billion British pounds were spent on recreational and sporting services in the UK .
gold_template: This statistic shows templateTitle[0] sector templateYLabel[0] on templateTitle[2] and templateTitle[3] templateTitle[4] in the United Kingdom from templateXValue[last] to 2018/19 . In the templateXLabel[0] templateXValue[4] , the United Kingdom spent the most during this period with approximately templateYValue[max] billion British pounds going toward templateTitle[2] and templateTitle[3] templateTitle[4] . In templateXValue[1] , templateYValue[min] billion British pounds were spent on templateTitle[2] and templateTitle[3] templateTitle[4] in the templateTitle[5] .

generated_template: This statistic shows the total templateYLabel[0] on templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] templateTitle[5] from templateXValue[last] to templateXValue[0] . In total over this period , the templateYLabel[0] on local templateTitle[1] templateTitle[2] templateTitle[3] and templateTitle[4] templateTitle[5] amounted to almost templateYValue[max] billion British pounds .
generated: This statistic shows the total Expenditure on expenditure recreational sporting in the services UK from 2013/14 to 2018//19 . In total over this period , the Expenditure on local expenditure recreational sporting and services UK amounted to almost 3997 billion British pounds .


Example 194:
data: Year|2019|x|line_chart Unemployment_rate|2.47|y|line_chart Year|2018|x|line_chart Unemployment_rate|2.4|y|line_chart Year|2017|x|line_chart Unemployment_rate|2.89|y|line_chart Year|2016|x|line_chart Unemployment_rate|3.95|y|line_chart Year|2015|x|line_chart Unemployment_rate|5.05|y|line_chart Year|2014|x|line_chart Unemployment_rate|6.11|y|line_chart Year|2013|x|line_chart Unemployment_rate|6.95|y|line_chart Year|2012|x|line_chart Unemployment_rate|6.98|y|line_chart Year|2011|x|line_chart Unemployment_rate|6.71|y|line_chart Year|2010|x|line_chart Unemployment_rate|7.28|y|line_chart Year|2009|x|line_chart Unemployment_rate|6.66|y|line_chart Year|2008|x|line_chart Unemployment_rate|4.39|y|line_chart Year|2007|x|line_chart Unemployment_rate|5.32|y|line_chart Year|2006|x|line_chart Unemployment_rate|7.15|y|line_chart Year|2005|x|line_chart Unemployment_rate|7.93|y|line_chart Year|2004|x|line_chart Unemployment_rate|8.21|y|line_chart Year|2003|x|line_chart Unemployment_rate|7.54|y|line_chart Year|2002|x|line_chart Unemployment_rate|7.02|y|line_chart Year|2001|x|line_chart Unemployment_rate|7.99|y|line_chart Year|2000|x|line_chart Unemployment_rate|8.76|y|line_chart Year|1999|x|line_chart Unemployment_rate|8.49|y|line_chart 
title: Unemployment rate in the Czech Republic 2019

gold: This statistic shows the unemployment rate in the Czech Republic from 1999 to 2019 . In 2019 , the unemployment rate in the Czech Republic was at approximately 2.47 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in the templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in the templateTitle[2] templateTitle[3] was at approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[min] percent .
generated: This statistic shows the Unemployment rate in Czech from 1999 to 2019 . In 2019 , the Unemployment rate in Czech was at approximately 2.4 percent .


Example 195:
data: Year|2024|x|line_chart Budget_balance_in_billion_euros|-51.31|y|line_chart Year|2023|x|line_chart Budget_balance_in_billion_euros|-49.15|y|line_chart Year|2022|x|line_chart Budget_balance_in_billion_euros|-48.57|y|line_chart Year|2021|x|line_chart Budget_balance_in_billion_euros|-47.61|y|line_chart Year|2020|x|line_chart Budget_balance_in_billion_euros|-45.51|y|line_chart Year|2019|x|line_chart Budget_balance_in_billion_euros|-35.77|y|line_chart Year|2018|x|line_chart Budget_balance_in_billion_euros|-37.51|y|line_chart Year|2017|x|line_chart Budget_balance_in_billion_euros|-41.29|y|line_chart Year|2016|x|line_chart Budget_balance_in_billion_euros|-42.66|y|line_chart Year|2015|x|line_chart Budget_balance_in_billion_euros|-43.14|y|line_chart Year|2014|x|line_chart Budget_balance_in_billion_euros|-49.34|y|line_chart 
title: Budget balance in Italy 2024

gold: The statistic shows the budget balance in Italy from 2014 to 2018 , with projections up until 2024 . A positive value indicates a state surplus ; a negative value , a state deficit . In 2018 , the state deficit of Italy was around 37.51 billion euros .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a state surplus ; a negative value , a state deficit . In templateXValue[6] , the state deficit of templateTitle[2] was around 37.51 templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a state surplus ; a negative value , a state deficit . templateYLabel[0] surplus ; a state deficit of templateTitle[2] was at around templateYValue[0] percent .
generated: The statistic shows the Budget balance in Italy from 2014 to 2018 , with projections up until 2024 . A positive value indicates a state surplus ; a negative value , a state deficit . Budget surplus ; a state deficit of Italy was at around -51.31 percent .


Example 196:
data: Year|2019_S1|x|line_chart Euro_cents_per_kilowatt-hour|30.88|y|line_chart Year|2018_S2|x|line_chart Euro_cents_per_kilowatt-hour|30.0|y|line_chart Year|2018_S1|x|line_chart Euro_cents_per_kilowatt-hour|29.87|y|line_chart Year|2017_S2|x|line_chart Euro_cents_per_kilowatt-hour|30.48|y|line_chart Year|2017_S1|x|line_chart Euro_cents_per_kilowatt-hour|30.48|y|line_chart Year|2016_S2|x|line_chart Euro_cents_per_kilowatt-hour|29.77|y|line_chart Year|2016_S1|x|line_chart Euro_cents_per_kilowatt-hour|29.69|y|line_chart Year|2015_S2|x|line_chart Euro_cents_per_kilowatt-hour|29.46|y|line_chart Year|2015_S1|x|line_chart Euro_cents_per_kilowatt-hour|29.51|y|line_chart Year|2014_S2|x|line_chart Euro_cents_per_kilowatt-hour|29.74|y|line_chart Year|2014_S1|x|line_chart Euro_cents_per_kilowatt-hour|29.81|y|line_chart Year|2013_S2|x|line_chart Euro_cents_per_kilowatt-hour|29.21|y|line_chart Year|2013_S1|x|line_chart Euro_cents_per_kilowatt-hour|29.19|y|line_chart Year|2012_S2|x|line_chart Euro_cents_per_kilowatt-hour|26.76|y|line_chart Year|2012_S1|x|line_chart Euro_cents_per_kilowatt-hour|25.95|y|line_chart Year|2011_S2|x|line_chart Euro_cents_per_kilowatt-hour|25.31|y|line_chart Year|2011_S1|x|line_chart Euro_cents_per_kilowatt-hour|25.28|y|line_chart Year|2010_S2|x|line_chart Euro_cents_per_kilowatt-hour|24.38|y|line_chart Year|2010_S1|x|line_chart Euro_cents_per_kilowatt-hour|23.75|y|line_chart 
title: Electricity prices for households in Germany 2010 - 2019 , semi-annually

gold: Between 2010 and 2013 , there was a noticeable increase in the price of electricity for households in Germany . At the start of the decade households paid less than 24 euro cents per kilowatt-hour . However , this increased by more than five euro cents by the second half of 2013 .
gold_template: Between templateXValue[17] and templateXValue[11] , there was a noticeable increase in the price of templateTitle[0] templateTitle[2] templateTitle[3] in templateTitle[4] . At the start of the decade templateTitle[3] paid less than templateYValue[17] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . However , this increased by more than five templateYLabel[0] templateYLabel[1] by the second half of templateXValue[11] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitle[4] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: This statistic shows the Electricity prices for household end users in Germany semi-annually from 2010_S2 to 2019_S1 . In the second half of 2018_S2 , the average Electricity price for households was 30.0 Euro cents per kWh .


Example 197:
data: Year|2024|x|line_chart GDP_growth_compared_to_previous_year|2.51|y|line_chart Year|2023|x|line_chart GDP_growth_compared_to_previous_year|2.46|y|line_chart Year|2022|x|line_chart GDP_growth_compared_to_previous_year|2.61|y|line_chart Year|2021|x|line_chart GDP_growth_compared_to_previous_year|2.71|y|line_chart Year|2020|x|line_chart GDP_growth_compared_to_previous_year|2.89|y|line_chart Year|2019|x|line_chart GDP_growth_compared_to_previous_year|3.11|y|line_chart Year|2018|x|line_chart GDP_growth_compared_to_previous_year|3.88|y|line_chart Year|2017|x|line_chart GDP_growth_compared_to_previous_year|4.47|y|line_chart Year|2016|x|line_chart GDP_growth_compared_to_previous_year|4.81|y|line_chart Year|2015|x|line_chart GDP_growth_compared_to_previous_year|1.96|y|line_chart Year|2014|x|line_chart GDP_growth_compared_to_previous_year|-1.31|y|line_chart 
title: Gross domestic product ( GDP ) growth rate in Cyprus 2024*

gold: The statistic shows the growth in real GDP in Cyprus from 2014 to 2018 , with projections up to 2024 . In 2018 , Cyprus 's real gross domestic product increased by around 3.88 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up to templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Cyprus from 2014 to 2018 , with projections up until 2024 . In 2018 , Cyprus 's real Gross domestic product grew by around 3.88 percent compared to the previous Year .


Example 198:
data: Year|2019|x|line_chart Viewers_in_millions|15.4|y|line_chart Year|2018|x|line_chart Viewers_in_millions|14.9|y|line_chart Year|2017|x|line_chart Viewers_in_millions|10.94|y|line_chart Year|2016|x|line_chart Viewers_in_millions|12.44|y|line_chart Year|2015|x|line_chart Viewers_in_millions|12.43|y|line_chart 
title: NFL Thursday Night Football average viewers 2015 - 2019

gold: An average of 15.4 million viewers tuned in to watch Thursday Night Football in the United States during the 2019 NFL season , an increase of half a million on the previous year . Ratings for Monday Night Football also increased in 2019 , with the average number of viewers rising to 12.6 million . Prime-time packages receive ratings boost Thursday Night Football is part of the prime-time television schedule that also includes Monday Night Football and Sunday Night Football – these NFL games are broadcast across the entire United States by one major national network .
gold_template: An templateTitle[4] of templateYValue[max] million templateYLabel[0] tuned in to watch templateTitle[1] templateTitle[2] templateTitle[3] in the United States during the templateXValue[max] templateTitle[0] season , an increase of half a million on the previous templateXLabel[0] . Ratings for Monday templateTitle[2] templateTitle[3] also increased in templateXValue[max] , with the templateTitle[4] number of templateYLabel[0] rising to 12.6 million . Prime-time packages receive ratings boost templateTitle[1] templateTitle[2] templateTitle[3] is part of the prime-time television schedule that also includes Monday templateTitle[2] templateTitle[3] and Sunday templateTitle[2] templateTitle[3] – these templateTitle[0] games are broadcast across the entire United States by one major national network .

generated_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[0] has been around templateYValue[min] million templateTitle[2] , an increase of around templateYValue[max] million on the previous templateXLabel[0] . Although viewing figures have increased , they are still down on the templateYValue[max] million templateYLabel[0] in templateXValue[min] . Sunday , templateTitle[1] , happy nights The templateTitle[0] has prime-time games that are broadcast across the entire United States by one major network on Thursday templateTitle[2] , Sunday templateTitle[2] , and templateTitle[1] templateTitle[2] – these games start at around 8:30 pm Eastern Time .
generated: Between 2015 and 2019 , the Viewers millions in NFL has been around 10.94 million Night , an increase of around 15.4 million on the previous Year . Although viewing figures have increased , they are still down on the 15.4 million Viewers in 2015 . Sunday , Thursday , happy nights The NFL has prime-time games that are broadcast across the entire United States by one major network on Thursday Night , Sunday Night , and Thursday Night – these games start at around 8:30 pm Eastern Time .


Example 199:
data: Year|2018|x|line_chart Passenger_enplanements_(in_millions)|777.91|y|line_chart Year|2017|x|line_chart Passenger_enplanements_(in_millions)|741.73|y|line_chart Year|2016|x|line_chart Passenger_enplanements_(in_millions)|719.99|y|line_chart Year|2015|x|line_chart Passenger_enplanements_(in_millions)|696.01|y|line_chart Year|2014|x|line_chart Passenger_enplanements_(in_millions)|662.82|y|line_chart Year|2013|x|line_chart Passenger_enplanements_(in_millions)|645.68|y|line_chart Year|2012|x|line_chart Passenger_enplanements_(in_millions)|642.29|y|line_chart Year|2011|x|line_chart Passenger_enplanements_(in_millions)|638.25|y|line_chart Year|2010|x|line_chart Passenger_enplanements_(in_millions)|629.54|y|line_chart Year|2009|x|line_chart Passenger_enplanements_(in_millions)|618.05|y|line_chart Year|2008|x|line_chart Passenger_enplanements_(in_millions)|651.71|y|line_chart Year|2007|x|line_chart Passenger_enplanements_(in_millions)|679.17|y|line_chart Year|2006|x|line_chart Passenger_enplanements_(in_millions)|658.36|y|line_chart Year|2005|x|line_chart Passenger_enplanements_(in_millions)|657.26|y|line_chart Year|2004|x|line_chart Passenger_enplanements_(in_millions)|629.77|y|line_chart 
title: U.S. airlines - domestic passenger enplanements 2004 - 2018

gold: This statistic represents the number of domestic passengers on U.S. airlines between 2004 and 2018 . U.S. airlines carried about 777.9 million passengers on domestic flights across the United States in 2018 .
gold_template: This statistic represents the number of templateTitle[2] passengers on templateTitle[0] templateTitle[1] between templateXValue[min] and templateXValue[max] . templateTitle[0] templateTitle[1] carried about templateYValue[max] million passengers on templateTitle[2] flights across the templateTitle[0] in templateXValue[max] .

generated_template: U.S. fashion fashion retailer templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] United States between templateXValue[min] and templateXValue[max] , templateYValue[0] percent of the United States were between templateYValue[0] percent of templateYLabel[1] throughout the templateXLabel[0] . The best percent of templateYLabel[1] with many years in templateYLabel[1] just templateYValue[min] percent of templateYLabel[1] in templateXValue[max] .
generated: U.S. fashion retailer passenger enplanements 2004 in (in United States between 2004 and 2018 , 777.91 percent of the United States were between 777.91 percent of enplanements throughout the Year . The best percent of enplanements with many years in enplanements just 618.05 percent of enplanements in 2018 .


Example 200:
data: Year|2018|x|line_chart Revenue_in_billion_U.S._dollars|4.06|y|line_chart Year|2017|x|line_chart Revenue_in_billion_U.S._dollars|4.21|y|line_chart Year|2016|x|line_chart Revenue_in_billion_U.S._dollars|4.25|y|line_chart Year|2015|x|line_chart Revenue_in_billion_U.S._dollars|4.38|y|line_chart Year|2014|x|line_chart Revenue_in_billion_U.S._dollars|4.44|y|line_chart Year|2013|x|line_chart Revenue_in_billion_U.S._dollars|4.13|y|line_chart Year|2012|x|line_chart Revenue_in_billion_U.S._dollars|3.99|y|line_chart Year|2011|x|line_chart Revenue_in_billion_U.S._dollars|3.84|y|line_chart Year|2010|x|line_chart Revenue_in_billion_U.S._dollars|3.63|y|line_chart Year|2009|x|line_chart Revenue_in_billion_U.S._dollars|3.6|y|line_chart 
title: Revenue of Bloomin ' Brands , Inc. worldwide from 2009 to 2018

gold: This statistic shows the revenue of Bloomin ' Brands , Inc. worldwide from 2009 to 2018 . In 2018 , Bloomin ' Brands generated approximately 4.06 billion U.S. dollars in revenue . Bloomin ' Brands , Inc. is a U.S.-based restaurant company that owns and operates the casual dining chains Bonefish Grill , Carrabba 's Italian Grill , Fleming 's Prime Steakhouse & Wine Bar , Outback Steakhouse and Roy 's .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] , templateTitle[4] templateTitle[5] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[1] templateTitle[2] templateTitle[3] generated approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] . templateTitle[1] templateTitle[2] templateTitle[3] , templateTitle[4] is a U.S.-based restaurant company that owns and operates the casual dining chains Bonefish Grill , Carrabba 's Italian Grill , Fleming 's Prime Steakhouse & Wine Bar , Outback Steakhouse and Roy 's .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . Global hotel company templateTitle[1] templateTitle[2] Corporation generated approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] in templateXValue[max] .
generated: This statistic shows the Revenue of Bloomin ' from 2009 to 2018 . Global hotel company Bloomin ' Corporation generated approximately 4.06 billion U.S. dollars in Revenue in 2018 .


Example 201:
data: Year|2030|x|line_chart Market_size_in_billion_U.S._dollars|25.0|y|line_chart Year|2025|x|line_chart Market_size_in_billion_U.S._dollars|15.0|y|line_chart Year|2020|x|line_chart Market_size_in_billion_U.S._dollars|4.0|y|line_chart Year|2015|x|line_chart Market_size_in_billion_U.S._dollars|0.4|y|line_chart 
title: Autonomous driving sensor components - global market size 2015 - 2030

gold: This statistic represents the projected size of the global market for autonomous driving sensor components from 2015 to 2030 . In 2025 , the market for autonomous driving sensor components is projected to be sized at 15 billion U.S. dollars in an optimistic scenario .
gold_template: This statistic represents the projected templateYLabel[1] of the templateTitle[4] templateYLabel[0] for templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateYLabel[0] for templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] is projected to be sized at templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in an optimistic scenario .

generated_template: The statistic depicts the templateYLabel[1] of the templateTitle[0] and templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateXValue[min] , with a forecast until templateXValue[max] . It is expected that the templateTitle[0] templateYLabel[0] templateYLabel[1] will be worth templateYValue[8] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateTitle[5] .
generated: The statistic depicts the size of the Autonomous and driving sensor components global in 2015 , with a forecast until 2030 . It is expected that the Autonomous Market size will be worth yValErr billion U.S. dollars market .


Example 202:
data: Year|2018|x|line_chart Per_capita_consumption_in_pounds|4.8|y|line_chart Year|2017|x|line_chart Per_capita_consumption_in_pounds|4.8|y|line_chart Year|2016|x|line_chart Per_capita_consumption_in_pounds|4.6|y|line_chart Year|2015|x|line_chart Per_capita_consumption_in_pounds|4.6|y|line_chart Year|2014|x|line_chart Per_capita_consumption_in_pounds|4.5|y|line_chart Year|2013|x|line_chart Per_capita_consumption_in_pounds|4.5|y|line_chart Year|2012|x|line_chart Per_capita_consumption_in_pounds|4.7|y|line_chart Year|2011|x|line_chart Per_capita_consumption_in_pounds|4.8|y|line_chart Year|2010|x|line_chart Per_capita_consumption_in_pounds|4.7|y|line_chart Year|2009|x|line_chart Per_capita_consumption_in_pounds|4.7|y|line_chart Year|2008|x|line_chart Per_capita_consumption_in_pounds|4.7|y|line_chart Year|2007|x|line_chart Per_capita_consumption_in_pounds|4.7|y|line_chart Year|2006|x|line_chart Per_capita_consumption_in_pounds|4.7|y|line_chart Year|2005|x|line_chart Per_capita_consumption_in_pounds|4.6|y|line_chart Year|2004|x|line_chart Per_capita_consumption_in_pounds|4.7|y|line_chart Year|2003|x|line_chart Per_capita_consumption_in_pounds|4.7|y|line_chart Year|2002|x|line_chart Per_capita_consumption_in_pounds|4.5|y|line_chart Year|2001|x|line_chart Per_capita_consumption_in_pounds|4.5|y|line_chart Year|2000|x|line_chart Per_capita_consumption_in_pounds|4.4|y|line_chart 
title: U.S. per capita consumption of oat products 2000 - 2018

gold: The timeline shows the per capita consumption of oat products in the United States from 2000 to 2018 . The U.S. per capita consumption of oat products amounted to 4.8 pounds in 2018 .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to templateYValue[max] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] templateTitle[6] in the United States from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] templateTitle[6] amounted to around templateYValue[max] templateYLabel[3] in templateXValue[1] .
generated: The timeline shows the Per capita consumption of oat products 2000 in the United States from 2000 to 2018 . According to the report , the U.S. Per capita consumption of oat products 2000 amounted to around 4.8 pounds in 2017 .


Example 203:
data: State|New_York|x|bar_chart Number_of_Ultra-High-Net-Worth_people|8655|y|bar_chart State|San_Francisco|x|bar_chart Number_of_Ultra-High-Net-Worth_people|5460|y|bar_chart State|Los_Angeles|x|bar_chart Number_of_Ultra-High-Net-Worth_people|5135|y|bar_chart State|Chicago|x|bar_chart Number_of_Ultra-High-Net-Worth_people|2885|y|bar_chart State|Washington|x|bar_chart Number_of_Ultra-High-Net-Worth_people|2730|y|bar_chart State|Houston|x|bar_chart Number_of_Ultra-High-Net-Worth_people|2545|y|bar_chart State|Dallas|x|bar_chart Number_of_Ultra-High-Net-Worth_people|2330|y|bar_chart State|Toronto|x|bar_chart Number_of_Ultra-High-Net-Worth_people|1840|y|bar_chart State|Atlanta|x|bar_chart Number_of_Ultra-High-Net-Worth_people|1230|y|bar_chart State|Seattle|x|bar_chart Number_of_Ultra-High-Net-Worth_people|1095|y|bar_chart 
title: Wealth in the U.S. - UHNW ( super rich ) population in 2014 , by city

gold: This statistic shows the number of the super-rich , or Ultra-High-Net-Worth , persons in the United States in 2014 , sorted by city . New York has the largest concentration of super-rich individuals ; about 8,655 UHNW ( Ultra High Net Worth ) people are living in the metro area .
gold_template: This statistic shows the templateYLabel[0] of the super-rich , or templateYLabel[1] , persons in the templateTitle[1] in templateTitle[6] , sorted templateTitle[7] templateTitle[8] . templateXValue[0] templateXValue[0] has the largest concentration of super-rich individuals ; about templateYValue[max] templateTitle[2] ( Ultra High Net Worth ) templateYLabel[2] are living in the metro area .

generated_template: The statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the United States in templateTitle[7] , templateTitle[4] templateXLabel[0] . In the most templateYLabel[1] templateYValue[max] people lived in the templateXValue[0] .
generated: The statistic shows the Number U.S. Ultra-High-Net-Worth in the United States in by , rich State . In the most Ultra-High-Net-Worth 8655 people lived in the New_York .


Example 204:
data: Artist_/_Band|The_Rolling_Stones|x|bar_chart Gross_revenue_in_million_U.S._dollars|177.8|y|bar_chart Artist_/_Band|Elton_John|x|bar_chart Gross_revenue_in_million_U.S._dollars|157.4|y|bar_chart Artist_/_Band|Bob_Seger_&_The_Silver_Bullet_Band|x|bar_chart Gross_revenue_in_million_U.S._dollars|97.0|y|bar_chart Artist_/_Band|Pink|x|bar_chart Gross_revenue_in_million_U.S._dollars|87.8|y|bar_chart Artist_/_Band|Ariana_Grande|x|bar_chart Gross_revenue_in_million_U.S._dollars|82.6|y|bar_chart Artist_/_Band|Jonas_Brothers|x|bar_chart Gross_revenue_in_million_U.S._dollars|81.7|y|bar_chart Artist_/_Band|Kiss|x|bar_chart Gross_revenue_in_million_U.S._dollars|81.6|y|bar_chart Artist_/_Band|Fleetwood_Mac|x|bar_chart Gross_revenue_in_million_U.S._dollars|77.5|y|bar_chart Artist_/_Band|Garth_Brooks|x|bar_chart Gross_revenue_in_million_U.S._dollars|76.1|y|bar_chart Artist_/_Band|Justin_Timberlake|x|bar_chart Gross_revenue_in_million_U.S._dollars|75.6|y|bar_chart Artist_/_Band|Billy_Joel|x|bar_chart Gross_revenue_in_million_U.S._dollars|70.4|y|bar_chart Artist_/_Band|Dead_&_Company|x|bar_chart Gross_revenue_in_million_U.S._dollars|68.6|y|bar_chart Artist_/_Band|Eric_Church|x|bar_chart Gross_revenue_in_million_U.S._dollars|68.6|y|bar_chart Artist_/_Band|Michael_Buble|x|bar_chart Gross_revenue_in_million_U.S._dollars|66.7|y|bar_chart Artist_/_Band|Trans-Siberian_Orchesta|x|bar_chart Gross_revenue_in_million_U.S._dollars|65.7|y|bar_chart 
title: The most successful music tours in North America in 2019

gold: In 2019 , the most successful music tour in North America based on gross revenue was that of The Rolling Stones , which generated 177.8 million U.S. dollars . Following closely behind was Elton John 's tour , which made 157.4 million dollars in revenue and sold over 1.15 million tickets .
gold_template: In templateTitle[6] , the templateTitle[0] templateTitle[1] templateTitle[2] tour in templateTitle[4] templateTitle[5] based on templateYLabel[0] templateYLabel[1] was that of The templateXValue[0] templateXValue[0] , which generated templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Following closely behind was templateXValue[1] templateXValue[1] 's tour , which made templateYValue[1] templateYLabel[2] templateYLabel[4] in templateYLabel[1] and sold over 1.15 templateYLabel[2] tickets .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States as of April templateTitle[6] , templateTitle[7] templateXLabel[0] . During the survey period , it was found that templateYValue[0] percent of the people templateTitle[3] templateTitle[4] templateTitle[5] in the United States accessed here .
generated: This statistic shows the Gross revenue of music tours North America in the United States as of April 2019 , titleErr Artist . During the survey period , it was found that 177.8 percent of the people tours North America in the United States accessed here .


Example 205:
data: Year|2019|x|line_chart Unemployment_rate|8.46|y|line_chart Year|2018|x|line_chart Unemployment_rate|8.33|y|line_chart Year|2017|x|line_chart Unemployment_rate|8.32|y|line_chart Year|2016|x|line_chart Unemployment_rate|8.61|y|line_chart Year|2015|x|line_chart Unemployment_rate|8.62|y|line_chart Year|2014|x|line_chart Unemployment_rate|8.72|y|line_chart Year|2013|x|line_chart Unemployment_rate|8.8|y|line_chart Year|2012|x|line_chart Unemployment_rate|8.75|y|line_chart Year|2011|x|line_chart Unemployment_rate|5.68|y|line_chart Year|2010|x|line_chart Unemployment_rate|5.52|y|line_chart Year|2009|x|line_chart Unemployment_rate|5.35|y|line_chart Year|2008|x|line_chart Unemployment_rate|4.77|y|line_chart Year|2007|x|line_chart Unemployment_rate|4.75|y|line_chart Year|2006|x|line_chart Unemployment_rate|4.96|y|line_chart Year|2005|x|line_chart Unemployment_rate|5.14|y|line_chart Year|2004|x|line_chart Unemployment_rate|5.14|y|line_chart Year|2003|x|line_chart Unemployment_rate|5.15|y|line_chart Year|2002|x|line_chart Unemployment_rate|5.11|y|line_chart Year|2001|x|line_chart Unemployment_rate|4.96|y|line_chart Year|2000|x|line_chart Unemployment_rate|4.72|y|line_chart Year|1999|x|line_chart Unemployment_rate|4.43|y|line_chart 
title: Unemployment rate in Samoa 2019

gold: This statistic shows the unemployment rate in Samoa from 1999 to 2019 . In 2019 , the unemployment rate in Samoa was at approximately 8.46 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: The statistic shows the Unemployment rate in Samoa from 1999 to 2019 . In 2019 , the Unemployment rate in Samoa was at approximately 8.46 percent .


Example 206:
data: Country|Total|x|bar_chart Number_of_H&M_store_openings|375|y|bar_chart Country|Franchise|x|bar_chart Number_of_H&M_store_openings|28|y|bar_chart Country|USA|x|bar_chart Number_of_H&M_store_openings|28|y|bar_chart Country|China|x|bar_chart Number_of_H&M_store_openings|21|y|bar_chart Country|Japan|x|bar_chart Number_of_H&M_store_openings|15|y|bar_chart Country|UK|x|bar_chart Number_of_H&M_store_openings|13|y|bar_chart Country|Sweden|x|bar_chart Number_of_H&M_store_openings|12|y|bar_chart Country|Germany|x|bar_chart Number_of_H&M_store_openings|11|y|bar_chart Country|Italy|x|bar_chart Number_of_H&M_store_openings|10|y|bar_chart Country|Russia|x|bar_chart Number_of_H&M_store_openings|10|y|bar_chart Country|France|x|bar_chart Number_of_H&M_store_openings|10|y|bar_chart Country|South_Korea|x|bar_chart Number_of_H&M_store_openings|8|y|bar_chart Country|India|x|bar_chart Number_of_H&M_store_openings|8|y|bar_chart Country|Mexico|x|bar_chart Number_of_H&M_store_openings|7|y|bar_chart Country|Philippines|x|bar_chart Number_of_H&M_store_openings|6|y|bar_chart Country|Netherlands|x|bar_chart Number_of_H&M_store_openings|6|y|bar_chart Country|Belgium|x|bar_chart Number_of_H&M_store_openings|6|y|bar_chart Country|Canada|x|bar_chart Number_of_H&M_store_openings|5|y|bar_chart Country|Australia|x|bar_chart Number_of_H&M_store_openings|5|y|bar_chart Country|Finland|x|bar_chart Number_of_H&M_store_openings|5|y|bar_chart Country|Luxembourg|x|bar_chart Number_of_H&M_store_openings|5|y|bar_chart Country|Poland|x|bar_chart Number_of_H&M_store_openings|4|y|bar_chart Country|New_Zealand|x|bar_chart Number_of_H&M_store_openings|4|y|bar_chart Country|Spain|x|bar_chart Number_of_H&M_store_openings|4|y|bar_chart Country|South_Africa|x|bar_chart Number_of_H&M_store_openings|4|y|bar_chart Country|Norway|x|bar_chart Number_of_H&M_store_openings|3|y|bar_chart Country|Iceland|x|bar_chart Number_of_H&M_store_openings|3|y|bar_chart Country|Switzerland|x|bar_chart Number_of_H&M_store_openings|3|y|bar_chart Country|Colombia|x|bar_chart Number_of_H&M_store_openings|3|y|bar_chart Country|Kazakhstan|x|bar_chart Number_of_H&M_store_openings|3|y|bar_chart Country|Austria|x|bar_chart Number_of_H&M_store_openings|3|y|bar_chart Country|Belarus|x|bar_chart Number_of_H&M_store_openings|2|y|bar_chart Country|Uruguay|x|bar_chart Number_of_H&M_store_openings|2|y|bar_chart Country|Malaysia|x|bar_chart Number_of_H&M_store_openings|2|y|bar_chart Country|Chile|x|bar_chart Number_of_H&M_store_openings|2|y|bar_chart Country|Peru|x|bar_chart Number_of_H&M_store_openings|2|y|bar_chart Country|Hong_Kong|x|bar_chart Number_of_H&M_store_openings|2|y|bar_chart Country|Vietnam|x|bar_chart Number_of_H&M_store_openings|2|y|bar_chart Country|Singapore|x|bar_chart Number_of_H&M_store_openings|2|y|bar_chart Country|Slovakia|x|bar_chart Number_of_H&M_store_openings|2|y|bar_chart Country|Latvia|x|bar_chart Number_of_H&M_store_openings|1|y|bar_chart Country|Lithuania|x|bar_chart Number_of_H&M_store_openings|1|y|bar_chart Country|Denmark|x|bar_chart Number_of_H&M_store_openings|1|y|bar_chart Country|Georgia|x|bar_chart Number_of_H&M_store_openings|1|y|bar_chart Country|Bosnia-Herzegovina|x|bar_chart Number_of_H&M_store_openings|1|y|bar_chart Country|Turkey|x|bar_chart Number_of_H&M_store_openings|1|y|bar_chart Country|Romania|x|bar_chart Number_of_H&M_store_openings|1|y|bar_chart Country|Estonia|x|bar_chart Number_of_H&M_store_openings|1|y|bar_chart Country|Serbia|x|bar_chart Number_of_H&M_store_openings|1|y|bar_chart Country|Ukraine|x|bar_chart Number_of_H&M_store_openings|1|y|bar_chart Country|Portugal|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart Country|Greece|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart Country|Puerto_Rico|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart Country|Slovenia|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart Country|Hungary|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart Country|Taiwan|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart Country|Bulgaria|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart Country|Cyprus|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart Country|Macau|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart Country|Croatia|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart Country|Ireland|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart Country|Czech_Republic|x|bar_chart Number_of_H&M_store_openings|0|y|bar_chart 
title: Number of H & M store openings worldwide by country in 2019

gold: This statistic shows the number of new openings of the H & M Group stores in 2019 , by country . In 2019 , 28 new H & M stores were opened in the United States . H & M is a Swedish clothing manufacturer and retailer , based in Stockholm , Sweden .
gold_template: This statistic shows the templateYLabel[0] of templateXValue[22] templateYLabel[3] of the templateTitle[1] templateTitle[2] templateTitle[3] Group stores in templateTitle[9] , templateTitle[7] templateXLabel[0] . In templateTitle[9] , templateYValue[1] templateXValue[22] templateTitle[1] templateTitle[2] templateTitle[3] stores were opened in the United States . templateTitle[1] templateTitle[2] templateTitle[3] is a Swedish clothing manufacturer and retailer , based in Stockholm , templateXValue[6] .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] . During the time , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] templateTitle[2] templateTitle[3] templateYValue[max] , followed by templateXValue[1] .
generated: This statistic presents the Number of H&M store M store openings in worldwide . During the time , Total had the highest Number H&M store of store openings & M 375 , followed by Franchise .


Example 207:
data: Year|2016|x|line_chart Production_in_million_cubic_feet|683|y|line_chart Year|2015|x|line_chart Production_in_million_cubic_feet|693|y|line_chart Year|2014|x|line_chart Production_in_million_cubic_feet|693|y|line_chart Year|2013|x|line_chart Production_in_million_cubic_feet|703|y|line_chart Year|2012|x|line_chart Production_in_million_cubic_feet|679|y|line_chart Year|2011|x|line_chart Production_in_million_cubic_feet|653|y|line_chart Year|2010|x|line_chart Production_in_million_cubic_feet|655|y|line_chart Year|2009|x|line_chart Production_in_million_cubic_feet|616|y|line_chart Year|2008|x|line_chart Production_in_million_cubic_feet|754|y|line_chart Year|2007|x|line_chart Production_in_million_cubic_feet|898|y|line_chart Year|2006|x|line_chart Production_in_million_cubic_feet|989|y|line_chart Year|2005|x|line_chart Production_in_million_cubic_feet|1068|y|line_chart Year|2004|x|line_chart Production_in_million_cubic_feet|1082|y|line_chart Year|2003|x|line_chart Production_in_million_cubic_feet|1052|y|line_chart 
title: Plywood and veneer production in the U.S. 2003 - 2016

gold: This statistic depicts the plywood and veneer production in the United States from 2001 to 2016 . In 2015 , the total U.S. plywood and veneer production amounted to some 683 million cubic feet .
gold_template: This statistic depicts the templateTitle[0] and templateTitle[1] templateYLabel[0] in the templateTitle[3] from 2001 to templateXValue[max] . In templateXValue[1] , the total templateTitle[3] templateTitle[0] and templateTitle[1] templateYLabel[0] amounted to some templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] million people living in templateTitle[3] templateTitle[4] , up from templateYValue[1] million in the previous templateXLabel[0] .
generated: This statistic shows the Production million of veneer production U.S. 2003 in the United States from 2003 to 2016 . In 2016 , there were 683 million people living in U.S. 2003 , up from 693 million in the previous Year .


Example 208:
data: Quarter|Q2_2016|x|bar_chart Users_in_millions|42.77|y|bar_chart Quarter|Q1_2016|x|bar_chart Users_in_millions|42.03|y|bar_chart Quarter|Q4_2015|x|bar_chart Users_in_millions|41.27|y|bar_chart Quarter|Q3_2015|x|bar_chart Users_in_millions|40.36|y|bar_chart Quarter|Q2_2015|x|bar_chart Users_in_millions|39.56|y|bar_chart Quarter|Q1_2015|x|bar_chart Users_in_millions|38.7|y|bar_chart Quarter|Q4_2014|x|bar_chart Users_in_millions|37.82|y|bar_chart Quarter|Q3_2014|x|bar_chart Users_in_millions|36.73|y|bar_chart Quarter|Q2_2014|x|bar_chart Users_in_millions|35.78|y|bar_chart Quarter|Q1_2014|x|bar_chart Users_in_millions|34.75|y|bar_chart Quarter|Q4_2013|x|bar_chart Users_in_millions|33.69|y|bar_chart Quarter|Q3_2013|x|bar_chart Users_in_millions|32.57|y|bar_chart 
title: Number of mobile internet users in the United Kingdom ( UK ) Q3 2013-Q2 2016

gold: This statistic displays a forecast of the number of mobile internet users in the UK from third quarter 2013 to second quarter 2016 . It is forecast that there will be 43 million mobile internet users as of quarter two 2016 .
gold_template: This statistic displays a forecast of the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitle[6] from third templateXLabel[0] templateXValue[10] to second templateXLabel[0] templateXValue[0] . It is forecast that there will be templateYValue[max] million templateTitle[1] templateTitle[2] templateYLabel[0] as of templateXLabel[0] two templateXValue[0] .

generated_template: In the fourth templateXLabel[0] of templateXValue[0] , there were templateYValue[max] percent of templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] in the United States , up by the templateYValue[max] percent of the templateXLabel[0] of templateXValue[0] . However , the company had been templateTitle[1] templateTitle[2] in templateTitle[4] at around templateYValue[1] million U.S. dollars .
generated: In the fourth Quarter of Q2_2016 , there were 42.77 percent of Number mobile internet in United in the United States , up by the 42.77 percent of the Quarter of Q2_2016 . However , the company had been mobile internet in United at around 42.03 million U.S. dollars .


Example 209:
data: Year|2018|x|line_chart Number_of_participants_in_millions|3.75|y|line_chart Year|2017|x|line_chart Number_of_participants_in_millions|3.97|y|line_chart Year|2016|x|line_chart Number_of_participants_in_millions|4.1|y|line_chart Year|2015|x|line_chart Number_of_participants_in_millions|4.1|y|line_chart Year|2014|x|line_chart Number_of_participants_in_millions|3.92|y|line_chart Year|2013|x|line_chart Number_of_participants_in_millions|3.92|y|line_chart Year|2012|x|line_chart Number_of_participants_in_millions|3.96|y|line_chart Year|2011|x|line_chart Number_of_participants_in_millions|3.73|y|line_chart Year|2010|x|line_chart Number_of_participants_in_millions|3.87|y|line_chart Year|2009|x|line_chart Number_of_participants_in_millions|4.34|y|line_chart Year|2008|x|line_chart Number_of_participants_in_millions|4.23|y|line_chart Year|2007|x|line_chart Number_of_participants_in_millions|3.79|y|line_chart Year|2006|x|line_chart Number_of_participants_in_millions|3.39|y|line_chart 
title: Number of participants in sailing in the U.S. 2006 - 2018

gold: This statistic shows the number of participants in sailing in the United States from 2006 to 2018 . In 2018 , there were approximately 3.75 million participants in sailing in the U.S. , down from 3.97 million the previous year .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[0] million templateYLabel[1] in templateTitle[2] in the templateTitle[3] , down from templateYValue[1] million the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the United States templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[0] million .
generated: This statistic shows the Number of participants in participants sailing in the United States 2006 2006 to 2018 . In 2018 , the Number of participants ( aged six years and older ) in participants sailing amounted to approximately 3.75 million .


Example 210:
data: Year|2028|x|line_chart Spending_in_billion_euros|131.4|y|line_chart Year|2018|x|line_chart Spending_in_billion_euros|114.9|y|line_chart Year|2017|x|line_chart Spending_in_billion_euros|112.3|y|line_chart Year|2016|x|line_chart Spending_in_billion_euros|110.9|y|line_chart Year|2015|x|line_chart Spending_in_billion_euros|108.1|y|line_chart Year|2014|x|line_chart Spending_in_billion_euros|108.3|y|line_chart Year|2013|x|line_chart Spending_in_billion_euros|107.5|y|line_chart Year|2012|x|line_chart Spending_in_billion_euros|107.2|y|line_chart 
title: Domestic travel spending in France 2012 - 2028

gold: Domestic tourism expenditure is expected to reach 131.4 billion euros in 2028 . The spending of French tourists in their country has increased since the beginning of the 2010s and has already reached nearly 115 billion euros in 2018 . Similarly , the spending of international tourists in France is estimated to grow in the coming years .
gold_template: templateTitle[0] tourism expenditure is expected to reach templateYValue[max] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The templateYLabel[0] of French tourists in their country has increased since the beginning of the 2010s and has already reached nearly templateYValue[1] templateYLabel[1] templateYLabel[2] in templateXValue[1] . Similarly , the templateYLabel[0] of international tourists in templateTitle[3] is estimated to grow in the coming years .

generated_template: This statistic shows the total annual spend of templateTitle[0] tourists ( 'visitor exports ' _ ) in templateTitle[3] from templateXValue[min] to templateXValue[max] . As of templateXValue[2] , there were around templateYValue[2] templateYLabel[1] templateYLabel[2] in templateTitle[3] .
generated: This statistic shows the total annual spend of Domestic tourists ( 'visitor exports ' _ ) in France from 2012 to 2028 . As of 2017 , there were around 112.3 billion euros in France .


Example 211:
data: Year|2017|x|line_chart Death_rate_per_100,000_population|20.1|y|line_chart Year|2016|x|line_chart Death_rate_per_100,000_population|18.2|y|line_chart Year|2015|x|line_chart Death_rate_per_100,000_population|14.8|y|line_chart Year|2014|x|line_chart Death_rate_per_100,000_population|13.1|y|line_chart Year|2013|x|line_chart Death_rate_per_100,000_population|12.2|y|line_chart Year|2012|x|line_chart Death_rate_per_100,000_population|11.5|y|line_chart Year|2011|x|line_chart Death_rate_per_100,000_population|11.6|y|line_chart Year|2010|x|line_chart Death_rate_per_100,000_population|10.6|y|line_chart Year|2009|x|line_chart Death_rate_per_100,000_population|10.3|y|line_chart Year|2008|x|line_chart Death_rate_per_100,000_population|10.2|y|line_chart Year|2007|x|line_chart Death_rate_per_100,000_population|9.9|y|line_chart Year|2006|x|line_chart Death_rate_per_100,000_population|9.2|y|line_chart Year|2005|x|line_chart Death_rate_per_100,000_population|8.0|y|line_chart Year|2004|x|line_chart Death_rate_per_100,000_population|7.2|y|line_chart Year|2003|x|line_chart Death_rate_per_100,000_population|6.7|y|line_chart Year|2002|x|line_chart Death_rate_per_100,000_population|6.1|y|line_chart Year|2001|x|line_chart Death_rate_per_100,000_population|4.9|y|line_chart Year|2000|x|line_chart Death_rate_per_100,000_population|4.5|y|line_chart Year|1999|x|line_chart Death_rate_per_100,000_population|4.4|y|line_chart Year|1998|x|line_chart Death_rate_per_100,000_population|3.9|y|line_chart Year|1997|x|line_chart Death_rate_per_100,000_population|3.7|y|line_chart Year|1996|x|line_chart Death_rate_per_100,000_population|3.5|y|line_chart Year|1995|x|line_chart Death_rate_per_100,000_population|3.4|y|line_chart Year|1990|x|line_chart Death_rate_per_100,000_population|2.3|y|line_chart Year|1980|x|line_chart Death_rate_per_100,000_population|1.9|y|line_chart Year|1970|x|line_chart Death_rate_per_100,000_population|2.8|y|line_chart Year|1960|x|line_chart Death_rate_per_100,000_population|1.7|y|line_chart Year|1950|x|line_chart Death_rate_per_100,000_population|2.5|y|line_chart 
title: Deaths by unintentional drug overdose in the U.S. 1950 - 2017

gold: This statistic shows the death rate due to unintentional drug overdose in the United States from 1950 to 2017 . In 2017 , there were 20.1 deaths by unintentional poisoning per 100,000 people in the United States .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] due to templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateTitle[0] templateTitle[1] templateTitle[2] poisoning templateYLabel[2] 100,000 people in the templateTitle[5] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of the United States had an increase of the previous templateXLabel[0] .
generated: This statistic shows the Death rate in unintentional drug from 1950 to 2017 . In 2017 , about 20.1 percent of the United States had an increase of the previous Year .


Example 212:
data: Country_and_Year|China_(1928)|x|bar_chart Number_of_deaths|3000000|y|bar_chart Country_and_Year|Bangladesh_(1943)|x|bar_chart Number_of_deaths|1900000|y|bar_chart Country_and_Year|India_(1942)|x|bar_chart Number_of_deaths|1500000|y|bar_chart Country_and_Year|India_(1965)|x|bar_chart Number_of_deaths|1500000|y|bar_chart Country_and_Year|India_(1900)|x|bar_chart Number_of_deaths|1250000|y|bar_chart Country_and_Year|Soviet_Union_(1921)|x|bar_chart Number_of_deaths|1200000|y|bar_chart Country_and_Year|China_(1920)|x|bar_chart Number_of_deaths|500000|y|bar_chart Country_and_Year|Ethiopia_(May_1983)|x|bar_chart Number_of_deaths|300000|y|bar_chart Country_and_Year|Sudan_(April_1983)|x|bar_chart Number_of_deaths|150000|y|bar_chart Country_and_Year|Ethiopia_(December_1973)|x|bar_chart Number_of_deaths|100000|y|bar_chart 
title: Number of deaths caused by majors droughts worldwide up to 2016

gold: This statistic illustrates deaths due to drought worldwide from 1900 to 2016* . The dry period of April 1983 in Sudan caused around 150,000 deaths . Deaths due to drought worldwide The 1928 drought in the People 's Republic of China was the deadliest drought during the period between 1900 and 2016 , causing the death of an estimated three million people .
gold_template: This statistic illustrates templateYLabel[1] due to drought templateTitle[6] from 1900 to 2016* . The dry period of April 1983 in templateXValue[8] templateTitle[2] around templateYValue[8] templateYLabel[1] . templateYLabel[1] due to drought templateTitle[6] The 1928 drought in the People 's Republic of templateXValue[0] was the deadliest drought during the period between 1900 and templateTitle[8] , causing the death of an estimated templateYValue[max] million people .

generated_template: In templateTitle[4] , the templateYLabel[0] of templateYLabel[1] of templateTitle[5] templateTitle[2] in the United States in templateTitle[6] , templateTitle[7] templateXLabel[0] templateXLabel[1] of almost templateYValue[max] people were killed by terrorists in the templateTitle[7] . The U.S. and that year year of templateTitle[2] were recorded in the UK U.S. templateTitle[5] .
generated: In majors , the Number of deaths of droughts caused in the United States in worldwide , up Country Year of almost 3000000 people were killed by terrorists in the up . The U.S. and that year of caused were recorded in the UK U.S. droughts .


Example 213:
data: Quarter|Q2_'16|x|bar_chart Percentage_of_mobile_GMV|75|y|bar_chart Quarter|Q1_'16|x|bar_chart Percentage_of_mobile_GMV|73|y|bar_chart Quarter|Q4_'15|x|bar_chart Percentage_of_mobile_GMV|68|y|bar_chart Quarter|Q3_'15|x|bar_chart Percentage_of_mobile_GMV|62|y|bar_chart Quarter|Q2_'15|x|bar_chart Percentage_of_mobile_GMV|55|y|bar_chart Quarter|Q1_'15|x|bar_chart Percentage_of_mobile_GMV|51|y|bar_chart Quarter|Q4_'14|x|bar_chart Percentage_of_mobile_GMV|42|y|bar_chart Quarter|Q3_'14|x|bar_chart Percentage_of_mobile_GMV|36|y|bar_chart Quarter|Q2_'14|x|bar_chart Percentage_of_mobile_GMV|33|y|bar_chart Quarter|Q1_'14|x|bar_chart Percentage_of_mobile_GMV|27|y|bar_chart Quarter|Q4_'13|x|bar_chart Percentage_of_mobile_GMV|20|y|bar_chart Quarter|Q3_'13|x|bar_chart Percentage_of_mobile_GMV|15|y|bar_chart Quarter|Q2_'13|x|bar_chart Percentage_of_mobile_GMV|12|y|bar_chart Quarter|Q1_'13|x|bar_chart Percentage_of_mobile_GMV|10.7|y|bar_chart Quarter|Q4_'12|x|bar_chart Percentage_of_mobile_GMV|7.4|y|bar_chart Quarter|Q3_'12|x|bar_chart Percentage_of_mobile_GMV|5.6|y|bar_chart Quarter|Q2_'12|x|bar_chart Percentage_of_mobile_GMV|4.6|y|bar_chart 
title: Alibaba : mobile share of gross merchandise volume Q2 2012-Q2 2016

gold: This statistic gives information on the mobile share of Alibaba 's gross merchandise volume from the second quarter of 2012 to the second quarter of 2016 . Last reported quarter , mobile sales accounted for 75 percent of the group 's China retail marketplaces GMV .
gold_template: This statistic gives information on the templateYLabel[1] templateTitle[2] of templateTitle[0] 's templateTitle[3] templateTitle[4] templateTitle[5] from the second templateXLabel[0] of 2012 to the second templateXLabel[0] of templateTitle[8] . Last reported templateXLabel[0] , templateYLabel[1] sales accounted for templateYValue[max] percent of the group 's China retail marketplaces templateYLabel[2] .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] of templateYLabel[2] in the United States from the first templateXLabel[0] of templateTitle[4] to the third templateXLabel[0] of templateTitle[4] templateTitle[5] . In the most recently reported templateXLabel[0] , templateYValue[max] million templateYLabel[1] templateYLabel[2] were sold in the United States .
generated: This statistic displays the Percentage of mobile of GMV in the United States from the first Quarter of merchandise to the third Quarter of merchandise volume . In the most recently reported Quarter , 75 million mobile GMV were sold in the United States .


Example 214:
data: Year|2019_S1|x|line_chart Euro_cents_per_kilowatt-hour|16.29|y|line_chart Year|2018_S2|x|line_chart Euro_cents_per_kilowatt-hour|15.11|y|line_chart Year|2018_S1|x|line_chart Euro_cents_per_kilowatt-hour|15.31|y|line_chart Year|2017_S2|x|line_chart Euro_cents_per_kilowatt-hour|15.82|y|line_chart Year|2017_S1|x|line_chart Euro_cents_per_kilowatt-hour|15.86|y|line_chart Year|2016_S2|x|line_chart Euro_cents_per_kilowatt-hour|16.24|y|line_chart Year|2016_S1|x|line_chart Euro_cents_per_kilowatt-hour|16.28|y|line_chart Year|2015_S2|x|line_chart Euro_cents_per_kilowatt-hour|16.5|y|line_chart Year|2015_S1|x|line_chart Euro_cents_per_kilowatt-hour|16.35|y|line_chart Year|2014_S2|x|line_chart Euro_cents_per_kilowatt-hour|13.01|y|line_chart Year|2014_S1|x|line_chart Euro_cents_per_kilowatt-hour|13.65|y|line_chart Year|2013_S2|x|line_chart Euro_cents_per_kilowatt-hour|13.58|y|line_chart Year|2013_S1|x|line_chart Euro_cents_per_kilowatt-hour|13.78|y|line_chart Year|2012_S2|x|line_chart Euro_cents_per_kilowatt-hour|13.69|y|line_chart Year|2012_S1|x|line_chart Euro_cents_per_kilowatt-hour|13.82|y|line_chart Year|2011_S2|x|line_chart Euro_cents_per_kilowatt-hour|13.42|y|line_chart Year|2011_S1|x|line_chart Euro_cents_per_kilowatt-hour|11.68|y|line_chart Year|2010_S2|x|line_chart Euro_cents_per_kilowatt-hour|10.48|y|line_chart Year|2010_S1|x|line_chart Euro_cents_per_kilowatt-hour|10.49|y|line_chart 
title: Electricity prices for households in Latvia 2010 - 2019 , semi-annually

gold: This statistic shows the electricity prices for household end users in Latvia semi-annually from 2010 to 2019 . In the second half of 2018 , the average electricity price for households was 15.11 euro cents per kWh .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitle[4] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitle[4] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: This statistic shows the Electricity prices for household end users in Latvia semi-annually from 2010_S2 to 2019_S1 . In the second half of 2018_S2 , the average Electricity price for households was 15.11 Euro cents per kWh .


Example 215:
data: Year|2019_S1|x|line_chart Euro_cents_per_kilowatt-hour|17.98|y|line_chart Year|2018_S2|x|line_chart Euro_cents_per_kilowatt-hour|16.91|y|line_chart Year|2018_S1|x|line_chart Euro_cents_per_kilowatt-hour|16.71|y|line_chart Year|2017_S2|x|line_chart Euro_cents_per_kilowatt-hour|16.18|y|line_chart Year|2017_S1|x|line_chart Euro_cents_per_kilowatt-hour|16.15|y|line_chart Year|2016_S2|x|line_chart Euro_cents_per_kilowatt-hour|16.98|y|line_chart Year|2016_S1|x|line_chart Euro_cents_per_kilowatt-hour|16.98|y|line_chart Year|2015_S2|x|line_chart Euro_cents_per_kilowatt-hour|17.67|y|line_chart Year|2015_S1|x|line_chart Euro_cents_per_kilowatt-hour|17.67|y|line_chart Year|2014_S2|x|line_chart Euro_cents_per_kilowatt-hour|17.38|y|line_chart Year|2014_S1|x|line_chart Euro_cents_per_kilowatt-hour|17.38|y|line_chart Year|2013_S2|x|line_chart Euro_cents_per_kilowatt-hour|16.46|y|line_chart Year|2013_S1|x|line_chart Euro_cents_per_kilowatt-hour|16.65|y|line_chart Year|2012_S2|x|line_chart Euro_cents_per_kilowatt-hour|17.06|y|line_chart Year|2012_S1|x|line_chart Euro_cents_per_kilowatt-hour|16.96|y|line_chart Year|2011_S2|x|line_chart Euro_cents_per_kilowatt-hour|16.62|y|line_chart Year|2011_S1|x|line_chart Euro_cents_per_kilowatt-hour|16.78|y|line_chart Year|2010_S2|x|line_chart Euro_cents_per_kilowatt-hour|17.47|y|line_chart Year|2010_S1|x|line_chart Euro_cents_per_kilowatt-hour|17.26|y|line_chart 
title: Electricity prices for households in Luxembourg 2010 - 2019 , semi-annually

gold: This statistic shows the electricity prices for household end users in Luxembourg semi-annually from 2010 to 2019 . In the second half of 2018 , the average electricity price for households was 16.91 euro cents per kWh .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitle[4] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitle[4] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: This statistic shows the Electricity prices for household end users in Luxembourg semi-annually from 2010_S2 to 2019_S1 . In the second half of 2018_S2 , the average Electricity price for households was 16.91 Euro cents per kWh .


Example 216:
data: Year|2018|x|line_chart Number_of_fatalities|233|y|line_chart Year|2017|x|line_chart Number_of_fatalities|230|y|line_chart Year|2016|x|line_chart Number_of_fatalities|216|y|line_chart Year|2015|x|line_chart Number_of_fatalities|253|y|line_chart Year|2014|x|line_chart Number_of_fatalities|243|y|line_chart Year|2013|x|line_chart Number_of_fatalities|269|y|line_chart Year|2012|x|line_chart Number_of_fatalities|339|y|line_chart Year|2011|x|line_chart Number_of_fatalities|320|y|line_chart Year|2010|x|line_chart Number_of_fatalities|327|y|line_chart Year|2009|x|line_chart Number_of_fatalities|349|y|line_chart Year|2008|x|line_chart Number_of_fatalities|357|y|line_chart Year|2007|x|line_chart Number_of_fatalities|384|y|line_chart Year|2006|x|line_chart Number_of_fatalities|370|y|line_chart 
title: Number of road deaths in Switzerland 2006 - 2018

gold: In 2018 , 233 fatalities were recorded on Swiss roads . Between 2006 and 2018 , traffic related deaths declined by over one third , with the lowest number seen in 2016 at 216 such incidences . Switzerland was one of the safest countries in Europe for road users .
gold_template: In templateXValue[max] , templateYValue[0] templateYLabel[1] were recorded on Swiss roads . Between templateXValue[min] and templateXValue[max] , traffic related templateTitle[2] declined by over one third , with the lowest templateYLabel[0] seen in templateXValue[2] at templateYValue[min] such incidences . templateTitle[3] was one of the safest countries in Europe for templateTitle[1] users .

generated_template: In templateXValue[max] , there were templateYValue[min] recorded traffic templateYLabel[1] in templateTitle[3] . The Scandinavian country had seen the templateYLabel[0] of templateTitle[1] templateTitle[2] drop significantly in the last ten years and by templateXValue[max] reported 20 incidents per one million inhabitants . This was the lowest templateTitle[1] fatality prevalence in Europe .
generated: In 2018 , there were 216 recorded traffic fatalities in Switzerland . The Scandinavian country had seen the Number of road deaths drop significantly in the last ten years and by 2018 reported 20 incidents per one million inhabitants . This was the lowest road fatality prevalence in Europe .


Example 217:
data: Fiscal_year|2019|x|bar_chart Operating_income_in_billion_U.S._dollars|21.96|y|bar_chart Fiscal_year|2018|x|bar_chart Operating_income_in_billion_U.S._dollars|20.44|y|bar_chart Fiscal_year|2017|x|bar_chart Operating_income_in_billion_U.S._dollars|22.76|y|bar_chart Fiscal_year|2016|x|bar_chart Operating_income_in_billion_U.S._dollars|24.11|y|bar_chart Fiscal_year|2015|x|bar_chart Operating_income_in_billion_U.S._dollars|27.15|y|bar_chart Fiscal_year|2014|x|bar_chart Operating_income_in_billion_U.S._dollars|26.87|y|bar_chart Fiscal_year|2013|x|bar_chart Operating_income_in_billion_U.S._dollars|27.73|y|bar_chart Fiscal_year|2012|x|bar_chart Operating_income_in_billion_U.S._dollars|26.49|y|bar_chart Fiscal_year|2011|x|bar_chart Operating_income_in_billion_U.S._dollars|25.51|y|bar_chart Fiscal_year|2010|x|bar_chart Operating_income_in_billion_U.S._dollars|23.97|y|bar_chart Fiscal_year|2009|x|bar_chart Operating_income_in_billion_U.S._dollars|22.77|y|bar_chart Fiscal_year|2008|x|bar_chart Operating_income_in_billion_U.S._dollars|21.92|y|bar_chart Fiscal_year|2007|x|bar_chart Operating_income_in_billion_U.S._dollars|20.5|y|bar_chart Fiscal_year|2006|x|bar_chart Operating_income_in_billion_U.S._dollars|18.69|y|bar_chart 
title: Walmart 's operating income worldwide 2006 - 2019

gold: The timeline shows Walmart 's operating income worldwide from 2006 to 2019 . In 2016 , Walmart 's global operating income amounted to about 24.11 billion U.S. dollars . Walmart , founded in 1962 , is an American multinational retailer corporation that runs chains of large discount department stores and warehouse stores .
gold_template: The timeline shows templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitle[0] templateTitle[1] global templateYLabel[0] templateYLabel[1] amounted to about templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitle[0] , founded in 1962 , is an American multinational retailer corporation that runs chains of large discount department stores and warehouse stores .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of the United States from templateXValue[min] to templateXValue[max] . In templateXValue[1] , commercial templateTitle[2] templateYLabel[0] amounted to around templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The figures are projected to have increased by around 796 templateYLabel[2] templateYLabel[3] templateYLabel[4] in that same templateXLabel[0] .
generated: This statistic shows the Walmart 's operating income of the United States from 2006 to 2019 . In 2018 , commercial operating Operating amounted to around 20.44 billion U.S. dollars . The figures are projected to have increased by around 796 billion U.S. dollars in that same Fiscal .


Example 218:
data: Year|2018|x|line_chart Return_on_equity|8.2|y|line_chart Year|2017|x|line_chart Return_on_equity|8.9|y|line_chart Year|2016|x|line_chart Return_on_equity|9.3|y|line_chart Year|2015|x|line_chart Return_on_equity|8.3|y|line_chart Year|2014|x|line_chart Return_on_equity|7.7|y|line_chart Year|2013|x|line_chart Return_on_equity|6.1|y|line_chart Year|2012|x|line_chart Return_on_equity|8.9|y|line_chart Year|2011|x|line_chart Return_on_equity|8.8|y|line_chart Year|2010|x|line_chart Return_on_equity|12.3|y|line_chart Year|2009|x|line_chart Return_on_equity|10.8|y|line_chart Year|2008|x|line_chart Return_on_equity|6.6|y|line_chart Year|2007|x|line_chart Return_on_equity|19.6|y|line_chart Year|2006|x|line_chart Return_on_equity|21.2|y|line_chart Year|2005|x|line_chart Return_on_equity|20.2|y|line_chart Year|2004|x|line_chart Return_on_equity|16.8|y|line_chart Year|2003|x|line_chart Return_on_equity|15.3|y|line_chart 
title: Return on equity of BNP Paribas 2003 - 2018

gold: The statistic shows the return on equity of BNP Paribas from 2003 to 2018 . In 2018 , the return on equity of the global banking group headquartered in Paris was equal to 8.2 percent .
gold_template: The statistic shows the templateYLabel[0] on templateYLabel[1] of templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] on templateYLabel[1] of the global banking group headquartered in Paris was equal to templateYValue[0] percent .

generated_template: In templateXValue[max] , there were templateYValue[0] million people employed in the United States . This figure had an increase from the previous templateXLabel[0] 's fact that in the previous templateXLabel[0] . Over the past few years later later in decades , the templateYLabel[0] templateYLabel[1] has remained relatively high level since templateXValue[6] , templateTitle[4] has seen steady growth in the last ten years .
generated: In 2018 , there were 8.2 million people employed in the United States . This figure had an increase from the previous Year 's fact that in the previous Year . Over the past few years later in decades , the Return equity has remained relatively high level since 2012 , 2003 has seen steady growth in the last ten years .


Example 219:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|2.01|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|1.97|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|1.97|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|2.09|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|2.06|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|2.6|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|2.53|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|1.39|y|line_chart Year|2016|x|line_chart Inflation_rate_compared_to_previous_year|-0.47|y|line_chart Year|2015|x|line_chart Inflation_rate_compared_to_previous_year|-0.33|y|line_chart Year|2014|x|line_chart Inflation_rate_compared_to_previous_year|-0.1|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|1.46|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|3.73|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|4.07|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|0.7|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|0.94|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|3.95|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|1.9|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|4.27|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|2.78|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|7.45|y|line_chart Year|2003|x|line_chart Inflation_rate_compared_to_previous_year|8.42|y|line_chart Year|2002|x|line_chart Inflation_rate_compared_to_previous_year|3.49|y|line_chart Year|2001|x|line_chart Inflation_rate_compared_to_previous_year|7.14|y|line_chart Year|2000|x|line_chart Inflation_rate_compared_to_previous_year|12.16|y|line_chart Year|1999|x|line_chart Inflation_rate_compared_to_previous_year|10.46|y|line_chart Year|1998|x|line_chart Inflation_rate_compared_to_previous_year|6.69|y|line_chart Year|1997|x|line_chart Inflation_rate_compared_to_previous_year|6.03|y|line_chart Year|1996|x|line_chart Inflation_rate_compared_to_previous_year|5.78|y|line_chart Year|1995|x|line_chart Inflation_rate_compared_to_previous_year|9.9|y|line_chart Year|1994|x|line_chart Inflation_rate_compared_to_previous_year|13.46|y|line_chart 
title: Inflation rate in Slovakia 2024

gold: This statistic shows the average inflation rate in Slovakia from 1994 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Slovakia increased about 2.53 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] increased about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Slovakia from 1994 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Slovakia amounted to about 2.53 percent compared to the previous Year .


Example 220:
data: Year|2024|x|line_chart GDP_per_capita_in_U.S._dollars|1411.77|y|line_chart Year|2023|x|line_chart GDP_per_capita_in_U.S._dollars|1328.18|y|line_chart Year|2022|x|line_chart GDP_per_capita_in_U.S._dollars|1248.69|y|line_chart Year|2021|x|line_chart GDP_per_capita_in_U.S._dollars|1161.57|y|line_chart Year|2020|x|line_chart GDP_per_capita_in_U.S._dollars|1066.2|y|line_chart Year|2019|x|line_chart GDP_per_capita_in_U.S._dollars|953.18|y|line_chart Year|2018|x|line_chart GDP_per_capita_in_U.S._dollars|852.88|y|line_chart Year|2017|x|line_chart GDP_per_capita_in_U.S._dollars|817.49|y|line_chart Year|2016|x|line_chart GDP_per_capita_in_U.S._dollars|777.29|y|line_chart Year|2015|x|line_chart GDP_per_capita_in_U.S._dollars|702.77|y|line_chart Year|2014|x|line_chart GDP_per_capita_in_U.S._dollars|613.1|y|line_chart Year|2013|x|line_chart GDP_per_capita_in_U.S._dollars|535.26|y|line_chart Year|2012|x|line_chart GDP_per_capita_in_U.S._dollars|493.32|y|line_chart Year|2011|x|line_chart GDP_per_capita_in_U.S._dollars|379.38|y|line_chart Year|2010|x|line_chart GDP_per_capita_in_U.S._dollars|360.83|y|line_chart Year|2009|x|line_chart GDP_per_capita_in_U.S._dollars|397.81|y|line_chart Year|2008|x|line_chart GDP_per_capita_in_U.S._dollars|334.15|y|line_chart Year|2007|x|line_chart GDP_per_capita_in_U.S._dollars|249.21|y|line_chart Year|2006|x|line_chart GDP_per_capita_in_U.S._dollars|198.34|y|line_chart Year|2005|x|line_chart GDP_per_capita_in_U.S._dollars|165.22|y|line_chart Year|2004|x|line_chart GDP_per_capita_in_U.S._dollars|138.55|y|line_chart Year|2003|x|line_chart GDP_per_capita_in_U.S._dollars|120.87|y|line_chart Year|2002|x|line_chart GDP_per_capita_in_U.S._dollars|116.79|y|line_chart Year|2001|x|line_chart GDP_per_capita_in_U.S._dollars|125.72|y|line_chart Year|2000|x|line_chart GDP_per_capita_in_U.S._dollars|129.68|y|line_chart Year|1999|x|line_chart GDP_per_capita_in_U.S._dollars|128.21|y|line_chart Year|1998|x|line_chart GDP_per_capita_in_U.S._dollars|134.23|y|line_chart Year|1997|x|line_chart GDP_per_capita_in_U.S._dollars|152.17|y|line_chart Year|1996|x|line_chart GDP_per_capita_in_U.S._dollars|155.12|y|line_chart Year|1995|x|line_chart GDP_per_capita_in_U.S._dollars|152.66|y|line_chart Year|1994|x|line_chart GDP_per_capita_in_U.S._dollars|151.97|y|line_chart Year|1993|x|line_chart GDP_per_capita_in_U.S._dollars|176.47|y|line_chart Year|1992|x|line_chart GDP_per_capita_in_U.S._dollars|293.35|y|line_chart Year|1991|x|line_chart GDP_per_capita_in_U.S._dollars|287.65|y|line_chart Year|1990|x|line_chart GDP_per_capita_in_U.S._dollars|269.08|y|line_chart Year|1989|x|line_chart GDP_per_capita_in_U.S._dollars|262.23|y|line_chart Year|1988|x|line_chart GDP_per_capita_in_U.S._dollars|257.56|y|line_chart Year|1987|x|line_chart GDP_per_capita_in_U.S._dollars|256.7|y|line_chart Year|1986|x|line_chart GDP_per_capita_in_U.S._dollars|247.98|y|line_chart Year|1985|x|line_chart GDP_per_capita_in_U.S._dollars|246.51|y|line_chart Year|1984|x|line_chart GDP_per_capita_in_U.S._dollars|217.42|y|line_chart 
title: Gross domestic product ( GDP ) per capita in Ethiopia 2024

gold: The statistic shows gross domestic product ( GDP ) per capita in Ethiopia from 1984 to 2024 . All figures are estimates . GDP is the total value of all goods and services produced in a country in a year .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] from templateXValue[min] to templateXValue[max] . All figures are estimates . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in Ethiopia 2024 from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a Year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .


Example 221:
data: Sex|Male|x|bar_chart Share_of_respondents|18.4|y|bar_chart Sex|Female|x|bar_chart Share_of_respondents|15.1|y|bar_chart 
title: Marijuana and cannabis consumption in the past three months Canada by gender 2019

gold: As of December 2019 , some 18.4 percent of surveyed males had consumed marijuana or cannabis in the past three months in Canada . In comparison , 15.1 percent of female respondents had used the recently legalized drug in the same time period . The legalization of cannabis in Canada Following a similar trend in the United States , the legalization of recreational marijuana in Canada has become a hot topic in politics and in the public realm .
gold_template: As of December templateTitle[9] , some templateYValue[max] percent of surveyed males had consumed templateTitle[0] or templateTitle[1] in the templateTitle[3] three templateTitle[5] in templateTitle[6] . In comparison , templateYValue[min] percent of templateXValue[last] templateYLabel[1] had used the recently legalized drug in the same time period . The legalization of templateTitle[1] in templateTitle[6] Following a similar trend in the United States , the legalization of recreational templateTitle[0] in templateTitle[6] has become a hot topic in politics and in the public realm .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] in the United States as of January templateTitle[4] , sorted templateTitle[5] templateTitle[6] . During the survey period it was found that templateYValue[max] percent of templateTitle[2] templateTitle[0] templateYLabel[1] were templateXValue[0] .
generated: This statistic gives information on the Marijuana cannabis in the United States as of January three , sorted months Canada . During the survey period it was found that 18.4 percent of consumption Marijuana respondents were Male .


Example 222:
data: Year|2019|x|line_chart Average_price_in_U.S._dollars|29.91|y|line_chart Year|2018|x|line_chart Average_price_in_U.S._dollars|29.71|y|line_chart Year|2017|x|line_chart Average_price_in_U.S._dollars|30.57|y|line_chart Year|2015|x|line_chart Average_price_in_U.S._dollars|27.86|y|line_chart Year|2014|x|line_chart Average_price_in_U.S._dollars|27.14|y|line_chart Year|2013|x|line_chart Average_price_in_U.S._dollars|26.18|y|line_chart Year|2012|x|line_chart Average_price_in_U.S._dollars|26.57|y|line_chart Year|2011|x|line_chart Average_price_in_U.S._dollars|26.99|y|line_chart Year|2010|x|line_chart Average_price_in_U.S._dollars|25.87|y|line_chart Year|2009|x|line_chart Average_price_in_U.S._dollars|25.85|y|line_chart Year|2008|x|line_chart Average_price_in_U.S._dollars|28.96|y|line_chart Year|2007|x|line_chart Average_price_in_U.S._dollars|27.63|y|line_chart Year|2006|x|line_chart Average_price_in_U.S._dollars|24.36|y|line_chart 
title: Average price of an acrylic fill in nail salons in the U.S. 2006 - 2019

gold: The average price of an acrylic fill in nail salons in the United States came at just under 30 U.S. dollars in 2019 . Many different types of manicures are offered at nail salons . One type of manicure is the acrylic nail .
gold_template: The templateYLabel[0] templateYLabel[1] of an templateTitle[2] templateTitle[3] in templateTitle[4] templateTitle[5] in the templateTitle[6] came at just under templateYValue[0] templateYLabel[2] templateYLabel[3] in templateXValue[max] . Many different types of manicures are offered at templateTitle[4] templateTitle[5] . One type of manicure is the templateTitle[2] templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] was at templateYValue[0] templateYLabel[2] templateYLabel[3] in templateTitle[5] .
generated: This statistic shows the Average price U.S. of fill nail in salons from 2006 to 2019 . In 2019 , the Average price of acrylic fill nail was at 29.91 U.S. dollars in salons .


Example 223:
data: Year|2018|x|line_chart Turnover_in_million_euros|1360|y|line_chart Year|2017|x|line_chart Turnover_in_million_euros|1515|y|line_chart Year|2016|x|line_chart Turnover_in_million_euros|1676|y|line_chart Year|2015|x|line_chart Turnover_in_million_euros|1702|y|line_chart Year|2014|x|line_chart Turnover_in_million_euros|1644|y|line_chart Year|2013|x|line_chart Turnover_in_million_euros|1650|y|line_chart Year|2012|x|line_chart Turnover_in_million_euros|1584|y|line_chart Year|2011|x|line_chart Turnover_in_million_euros|1275|y|line_chart 
title: Turnover of Italian fashion company Giorgio Armani 2011 - 2018

gold: The turnover of the Italian luxury fashion company Giorgio Armani S.p.A. amounted to 1.3 billion euros in 2018 . This figure represents a decrease compared to the peak reached by the company in 2015 , when the turnover reported amounted to 1.7 billion euros . The reduction in turnover coincided with lower profits for the company during the same period .
gold_template: The templateYLabel[0] of the templateTitle[1] luxury templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] S.p.A. amounted to templateYValue[min] billion templateYLabel[2] in templateXValue[max] . This figure represents a decrease compared to the peak reached by the templateTitle[3] in templateXValue[3] , when the templateYLabel[0] reported amounted to templateYValue[2] billion templateYLabel[2] . The reduction in templateYLabel[0] coincided with lower profits for the templateTitle[3] during the same period .

generated_template: The templateYLabel[0] of the templateTitle[1] luxury brand templateTitle[4] templateTitle[5] has increased twofold over the period surveyed , growing from roughly templateYValue[min] templateYLabel[1] templateYLabel[2] in templateXValue[min] to templateYValue[max] templateYLabel[1] templateYLabel[2] in the templateXLabel[0] templateXValue[max] . Despite the steady increase in templateYLabel[0] during the period considered , the templateTitle[3] reported a net loss of approximately 25 templateYLabel[1] templateYLabel[2] in templateXValue[max] .
generated: The Turnover of the Italian luxury brand Giorgio Armani has increased twofold over the period surveyed , growing from roughly 1275 million euros in 2011 to 1702 million euros in the Year 2018 . Despite the steady increase in Turnover during the period considered , the company reported a net loss of approximately 25 million euros in 2018 .


Example 224:
data: Country|Brazil|x|bar_chart Value_in_million_U.S._dollars|981.0|y|bar_chart Country|Argentina|x|bar_chart Value_in_million_U.S._dollars|699.0|y|bar_chart Country|Uruguay|x|bar_chart Value_in_million_U.S._dollars|373.0|y|bar_chart Country|Colombia|x|bar_chart Value_in_million_U.S._dollars|251.1|y|bar_chart Country|Mexico|x|bar_chart Value_in_million_U.S._dollars|154.6|y|bar_chart Country|Costa_Rica|x|bar_chart Value_in_million_U.S._dollars|40.15|y|bar_chart Country|Peru|x|bar_chart Value_in_million_U.S._dollars|38.53|y|bar_chart Country|Panama|x|bar_chart Value_in_million_U.S._dollars|8.23|y|bar_chart 
title: 2018 FIFA World Cup : most valued Latin American teams

gold: The statistic presents the market value of all Latin American soccer teams participating in the 2018 FIFA World Cup in Russia . The Brazilian squad was the Latin American team with the highest market value , amounting to 981 million euros . Argentina ranked second , with a market value of 699 million euros .
gold_template: The statistic presents the market templateYLabel[0] of all templateTitle[6] templateTitle[7] soccer templateTitle[8] participating in the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in Russia . The Brazilian squad was the templateTitle[6] templateTitle[7] team with the highest market templateYLabel[0] , amounting to templateYValue[max] templateYLabel[1] euros . templateXValue[1] ranked second , with a market templateYLabel[0] of templateYValue[1] templateYLabel[1] euros .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] templateYLabel[2] of templateTitle[5] as of templateTitle[7] , templateTitle[7] . During the survey period , it was found that templateYValue[min] percent of the templateYLabel[1] templateYLabel[2] templateTitle[3] .
generated: This statistic shows the 2018 Value of World Cup most million U.S. of valued as of American , American . During the survey period , it was found that 8.23 percent of the million U.S. Cup .


Example 225:
data: Year|2018|x|line_chart Revenue_in_billion_euros|43.52|y|line_chart Year|2017|x|line_chart Revenue_in_billion_euros|40.25|y|line_chart Year|2016|x|line_chart Revenue_in_billion_euros|38.07|y|line_chart Year|2015|x|line_chart Revenue_in_billion_euros|38.5|y|line_chart Year|2014|x|line_chart Revenue_in_billion_euros|38.7|y|line_chart Year|2013|x|line_chart Revenue_in_billion_euros|40.34|y|line_chart Year|2012|x|line_chart Revenue_in_billion_euros|38.63|y|line_chart Year|2011|x|line_chart Revenue_in_billion_euros|36.96|y|line_chart Year|2010|x|line_chart Revenue_in_billion_euros|33.38|y|line_chart Year|2009|x|line_chart Revenue_in_billion_euros|30.74|y|line_chart 
title: Vinci Group - revenue 2009 - 2018

gold: The statistic represents Vinci 's total revenue from the fiscal year of 2009 to the fiscal year of 2018 . In the fiscal year of 2018 , the French concessions and construction company generated around 43.5 billion euros ( or about 47.8 billion US dollars ) in revenue .
gold_template: The statistic represents templateTitle[0] 's total templateYLabel[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the French concessions and construction company generated around templateYValue[max] templateYLabel[1] templateYLabel[2] ( or about 47.8 templateYLabel[1] US dollars ) in templateYLabel[0] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the templateXValue[max] fiscal templateXLabel[0] , templateTitle[0] generated around templateYValue[max] templateYLabel[1] templateYLabel[2] ( or about 27 templateYLabel[1] templateYLabel[2] in templateYLabel[0] .
generated: The statistic shows Vinci Group Revenue from the fiscal Year of 2009 to the fiscal Year of 2018 . In the 2018 fiscal Year , Vinci generated around 43.52 billion euros ( or about 27 billion euros in Revenue .


Example 226:
data: Year|2028|x|line_chart Spending_in_billion_euros|26.4|y|line_chart Year|2018|x|line_chart Spending_in_billion_euros|19.4|y|line_chart Year|2017|x|line_chart Spending_in_billion_euros|18.1|y|line_chart Year|2016|x|line_chart Spending_in_billion_euros|15.6|y|line_chart Year|2015|x|line_chart Spending_in_billion_euros|14.5|y|line_chart Year|2014|x|line_chart Spending_in_billion_euros|13.9|y|line_chart Year|2013|x|line_chart Spending_in_billion_euros|12.8|y|line_chart Year|2012|x|line_chart Spending_in_billion_euros|12.2|y|line_chart 
title: International tourism spending in Portugal 2012 - 2028

gold: This statistic shows the total annual spend of international tourists ( visitor exports ) in Portugal from 2012 to 2018 and a forecast for 2028 . In 2017 international visitors in Portugal spent a total of 18.1 billion euros . This is expected to increase in 2018 to 19.4 billion euros .
gold_template: This statistic shows the total annual spend of templateTitle[0] tourists ( visitor exports ) in templateTitle[3] from templateXValue[min] to templateXValue[1] and a forecast for templateXValue[max] . In templateXValue[2] templateTitle[0] visitors in templateTitle[3] spent a total of templateYValue[2] templateYLabel[1] templateYLabel[2] . This is expected to increase in templateXValue[1] to templateYValue[1] templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the annual templateYLabel[0] of templateTitle[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateTitle[0] templateTitle[1] templateTitle[2] generated some templateYValue[2] templateYLabel[1] templateYLabel[2] in templateTitle[3] .
generated: The statistic shows the annual Spending of spending in Portugal from 2012 to 2028 . In 2017 , International tourism spending generated some 18.1 billion euros in Portugal .


Example 227:
data: Year|2013|x|line_chart Market_share|4.3|y|line_chart Year|2014|x|line_chart Market_share|4.2|y|line_chart Year|2015|x|line_chart Market_share|4.15|y|line_chart Year|2016|x|line_chart Market_share|4.1|y|line_chart Year|2017|x|line_chart Market_share|4.05|y|line_chart Year|2018|x|line_chart Market_share|3.95|y|line_chart Year|2019|x|line_chart Market_share|3.85|y|line_chart Year|2020|x|line_chart Market_share|3.75|y|line_chart Year|2021|x|line_chart Market_share|3.65|y|line_chart 
title: Johnson & Johnson 's share of the skin care products market worldwide 2013 - 2021

gold: This statistic shows Johnson & Johnson 's share of the skin care products market worldwide from 2013 to 2021 . In 2015 , Johnson & Johnson 's share of the global skin care products market is estimated to be 4.15 percent .
gold_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[0] templateTitle[3] templateYLabel[1] of the templateTitle[5] templateTitle[6] templateTitle[7] templateYLabel[0] templateTitle[9] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateTitle[0] templateTitle[1] templateTitle[0] templateTitle[3] templateYLabel[1] of the global templateTitle[5] templateTitle[6] templateTitle[7] templateYLabel[0] is estimated to be templateYValue[2] percent .

generated_template: In templateXValue[4] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of the United States amounted to templateYValue[max] percent . This figure is expected to grow over templateYValue[0] percent in templateXValue[max] . Despite its steady rise in the coming years , templateTitle[0] templateTitle[1] vehicles also known as high as can be seen that templateTitle[0] market , templateTitle[0] templateTitle[1] 's vehicle manufacturers manufacturers in the United States .
generated: In 2017 , Johnson & Johnson Market share of the United States amounted to 4.3 percent . This figure is expected to grow over 4.3 percent in 2021 . Despite its steady rise in the coming years , Johnson & vehicles also known as high as can be seen that Johnson market , Johnson & 's vehicle manufacturers in the United States .


Example 228:
data: Country|Japan|x|bar_chart Share_in_total_exports|5.3|y|bar_chart Country|Italy|x|bar_chart Share_in_total_exports|5.7|y|bar_chart Country|Turkey|x|bar_chart Share_in_total_exports|11.1|y|bar_chart Country|South_Korea|x|bar_chart Share_in_total_exports|11.4|y|bar_chart Country|India|x|bar_chart Share_in_total_exports|15.1|y|bar_chart Country|China|x|bar_chart Share_in_total_exports|27.5|y|bar_chart 
title: Main export partners of Iran 2017

gold: This statistic shows Iran 's main export partners in 2017 , sorted by their share in total exports . In 2017 , Iran 's main export partner was China with a share of 27.5 percent in all exports .
gold_template: This statistic shows templateTitle[3] 's templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] , sorted by their templateYLabel[0] in templateYLabel[1] templateYLabel[2] . In templateTitle[4] , templateTitle[3] 's templateTitle[0] templateTitle[1] partner was templateXValue[last] with a templateYLabel[0] of templateYValue[max] percent in all templateYLabel[2] .

generated_template: The statistic shows the estimated templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] . That year , templateTitle[4] 's templateTitle[0] templateTitle[1] partner was templateXValue[0] , accounting templateTitle[3] templateYValue[max] percent of all templateYLabel[2] .
generated: The statistic shows the estimated Main export partners Iran 2017 in titleErr . That year , 2017 's Main export partner was Japan , accounting Iran 27.5 percent of all exports .


Example 229:
data: Year|2018|x|line_chart Share_of_urban_population_in_total_population|55.67|y|line_chart Year|2017|x|line_chart Share_of_urban_population_in_total_population|55.38|y|line_chart Year|2016|x|line_chart Share_of_urban_population_in_total_population|55.1|y|line_chart Year|2015|x|line_chart Share_of_urban_population_in_total_population|54.83|y|line_chart Year|2014|x|line_chart Share_of_urban_population_in_total_population|54.58|y|line_chart Year|2013|x|line_chart Share_of_urban_population_in_total_population|54.35|y|line_chart Year|2012|x|line_chart Share_of_urban_population_in_total_population|54.13|y|line_chart Year|2011|x|line_chart Share_of_urban_population_in_total_population|53.93|y|line_chart Year|2010|x|line_chart Share_of_urban_population_in_total_population|53.74|y|line_chart Year|2009|x|line_chart Share_of_urban_population_in_total_population|53.56|y|line_chart Year|2008|x|line_chart Share_of_urban_population_in_total_population|53.37|y|line_chart 
title: Urbanization in Jamaica 2018

gold: This statistic shows the degree of urbanization in Jamaica from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 55.67 percent of Jamaica 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] templateTitle[2] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Jamaica 2018 from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 55.67 percent of Jamaica 2018 's total population lived in urban areas and cities .


Example 230:
data: Country|United_States|x|bar_chart Share_of_traffic|49.57|y|bar_chart Country|United_Kingdom|x|bar_chart Share_of_traffic|7.79|y|bar_chart Country|Canada|x|bar_chart Share_of_traffic|7.75|y|bar_chart Country|Australia|x|bar_chart Share_of_traffic|4.28|y|bar_chart Country|Germany|x|bar_chart Share_of_traffic|3.27|y|bar_chart 
title: Distribution of Reddit.com traffic 2019 , by country

gold: Reddit is one of the world 's most popular websites and as of October 2019 , the United States generated 49.57 percent of desktop traffic to the forum site . Reddit in the United States In August 2019 , Reddit accounted for over less than one percent of social media website traffic in the United States , still ranking the platform one of the leading social networks based on visits . Founded in 2005 , Reddit is a discussion website which enables users to aggregate news by posting links and let other users vote and comment on them .
gold_template: Reddit is one of the world 's most popular websites and as of October templateTitle[3] , the templateXValue[0] templateXValue[0] generated templateYValue[max] percent of desktop templateYLabel[1] to the forum site . Reddit in the templateXValue[0] templateXValue[0] In August templateTitle[3] , Reddit accounted for over less than one percent of social media website templateYLabel[1] in the templateXValue[0] templateXValue[0] , still ranking the platform one of the leading social networks based on visits . Founded in 2005 , Reddit is a discussion website which enables users to aggregate news templateTitle[4] posting links and let other users vote and comment on them .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in the last templateYValue[min] months . As of October templateTitle[3] , the templateXValue[0] templateXValue[0] accounted for templateYValue[max] percent of templateYLabel[1] to the website and templateXValue[2] accounted for templateYValue[2] percent of templateYLabel[1] .
generated: This statistic shows the Distribution of Reddit.com traffic traffic in the last 3.27 months . As of October 2019 , the United_States accounted for 49.57 percent of traffic to the website and Canada accounted for 7.75 percent of traffic .


Example 231:
data: Year|2018|x|line_chart Revenue_in_million_U.S._dollars|456|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|434|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|421|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|391|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|347|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|299|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|282|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|276|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|259|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|242|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|232|y|line_chart Year|2007|x|line_chart Revenue_in_million_U.S._dollars|218|y|line_chart Year|2006|x|line_chart Revenue_in_million_U.S._dollars|197|y|line_chart Year|2005|x|line_chart Revenue_in_million_U.S._dollars|194|y|line_chart Year|2004|x|line_chart Revenue_in_million_U.S._dollars|189|y|line_chart Year|2003|x|line_chart Revenue_in_million_U.S._dollars|138|y|line_chart Year|2002|x|line_chart Revenue_in_million_U.S._dollars|152|y|line_chart Year|2001|x|line_chart Revenue_in_million_U.S._dollars|132|y|line_chart 
title: Revenue of the Green Bay Packers ( NFL ) 2001 - 2018

gold: The statistic depicts the revenue of the Green Bay Packers , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Green Bay Packers was 456 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitle[0] templateTitle[1] are owned by the Ricketts family , who bought the franchise for 700 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[9] .
generated: The statistic depicts the Revenue of the Revenue Green from 2001 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 456 million U.S. dollars.The Revenue Green are owned by the Ricketts family , who bought the franchise for 700 million U.S. dollars in 2009 .


Example 232:
data: Year|2018|x|line_chart Revenue_in_million_U.S._dollars|438|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|417|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|403|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|378|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|345|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|304|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|292|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|279|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|262|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|255|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|240|y|line_chart Year|2007|x|line_chart Revenue_in_million_U.S._dollars|226|y|line_chart Year|2006|x|line_chart Revenue_in_million_U.S._dollars|205|y|line_chart Year|2005|x|line_chart Revenue_in_million_U.S._dollars|201|y|line_chart Year|2004|x|line_chart Revenue_in_million_U.S._dollars|192|y|line_chart Year|2003|x|line_chart Revenue_in_million_U.S._dollars|172|y|line_chart Year|2002|x|line_chart Revenue_in_million_U.S._dollars|155|y|line_chart Year|2001|x|line_chart Revenue_in_million_U.S._dollars|148|y|line_chart 
title: Revenue of the Baltimore Ravens ( NFL ) 2001 - 2018

gold: The statistic depicts the revenue of the Baltimore Ravens , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Baltimore Ravens was 438 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitle[1] templateTitle[2] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[1] templateTitle[2] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Baltimore Ravens NFL , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the Revenue of the Baltimore Ravens NFL was 438 million U.S. dollars .


Example 233:
data: Year|2008|x|line_chart GDP_in_million_Danish_kroner|1801470|y|line_chart Year|2009|x|line_chart GDP_in_million_Danish_kroner|1722143|y|line_chart Year|2010|x|line_chart GDP_in_million_Danish_kroner|1810926|y|line_chart Year|2011|x|line_chart GDP_in_million_Danish_kroner|1846854|y|line_chart Year|2012|x|line_chart GDP_in_million_Danish_kroner|1895002|y|line_chart Year|2013|x|line_chart GDP_in_million_Danish_kroner|1929677|y|line_chart Year|2014|x|line_chart GDP_in_million_Danish_kroner|1981165|y|line_chart Year|2015|x|line_chart GDP_in_million_Danish_kroner|2036356|y|line_chart Year|2016|x|line_chart GDP_in_million_Danish_kroner|2107808|y|line_chart Year|2017|x|line_chart GDP_in_million_Danish_kroner|2175106|y|line_chart Year|2018|x|line_chart GDP_in_million_Danish_kroner|2245954|y|line_chart 
title: Gross domestic product ( GDP ) at current prices in Denmark 2008 - 2018

gold: The statistic shows the gross domestic product ( GDP ) in Denmark from 2008 to 2018 . From 2009 and onwards , the GDP increased on an annual basis . As of 2018 , it amounted to around 2,246 billion Danish kroner .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitle[6] from templateXValue[min] to templateXValue[max] . From templateXValue[1] and onwards , the templateYLabel[0] increased on an annual basis . As of templateXValue[max] , it amounted to around templateYValue[max] billion templateYLabel[2] templateYLabel[3] .

generated_template: U.S. fashion fashion retailer templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] 100,000 of templateYValue[0] in templateXValue[max] , down from templateYValue[1] percent in the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] were recorded at templateYValue[max] percent in templateTitle[3] templateTitle[4] templateTitle[5] U.S. dollars at least once since many templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateXValue[max] . templateTitle[2] templateYLabel[2] rose a decline of at least 2024 .
generated: U.S. fashion retailer GDP current prices in Danish 100,000 of 1801470 in 2018 , down from 1722143 percent in the previous Year . The GDP million were recorded at 2245954 percent in GDP current prices U.S. dollars at least once since many product GDP current prices in 2018 . product Danish rose a decline of at least 2024 .


Example 234:
data: Year|2017|x|line_chart Percentage_of_individuals_using_the_internet|13.78|y|line_chart Year|2016|x|line_chart Percentage_of_individuals_using_the_internet|11.47|y|line_chart Year|2015|x|line_chart Percentage_of_individuals_using_the_internet|5.3|y|line_chart Year|2014|x|line_chart Percentage_of_individuals_using_the_internet|5.83|y|line_chart Year|2013|x|line_chart Percentage_of_individuals_using_the_internet|5.05|y|line_chart Year|2012|x|line_chart Percentage_of_individuals_using_the_internet|4.35|y|line_chart Year|2011|x|line_chart Percentage_of_individuals_using_the_internet|3.33|y|line_chart Year|2010|x|line_chart Percentage_of_individuals_using_the_internet|2.26|y|line_chart Year|2009|x|line_chart Percentage_of_individuals_using_the_internet|1.07|y|line_chart Year|2008|x|line_chart Percentage_of_individuals_using_the_internet|0.7|y|line_chart Year|2007|x|line_chart Percentage_of_individuals_using_the_internet|0.97|y|line_chart Year|2006|x|line_chart Percentage_of_individuals_using_the_internet|0.43|y|line_chart Year|2005|x|line_chart Percentage_of_individuals_using_the_internet|0.38|y|line_chart Year|2004|x|line_chart Percentage_of_individuals_using_the_internet|0.35|y|line_chart Year|2003|x|line_chart Percentage_of_individuals_using_the_internet|0.28|y|line_chart Year|2002|x|line_chart Percentage_of_individuals_using_the_internet|0.22|y|line_chart Year|2001|x|line_chart Percentage_of_individuals_using_the_internet|0.16|y|line_chart Year|2000|x|line_chart Percentage_of_individuals_using_the_internet|0.13|y|line_chart 
title: Malawi : internet penetration 2000 - 2017

gold: This statistic gives information on the internet penetration in Malawi from 2000 to 2017 . In 2017 , 13.78 percent of the population accessed the internet , up from 0.13 percent in 2000 . In 2015 , Malawi 's population amounted to 18.11 million inhabitants .
gold_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the population accessed the templateYLabel[3] , up from templateYValue[min] percent in templateXValue[min] . In templateXValue[2] , templateTitle[0] 's population amounted to 18.11 million inhabitants .

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In the most recently measured period , templateYValue[max] percent of the population accessed the templateYLabel[3] , up from templateYValue[min] percent in templateXValue[min] . In templateXValue[2] , templateTitle[0] 's population increased by approximately 2.48 percent compared to the previous templateXLabel[0] .
generated: This statistic gives information on the internet penetration in Malawi from 2000 to 2017 . In the most recently measured period , 13.78 percent of the population accessed the internet , up from 0.13 percent in 2000 . In 2015 , Malawi 's population increased by approximately 2.48 percent compared to the previous Year .


Example 235:
data: Year|2018|x|line_chart Deaths_per_1,000_live_births|63.7|y|line_chart Year|2017|x|line_chart Deaths_per_1,000_live_births|63.7|y|line_chart Year|2016|x|line_chart Deaths_per_1,000_live_births|63.7|y|line_chart Year|2015|x|line_chart Deaths_per_1,000_live_births|63.7|y|line_chart Year|2014|x|line_chart Deaths_per_1,000_live_births|63.7|y|line_chart Year|2013|x|line_chart Deaths_per_1,000_live_births|64.0|y|line_chart Year|2012|x|line_chart Deaths_per_1,000_live_births|64.9|y|line_chart Year|2011|x|line_chart Deaths_per_1,000_live_births|66.9|y|line_chart Year|2010|x|line_chart Deaths_per_1,000_live_births|69.7|y|line_chart Year|2009|x|line_chart Deaths_per_1,000_live_births|72.8|y|line_chart Year|2008|x|line_chart Deaths_per_1,000_live_births|76.1|y|line_chart 
title: Infant mortality rate in South Sudan 2018

gold: The statistic shows the infant mortality rate in South Sudan from 2008 to 2018 . In 2018 , the infant mortality rate in South Sudan was at about 63.7 deaths per 1,000 live births .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[3] templateTitle[4] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[3] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality rate in South from 2008 to 2018 . In 2018 , the Infant mortality rate in South was at about 63.7 Deaths per 1,000 live births .


Example 236:
data: Year|2019|x|line_chart Production_in_thousand_metric_tons|1100|y|line_chart Year|2018|x|line_chart Production_in_thousand_metric_tons|1150|y|line_chart Year|2017|x|line_chart Production_in_thousand_metric_tons|1170|y|line_chart Year|2016|x|line_chart Production_in_thousand_metric_tons|1280|y|line_chart Year|2015|x|line_chart Production_in_thousand_metric_tons|2000|y|line_chart Year|2014|x|line_chart Production_in_thousand_metric_tons|2020|y|line_chart Year|2013|x|line_chart Production_in_thousand_metric_tons|2020|y|line_chart Year|2012|x|line_chart Production_in_thousand_metric_tons|1970|y|line_chart Year|2011|x|line_chart Production_in_thousand_metric_tons|2030|y|line_chart Year|2010|x|line_chart Production_in_thousand_metric_tons|2010|y|line_chart Year|2009|x|line_chart Production_in_thousand_metric_tons|2070|y|line_chart Year|2008|x|line_chart Production_in_thousand_metric_tons|2090|y|line_chart Year|2007|x|line_chart Production_in_thousand_metric_tons|2200|y|line_chart 
title: Global mine production of asbestos 2007 - 2019

gold: This statistic depicts the total world mine production of asbestos from 2007 to 2019 . The world mine production of asbestos amounted to approximately 2.2 million metric tons in 2007 . In 2019 , total production dropped just above 1.1 million metric tons .
gold_template: This statistic depicts the total world templateTitle[1] templateYLabel[0] of templateTitle[3] from templateXValue[min] to templateXValue[max] . The world templateTitle[1] templateYLabel[0] of templateTitle[3] amounted to approximately templateYValue[max] million templateYLabel[2] templateYLabel[3] in templateXValue[min] . In templateXValue[max] , total templateYLabel[0] dropped just above templateYValue[min] million templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the total amount of templateTitle[2] mined in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] thousand templateYLabel[2] templateYLabel[3] of templateTitle[2] was mined across the country . Since then , this figure increased to some templateYValue[max] thousand in templateXValue[max] .
generated: This statistic shows the total amount of production mined in the Global from 2007 to 2019 . In 2017 , 1170 thousand metric tons of production was mined across the country . Since then , this figure increased to some 2200 thousand in 2019 .


Example 237:
data: Platform|Television|x|bar_chart Share_of_time_spent|70|y|bar_chart Platform|PC/laptop|x|bar_chart Share_of_time_spent|15|y|bar_chart Platform|Mobile|x|bar_chart Share_of_time_spent|10|y|bar_chart Platform|Tablet|x|bar_chart Share_of_time_spent|5|y|bar_chart 
title: Netflix content watching worldwide by device 2017

gold: The figure shows the share of time spent watching Netflix worldwide in 2017 , by device . According to the source , 70 percent of content streamed on Netflix worldwide in 2017 was via a connected TV .
gold_template: The figure shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] templateTitle[0] templateTitle[3] in templateTitle[6] , templateTitle[4] templateTitle[5] . According to the source , templateYValue[max] percent of templateTitle[1] streamed on templateTitle[0] templateTitle[3] in templateTitle[6] was via a connected TV .

generated_template: This statistic presents the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] employed templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] in the United States as of September templateTitle[8] . During the survey period , it was found that templateYValue[max] percent of templateXValue[0] app had an increase compared to the previous year .
generated: This statistic presents the Share of content watching worldwide by employed device 2017 titleErr titleErr in the United States as of September titleErr . During the survey period , it was found that 70 percent of Television app had an increase compared to the previous year .


Example 238:
data: Year|18-29|x|line_chart Share_of_respondents|81|y|line_chart Year|30-49|x|line_chart Share_of_respondents|72|y|line_chart Year|50-64|x|line_chart Share_of_respondents|67|y|line_chart Year|65+|x|line_chart Share_of_respondents|68|y|line_chart 
title: Book readers in the U.S. 2019 , by age group

gold: During a survey held in early 2019 , it was found that 81 percent of adults aged between 18 and 29 years old had read a book in any format in the previous year . The survey results showed that adults within this age category were more likely than older respondents to have read a book within the last twelve months . Book readers in the U.S. – additional information While it is mostly believed that book reading is a vanishing pastime , particularly among Millennials , surveys among consumers in the U.S. have shown the opposite .
gold_template: During a survey held in early templateTitle[3] , it was found that templateYValue[max] percent of adults aged between 18 and 29 years old had read a templateTitle[0] in any format in the previous templateXLabel[0] . The survey results showed that adults within this templateTitle[5] category were more likely than older templateYLabel[1] to have read a templateTitle[0] within the last twelve months . templateTitle[0] templateTitle[1] in the templateTitle[2] – additional information While it is mostly believed that templateTitle[0] reading is a vanishing pastime , particularly among Millennials , surveys among consumers in the templateTitle[2] have shown the opposite .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitle[3] templateTitle[4] who were using templateTitle[0] as of February templateTitle[5] , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] percent of templateYLabel[1] between 30 and 29 years used the social networking site .
generated: This statistic shows the Share of adults in the 2019 by who were using Book as of February age , sorted group titleErr titleErr . During that period of time , 81 percent of respondents between 30 and 29 years used the social networking site .


Example 239:
data: Platform|Facebook|x|bar_chart Share_of_daily_active_users|63.7|y|bar_chart Platform|Instagram|x|bar_chart Share_of_daily_active_users|58.8|y|bar_chart Platform|Snapchat|x|bar_chart Share_of_daily_active_users|55.3|y|bar_chart Platform|Facebook_Messenger|x|bar_chart Share_of_daily_active_users|54.9|y|bar_chart Platform|WhatsApp|x|bar_chart Share_of_daily_active_users|46.2|y|bar_chart Platform|Reddit|x|bar_chart Share_of_daily_active_users|43.8|y|bar_chart Platform|TikTok|x|bar_chart Share_of_daily_active_users|40|y|bar_chart Platform|Twitter|x|bar_chart Share_of_daily_active_users|38.3|y|bar_chart Platform|Tumblr|x|bar_chart Share_of_daily_active_users|23.6|y|bar_chart Platform|Pinterest|x|bar_chart Share_of_daily_active_users|19.3|y|bar_chart 
title: Daily engagement rate of U.S. mobile social users 2019

gold: As of December 2019 , it was found that 46.2 percent of U.S. Android users who had installed WhatsApp were also daily active users . According to App Ape , mobile Facebook app audiences in the United States showed the highest daily app engagement rate with almost 64 percent .
gold_template: As of December templateTitle[7] , it was found that templateYValue[4] percent of templateTitle[3] Android templateYLabel[3] who had installed templateXValue[4] were also templateYLabel[1] templateYLabel[2] templateYLabel[3] . According to App Ape , templateTitle[4] templateXValue[0] app audiences in the templateTitle[3] showed the highest templateYLabel[1] app templateTitle[1] templateTitle[2] with almost templateYValue[max] percent .

generated_template: This statistic gives information on the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] . During the measured period , it was found that templateYValue[max] percent of templateYLabel[1] had an increase from the previous year .
generated: This statistic gives information on the Share daily of the Daily engagement rate U.S. mobile in social . During the measured period , it was found that 63.7 percent of daily had an increase from the previous year .


Example 240:
data: Response|Node.js|x|bar_chart Share_of_respondents|49.9|y|bar_chart Response|.NET|x|bar_chart Share_of_respondents|37.4|y|bar_chart Response|.NET_Core|x|bar_chart Share_of_respondents|23.7|y|bar_chart Response|Pandas|x|bar_chart Share_of_respondents|12.7|y|bar_chart Response|Unity_3D|x|bar_chart Share_of_respondents|11.3|y|bar_chart Response|React_Native|x|bar_chart Share_of_respondents|10.5|y|bar_chart Response|TensorFlow|x|bar_chart Share_of_respondents|10.3|y|bar_chart Response|Ansible|x|bar_chart Share_of_respondents|9.4|y|bar_chart Response|Cordova|x|bar_chart Share_of_respondents|7.1|y|bar_chart Response|Xamarin|x|bar_chart Share_of_respondents|6.5|y|bar_chart Response|Apache_Spark|x|bar_chart Share_of_respondents|5.8|y|bar_chart Response|Hadoop|x|bar_chart Share_of_respondents|4.9|y|bar_chart Response|Unreal_Engine|x|bar_chart Share_of_respondents|3.5|y|bar_chart Response|Flutter|x|bar_chart Share_of_respondents|3.4|y|bar_chart Response|Torch/PyTorch|x|bar_chart Share_of_respondents|3.3|y|bar_chart Response|Puppet|x|bar_chart Share_of_respondents|2.7|y|bar_chart Response|Chef|x|bar_chart Share_of_respondents|2.5|y|bar_chart Response|CryEngine|x|bar_chart Share_of_respondents|0.6|y|bar_chart 
title: Most utilized frameworks among developers worldwide 2019

gold: The statistic shows the most used libraries , frameworks , and tools among software developers worldwide , as of early 2019 . According to the survey , 49.9 percent of respondents used Node.js , while 37.4 percent used .NET . The least used framework was CryEngine with only 0.6 percent of respondents reporting to use it .
gold_template: The statistic shows the templateTitle[0] used libraries , templateTitle[2] , and tools templateTitle[3] software templateTitle[4] templateTitle[5] , as of early templateTitle[6] . According to the survey , templateYValue[max] percent of templateYLabel[1] used templateXValue[0] , while templateYValue[1] percent used templateXValue[1] . The least used framework was templateXValue[last] with only templateYValue[min] percent of templateYLabel[1] reporting to use it .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] according to the United States in templateTitle[4] templateTitle[5] . During the survey , templateYValue[max] percent of templateYLabel[1] stated that they templateTitle[0] templateTitle[1] in a templateXValue[0] templateXValue[0] templateTitle[5] templateXValue[8] .
generated: This statistic shows the Most utilized in frameworks among according to the United States in developers worldwide . During the survey , 49.9 percent of respondents stated that they Most utilized in a Node.js worldwide Cordova .


Example 241:
data: Year|2019|x|line_chart Net_profit_in_million_euros|3321|y|line_chart Year|2018|x|line_chart Net_profit_in_million_euros|4083|y|line_chart Year|2017|x|line_chart Net_profit_in_million_euros|4008|y|line_chart Year|2016|x|line_chart Net_profit_in_million_euros|3646|y|line_chart Year|2015|x|line_chart Net_profit_in_million_euros|3064|y|line_chart Year|2014|x|line_chart Net_profit_in_million_euros|3280|y|line_chart Year|2013|x|line_chart Net_profit_in_million_euros|3326|y|line_chart Year|2012|x|line_chart Net_profit_in_million_euros|2803|y|line_chart Year|2011|x|line_chart Net_profit_in_million_euros|3437|y|line_chart Year|2010|x|line_chart Net_profit_in_million_euros|1813|y|line_chart Year|2009|x|line_chart Net_profit_in_million_euros|1750|y|line_chart Year|2008|x|line_chart Net_profit_in_million_euros|1847|y|line_chart Year|2007|x|line_chart Net_profit_in_million_euros|1906|y|line_chart Year|2006|x|line_chart Net_profit_in_million_euros|1871|y|line_chart 
title: SAP 's net profit 2006 - 2018

gold: This statistic shows the net profit generated by software company SAP from 2006 to 2019 . In 2019 , the profit attributable to the SAP parent company came to approximately 3.3 billion euros . That same year , SAP generated 27.55 billion euros in revenue worldwide .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] generated by software company templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] attributable to the templateTitle[0] parent company came to approximately templateYValue[0] billion templateYLabel[3] . That same templateXLabel[0] , templateTitle[0] generated 27.55 billion templateYLabel[3] in revenue worldwide .

generated_template: The Irish low-cost carrier templateTitle[0] saw an astonishing growth in the amount of templateYLabel[0] profits generated , before suffering a roughly 39 percent decline in its latest financial templateXLabel[0] . Between templateXValue[min] and templateXValue[max] , the Dublin based company was able to nearly templateYValue[0] templateYLabel[1] .
generated: The Irish low-cost carrier SAP saw an astonishing growth in the amount of Net profits generated , before suffering a roughly 39 percent decline in its latest financial Year . Between 2006 and 2019 , the Dublin based company was able to nearly 3321 profit .


Example 242:
data: Year|2018|x|line_chart Percentage_of_population|14.9|y|line_chart Year|2017|x|line_chart Percentage_of_population|14.7|y|line_chart Year|2016|x|line_chart Percentage_of_population|15.6|y|line_chart Year|2015|x|line_chart Percentage_of_population|15.9|y|line_chart Year|2014|x|line_chart Percentage_of_population|17.2|y|line_chart Year|2013|x|line_chart Percentage_of_population|17.5|y|line_chart Year|2012|x|line_chart Percentage_of_population|17.9|y|line_chart Year|2011|x|line_chart Percentage_of_population|18.5|y|line_chart Year|2010|x|line_chart Percentage_of_population|17.9|y|line_chart Year|2009|x|line_chart Percentage_of_population|17.2|y|line_chart Year|2008|x|line_chart Percentage_of_population|15.8|y|line_chart Year|2007|x|line_chart Percentage_of_population|16.3|y|line_chart Year|2006|x|line_chart Percentage_of_population|16.9|y|line_chart Year|2005|x|line_chart Percentage_of_population|17.6|y|line_chart Year|2004|x|line_chart Percentage_of_population|16.6|y|line_chart Year|2003|x|line_chart Percentage_of_population|16.3|y|line_chart Year|2002|x|line_chart Percentage_of_population|15.6|y|line_chart Year|2001|x|line_chart Percentage_of_population|15|y|line_chart Year|2000|x|line_chart Percentage_of_population|15.1|y|line_chart 
title: Texas - poverty rate 2000 - 2018

gold: This statistic shows the poverty rate in Texas from 2000 to 2018 . For instance , 14.9 percent of Texas 's population lived below the poverty line in 2018 .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . For instance , templateYValue[0] percent of templateTitle[0] 's templateYLabel[1] lived below the templateTitle[1] line in templateXValue[max] .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitle[0] templateTitle[1] 's templateYLabel[1] lived below the templateTitle[2] line .
generated: This statistic shows the rate 2000 in Texas poverty from 2000 to 2018 . In 2018 , 14.9 percent of Texas poverty 's population lived below the rate line .


Example 243:
data: Month|FIFA_19|x|bar_chart Number_of_units_sold_in_thousands|1353.4|y|bar_chart Month|Red_Dead_Redemption_2|x|bar_chart Number_of_units_sold_in_thousands|1011.0|y|bar_chart Month|Call_of_Duty:_Black_Ops_4|x|bar_chart Number_of_units_sold_in_thousands|565.0|y|bar_chart Month|Mario_Kart_8_Deluxe|x|bar_chart Number_of_units_sold_in_thousands|542.4|y|bar_chart Month|Super_Mario_Party|x|bar_chart Number_of_units_sold_in_thousands|380.3|y|bar_chart Month|Spider-Man|x|bar_chart Number_of_units_sold_in_thousands|345.3|y|bar_chart Month|Super_Smash_Bros._Ultimate|x|bar_chart Number_of_units_sold_in_thousands|335.6|y|bar_chart Month|Super_Mario_Odyssey|x|bar_chart Number_of_units_sold_in_thousands|328.7|y|bar_chart Month|Assassin’s_Creed_Odyssey|x|bar_chart Number_of_units_sold_in_thousands|322.8|y|bar_chart Month|God_of_War|x|bar_chart Number_of_units_sold_in_thousands|301.4|y|bar_chart 
title: Best selling video games on all gaming platforms in France 2018 , by sales volume

gold: This statistic represents the ten best selling video games across all gaming platforms combined in France in 2018 , by number of physical software units sold . With around 1.4 million units sold , FIFA 19 was the most sold video game in France that year .
gold_template: This statistic represents the ten templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] across templateTitle[4] templateTitle[5] templateTitle[6] combined in templateTitle[7] in templateTitle[8] , templateTitle[9] templateYLabel[0] of physical software templateYLabel[1] templateYLabel[2] . With around templateYValue[max] million templateYLabel[1] templateYLabel[2] , templateXValue[0] templateXValue[0] was the most templateYLabel[2] templateTitle[2] game in templateTitle[7] that year .

generated_template: During the year templateTitle[5] , templateXValue[0] templateXValue[0] was the most templateYLabel[1] with the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States , according to templateYValue[max] templateYLabel[0] templateYLabel[1] in the United States . The current level of people valued at 100 percent in templateXValue[6] .
generated: During the year gaming , FIFA_19 was the most units with the highest Number units sold in the United States , according to 1353.4 Number units in the United States . The current level of people valued at 100 percent in Super_Smash_Bros._Ultimate .


Example 244:
data: Year|2024|x|line_chart GDP_per_capita_in_U.S._dollars|31483.8|y|line_chart Year|2023|x|line_chart GDP_per_capita_in_U.S._dollars|29870.22|y|line_chart Year|2022|x|line_chart GDP_per_capita_in_U.S._dollars|28324.43|y|line_chart Year|2021|x|line_chart GDP_per_capita_in_U.S._dollars|26861.07|y|line_chart Year|2020|x|line_chart GDP_per_capita_in_U.S._dollars|25525.81|y|line_chart Year|2019|x|line_chart GDP_per_capita_in_U.S._dollars|24827.9|y|line_chart Year|2018|x|line_chart GDP_per_capita_in_U.S._dollars|25007.75|y|line_chart Year|2017|x|line_chart GDP_per_capita_in_U.S._dollars|24389.68|y|line_chart Year|2016|x|line_chart GDP_per_capita_in_U.S._dollars|22572.7|y|line_chart Year|2015|x|line_chart GDP_per_capita_in_U.S._dollars|22373.56|y|line_chart Year|2014|x|line_chart GDP_per_capita_in_U.S._dollars|22638.92|y|line_chart Year|2013|x|line_chart GDP_per_capita_in_U.S._dollars|21887.99|y|line_chart Year|2012|x|line_chart GDP_per_capita_in_U.S._dollars|21269.61|y|line_chart Year|2011|x|line_chart GDP_per_capita_in_U.S._dollars|20911.64|y|line_chart Year|2010|x|line_chart GDP_per_capita_in_U.S._dollars|19261.67|y|line_chart Year|2009|x|line_chart GDP_per_capita_in_U.S._dollars|16959.78|y|line_chart Year|2008|x|line_chart GDP_per_capita_in_U.S._dollars|18102.95|y|line_chart Year|2007|x|line_chart GDP_per_capita_in_U.S._dollars|17780.93|y|line_chart Year|2006|x|line_chart GDP_per_capita_in_U.S._dollars|16984.54|y|line_chart Year|2005|x|line_chart GDP_per_capita_in_U.S._dollars|16503.31|y|line_chart Year|2004|x|line_chart GDP_per_capita_in_U.S._dollars|15360.72|y|line_chart Year|2003|x|line_chart GDP_per_capita_in_U.S._dollars|14094.37|y|line_chart Year|2002|x|line_chart GDP_per_capita_in_U.S._dollars|13715.53|y|line_chart Year|2001|x|line_chart GDP_per_capita_in_U.S._dollars|13408.38|y|line_chart Year|2000|x|line_chart GDP_per_capita_in_U.S._dollars|14876.88|y|line_chart Year|1999|x|line_chart GDP_per_capita_in_U.S._dollars|13768.27|y|line_chart Year|1998|x|line_chart GDP_per_capita_in_U.S._dollars|12787.26|y|line_chart Year|1997|x|line_chart GDP_per_capita_in_U.S._dollars|13968.1|y|line_chart Year|1996|x|line_chart GDP_per_capita_in_U.S._dollars|13597.25|y|line_chart Year|1995|x|line_chart GDP_per_capita_in_U.S._dollars|13706.01|y|line_chart Year|1994|x|line_chart GDP_per_capita_in_U.S._dollars|12108.75|y|line_chart Year|1993|x|line_chart GDP_per_capita_in_U.S._dollars|11200.81|y|line_chart Year|1992|x|line_chart GDP_per_capita_in_U.S._dollars|10725.7|y|line_chart Year|1991|x|line_chart GDP_per_capita_in_U.S._dollars|9092.11|y|line_chart Year|1990|x|line_chart GDP_per_capita_in_U.S._dollars|8178.15|y|line_chart Year|1989|x|line_chart GDP_per_capita_in_U.S._dollars|7577.05|y|line_chart Year|1988|x|line_chart GDP_per_capita_in_U.S._dollars|6337.5|y|line_chart Year|1987|x|line_chart GDP_per_capita_in_U.S._dollars|5325.22|y|line_chart Year|1986|x|line_chart GDP_per_capita_in_U.S._dollars|4010.11|y|line_chart Year|1985|x|line_chart GDP_per_capita_in_U.S._dollars|3295.11|y|line_chart Year|1984|x|line_chart GDP_per_capita_in_U.S._dollars|3203.47|y|line_chart 
title: Gross domestic product ( GDP ) per capita in Taiwan 2024

gold: The statistic shows gross domestic product ( GDP ) per capita in Taiwan from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services templateYLabel[2] in a country in a templateXLabel[0] . templateYLabel[0] templateYLabel[1] templateYLabel[2] of a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth growth .
generated: The statistic shows Gross domestic product ( GDP ) per capita in Taiwan 2024 from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services capita in a country in a Year . GDP per capita of a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .


Example 245:
data: Year|17|x|line_chart Billion_U.S._dollars|33.92|y|line_chart Year|16|x|line_chart Billion_U.S._dollars|33.97|y|line_chart Year|15|x|line_chart Billion_U.S._dollars|34.14|y|line_chart Year|14|x|line_chart Billion_U.S._dollars|33.83|y|line_chart Year|13|x|line_chart Billion_U.S._dollars|33.21|y|line_chart Year|12|x|line_chart Billion_U.S._dollars|32.32|y|line_chart Year|11|x|line_chart Billion_U.S._dollars|31.25|y|line_chart Year|10|x|line_chart Billion_U.S._dollars|27.78|y|line_chart Year|9|x|line_chart Billion_U.S._dollars|27.46|y|line_chart Year|8|x|line_chart Billion_U.S._dollars|30.91|y|line_chart Year|7|x|line_chart Billion_U.S._dollars|32.77|y|line_chart Year|6|x|line_chart Billion_U.S._dollars|31.96|y|line_chart Year|5|x|line_chart Billion_U.S._dollars|30.13|y|line_chart Year|4|x|line_chart Billion_U.S._dollars|29.28|y|line_chart Year|3|x|line_chart Billion_U.S._dollars|27.17|y|line_chart Year|2|x|line_chart Billion_U.S._dollars|26.27|y|line_chart Year|1|x|line_chart Billion_U.S._dollars|25.2|y|line_chart Year|0|x|line_chart Billion_U.S._dollars|26.7|y|line_chart Year|99|x|line_chart Billion_U.S._dollars|25.38|y|line_chart Year|98|x|line_chart Billion_U.S._dollars|22.79|y|line_chart Year|97|x|line_chart Billion_U.S._dollars|21.12|y|line_chart Year|96|x|line_chart Billion_U.S._dollars|21.63|y|line_chart Year|95|x|line_chart Billion_U.S._dollars|20.28|y|line_chart Year|94|x|line_chart Billion_U.S._dollars|19.02|y|line_chart Year|93|x|line_chart Billion_U.S._dollars|17.59|y|line_chart Year|92|x|line_chart Billion_U.S._dollars|16.12|y|line_chart 
title: Luggage and leather goods store sales in the U.S. from 1992 to 2017

gold: This timeline shows jewelry , luggage , and leather goods store sales in the United States from 1992 to 2017 . In 2017 , U.S. jewelry , luggage , and leather goods store sales amounted to about 33.92 billion U.S. dollars .
gold_template: This timeline shows jewelry , templateTitle[0] , and templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] templateTitle[6] templateTitle[7] to templateTitle[8] . In templateTitle[8] , templateYLabel[1] jewelry , templateTitle[0] , and templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] from templateTitle[6] to templateTitle[7] . In templateTitle[7] , templateYLabel[1] templateTitle[0] templateTitle[1] templateTitle[2] amounted to about templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Luggage leather in goods from from to 1992 . In 1992 , U.S. Luggage leather goods amounted to about 33.92 Billion U.S. dollars .


Example 246:
data: Year|2018|x|line_chart Number_of_banks|505|y|line_chart Year|2017|x|line_chart Number_of_banks|538|y|line_chart Year|2016|x|line_chart Number_of_banks|604|y|line_chart Year|2015|x|line_chart Number_of_banks|643|y|line_chart Year|2014|x|line_chart Number_of_banks|664|y|line_chart Year|2013|x|line_chart Number_of_banks|684|y|line_chart Year|2012|x|line_chart Number_of_banks|706|y|line_chart Year|2011|x|line_chart Number_of_banks|740|y|line_chart 
title: Italy : number of banks 2011 - 2018

gold: The total number of banks and credit institutions in Italy steadily decreased from 2011 to 2018 , sinking from 740 to 505 units . Such a decrease in the number of banks in the country might be due to the financial crisis of the years 2007 and 2008 , which hit Italy quite harshly also in the following years . During the same period , the number of bank branches also decreased steadily , and , as a consequence , the population size per bank branch increased from 1,734 in 2008 to 2,067 in 2016 .
gold_template: The total templateYLabel[0] of templateYLabel[1] and credit institutions in templateTitle[0] steadily decreased from templateXValue[min] to templateXValue[max] , sinking from templateYValue[max] to templateYValue[min] units . Such a decrease in the templateYLabel[0] of templateYLabel[1] in the country might be due to the financial crisis of the years 2007 and 2008 , which hit templateTitle[0] quite harshly also in the following years . During the same period , the templateYLabel[0] of bank branches also decreased steadily , and , as a consequence , the population size per bank branch increased from 1,734 in 2008 to 2,067 in templateXValue[2] .

generated_template: In templateXValue[max] , there were templateYValue[min] templateTitle[1] templateTitle[2] templateYLabel[1] in the United States . The FDIC , of Federal Deposit Insurance Corporation , is an agency that insures the banking system in the U.S . The templateYLabel[0] of such registered templateYLabel[1] has increased annually since templateXValue[min] , when it there were over 7,800 templateTitle[1] templateYLabel[1] in the country .
generated: In 2018 , there were 505 number banks banks in the United States . The FDIC , of Federal Deposit Insurance Corporation , is an agency that insures the banking system in the U.S . The Number of such registered banks has increased annually since 2011 , when it there were over 7,800 number banks in the country .


Example 247:
data: Year|2018|x|line_chart Export_volume_in_billion_U.S._dollars|19453.36|y|line_chart Year|2017|x|line_chart Export_volume_in_billion_U.S._dollars|17731.08|y|line_chart Year|2016|x|line_chart Export_volume_in_billion_U.S._dollars|16021.98|y|line_chart Year|2015|x|line_chart Export_volume_in_billion_U.S._dollars|16539.16|y|line_chart Year|2014|x|line_chart Export_volume_in_billion_U.S._dollars|18986.54|y|line_chart Year|2013|x|line_chart Export_volume_in_billion_U.S._dollars|18950.64|y|line_chart Year|2012|x|line_chart Export_volume_in_billion_U.S._dollars|18513.19|y|line_chart Year|2011|x|line_chart Export_volume_in_billion_U.S._dollars|18339.81|y|line_chart Year|2010|x|line_chart Export_volume_in_billion_U.S._dollars|15302.68|y|line_chart Year|2009|x|line_chart Export_volume_in_billion_U.S._dollars|12556.2|y|line_chart Year|2008|x|line_chart Export_volume_in_billion_U.S._dollars|16149.3|y|line_chart Year|2007|x|line_chart Export_volume_in_billion_U.S._dollars|14021.16|y|line_chart Year|2006|x|line_chart Export_volume_in_billion_U.S._dollars|12128.03|y|line_chart Year|2005|x|line_chart Export_volume_in_billion_U.S._dollars|10502.74|y|line_chart Year|2000|x|line_chart Export_volume_in_billion_U.S._dollars|6452.32|y|line_chart Year|1995|x|line_chart Export_volume_in_billion_U.S._dollars|5176.2|y|line_chart Year|1990|x|line_chart Export_volume_in_billion_U.S._dollars|3495.69|y|line_chart Year|1985|x|line_chart Export_volume_in_billion_U.S._dollars|1964.84|y|line_chart Year|1980|x|line_chart Export_volume_in_billion_U.S._dollars|2049.41|y|line_chart Year|1975|x|line_chart Export_volume_in_billion_U.S._dollars|876.89|y|line_chart Year|1970|x|line_chart Export_volume_in_billion_U.S._dollars|318.02|y|line_chart Year|1965|x|line_chart Export_volume_in_billion_U.S._dollars|189.62|y|line_chart Year|1960|x|line_chart Export_volume_in_billion_U.S._dollars|130.09|y|line_chart Year|1955|x|line_chart Export_volume_in_billion_U.S._dollars|93.92|y|line_chart Year|1950|x|line_chart Export_volume_in_billion_U.S._dollars|61.81|y|line_chart 
title: Trade : export volume worldwide 1950 - 2018

gold: In 2018 , the global trade value of goods exported throughout the world amounted to approximately 19.5 trillion U.S. dollars . In comparison , this figure stood at around 6.45 trillion U.S. dollars in 2000 . The rise in the value of goods exported around the world reflects developments in international trade , globalization , and advances in technology .
gold_template: In templateXValue[max] , the global templateTitle[0] value of goods exported throughout the world amounted to approximately templateYValue[max] trillion templateYLabel[3] templateYLabel[4] . In comparison , this figure stood at around templateYValue[14] trillion templateYLabel[3] templateYLabel[4] in templateXValue[14] . The rise in the value of goods exported around the world reflects developments in international templateTitle[0] , globalization , and advances in technology .

generated_template: There were templateYValue[0] templateYLabel[0] templateYLabel[1] in the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States between templateXValue[min] and templateXValue[max] . In templateXValue[6] , templateYLabel[3] share of templateYLabel[1] to the templateTitle[4] templateTitle[5] amounted to templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . The highest share of templateYLabel[1] templateYLabel[2] consultancy in the U.S. dollars . templateYLabel[0] templateYLabel[1] templateYLabel[2] decreased in templateXValue[8] , when it stood at templateYValue[0] .
generated: There were 19453.36 Export volume in the volume worldwide 1950 2018 in the United States between 1950 and 2018 . In 2012 , U.S. share of volume to the 1950 2018 amounted to 19453.36 Export volume billion U.S. in 2018 . The highest share of volume billion consultancy in the U.S. dollars . Export volume billion decreased in 2010 , when it stood at 19453.36 .


Example 248:
data: Year|2019|x|line_chart Spending_in_million_U.S._dollars|1911|y|line_chart Year|2018|x|line_chart Spending_in_million_U.S._dollars|1821|y|line_chart Year|2017|x|line_chart Spending_in_million_U.S._dollars|1870|y|line_chart Year|2016|x|line_chart Spending_in_million_U.S._dollars|1735|y|line_chart Year|2015|x|line_chart Spending_in_million_U.S._dollars|1763|y|line_chart Year|2014|x|line_chart Spending_in_million_U.S._dollars|1770|y|line_chart Year|2013|x|line_chart Spending_in_million_U.S._dollars|1715|y|line_chart Year|2012|x|line_chart Spending_in_million_U.S._dollars|1634|y|line_chart Year|2011|x|line_chart Spending_in_million_U.S._dollars|1570|y|line_chart Year|2010|x|line_chart Spending_in_million_U.S._dollars|1434|y|line_chart 
title: Research and development spending of 3M from 2010 to 2019

gold: This statistic shows 3M 's spending on research , development , and related expenses from 2010 to 2019 . 3M Company , formerly known as the Minnesota Mining and Manufacturing Company , is a U.S.-based multinational company that produces some 55 thousand products , ranging from adhesives to electronic materials . 3M 's spending research and development in 2019 came to around 1.9 billion U.S. dollars .
gold_template: This statistic shows templateTitle[3] 's templateYLabel[0] on templateTitle[0] , templateTitle[1] , and related expenses templateTitle[4] templateXValue[min] to templateXValue[max] . templateTitle[3] Company , formerly known as the Minnesota Mining and Manufacturing Company , is a U.S.-based multinational company that produces some 55 thousand products , ranging templateTitle[4] adhesives to electronic materials . templateTitle[3] 's templateYLabel[0] templateTitle[0] and templateTitle[1] in templateXValue[max] came to around templateYValue[max] billion templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the worldwide templateYLabel[0] for templateTitle[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the company spent some templateYValue[8] billion templateYLabel[2] templateYLabel[3] on R & D . templateTitle[0] templateTitle[1] in templateTitle[2] is one of the leading oil and gas companies worldwide .
generated: This statistic shows the worldwide Spending for spending in the United States from 2010 to 2019 . In 2010 , the company spent some 1570 billion U.S. dollars on R & D . Research development in spending is one of the leading oil and gas companies worldwide .


Example 249:
data: Country|Nicaragua|x|bar_chart Index_score|0.81|y|bar_chart Country|Costa_Rica|x|bar_chart Index_score|0.78|y|bar_chart Country|Colombia|x|bar_chart Index_score|0.76|y|bar_chart Country|Trinidad_and_Tobago|x|bar_chart Index_score|0.76|y|bar_chart Country|Mexico|x|bar_chart Index_score|0.75|y|bar_chart Country|Barbados|x|bar_chart Index_score|0.75|y|bar_chart Country|Argentina|x|bar_chart Index_score|0.75|y|bar_chart Country|Cuba|x|bar_chart Index_score|0.75|y|bar_chart Country|Uruguay|x|bar_chart Index_score|0.74|y|bar_chart Country|Jamaica|x|bar_chart Index_score|0.74|y|bar_chart Country|Bolivia|x|bar_chart Index_score|0.73|y|bar_chart Country|Panama|x|bar_chart Index_score|0.73|y|bar_chart Country|Ecuador|x|bar_chart Index_score|0.73|y|bar_chart Country|Chile|x|bar_chart Index_score|0.72|y|bar_chart Country|Honduras|x|bar_chart Index_score|0.72|y|bar_chart Country|Bahamas|x|bar_chart Index_score|0.72|y|bar_chart Country|Peru|x|bar_chart Index_score|0.71|y|bar_chart Country|Venezuela|x|bar_chart Index_score|0.71|y|bar_chart Country|Suriname|x|bar_chart Index_score|0.71|y|bar_chart Country|El_Salvador|x|bar_chart Index_score|0.71|y|bar_chart Country|Dominican_Republic|x|bar_chart Index_score|0.7|y|bar_chart Country|Brazil|x|bar_chart Index_score|0.69|y|bar_chart Country|Paraguay|x|bar_chart Index_score|0.68|y|bar_chart Country|Belize|x|bar_chart Index_score|0.67|y|bar_chart Country|Guatemala|x|bar_chart Index_score|0.67|y|bar_chart 
title: Latin America : gender gap index 2020 , by country

gold: In 2020 , Nicaragua was the Latin American country with the highest gender gap index , with 0.81 points . Guatemala , on the other hand , had the worst score in the region with 0.67 points , which shows a gender pay gap of 33 percent ( on average , women had 33 percent less opportunities than men in Guatemala ) .
gold_template: In templateTitle[5] , templateXValue[0] was the templateTitle[0] American templateXLabel[0] with the highest templateTitle[2] templateTitle[3] templateYLabel[0] , with templateYValue[max] points . templateXValue[last] , on the other hand , had the worst templateYLabel[1] in the region with templateYValue[min] points , which shows a templateTitle[2] pay templateTitle[3] of 33 percent ( on average , women had 33 percent less opportunities than men in templateXValue[last] ) .

generated_template: In templateTitle[8] , templateXValue[0] was the templateTitle[0] American templateXLabel[0] with the highest templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] , with templateYValue[max] points . templateXValue[last] , on the other hand , had the worst templateYLabel[1] in the region with templateYValue[min] points , which shows a templateTitle[4] templateTitle[5] templateTitle[6] of 54 percent ( on average , women 's income in templateXValue[last] was estimated to be 54 percent lower than men 's ) .
generated: In titleErr , Nicaragua was the Latin American Country with the highest index 2020 by Index , with 0.81 points . Guatemala , on the other hand , had the worst score in the region with 0.67 points , which shows a index 2020 by of 54 percent ( on average , women 's income in Guatemala was estimated to be 54 percent lower than men 's ) .


Example 250:
data: Year|2017|x|line_chart Number_of_children_born_per_woman|1.97|y|line_chart Year|2016|x|line_chart Number_of_children_born_per_woman|2.03|y|line_chart Year|2015|x|line_chart Number_of_children_born_per_woman|2.1|y|line_chart Year|2014|x|line_chart Number_of_children_born_per_woman|2.18|y|line_chart Year|2013|x|line_chart Number_of_children_born_per_woman|2.26|y|line_chart Year|2012|x|line_chart Number_of_children_born_per_woman|2.35|y|line_chart Year|2011|x|line_chart Number_of_children_born_per_woman|2.44|y|line_chart Year|2010|x|line_chart Number_of_children_born_per_woman|2.54|y|line_chart Year|2009|x|line_chart Number_of_children_born_per_woman|2.64|y|line_chart Year|2008|x|line_chart Number_of_children_born_per_woman|2.75|y|line_chart Year|2007|x|line_chart Number_of_children_born_per_woman|2.87|y|line_chart 
title: Fertility rate in Nepal 2017

gold: This statistic shows the fertility rate of Nepal from 2007 to 2017 . The fertility rate is the average number of children a woman will have during her child-bearing years . In 2017 , the fertility rate in Nepal was 1.97 children per woman .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] a templateYLabel[4] will have during her child-bearing years . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[2] was templateYValue[min] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[2] amounted to about templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate in Nepal from 2007 to 2017 . In 2017 , the Fertility rate in Nepal amounted to about 1.97 children born per woman .


Example 251:
data: Year|2010|x|line_chart Price_in_U.S._dollars_per_metric_ton|10707|y|line_chart Year|2011|x|line_chart Price_in_U.S._dollars_per_metric_ton|62724|y|line_chart Year|2012|x|line_chart Price_in_U.S._dollars_per_metric_ton|24167|y|line_chart Year|2013|x|line_chart Price_in_U.S._dollars_per_metric_ton|24033|y|line_chart Year|2014|x|line_chart Price_in_U.S._dollars_per_metric_ton|25485|y|line_chart Year|2015|x|line_chart Price_in_U.S._dollars_per_metric_ton|26046|y|line_chart Year|2016|x|line_chart Price_in_U.S._dollars_per_metric_ton|26447|y|line_chart Year|2017|x|line_chart Price_in_U.S._dollars_per_metric_ton|28055|y|line_chart Year|2018|x|line_chart Price_in_U.S._dollars_per_metric_ton|28473|y|line_chart Year|2019|x|line_chart Price_in_U.S._dollars_per_metric_ton|23916|y|line_chart Year|2020|x|line_chart Price_in_U.S._dollars_per_metric_ton|19700|y|line_chart Year|2021|x|line_chart Price_in_U.S._dollars_per_metric_ton|19700|y|line_chart Year|2022|x|line_chart Price_in_U.S._dollars_per_metric_ton|19982|y|line_chart Year|2023|x|line_chart Price_in_U.S._dollars_per_metric_ton|20603|y|line_chart Year|2024|x|line_chart Price_in_U.S._dollars_per_metric_ton|21907|y|line_chart Year|2025|x|line_chart Price_in_U.S._dollars_per_metric_ton|23437|y|line_chart 
title: Forecast of rare earth oxide gadolinium oxide price globally 2009 - 2025

gold: This statistic shows the price development of rare earth oxide gadolinium oxide globally from 2009 to 2025 . It is expected that the price of gadolinium oxide will reach some 28,055 U.S. dollars per metric ton in 2017 .
gold_template: This statistic shows the templateYLabel[0] development of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[3] templateTitle[7] from templateTitle[8] to templateXValue[max] . It is expected that the templateYLabel[0] of templateTitle[4] templateTitle[3] will reach some templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[7] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateTitle[3] from templateXValue[min] to templateXValue[8] , with a forecast until templateXValue[max] . According to the source , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[5] in templateXValue[1] . In templateXValue[8] , the templateYLabel[1] templateYLabel[2] of templateTitle[2] templateTitle[3] was valued at over templateYValue[8] templateYLabel[2] .
generated: This statistic shows the Price of U.S. dollars per metric ton in oxide from 2010 to 2018 , with a forecast until 2025 . According to the source , the Price of U.S. dollars per metric ton in 2011 . In 2018 , the U.S. dollars of earth oxide was valued at over 28473 dollars .


Example 252:
data: Hours_used_per_day|Less_than_1_hour|x|bar_chart Housing_units_in_millions|16.6|y|bar_chart Hours_used_per_day|1_to_3_hours|x|bar_chart Housing_units_in_millions|32.0|y|bar_chart Hours_used_per_day|3_to_6_hours|x|bar_chart Housing_units_in_millions|17.5|y|bar_chart Hours_used_per_day|6_to_10_hours|x|bar_chart Housing_units_in_millions|7.7|y|bar_chart Hours_used_per_day|More_than_10_hours|x|bar_chart Housing_units_in_millions|12.5|y|bar_chart Hours_used_per_day|No_computers|x|bar_chart Housing_units_in_millions|27.4|y|bar_chart 
title: U.S. households - average hours per day of computer usage in 2009

gold: This statistic shows the hours per day a computer is used in a U.S. home in 2009 . In 17.5 million U.S. households , a computer is used 3 to 6 hours per day .
gold_template: This statistic shows the templateXValue[1] templateXLabel[2] templateXLabel[3] a templateTitle[6] is templateXLabel[1] in a templateTitle[0] home in templateTitle[8] . In templateYValue[2] million templateTitle[0] templateTitle[1] , a templateTitle[6] is templateXLabel[1] templateXValue[1] to templateXValue[2] templateXValue[1] templateXLabel[2] templateXLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] in templateTitle[3] . In that year , the templateXValue[0] of templateXValue[0] produced some templateYValue[max] billion templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the U.S. households in average in hours . In that year , the Less_than_1_hour of Less_than_1_hour produced some 32.0 billion millions yLabelErr .


Example 253:
data: Year|2018|x|line_chart Household_income_in_current_U.S._dollars|70145|y|line_chart Year|2017|x|line_chart Household_income_in_current_U.S._dollars|64609|y|line_chart Year|2016|x|line_chart Household_income_in_current_U.S._dollars|61386|y|line_chart Year|2015|x|line_chart Household_income_in_current_U.S._dollars|60413|y|line_chart Year|2014|x|line_chart Household_income_in_current_U.S._dollars|54916|y|line_chart Year|2013|x|line_chart Household_income_in_current_U.S._dollars|57196|y|line_chart Year|2012|x|line_chart Household_income_in_current_U.S._dollars|51738|y|line_chart Year|2011|x|line_chart Household_income_in_current_U.S._dollars|50637|y|line_chart Year|2010|x|line_chart Household_income_in_current_U.S._dollars|50728|y|line_chart Year|2009|x|line_chart Household_income_in_current_U.S._dollars|52870|y|line_chart Year|2008|x|line_chart Household_income_in_current_U.S._dollars|53254|y|line_chart Year|2007|x|line_chart Household_income_in_current_U.S._dollars|52506|y|line_chart Year|2006|x|line_chart Household_income_in_current_U.S._dollars|48671|y|line_chart Year|2005|x|line_chart Household_income_in_current_U.S._dollars|48398|y|line_chart Year|2004|x|line_chart Household_income_in_current_U.S._dollars|46077|y|line_chart Year|2003|x|line_chart Household_income_in_current_U.S._dollars|45153|y|line_chart Year|2002|x|line_chart Household_income_in_current_U.S._dollars|42710|y|line_chart Year|2001|x|line_chart Household_income_in_current_U.S._dollars|46171|y|line_chart Year|2000|x|line_chart Household_income_in_current_U.S._dollars|46064|y|line_chart Year|1999|x|line_chart Household_income_in_current_U.S._dollars|46330|y|line_chart Year|1998|x|line_chart Household_income_in_current_U.S._dollars|43178|y|line_chart Year|1997|x|line_chart Household_income_in_current_U.S._dollars|41283|y|line_chart Year|1996|x|line_chart Household_income_in_current_U.S._dollars|39554|y|line_chart Year|1995|x|line_chart Household_income_in_current_U.S._dollars|38071|y|line_chart Year|1994|x|line_chart Household_income_in_current_U.S._dollars|35081|y|line_chart Year|1993|x|line_chart Household_income_in_current_U.S._dollars|32857|y|line_chart Year|1992|x|line_chart Household_income_in_current_U.S._dollars|31551|y|line_chart Year|1991|x|line_chart Household_income_in_current_U.S._dollars|31884|y|line_chart Year|1990|x|line_chart Household_income_in_current_U.S._dollars|32542|y|line_chart 
title: Illinois - median household income 1990 - 2018

gold: This statistic shows the median household income in Illinois from 1990 to 2018 . In 2018 , the median household income in Illinois amounted to 70,145 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at templateYValue[0] templateYLabel[3] .
generated: The statistic shows the Household income in household from 1990 to 2018 . In 2018 , the Household income in household was at 70145 U.S. .


Example 254:
data: Accounting_firm|Deloitte|x|bar_chart Number_of_professionals|73855|y|bar_chart Accounting_firm|PwC|x|bar_chart Number_of_professionals|35350|y|bar_chart Accounting_firm|Ernst_&_Young|x|bar_chart Number_of_professionals|33600|y|bar_chart Accounting_firm|KPMG|x|bar_chart Number_of_professionals|26447|y|bar_chart Accounting_firm|RSM_US|x|bar_chart Number_of_professionals|7252|y|bar_chart Accounting_firm|Grant_Thornton|x|bar_chart Number_of_professionals|6616|y|bar_chart Accounting_firm|BDO_USA|x|bar_chart Number_of_professionals|4958|y|bar_chart Accounting_firm|CliftonLarsonAllen|x|bar_chart Number_of_professionals|4056|y|bar_chart Accounting_firm|Crowe_Horwath|x|bar_chart Number_of_professionals|3402|y|bar_chart Accounting_firm|CBIZ_/_Mayer_Hoffman_McCann|x|bar_chart Number_of_professionals|2470|y|bar_chart Accounting_firm|Moss_Adams|x|bar_chart Number_of_professionals|2066|y|bar_chart Accounting_firm|CohnReznick|x|bar_chart Number_of_professionals|1908|y|bar_chart Accounting_firm|BKD|x|bar_chart Number_of_professionals|1824|y|bar_chart Accounting_firm|Baker_Tilly_Virchow_Krause|x|bar_chart Number_of_professionals|2095|y|bar_chart Accounting_firm|Marcum|x|bar_chart Number_of_professionals|1219|y|bar_chart Accounting_firm|Plante_Moran|x|bar_chart Number_of_professionals|1796|y|bar_chart Accounting_firm|Dixon_Hughes_Goodman|x|bar_chart Number_of_professionals|1385|y|bar_chart Accounting_firm|EisnerAmper|x|bar_chart Number_of_professionals|979|y|bar_chart Accounting_firm|Wipfli|x|bar_chart Number_of_professionals|1229|y|bar_chart Accounting_firm|Carr_Riggs_&_Ingram|x|bar_chart Number_of_professionals|1346|y|bar_chart Accounting_firm|Eide_Bailly|x|bar_chart Number_of_professionals|1386|y|bar_chart Accounting_firm|Citrin_Cooperman_&_Co.|x|bar_chart Number_of_professionals|675|y|bar_chart Accounting_firm|Armanino|x|bar_chart Number_of_professionals|900|y|bar_chart Accounting_firm|Withum|x|bar_chart Number_of_professionals|720|y|bar_chart Accounting_firm|Mazars_USA|x|bar_chart Number_of_professionals|601|y|bar_chart 
title: Number of professionals at the leading accounting firms in the U.S. 2019

gold: This statistic shows the number of professionals at the leading accounting firms in the United States as of March 2019 . The largest accounting firm in the U.S. , Deloitte , employed 73,855 professionals at the end of their fiscal year in June 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] at the templateTitle[2] templateXLabel[0] templateTitle[4] in the templateTitle[5] as of March templateTitle[6] . The largest templateXLabel[0] templateXLabel[1] in the templateTitle[5] , templateXValue[0] , employed templateYValue[max] templateYLabel[1] at the end of their fiscal year in June 2018 .

generated_template: This statistic shows the results of a survey among people in the United States in templateTitle[4] , sorted templateTitle[5] templateXLabel[0] . In templateTitle[4] , about templateYValue[max] percent of the people lived in templateXValue[0] templateXValue[0] .
generated: This statistic shows the results of a survey among people in the United States in firms , sorted U.S. Accounting . In firms , about 73855 percent of the people lived in Deloitte .


Example 255:
data: Year|2018|x|line_chart Share_of_urban_population_in_total_population|63.17|y|line_chart Year|2017|x|line_chart Share_of_urban_population_in_total_population|62.95|y|line_chart Year|2016|x|line_chart Share_of_urban_population_in_total_population|62.74|y|line_chart Year|2015|x|line_chart Share_of_urban_population_in_total_population|62.54|y|line_chart Year|2014|x|line_chart Share_of_urban_population_in_total_population|62.34|y|line_chart Year|2013|x|line_chart Share_of_urban_population_in_total_population|62.14|y|line_chart Year|2012|x|line_chart Share_of_urban_population_in_total_population|61.94|y|line_chart Year|2011|x|line_chart Share_of_urban_population_in_total_population|61.74|y|line_chart Year|2010|x|line_chart Share_of_urban_population_in_total_population|61.54|y|line_chart Year|2009|x|line_chart Share_of_urban_population_in_total_population|61.34|y|line_chart Year|2008|x|line_chart Share_of_urban_population_in_total_population|61.14|y|line_chart 
title: Urbanization in Ireland 2018

gold: This statistic shows the degree of urbanization in Ireland from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 63.17 percent of Ireland 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Ireland from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 63.17 percent of Ireland 's total population lived in urban areas and cities .


Example 256:
data: Year|2023|x|line_chart Number_of_mobile_phone_internet_users_in_millions|4.9|y|line_chart Year|2022|x|line_chart Number_of_mobile_phone_internet_users_in_millions|4.9|y|line_chart Year|2021|x|line_chart Number_of_mobile_phone_internet_users_in_millions|4.8|y|line_chart Year|2020|x|line_chart Number_of_mobile_phone_internet_users_in_millions|4.7|y|line_chart Year|2019|x|line_chart Number_of_mobile_phone_internet_users_in_millions|4.6|y|line_chart Year|2018|x|line_chart Number_of_mobile_phone_internet_users_in_millions|4.5|y|line_chart Year|2017|x|line_chart Number_of_mobile_phone_internet_users_in_millions|4.3|y|line_chart 
title: Singapore : mobile phone internet users 2017 - 2023

gold: The statistic shows the number of mobile internet users in Singapore from 2017 to 2023 . In 2017 , 4.3 million people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 4.9 million mobile phone internet users .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] million people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] million templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] million people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] million templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number of mobile internet users in Singapore from 2017 to 2023 . In 2017 , 4.3 million people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 4.9 million mobile phone internet users .


Example 257:
data: Year|2024|x|line_chart National_debt_to_GDP_ratio|65.61|y|line_chart Year|2023|x|line_chart National_debt_to_GDP_ratio|66.19|y|line_chart Year|2022|x|line_chart National_debt_to_GDP_ratio|66.93|y|line_chart Year|2021|x|line_chart National_debt_to_GDP_ratio|67.75|y|line_chart Year|2020|x|line_chart National_debt_to_GDP_ratio|68.52|y|line_chart Year|2019|x|line_chart National_debt_to_GDP_ratio|69.04|y|line_chart Year|2018|x|line_chart National_debt_to_GDP_ratio|68.05|y|line_chart Year|2017|x|line_chart National_debt_to_GDP_ratio|67.83|y|line_chart Year|2016|x|line_chart National_debt_to_GDP_ratio|67.67|y|line_chart Year|2015|x|line_chart National_debt_to_GDP_ratio|68.78|y|line_chart Year|2014|x|line_chart National_debt_to_GDP_ratio|66.83|y|line_chart 
title: National debt of India in relation to gross domestic product ( GDP ) 2024

gold: The statistic shows the national debt in India from 2014 to 2018 in relation to gross domestic product ( GDP ) , with projections up until 2024 . In 2018 , the national debt of India amounted to about 68.05 percent of the gross domestic product . India 's economy on the rise India is one of the most populous countries in the world , and although a large share of inhabitants are living below the poverty line – or probably due to this fact – , the country 's economy is growing steadily .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] in templateTitle[3] to templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] amounted to about templateYValue[6] percent of the templateTitle[4] templateTitle[5] templateTitle[6] . templateTitle[2] 's economy on the rise templateTitle[2] is one of the most populous countries in the world , and although a large share of inhabitants are living below the poverty line – or probably due to this fact – , the country 's economy is growing steadily .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] in templateTitle[3] to templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] amounted to about templateYValue[6] percent of the templateTitle[4] templateTitle[5] templateTitle[6] . templateYLabel[0] templateYLabel[1] of templateTitle[2] – additional information In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] is a country 's debt-to-GDP templateYLabel[3] .
generated: The statistic shows the National debt in India from 2014 to 2018 in relation to gross domestic product ( GDP ) , with projections up until 2024 . In 2018 , the National debt of India amounted to about 68.05 percent of the gross domestic product . National debt of India – additional information In 2018 , the National debt of India is a country 's debt-to-GDP ratio .


Example 258:
data: Country|Somalia|x|bar_chart Number_of_kidnappings|2527|y|bar_chart Country|Afghanistan|x|bar_chart Number_of_kidnappings|902|y|bar_chart Country|Pakistan|x|bar_chart Number_of_kidnappings|430|y|bar_chart Country|India|x|bar_chart Number_of_kidnappings|341|y|bar_chart Country|Colombia|x|bar_chart Number_of_kidnappings|285|y|bar_chart Country|Congo_Democratic_Republic|x|bar_chart Number_of_kidnappings|189|y|bar_chart Country|Sudan|x|bar_chart Number_of_kidnappings|159|y|bar_chart Country|Yemen|x|bar_chart Number_of_kidnappings|117|y|bar_chart Country|Central_African_Republic|x|bar_chart Number_of_kidnappings|115|y|bar_chart Country|Iraq|x|bar_chart Number_of_kidnappings|111|y|bar_chart Country|Gaza_Strip|x|bar_chart Number_of_kidnappings|107|y|bar_chart Country|Philippines|x|bar_chart Number_of_kidnappings|102|y|bar_chart Country|Turkey|x|bar_chart Number_of_kidnappings|63|y|bar_chart Country|Burma|x|bar_chart Number_of_kidnappings|25|y|bar_chart Country|Nigeria|x|bar_chart Number_of_kidnappings|17|y|bar_chart 
title: Terrorism - kidnappings grouped by country

gold: The statistic shows the number of kidnappings due to terrorism grouped by country in 2011 . 2,527 people werde kidnapped in Somalia .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] due to templateTitle[0] templateTitle[2] templateTitle[3] templateXLabel[0] in 2011 . templateYValue[max] people werde kidnapped in templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] due to templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[3] as of January templateTitle[4] , sorted templateTitle[5] templateXLabel[0] . The survey period it was found that templateXValue[0] had the highest templateTitle[0] templateTitle[1] templateTitle[2] with a templateYLabel[1] of templateYValue[max] people in templateTitle[6] .
generated: This statistic shows the Number of kidnappings due to Terrorism kidnappings grouped in by as of January country , sorted titleErr Country . The survey period it was found that Somalia had the highest Terrorism kidnappings grouped with a kidnappings of 2527 people in titleErr .


Example 259:
data: Year|2024|x|line_chart National_debt_in_relation_to_GDP|65.41|y|line_chart Year|2023|x|line_chart National_debt_in_relation_to_GDP|68.98|y|line_chart Year|2022|x|line_chart National_debt_in_relation_to_GDP|72.48|y|line_chart Year|2021|x|line_chart National_debt_in_relation_to_GDP|76.11|y|line_chart Year|2020|x|line_chart National_debt_in_relation_to_GDP|78.65|y|line_chart Year|2019|x|line_chart National_debt_in_relation_to_GDP|76.73|y|line_chart Year|2018|x|line_chart National_debt_in_relation_to_GDP|71.69|y|line_chart Year|2017|x|line_chart National_debt_in_relation_to_GDP|67.05|y|line_chart Year|2016|x|line_chart National_debt_in_relation_to_GDP|67.63|y|line_chart Year|2015|x|line_chart National_debt_in_relation_to_GDP|63.32|y|line_chart Year|2014|x|line_chart National_debt_in_relation_to_GDP|63.47|y|line_chart 
title: National debt of Pakistan in relation to gross domestic product ( GDP ) 2024*

gold: This statistic shows the national debt of Pakistan from 2014 to 2018 , in relation to the gross domestic product ( GDP ) , with projections up until 2024 . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In 2018 , the national debt of Pakistan amounted to approximately 71.69 percent of the GDP .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , in templateYLabel[2] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] amounted to approximately templateYValue[6] percent of the templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the United States from templateXValue[min] to templateXValue[6] in templateYLabel[2] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of the United States was at around templateYValue[6] percent of the templateTitle[4] templateTitle[5] templateTitle[6] . See the templateTitle[2] templateYLabel[3] for further information .
generated: The statistic shows the National debt of the United States from 2014 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . In 2018 , the National debt of the United States was at around 71.69 percent of the gross domestic product . See the Pakistan GDP for further information .


Example 260:
data: Year|2024|x|line_chart National_debt_as_percent_of_GDP|43.72|y|line_chart Year|2023|x|line_chart National_debt_as_percent_of_GDP|46.24|y|line_chart Year|2022|x|line_chart National_debt_as_percent_of_GDP|49.77|y|line_chart Year|2021|x|line_chart National_debt_as_percent_of_GDP|53.83|y|line_chart Year|2020|x|line_chart National_debt_as_percent_of_GDP|57.89|y|line_chart Year|2019|x|line_chart National_debt_as_percent_of_GDP|62.03|y|line_chart Year|2018|x|line_chart National_debt_as_percent_of_GDP|63.86|y|line_chart Year|2017|x|line_chart National_debt_as_percent_of_GDP|71.92|y|line_chart Year|2016|x|line_chart National_debt_as_percent_of_GDP|81.18|y|line_chart Year|2015|x|line_chart National_debt_as_percent_of_GDP|79.5|y|line_chart Year|2014|x|line_chart National_debt_as_percent_of_GDP|70.32|y|line_chart Year|2013|x|line_chart National_debt_as_percent_of_GDP|40.52|y|line_chart Year|2012|x|line_chart National_debt_as_percent_of_GDP|37.54|y|line_chart Year|2011|x|line_chart National_debt_as_percent_of_GDP|36.88|y|line_chart Year|2010|x|line_chart National_debt_as_percent_of_GDP|40.63|y|line_chart 
title: National debt of Ukraine in relation to gross domestic product 2010 - 2024

gold: This statistic shows the national debt of Ukraine from 2010 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In 2018 , the national debt of Ukraine amounted to an estimate of approximately 63.86 percent of the GDP .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] amounted to an estimate of approximately templateYValue[6] templateYLabel[2] of the templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] amounted to approximately templateYValue[6] templateYLabel[2] of the templateYLabel[3] .
generated: This statistic shows the National debt of Ukraine from 2010 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In 2018 , the National debt of Ukraine amounted to approximately 63.86 percent of the GDP .


Example 261:
data: Year|2024|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|57.13|y|line_chart Year|2023|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|54.15|y|line_chart Year|2022|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|51.52|y|line_chart Year|2021|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|49.4|y|line_chart Year|2020|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|48.04|y|line_chart Year|2019|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|47.17|y|line_chart Year|2018|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|46.94|y|line_chart Year|2017|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|41.38|y|line_chart Year|2016|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|37.83|y|line_chart Year|2015|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|50.84|y|line_chart Year|2014|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|75.24|y|line_chart Year|2013|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|74.16|y|line_chart Year|2012|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|69.69|y|line_chart Year|2011|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|65.99|y|line_chart Year|2010|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|52.91|y|line_chart Year|2009|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|44.29|y|line_chart Year|2008|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|48.98|y|line_chart Year|2007|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|33.09|y|line_chart Year|2006|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|21.03|y|line_chart Year|2005|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|13.27|y|line_chart Year|2004|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|8.68|y|line_chart Year|2003|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|7.28|y|line_chart Year|2002|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|6.23|y|line_chart Year|2001|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|5.48|y|line_chart Year|2000|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|5.27|y|line_chart Year|1999|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|4.58|y|line_chart Year|1998|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|4.28|y|line_chart Year|1997|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.96|y|line_chart Year|1996|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.18|y|line_chart Year|1995|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2.42|y|line_chart Year|1994|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2.26|y|line_chart 
title: Gross domestic product ( GDP ) in Azerbaijan 2024

gold: The statistic shows gross domestic product ( GDP ) in Azerbaijan from 1994 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Azerbaijan from 1994 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year . GDP is an important indicator of a country 's economic power .


Example 262:
data: Country|Turkey|x|bar_chart Volume_in_thousand_tonnes|12150.0|y|bar_chart Country|Italy|x|bar_chart Volume_in_thousand_tonnes|6055.43|y|bar_chart Country|Spain|x|bar_chart Volume_in_thousand_tonnes|4768.82|y|bar_chart Country|Portugal|x|bar_chart Volume_in_thousand_tonnes|1329.76|y|bar_chart Country|Poland|x|bar_chart Volume_in_thousand_tonnes|928.83|y|bar_chart Country|Netherlands|x|bar_chart Volume_in_thousand_tonnes|910.0|y|bar_chart Country|Greece|x|bar_chart Volume_in_thousand_tonnes|885.15|y|bar_chart Country|France|x|bar_chart Volume_in_thousand_tonnes|712.02|y|bar_chart Country|Romania|x|bar_chart Volume_in_thousand_tonnes|451.76|y|bar_chart Country|Albania_|x|bar_chart Volume_in_thousand_tonnes|286.8|y|bar_chart Country|Belgium|x|bar_chart Volume_in_thousand_tonnes|258.68|y|bar_chart Country|Hungary|x|bar_chart Volume_in_thousand_tonnes|204.0|y|bar_chart Country|Serbia_|x|bar_chart Volume_in_thousand_tonnes|170.76|y|bar_chart Country|North_Macedonia|x|bar_chart Volume_in_thousand_tonnes|161.62|y|bar_chart Country|Bulgaria|x|bar_chart Volume_in_thousand_tonnes|148.08|y|bar_chart Country|Germany|x|bar_chart Volume_in_thousand_tonnes|103.27|y|bar_chart Country|United_Kingdom|x|bar_chart Volume_in_thousand_tonnes|66.9|y|bar_chart Country|Austria|x|bar_chart Volume_in_thousand_tonnes|58.15|y|bar_chart Country|Switzerland|x|bar_chart Volume_in_thousand_tonnes|48.24|y|bar_chart Country|Bosnia_and_Herzegovina_|x|bar_chart Volume_in_thousand_tonnes|43.92|y|bar_chart Country|Finland|x|bar_chart Volume_in_thousand_tonnes|39.32|y|bar_chart Country|Croatia|x|bar_chart Volume_in_thousand_tonnes|22.59|y|bar_chart Country|Slovakia|x|bar_chart Volume_in_thousand_tonnes|22.29|y|bar_chart Country|Kosovo|x|bar_chart Volume_in_thousand_tonnes|18.8|y|bar_chart Country|Sweden|x|bar_chart Volume_in_thousand_tonnes|18.23|y|bar_chart Country|Cyprus_|x|bar_chart Volume_in_thousand_tonnes|15.7|y|bar_chart Country|Lithuania|x|bar_chart Volume_in_thousand_tonnes|12.19|y|bar_chart Country|Denmark|x|bar_chart Volume_in_thousand_tonnes|11.76|y|bar_chart Country|Malta|x|bar_chart Volume_in_thousand_tonnes|11.19|y|bar_chart Country|Norway|x|bar_chart Volume_in_thousand_tonnes|10.57|y|bar_chart Country|Czechia|x|bar_chart Volume_in_thousand_tonnes|10.08|y|bar_chart Country|Slovenia|x|bar_chart Volume_in_thousand_tonnes|8.39|y|bar_chart Country|Latvia|x|bar_chart Volume_in_thousand_tonnes|5.2|y|bar_chart Country|Ireland|x|bar_chart Volume_in_thousand_tonnes|3.92|y|bar_chart Country|Montenegro_|x|bar_chart Volume_in_thousand_tonnes|3.27|y|bar_chart Country|Iceland|x|bar_chart Volume_in_thousand_tonnes|1.21|y|bar_chart Country|Estonia|x|bar_chart Volume_in_thousand_tonnes|0.29|y|bar_chart Country|Luxembourg|x|bar_chart Volume_in_thousand_tonnes|0.01|y|bar_chart 
title: Production volume of tomatoes in Europe 2018

gold: This statistic displays the volume of harvested tomato production in Europe in 2018 , by country . In this year , Turkey produced the largest volume of tomatoes at over 12.15 million tonnes , followed by Italy with 6.06 million tonnes produced .
gold_template: This statistic displays the templateYLabel[0] of harvested tomato templateTitle[0] in templateTitle[3] in templateTitle[4] , by templateXLabel[0] . In this year , templateXValue[0] produced the largest templateYLabel[0] of templateTitle[2] at over templateYValue[max] million templateYLabel[2] , followed by templateXValue[1] with templateYValue[1] million templateYLabel[2] produced .

generated_template: In templateTitle[4] , templateXValue[0] was the highest templateYLabel[0] templateYLabel[1] in selected European countries with templateYValue[max] people . The templateYLabel[0] templateYLabel[1] had the highest templateYLabel[0] rates of templateTitle[4] templateTitle[5] the templateXValue[0] templateXValue[0] , followed by templateXValue[1] at templateYValue[1] percent . The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[1] templateTitle[1] and templateXValue[3] had .
generated: In 2018 , Turkey was the highest Volume thousand in selected European countries with 12150.0 people . The Volume thousand had the highest Volume rates of 2018 titleErr the Turkey , followed by Italy at 6055.43 percent . The Volume thousand tonnes thousand volume and Portugal had .


Example 263:
data: Year|2012|x|line_chart Growth_in_the_number_of_CFPs|60|y|line_chart Year|2011|x|line_chart Growth_in_the_number_of_CFPs|54|y|line_chart Year|2010|x|line_chart Growth_in_the_number_of_CFPs|47|y|line_chart Year|2009|x|line_chart Growth_in_the_number_of_CFPs|45|y|line_chart Year|2008|x|line_chart Growth_in_the_number_of_CFPs|38|y|line_chart 
title: Growth of crowdfunding platforms worldwide 2008 - 2012

gold: The statistic shows the percentage growth in the number of crowdfunding platforms worldwide from 2008 to 2012 . In 2008 , the number of crowdfunding platforms worldwide increased by 38 percent in comparison to the previous year . The rate of crowdfunding platform growth continued in all of the following years and the growth rate reached 60 percent in 2012 .
gold_template: The statistic shows the percentage templateYLabel[0] in the templateYLabel[1] of templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[1] of templateTitle[1] templateTitle[2] templateTitle[3] increased by templateYValue[min] percent in comparison to the previous templateXLabel[0] . The rate of templateTitle[1] platform templateYLabel[0] continued in all of the following years and the templateYLabel[0] rate reached templateYValue[max] percent in templateXValue[max] .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[0] million templateYLabel[1] reported in the United States .
generated: This statistic presents the Growth number CFPs of worldwide in the United States from 2008 to 2012 . In 2012 , there were approximately 60 million number reported in the United States .


Example 264:
data: Year|1990|x|line_chart Production_in_million_metric_tons|401.61|y|line_chart Year|2000|x|line_chart Production_in_million_metric_tons|576.65|y|line_chart Year|2001|x|line_chart Production_in_million_metric_tons|591.63|y|line_chart Year|2002|x|line_chart Production_in_million_metric_tons|609.53|y|line_chart Year|2003|x|line_chart Production_in_million_metric_tons|621.92|y|line_chart Year|2004|x|line_chart Production_in_million_metric_tons|645.87|y|line_chart Year|2005|x|line_chart Production_in_million_metric_tons|661.62|y|line_chart Year|2006|x|line_chart Production_in_million_metric_tons|687.42|y|line_chart Year|2007|x|line_chart Production_in_million_metric_tons|702.61|y|line_chart Year|2008|x|line_chart Production_in_million_metric_tons|718.82|y|line_chart Year|2009|x|line_chart Production_in_million_metric_tons|736.13|y|line_chart Year|2010|x|line_chart Production_in_million_metric_tons|740.54|y|line_chart Year|2011|x|line_chart Production_in_million_metric_tons|766.21|y|line_chart Year|2012|x|line_chart Production_in_million_metric_tons|778.94|y|line_chart Year|2013|x|line_chart Production_in_million_metric_tons|810.44|y|line_chart Year|2014|x|line_chart Production_in_million_metric_tons|822.81|y|line_chart Year|2015|x|line_chart Production_in_million_metric_tons|833.74|y|line_chart Year|2016|x|line_chart Production_in_million_metric_tons|835.34|y|line_chart Year|2017|x|line_chart Production_in_million_metric_tons|842.84|y|line_chart Year|2018|x|line_chart Production_in_million_metric_tons|868.09|y|line_chart 
title: Fresh fruit production worldwide 1990 - 2018

gold: This statistic illustrates the total global production of fresh fruit from 1990 to 2018 . In 2018 , the global production of fresh fruit amounted to some 868.1 million metric tons , increasing from 576.65 million metric tons in 2000 .
gold_template: This statistic illustrates the total global templateYLabel[0] of templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateYLabel[0] of templateTitle[0] templateTitle[1] amounted to some templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , increasing from templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[1] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] in templateYLabel[1] templateYLabel[2] templateYLabel[3] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[4] were produced in the United States .
generated: This statistic shows the Fresh Production in million metric tons in the United States from 1990 to 2018 . In 2018 , 868.09 million metric tons of 1990 were produced in the United States .


Example 265:
data: Month|Mar_19|x|bar_chart Percentage_change|12.4|y|bar_chart Month|Feb_19|x|bar_chart Percentage_change|11.8|y|bar_chart Month|Jan_19|x|bar_chart Percentage_change|11.6|y|bar_chart Month|Dec_18|x|bar_chart Percentage_change|13.3|y|bar_chart Month|Nov_18|x|bar_chart Percentage_change|13|y|bar_chart Month|Oct_18|x|bar_chart Percentage_change|12.9|y|bar_chart Month|Sep_18|x|bar_chart Percentage_change|12|y|bar_chart Month|Aug_18|x|bar_chart Percentage_change|14.2|y|bar_chart Month|Jul_18|x|bar_chart Percentage_change|16.1|y|bar_chart Month|Jun_18|x|bar_chart Percentage_change|15.8|y|bar_chart Month|May_18|x|bar_chart Percentage_change|21|y|bar_chart Month|Apr_18|x|bar_chart Percentage_change|13|y|bar_chart Month|Mar_18|x|bar_chart Percentage_change|16.5|y|bar_chart Month|Feb_18|x|bar_chart Percentage_change|15.3|y|bar_chart Month|Jan_18|x|bar_chart Percentage_change|13.6|y|bar_chart Month|Dec_17|x|bar_chart Percentage_change|9|y|bar_chart Month|Nov_17|x|bar_chart Percentage_change|10.1|y|bar_chart Month|Oct_17|x|bar_chart Percentage_change|9.6|y|bar_chart Month|Sep_17|x|bar_chart Percentage_change|13.6|y|bar_chart Month|Aug_17|x|bar_chart Percentage_change|19|y|bar_chart Month|Jul_17|x|bar_chart Percentage_change|20.1|y|bar_chart Month|Jun_17|x|bar_chart Percentage_change|20.1|y|bar_chart Month|May_17|x|bar_chart Percentage_change|17.1|y|bar_chart Month|Apr_17|x|bar_chart Percentage_change|22.1|y|bar_chart Month|Mar_17|x|bar_chart Percentage_change|24.3|y|bar_chart Month|Feb_17|x|bar_chart Percentage_change|22.2|y|bar_chart Month|Jan_17|x|bar_chart Percentage_change|16.1|y|bar_chart Month|Dec_16|x|bar_chart Percentage_change|30.1|y|bar_chart Month|Nov_16|x|bar_chart Percentage_change|28.4|y|bar_chart Month|Oct_16|x|bar_chart Percentage_change|30|y|bar_chart Month|Sep_16|x|bar_chart Percentage_change|25.8|y|bar_chart Month|Aug_16|x|bar_chart Percentage_change|23.6|y|bar_chart Month|Jul_16|x|bar_chart Percentage_change|17.8|y|bar_chart Month|Jun_16|x|bar_chart Percentage_change|17.2|y|bar_chart Month|May_16|x|bar_chart Percentage_change|19.6|y|bar_chart Month|Apr_16|x|bar_chart Percentage_change|12.9|y|bar_chart Month|Mar_16|x|bar_chart Percentage_change|11.6|y|bar_chart Month|Feb_16|x|bar_chart Percentage_change|12.4|y|bar_chart Month|Jan_16|x|bar_chart Percentage_change|13.8|y|bar_chart Month|Dec_15|x|bar_chart Percentage_change|8.4|y|bar_chart Month|Nov_15|x|bar_chart Percentage_change|14.9|y|bar_chart Month|Oct_15|x|bar_chart Percentage_change|11.6|y|bar_chart Month|Sep_15|x|bar_chart Percentage_change|14.4|y|bar_chart Month|Aug_15|x|bar_chart Percentage_change|8.7|y|bar_chart Month|Jul_15|x|bar_chart Percentage_change|13.2|y|bar_chart Month|Jun_15|x|bar_chart Percentage_change|13.9|y|bar_chart Month|May_15|x|bar_chart Percentage_change|9.4|y|bar_chart Month|Apr_15|x|bar_chart Percentage_change|14.3|y|bar_chart Month|Mar_15|x|bar_chart Percentage_change|12.5|y|bar_chart Month|Feb_15|x|bar_chart Percentage_change|11.4|y|bar_chart Month|Jan_15|x|bar_chart Percentage_change|19.3|y|bar_chart Month|Dec_14|x|bar_chart Percentage_change|11.1|y|bar_chart Month|Nov_14|x|bar_chart Percentage_change|19.7|y|bar_chart Month|Oct_14|x|bar_chart Percentage_change|12|y|bar_chart Month|Sep_14|x|bar_chart Percentage_change|10.1|y|bar_chart Month|Aug_14|x|bar_chart Percentage_change|13.7|y|bar_chart Month|Jul_14|x|bar_chart Percentage_change|14.8|y|bar_chart Month|Jun_14|x|bar_chart Percentage_change|15|y|bar_chart Month|May_14|x|bar_chart Percentage_change|16.6|y|bar_chart Month|Apr_14|x|bar_chart Percentage_change|15|y|bar_chart Month|Mar_14|x|bar_chart Percentage_change|9.1|y|bar_chart Month|Feb_14|x|bar_chart Percentage_change|11.9|y|bar_chart Month|Jan_14|x|bar_chart Percentage_change|11|y|bar_chart Month|Dec_13|x|bar_chart Percentage_change|13.5|y|bar_chart Month|Nov_13|x|bar_chart Percentage_change|17.5|y|bar_chart Month|Oct_13|x|bar_chart Percentage_change|17.8|y|bar_chart Month|Sep_13|x|bar_chart Percentage_change|17.4|y|bar_chart Month|Aug_13|x|bar_chart Percentage_change|21.7|y|bar_chart Month|Jul_13|x|bar_chart Percentage_change|10.2|y|bar_chart Month|Jun_13|x|bar_chart Percentage_change|18.1|y|bar_chart Month|May_13|x|bar_chart Percentage_change|10.7|y|bar_chart Month|Apr_13|x|bar_chart Percentage_change|13.9|y|bar_chart Month|Mar_13|x|bar_chart Percentage_change|18.8|y|bar_chart Month|Feb_13|x|bar_chart Percentage_change|12.9|y|bar_chart Month|Jan_13|x|bar_chart Percentage_change|12.8|y|bar_chart 
title: Internet retail sales value trend monthly in the United Kingdom ( UK ) 2013 - 2019

gold: This statistic displays the monthly trend of the value of retail internet sales in the United Kingdom ( UK ) from January 2013 to March 2019 . In March 2019 , retail sales increased by 12.4 percent .
gold_template: This statistic displays the templateTitle[5] templateTitle[4] of the templateTitle[3] of templateTitle[1] templateTitle[0] templateTitle[2] in the templateTitle[6] templateTitle[7] ( templateTitle[8] ) from January templateTitle[9] to March templateTitle[10] . In March templateTitle[10] , templateTitle[1] templateTitle[2] increased by templateYValue[0] percent .

generated_template: The number of templateTitle[1] templateTitle[3] templateTitle[4] in the United Kingdom ( templateTitle[5] ) increased by templateYValue[0] percent from December templateTitle[6] to December templateTitle[7] . The United Kingdom is due to a low baseline from January 2018 to the source predicts spend templateXValue[0] templateXValue[0] to templateYValue[max] percent in January templateTitle[8] . The United Kingdom ( templateTitle[5] ) issues new registration plates .
generated: The number of retail value trend in the United Kingdom ( monthly ) increased by 12.4 percent from December United to December Kingdom . The United Kingdom is due to a low baseline from January 2018 to the source predicts spend Mar_19 to 30.1 percent in January UK . The United Kingdom ( monthly ) issues new registration plates .


Example 266:
data: Year|2017|x|line_chart Life_expectancy_at_birth_in_years|80.99|y|line_chart Year|2016|x|line_chart Life_expectancy_at_birth_in_years|80.99|y|line_chart Year|2015|x|line_chart Life_expectancy_at_birth_in_years|80.64|y|line_chart Year|2014|x|line_chart Life_expectancy_at_birth_in_years|81.09|y|line_chart Year|2013|x|line_chart Life_expectancy_at_birth_in_years|80.49|y|line_chart Year|2012|x|line_chart Life_expectancy_at_birth_in_years|80.54|y|line_chart Year|2011|x|line_chart Life_expectancy_at_birth_in_years|80.44|y|line_chart Year|2010|x|line_chart Life_expectancy_at_birth_in_years|79.99|y|line_chart Year|2009|x|line_chart Life_expectancy_at_birth_in_years|79.84|y|line_chart Year|2008|x|line_chart Life_expectancy_at_birth_in_years|79.74|y|line_chart Year|2007|x|line_chart Life_expectancy_at_birth_in_years|79.53|y|line_chart 
title: Life expectancy at birth in Germany 2017

gold: This statistic shows the average life expectancy at birth in Germany from 2007 to 2017 . A child born in the year 2017 had an average life expectancy of 80.99 years .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . A child born in the templateXLabel[0] templateXValue[max] had an average templateYLabel[0] templateYLabel[1] of templateYValue[0] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] at templateYLabel[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . The average templateYLabel[0] templateYLabel[1] of templateTitle[2] in templateXValue[max] was templateYValue[max] templateYLabel[3] . Standard of living in templateTitle[2] is one of the so-called BRIC countries , an acronym which stands for Brazil , Russia , templateTitle[2] and China .
generated: The statistic shows the Life expectancy of birth at birth in Germany from 2007 to 2017 . The average Life expectancy of birth in 2017 was 81.09 years . Standard of living in birth is one of the so-called BRIC countries , an acronym which stands for Brazil , Russia , birth and China .


Example 267:
data: Year|2018|x|line_chart Number_of_residents_per_square_mile|229.5|y|line_chart Year|2017|x|line_chart Number_of_residents_per_square_mile|230.6|y|line_chart Year|2016|x|line_chart Number_of_residents_per_square_mile|230.6|y|line_chart Year|2015|x|line_chart Number_of_residents_per_square_mile|231.6|y|line_chart Year|2014|x|line_chart Number_of_residents_per_square_mile|232.0|y|line_chart Year|2013|x|line_chart Number_of_residents_per_square_mile|232.0|y|line_chart Year|2012|x|line_chart Number_of_residents_per_square_mile|231.9|y|line_chart Year|2010|x|line_chart Number_of_residents_per_square_mile|231.1|y|line_chart Year|2000|x|line_chart Number_of_residents_per_square_mile|223.7|y|line_chart Year|1990|x|line_chart Number_of_residents_per_square_mile|205.9|y|line_chart Year|1980|x|line_chart Number_of_residents_per_square_mile|205.8|y|line_chart Year|1970|x|line_chart Number_of_residents_per_square_mile|200.1|y|line_chart Year|1960|x|line_chart Number_of_residents_per_square_mile|181.3|y|line_chart 
title: Population density in Illinois 1960 - 2018

gold: This graph shows the population density in the federal state of Illinois from 1960 to 2018 . In 2018 , the population density of Illinois stood at 229.5 residents per square mile of land area .
gold_template: This graph shows the templateTitle[0] templateTitle[1] in the federal state of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[2] stood at templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .

generated_template: This graph shows the templateTitle[0] templateTitle[1] in the federal state of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[2] stood at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .
generated: This graph shows the Population density in the federal state of Illinois from 1960 to 2018 . In 2018 , the Population density of Illinois stood at 232.0 residents per square mile of land area .


Example 268:
data: Year|2018|x|line_chart Number_of_companies|171072|y|line_chart Year|2017|x|line_chart Number_of_companies|176354|y|line_chart Year|2016|x|line_chart Number_of_companies|177684|y|line_chart Year|2015|x|line_chart Number_of_companies|174862|y|line_chart Year|2014|x|line_chart Number_of_companies|171437|y|line_chart Year|2013|x|line_chart Number_of_companies|174254|y|line_chart Year|2012|x|line_chart Number_of_companies|180200|y|line_chart Year|2011|x|line_chart Number_of_companies|186101|y|line_chart Year|2010|x|line_chart Number_of_companies|191459|y|line_chart Year|2009|x|line_chart Number_of_companies|188464|y|line_chart 
title: Number of textile and clothing manufacturers in the European Union ( EU28 ) 2009 - 2018

gold: In the European Union , the number of companies in the business of textile and clothing manufacturing has ebbed and flowed over the period of time between 2009 and 2018 . In 2018 , the total number of textile and clothing manufacturers was recorded as 171,072 in EU28 countries , marking a loss of about 17,000 of them since 2009 . In contrast , the turnover figures of the industry demonstrated a consistently positive trend before peaking at 181 billion euros in 2017 .
gold_template: In the templateTitle[4] templateTitle[5] , the templateYLabel[0] of templateYLabel[1] in the business of templateTitle[1] and templateTitle[2] manufacturing has ebbed and flowed over the period of time between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the total templateYLabel[0] of templateTitle[1] and templateTitle[2] templateTitle[3] was recorded as templateYValue[min] in templateTitle[6] countries , marking a loss of about 17,000 of them since templateXValue[min] . In contrast , the turnover figures of the industry demonstrated a consistently positive trend before peaking at 181 billion euros in templateXValue[1] .

generated_template: templateTitle[3] has seen the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[4] templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[2] , there were templateYValue[2] templateYLabel[1] templateYLabel[0] of templateYLabel[1] in the United States . templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[1] templateTitle[2] templateTitle[3] in the United States are back to be observed in recent years .
generated: manufacturers has seen the Number Number of companies yLabelErr in the European Union 2009 to 2018 . In 2016 , there were 177684 companies Number of companies in the United States . clothing manufacturers European in the textile clothing manufacturers in the United States are back to be observed in recent years .


Example 269:
data: Month|Marco_Fabian|x|bar_chart Payroll_in_thousand_U.S._dollars|2274.09|y|bar_chart Month|Alejandro_Bedoya|x|bar_chart Payroll_in_thousand_U.S._dollars|1266.25|y|bar_chart Month|Sergio_Santos|x|bar_chart Payroll_in_thousand_U.S._dollars|668.5|y|bar_chart Month|Haris_Medunjanin|x|bar_chart Payroll_in_thousand_U.S._dollars|595.01|y|bar_chart Month|Jamiro_Monteiro|x|bar_chart Payroll_in_thousand_U.S._dollars|569.2|y|bar_chart Month|Andre_Blake|x|bar_chart Payroll_in_thousand_U.S._dollars|550.0|y|bar_chart Month|Kai_Wagner|x|bar_chart Payroll_in_thousand_U.S._dollars|360.15|y|bar_chart Month|Ilsinho|x|bar_chart Payroll_in_thousand_U.S._dollars|357.0|y|bar_chart Month|Kacper_Przybylko|x|bar_chart Payroll_in_thousand_U.S._dollars|277.0|y|bar_chart Month|Jack_Elliott|x|bar_chart Payroll_in_thousand_U.S._dollars|265.0|y|bar_chart Month|Raymon_Gaddis|x|bar_chart Payroll_in_thousand_U.S._dollars|190.0|y|bar_chart Month|Warren_Creavalle|x|bar_chart Payroll_in_thousand_U.S._dollars|175.3|y|bar_chart Month|Aurelien_Collin|x|bar_chart Payroll_in_thousand_U.S._dollars|175.0|y|bar_chart Month|Fafa_Picault|x|bar_chart Payroll_in_thousand_U.S._dollars|173.67|y|bar_chart Month|Carlos_Miguel_Coronel|x|bar_chart Payroll_in_thousand_U.S._dollars|148.43|y|bar_chart Month|Auston_Trusty|x|bar_chart Payroll_in_thousand_U.S._dollars|124.1|y|bar_chart Month|Fabinho_Alves|x|bar_chart Payroll_in_thousand_U.S._dollars|120.0|y|bar_chart Month|Brenden_Aaronson|x|bar_chart Payroll_in_thousand_U.S._dollars|95.81|y|bar_chart Month|Mark_McKenzie|x|bar_chart Payroll_in_thousand_U.S._dollars|82.23|y|bar_chart Month|Derrick_Jones|x|bar_chart Payroll_in_thousand_U.S._dollars|80.9|y|bar_chart Month|Cory_Burke|x|bar_chart Payroll_in_thousand_U.S._dollars|79.72|y|bar_chart Month|Matt_Freese|x|bar_chart Payroll_in_thousand_U.S._dollars|77.65|y|bar_chart Month|Olivier_Mbaizo|x|bar_chart Payroll_in_thousand_U.S._dollars|70.88|y|bar_chart Month|Anthony_Fontana|x|bar_chart Payroll_in_thousand_U.S._dollars|70.26|y|bar_chart Month|Matthew_Real|x|bar_chart Payroll_in_thousand_U.S._dollars|57.23|y|bar_chart Month|Michee_Ngalina|x|bar_chart Payroll_in_thousand_U.S._dollars|56.25|y|bar_chart 
title: Player expenses ( payroll ) of Philadelphia Union 2019

gold: The statistic shows the player expenses ( payroll ) of the Philadelphia Union club of Major League Soccer by player in 2019 . Marco Fabian received a salary of 2.27 million U.S. dollars .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitle[3] templateTitle[4] club of Major League Soccer by templateTitle[0] in templateTitle[5] . templateXValue[0] templateXValue[0] received a salary of templateYValue[max] million templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitle[3] templateTitle[4] club of Major League Soccer by templateTitle[0] in templateTitle[5] . templateXValue[0] templateXValue[0] received a salary of templateYValue[max] million templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Player expenses ( Payroll ) of the Philadelphia Union club of Major League Soccer by Player in 2019 . Marco_Fabian received a salary of 2274.09 million U.S. dollars .


Example 270:
data: Year|2017|x|line_chart Savings_rate|14.6|y|line_chart Year|2016|x|line_chart Savings_rate|14|y|line_chart Year|2015|x|line_chart Savings_rate|14.5|y|line_chart Year|2014|x|line_chart Savings_rate|14.4|y|line_chart Year|2013|x|line_chart Savings_rate|14.3|y|line_chart Year|2012|x|line_chart Savings_rate|15.1|y|line_chart Year|2011|x|line_chart Savings_rate|15.6|y|line_chart Year|2010|x|line_chart Savings_rate|15.8|y|line_chart 
title: French households savings rate 2010 - 2017

gold: The statistic presents the personal savings rate in France from 2010 to 2017 . The personal savings rate amounted to 14.6 percent in the first quarter of 2017 in France . The savings rate refers to the amount of money , expressed as a percentage or ratio , that a person deducts from his disposable personal income .
gold_template: The statistic presents the personal templateYLabel[0] templateYLabel[1] in France from templateXValue[min] to templateXValue[max] . The personal templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] percent in the first quarter of templateXValue[max] in France . The templateYLabel[0] templateYLabel[1] refers to the amount of money , expressed as a percentage or ratio , that a person deducts from his disposable personal income .

generated_template: Over templateYValue[max] million people consumed in templateXValue[max] , up from templateYValue[1] million in the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] has increased by over the last decade .
generated: Over 15.8 million people consumed in 2017 , up from 14 million in the previous Year . The Savings rate yLabelErr savings rate 2010 has increased by over the last decade .


Example 271:
data: Year|2020|x|line_chart Franchise_value_in_million_U.S._dollars|2000|y|line_chart Year|2019|x|line_chart Franchise_value_in_million_U.S._dollars|1650|y|line_chart Year|2018|x|line_chart Franchise_value_in_million_U.S._dollars|1180|y|line_chart Year|2017|x|line_chart Franchise_value_in_million_U.S._dollars|800|y|line_chart Year|2016|x|line_chart Franchise_value_in_million_U.S._dollars|700|y|line_chart Year|2015|x|line_chart Franchise_value_in_million_U.S._dollars|700|y|line_chart Year|2014|x|line_chart Franchise_value_in_million_U.S._dollars|469|y|line_chart Year|2013|x|line_chart Franchise_value_in_million_U.S._dollars|418|y|line_chart Year|2012|x|line_chart Franchise_value_in_million_U.S._dollars|314|y|line_chart Year|2011|x|line_chart Franchise_value_in_million_U.S._dollars|330|y|line_chart Year|2010|x|line_chart Franchise_value_in_million_U.S._dollars|344|y|line_chart Year|2009|x|line_chart Franchise_value_in_million_U.S._dollars|360|y|line_chart Year|2008|x|line_chart Franchise_value_in_million_U.S._dollars|380|y|line_chart Year|2007|x|line_chart Franchise_value_in_million_U.S._dollars|373|y|line_chart Year|2006|x|line_chart Franchise_value_in_million_U.S._dollars|351|y|line_chart Year|2005|x|line_chart Franchise_value_in_million_U.S._dollars|342|y|line_chart Year|2004|x|line_chart Franchise_value_in_million_U.S._dollars|328|y|line_chart Year|2003|x|line_chart Franchise_value_in_million_U.S._dollars|298|y|line_chart 
title: Franchise value of the Philadelphia 76ers ( NBA ) 2003 - 2020

gold: This graph depicts the value of the Philadelphia 76ers franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the franchise had an estimated value of 2 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the value of the Philadelphia 76ers NBA Franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the Franchise had an estimated value of 2000 billion U.S. dollars .


Example 272:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|4.33|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|4.23|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|4.18|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|4.15|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|4.18|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|4.21|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|3.75|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|4.43|y|line_chart Year|2016|x|line_chart Inflation_rate_compared_to_previous_year|4.45|y|line_chart Year|2015|x|line_chart Inflation_rate_compared_to_previous_year|2.39|y|line_chart Year|2014|x|line_chart Inflation_rate_compared_to_previous_year|3.42|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|4.34|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|3.78|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|6.22|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|3.86|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|1.86|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|11.36|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|6.82|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|6.56|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|9.11|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|7.58|y|line_chart Year|2003|x|line_chart Inflation_rate_compared_to_previous_year|5.6|y|line_chart Year|2002|x|line_chart Inflation_rate_compared_to_previous_year|8.14|y|line_chart Year|2001|x|line_chart Inflation_rate_compared_to_previous_year|7.28|y|line_chart Year|2000|x|line_chart Inflation_rate_compared_to_previous_year|5.98|y|line_chart Year|1999|x|line_chart Inflation_rate_compared_to_previous_year|5.21|y|line_chart Year|1998|x|line_chart Inflation_rate_compared_to_previous_year|6.62|y|line_chart Year|1997|x|line_chart Inflation_rate_compared_to_previous_year|9.24|y|line_chart Year|1996|x|line_chart Inflation_rate_compared_to_previous_year|11.06|y|line_chart Year|1995|x|line_chart Inflation_rate_compared_to_previous_year|8.41|y|line_chart Year|1994|x|line_chart Inflation_rate_compared_to_previous_year|12.51|y|line_chart Year|1993|x|line_chart Inflation_rate_compared_to_previous_year|13.36|y|line_chart Year|1992|x|line_chart Inflation_rate_compared_to_previous_year|10.24|y|line_chart Year|1991|x|line_chart Inflation_rate_compared_to_previous_year|35.11|y|line_chart Year|1990|x|line_chart Inflation_rate_compared_to_previous_year|37.98|y|line_chart Year|1989|x|line_chart Inflation_rate_compared_to_previous_year|12.99|y|line_chart Year|1988|x|line_chart Inflation_rate_compared_to_previous_year|10.3|y|line_chart Year|1987|x|line_chart Inflation_rate_compared_to_previous_year|10.85|y|line_chart Year|1986|x|line_chart Inflation_rate_compared_to_previous_year|32.78|y|line_chart Year|1985|x|line_chart Inflation_rate_compared_to_previous_year|19.18|y|line_chart Year|1984|x|line_chart Inflation_rate_compared_to_previous_year|3.21|y|line_chart 
title: Inflation rate in Guatemala 2024*

gold: This statistic shows the average inflation rate in Guatemala from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Guatemala amounted to about 3.75 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Guatemala from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Guatemala amounted to about 3.75 percent compared to the previous Year .


Example 273:
data: Month|Fannie_Mae|x|bar_chart Revenue_in_billion_U.S._dollars|120.1|y|bar_chart Month|Freddie_Mac|x|bar_chart Revenue_in_billion_U.S._dollars|73.6|y|bar_chart Month|American_Express|x|bar_chart Revenue_in_billion_U.S._dollars|43.28|y|bar_chart Month|INTL_FCStone|x|bar_chart Revenue_in_billion_U.S._dollars|27.66|y|bar_chart Month|Icahn_Enterprises|x|bar_chart Revenue_in_billion_U.S._dollars|18.98|y|bar_chart Month|Synchrony_Financial|x|bar_chart Revenue_in_billion_U.S._dollars|18.25|y|bar_chart Month|Marsh_&_McLennan|x|bar_chart Revenue_in_billion_U.S._dollars|14.95|y|bar_chart Month|Ameriprise_Financial|x|bar_chart Revenue_in_billion_U.S._dollars|12.92|y|bar_chart Month|Ally_Financial|x|bar_chart Revenue_in_billion_U.S._dollars|10.47|y|bar_chart Month|Voya_Financial|x|bar_chart Revenue_in_billion_U.S._dollars|8.93|y|bar_chart 
title: Leading diversified financial service companies in the U.S. 2018 , by revenue

gold: The statistic displays the leading diversified financial service companies in the United States in 2018 , by revenue . In that year , Fannie Mae was ranked first with revenue of around 120.1 billion U.S. dollars .
gold_template: The statistic displays the templateTitle[0] templateTitle[1] templateXValue[5] templateTitle[3] templateTitle[4] in the templateTitle[5] in templateTitle[6] , templateTitle[7] templateYLabel[0] . In that year , templateXValue[0] templateXValue[0] was ranked first with templateYLabel[0] of around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[2] templateYLabel[3] in the United States in templateTitle[5] , templateTitle[3] templateXLabel[0] . In templateTitle[4] , templateXValue[0] templateXValue[0] generated templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[2] .
generated: The statistic shows the Revenue of diversified U.S. dollars in the United States in U.S. , service Month . In companies , Fannie_Mae generated 120.1 billion U.S. dollars in financial .


Example 274:
data: Year|2019|x|line_chart Percentage_of_change|-1.3|y|line_chart Year|2018|x|line_chart Percentage_of_change|7.8|y|line_chart Year|2017|x|line_chart Percentage_of_change|6.6|y|line_chart Year|2016|x|line_chart Percentage_of_change|-3.6|y|line_chart Year|2015|x|line_chart Percentage_of_change|-7.6|y|line_chart Year|2014|x|line_chart Percentage_of_change|2.6|y|line_chart Year|2013|x|line_chart Percentage_of_change|2|y|line_chart Year|2012|x|line_chart Percentage_of_change|4.3|y|line_chart Year|2011|x|line_chart Percentage_of_change|16.2|y|line_chart Year|2010|x|line_chart Percentage_of_change|20.5|y|line_chart Year|2009|x|line_chart Percentage_of_change|-18.2|y|line_chart Year|2008|x|line_chart Percentage_of_change|12.3|y|line_chart Year|2007|x|line_chart Percentage_of_change|11.9|y|line_chart Year|2006|x|line_chart Percentage_of_change|14|y|line_chart Year|2005|x|line_chart Percentage_of_change|10.9|y|line_chart Year|2004|x|line_chart Percentage_of_change|12.8|y|line_chart Year|2003|x|line_chart Percentage_of_change|4.6|y|line_chart Year|2002|x|line_chart Percentage_of_change|-4.6|y|line_chart Year|2001|x|line_chart Percentage_of_change|-6.8|y|line_chart Year|2000|x|line_chart Percentage_of_change|12.4|y|line_chart Year|1999|x|line_chart Percentage_of_change|4.2|y|line_chart Year|1998|x|line_chart Percentage_of_change|-1.2|y|line_chart Year|1997|x|line_chart Percentage_of_change|10.8|y|line_chart Year|1996|x|line_chart Percentage_of_change|6.4|y|line_chart Year|1995|x|line_chart Percentage_of_change|14.4|y|line_chart Year|1994|x|line_chart Percentage_of_change|10|y|line_chart Year|1993|x|line_chart Percentage_of_change|3.9|y|line_chart Year|1992|x|line_chart Percentage_of_change|6.2|y|line_chart Year|1991|x|line_chart Percentage_of_change|6.9|y|line_chart Year|1990|x|line_chart Percentage_of_change|7.6|y|line_chart 
title: Change in U.S. exports of trade goods 1990 - 2018

gold: This graph shows the percentage of change in the U.S. export volume of trade goods from 1990 to 2019 . In 2019 , U.S. exports decreased by 1.3 percent from the previous year .
gold_template: This graph shows the templateYLabel[0] of templateYLabel[1] in the templateTitle[1] export volume of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[1] templateTitle[2] decreased by 1.3 percent from the previous templateXLabel[0] .

generated_template: In templateXValue[max] , there were templateYValue[0] templateTitle[1] templateTitle[2] templateYLabel[1] in the United States . This was a slight decrease from the previous templateXLabel[0] 's history with the highest rate compared to the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] among the templateYLabel[1] rate was the highest templateTitle[1] templateTitle[2] templateYLabel[1] 100,000 people in the United States .
generated: In 2019 , there were -1.3 U.S. exports change in the United States . This was a slight decrease from the previous Year 's history with the highest rate compared to the previous Year . The Percentage change among the change rate was the highest U.S. exports change 100,000 people in the United States .


Example 275:
data: Year|2016|x|line_chart Consumption_in_thousand_metric_tons|2395070|y|line_chart Year|2015|x|line_chart Consumption_in_thousand_metric_tons|2339000|y|line_chart Year|2014|x|line_chart Consumption_in_thousand_metric_tons|2454081|y|line_chart Year|2013|x|line_chart Consumption_in_thousand_metric_tons|2206000|y|line_chart Year|2012|x|line_chart Consumption_in_thousand_metric_tons|2160000|y|line_chart Year|2011|x|line_chart Consumption_in_thousand_metric_tons|2055224|y|line_chart Year|2010|x|line_chart Consumption_in_thousand_metric_tons|1872921|y|line_chart Year|2009|x|line_chart Consumption_in_thousand_metric_tons|1642216|y|line_chart Year|2008|x|line_chart Consumption_in_thousand_metric_tons|1375720|y|line_chart Year|2007|x|line_chart Consumption_in_thousand_metric_tons|1345338|y|line_chart Year|2006|x|line_chart Consumption_in_thousand_metric_tons|1218128|y|line_chart 
title: Consumption of cement in China 2016

gold: The statistic displays the consumption of cement in China from 2006 to 2016 . In 2016 , the country consumed around 2.4 billion metric tons of cement .
gold_template: The statistic displays the templateYLabel[0] of templateTitle[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the country consumed around templateYValue[0] billion templateYLabel[2] templateYLabel[3] of templateTitle[1] .

generated_template: There were templateYValue[0] thousand templateYLabel[2] templateYLabel[3] in the United States in templateXValue[max] . This was a decrease from templateXValue[1] to the previous templateXLabel[0] . The templateYLabel[0] volume of templateYLabel[2] templateYLabel[3] in Japan , but has increased significantly significantly in recent years and peaked in templateXValue[1] .
generated: There were 2395070 thousand metric tons in the United States in 2016 . This was a decrease from 2015 to the previous Year . The Consumption volume of metric tons in Japan , but has increased significantly in recent years and peaked in 2015 .


Example 276:
data: Industry|Social_protection|x|bar_chart Amount_budgeted_in_billion_GBP|256|y|bar_chart Industry|Health|x|bar_chart Amount_budgeted_in_billion_GBP|166|y|bar_chart Industry|Education|x|bar_chart Amount_budgeted_in_billion_GBP|103|y|bar_chart Industry|Other_(including_EU_transactions|x|bar_chart Amount_budgeted_in_billion_GBP|58|y|bar_chart Industry|Defense|x|bar_chart Amount_budgeted_in_billion_GBP|52|y|bar_chart Industry|Net_debt_increase|x|bar_chart Amount_budgeted_in_billion_GBP|43|y|bar_chart Industry|Transport|x|bar_chart Amount_budgeted_in_billion_GBP|37|y|bar_chart Industry|Public_order_and_safety|x|bar_chart Amount_budgeted_in_billion_GBP|35|y|bar_chart Industry|Personal_social_services|x|bar_chart Amount_budgeted_in_billion_GBP|34|y|bar_chart Industry|Housing_and_environment|x|bar_chart Amount_budgeted_in_billion_GBP|32|y|bar_chart Industry|Housing_and_community_amenities|x|bar_chart Amount_budgeted_in_billion_GBP|32|y|bar_chart Industry|Industry_agriculture_and_employment|x|bar_chart Amount_budgeted_in_billion_GBP|25|y|bar_chart 
title: Budgeted public sector spending in the United Kingdom 2019/20 by function

gold: This statistic presents what the government of the United Kingdom has budgeted for public sector spending for the fiscal year 2019/20 , in billion British Pounds . During this year the government has budgeted 256 billion pounds for social protection , which includes spending on pensions and other welfare benefits . Government spending on health is expected to be 166 billion pounds , the second highest spending function in this fiscal year .
gold_template: This statistic presents what the government of the templateTitle[4] templateTitle[5] has templateYLabel[1] for templateXValue[7] templateTitle[2] templateTitle[3] for the fiscal year templateTitle[6] , in templateYLabel[2] British Pounds . During this year the government has templateYLabel[1] templateYValue[max] templateYLabel[2] pounds for templateXValue[0] templateXValue[0] , which includes templateTitle[3] on pensions and templateXValue[3] welfare benefits . Government templateTitle[3] on templateXValue[1] is expected to be templateYValue[1] templateYLabel[2] pounds , the second highest templateTitle[3] templateTitle[8] in this fiscal year .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of crimes templateYLabel[3] in templateTitle[3] United States in templateTitle[4] , sorted templateTitle[5] templateXLabel[0] . In templateTitle[4] , there were templateYValue[max] percent of the templateXLabel[0] templateYLabel[1] in templateTitle[4] templateXValue[0] templateXValue[0] in the United States .
generated: This statistic shows the Amount budgeted of crimes GBP in spending United States in United , sorted Kingdom Industry . In United , there were 256 percent of the Industry budgeted in United Social_protection in the United States .


Example 277:
data: Entrepreneur_(company)|Ma_Huateng_(Tencent)|x|bar_chart Net_worth_in_billion_U.S._dollars|21.9|y|bar_chart Entrepreneur_(company)|William_Ding_(NetEase)|x|bar_chart Net_worth_in_billion_U.S._dollars|11.5|y|bar_chart Entrepreneur_(company)|Shi_Yuzhu_(Giant_Interactive)|x|bar_chart Net_worth_in_billion_U.S._dollars|5.4|y|bar_chart Entrepreneur_(company)|Kwon_Hyuk-Bin_(SmileGate)|x|bar_chart Net_worth_in_billion_U.S._dollars|4.9|y|bar_chart Entrepreneur_(company)|Kim_Jung-Ju_(Nexon)|x|bar_chart Net_worth_in_billion_U.S._dollars|3.5|y|bar_chart 
title: Video game industry 's wealthiest entrepreneurs 2016

gold: The graph shows the estimated net worth of the wealthiest entrepreneurs in the video game industry worldwide as of July 2016 . According to the source , Ma Huateng , the founder and chairman as well as CEO of Tencent , was worth 21.9 billion U.S. dollars in the measured period . Overall , Tencent reported 70.84 billion Chinese yuan revenue from its online games in 2016 .
gold_template: The graph shows the estimated templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] in the templateTitle[0] templateTitle[1] templateTitle[2] worldwide as of July templateTitle[6] . According to the source , templateXValue[0] templateXValue[0] , the founder and chairman as well as CEO of Tencent , was templateYLabel[1] templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the measured period . Overall , Tencent reported 70.84 templateYLabel[2] Chinese yuan revenue from its online games in templateTitle[6] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] on templateTitle[4] templateTitle[5] in the United States as of templateTitle[7] . During the survey period , it was found that templateYValue[0] percent of the templateYLabel[1] with a templateXValue[0] templateXValue[0] 's templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Video game in industry 's on wealthiest entrepreneurs in the United States as of titleErr . During the survey period , it was found that 21.9 percent of the worth with a Ma_Huateng_(Tencent) 's U.S. dollars yLabelErr .


Example 278:
data: Year|2019|x|line_chart Unemployment_rate|6.52|y|line_chart Year|2018|x|line_chart Unemployment_rate|6.46|y|line_chart Year|2017|x|line_chart Unemployment_rate|6.44|y|line_chart Year|2016|x|line_chart Unemployment_rate|6.61|y|line_chart Year|2015|x|line_chart Unemployment_rate|6.76|y|line_chart Year|2014|x|line_chart Unemployment_rate|7.63|y|line_chart Year|2013|x|line_chart Unemployment_rate|8.51|y|line_chart Year|2012|x|line_chart Unemployment_rate|9.31|y|line_chart Year|2011|x|line_chart Unemployment_rate|10.36|y|line_chart Year|2010|x|line_chart Unemployment_rate|10.54|y|line_chart Year|2009|x|line_chart Unemployment_rate|10.36|y|line_chart Year|2008|x|line_chart Unemployment_rate|9.32|y|line_chart Year|2007|x|line_chart Unemployment_rate|9.5|y|line_chart Year|2006|x|line_chart Unemployment_rate|10.03|y|line_chart Year|2005|x|line_chart Unemployment_rate|9.09|y|line_chart Year|2004|x|line_chart Unemployment_rate|7.96|y|line_chart Year|2003|x|line_chart Unemployment_rate|6.78|y|line_chart Year|2002|x|line_chart Unemployment_rate|5.65|y|line_chart Year|2001|x|line_chart Unemployment_rate|5.61|y|line_chart Year|2000|x|line_chart Unemployment_rate|5.6|y|line_chart Year|1999|x|line_chart Unemployment_rate|5.7|y|line_chart 
title: Unemployment rate in Senegal 2019

gold: This statistic shows the unemployment rate in Senegal from 1999 to 2019 . The figures are ILO estimates . In 2019 , the estimated unemployment rate in Senegal was 6.52 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . The figures are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] in templateTitle[2] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Senegal from 1999 to 2019 . In 2019 , the Unemployment rate in Senegal was at approximately 6.52 percent .


Example 279:
data: Year|2024|x|line_chart Share_of_global_GDP|1.23|y|line_chart Year|2023|x|line_chart Share_of_global_GDP|1.25|y|line_chart Year|2022|x|line_chart Share_of_global_GDP|1.27|y|line_chart Year|2021|x|line_chart Share_of_global_GDP|1.3|y|line_chart Year|2020|x|line_chart Share_of_global_GDP|1.32|y|line_chart Year|2019|x|line_chart Share_of_global_GDP|1.34|y|line_chart Year|2018|x|line_chart Share_of_global_GDP|1.36|y|line_chart Year|2017|x|line_chart Share_of_global_GDP|1.38|y|line_chart Year|2016|x|line_chart Share_of_global_GDP|1.39|y|line_chart Year|2015|x|line_chart Share_of_global_GDP|1.42|y|line_chart Year|2014|x|line_chart Share_of_global_GDP|1.46|y|line_chart 
title: Canada 's share of global gross domestic product ( GDP ) 2024

gold: The statistic shows Canada 's share of the global gross domestic product ( GDP ) adjusted for Purchasing Power Parity ( PPP ) from 2014 to 2024 . In 2018 , Canada 's share of the global GDP ( in relation to PPP dollars ) amounted to about 1.36 percent .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] of the templateYLabel[1] templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitle[0] templateTitle[1] templateYLabel[0] of the templateYLabel[1] templateYLabel[2] ( in relation to PPP dollars ) amounted to about templateYValue[6] percent .

generated_template: The statistic shows the 20 templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of the templateYLabel[1] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] ( in relation to PPP dollars ) amounted to about templateYValue[6] percent .
generated: The statistic shows the 20 Canada 's share global of the global domestic product ( GDP ) adjusted for Purchasing Power Parity ( PPP ) from 2014 to 2024 . In 2018 , the Canada 's share Share of global GDP ( in relation to PPP dollars ) amounted to about 1.36 percent .


Example 280:
data: Year|2018|x|line_chart At-risk-of-poverty_rate|16.4|y|line_chart Year|2017|x|line_chart At-risk-of-poverty_rate|15.9|y|line_chart Year|2016|x|line_chart At-risk-of-poverty_rate|15.5|y|line_chart Year|2015|x|line_chart At-risk-of-poverty_rate|14.9|y|line_chart Year|2014|x|line_chart At-risk-of-poverty_rate|15.5|y|line_chart Year|2013|x|line_chart At-risk-of-poverty_rate|15.1|y|line_chart Year|2012|x|line_chart At-risk-of-poverty_rate|15.3|y|line_chart Year|2011|x|line_chart At-risk-of-poverty_rate|15.3|y|line_chart Year|2010|x|line_chart At-risk-of-poverty_rate|14.6|y|line_chart Year|2009|x|line_chart At-risk-of-poverty_rate|14.6|y|line_chart Year|2008|x|line_chart At-risk-of-poverty_rate|14.7|y|line_chart 
title: Poverty risk rate in Belgium 2008 - 2018

gold: Between 2008 and 2018 , roughly 15 percent of the Belgium population was at risk of poverty , defined by Statistics Belgium as 60 percent of the average disposable income in the country . People with an income lower than this are considered poor . In 2018 , this definition matched nearly 16 percent of the Belgians .
gold_template: Between templateXValue[min] and templateXValue[max] , roughly templateYValue[3] percent of the templateTitle[3] population was at templateTitle[1] of templateTitle[0] , defined by Statistics templateTitle[3] as 60 percent of the average disposable income in the country . People with an income lower than this are considered poor . In templateXValue[max] , this definition matched nearly templateYValue[max] percent of the Belgians .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[max] million people attended by the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the At-risk-of-poverty rate of 2008 2018 in the United States from 2008 to 2018 . In 2018 , about 16.4 million people attended by the rate Belgium 2008 2018 in the United States .


Example 281:
data: Response|Swish|x|bar_chart Share_of_respondents|75|y|bar_chart Response|PayPal_Mobile|x|bar_chart Share_of_respondents|14|y|bar_chart Response|WyWallet|x|bar_chart Share_of_respondents|3|y|bar_chart Response|Other|x|bar_chart Share_of_respondents|5|y|bar_chart Response|None|x|bar_chart Share_of_respondents|21|y|bar_chart 
title: Most popular mobile payment services in Sweden 2017

gold: Swish was the most popular mobile payment service in Sweden in 2017 . The share of respondents who used Swish amounted to 75 percent . It was followed by PayPal Mobile , which was used by 14 percent of respondents that year .
gold_template: templateXValue[0] was the templateTitle[0] templateTitle[1] templateXValue[1] templateTitle[3] service in templateTitle[5] in templateTitle[6] . The templateYLabel[0] of templateYLabel[1] who used templateXValue[0] amounted to templateYValue[max] percent . It was followed by templateXValue[1] templateXValue[1] , which was used by templateYValue[1] percent of templateYLabel[1] that year .

generated_template: This statistic shows the results of a survey conducted in the templateTitle[0] templateTitle[1] in templateTitle[3] templateTitle[4] on whether templateTitle[5] templateTitle[6] templateTitle[7] templateXValue[1] templateXValue[0] templateXValue[0] . During the survey , templateYValue[max] percent of templateYLabel[1] stated that they would be willing to templateXValue[1] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] and templateXValue[0] templateXValue[0] .
generated: This statistic shows the results of a survey conducted in the Most popular in payment services on whether Sweden 2017 titleErr PayPal_Mobile Swish . During the survey , 75 percent of respondents stated that they would be willing to PayPal_Mobile Swish Swish and Swish .


Example 282:
data: Quarter|Q3_2017|x|bar_chart Price_difference_in_percent|-18.3|y|bar_chart Quarter|Q2_2017|x|bar_chart Price_difference_in_percent|-15.7|y|bar_chart Quarter|Q1_2017|x|bar_chart Price_difference_in_percent|-20.1|y|bar_chart Quarter|Q4_2016|x|bar_chart Price_difference_in_percent|-18.1|y|bar_chart Quarter|Q3_2016|x|bar_chart Price_difference_in_percent|-15.3|y|bar_chart Quarter|Q2_2016|x|bar_chart Price_difference_in_percent|-17.8|y|bar_chart Quarter|Q1_2016|x|bar_chart Price_difference_in_percent|-17.6|y|bar_chart 
title: Natural and synthetic diamonds price difference Q1 2016-Q3 2017

gold: This statistic shows the price difference between synthetic/lab-created diamonds and natural diamonds from the first quarter of 2016 ( Q1 2016 ) to the third quarter of 2017 ( Q3 2017 ) in percent . In the second quarter of 2017 , lab-created diamonds cost some 15.7 percent less than naturally occurring diamonds .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] between synthetic/lab-created templateTitle[2] and templateTitle[0] templateTitle[2] from the first templateXLabel[0] of templateXValue[3] ( templateXValue[2] templateXValue[3] ) to the third templateXLabel[0] of templateXValue[0] ( templateXValue[0] templateXValue[0] ) in templateYLabel[2] . In the second templateXLabel[0] of templateXValue[0] , lab-created templateTitle[2] cost some 15.7 templateYLabel[2] less than naturally occurring templateTitle[2] .

generated_template: During the first templateXLabel[0] of templateXValue[0] , it was found that templateYValue[max] percent of templateYLabel[1] templateYLabel[2] internet users in the United States were between the first templateXLabel[0] of templateXValue[0] . A among the company 's templateTitle[1] templateYLabel[1] selected countries was reached its templateXValue[2] the first templateXLabel[0] of templateXValue[0] , which has seen a high level of history when it by the number of people . However , which has paid out in the United States has seen in the first templateXLabel[0] .
generated: During the first Quarter of Q3_2017 , it was found that -15.3 percent of difference percent internet users in the United States were between the first Quarter of Q3_2017 . A among the company 's synthetic difference selected countries was reached its Q1_2017 the first Quarter of Q3_2017 , which has seen a high level of history when it by the number of people . However , which has paid out in the United States has seen in the first Quarter .


Example 283:
data: Year|2020|x|line_chart Spending_in_billion_U.S._dollars|94.3|y|line_chart Year|2019|x|line_chart Spending_in_billion_U.S._dollars|97.8|y|line_chart Year|2018|x|line_chart Spending_in_billion_U.S._dollars|105.9|y|line_chart Year|2017|x|line_chart Spending_in_billion_U.S._dollars|95.6|y|line_chart Year|2016|x|line_chart Spending_in_billion_U.S._dollars|67.8|y|line_chart Year|2015|x|line_chart Spending_in_billion_U.S._dollars|65.2|y|line_chart Year|2014|x|line_chart Spending_in_billion_U.S._dollars|66.1|y|line_chart Year|2013|x|line_chart Spending_in_billion_U.S._dollars|55.3|y|line_chart Year|2012|x|line_chart Spending_in_billion_U.S._dollars|59.0|y|line_chart Year|2011|x|line_chart Spending_in_billion_U.S._dollars|67.4|y|line_chart Year|2010|x|line_chart Spending_in_billion_U.S._dollars|54.0|y|line_chart Year|2009|x|line_chart Spending_in_billion_U.S._dollars|26.1|y|line_chart Year|2008|x|line_chart Spending_in_billion_U.S._dollars|43.4|y|line_chart Year|2007|x|line_chart Spending_in_billion_U.S._dollars|61.0|y|line_chart Year|2006|x|line_chart Spending_in_billion_U.S._dollars|57.5|y|line_chart Year|2005|x|line_chart Spending_in_billion_U.S._dollars|48.1|y|line_chart Year|2004|x|line_chart Spending_in_billion_U.S._dollars|47.7|y|line_chart Year|2003|x|line_chart Spending_in_billion_U.S._dollars|31.3|y|line_chart Year|2002|x|line_chart Spending_in_billion_U.S._dollars|27.5|y|line_chart Year|2001|x|line_chart Spending_in_billion_U.S._dollars|38.7|y|line_chart Year|2000|x|line_chart Spending_in_billion_U.S._dollars|61.3|y|line_chart 
title: Capital spending in the semiconductor industry worldwide 2000 - 2020

gold: The statistic depicts the total capital spending in the global semiconductor industry from 2000 to 2018 and shows a forecast for 2019 and 2020 . In 2019 , global semiconductor capital spending is projected to amount to 97.8 billion U.S. dollars , down from the all-time highest spending of 105.9 billion U.S. dollars in 2018 .
gold_template: The statistic depicts the total templateTitle[0] templateYLabel[0] in the global templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[2] and shows a forecast for templateXValue[1] and templateXValue[max] . In templateXValue[1] , global templateTitle[2] templateTitle[0] templateYLabel[0] is projected to amount to templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] , down from the all-time highest templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[2] .

generated_template: This statistic shows the total templateTitle[0] templateYLabel[0] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[2] , global templateTitle[2] templateTitle[3] templateYLabel[0] amounted to templateYValue[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the total Capital Spending in the United States from 2000 to 2020 . In 2018 , global semiconductor industry Spending amounted to 105.9 billion U.S. dollars .


Example 284:
data: State|California|x|bar_chart Number_of_forcible_rapes|15505|y|bar_chart State|Texas|x|bar_chart Number_of_forcible_rapes|14693|y|bar_chart State|Florida|x|bar_chart Number_of_forcible_rapes|8438|y|bar_chart State|Michigan|x|bar_chart Number_of_forcible_rapes|7690|y|bar_chart State|New_York|x|bar_chart Number_of_forcible_rapes|6575|y|bar_chart State|Illinois|x|bar_chart Number_of_forcible_rapes|5859|y|bar_chart State|Ohio|x|bar_chart Number_of_forcible_rapes|5300|y|bar_chart State|Pennsylvania|x|bar_chart Number_of_forcible_rapes|4483|y|bar_chart State|Colorado|x|bar_chart Number_of_forcible_rapes|4070|y|bar_chart State|Arizona|x|bar_chart Number_of_forcible_rapes|3638|y|bar_chart State|Washington|x|bar_chart Number_of_forcible_rapes|3413|y|bar_chart State|Virginia|x|bar_chart Number_of_forcible_rapes|2924|y|bar_chart State|Missouri|x|bar_chart Number_of_forcible_rapes|2912|y|bar_chart State|Tennessee|x|bar_chart Number_of_forcible_rapes|2821|y|bar_chart State|Georgia|x|bar_chart Number_of_forcible_rapes|2651|y|bar_chart State|North_Carolina|x|bar_chart Number_of_forcible_rapes|2633|y|bar_chart State|Minnesota|x|bar_chart Number_of_forcible_rapes|2462|y|bar_chart State|South_Carolina|x|bar_chart Number_of_forcible_rapes|2434|y|bar_chart State|Massachusetts|x|bar_chart Number_of_forcible_rapes|2410|y|bar_chart State|Indiana|x|bar_chart Number_of_forcible_rapes|2370|y|bar_chart State|Nevada|x|bar_chart Number_of_forcible_rapes|2329|y|bar_chart State|Oklahoma|x|bar_chart Number_of_forcible_rapes|2299|y|bar_chart State|Wisconsin|x|bar_chart Number_of_forcible_rapes|2248|y|bar_chart State|Arkansas|x|bar_chart Number_of_forcible_rapes|2196|y|bar_chart State|Louisiana|x|bar_chart Number_of_forcible_rapes|2085|y|bar_chart State|Alabama|x|bar_chart Number_of_forcible_rapes|1996|y|bar_chart State|Maryland|x|bar_chart Number_of_forcible_rapes|1979|y|bar_chart State|Oregon|x|bar_chart Number_of_forcible_rapes|1975|y|bar_chart State|Utah|x|bar_chart Number_of_forcible_rapes|1753|y|bar_chart State|Kentucky|x|bar_chart Number_of_forcible_rapes|1707|y|bar_chart State|Kansas|x|bar_chart Number_of_forcible_rapes|1567|y|bar_chart State|New_Jersey|x|bar_chart Number_of_forcible_rapes|1424|y|bar_chart State|New_Mexico|x|bar_chart Number_of_forcible_rapes|1354|y|bar_chart State|Nebraska|x|bar_chart Number_of_forcible_rapes|1233|y|bar_chart State|Alaska|x|bar_chart Number_of_forcible_rapes|1192|y|bar_chart State|Iowa|x|bar_chart Number_of_forcible_rapes|976|y|bar_chart State|Connecticut|x|bar_chart Number_of_forcible_rapes|840|y|bar_chart State|Idaho|x|bar_chart Number_of_forcible_rapes|791|y|bar_chart State|West_Virginia|x|bar_chart Number_of_forcible_rapes|652|y|bar_chart State|Hawaii|x|bar_chart Number_of_forcible_rapes|625|y|bar_chart State|South_Dakota|x|bar_chart Number_of_forcible_rapes|614|y|bar_chart State|Montana|x|bar_chart Number_of_forcible_rapes|551|y|bar_chart State|Mississippi|x|bar_chart Number_of_forcible_rapes|537|y|bar_chart State|New_Hampshire|x|bar_chart Number_of_forcible_rapes|534|y|bar_chart State|Rhode_Island|x|bar_chart Number_of_forcible_rapes|481|y|bar_chart State|District_of_Columbia|x|bar_chart Number_of_forcible_rapes|450|y|bar_chart State|Maine|x|bar_chart Number_of_forcible_rapes|446|y|bar_chart State|North_Dakota|x|bar_chart Number_of_forcible_rapes|397|y|bar_chart State|Delaware|x|bar_chart Number_of_forcible_rapes|338|y|bar_chart State|Vermont|x|bar_chart Number_of_forcible_rapes|287|y|bar_chart State|Wyoming|x|bar_chart Number_of_forcible_rapes|243|y|bar_chart 
title: Number of forcible rape cases by state U.S. 2018

gold: In 2018 , California had the highest number of forcible rape cases in the United States , with 15,505 reported rapes . Vermont had the lowest number of reported forcible rape cases at 243 . Number vs. rate It is perhaps unsurprising that California had the highest number of reported rapes in the United States in 2018 , as California is the state with the highest population .
gold_template: In templateTitle[7] , templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] in the templateTitle[6] , with templateYValue[max] reported templateYLabel[2] . templateXValue[49] had the lowest templateYLabel[0] of reported templateYLabel[1] templateTitle[2] templateTitle[3] at templateYValue[min] . templateYLabel[0] vs. rate It is perhaps unsurprising that templateXValue[0] had the highest templateYLabel[0] of reported templateYLabel[2] in the templateTitle[6] in templateTitle[7] , as templateXValue[0] is the templateXLabel[0] with the highest population .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the United States from templateXValue[last] to templateXValue[0] . In templateXValue[0] , about templateYValue[max] percent of the United States had the highest templateYLabel[0] templateYLabel[1] throughout the templateXLabel[0] .
generated: This statistic shows the Number forcible forcible in the United States from Wyoming to California . In California , about 15505 percent of the United States had the highest Number forcible throughout the State .


Example 285:
data: Year|2050|x|line_chart Emissions_in_metric_tons_of_CO2_equivalent/_person|12.6|y|line_chart Year|2045|x|line_chart Emissions_in_metric_tons_of_CO2_equivalent/_person|12.5|y|line_chart Year|2040|x|line_chart Emissions_in_metric_tons_of_CO2_equivalent/_person|12.6|y|line_chart Year|2035|x|line_chart Emissions_in_metric_tons_of_CO2_equivalent/_person|12.8|y|line_chart Year|2030|x|line_chart Emissions_in_metric_tons_of_CO2_equivalent/_person|13.1|y|line_chart Year|2025|x|line_chart Emissions_in_metric_tons_of_CO2_equivalent/_person|13.7|y|line_chart Year|2020|x|line_chart Emissions_in_metric_tons_of_CO2_equivalent/_person|15.0|y|line_chart Year|2019|x|line_chart Emissions_in_metric_tons_of_CO2_equivalent/_person|15.5|y|line_chart 
title: Carbon dioxide emissions per person in the U.S. 2019 - 2050

gold: Per capita carbon dioxide ( CO2 ) emissions in the United States reached an estimate of 15.5 metric tons of carbon dioxide in 2019 . The United States has forecast that a decrease in CO2 emissions will occur through 2050 , reaching 12.6 metric tons of carbon dioxide equivalent per person . This forecast is driven by increasing consumption of natural gas due to lower cost and emissions with higher efficiency .
gold_template: templateTitle[3] capita templateTitle[0] templateTitle[1] ( templateYLabel[3] ) templateYLabel[0] in the templateTitle[5] reached an estimate of templateYValue[max] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] in templateXValue[min] . The templateTitle[5] has forecast that a decrease in templateYLabel[3] templateYLabel[0] will occur through templateXValue[max] , reaching templateYValue[0] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] equivalent templateTitle[3] templateYLabel[5] . This forecast is driven by increasing consumption of natural gas due to lower cost and templateYLabel[0] with higher efficiency .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[0] thousand people living in the templateTitle[6] .
generated: This statistic presents the Emissions metric of person U.S. in the United States from 2019 to 2050 . In 2050 , there were approximately 12.6 thousand people living in the 2019 .


Example 286:
data: Year|2016|x|line_chart Data_volume_in_petabytes|96|y|line_chart Year|2015|x|line_chart Data_volume_in_petabytes|62|y|line_chart Year|2014|x|line_chart Data_volume_in_petabytes|44|y|line_chart Year|2013|x|line_chart Data_volume_in_petabytes|37|y|line_chart Year|2012|x|line_chart Data_volume_in_petabytes|29|y|line_chart Year|2011|x|line_chart Data_volume_in_petabytes|22|y|line_chart 
title: Online gaming traffic in North America 2011 - 2016

gold: The statistic shows a forecast for the traffic caused by online gaming in North America from 2011 until 2016 , in petabytes per month . In 2014 , online gaming IP traffic is expected to reach 44 petabytes per month .
gold_template: The statistic shows a forecast for the templateTitle[2] caused by templateTitle[0] templateTitle[1] in templateTitle[3] templateTitle[4] from templateXValue[min] until templateXValue[max] , in templateYLabel[2] per month . In templateXValue[2] , templateTitle[0] templateTitle[1] IP templateTitle[2] is expected to reach templateYValue[2] templateYLabel[2] per month .

generated_template: This statistic shows the distribution of templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[min] percent of the United States had an increase of templateYValue[max] percent .
generated: This statistic shows the distribution of gaming Data volume in North from 2011 to 2016 . In 2016 , 22 percent of the United States had an increase of 96 percent .


Example 287:
data: Country|Mexico|x|bar_chart Share_of_respondents|97|y|bar_chart Country|Brazil|x|bar_chart Share_of_respondents|95|y|bar_chart Country|Italy|x|bar_chart Share_of_respondents|90|y|bar_chart Country|Spain|x|bar_chart Share_of_respondents|90|y|bar_chart Country|South_Korea|x|bar_chart Share_of_respondents|86|y|bar_chart Country|Canada|x|bar_chart Share_of_respondents|84|y|bar_chart Country|Total|x|bar_chart Share_of_respondents|83|y|bar_chart Country|Germany|x|bar_chart Share_of_respondents|82|y|bar_chart Country|United_States|x|bar_chart Share_of_respondents|82|y|bar_chart Country|France|x|bar_chart Share_of_respondents|81|y|bar_chart Country|Great_Britain|x|bar_chart Share_of_respondents|79|y|bar_chart Country|Sweden|x|bar_chart Share_of_respondents|79|y|bar_chart Country|Australia|x|bar_chart Share_of_respondents|77|y|bar_chart Country|Japan|x|bar_chart Share_of_respondents|72|y|bar_chart 
title: Global YouTube usage for music consumption 2017 , by country

gold: This statistic shows the share of global users who have accessed YouTube to consume music as of 2017 , sorted by country . During the survey period , 82 percent of respondents from the United States said that they had used YouTube for music .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] users who have accessed templateTitle[1] to consume templateTitle[4] as of templateTitle[6] , sorted templateTitle[7] templateXLabel[0] . During the survey period , templateYValue[7] percent of templateYLabel[1] from the templateXValue[8] templateXValue[8] said that they had used templateTitle[1] templateTitle[3] templateTitle[4] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateTitle[2] templateTitle[3] as of templateTitle[5] . templateYValue[2] percent of templateYLabel[1] stated templateXValue[2] was currently the templateTitle[0] templateXLabel[0] with a templateTitle[1] templateTitle[2] templateTitle[3] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Global nation in usage for as of consumption . 90 percent of respondents stated Italy was currently the Global Country with a YouTube usage for .


Example 288:
data: Year|2019|x|line_chart Youth_unemployment_rate|36.68|y|line_chart Year|2018|x|line_chart Youth_unemployment_rate|37.24|y|line_chart Year|2017|x|line_chart Youth_unemployment_rate|34.86|y|line_chart Year|2016|x|line_chart Youth_unemployment_rate|35.63|y|line_chart Year|2015|x|line_chart Youth_unemployment_rate|30.9|y|line_chart Year|2014|x|line_chart Youth_unemployment_rate|28.43|y|line_chart Year|2013|x|line_chart Youth_unemployment_rate|29.87|y|line_chart Year|2012|x|line_chart Youth_unemployment_rate|28.84|y|line_chart Year|2011|x|line_chart Youth_unemployment_rate|30.03|y|line_chart Year|2010|x|line_chart Youth_unemployment_rate|28.88|y|line_chart Year|2009|x|line_chart Youth_unemployment_rate|29.12|y|line_chart Year|2008|x|line_chart Youth_unemployment_rate|28.51|y|line_chart Year|2007|x|line_chart Youth_unemployment_rate|29.04|y|line_chart Year|2006|x|line_chart Youth_unemployment_rate|30.45|y|line_chart Year|2005|x|line_chart Youth_unemployment_rate|31.75|y|line_chart Year|2004|x|line_chart Youth_unemployment_rate|31.37|y|line_chart Year|2003|x|line_chart Youth_unemployment_rate|31.06|y|line_chart Year|2002|x|line_chart Youth_unemployment_rate|32.62|y|line_chart Year|2001|x|line_chart Youth_unemployment_rate|31.08|y|line_chart Year|2000|x|line_chart Youth_unemployment_rate|29.4|y|line_chart Year|1999|x|line_chart Youth_unemployment_rate|29.65|y|line_chart 
title: Youth unemployment rate in Jordan in 2019

gold: The statistic shows the youth unemployment rate in Jordan from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Jordan was at 36.68 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] was at templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Jordan from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Jordan was at 36.68 percent .


Example 289:
data: Country|China|x|bar_chart Share_of_total_exports|10.9|y|bar_chart Country|Netherlands|x|bar_chart Share_of_total_exports|10|y|bar_chart Country|Germany|x|bar_chart Share_of_total_exports|7.1|y|bar_chart Country|Belarus|x|bar_chart Share_of_total_exports|5.1|y|bar_chart Country|Turkey|x|bar_chart Share_of_total_exports|4.9|y|bar_chart 
title: Main export partners for Russia 2017

gold: This statistic shows the main export partners for Russia in 2017 . In 2017 , the most important export partner for Russia was China , accounting for 10.9 percent of all exports .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] . In templateTitle[5] , the most important templateTitle[1] partner templateTitle[3] templateTitle[4] was templateXValue[0] , accounting templateTitle[3] templateYValue[max] percent of all templateYLabel[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] . In templateTitle[5] , the most important templateTitle[1] partner templateTitle[3] templateTitle[4] were the templateXValue[0] templateXValue[0] , accounting templateTitle[3] templateYValue[max] percent of all templateYLabel[2] .
generated: This statistic shows the Main export partners for Russia in 2017 . In 2017 , the most important export partner for Russia were the China , accounting for 10.9 percent of all exports .


Example 290:
data: Year|2013|x|line_chart Television_revenue_(in_million_U.S._dollars)|684.3|y|line_chart Year|2012|x|line_chart Television_revenue_(in_million_U.S._dollars)|670.0|y|line_chart Year|2011|x|line_chart Television_revenue_(in_million_U.S._dollars)|653.0|y|line_chart Year|2010|x|line_chart Television_revenue_(in_million_U.S._dollars)|633.8|y|line_chart Year|2009|x|line_chart Television_revenue_(in_million_U.S._dollars)|571.0|y|line_chart Year|2008|x|line_chart Television_revenue_(in_million_U.S._dollars)|543.8|y|line_chart Year|2007|x|line_chart Television_revenue_(in_million_U.S._dollars)|503.0|y|line_chart Year|2006|x|line_chart Television_revenue_(in_million_U.S._dollars)|453.0|y|line_chart Year|2005|x|line_chart Television_revenue_(in_million_U.S._dollars)|432.0|y|line_chart Year|2004|x|line_chart Television_revenue_(in_million_U.S._dollars)|400.0|y|line_chart Year|2003|x|line_chart Television_revenue_(in_million_U.S._dollars)|370.04|y|line_chart Year|2002|x|line_chart Television_revenue_(in_million_U.S._dollars)|272.78|y|line_chart Year|2001|x|line_chart Television_revenue_(in_million_U.S._dollars)|242.1|y|line_chart Year|2000|x|line_chart Television_revenue_(in_million_U.S._dollars)|227.7|y|line_chart Year|1999|x|line_chart Television_revenue_(in_million_U.S._dollars)|213.8|y|line_chart Year|1998|x|line_chart Television_revenue_(in_million_U.S._dollars)|200.9|y|line_chart Year|1997|x|line_chart Television_revenue_(in_million_U.S._dollars)|188.4|y|line_chart Year|1996|x|line_chart Television_revenue_(in_million_U.S._dollars)|178.3|y|line_chart Year|1995|x|line_chart Television_revenue_(in_million_U.S._dollars)|166.2|y|line_chart Year|1994|x|line_chart Television_revenue_(in_million_U.S._dollars)|137.06|y|line_chart Year|1993|x|line_chart Television_revenue_(in_million_U.S._dollars)|129.06|y|line_chart Year|1992|x|line_chart Television_revenue_(in_million_U.S._dollars)|120.06|y|line_chart Year|1991|x|line_chart Television_revenue_(in_million_U.S._dollars)|112.44|y|line_chart Year|1990|x|line_chart Television_revenue_(in_million_U.S._dollars)|63.51|y|line_chart Year|1989|x|line_chart Television_revenue_(in_million_U.S._dollars)|57.16|y|line_chart Year|1988|x|line_chart Television_revenue_(in_million_U.S._dollars)|57.79|y|line_chart Year|1987|x|line_chart Television_revenue_(in_million_U.S._dollars)|36.64|y|line_chart Year|1986|x|line_chart Television_revenue_(in_million_U.S._dollars)|33.03|y|line_chart Year|1985|x|line_chart Television_revenue_(in_million_U.S._dollars)|28.33|y|line_chart Year|1984|x|line_chart Television_revenue_(in_million_U.S._dollars)|20.14|y|line_chart Year|1983|x|line_chart Television_revenue_(in_million_U.S._dollars)|16.88|y|line_chart Year|1982|x|line_chart Television_revenue_(in_million_U.S._dollars)|14.63|y|line_chart Year|1981|x|line_chart Television_revenue_(in_million_U.S._dollars)|10.32|y|line_chart Year|1980|x|line_chart Television_revenue_(in_million_U.S._dollars)|8.86|y|line_chart 
title: NCAA college basketball tournament TV/television revenue 2013

gold: The graph shows the television revenue generated through NCAA college basketball tournament from 2000 to 2013 . In 2010 , the television revenue was at 633.8 million U.S. dollars .
gold_template: The graph shows the templateYLabel[0] templateYLabel[1] generated through templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[13] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] was at templateYValue[3] templateYLabel[3] templateYLabel[4] dollars .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people living in the templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Television revenue (in of tournament TV/television revenue in the United States from 1980 to 2013 . In 2013 , there were 684.3 people living in the TV/television revenue in the United States .


Example 291:
data: Year|2019|x|line_chart Unemployment_rate|4.15|y|line_chart Year|2018|x|line_chart Unemployment_rate|4.15|y|line_chart Year|2017|x|line_chart Unemployment_rate|4.14|y|line_chart Year|2016|x|line_chart Unemployment_rate|4.32|y|line_chart Year|2015|x|line_chart Unemployment_rate|4.55|y|line_chart Year|2014|x|line_chart Unemployment_rate|4.53|y|line_chart Year|2013|x|line_chart Unemployment_rate|4.36|y|line_chart Year|2012|x|line_chart Unemployment_rate|4.52|y|line_chart Year|2011|x|line_chart Unemployment_rate|4.41|y|line_chart Year|2010|x|line_chart Unemployment_rate|4.35|y|line_chart Year|2009|x|line_chart Unemployment_rate|4.16|y|line_chart Year|2008|x|line_chart Unemployment_rate|3.62|y|line_chart Year|2007|x|line_chart Unemployment_rate|3.76|y|line_chart Year|2006|x|line_chart Unemployment_rate|3.83|y|line_chart Year|2005|x|line_chart Unemployment_rate|3.89|y|line_chart Year|2004|x|line_chart Unemployment_rate|4.06|y|line_chart Year|2003|x|line_chart Unemployment_rate|4.07|y|line_chart Year|2002|x|line_chart Unemployment_rate|4.16|y|line_chart Year|2001|x|line_chart Unemployment_rate|4.15|y|line_chart Year|2000|x|line_chart Unemployment_rate|4.15|y|line_chart Year|1999|x|line_chart Unemployment_rate|4.12|y|line_chart 
title: Unemployment rate in Fiji 2019

gold: This statistic shows the unemployment rate in Fiji from 1999 to 2019 . In 2019 , the unemployment rate in Fiji was at approximately 4.15 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Fiji from 1999 to 2019 . In 2019 , the Unemployment rate in Fiji was at approximately 4.15 percent .


Example 292:
data: Year|2018|x|line_chart Percentage_of_population|10.4|y|line_chart Year|2017|x|line_chart Percentage_of_population|9.6|y|line_chart Year|2016|x|line_chart Percentage_of_population|9.8|y|line_chart Year|2015|x|line_chart Percentage_of_population|10.5|y|line_chart Year|2014|x|line_chart Percentage_of_population|10.8|y|line_chart Year|2013|x|line_chart Percentage_of_population|10.7|y|line_chart Year|2012|x|line_chart Percentage_of_population|10.7|y|line_chart Year|2011|x|line_chart Percentage_of_population|10.9|y|line_chart Year|2010|x|line_chart Percentage_of_population|10.1|y|line_chart Year|2009|x|line_chart Percentage_of_population|9.4|y|line_chart Year|2008|x|line_chart Percentage_of_population|9.3|y|line_chart Year|2007|x|line_chart Percentage_of_population|7.9|y|line_chart Year|2006|x|line_chart Percentage_of_population|8.3|y|line_chart Year|2005|x|line_chart Percentage_of_population|8.3|y|line_chart Year|2004|x|line_chart Percentage_of_population|7.6|y|line_chart Year|2003|x|line_chart Percentage_of_population|8.1|y|line_chart Year|2002|x|line_chart Percentage_of_population|7.5|y|line_chart Year|2001|x|line_chart Percentage_of_population|7.3|y|line_chart Year|2000|x|line_chart Percentage_of_population|7.7|y|line_chart 
title: Connecticut - Poverty rate 2000 - 2018

gold: This statistic shows the poverty rate in Connecticut from 2000 to 2018 . In 2018 , about 10.4 percent of Connecticut 's population lived below the poverty line .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateTitle[0] 's templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the Connecticut Poverty rate in the United States from 2000 to 2018 . In 2018 , 10.4 percent of the population lived below the Poverty line .


Example 293:
data: Quarter|Q2_'16|x|bar_chart Number_of_mobile_visiting_members_in_millions|63|y|bar_chart Quarter|Q1_'16|x|bar_chart Number_of_mobile_visiting_members_in_millions|61|y|bar_chart Quarter|Q4_'15|x|bar_chart Number_of_mobile_visiting_members_in_millions|57|y|bar_chart Quarter|Q3_'15|x|bar_chart Number_of_mobile_visiting_members_in_millions|55|y|bar_chart Quarter|Q2_'15|x|bar_chart Number_of_mobile_visiting_members_in_millions|51|y|bar_chart Quarter|Q1_'15|x|bar_chart Number_of_mobile_visiting_members_in_millions|49|y|bar_chart Quarter|Q4_'14|x|bar_chart Number_of_mobile_visiting_members_in_millions|45|y|bar_chart Quarter|Q3_'14|x|bar_chart Number_of_mobile_visiting_members_in_millions|42|y|bar_chart Quarter|Q2_'14|x|bar_chart Number_of_mobile_visiting_members_in_millions|38|y|bar_chart Quarter|Q1_'14|x|bar_chart Number_of_mobile_visiting_members_in_millions|35|y|bar_chart Quarter|Q4_'13|x|bar_chart Number_of_mobile_visiting_members_in_millions|31|y|bar_chart Quarter|Q3_'13|x|bar_chart Number_of_mobile_visiting_members_in_millions|29|y|bar_chart Quarter|Q2_'13|x|bar_chart Number_of_mobile_visiting_members_in_millions|26|y|bar_chart Quarter|Q1_'13|x|bar_chart Number_of_mobile_visiting_members_in_millions|20|y|bar_chart 
title: LinkedIn : unique mobile visiting members 2013 - 2016

gold: This timeline displays the number of unique mobile visiting members to social network LinkedIn . As of the second quarter of 2016 , LinkedIn had an average of 63 million unique visiting members via mobile . These accounted for 59 percent of all unique visiting members .
gold_template: This timeline displays the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] to social network templateTitle[0] . As of the second templateXLabel[0] of templateTitle[6] , templateTitle[0] had an average of templateYValue[max] million templateTitle[1] templateYLabel[2] templateYLabel[3] via templateYLabel[1] . These accounted for 59 percent of all templateTitle[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic gives information on the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[0] from the first templateXLabel[0] of templateTitle[4] to the fourth templateXLabel[0] of templateTitle[6] . In the most recently reported templateXLabel[0] , templateYValue[max] percent of the United States had been carried out in the United States , up from templateYValue[1] percent of the previous templateXLabel[0] .
generated: This statistic gives information on the Number mobile visiting in LinkedIn from the first Quarter of members to the fourth Quarter of 2016 . In the most recently reported Quarter , 63 percent of the United States had been carried out in the United States , up from 61 percent of the previous Quarter .


Example 294:
data: Country|India|x|bar_chart Number_of_Facebook_users_in_millions|260|y|bar_chart Country|United_States|x|bar_chart Number_of_Facebook_users_in_millions|180|y|bar_chart Country|Indonesia|x|bar_chart Number_of_Facebook_users_in_millions|130|y|bar_chart Country|Brazil|x|bar_chart Number_of_Facebook_users_in_millions|120|y|bar_chart Country|Mexico|x|bar_chart Number_of_Facebook_users_in_millions|84|y|bar_chart Country|Philippines|x|bar_chart Number_of_Facebook_users_in_millions|70|y|bar_chart Country|Vietnam|x|bar_chart Number_of_Facebook_users_in_millions|61|y|bar_chart Country|Thailand|x|bar_chart Number_of_Facebook_users_in_millions|47|y|bar_chart Country|Egypt|x|bar_chart Number_of_Facebook_users_in_millions|38|y|bar_chart Country|Turkey|x|bar_chart Number_of_Facebook_users_in_millions|37|y|bar_chart Country|United_Kingdom|x|bar_chart Number_of_Facebook_users_in_millions|37|y|bar_chart Country|Bangladesh|x|bar_chart Number_of_Facebook_users_in_millions|34|y|bar_chart Country|Pakistan|x|bar_chart Number_of_Facebook_users_in_millions|33|y|bar_chart Country|Colombia|x|bar_chart Number_of_Facebook_users_in_millions|32|y|bar_chart Country|France|x|bar_chart Number_of_Facebook_users_in_millions|31|y|bar_chart Country|Argentina|x|bar_chart Number_of_Facebook_users_in_millions|29|y|bar_chart Country|Italy|x|bar_chart Number_of_Facebook_users_in_millions|29|y|bar_chart Country|Germany|x|bar_chart Number_of_Facebook_users_in_millions|28|y|bar_chart Country|Nigeria|x|bar_chart Number_of_Facebook_users_in_millions|24|y|bar_chart Country|Malaysia|x|bar_chart Number_of_Facebook_users_in_millions|22|y|bar_chart Country|Peru|x|bar_chart Number_of_Facebook_users_in_millions|22|y|bar_chart Country|Canada|x|bar_chart Number_of_Facebook_users_in_millions|21|y|bar_chart Country|Maynmar|x|bar_chart Number_of_Facebook_users_in_millions|21|y|bar_chart Country|Spain|x|bar_chart Number_of_Facebook_users_in_millions|21|y|bar_chart 
title: Countries with the most Facebook users 2020

gold: There are over 260 million Facebook users in India alone , making it the leading country in terms of Facebook audience size . To put this into context , if India 's Facebook audience were a country then it would be ranked fourth in terms of largest population worldwide . Apart from India , there are several other markets with more than 100 million Facebook users each : The United States , Indonesia , and Brazil with 180 million , 130 million , and 120 million Facebook users respectively .
gold_template: There are over templateYValue[max] million templateYLabel[1] templateYLabel[2] in templateXValue[0] alone , making it the leading templateXLabel[0] in terms of templateYLabel[1] audience size . To put this into context , if templateXValue[0] 's templateYLabel[1] audience were a templateXLabel[0] then it would be ranked fourth in terms of largest population worldwide . Apart from templateXValue[0] , there are several other markets templateTitle[1] more than 100 million templateYLabel[1] templateYLabel[2] each : The templateXValue[1] templateXValue[1] , templateXValue[2] , and templateXValue[3] templateTitle[1] templateYValue[1] million , templateYValue[2] million , and templateYValue[3] million templateYLabel[1] templateYLabel[2] respectively .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States as of April templateTitle[6] . During the survey , templateYValue[2] percent of templateYLabel[1] had the highest active users of U.S. dollars .
generated: This statistic shows the Number of Facebook of users 2020 titleErr in the United States as of April titleErr . During the survey , 130 percent of Facebook had the highest active users of U.S. dollars .


Example 295:
data: Year|2018|x|line_chart Revenue_in_million_U.S._dollars|275|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|258|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|253|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|223|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|211|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|192|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|195|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|186|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|180|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|172|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|177|y|line_chart Year|2007|x|line_chart Revenue_in_million_U.S._dollars|165|y|line_chart Year|2006|x|line_chart Revenue_in_million_U.S._dollars|154|y|line_chart Year|2005|x|line_chart Revenue_in_million_U.S._dollars|145|y|line_chart Year|2004|x|line_chart Revenue_in_million_U.S._dollars|136|y|line_chart Year|2003|x|line_chart Revenue_in_million_U.S._dollars|126|y|line_chart Year|2002|x|line_chart Revenue_in_million_U.S._dollars|122|y|line_chart Year|2001|x|line_chart Revenue_in_million_U.S._dollars|127|y|line_chart 
title: Arizona Diamondbacks revenue 2001 - 2018

gold: The statistic depicts the revenue of the Arizona Diamondbacks from 2001 to 2018 . In 2018 , the revenue of the Major League Baseball franchise amounted to 275 million U.S. dollars.The Arizona Diamondbacks are owned by Ken Kendrick , who bought the franchise for 238 million U.S. dollars in 2004 .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitle[0] templateTitle[1] are owned by Ken Kendrick , who bought the franchise for 238 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[14] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitle[0] templateTitle[1] are owned by Ted Lerner who bought the franchise for 450 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[12] .
generated: The statistic depicts the Revenue of the Arizona Diamondbacks from 2001 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 275 million U.S. dollars.The Arizona Diamondbacks are owned by Ted Lerner who bought the franchise for 450 million U.S. dollars in 2006 .


Example 296:
data: Response|Daily|x|bar_chart Share_of_respondents|5.2|y|bar_chart Response|A_few_times_per_week|x|bar_chart Share_of_respondents|19|y|bar_chart Response|About_once_per_week|x|bar_chart Share_of_respondents|21|y|bar_chart Response|A_few_times_per_month|x|bar_chart Share_of_respondents|22.7|y|bar_chart Response|About_once_per_month|x|bar_chart Share_of_respondents|12.2|y|bar_chart Response|Rarely|x|bar_chart Share_of_respondents|17.5|y|bar_chart Response|Never|x|bar_chart Share_of_respondents|2.2|y|bar_chart 
title: Frequency of eating fast food in the U.S. as of August 2014

gold: This statistic shows the frequency with which consumers ate fast food in the United States as of August 2014 . During the survey , 21 percent of respondents said that they ate fast food about once per week .
gold_template: This statistic shows the templateTitle[0] with which consumers ate templateTitle[2] templateTitle[3] in the templateTitle[4] as of templateTitle[5] templateTitle[6] . During the survey , templateYValue[2] percent of templateYLabel[1] said that they ate templateTitle[2] templateTitle[3] templateXValue[2] templateXValue[2] templateXValue[1] templateXValue[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States as of March templateTitle[7] . During the survey , templateYValue[max] percent of the templateYLabel[1] stated they planned to visit a templateXValue[1] via templateXValue[2] .
generated: This statistic shows the Frequency eating fast food U.S. August in the United States as of March titleErr . During the survey , 22.7 percent of the respondents stated they planned to visit a A_few_times_per_week via About_once_per_week .


Example 297:
data: Response|Yes_all_of_my_social_media_accounts_are_private|x|bar_chart Share_of_respondents|45|y|bar_chart Response|Yes_a_few_of_my_social_meda_accounts_are_private|x|bar_chart Share_of_respondents|20|y|bar_chart Response|Yes_one_of_my_social_media_accounts_is_private|x|bar_chart Share_of_respondents|7|y|bar_chart Response|No_none_of_my_social_media_accounts_are_private|x|bar_chart Share_of_respondents|19|y|bar_chart Response|Don't_know/No_opinion|x|bar_chart Share_of_respondents|8|y|bar_chart 
title: U.S. social media user account privacy 2018

gold: This statistic presents the percentage of social media users in the United States who have a private social media account as of September 2018 . According to the findings , 45 percent of respondents reported that all of their social media accounts were private , while 19 percent of respondents stated the opposite saying none of their social media accounts were private at all .
gold_template: This statistic presents the percentage of templateXValue[0] templateXValue[0] users in the templateTitle[0] who have a templateXValue[0] templateXValue[0] templateXValue[0] templateTitle[4] as of September templateTitle[6] . According to the findings , templateYValue[max] percent of templateYLabel[1] reported that templateXValue[0] of their templateXValue[0] templateXValue[0] templateXValue[0] were templateXValue[0] , while templateYValue[3] percent of templateYLabel[1] stated the opposite saying templateXValue[3] of their templateXValue[0] templateXValue[0] templateXValue[0] were templateXValue[0] at templateXValue[0] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] as of templateTitle[5] . During the survey period , templateYValue[1] percent of templateYLabel[1] stated that they used templateXValue[0] templateXValue[0] in the United States .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the U.S. of social media user as of privacy . During the survey period , 20 percent of respondents stated that they used Yes_all_of_my_social_media_accounts_are_private in the United States .


Example 298:
data: Year|2018|x|line_chart Revenue_in_million_U.S._dollars|7253|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|7379|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|6680|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|6085|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|6819|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|7891|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|9297|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|10441|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|9610|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|7705|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|6124|y|line_chart Year|2007|x|line_chart Revenue_in_million_U.S._dollars|5465|y|line_chart 
title: Newmont Mining 's revenue 2007 - 2018

gold: This statistic shows Newmont Mining 's revenue from 2007 to 2018 , in million U.S. dollars . United States-based Newmont is one of the world 's largest gold producers . In 2018 , the company generated some 7.25 billion U.S. dollars of revenue .
gold_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] from templateXValue[min] to templateXValue[max] , in templateYLabel[1] templateYLabel[2] templateYLabel[3] . United States-based templateTitle[0] is one of the world templateTitle[2] largest gold producers . In templateXValue[max] , the company generated some templateYValue[0] billion templateYLabel[2] templateYLabel[3] of templateYLabel[0] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] dollars.The templateTitle[0] templateTitle[1] templateTitle[2] are owned by David Glass , who bought the franchise for 96 templateYLabel[1] templateYLabel[2] templateYLabel[3] in 2000 .
generated: The statistic depicts the Revenue of the Newmont Mining 's from 2007 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 7253 million U.S. dollars.The Newmont Mining 's are owned by David Glass , who bought the franchise for 96 million U.S. dollars in 2000 .


Example 299:
data: Quarter|Q4_'19|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|1053.1|y|bar_chart Quarter|Q3_'19|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|1012.8|y|bar_chart Quarter|Q2_'19|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|921.9|y|bar_chart Quarter|Q1_'19|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|871.7|y|bar_chart Quarter|Q4_'18|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|974.4|y|bar_chart Quarter|Q3_'18|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|855.3|y|bar_chart Quarter|Q2_'18|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|805.2|y|bar_chart Quarter|Q1_'18|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|770.0|y|bar_chart Quarter|Q4_'17|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|868.0|y|bar_chart Quarter|Q3_'17|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|771.3|y|bar_chart Quarter|Q2_'17|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|735.8|y|bar_chart Quarter|Q1_'17|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|704.0|y|bar_chart Quarter|Q4_'16|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|778.4|y|bar_chart Quarter|Q3_'16|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|748.9|y|bar_chart Quarter|Q2_'16|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|705.4|y|bar_chart Quarter|Q1_'16|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|682.8|y|bar_chart Quarter|Q4_'15|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|741.8|y|bar_chart Quarter|Q3_'15|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|685.3|y|bar_chart Quarter|Q2_'15|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|640.8|y|bar_chart Quarter|Q1_'15|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|611.9|y|bar_chart Quarter|Q4_'14|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|671.0|y|bar_chart Quarter|Q3_'14|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|607.7|y|bar_chart Quarter|Q2_'14|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|548.6|y|bar_chart Quarter|Q1_'14|x|bar_chart Gross_merchandise_sales_in_billion_Japanese_yen|607.0|y|bar_chart 
title: Rakuten Group : quarterly domestic e-commerce GMS 2014 - 2019

gold: In the fourth quarter of 2019 , the domestic gross transaction value of the Rakuten Group 's e-commerce sectors amounted to approximately 1.05 trillion Japanese yen , up 8.1 percent compared to the fourth quarter of the previous year . The company is headquartered in Tokyo , where it was founded in 1997 .
gold_template: In the fourth templateXLabel[0] of templateTitle[7] , the templateTitle[3] templateYLabel[0] transaction value of the templateTitle[0] templateTitle[1] 's templateTitle[4] sectors amounted to approximately templateYValue[max] trillion templateYLabel[4] templateYLabel[5] , up 8.1 percent compared to the fourth templateXLabel[0] of the previous year . The company is headquartered in Tokyo , where it was founded in 1997 .

generated_template: This statistic gives information on the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[5] in the United States from the first templateXLabel[0] of templateTitle[6] to the third templateXLabel[0] of templateTitle[8] . During the last reported templateXLabel[0] , templateYValue[0] percent of templateYLabel[1] were reported templateXLabel[0] .
generated: This statistic gives information on the Gross merchandise sales of domestic e-commerce GMS in the United States from the first Quarter of 2014 to the third Quarter of titleErr . During the last reported Quarter , 1053.1 percent of merchandise were reported Quarter .


Example 300:
data: Year|2017|x|line_chart Number_of_children_born_per_woman|1.74|y|line_chart Year|2016|x|line_chart Number_of_children_born_per_woman|1.75|y|line_chart Year|2015|x|line_chart Number_of_children_born_per_woman|1.75|y|line_chart Year|2014|x|line_chart Number_of_children_born_per_woman|1.76|y|line_chart Year|2013|x|line_chart Number_of_children_born_per_woman|1.76|y|line_chart Year|2012|x|line_chart Number_of_children_born_per_woman|1.77|y|line_chart Year|2011|x|line_chart Number_of_children_born_per_woman|1.78|y|line_chart Year|2010|x|line_chart Number_of_children_born_per_woman|1.8|y|line_chart Year|2009|x|line_chart Number_of_children_born_per_woman|1.82|y|line_chart Year|2008|x|line_chart Number_of_children_born_per_woman|1.85|y|line_chart Year|2007|x|line_chart Number_of_children_born_per_woman|1.88|y|line_chart 
title: Fertility rate in Brazil

gold: This statistic shows the fertility rate in Brazil from 2007 to 2017 . The fertility rate is the average number of children born by one woman while being of child-bearing age . In 2017 , the fertility rate among Brazil 's population amounted to 1.74 children per woman .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] among templateTitle[2] 's population amounted to templateYValue[min] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] borne by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[2] amounted to templateYValue[min] templateYLabel[1] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate in Brazil from 2007 to 2017 The Fertility rate is the average Number of children borne by one woman while being of child-bearing age . In 2017 , the Fertility rate in Brazil amounted to 1.74 children per woman .


Example 301:
data: Country|Rest_of_world|x|bar_chart Average_annual_growth|19|y|bar_chart Country|World|x|bar_chart Average_annual_growth|12|y|bar_chart Country|North_America|x|bar_chart Average_annual_growth|11|y|bar_chart Country|Europe|x|bar_chart Average_annual_growth|9|y|bar_chart Country|Japan|x|bar_chart Average_annual_growth|6|y|bar_chart 
title: Annual growth of the global generic market 2009 - 2015 by region

gold: This statistic depicts the average annual growth of the global generic market between 2009 and 2015 , by region . In Japan , the average annual growth is estimated to be six percent in that period .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[2] templateTitle[3] templateTitle[4] between templateTitle[5] and templateTitle[6] , templateTitle[7] templateTitle[8] . In templateXValue[last] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] is estimated to be templateYValue[min] percent in that period .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[5] in templateTitle[8] , broken down templateTitle[4] templateXLabel[0] . According to the report , it was found that templateYValue[max] percent of the United States were located in templateXValue[0] templateXValue[0] , up from templateYValue[1] percent in the previous year .
generated: The statistic shows the Average annual of the Annual growth 2009 in region , broken down market Country . According to the report , it was found that 19 percent of the United States were located in Rest_of_world , up from 12 percent in the previous year .


Example 302:
data: Year|'18|x|line_chart Unemployment_rate|4.3|y|line_chart Year|'17|x|line_chart Unemployment_rate|4.9|y|line_chart Year|'16|x|line_chart Unemployment_rate|5.4|y|line_chart Year|'15|x|line_chart Unemployment_rate|5.3|y|line_chart Year|'14|x|line_chart Unemployment_rate|5.9|y|line_chart Year|'13|x|line_chart Unemployment_rate|7.4|y|line_chart Year|'12|x|line_chart Unemployment_rate|7.8|y|line_chart Year|'11|x|line_chart Unemployment_rate|7.9|y|line_chart Year|'10|x|line_chart Unemployment_rate|8.5|y|line_chart Year|'09|x|line_chart Unemployment_rate|8|y|line_chart Year|'08|x|line_chart Unemployment_rate|5.3|y|line_chart Year|'07|x|line_chart Unemployment_rate|4.4|y|line_chart Year|'06|x|line_chart Unemployment_rate|4.6|y|line_chart Year|'05|x|line_chart Unemployment_rate|5|y|line_chart Year|'04|x|line_chart Unemployment_rate|5.4|y|line_chart Year|'03|x|line_chart Unemployment_rate|5.7|y|line_chart Year|'02|x|line_chart Unemployment_rate|5.6|y|line_chart Year|'01|x|line_chart Unemployment_rate|4.8|y|line_chart Year|'00|x|line_chart Unemployment_rate|4.1|y|line_chart Year|'99|x|line_chart Unemployment_rate|4.4|y|line_chart Year|'98|x|line_chart Unemployment_rate|4.6|y|line_chart Year|'97|x|line_chart Unemployment_rate|5.1|y|line_chart Year|'96|x|line_chart Unemployment_rate|5.4|y|line_chart Year|'95|x|line_chart Unemployment_rate|5.9|y|line_chart Year|'94|x|line_chart Unemployment_rate|6.3|y|line_chart Year|'93|x|line_chart Unemployment_rate|7|y|line_chart Year|'92|x|line_chart Unemployment_rate|7.6|y|line_chart 
title: Pennsylvania - Unemployment rate 1992 - 2018

gold: This statistic displays the unemployment rate in Pennsylvania from 1992 to 2018 . In 2018 , unemployment in Pennsylvania was 4.3 percent .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateTitle[3] to templateTitle[4] . In templateTitle[4] , templateYLabel[0] in templateTitle[0] was templateYValue[0] percent .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] of the state of templateTitle[0] templateTitle[1] from templateTitle[4] to templateTitle[5] . In templateTitle[5] , templateYLabel[0] in templateTitle[0] templateTitle[1] was templateYValue[min] percent .
generated: This statistic displays the Unemployment rate of the state of Pennsylvania Unemployment from 2018 to titleErr . In titleErr , Unemployment in Pennsylvania Unemployment was 4.1 percent .


Example 303:
data: Year|2017|x|line_chart Unit_shipments_in_millions|64.61|y|line_chart Year|2016|x|line_chart Unit_shipments_in_millions|62.29|y|line_chart Year|2015|x|line_chart Unit_shipments_in_millions|60.52|y|line_chart Year|2014|x|line_chart Unit_shipments_in_millions|58.76|y|line_chart Year|2013|x|line_chart Unit_shipments_in_millions|55.71|y|line_chart Year|2012|x|line_chart Unit_shipments_in_millions|52.11|y|line_chart Year|2011|x|line_chart Unit_shipments_in_millions|53.53|y|line_chart Year|2010|x|line_chart Unit_shipments_in_millions|53.51|y|line_chart Year|2009|x|line_chart Unit_shipments_in_millions|52.02|y|line_chart Year|2008|x|line_chart Unit_shipments_in_millions|57.54|y|line_chart Year|2007|x|line_chart Unit_shipments_in_millions|63.41|y|line_chart Year|2006|x|line_chart Unit_shipments_in_millions|68.45|y|line_chart Year|2005|x|line_chart Unit_shipments_in_millions|69.13|y|line_chart 
title: Major kitchen/laundry appliances : unit shipments in the U.S. 2005 - 2017

gold: The statistic illustrates the total unit shipments of kitchen and laundry appliances* in the United States from 2005 to 2012 and forecasts up to and including 2017 . For 2015 the Appliance Magazine projects total unit shipments of major kitchen/laundry appliances to reach 60.52 million units . U.S. unit shipments of major appliances - additional information Total unit shipments of major kitchen and laundry appliances in the United States are expected to reach 64.6 million units in 2017 .
gold_template: The statistic illustrates the total templateYLabel[0] templateYLabel[1] of kitchen and laundry appliances* in the templateTitle[5] from templateXValue[min] to templateXValue[5] and forecasts up to and including templateXValue[max] . For templateXValue[2] the Appliance Magazine projects total templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] to reach templateYValue[2] million units . templateTitle[5] templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[2] - additional information Total templateYLabel[0] templateYLabel[1] of templateTitle[0] kitchen and laundry templateTitle[2] in the templateTitle[5] are expected to reach templateYValue[0] million units in templateXValue[max] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of e-Readers from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] million units of e-Readers were shipped worldwide .
generated: This statistic shows the Major Unit shipments of e-Readers from 2005 to 2017 . In 2015 , 60.52 million units of e-Readers were shipped worldwide .


Example 304:
data: Year|2018|x|line_chart Sales_in_billion_U.S._dollars|12.66|y|line_chart Year|2017|x|line_chart Sales_in_billion_U.S._dollars|12.5|y|line_chart Year|2016|x|line_chart Sales_in_billion_U.S._dollars|12.0|y|line_chart Year|2015|x|line_chart Sales_in_billion_U.S._dollars|11.5|y|line_chart Year|2014|x|line_chart Sales_in_billion_U.S._dollars|10.84|y|line_chart Year|2013|x|line_chart Sales_in_billion_U.S._dollars|10.41|y|line_chart Year|2012|x|line_chart Sales_in_billion_U.S._dollars|9.79|y|line_chart Year|1990|x|line_chart Sales_in_billion_U.S._dollars|1.84|y|line_chart 
title: U.S. tea market : total wholesale value 1990 - 2018

gold: The statistic shows the wholesale tea value in the United States from 1990 to 2018 . U.S. wholesale tea sales amounted to approximately 1.84 billion U.S. dollars in 1990 and increased to about 12.66 billion U.S. dollars in 2018 .
gold_template: The statistic shows the templateTitle[4] templateTitle[1] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . templateYLabel[2] templateTitle[4] templateTitle[1] templateYLabel[0] amounted to approximately templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[min] and increased to about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows templateTitle[0] templateTitle[1] templateYLabel[0] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateYLabel[0] of around templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The timeline shows U.S. tea Sales in the United States from 1990 to 2018 . In 2018 , U.S. tea Sales of around 1.84 billion U.S. dollars .


Example 305:
data: Year|2024|x|line_chart Ratio_of_government_expenditure_to_GDP|38.28|y|line_chart Year|2023|x|line_chart Ratio_of_government_expenditure_to_GDP|37.74|y|line_chart Year|2022|x|line_chart Ratio_of_government_expenditure_to_GDP|37.28|y|line_chart Year|2021|x|line_chart Ratio_of_government_expenditure_to_GDP|37.04|y|line_chart Year|2020|x|line_chart Ratio_of_government_expenditure_to_GDP|36.86|y|line_chart Year|2019|x|line_chart Ratio_of_government_expenditure_to_GDP|37.56|y|line_chart Year|2018|x|line_chart Ratio_of_government_expenditure_to_GDP|38.9|y|line_chart Year|2017|x|line_chart Ratio_of_government_expenditure_to_GDP|41.18|y|line_chart Year|2016|x|line_chart Ratio_of_government_expenditure_to_GDP|41.52|y|line_chart Year|2015|x|line_chart Ratio_of_government_expenditure_to_GDP|41.37|y|line_chart Year|2014|x|line_chart Ratio_of_government_expenditure_to_GDP|38.85|y|line_chart 
title: Ratio of government expenditure to gross domestic product ( GDP ) in Argentina 2024*

gold: The statistic shows the ratio of government expenditure to the gross domestic product ( GDP ) in Argentina from 2014 to 2018 , with projections up until 2024 . In 2018 , the government expenditure in Argentina amounted to about 38.9 percent of the country 's gross domestic product .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to the templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[1] templateYLabel[2] in templateTitle[7] amounted to about templateYValue[6] percent of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to the templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitle[7] amounted to about templateYValue[6] percent of the templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to the gross domestic product ( GDP ) in Argentina from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in Argentina amounted to about 38.9 percent of the gross domestic product .


Example 306:
data: Response|Hacking_by_foreign_governments|x|bar_chart Share_of_respondents|72|y|bar_chart Response|Securing_confidential_intelligence_reports|x|bar_chart Share_of_respondents|23|y|bar_chart Response|Securing_citizen_records_(ex._IRS_filings)|x|bar_chart Share_of_respondents|17|y|bar_chart Response|Eavesdropping_through_smart_technology|x|bar_chart Share_of_respondents|11|y|bar_chart Response|Securing_records_of_military_personnel|x|bar_chart Share_of_respondents|11|y|bar_chart Response|Interfering_with_elections_through_propaganda|x|bar_chart Share_of_respondents|10|y|bar_chart Response|Interfering_with_elections_by_hacking_the_counting_of_ballots|x|bar_chart Share_of_respondents|8|y|bar_chart 
title: Biggest U.S. government cyber security problems according to U.S. adults 2017

gold: This statistic presents a ranking of the biggest cyber security problems facing the U.S. government according to adults in the United States . During the January 2017 survey period , 72 percent of respondents stated that hacking by foreign governments was the U.S. government 's biggest cyber security problem .
gold_template: This statistic presents a ranking of the templateTitle[0] templateTitle[3] templateTitle[4] templateTitle[5] facing the templateTitle[1] templateTitle[2] templateTitle[6] to templateTitle[8] in the templateTitle[1] . During the January templateTitle[9] survey period , templateYValue[max] percent of templateYLabel[1] stated that templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] was the templateTitle[1] templateTitle[2] 's templateTitle[0] templateTitle[3] templateTitle[4] problem .

generated_template: This statistic shows the results of a survey among female templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . According to the survey , templateYValue[max] percent of templateYLabel[1] cited templateXValue[1] templateXValue[1] templateTitle[4] and templateYValue[1] percent of the United States .
generated: This statistic shows the results of a survey among female U.S. government cyber security problems according in U.S. . According to the survey , 72 percent of respondents cited Securing_confidential_intelligence_reports security and 23 percent of the United States .


Example 307:
data: Year|2019|x|line_chart Average_number_of_days|32|y|line_chart Year|2018|x|line_chart Average_number_of_days|34|y|line_chart Year|2017|x|line_chart Average_number_of_days|34|y|line_chart Year|2016|x|line_chart Average_number_of_days|34|y|line_chart Year|2015|x|line_chart Average_number_of_days|36|y|line_chart Year|2014|x|line_chart Average_number_of_days|37|y|line_chart Year|2013|x|line_chart Average_number_of_days|41|y|line_chart Year|2012|x|line_chart Average_number_of_days|46|y|line_chart Year|2011|x|line_chart Average_number_of_days|45|y|line_chart Year|2010|x|line_chart Average_number_of_days|40|y|line_chart Year|2009|x|line_chart Average_number_of_days|41|y|line_chart Year|2008|x|line_chart Average_number_of_days|38|y|line_chart Year|2007|x|line_chart Average_number_of_days|40|y|line_chart Year|2006|x|line_chart Average_number_of_days|39|y|line_chart Year|2005|x|line_chart Average_number_of_days|38|y|line_chart Year|2004|x|line_chart Average_number_of_days|42|y|line_chart Year|2003|x|line_chart Average_number_of_days|40|y|line_chart Year|2002|x|line_chart Average_number_of_days|33|y|line_chart Year|2001|x|line_chart Average_number_of_days|37|y|line_chart Year|2000|x|line_chart Average_number_of_days|44|y|line_chart Year|1999|x|line_chart Average_number_of_days|40|y|line_chart Year|1998|x|line_chart Average_number_of_days|51|y|line_chart Year|1997|x|line_chart Average_number_of_days|81|y|line_chart Year|1996|x|line_chart Average_number_of_days|66|y|line_chart Year|1995|x|line_chart Average_number_of_days|66|y|line_chart 
title: U.S. nuclear refueling outage days 1995 - 2019

gold: The statistic represents the average length of outages for nuclear power plants in the United States between 1995 and 2019 . In 2019 , as of October 2019 , this figure stood at 32 outage days . US nuclear refueling outage days America 's electricity needs are met by a selection of energy carriers including coal , oil and natural gas , as well as nuclear fuel and renewable energy sources .
gold_template: The statistic represents the templateYLabel[0] length of outages for templateTitle[1] power plants in the templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , as of October templateXValue[max] , this figure stood at templateYValue[min] templateTitle[3] templateYLabel[2] . US templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] America 's electricity needs are met by a selection of energy carriers including coal , oil and natural gas , as well as templateTitle[1] fuel and renewable energy sources .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] percent of the United States .
generated: This statistic shows the Average number of refueling outage days 1995 in the United States from 1995 to 2019 . In 2019 , there were 32 percent of the United States .


Example 308:
data: Year|18/19|x|line_chart Revenue_in_million_U.S._dollars|287|y|line_chart Year|17/18|x|line_chart Revenue_in_million_U.S._dollars|246|y|line_chart Year|16/17|x|line_chart Revenue_in_million_U.S._dollars|223|y|line_chart Year|15/16|x|line_chart Revenue_in_million_U.S._dollars|178|y|line_chart Year|14/15|x|line_chart Revenue_in_million_U.S._dollars|157|y|line_chart Year|13/14|x|line_chart Revenue_in_million_U.S._dollars|153|y|line_chart Year|12/13|x|line_chart Revenue_in_million_U.S._dollars|140|y|line_chart Year|11/12|x|line_chart Revenue_in_million_U.S._dollars|117|y|line_chart Year|10/11|x|line_chart Revenue_in_million_U.S._dollars|132|y|line_chart Year|09/10|x|line_chart Revenue_in_million_U.S._dollars|127|y|line_chart Year|08/09|x|line_chart Revenue_in_million_U.S._dollars|121|y|line_chart Year|07/08|x|line_chart Revenue_in_million_U.S._dollars|114|y|line_chart Year|06/07|x|line_chart Revenue_in_million_U.S._dollars|82|y|line_chart Year|05/06|x|line_chart Revenue_in_million_U.S._dollars|77|y|line_chart Year|04/05|x|line_chart Revenue_in_million_U.S._dollars|78|y|line_chart Year|03/04|x|line_chart Revenue_in_million_U.S._dollars|88|y|line_chart Year|02/03|x|line_chart Revenue_in_million_U.S._dollars|97|y|line_chart Year|01/02|x|line_chart Revenue_in_million_U.S._dollars|96|y|line_chart 
title: Portland Trail Blazers ' revenue 2001 - 2019

gold: The statistic shows the revenue of the Portland Trail Blazers franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 287 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Portland Trail franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 287 million U.S. dollars .


Example 309:
data: Year|2019|x|line_chart Expenditure_in_million_U.S._dollars|1010|y|line_chart Year|2018|x|line_chart Expenditure_in_million_U.S._dollars|1213|y|line_chart Year|2017|x|line_chart Expenditure_in_million_U.S._dollars|1778|y|line_chart Year|2016|x|line_chart Expenditure_in_million_U.S._dollars|2077|y|line_chart Year|2015|x|line_chart Expenditure_in_million_U.S._dollars|1525|y|line_chart Year|2014|x|line_chart Expenditure_in_million_U.S._dollars|1488|y|line_chart Year|2013|x|line_chart Expenditure_in_million_U.S._dollars|1427|y|line_chart Year|2012|x|line_chart Expenditure_in_million_U.S._dollars|1356|y|line_chart Year|2011|x|line_chart Expenditure_in_million_U.S._dollars|1095|y|line_chart Year|2010|x|line_chart Expenditure_in_million_U.S._dollars|951|y|line_chart Year|2009|x|line_chart Expenditure_in_million_U.S._dollars|825|y|line_chart Year|2008|x|line_chart Expenditure_in_million_U.S._dollars|786|y|line_chart Year|2007|x|line_chart Expenditure_in_million_U.S._dollars|581|y|line_chart Year|2006|x|line_chart Expenditure_in_million_U.S._dollars|495|y|line_chart 
title: Teva : expenditure on research and development 2006 - 2019

gold: This statistic shows the expenditure of pharmaceutical company Teva for research and development from 2006 to 2019 . Teva Pharmaceutical Industries is one of the largest generic drug manufacturers in the world . In 2019 , the company invested about one billion U.S. dollars in research and development .
gold_template: This statistic shows the templateYLabel[0] of pharmaceutical company templateTitle[0] for templateTitle[2] and templateTitle[3] from templateXValue[min] to templateXValue[max] . templateTitle[0] Pharmaceutical Industries is one of the largest generic drug manufacturers in the world . In templateXValue[max] , the company invested about templateYValue[0] billion templateYLabel[2] templateYLabel[3] in templateTitle[2] and templateTitle[3] .

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] ( templateTitle[2] ) of the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company invested approximately templateYValue[0] billion templateYLabel[2] templateYLabel[3] . templateTitle[2] templateTitle[3] templateTitle[4] research and development as an agricultural company largest company generally worldwide as of templateXValue[1] , measured by revenue .
generated: The statistic depicts the Expenditure million of research development 2006 2019 titleErr ( research ) of the United States from 2006 to 2019 . In 2019 , the company invested approximately 1010 billion U.S. dollars . research development 2006 research and development as an agricultural company largest company generally worldwide as of 2018 , measured by revenue .


Example 310:
data: Year|2024|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2296.03|y|line_chart Year|2023|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2188.89|y|line_chart Year|2022|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2084.3|y|line_chart Year|2021|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1987.82|y|line_chart Year|2020|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1893.01|y|line_chart Year|2019|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1847.02|y|line_chart Year|2018|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1867.82|y|line_chart Year|2017|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2052.81|y|line_chart Year|2016|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1795.37|y|line_chart Year|2015|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1799.88|y|line_chart Year|2014|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2456.11|y|line_chart Year|2013|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2471.56|y|line_chart Year|2012|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2464.4|y|line_chart Year|2011|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2613.99|y|line_chart Year|2010|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2207.62|y|line_chart Year|2009|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1667.68|y|line_chart Year|2008|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1694.87|y|line_chart Year|2007|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1396.11|y|line_chart Year|2006|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1106.37|y|line_chart Year|2005|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|890.67|y|line_chart Year|2004|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|668.43|y|line_chart Year|2003|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|557.68|y|line_chart Year|2002|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|509.36|y|line_chart Year|2001|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|559.96|y|line_chart Year|2000|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|655.44|y|line_chart Year|1999|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|599.87|y|line_chart Year|1998|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|865.12|y|line_chart Year|1997|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|884.31|y|line_chart Year|1996|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|850.42|y|line_chart Year|1995|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|786.54|y|line_chart Year|1994|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|546.57|y|line_chart Year|1993|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|429.03|y|line_chart Year|1992|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|382.33|y|line_chart Year|1991|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|399.11|y|line_chart Year|1990|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|455.17|y|line_chart Year|1989|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|439.28|y|line_chart Year|1988|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|319.99|y|line_chart Year|1987|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|286.44|y|line_chart Year|1986|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|263.16|y|line_chart Year|1985|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|226.86|y|line_chart Year|1984|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|142.91|y|line_chart 
title: Gross domestic product ( GDP ) in Brazil 2024

gold: The statistic shows gross domestic product ( GDP ) in Brazil from 1984 to 2018 , with projections up until 2024 . Gross domestic product denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Brazil 2024 from 1984 to 2018 , with projections up until 2024 . Gross domestic product denotes the aggregate value of all services and goods produced within a country in any given Year . GDP is an important indicator of a country 's economic power .


Example 311:
data: Year|2018|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|30446.6|y|line_chart Year|2017|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|30223.9|y|line_chart Year|2016|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|29546.0|y|line_chart Year|2015|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|29275.7|y|line_chart Year|2014|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|29039.6|y|line_chart Year|2013|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|29017.0|y|line_chart Year|2012|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|29117.4|y|line_chart Year|2011|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|29467.6|y|line_chart Year|2010|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|29404.4|y|line_chart Year|2009|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|28802.1|y|line_chart Year|2008|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|29215.2|y|line_chart Year|2007|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|28962.4|y|line_chart Year|2006|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|28787.2|y|line_chart Year|2005|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|28281.0|y|line_chart Year|2004|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|28008.6|y|line_chart Year|2003|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|27240.8|y|line_chart Year|2002|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|26653.8|y|line_chart Year|2001|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|25444.8|y|line_chart Year|2000|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|25058.0|y|line_chart 
title: GDP of New Brunswick , Canada 2000 - 2018

gold: This statistic shows the gross domestic product ( GDP ) of New Brunswick from 2000 to 2018 . In 2018 , New Brunswick 's GDP was 30.44 billion chained 2012 Canadian dollars .
gold_template: This statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[1] templateTitle[2] 's templateYLabel[0] was 30.44 billion templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] 's templateYLabel[0] was templateYValue[0] templateYLabel[1] templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the GDP New Brunswick ( GDP ) million United States from 2000 to 2018 . In 2018 , Canada 's GDP was 30446.6 million chained 2012 Canadian dollars .


Example 312:
data: Country|China|x|bar_chart Number_of_employees|2301534|y|bar_chart Country|India|x|bar_chart Number_of_employees|567469|y|bar_chart Country|United_States|x|bar_chart Number_of_employees|251995|y|bar_chart Country|Germany|x|bar_chart Number_of_employees|128000|y|bar_chart Country|Brazil|x|bar_chart Number_of_employees|105253|y|bar_chart Country|Japan|x|bar_chart Number_of_employees|91529|y|bar_chart Country|Russian_Federation|x|bar_chart Number_of_employees|66523|y|bar_chart Country|Indonesia|x|bar_chart Number_of_employees|64059|y|bar_chart Country|Italy|x|bar_chart Number_of_employees|59300|y|bar_chart Country|Mexico|x|bar_chart Number_of_employees|51125|y|bar_chart Country|Thailand|x|bar_chart Number_of_employees|48000|y|bar_chart Country|Egypt|x|bar_chart Number_of_employees|47753|y|bar_chart Country|France|x|bar_chart Number_of_employees|47000|y|bar_chart Country|Spain|x|bar_chart Number_of_employees|43723|y|bar_chart Country|Switzerland|x|bar_chart Number_of_employees|43258|y|bar_chart Country|United_Kingdom|x|bar_chart Number_of_employees|41690|y|bar_chart Country|Vietnam|x|bar_chart Number_of_employees|39749|y|bar_chart Country|Poland|x|bar_chart Number_of_employees|38000|y|bar_chart Country|Pakistan|x|bar_chart Number_of_employees|36336|y|bar_chart Country|Turkey|x|bar_chart Number_of_employees|35100|y|bar_chart 
title: Top countries by pharmaceutical industry employment 2014

gold: This statistic displays the top 20 countries worldwide based on the number of people employed in the pharmaceutical industry as of 2014 . The pharmaceutical industry in Japan counted over 91,500 employees that year .
gold_template: This statistic displays the templateTitle[0] 20 templateTitle[1] worldwide based on the templateYLabel[0] of people employed in the templateTitle[3] templateTitle[4] as of templateTitle[6] . The templateTitle[3] templateTitle[4] in templateXValue[5] counted over 91,500 templateYLabel[1] that year .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of templateYLabel[1] templateTitle[3] templateTitle[4] in templateTitle[5] , templateTitle[3] templateXLabel[0] . templateXValue[0] ranked first among U.S. fashion company around templateYValue[max] templateYLabel[1] in templateXValue[1] . templateTitle[2] templateTitle[3] group more than templateXValue[1] , the U.S. employed more than Japan , the U.S. dollars in templateXValue[1] .
generated: This statistic shows the Top countries Number of employees pharmaceutical industry in employment , pharmaceutical Country . China ranked first among U.S. fashion company around 2301534 employees in India . by pharmaceutical group more than India , the U.S. employed more than Japan , the U.S. dollars in India .


Example 313:
data: Year|2019|x|line_chart ASMs_in_billions|157.25|y|line_chart Year|2018|x|line_chart ASMs_in_billions|159.8|y|line_chart Year|2017|x|line_chart ASMs_in_billions|153.81|y|line_chart Year|2016|x|line_chart ASMs_in_billions|148.52|y|line_chart Year|2015|x|line_chart ASMs_in_billions|140.5|y|line_chart Year|2014|x|line_chart ASMs_in_billions|131.0|y|line_chart Year|2013|x|line_chart ASMs_in_billions|130.34|y|line_chart Year|2012|x|line_chart ASMs_in_billions|128.14|y|line_chart Year|2011|x|line_chart ASMs_in_billions|120.58|y|line_chart 
title: Southwest Airlines - available seat miles 2011 - 2019

gold: Southwest Airlines grew its available seat miles ( ASMs ) from 120.58 billion in 2011 to 157.25 billion in 2019 . ASMs are calculated by the total number of seats multiplied by the total distance traveled . When describing the airline industry , people consider ASMs as a measure of capacity .
gold_template: templateTitle[0] templateTitle[1] grew its templateTitle[2] templateTitle[3] templateTitle[4] ( templateYLabel[0] ) from templateYValue[min] billion in templateXValue[min] to templateYValue[0] billion in templateXValue[max] . templateYLabel[0] are calculated by the total number of seats multiplied by the total distance traveled . When describing the airline industry , people consider templateYLabel[0] as a measure of capacity .

generated_template: templateYLabel[1] at templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] , according to templateYValue[max] percent of templateYLabel[1] in templateXValue[max] , an increase from templateYValue[1] percent in templateXValue[1] . The templateYLabel[0] templateYLabel[1] compared to the previous templateXLabel[0] templateTitle[3] templateTitle[4] templateTitle[5] 's templateYValue[1] percent in templateXValue[1] . templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[3] templateTitle[4] can be attributed to the number of people in the last ten years .
generated: billions at available seat miles 2011 in 2019 , according to 159.8 percent of billions in 2019 , an increase from 159.8 percent in 2018 . The ASMs billions compared to the previous Year seat miles 2011 's 159.8 percent in 2018 . available seat miles 2011 seat miles can be attributed to the number of people in the last ten years .


Example 314:
data: Year|2018|x|line_chart Number_of_recalls|52|y|line_chart Year|2017|x|line_chart Number_of_recalls|93|y|line_chart Year|2016|x|line_chart Number_of_recalls|76|y|line_chart Year|2015|x|line_chart Number_of_recalls|68|y|line_chart Year|2014|x|line_chart Number_of_recalls|75|y|line_chart Year|2013|x|line_chart Number_of_recalls|114|y|line_chart Year|2012|x|line_chart Number_of_recalls|97|y|line_chart Year|2011|x|line_chart Number_of_recalls|124|y|line_chart Year|2010|x|line_chart Number_of_recalls|148|y|line_chart Year|2009|x|line_chart Number_of_recalls|143|y|line_chart Year|2008|x|line_chart Number_of_recalls|209|y|line_chart Year|2007|x|line_chart Number_of_recalls|232|y|line_chart Year|2006|x|line_chart Number_of_recalls|111|y|line_chart Year|2005|x|line_chart Number_of_recalls|122|y|line_chart Year|2004|x|line_chart Number_of_recalls|87|y|line_chart Year|2003|x|line_chart Number_of_recalls|66|y|line_chart Year|2002|x|line_chart Number_of_recalls|90|y|line_chart Year|2001|x|line_chart Number_of_recalls|118|y|line_chart 
title: Total recalls of children 's products in the U.S. 2001 - 2018

gold: The statistic depicts the number of children 's products that were recalled in the United States from 2001 to 2018 . In 2018 , 52 children 's products were the subject of a recall .
gold_template: The statistic depicts the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] that were recalled in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[min] templateTitle[2] templateTitle[3] templateTitle[4] were the subject of a recall .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people throughout the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: The statistic shows the Number recalls of 's products U.S. 2001 the United States from 2001 to 2018 . In 2018 , there were 52 people throughout the children 's products U.S. in the United States .


Example 315:
data: urban_population_(_of_total)|2018|x|bar_chart Unnamed:_1|69.45|y|bar_chart urban_population_(_of_total)|2017|x|bar_chart Unnamed:_1|68.7|y|bar_chart urban_population_(_of_total)|2016|x|bar_chart Unnamed:_1|67.93|y|bar_chart urban_population_(_of_total)|2015|x|bar_chart Unnamed:_1|67.16|y|bar_chart urban_population_(_of_total)|2014|x|bar_chart Unnamed:_1|66.37|y|bar_chart urban_population_(_of_total)|2013|x|bar_chart Unnamed:_1|65.57|y|bar_chart urban_population_(_of_total)|2012|x|bar_chart Unnamed:_1|64.77|y|bar_chart urban_population_(_of_total)|2011|x|bar_chart Unnamed:_1|63.87|y|bar_chart urban_population_(_of_total)|2010|x|bar_chart Unnamed:_1|62.41|y|bar_chart urban_population_(_of_total)|2009|x|bar_chart Unnamed:_1|60.94|y|bar_chart urban_population_(_of_total)|2008|x|bar_chart Unnamed:_1|59.44|y|bar_chart 
title: Urbanization in Botswana 2018

gold: This statistic shows the percentage of the total population living in urban areas in Botswana from 2008 to 2018 . In 2018 , 69.45 percent of the total population of Botswana was living in urban areas .
gold_template: This statistic shows the percentage of the total templateXLabel[1] living in templateXLabel[0] areas in templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the total templateXLabel[1] of templateTitle[1] was living in templateXLabel[0] areas .

generated_template: The statistic shows the templateXLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the templateYLabel[1] templateYLabel[2] in templateTitle[2] was produced .
generated: The statistic shows the urban 1 yLabelErr in 2018 from 2008 to 2018 . In 2018 , 69.45 percent of the 1 yLabelErr in 2018 was produced .


Example 316:
data: Year|2019|x|line_chart Unemployment_rate|13|y|line_chart Year|2018|x|line_chart Unemployment_rate|12.88|y|line_chart Year|2017|x|line_chart Unemployment_rate|12.77|y|line_chart Year|2016|x|line_chart Unemployment_rate|13.01|y|line_chart Year|2015|x|line_chart Unemployment_rate|13.16|y|line_chart Year|2014|x|line_chart Unemployment_rate|13.21|y|line_chart Year|2013|x|line_chart Unemployment_rate|13.26|y|line_chart Year|2012|x|line_chart Unemployment_rate|13.18|y|line_chart Year|2011|x|line_chart Unemployment_rate|13.04|y|line_chart Year|2010|x|line_chart Unemployment_rate|13.35|y|line_chart Year|2009|x|line_chart Unemployment_rate|13|y|line_chart Year|2008|x|line_chart Unemployment_rate|14.8|y|line_chart Year|2007|x|line_chart Unemployment_rate|14.9|y|line_chart Year|2006|x|line_chart Unemployment_rate|15.26|y|line_chart Year|2005|x|line_chart Unemployment_rate|16.06|y|line_chart Year|2004|x|line_chart Unemployment_rate|16.34|y|line_chart Year|2003|x|line_chart Unemployment_rate|16.61|y|line_chart Year|2002|x|line_chart Unemployment_rate|16.49|y|line_chart Year|2001|x|line_chart Unemployment_rate|16.62|y|line_chart Year|2000|x|line_chart Unemployment_rate|16.94|y|line_chart Year|1999|x|line_chart Unemployment_rate|16.71|y|line_chart 
title: Unemployment rate in Sudan 2019

gold: This statistic shows the unemployment rate in Sudan from 1999 to 2019 . In 2019 , the estimated unemployment rate in Sudan was at approximately 13 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Sudan from 1999 to 2019 . In 2019 , the Unemployment rate in Sudan was at approximately 13 percent .


Example 317:
data: Year|2019|x|line_chart Youth_unemployment_rate|3.43|y|line_chart Year|2018|x|line_chart Youth_unemployment_rate|3.45|y|line_chart Year|2017|x|line_chart Youth_unemployment_rate|3.43|y|line_chart Year|2016|x|line_chart Youth_unemployment_rate|3.66|y|line_chart Year|2015|x|line_chart Youth_unemployment_rate|3.7|y|line_chart Year|2014|x|line_chart Youth_unemployment_rate|3.71|y|line_chart Year|2013|x|line_chart Youth_unemployment_rate|5.69|y|line_chart Year|2012|x|line_chart Youth_unemployment_rate|6.34|y|line_chart Year|2011|x|line_chart Youth_unemployment_rate|6.76|y|line_chart Year|2010|x|line_chart Youth_unemployment_rate|5.9|y|line_chart Year|2009|x|line_chart Youth_unemployment_rate|4.88|y|line_chart Year|2008|x|line_chart Youth_unemployment_rate|4.58|y|line_chart Year|2007|x|line_chart Youth_unemployment_rate|5.04|y|line_chart Year|2006|x|line_chart Youth_unemployment_rate|6.07|y|line_chart Year|2005|x|line_chart Youth_unemployment_rate|6.29|y|line_chart Year|2004|x|line_chart Youth_unemployment_rate|6.19|y|line_chart Year|2003|x|line_chart Youth_unemployment_rate|6.04|y|line_chart Year|2002|x|line_chart Youth_unemployment_rate|5.81|y|line_chart Year|2001|x|line_chart Youth_unemployment_rate|5.36|y|line_chart Year|2000|x|line_chart Youth_unemployment_rate|5.64|y|line_chart Year|1999|x|line_chart Youth_unemployment_rate|5.88|y|line_chart 
title: Youth unemployment rate in Tanzania in 2019

gold: The statistic shows the youth unemployment rate in Tanzania from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Tanzania was at 3.43 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] was at templateYValue[min] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Tanzania from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Tanzania was at 3.43 percent .


Example 318:
data: Company|Motorola_Mobility_(2012)|x|bar_chart Price_in_million_U.S._dollars|12500.0|y|bar_chart Company|Nest_Labs_(2014)|x|bar_chart Price_in_million_U.S._dollars|3200.0|y|bar_chart Company|DoubleClick_(2008)|x|bar_chart Price_in_million_U.S._dollars|3100.0|y|bar_chart Company|YouTube_(2006)|x|bar_chart Price_in_million_U.S._dollars|1650.0|y|bar_chart Company|Part_of_HTC_mobile_division_and_licenses_(2017)|x|bar_chart Price_in_million_U.S._dollars|1100.0|y|bar_chart Company|Waze_(2013)|x|bar_chart Price_in_million_U.S._dollars|1100.0|y|bar_chart Company|AdMob_(2009)|x|bar_chart Price_in_million_U.S._dollars|750.0|y|bar_chart Company|ITA_Software_(2012)|x|bar_chart Price_in_million_U.S._dollars|700.0|y|bar_chart Company|Postini_(2007)|x|bar_chart Price_in_million_U.S._dollars|625.0|y|bar_chart Company|Apigee_(2016)|x|bar_chart Price_in_million_U.S._dollars|625.0|y|bar_chart Company|DeepMind_(2014)|x|bar_chart Price_in_million_U.S._dollars|500.0|y|bar_chart Company|Skybox_Imaging_(2014)|x|bar_chart Price_in_million_U.S._dollars|500.0|y|bar_chart Company|Admeld_(2011)|x|bar_chart Price_in_million_U.S._dollars|391.08|y|bar_chart Company|Bebop_(2015)|x|bar_chart Price_in_million_U.S._dollars|380.2|y|bar_chart Company|Wildfire_(2012)|x|bar_chart Price_in_million_U.S._dollars|350.0|y|bar_chart Company|Slide_(2010)|x|bar_chart Price_in_million_U.S._dollars|182.0|y|bar_chart Company|Widevine_Technologies_(2010)|x|bar_chart Price_in_million_U.S._dollars|160.0|y|bar_chart Company|Zagat_(2011)|x|bar_chart Price_in_million_U.S._dollars|151.0|y|bar_chart Company|On2_Technologies_(2010)|x|bar_chart Price_in_million_U.S._dollars|130.0|y|bar_chart Company|Channel_Intelligence_(2013)|x|bar_chart Price_in_million_U.S._dollars|125.0|y|bar_chart Company|Divide_(2014)|x|bar_chart Price_in_million_U.S._dollars|120.0|y|bar_chart Company|dMarc_Broadcasting_(2006)|x|bar_chart Price_in_million_U.S._dollars|102.0|y|bar_chart Company|Applied_Semantics_(2003)|x|bar_chart Price_in_million_U.S._dollars|102.0|y|bar_chart Company|Meebo_(2012)|x|bar_chart Price_in_million_U.S._dollars|100.0|y|bar_chart Company|FeedBurner_(2007)|x|bar_chart Price_in_million_U.S._dollars|100.0|y|bar_chart Company|Invite_Media_(2010)|x|bar_chart Price_in_million_U.S._dollars|80.0|y|bar_chart Company|Global_IP_Solutions_(2010)|x|bar_chart Price_in_million_U.S._dollars|68.2|y|bar_chart Company|Android_(2012)|x|bar_chart Price_in_million_U.S._dollars|50.0|y|bar_chart 
title: Price of selected acquisitions by Google 2017

gold: This statistic shows a selection of companies Google , Inc. has acquired since 2003 , and their respective price . In June 2013 , Google acquired social traffic app Waze for 1.1 billion U.S. dollars . The internet company 's most expensive acquisition was Motorola Mobility in August 2011 , tallying 12.5 billion U.S. dollars .
gold_template: This statistic shows a selection of companies templateTitle[4] , Inc. has acquired since 2003 , and their respective templateYLabel[0] . In June 2013 , templateTitle[4] acquired social traffic app templateXValue[5] for templateYValue[4] billion templateYLabel[2] templateYLabel[3] . The internet templateXLabel[0] 's most expensive acquisition was templateXValue[0] templateXValue[0] in August 2011 , tallying templateYValue[max] billion templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[5] . templateXValue[2] templateXValue[1] had a templateYLabel[0] of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[5] .
generated: This statistic shows the Price of Price selected acquisitions by Google million U.S. dollars 2017 . DoubleClick_(2008) Nest_Labs_(2014) had a Price of 12500.0 million U.S. dollars in 2017 .


Example 319:
data: Year|2018/19|x|line_chart Number_of_players|621026|y|line_chart Year|2017/18|x|line_chart Number_of_players|637000|y|line_chart Year|2016/17|x|line_chart Number_of_players|631295|y|line_chart Year|2015/16|x|line_chart Number_of_players|639500|y|line_chart Year|2014/15|x|line_chart Number_of_players|721504|y|line_chart Year|2013/14|x|line_chart Number_of_players|721504|y|line_chart Year|2012/13|x|line_chart Number_of_players|625152|y|line_chart Year|2011/12|x|line_chart Number_of_players|617107|y|line_chart Year|2010/11|x|line_chart Number_of_players|572411|y|line_chart 
title: Ice hockey players in Canada 2010 - 2019

gold: The statistics depicts the number of registered ice hockey players in Canada from 2010/11 to 2017/18 . In the 2018/19 season , there were a total of 621,026 registered ice hockey players in Canada according to the International Ice Hockey Federation .
gold_template: The statistics depicts the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitle[3] from templateXValue[last] to templateXValue[1] . In the templateXValue[0] season , there were a total of templateYValue[0] registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitle[3] according to the International templateTitle[0] templateTitle[1] Federation .

generated_template: The graph depicts the templateTitle[3] templateTitle[4] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] templateTitle[4] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a templateYLabel[1] of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] templateTitle[4] according to the International templateTitle[0] templateTitle[1] Federation .
generated: The graph depicts the Canada 2010 of Ice hockey players in the Canada 2010 from 2010/11 to 2018/19 . In the 2018/19 season , there were a players of 721504 registered Ice hockey players in the Canada 2010 according to the International Ice hockey Federation .


Example 320:
data: Year|2024|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|10.48|y|line_chart Year|2023|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|9.77|y|line_chart Year|2022|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|9.12|y|line_chart Year|2021|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|8.54|y|line_chart Year|2020|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|8.05|y|line_chart Year|2019|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|7.52|y|line_chart Year|2018|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|6.9|y|line_chart Year|2017|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|6.23|y|line_chart Year|2016|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|5.49|y|line_chart Year|2015|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|6.4|y|line_chart Year|2014|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|6.06|y|line_chart Year|2013|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|5.43|y|line_chart Year|2012|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|5.98|y|line_chart Year|2011|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|7.98|y|line_chart Year|2010|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|6.96|y|line_chart Year|2009|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|6.2|y|line_chart Year|2008|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|5.32|y|line_chart Year|2007|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|4.43|y|line_chart Year|2006|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|4.0|y|line_chart Year|2005|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.66|y|line_chart Year|2004|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.48|y|line_chart Year|2003|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.21|y|line_chart Year|2002|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.5|y|line_chart Year|2001|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2.97|y|line_chart Year|2000|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.02|y|line_chart Year|1999|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.08|y|line_chart Year|1998|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.04|y|line_chart Year|1997|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|4.62|y|line_chart Year|1996|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.96|y|line_chart Year|1995|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2.42|y|line_chart Year|1994|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2.08|y|line_chart Year|1993|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.59|y|line_chart Year|1992|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.12|y|line_chart Year|1991|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.82|y|line_chart Year|1990|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.0|y|line_chart Year|1989|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2.64|y|line_chart Year|1988|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2.31|y|line_chart Year|1987|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2.01|y|line_chart Year|1986|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2.05|y|line_chart Year|1985|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1.96|y|line_chart Year|1984|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|2.1|y|line_chart 
title: Gross domestic product ( GDP ) in Malawi 2024

gold: The statistic shows gross domestic product ( GDP ) in Malawi from 1984 to 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Malawi from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year . GDP is an important indicator of a country 's economic power .


Example 321:
data: Year|2019|x|line_chart Number_of_female_directors|292|y|line_chart Year|2018|x|line_chart Number_of_female_directors|264|y|line_chart Year|2017|x|line_chart Number_of_female_directors|259|y|line_chart Year|2016|x|line_chart Number_of_female_directors|244|y|line_chart Year|2015|x|line_chart Number_of_female_directors|233|y|line_chart Year|2014|x|line_chart Number_of_female_directors|205|y|line_chart Year|2013|x|line_chart Number_of_female_directors|169|y|line_chart Year|2012|x|line_chart Number_of_female_directors|141|y|line_chart 
title: Number of female directors in FTSE 100 companies UK 2012 - 2019

gold: This statistic shows the total number of female directors working on the board of FTSE 100 companies in the United Kingdom ( UK ) from 2012 to 2019 . During the observed period the number of female directors ( both executive and non-executive ) in FTSE 100 companies increased by 151 to reach a total of 292 females holding a director position , as of June 2019 .
gold_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] working on the board of templateTitle[3] templateTitle[4] templateTitle[5] in the United Kingdom ( templateTitle[6] ) from templateXValue[min] to templateXValue[max] . During the observed period the templateYLabel[0] of templateYLabel[1] templateYLabel[2] ( both executive and non-executive ) in templateTitle[3] templateTitle[4] templateTitle[5] increased by 151 to reach a total of templateYValue[max] females holding a director position , as of June templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[max] thousand people living in the United States .
generated: This statistic shows the Number female of FTSE 100 companies in the United States from 2012 to 2019 . In 2019 , there were approximately 292 thousand people living in the United States .


Example 322:
data: Year|2019|x|line_chart Franchise_value_in_million_U.S._dollars|2100|y|line_chart Year|2018|x|line_chart Franchise_value_in_million_U.S._dollars|1900|y|line_chart Year|2017|x|line_chart Franchise_value_in_million_U.S._dollars|1800|y|line_chart Year|2016|x|line_chart Franchise_value_in_million_U.S._dollars|1600|y|line_chart Year|2015|x|line_chart Franchise_value_in_million_U.S._dollars|1400|y|line_chart Year|2014|x|line_chart Franchise_value_in_million_U.S._dollars|820|y|line_chart Year|2013|x|line_chart Franchise_value_in_million_U.S._dollars|716|y|line_chart Year|2012|x|line_chart Franchise_value_in_million_U.S._dollars|591|y|line_chart Year|2011|x|line_chart Franchise_value_in_million_U.S._dollars|518|y|line_chart Year|2010|x|line_chart Franchise_value_in_million_U.S._dollars|488|y|line_chart Year|2009|x|line_chart Franchise_value_in_million_U.S._dollars|486|y|line_chart Year|2008|x|line_chart Franchise_value_in_million_U.S._dollars|484|y|line_chart Year|2007|x|line_chart Franchise_value_in_million_U.S._dollars|460|y|line_chart Year|2006|x|line_chart Franchise_value_in_million_U.S._dollars|429|y|line_chart Year|2005|x|line_chart Franchise_value_in_million_U.S._dollars|370|y|line_chart Year|2004|x|line_chart Franchise_value_in_million_U.S._dollars|314|y|line_chart Year|2003|x|line_chart Franchise_value_in_million_U.S._dollars|308|y|line_chart Year|2002|x|line_chart Franchise_value_in_million_U.S._dollars|271|y|line_chart 
title: Franchise value of the St. Louis Cardinals 2002 - 2019

gold: This graph depicts the value of the St. Louis Cardinals franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 2.1 billion U.S. dollars . The St. Louis Cardinals are owned by William DeWitt Jr. , who bought the franchise for 150 million U.S. dollars in 1996 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[4] are owned by William DeWitt Jr. , who bought the templateYLabel[0] for 150 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1996 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The current owner Robert Kraft bought the templateYLabel[0] in 1994 for 172 templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Franchise value of the St. Louis Cardinals of the National Football League from 2002 to 2019 . In 2019 , the Franchise value of the St. Louis Cardinals was 2100 billion U.S. dollars . The current owner Robert Kraft bought the Franchise in 1994 for 172 million U.S. dollars .


Example 323:
data: Response|Morally_acceptable|x|bar_chart Share_of_respondents|43|y|bar_chart Response|Morally_wrong|x|bar_chart Share_of_respondents|49|y|bar_chart Response|Depends_on_situation|x|bar_chart Share_of_respondents|7|y|bar_chart Response|No_opinion|x|bar_chart Share_of_respondents|1|y|bar_chart 
title: Americans ' moral stance towards abortion in 2018

gold: This statistic shows the results of a survey among Americans regarding their moral stance towards abortion in 2018 . In 2018 , 43 percent of respondents stated that they think having an abortion is morally acceptable , while 48 percent considered it morally wrong .
gold_template: This statistic shows the results of a survey among templateTitle[0] regarding their templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] . In templateTitle[6] , templateYValue[0] percent of templateYLabel[1] stated that they think having an templateTitle[5] is templateXValue[0] templateXValue[0] , while 48 percent considered it templateXValue[0] templateXValue[1] .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitle[0] regarding templateTitle[5] or templateTitle[6] templateTitle[7] in templateTitle[8] . During this survey , templateYValue[max] percent of templateYLabel[1] stated they think templateTitle[5] or templateTitle[6] templateTitle[7] are templateXValue[0] templateXValue[0] , while templateYValue[min] percent said it templateXValue[2] on the situation .
generated: This statistic shows the moral stance of Americans regarding abortion or 2018 titleErr in titleErr . During this survey , 49 percent of respondents stated they think abortion or 2018 titleErr are Morally_acceptable , while 1 percent said it Depends_on_situation on the situation .


Example 324:
data: Country|Poland|x|bar_chart Operating_budgetary_balances_in_billion_euros|8.57|y|bar_chart Country|Greece|x|bar_chart Operating_budgetary_balances_in_billion_euros|3.74|y|bar_chart Country|Romania|x|bar_chart Operating_budgetary_balances_in_billion_euros|3.38|y|bar_chart Country|Hungary|x|bar_chart Operating_budgetary_balances_in_billion_euros|3.14|y|bar_chart Country|Czech_Republic|x|bar_chart Operating_budgetary_balances_in_billion_euros|2.48|y|bar_chart Country|Portugal|x|bar_chart Operating_budgetary_balances_in_billion_euros|2.44|y|bar_chart Country|Bulgaria|x|bar_chart Operating_budgetary_balances_in_billion_euros|1.47|y|bar_chart Country|Lithuania|x|bar_chart Operating_budgetary_balances_in_billion_euros|1.27|y|bar_chart Country|Slowakia|x|bar_chart Operating_budgetary_balances_in_billion_euros|0.98|y|bar_chart Country|Spain|x|bar_chart Operating_budgetary_balances_in_billion_euros|0.73|y|bar_chart Country|Latvia|x|bar_chart Operating_budgetary_balances_in_billion_euros|0.53|y|bar_chart Country|Estonia|x|bar_chart Operating_budgetary_balances_in_billion_euros|0.47|y|bar_chart Country|Croatia|x|bar_chart Operating_budgetary_balances_in_billion_euros|0.26|y|bar_chart Country|Slovenia|x|bar_chart Operating_budgetary_balances_in_billion_euros|0.15|y|bar_chart Country|Malta|x|bar_chart Operating_budgetary_balances_in_billion_euros|0.1|y|bar_chart Country|Cyprus|x|bar_chart Operating_budgetary_balances_in_billion_euros|0.05|y|bar_chart Country|Luxembourg|x|bar_chart Operating_budgetary_balances_in_billion_euros|0.01|y|bar_chart Country|Ireland|x|bar_chart Operating_budgetary_balances_in_billion_euros|-0.17|y|bar_chart Country|Finland|x|bar_chart Operating_budgetary_balances_in_billion_euros|-0.28|y|bar_chart Country|Denmark|x|bar_chart Operating_budgetary_balances_in_billion_euros|-0.7|y|bar_chart Country|Belgium|x|bar_chart Operating_budgetary_balances_in_billion_euros|-0.72|y|bar_chart Country|Austria|x|bar_chart Operating_budgetary_balances_in_billion_euros|-0.93|y|bar_chart Country|Netherlands|x|bar_chart Operating_budgetary_balances_in_billion_euros|-1.39|y|bar_chart Country|Sweden|x|bar_chart Operating_budgetary_balances_in_billion_euros|-1.4|y|bar_chart Country|Italy|x|bar_chart Operating_budgetary_balances_in_billion_euros|-3.58|y|bar_chart Country|France|x|bar_chart Operating_budgetary_balances_in_billion_euros|-4.57|y|bar_chart Country|United_Kingdom|x|bar_chart Operating_budgetary_balances_in_billion_euros|-5.35|y|bar_chart Country|Germany|x|bar_chart Operating_budgetary_balances_in_billion_euros|-10.68|y|bar_chart 
title: EU operating budgetary balances in 2017 , by member state

gold: This statistic shows the operating budgetary balances of the EU member states in 2017 . A negative budgetary balance means that a country contributes more to the EU budget than it receives from it , a positive balance means the country contributes less than it receives . In 2017 , Germany contributed the most with approximately 10.68 billion euros .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[0] templateTitle[6] states in templateTitle[4] . A negative templateYLabel[1] balance means that a templateXLabel[0] contributes more to the templateTitle[0] budget than it receives from it , a positive balance means the templateXLabel[0] contributes less than it receives . In templateTitle[4] , templateXValue[last] contributed the most with approximately 10.68 templateYLabel[3] templateYLabel[4] .

generated_template: In templateTitle[4] templateTitle[5] , templateXValue[0] was the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] in the European Union , according to templateYValue[max] templateYLabel[4] . This figure only templateYValue[1] templateYLabel[3] in the previous year . The templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in this statistic above 100 people lived in which countries .
generated: In 2017 by , Poland was the highest Operating budgetary balances in the European Union , according to 8.57 euros . This figure only 3.74 billion in the previous year . The Operating budgetary balances of 2017 by in this statistic above 100 people lived in which countries .


Example 325:
data: 12_week_period_ending|14_Jul_19|x|bar_chart Percentage_growth_(year-on-year)|-1.9|y|bar_chart 12_week_period_ending|16_Jun_19|x|bar_chart Percentage_growth_(year-on-year)|-|y|bar_chart 12_week_period_ending|21_Apr_19|x|bar_chart Percentage_growth_(year-on-year)|0.7|y|bar_chart 12_week_period_ending|24_Mar_19|x|bar_chart Percentage_growth_(year-on-year)|1.3|y|bar_chart 12_week_period_ending|24_Feb_19|x|bar_chart Percentage_growth_(year-on-year)|1|y|bar_chart 12_week_period_ending|28_Jan_19|x|bar_chart Percentage_growth_(year-on-year)|0.2|y|bar_chart 12_week_period_ending|30_Dec_18|x|bar_chart Percentage_growth_(year-on-year)|-1.7|y|bar_chart 12_week_period_ending|2_Dec_18|x|bar_chart Percentage_growth_(year-on-year)|-0.7|y|bar_chart 12_week_period_ending|6_Nov_18|x|bar_chart Percentage_growth_(year-on-year)|-0.1|y|bar_chart 12_week_period_ending|8_Oct_18|x|bar_chart Percentage_growth_(year-on-year)|0.1|y|bar_chart 12_week_period_ending|10_Sep_18|x|bar_chart Percentage_growth_(year-on-year)|0.8|y|bar_chart 12_week_period_ending|13_Aug_18|x|bar_chart Percentage_growth_(year-on-year)|2.4|y|bar_chart 12_week_period_ending|15_Jul_18|x|bar_chart Percentage_growth_(year-on-year)|2.8|y|bar_chart 12_week_period_ending|17_Jun_18|x|bar_chart Percentage_growth_(year-on-year)|0.1|y|bar_chart 12_week_period_ending|22_Apr_18|x|bar_chart Percentage_growth_(year-on-year)|0.2|y|bar_chart 12_week_period_ending|25_Mar_18|x|bar_chart Percentage_growth_(year-on-year)|1.5|y|bar_chart 12_week_period_ending|25_Feb_18|x|bar_chart Percentage_growth_(year-on-year)|2.3|y|bar_chart 12_week_period_ending|28_Jan_18|x|bar_chart Percentage_growth_(year-on-year)|1.5|y|bar_chart 12_week_period_ending|31_Dec_17|x|bar_chart Percentage_growth_(year-on-year)|2.3|y|bar_chart 12_week_period_ending|3_Dec_17|x|bar_chart Percentage_growth_(year-on-year)|1.6|y|bar_chart 12_week_period_ending|8_Oct_17|x|bar_chart Percentage_growth_(year-on-year)|2.3|y|bar_chart 12_week_period_ending|10_Sep_17|x|bar_chart Percentage_growth_(year-on-year)|2.4|y|bar_chart 12_week_period_ending|13_Aug_17|x|bar_chart Percentage_growth_(year-on-year)|2.8|y|bar_chart 12_week_period_ending|16_Jul_17|x|bar_chart Percentage_growth_(year-on-year)|2.8|y|bar_chart 12_week_period_ending|18_Jun_17|x|bar_chart Percentage_growth_(year-on-year)|5.3|y|bar_chart 12_week_period_ending|21_May_17|x|bar_chart Percentage_growth_(year-on-year)|3.3|y|bar_chart 12_week_period_ending|23_Apr_17|x|bar_chart Percentage_growth_(year-on-year)|3.1|y|bar_chart 12_week_period_ending|26_Mar_17|x|bar_chart Percentage_growth_(year-on-year)|0.3|y|bar_chart 12_week_period_ending|26_Feb_17|x|bar_chart Percentage_growth_(year-on-year)|2.9|y|bar_chart 12_week_period_ending|29_Jan_17|x|bar_chart Percentage_growth_(year-on-year)|3.4|y|bar_chart 12_week_period_ending|1_Jan_17|x|bar_chart Percentage_growth_(year-on-year)|3|y|bar_chart 12_week_period_ending|4_Dec_16|x|bar_chart Percentage_growth_(year-on-year)|1.1|y|bar_chart 12_week_period_ending|6_Nov_16|x|bar_chart Percentage_growth_(year-on-year)|3|y|bar_chart 12_week_period_ending|9_Oct_16|x|bar_chart Percentage_growth_(year-on-year)|3.5|y|bar_chart 12_week_period_ending|11_Sep_16|x|bar_chart Percentage_growth_(year-on-year)|3.4|y|bar_chart 12_week_period_ending|14_Aug_16|x|bar_chart Percentage_growth_(year-on-year)|1.4|y|bar_chart 12_week_period_ending|17_Jul_16|x|bar_chart Percentage_growth_(year-on-year)|1.6|y|bar_chart 12_week_period_ending|19_Jun_16|x|bar_chart Percentage_growth_(year-on-year)|1.3|y|bar_chart 12_week_period_ending|22_May_16|x|bar_chart Percentage_growth_(year-on-year)|2.1|y|bar_chart 12_week_period_ending|24_Apr_16|x|bar_chart Percentage_growth_(year-on-year)|1.5|y|bar_chart 12_week_period_ending|27_Mar_16|x|bar_chart Percentage_growth_(year-on-year)|1.7|y|bar_chart 12_week_period_ending|31_Jan_16|x|bar_chart Percentage_growth_(year-on-year)|0.1|y|bar_chart 12_week_period_ending|3_Jan_16|x|bar_chart Percentage_growth_(year-on-year)|1.5|y|bar_chart 12_week_period_ending|6_Dec_15|x|bar_chart Percentage_growth_(year-on-year)|2.7|y|bar_chart 12_week_period_ending|8_Nov_15|x|bar_chart Percentage_growth_(year-on-year)|2.7|y|bar_chart 12_week_period_ending|11_Oct_15|x|bar_chart Percentage_growth_(year-on-year)|2.1|y|bar_chart 12_week_period_ending|13_Sept_15|x|bar_chart Percentage_growth_(year-on-year)|2.9|y|bar_chart 12_week_period_ending|16_Aug_15|x|bar_chart Percentage_growth_(year-on-year)|3.7|y|bar_chart 12_week_period_ending|19_Jul_15|x|bar_chart Percentage_growth_(year-on-year)|3|y|bar_chart 
title: Waitrose sales growth year-on-year in Great Britain 2015 - 2019

gold: Waitrose sales have decreased by 1.9 percent in Great Britain over a 12-week period ending July 12 , 2019 compared to the same time period in 2018 . Waitrose has seen its sales grow during the last three and a half years . The second quarter of 2017 saw the highest growth , with sales going up over five percent .
gold_template: templateTitle[0] templateTitle[1] have decreased by 1.9 percent in templateTitle[4] templateTitle[5] over a 12-week templateXLabel[2] templateXLabel[3] July templateXLabel[0] , templateTitle[7] compared to the same time templateXLabel[2] in 2018 . templateTitle[0] has seen its templateTitle[1] grow during the last templateXValue[19] and a half years . The second quarter of 2017 saw the highest templateYLabel[1] , with templateTitle[1] going up over five percent .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[4] , sorted templateTitle[5] templateXLabel[0] . In templateTitle[4] , about templateYValue[max] percent of templateTitle[4] people lived in templateXValue[0] templateXValue[0] templateXValue[0] , one of the United States .
generated: This statistic shows the results of a survey conducted in the United States in Great , sorted Britain 12 . In Great , about -1.9 percent of Great people lived in 14_Jul_19 14_Jul_19 , one of the United States .


Example 326:
data: Year|2018|x|line_chart Percentage_of_population|18.6|y|line_chart Year|2017|x|line_chart Percentage_of_population|19.7|y|line_chart Year|2016|x|line_chart Percentage_of_population|20.2|y|line_chart Year|2015|x|line_chart Percentage_of_population|19.6|y|line_chart Year|2014|x|line_chart Percentage_of_population|19.8|y|line_chart Year|2013|x|line_chart Percentage_of_population|19.8|y|line_chart Year|2012|x|line_chart Percentage_of_population|19.9|y|line_chart Year|2011|x|line_chart Percentage_of_population|20.4|y|line_chart Year|2010|x|line_chart Percentage_of_population|18.7|y|line_chart Year|2009|x|line_chart Percentage_of_population|17.3|y|line_chart Year|2008|x|line_chart Percentage_of_population|17.3|y|line_chart Year|2007|x|line_chart Percentage_of_population|18.6|y|line_chart Year|2006|x|line_chart Percentage_of_population|19|y|line_chart Year|2005|x|line_chart Percentage_of_population|19.8|y|line_chart Year|2004|x|line_chart Percentage_of_population|19.4|y|line_chart Year|2003|x|line_chart Percentage_of_population|20.3|y|line_chart Year|2002|x|line_chart Percentage_of_population|18.8|y|line_chart Year|2001|x|line_chart Percentage_of_population|19.1|y|line_chart Year|2000|x|line_chart Percentage_of_population|20|y|line_chart 
title: Louisiana - poverty rate 2000 - 2018

gold: This statistic shows the poverty rate in Louisiana from 2000 to 2018 . In 2018 , about 18.6 percent of Louisiana 's population lived below the poverty line .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateTitle[0] 's templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitle[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the poverty rate in Louisiana from 2000 to 2018 . In 2018 , 18.6 percent of Louisiana 's population lived below the poverty line .


Example 327:
data: Months_from_June_2018_to_June_2019|Jun_'19|x|bar_chart Index_points_(50_=_neutral)|56.9|y|bar_chart Months_from_June_2018_to_June_2019|May_'19|x|bar_chart Index_points_(50_=_neutral)|56.5|y|bar_chart Months_from_June_2018_to_June_2019|Apr_'19|x|bar_chart Index_points_(50_=_neutral)|58.4|y|bar_chart Months_from_June_2018_to_June_2019|Mar_'19|x|bar_chart Index_points_(50_=_neutral)|61.9|y|bar_chart Months_from_June_2018_to_June_2019|Feb_'19|x|bar_chart Index_points_(50_=_neutral)|64.5|y|bar_chart Months_from_June_2018_to_June_2019|Jan_'19|x|bar_chart Index_points_(50_=_neutral)|64.7|y|bar_chart Months_from_June_2018_to_June_2019|Dec_'18|x|bar_chart Index_points_(50_=_neutral)|63.8|y|bar_chart Months_from_June_2018_to_June_2019|Nov_'18|x|bar_chart Index_points_(50_=_neutral)|63.2|y|bar_chart Months_from_June_2018_to_June_2019|Oct_'18|x|bar_chart Index_points_(50_=_neutral)|53.7|y|bar_chart Months_from_June_2018_to_June_2019|Sep_'18|x|bar_chart Index_points_(50_=_neutral)|52.8|y|bar_chart Months_from_June_2018_to_June_2019|Aug_'18|x|bar_chart Index_points_(50_=_neutral)|53.3|y|bar_chart Months_from_June_2018_to_June_2019|Jul_'18|x|bar_chart Index_points_(50_=_neutral)|50.2|y|bar_chart Months_from_June_2018_to_June_2019|Jun_'18|x|bar_chart Index_points_(50_=_neutral)|49.6|y|bar_chart 
title: Business climate index of June 2019

gold: This statistic shows the business climate index for Brazil from June 2018 to June 2019 . The index is based on a survey of approximately 2,500 companies . Figures above 50 represent an optimistic outlook , while figures below 50 show a pessimistic business climate .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] for Brazil templateXLabel[1] templateXLabel[2] templateXLabel[3] to templateXLabel[2] templateXLabel[5] . The templateYLabel[0] is based on a survey of approximately 2,500 companies . Figures above templateYValue[11] represent an optimistic outlook , while figures below templateYValue[11] show a pessimistic templateTitle[0] templateTitle[1] .

generated_template: This statistic shows the templateXLabel[0] templateTitle[3] templateTitle[4] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateTitle[6] to templateTitle[7] . In January templateTitle[8] , about templateYValue[max] templateYLabel[3] of the templateTitle[2] templateTitle[3] templateTitle[4] were located in the United States .
generated: This statistic shows the Months June 2019 ( UK ) 2019 Index points (50 in the United States from titleErr to titleErr . In January titleErr , about 64.7 = of the index June 2019 were located in the United States .


Example 328:
data: Response|King_James_Version|x|bar_chart Share_of_respondents|31|y|bar_chart Response|New_International_Version|x|bar_chart Share_of_respondents|13|y|bar_chart Response|English_Standard_Version|x|bar_chart Share_of_respondents|9|y|bar_chart Response|New_King_James_Version|x|bar_chart Share_of_respondents|7|y|bar_chart Response|Amplified|x|bar_chart Share_of_respondents|7|y|bar_chart Response|Christian_Community|x|bar_chart Share_of_respondents|4|y|bar_chart Response|New_American_Standard|x|bar_chart Share_of_respondents|3|y|bar_chart Response|New_Living_Translation|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Revised_Standard|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Contemporary_English_Version|x|bar_chart Share_of_respondents|2|y|bar_chart Response|New_American_Bible|x|bar_chart Share_of_respondents|2|y|bar_chart Response|All_others_(1_or_less_combined)|x|bar_chart Share_of_respondents|9|y|bar_chart Response|Not_sure|x|bar_chart Share_of_respondents|8|y|bar_chart 
title: Preferred Bible version in the U.S. 2017

gold: The graph presents data on the popularity of the versions of the Bible read in the United States as of January 2017 . During the survey , 31 percent of the respondents stated they most often read the King James Version of the Bible . During the same survey , 32 percent of respondents stated that they had never read the Bible , whilst 16 percent stated that they read the Bible every day .
gold_template: The graph presents data on the popularity of the versions of the templateXValue[10] read in the templateTitle[3] as of January templateTitle[4] . During the survey , templateYValue[max] percent of the templateYLabel[1] stated they most often read the templateXValue[0] templateXValue[0] templateXValue[0] of the templateXValue[10] . During the same survey , 32 percent of templateYLabel[1] stated that they had never read the templateXValue[10] , whilst 16 percent stated that they read the templateXValue[10] every day .

generated_template: This statistic shows the results of a survey conducted in the templateTitle[0] templateTitle[1] in templateTitle[5] on templateXValue[0] templateTitle[2] templateTitle[3] in templateTitle[4] templateTitle[5] according to survey conducted in templateTitle[8] . templateYValue[max] percent of templateYLabel[1] said that they would be willing to templateXValue[1] templateXValue[0] in the U.S .
generated: This statistic shows the results of a survey conducted in the Preferred Bible in titleErr on King_James_Version version U.S. in 2017 titleErr according to survey conducted in titleErr . 31 percent of respondents said that they would be willing to New_International_Version King_James_Version in the U.S .


Example 329:
data: Months_from_June_2018_to_June_2019|Dec_'18|x|bar_chart Index_points_(2001_=_100)|114.3|y|bar_chart Months_from_June_2018_to_June_2019|Nov_'18|x|bar_chart Index_points_(2001_=_100)|113.6|y|bar_chart Months_from_June_2018_to_June_2019|Oct_'18|x|bar_chart Index_points_(2001_=_100)|110.6|y|bar_chart Months_from_June_2018_to_June_2019|Sep_'18|x|bar_chart Index_points_(2001_=_100)|-|y|bar_chart Months_from_June_2018_to_June_2019|Aug_'18|x|bar_chart Index_points_(2001_=_100)|104.7|y|bar_chart Months_from_June_2018_to_June_2019|Jul_'18|x|bar_chart Index_points_(2001_=_100)|101.6|y|bar_chart Months_from_June_2018_to_June_2019|Jun_'18|x|bar_chart Index_points_(2001_=_100)|98.3|y|bar_chart Months_from_June_2018_to_June_2019|May_'18|x|bar_chart Index_points_(2001_=_100)|102.2|y|bar_chart Months_from_June_2018_to_June_2019|Apr_'18|x|bar_chart Index_points_(2001_=_100)|102.2|y|bar_chart Months_from_June_2018_to_June_2019|Mar_'18|x|bar_chart Index_points_(2001_=_100)|101.9|y|bar_chart Months_from_June_2018_to_June_2019|Feb_'18|x|bar_chart Index_points_(2001_=_100)|102.7|y|bar_chart Months_from_June_2018_to_June_2019|Jan_'18|x|bar_chart Index_points_(2001_=_100)|102.9|y|bar_chart Months_from_June_2018_to_June_2019|Dec_'17|x|bar_chart Index_points_(2001_=_100)|100.5|y|bar_chart 
title: Consumer confidence index of June 2019

gold: This statistic shows the consumer confidence index for Brazil from December 2017 to December 2018 . The index is composed of several different indices , including an assessment of one 's personal financial situation . In December 2018 , Brazil 's consumer confidence was at 114.3 points .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] for Brazil templateXLabel[1] December 2017 to December templateXLabel[3] . The templateYLabel[0] is composed of several different indices , including an assessment of one 's personal financial situation . In December templateXLabel[3] , Brazil 's templateTitle[0] templateTitle[1] was at templateYValue[0] templateYLabel[1] .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] templateYLabel[2] of the templateTitle[0] and templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] ( templateTitle[5] ) from templateTitle[6] to templateTitle[7] . In this period , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[4] templateTitle[5] amounted to templateYValue[0] templateYLabel[3] .
generated: This statistic displays the Index of points (2001 of the Consumer and confidence index of June 2019 ( titleErr ) from titleErr to titleErr . In this period , the Index points (2001 in the 2019 titleErr amounted to 114.3 = .


Example 330:
data: Industry|Forestry_fishing_mining_quarrying_oil_and_gas|x|bar_chart Average_usual_weekly_hours|45.0|y|bar_chart Industry|Agriculture|x|bar_chart Average_usual_weekly_hours|43.5|y|bar_chart Industry|Goods-producing_sector|x|bar_chart Average_usual_weekly_hours|40.4|y|bar_chart Industry|Construction|x|bar_chart Average_usual_weekly_hours|40.3|y|bar_chart Industry|Transportation_and_warehousing|x|bar_chart Average_usual_weekly_hours|40.2|y|bar_chart Industry|Manufacturing|x|bar_chart Average_usual_weekly_hours|39.3|y|bar_chart Industry|Utilities|x|bar_chart Average_usual_weekly_hours|38.6|y|bar_chart Industry|Professional_scientific_and_technical_services|x|bar_chart Average_usual_weekly_hours|37.0|y|bar_chart Industry|Public_administration|x|bar_chart Average_usual_weekly_hours|36.7|y|bar_chart Industry|Finance_insurance_real_estate_rental_and_leasing|x|bar_chart Average_usual_weekly_hours|36.7|y|bar_chart Industry|Total_employed_all_industries|x|bar_chart Average_usual_weekly_hours|35.7|y|bar_chart Industry|Other_services_(except_public_administration)|x|bar_chart Average_usual_weekly_hours|34.9|y|bar_chart Industry|Services-producing_sector|x|bar_chart Average_usual_weekly_hours|34.5|y|bar_chart Industry|Health_care_and_social_assistance|x|bar_chart Average_usual_weekly_hours|34.2|y|bar_chart Industry|Business_building_and_other_support_services|x|bar_chart Average_usual_weekly_hours|33.8|y|bar_chart Industry|Wholesale_and_retail_trade|x|bar_chart Average_usual_weekly_hours|33.5|y|bar_chart Industry|Information_culture_and_recreation|x|bar_chart Average_usual_weekly_hours|32.5|y|bar_chart Industry|Educational_services|x|bar_chart Average_usual_weekly_hours|31.8|y|bar_chart Industry|Accommodation_and_food_services|x|bar_chart Average_usual_weekly_hours|29.8|y|bar_chart 
title: Canada - average weekly hours worked at the main job , by industry 2019

gold: This statistic shows the average usual weekly hours worked in Canada in 2019 , distinguished by industry . In 2019 , Canadian employees in agriculture were working about 43.5 hours a week , which is above the national average of 35.7 hours .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[4] in templateTitle[0] in templateTitle[9] , distinguished templateTitle[7] templateXLabel[0] . In templateTitle[9] , Canadian employees in templateXValue[1] were working about templateYValue[1] templateYLabel[3] a week , which is above the national templateYLabel[0] of templateYValue[10] templateYLabel[3] .

generated_template: In templateTitle[6] , the templateYLabel[0] templateYLabel[1] of people employed in the United States , templateTitle[4] templateXLabel[0] templateXLabel[1] templateXLabel[2] templateXLabel[3] templateXLabel[4] in the United States , about templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] , up from templateYValue[1] people in the previous templateXLabel[0] . The United States templateTitle[3] templateTitle[4] can be accessed here .
generated: In job , the Average usual of people employed in the United States , worked Industry xLabelErr xLabelErr xLabelErr xLabelErr in the United States , about 45.0 hours yLabelErr yLabelErr , up from 43.5 people in the previous Industry . The United States hours worked can be accessed here .


Example 331:
data: Year|2004|x|line_chart Global_market_share|27.3|y|line_chart Year|2005|x|line_chart Global_market_share|26.6|y|line_chart Year|2006|x|line_chart Global_market_share|25.7|y|line_chart Year|2007|x|line_chart Global_market_share|24.6|y|line_chart Year|2008|x|line_chart Global_market_share|24.3|y|line_chart 
title: Global market share of the U.S. athletic and non-athletic footwear retail market 2004 - 2008

gold: The timeline shows the global market share of the U.S. athletic and non-athletic footwear retail market from 2004 to 2008 . In 2007 , the global market share of the U.S. athletic and non-athletic footwear retail market was 24.6 percent . Between 2004 and 2008 , the global market share of the U.S. footwear retail market decreased by 3.0 percent .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[3] templateTitle[4] and templateTitle[5] templateTitle[6] templateTitle[7] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[3] templateTitle[4] and templateTitle[5] templateTitle[6] templateTitle[7] templateYLabel[1] was templateYValue[3] percent . Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[3] templateTitle[6] templateTitle[7] templateYLabel[1] decreased by 3.0 percent .

generated_template: In terms of motor vehicle templateTitle[7] volume , templateTitle[0] templateTitle[1] templateYLabel[1] templateYLabel[2] reached 11.7 in templateXValue[max] . That templateXLabel[0] , templateTitle[0] produced between 10 and templateYValue[min] million vehicles worldwide .
generated: In terms of motor vehicle retail volume , Global market market share reached 11.7 in 2008 . That Year , Global produced between 10 and 24.3 million vehicles worldwide .


Example 332:
data: Country|Azarbaijan|x|bar_chart Import_volume_in_tons|12298989|y|bar_chart Country|Iran|x|bar_chart Import_volume_in_tons|9324007|y|bar_chart Country|Iraq|x|bar_chart Import_volume_in_tons|2041664|y|bar_chart Country|Libya|x|bar_chart Import_volume_in_tons|1840713|y|bar_chart Country|Saudi_Arabia|x|bar_chart Import_volume_in_tons|1825182|y|bar_chart Country|Russia|x|bar_chart Import_volume_in_tons|1052134|y|bar_chart Country|Nigeria|x|bar_chart Import_volume_in_tons|767828|y|bar_chart Country|Kazakhstan|x|bar_chart Import_volume_in_tons|703232|y|bar_chart Country|USA|x|bar_chart Import_volume_in_tons|504954|y|bar_chart Country|Angola|x|bar_chart Import_volume_in_tons|322012|y|bar_chart Country|Canada|x|bar_chart Import_volume_in_tons|317132|y|bar_chart Country|Kuwait|x|bar_chart Import_volume_in_tons|312218|y|bar_chart Country|Egypt|x|bar_chart Import_volume_in_tons|204085|y|bar_chart Country|Cameroon|x|bar_chart Import_volume_in_tons|185753|y|bar_chart Country|Algeria|x|bar_chart Import_volume_in_tons|155279|y|bar_chart Country|Equatorial_Guinea|x|bar_chart Import_volume_in_tons|89845|y|bar_chart Country|Tunisia|x|bar_chart Import_volume_in_tons|56891|y|bar_chart Country|Mauritania|x|bar_chart Import_volume_in_tons|33791|y|bar_chart Country|Albania|x|bar_chart Import_volume_in_tons|123|y|bar_chart 
title: Italy : volume of crude oil imported Q1 2018 , by country of origin

gold: During the first quarter of 2018 , Azerbaijan exported roughly 12.3 million tons of crude oil to Italy , establishing itself as the major crude oil supplier for the country . Iran followed with 9.3 million tons . The amount of crude oil imported from other suppliers was lower , during the first quarter of 2018 .
gold_template: During the first quarter of templateTitle[6] , Azerbaijan exported roughly templateYValue[max] million templateYLabel[2] of templateTitle[2] templateTitle[3] to templateTitle[0] , establishing itself as the major templateTitle[2] templateTitle[3] supplier for the templateXLabel[0] . templateXValue[1] followed with templateYValue[1] million templateYLabel[2] . The amount of templateTitle[2] templateTitle[3] templateTitle[4] from other suppliers was lower , during the first quarter of templateTitle[6] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[7] world templateTitle[5] templateXLabel[0] . Over the last countries with an templateYLabel[0] templateYLabel[1] templateYValue[max] U.S. templateYLabel[1] templateYLabel[0] people . templateXValue[1] , with templateYValue[1] percent of templateYLabel[1] templateYLabel[2] in templateXValue[2] U.S. dollars .
generated: This statistic shows the Italy volume Import volume oil imported Q1 in by world Q1 Country . Over the last countries with an Import volume 12298989 U.S. volume Import people . Iran , with 9324007 percent of volume tons in Iraq U.S. dollars .


Example 333:
data: Year|2019|x|line_chart Return_on_equity|3.6|y|line_chart Year|2018|x|line_chart Return_on_equity|7.7|y|line_chart Year|2017|x|line_chart Return_on_equity|5.9|y|line_chart Year|2016|x|line_chart Return_on_equity|0.8|y|line_chart Year|2015|x|line_chart Return_on_equity|7.2|y|line_chart Year|2014|x|line_chart Return_on_equity|7.3|y|line_chart Year|2013|x|line_chart Return_on_equity|9.2|y|line_chart Year|2012|x|line_chart Return_on_equity|8.4|y|line_chart Year|2011|x|line_chart Return_on_equity|10.9|y|line_chart Year|2010|x|line_chart Return_on_equity|9.5|y|line_chart Year|2009|x|line_chart Return_on_equity|5.1|y|line_chart 
title: Return on average ordinary shareholders ' equity at HSBC 2009 - 2019

gold: The statistic shows the return on average ordinary shareholders ' equity at HSBC from 2009 to 2019 . The return on average ordinary shareholders ' equity at HSBC amounted to 3.6 percent in 2019 .
gold_template: The statistic shows the templateYLabel[0] on templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] at templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] on templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] at templateTitle[6] amounted to templateYValue[0] percent in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] templateYLabel[3] of templateTitle[4] were sold by the United States .
generated: This statistic shows the Return equity yLabelErr in the United States from 2009 to 2019 . In 2019 , about 3.6 yLabelErr of ' were sold by the United States .


Example 334:
data: Year|2016|x|line_chart AIR_in_thousands|483|y|line_chart Year|2015|x|line_chart AIR_in_thousands|469|y|line_chart Year|2014|x|line_chart AIR_in_thousands|541|y|line_chart Year|2013|x|line_chart AIR_in_thousands|647|y|line_chart Year|2012|x|line_chart AIR_in_thousands|631|y|line_chart Year|2011|x|line_chart AIR_in_thousands|633|y|line_chart Year|2010|x|line_chart AIR_in_thousands|664|y|line_chart Year|2009|x|line_chart AIR_in_thousands|639|y|line_chart Year|2008|x|line_chart AIR_in_thousands|589|y|line_chart Year|2007|x|line_chart AIR_in_thousands|619|y|line_chart Year|2006|x|line_chart AIR_in_thousands|554|y|line_chart 
title: Readership of FourFourTwo magazine in the United Kingdom ( UK ) 2006 - 2016

gold: This statistic displays the readership trend of FourFourTwo magazine in the United Kingdom from 2006 to 2016 . In 2015 , the magazine was read by an average 469 thousand readers per issue .
gold_template: This statistic displays the templateTitle[0] trend of templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateTitle[2] was read by an average templateYValue[min] thousand readers per issue .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] such as templateYLabel[1] in this templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic presents the AIR thousands of magazine United Kingdom UK in the United States from 2006 to 2016 . In 2016 , there were 483 such as thousands in this United Kingdom UK in the United States .


Example 335:
data: Year|2018/19|x|line_chart Passenger_journeys_in_millions|43.7|y|line_chart Year|2017/18|x|line_chart Passenger_journeys_in_millions|41.2|y|line_chart Year|2016/17|x|line_chart Passenger_journeys_in_millions|37.8|y|line_chart Year|2015/16|x|line_chart Passenger_journeys_in_millions|34.3|y|line_chart Year|2014/15|x|line_chart Passenger_journeys_in_millions|31.2|y|line_chart Year|2013/14|x|line_chart Passenger_journeys_in_millions|29.2|y|line_chart Year|2012/13|x|line_chart Passenger_journeys_in_millions|25.0|y|line_chart Year|2011/12|x|line_chart Passenger_journeys_in_millions|22.3|y|line_chart Year|2010/11|x|line_chart Passenger_journeys_in_millions|19.2|y|line_chart Year|2009/10|x|line_chart Passenger_journeys_in_millions|19.6|y|line_chart Year|2008/09|x|line_chart Passenger_journeys_in_millions|21.1|y|line_chart Year|2007/08|x|line_chart Passenger_journeys_in_millions|20.0|y|line_chart Year|2006/07|x|line_chart Passenger_journeys_in_millions|19.8|y|line_chart Year|2005/06|x|line_chart Passenger_journeys_in_millions|19.9|y|line_chart Year|2004/05|x|line_chart Passenger_journeys_in_millions|19.7|y|line_chart Year|2003/04|x|line_chart Passenger_journeys_in_millions|18.9|y|line_chart Year|2002/03|x|line_chart Passenger_journeys_in_millions|18.8|y|line_chart Year|2001/02|x|line_chart Passenger_journeys_in_millions|18.2|y|line_chart Year|2000/01|x|line_chart Passenger_journeys_in_millions|17.2|y|line_chart Year|1999/00|x|line_chart Passenger_journeys_in_millions|14.2|y|line_chart Year|1998/99|x|line_chart Passenger_journeys_in_millions|13.2|y|line_chart Year|1997/98|x|line_chart Passenger_journeys_in_millions|13.8|y|line_chart Year|1996/97|x|line_chart Passenger_journeys_in_millions|13.4|y|line_chart Year|1995/96|x|line_chart Passenger_journeys_in_millions|12.6|y|line_chart Year|1994/95|x|line_chart Passenger_journeys_in_millions|12.3|y|line_chart Year|1993/94|x|line_chart Passenger_journeys_in_millions|11.3|y|line_chart Year|1992/93|x|line_chart Passenger_journeys_in_millions|8.1|y|line_chart 
title: Passenger journeys on the Manchester Metrolink in the United Kingdom 1992 - 2019

gold: The Manchester Metrolink recorded 43.7 million passenger journeys in 2018/19 . Since beginning its operation in April 1992 as the United Kingdom 's first modern tram system , the Metrolink has grown to become an integral part of public transportation within the city . The Metrolink is one of currently nine light rail systems in the country , next to those in London 's docklands , Croydon , Birmingham , Sheffield , Newcastle , Nottingham , Blackpool and Edinburgh .
gold_template: The templateTitle[2] templateTitle[3] recorded templateYValue[max] million templateYLabel[0] templateYLabel[1] in templateXValue[0] . Since beginning its operation in April templateTitle[6] as the templateTitle[4] templateTitle[5] 's first modern tram system , the templateTitle[3] has grown to become an integral part of public transportation within the city . The templateTitle[3] is one of currently nine light rail systems in the country , next to those in London 's docklands , Croydon , Birmingham , Sheffield , Newcastle , Nottingham , Blackpool and Edinburgh .

generated_template: In templateXValue[0] , templateYValue[max] percent of templateYLabel[0] templateYLabel[1] in the United States had been increasing from the previous templateXLabel[0] . This figure has steadily been steadily throughout the years , and in the past few years . In templateXValue[0] , there were templateYValue[0] people lived in the United States .
generated: In 2018/19 , 43.7 percent of Passenger journeys in the United States had been increasing from the previous Year . This figure has steadily been steadily throughout the years , and in the past few years . In 2018/19 , there were 43.7 people lived in the United States .


Example 336:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|2|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|1.8|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|1.6|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|1.2|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|0.92|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|0.86|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|1.06|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|0.67|y|line_chart Year|2016|x|line_chart Inflation_rate_compared_to_previous_year|0.19|y|line_chart Year|2015|x|line_chart Inflation_rate_compared_to_previous_year|-0.9|y|line_chart Year|2014|x|line_chart Inflation_rate_compared_to_previous_year|1.9|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|2.19|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|3.01|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|3.81|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|3.29|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|-0.85|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|5.46|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|2.2|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|4.66|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|4.52|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|2.76|y|line_chart 
title: Inflation rate in Thailand 2024

gold: In 2018 , the average inflation rate in Thailand amounted to about 1.06 percent compared to the previous year , when it was just recovering from a slump below the 0-percent-mark in 2015 . Political turmoil begets economic turmoil In 2014 , after a coup d'etat following months of political crisis , the Thai military took over the country , and the senate and government were dissolved . As a result , Thailand 's economy experienced a sudden downturn , GDP growth and inflation slumped , while unemployment , which is usually delayed in reflecting economic struggles , has been increasing ever since .
gold_template: In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] , when it was just recovering from a slump below the 0-percent-mark in templateXValue[9] . Political turmoil begets economic turmoil In templateXValue[10] , after a coup d'etat following months of political crisis , the Thai military took over the country , and the senate and government were dissolved . As a result , templateTitle[2] 's economy experienced a sudden downturn , GDP growth and templateYLabel[0] slumped , while unemployment , which is usually delayed in reflecting economic struggles , has been increasing ever since .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price increase of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: The statistic shows the Inflation rate in Thailand from 2004 to 2018 , with projections up until 2024 . The Inflation rate is calculated using the price increase of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the Year .


Example 337:
data: Year|2024|x|line_chart GDP_per_capita_in_U.S._dollars|72325.94|y|line_chart Year|2023|x|line_chart GDP_per_capita_in_U.S._dollars|69413.32|y|line_chart Year|2022|x|line_chart GDP_per_capita_in_U.S._dollars|66656.96|y|line_chart Year|2021|x|line_chart GDP_per_capita_in_U.S._dollars|64106.78|y|line_chart Year|2020|x|line_chart GDP_per_capita_in_U.S._dollars|61732.57|y|line_chart Year|2019|x|line_chart GDP_per_capita_in_U.S._dollars|59795.27|y|line_chart Year|2018|x|line_chart GDP_per_capita_in_U.S._dollars|60897.23|y|line_chart Year|2017|x|line_chart GDP_per_capita_in_U.S._dollars|57380.2|y|line_chart Year|2016|x|line_chart GDP_per_capita_in_U.S._dollars|54665.22|y|line_chart Year|2015|x|line_chart GDP_per_capita_in_U.S._dollars|53478.5|y|line_chart Year|2014|x|line_chart GDP_per_capita_in_U.S._dollars|62729.5|y|line_chart Year|2013|x|line_chart GDP_per_capita_in_U.S._dollars|61325.58|y|line_chart Year|2012|x|line_chart GDP_per_capita_in_U.S._dollars|58623.41|y|line_chart Year|2011|x|line_chart GDP_per_capita_in_U.S._dollars|61864.09|y|line_chart Year|2010|x|line_chart GDP_per_capita_in_U.S._dollars|58177.16|y|line_chart Year|2009|x|line_chart GDP_per_capita_in_U.S._dollars|58286.54|y|line_chart Year|2008|x|line_chart GDP_per_capita_in_U.S._dollars|64531.12|y|line_chart Year|2007|x|line_chart GDP_per_capita_in_U.S._dollars|58641.19|y|line_chart Year|2006|x|line_chart GDP_per_capita_in_U.S._dollars|52121.25|y|line_chart Year|2005|x|line_chart GDP_per_capita_in_U.S._dollars|48872.1|y|line_chart Year|2004|x|line_chart GDP_per_capita_in_U.S._dollars|46571.28|y|line_chart Year|2003|x|line_chart GDP_per_capita_in_U.S._dollars|40512.05|y|line_chart Year|2002|x|line_chart GDP_per_capita_in_U.S._dollars|33275.56|y|line_chart Year|2001|x|line_chart GDP_per_capita_in_U.S._dollars|30806.61|y|line_chart Year|2000|x|line_chart GDP_per_capita_in_U.S._dollars|30798.72|y|line_chart Year|1999|x|line_chart GDP_per_capita_in_U.S._dollars|33492.35|y|line_chart Year|1998|x|line_chart GDP_per_capita_in_U.S._dollars|33426.97|y|line_chart Year|1997|x|line_chart GDP_per_capita_in_U.S._dollars|32897.57|y|line_chart Year|1996|x|line_chart GDP_per_capita_in_U.S._dollars|35732.69|y|line_chart Year|1995|x|line_chart GDP_per_capita_in_U.S._dollars|35471.26|y|line_chart Year|1994|x|line_chart GDP_per_capita_in_U.S._dollars|30050.88|y|line_chart Year|1993|x|line_chart GDP_per_capita_in_U.S._dollars|27640.5|y|line_chart Year|1992|x|line_chart GDP_per_capita_in_U.S._dollars|29622.47|y|line_chart Year|1991|x|line_chart GDP_per_capita_in_U.S._dollars|27052.65|y|line_chart Year|1990|x|line_chart GDP_per_capita_in_U.S._dollars|26920.58|y|line_chart Year|1989|x|line_chart GDP_per_capita_in_U.S._dollars|21913.16|y|line_chart Year|1988|x|line_chart GDP_per_capita_in_U.S._dollars|22528.11|y|line_chart Year|1987|x|line_chart GDP_per_capita_in_U.S._dollars|21349.95|y|line_chart Year|1986|x|line_chart GDP_per_capita_in_U.S._dollars|17215.43|y|line_chart Year|1985|x|line_chart GDP_per_capita_in_U.S._dollars|12259.28|y|line_chart Year|1984|x|line_chart GDP_per_capita_in_U.S._dollars|11561.77|y|line_chart 
title: Gross domestic product ( GDP ) per capita in Denmark 2024

gold: The statistic shows gross domestic product ( GDP ) per capita in Denmark from 1984 to 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] from templateXValue[min] to templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services templateYLabel[2] in a country in a templateXLabel[0] . templateYLabel[0] templateYLabel[1] templateYLabel[2] of a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth growth .
generated: The statistic shows Gross domestic product ( GDP ) per capita in Denmark 2024 from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services capita in a country in a Year . GDP per capita of a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .


Example 338:
data: Year|2018|x|line_chart Thousands_of_hectoliters|6.1|y|line_chart Year|2017|x|line_chart Thousands_of_hectoliters|6.7|y|line_chart Year|2016|x|line_chart Thousands_of_hectoliters|6.0|y|line_chart Year|2015|x|line_chart Thousands_of_hectoliters|7.0|y|line_chart Year|2014|x|line_chart Thousands_of_hectoliters|6.2|y|line_chart Year|2013|x|line_chart Thousands_of_hectoliters|6.2|y|line_chart Year|2012|x|line_chart Thousands_of_hectoliters|6.3|y|line_chart Year|2011|x|line_chart Thousands_of_hectoliters|5.6|y|line_chart Year|2010|x|line_chart Thousands_of_hectoliters|7.13|y|line_chart Year|2009|x|line_chart Thousands_of_hectoliters|5.87|y|line_chart Year|2008|x|line_chart Thousands_of_hectoliters|5.69|y|line_chart Year|2007|x|line_chart Thousands_of_hectoliters|6.07|y|line_chart Year|2006|x|line_chart Thousands_of_hectoliters|7.54|y|line_chart 
title: Volume of wine produced in Portugal 2006 - 2018

gold: The volume of wine produced in Portugal was forecast to reach approximately 6.1 million hectoliters in 2018 . The production output does seem to be stagnating , with occasional spikes in production , which are likely due to better weather conditions . The data does not include juice and must .
gold_template: The templateTitle[0] of templateTitle[1] templateTitle[2] in templateTitle[3] was forecast to reach approximately templateYValue[0] million templateYLabel[1] in templateXValue[max] . The production output does seem to be stagnating , with occasional spikes in production , which are likely due to better weather conditions . The data does not include juice and must .

generated_template: In templateXValue[max] , there were templateYValue[0] templateTitle[0] templateTitle[1] templateTitle[2] ( UK ) templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] , an increase from the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] in the United States has fluctuated between a high in recent years and templateXValue[11] when it has increased in recent years . However , the number of inhabitants exceeded 40 percent in the European Union , which operates in 1999/2000 .
generated: In 2018 , there were 6.1 Volume wine produced ( UK ) wine produced Portugal 2006 , an increase from the previous Year . The Thousands hectoliters in the United States has fluctuated between a high in recent years and 2007 when it has increased in recent years . However , the number of inhabitants exceeded 40 percent in the European Union , which operates in 1999/2000 .


Example 339:
data: Year|2024|x|line_chart GDP_per_capita_in_U.S._dollars|25936.96|y|line_chart Year|2023|x|line_chart GDP_per_capita_in_U.S._dollars|24437.92|y|line_chart Year|2022|x|line_chart GDP_per_capita_in_U.S._dollars|23066.81|y|line_chart Year|2021|x|line_chart GDP_per_capita_in_U.S._dollars|21708.62|y|line_chart Year|2020|x|line_chart GDP_per_capita_in_U.S._dollars|20355.0|y|line_chart Year|2019|x|line_chart GDP_per_capita_in_U.S._dollars|19266.79|y|line_chart Year|2018|x|line_chart GDP_per_capita_in_U.S._dollars|18994.38|y|line_chart Year|2017|x|line_chart GDP_per_capita_in_U.S._dollars|16845.33|y|line_chart Year|2016|x|line_chart GDP_per_capita_in_U.S._dollars|14988.57|y|line_chart Year|2015|x|line_chart GDP_per_capita_in_U.S._dollars|14299.1|y|line_chart Year|2014|x|line_chart GDP_per_capita_in_U.S._dollars|16571.43|y|line_chart Year|2013|x|line_chart GDP_per_capita_in_U.S._dollars|15695.74|y|line_chart Year|2012|x|line_chart GDP_per_capita_in_U.S._dollars|14354.29|y|line_chart Year|2011|x|line_chart GDP_per_capita_in_U.S._dollars|14386.61|y|line_chart Year|2010|x|line_chart GDP_per_capita_in_U.S._dollars|12010.68|y|line_chart Year|2009|x|line_chart GDP_per_capita_in_U.S._dollars|11866.63|y|line_chart Year|2008|x|line_chart GDP_per_capita_in_U.S._dollars|15047.25|y|line_chart Year|2007|x|line_chart GDP_per_capita_in_U.S._dollars|12313.17|y|line_chart Year|2006|x|line_chart GDP_per_capita_in_U.S._dollars|9246.51|y|line_chart Year|2005|x|line_chart GDP_per_capita_in_U.S._dollars|7880.35|y|line_chart Year|2004|x|line_chart GDP_per_capita_in_U.S._dollars|6706.03|y|line_chart Year|2003|x|line_chart GDP_per_capita_in_U.S._dollars|5505.59|y|line_chart Year|2002|x|line_chart GDP_per_capita_in_U.S._dollars|4146.11|y|line_chart Year|2001|x|line_chart GDP_per_capita_in_U.S._dollars|3530.2|y|line_chart Year|2000|x|line_chart GDP_per_capita_in_U.S._dollars|3297.45|y|line_chart Year|1999|x|line_chart GDP_per_capita_in_U.S._dollars|3113.64|y|line_chart Year|1998|x|line_chart GDP_per_capita_in_U.S._dollars|3166.96|y|line_chart Year|1997|x|line_chart GDP_per_capita_in_U.S._dollars|2830.75|y|line_chart Year|1996|x|line_chart GDP_per_capita_in_U.S._dollars|2328.22|y|line_chart Year|1995|x|line_chart GDP_per_capita_in_U.S._dollars|1845.67|y|line_chart 
title: Gross domestic product ( GDP ) per capita in Lithuania 2024

gold: The statistic shows gross domestic product ( GDP ) per capita in Lithuania from 1995 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services templateYLabel[2] in a country in a templateXLabel[0] . templateYLabel[0] templateYLabel[1] templateYLabel[2] of current prices in templateTitle[7] was templateYValue[0] percent .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in 2024 from 1995 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services capita in a country in a Year . GDP per capita of current prices in 2024 was 25936.96 percent .


Example 340:
data: Country|Wefunder|x|bar_chart Amount_of_offerings|95|y|bar_chart Country|Start_Engine|x|bar_chart Amount_of_offerings|52|y|bar_chart Country|Seed_Invest|x|bar_chart Amount_of_offerings|29|y|bar_chart Country|uFunding|x|bar_chart Amount_of_offerings|18|y|bar_chart Country|Dream_Funded|x|bar_chart Amount_of_offerings|14|y|bar_chart Country|TruCrowd|x|bar_chart Amount_of_offerings|13|y|bar_chart Country|Nextseed|x|bar_chart Amount_of_offerings|13|y|bar_chart Country|Net_Capital|x|bar_chart Amount_of_offerings|12|y|bar_chart Country|Microventures|x|bar_chart Amount_of_offerings|11|y|bar_chart Country|Jumpstart_Micro|x|bar_chart Amount_of_offerings|10|y|bar_chart Country|Flashfunders|x|bar_chart Amount_of_offerings|9|y|bar_chart Country|Republic|x|bar_chart Amount_of_offerings|9|y|bar_chart Country|GridShare|x|bar_chart Amount_of_offerings|7|y|bar_chart Country|Growth_Fountain|x|bar_chart Amount_of_offerings|5|y|bar_chart Country|Venture.co|x|bar_chart Amount_of_offerings|4|y|bar_chart Country|Crowd_Source_Funded|x|bar_chart Amount_of_offerings|3|y|bar_chart Country|FundingWonder|x|bar_chart Amount_of_offerings|2|y|bar_chart Country|ibankers|x|bar_chart Amount_of_offerings|2|y|bar_chart Country|Local_Stake|x|bar_chart Amount_of_offerings|1|y|bar_chart Country|Open_Night_Capital|x|bar_chart Amount_of_offerings|1|y|bar_chart 
title: Leading crowdfunding platforms in the U.S. 2017 , by number of offerings

gold: This statistic shows the leading crowdfunding platforms in the United States as of May 2017 , by number of offerings . Wefunder had 95 offerings , which made it the largest platform in terms of offerings as of May 2017 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] as of May templateTitle[4] , templateTitle[5] templateTitle[6] of templateYLabel[1] . templateXValue[0] had templateYValue[max] templateYLabel[1] , which made it the largest platform in terms of templateYLabel[1] as of May templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States as of templateTitle[5] . During the last reported period , templateYValue[max] percent of templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] in templateXValue[0] .
generated: This statistic shows the Amount of Leading crowdfunding platforms U.S. 2017 in the United States as of by . During the last reported period , 95 percent of offerings U.S. 2017 by in Wefunder .


Example 341:
data: Year|2017|x|line_chart Value_per_head_in_U.S._dollars|203|y|line_chart Year|2016|x|line_chart Value_per_head_in_U.S._dollars|202|y|line_chart Year|2015|x|line_chart Value_per_head_in_U.S._dollars|214|y|line_chart Year|2014|x|line_chart Value_per_head_in_U.S._dollars|188|y|line_chart Year|2013|x|line_chart Value_per_head_in_U.S._dollars|177|y|line_chart Year|2012|x|line_chart Value_per_head_in_U.S._dollars|221|y|line_chart Year|2011|x|line_chart Value_per_head_in_U.S._dollars|170|y|line_chart Year|2010|x|line_chart Value_per_head_in_U.S._dollars|135|y|line_chart Year|2009|x|line_chart Value_per_head_in_U.S._dollars|133|y|line_chart Year|2008|x|line_chart Value_per_head_in_U.S._dollars|138|y|line_chart Year|2007|x|line_chart Value_per_head_in_U.S._dollars|134|y|line_chart Year|2006|x|line_chart Value_per_head_in_U.S._dollars|141|y|line_chart Year|2005|x|line_chart Value_per_head_in_U.S._dollars|130|y|line_chart Year|2004|x|line_chart Value_per_head_in_U.S._dollars|119|y|line_chart Year|2003|x|line_chart Value_per_head_in_U.S._dollars|104|y|line_chart Year|2002|x|line_chart Value_per_head_in_U.S._dollars|92|y|line_chart Year|2001|x|line_chart Value_per_head_in_U.S._dollars|100|y|line_chart 
title: Head value of sheep and lambs in the U.S. 2001 - 2017

gold: This statistic shows the average value per head of sheep and lambs in the United States from 2001 to 2017 . In 2001 , this figure stood at 100 U.S. dollars and rose to 203 U.S. dollars by 2017 .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[2] and templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , this figure stood at templateYValue[16] templateYLabel[3] templateYLabel[4] and rose to templateYValue[0] templateYLabel[3] templateYLabel[4] by templateXValue[max] .

generated_template: In templateXValue[max] , there were templateYValue[0] million people living in templateTitle[4] , up from templateYValue[1] million in the United States in the previous templateXLabel[0] . The government is a major airline templateYLabel[0] templateYLabel[1] of templateYValue[max] million people living in the United States .
generated: In 2017 , there were 203 million people living in U.S. , up from 202 million in the United States in the previous Year . The government is a major airline Value per of 221 million people living in the United States .


Example 342:
data: Year|2018/19|x|line_chart Percentage_of_GDP|85.2|y|line_chart Year|2017/18|x|line_chart Percentage_of_GDP|85.3|y|line_chart Year|2016/17|x|line_chart Percentage_of_GDP|86.5|y|line_chart Year|2015/16|x|line_chart Percentage_of_GDP|86.4|y|line_chart Year|2014/15|x|line_chart Percentage_of_GDP|86.5|y|line_chart Year|2013/14|x|line_chart Percentage_of_GDP|85.5|y|line_chart Year|2012/13|x|line_chart Percentage_of_GDP|83.3|y|line_chart Year|2011/12|x|line_chart Percentage_of_GDP|81.8|y|line_chart Year|2010/11|x|line_chart Percentage_of_GDP|75.6|y|line_chart Year|2009/10|x|line_chart Percentage_of_GDP|69.6|y|line_chart Year|2008/09|x|line_chart Percentage_of_GDP|52.6|y|line_chart Year|2007/08|x|line_chart Percentage_of_GDP|40.9|y|line_chart Year|2006/07|x|line_chart Percentage_of_GDP|40|y|line_chart Year|2005/06|x|line_chart Percentage_of_GDP|39.2|y|line_chart Year|2004/05|x|line_chart Percentage_of_GDP|38|y|line_chart Year|2003/04|x|line_chart Percentage_of_GDP|35.5|y|line_chart Year|2002/03|x|line_chart Percentage_of_GDP|33.8|y|line_chart Year|2001/02|x|line_chart Percentage_of_GDP|33.7|y|line_chart Year|2001/01|x|line_chart Percentage_of_GDP|35.2|y|line_chart 
title: United Kingdom ( UK ) : National debt as a percentage of GDP 2000 - 2019

gold: This statistic shows the general government gross consolidated debt ( national debt ) as a percentage of gross domestic product ( GDP ) in the United Kingdom ( UK ) from fiscal year 2000/01 to 2018/19 . After 2002/03 , national debt as a percentage of GDP rose continuously over the remainder of the period to a peak in 2016/17 .
gold_template: This statistic shows the general government gross consolidated templateTitle[4] ( templateTitle[3] templateTitle[4] ) as a templateYLabel[0] of gross domestic product ( templateYLabel[1] ) in the templateTitle[0] templateTitle[1] ( templateTitle[2] ) from fiscal templateXLabel[0] 2000/01 to templateXValue[0] . After templateXValue[16] , templateTitle[3] templateTitle[4] as a templateYLabel[0] of templateYLabel[1] rose continuously over the remainder of the period to a peak in templateXValue[2] .

generated_template: Recent figures tell us that the United States spent templateYValue[2] templateTitle[3] of its gross domestic product on templateTitle[1] care in templateXValue[2] . Projections show us that in the subsequent two years the templateTitle[1] spending in regards to templateYLabel[1] will stagnate . The United States has the highest templateTitle[1] spending based on templateYLabel[1] share among developed countries .
generated: Recent figures tell us that the United States spent 86.5 National of its gross domestic product on Kingdom care in 2016/17 . Projections show us that in the subsequent two years the Kingdom spending in regards to GDP will stagnate . The United States has the highest Kingdom spending based on GDP share among developed countries .


Example 343:
data: Year|2024|x|line_chart GDP_per_capita_in_U.S._dollars|27920.05|y|line_chart Year|2023|x|line_chart GDP_per_capita_in_U.S._dollars|26788.05|y|line_chart Year|2022|x|line_chart GDP_per_capita_in_U.S._dollars|25744.8|y|line_chart Year|2021|x|line_chart GDP_per_capita_in_U.S._dollars|24727.89|y|line_chart Year|2020|x|line_chart GDP_per_capita_in_U.S._dollars|23731.13|y|line_chart Year|2019|x|line_chart GDP_per_capita_in_U.S._dollars|23030.79|y|line_chart Year|2018|x|line_chart GDP_per_capita_in_U.S._dollars|23437.39|y|line_chart Year|2017|x|line_chart GDP_per_capita_in_U.S._dollars|21482.86|y|line_chart Year|2016|x|line_chart GDP_per_capita_in_U.S._dollars|19986.36|y|line_chart Year|2015|x|line_chart GDP_per_capita_in_U.S._dollars|19252.01|y|line_chart Year|2014|x|line_chart GDP_per_capita_in_U.S._dollars|22109.32|y|line_chart Year|2013|x|line_chart GDP_per_capita_in_U.S._dollars|21625.48|y|line_chart Year|2012|x|line_chart GDP_per_capita_in_U.S._dollars|20588.87|y|line_chart Year|2011|x|line_chart GDP_per_capita_in_U.S._dollars|23217.34|y|line_chart Year|2010|x|line_chart GDP_per_capita_in_U.S._dollars|22580.68|y|line_chart Year|2009|x|line_chart GDP_per_capita_in_U.S._dollars|23122.56|y|line_chart Year|2008|x|line_chart GDP_per_capita_in_U.S._dollars|24933.17|y|line_chart Year|2007|x|line_chart GDP_per_capita_in_U.S._dollars|22811.57|y|line_chart Year|2006|x|line_chart GDP_per_capita_in_U.S._dollars|19837.97|y|line_chart Year|2005|x|line_chart GDP_per_capita_in_U.S._dollars|18815.44|y|line_chart Year|2004|x|line_chart GDP_per_capita_in_U.S._dollars|18064.47|y|line_chart Year|2003|x|line_chart GDP_per_capita_in_U.S._dollars|15799.89|y|line_chart Year|2002|x|line_chart GDP_per_capita_in_U.S._dollars|12922.15|y|line_chart Year|2001|x|line_chart GDP_per_capita_in_U.S._dollars|11737.17|y|line_chart Year|2000|x|line_chart GDP_per_capita_in_U.S._dollars|11533.83|y|line_chart Year|1999|x|line_chart GDP_per_capita_in_U.S._dollars|12490.93|y|line_chart Year|1998|x|line_chart GDP_per_capita_in_U.S._dollars|12220.18|y|line_chart Year|1997|x|line_chart GDP_per_capita_in_U.S._dollars|11597.7|y|line_chart Year|1996|x|line_chart GDP_per_capita_in_U.S._dollars|12187.56|y|line_chart Year|1995|x|line_chart GDP_per_capita_in_U.S._dollars|11788.47|y|line_chart Year|1994|x|line_chart GDP_per_capita_in_U.S._dollars|9978.59|y|line_chart Year|1993|x|line_chart GDP_per_capita_in_U.S._dollars|9548.58|y|line_chart Year|1992|x|line_chart GDP_per_capita_in_U.S._dollars|10864.55|y|line_chart Year|1991|x|line_chart GDP_per_capita_in_U.S._dollars|9027.18|y|line_chart Year|1990|x|line_chart GDP_per_capita_in_U.S._dollars|7958.02|y|line_chart Year|1989|x|line_chart GDP_per_capita_in_U.S._dollars|5978.16|y|line_chart Year|1988|x|line_chart GDP_per_capita_in_U.S._dollars|5533.16|y|line_chart Year|1987|x|line_chart GDP_per_capita_in_U.S._dollars|4724.91|y|line_chart Year|1986|x|line_chart GDP_per_capita_in_U.S._dollars|3774.95|y|line_chart Year|1985|x|line_chart GDP_per_capita_in_U.S._dollars|2716.91|y|line_chart Year|1984|x|line_chart GDP_per_capita_in_U.S._dollars|2596.33|y|line_chart 
title: Gross domestic product ( GDP ) per capita in Portugal 2024

gold: The statistic shows gross domestic product ( GDP ) per capita in Portugal from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services templateYLabel[2] in a country in a templateXLabel[0] . templateYLabel[0] templateYLabel[1] templateYLabel[2] of a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth growth .
generated: The statistic shows Gross domestic product ( GDP ) per capita in Portugal 2024 from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services capita in a country in a Year . GDP per capita of a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .


Example 344:
data: Quarter|Q3_2019|x|bar_chart Revenue_in_billion_euros|13.54|y|bar_chart Quarter|Q2_2019|x|bar_chart Revenue_in_billion_euros|12.83|y|bar_chart Quarter|Q1_2019|x|bar_chart Revenue_in_billion_euros|16.56|y|bar_chart Quarter|Q4_2018|x|bar_chart Revenue_in_billion_euros|17.06|y|bar_chart Quarter|Q3_2018|x|bar_chart Revenue_in_billion_euros|13.6|y|bar_chart Quarter|Q2_2018|x|bar_chart Revenue_in_billion_euros|12.44|y|bar_chart Quarter|Q1_2018|x|bar_chart Revenue_in_billion_euros|14.24|y|bar_chart Quarter|Q4_2017|x|bar_chart Revenue_in_billion_euros|16.3|y|bar_chart Quarter|Q3_2017|x|bar_chart Revenue_in_billion_euros|13.22|y|bar_chart Quarter|Q2_2017|x|bar_chart Revenue_in_billion_euros|11.81|y|bar_chart Quarter|Q1_2017|x|bar_chart Revenue_in_billion_euros|13.46|y|bar_chart Quarter|Q4_2016|x|bar_chart Revenue_in_billion_euros|16.48|y|bar_chart Quarter|Q3_2016|x|bar_chart Revenue_in_billion_euros|12.66|y|bar_chart Quarter|Q2_2016|x|bar_chart Revenue_in_billion_euros|12.21|y|bar_chart Quarter|Q1_2016|x|bar_chart Revenue_in_billion_euros|13.7|y|bar_chart Quarter|Q4_2015|x|bar_chart Revenue_in_billion_euros|18.0|y|bar_chart 
title: Information Technology ( IT ) : revenue in Western Europe Q4 2015-Q3 2019

gold: The statistic shows trends in Information Technology ( IT ) revenues in the technical consumer goods market in Western Europe from the fourth quarter of 2015 to the third quarter of 2019 . Revenue reached 13.54 billion euros by the end of Q3 2019 .
gold_template: The statistic shows trends in templateTitle[0] templateTitle[1] ( IT ) revenues in the technical consumer goods market in templateTitle[3] templateTitle[4] from the fourth templateXLabel[0] of templateXValue[last] to the third templateXLabel[0] of templateXValue[0] . templateYLabel[0] reached templateYValue[0] templateYLabel[1] templateYLabel[2] by the end of templateXValue[0] templateXValue[0] .

generated_template: In the fourth templateXLabel[0] of templateXValue[0] , the templateTitle[0] templateTitle[1] templateTitle[2] generated a total templateYLabel[0] of around templateYValue[max] templateYLabel[1] templateYLabel[2] worldwide , marking a small increase from the previous templateXLabel[0] . templateTitle[1] templateTitle[2] is one of the most templateYLabel[1] templateYLabel[2] worldwide . templateTitle[1] templateTitle[2] largest sporting goods manufacturers in the world , templateTitle[0] has made a name for itself as a top vendor of business and enterprise software tools .
generated: In the fourth Quarter of Q3_2019 , the Information Technology revenue generated a total Revenue of around 18.0 billion euros worldwide , marking a small increase from the previous Quarter . Technology revenue is one of the most billion euros worldwide . Technology revenue largest sporting goods manufacturers in the world , Information has made a name for itself as a top vendor of business and enterprise software tools .


Example 345:
data: Quarter|Q4_2016|x|bar_chart Rent_growth|1|y|bar_chart Quarter|Q3_2016|x|bar_chart Rent_growth|0.9|y|bar_chart Quarter|Q2_2016|x|bar_chart Rent_growth|0.9|y|bar_chart Quarter|Q1_2016|x|bar_chart Rent_growth|0.9|y|bar_chart Quarter|Q4_2015|x|bar_chart Rent_growth|0.9|y|bar_chart Quarter|Q3_2015|x|bar_chart Rent_growth|0.9|y|bar_chart Quarter|Q2_2015|x|bar_chart Rent_growth|0.8|y|bar_chart 
title: Forecast of office rent growth in the U.S. 2015 - 2016

gold: This statistic presents a forecast of office rent growth in the United States from second quarter of 2015 to fourth quarter of 2016 . It was expected that office rent would grow by one percent in the fourth quarter of 2016 in the United States . Coworking worldwide – additional information Coworking is an alternative to the traditional office space , wherein independent workers , such as freelancers and remote workers , share a working environment .
gold_template: This statistic presents a templateTitle[0] of templateTitle[1] templateYLabel[0] templateYLabel[1] in the templateTitle[4] from second templateXLabel[0] of templateXValue[4] to fourth templateXLabel[0] of templateXValue[0] . It was expected that templateTitle[1] templateYLabel[0] would grow by templateYValue[max] percent in the fourth templateXLabel[0] of templateXValue[0] in the templateTitle[4] . Coworking worldwide – additional information Coworking is an alternative to the traditional templateTitle[1] space , wherein independent workers , such as freelancers and remote workers , share a working environment .

generated_template: In the fourth templateXLabel[0] of templateXValue[0] , this templateYLabel[1] of templateTitle[4] templateTitle[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the United States amounted to templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the corresponding templateXLabel[0] of templateXValue[0] . The majority of templateYLabel[1] As of the fourth templateXLabel[0] of templateXValue[0] , templateXValue[0] was followed by the company 's gross domestic product .
generated: In the fourth Quarter of Q4_2016 , this growth of U.S. 2015 growth yLabelErr yLabelErr in the United States amounted to 1 Rent growth yLabelErr yLabelErr in the corresponding Quarter of Q4_2016 . The majority of growth As of the fourth Quarter of Q4_2016 , Q4_2016 was followed by the company 's gross domestic product .


Example 346:
data: Country|United_States|x|bar_chart Share_of_Twitter_users|18.9|y|bar_chart Country|Japan|x|bar_chart Share_of_Twitter_users|14.6|y|bar_chart Country|Venezuela|x|bar_chart Share_of_Twitter_users|5.8|y|bar_chart Country|United_Kingdom|x|bar_chart Share_of_Twitter_users|5.5|y|bar_chart Country|Saudi_Arabia|x|bar_chart Share_of_Twitter_users|4|y|bar_chart Country|Turkey|x|bar_chart Share_of_Twitter_users|3.3|y|bar_chart Country|Brazil|x|bar_chart Share_of_Twitter_users|3|y|bar_chart Country|Mexico|x|bar_chart Share_of_Twitter_users|2.8|y|bar_chart Country|India|x|bar_chart Share_of_Twitter_users|2.6|y|bar_chart Country|Spain|x|bar_chart Share_of_Twitter_users|2.6|y|bar_chart 
title: Twitter user share in selected countries 2018

gold: This statistic represents a ranking of the countries with the largest Twitter audiences as of July 2018 . During the measured period , the United States accounted for 18.9 percent of Twitter audiences . Japan was ranked second with a 14.6 percent share .
gold_template: This statistic represents a ranking of the templateTitle[4] with the largest templateYLabel[1] audiences as of July templateTitle[5] . During the measured period , the templateXValue[0] templateXValue[0] accounted for templateYValue[max] percent of templateYLabel[1] audiences . templateXValue[1] was ranked second with a templateYValue[1] percent templateYLabel[0] .

generated_template: This statistic gives information on the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] market templateTitle[5] in the United States as of templateTitle[6] . During the survey period it was found that templateYValue[2] percent of the templateYLabel[1] templateYLabel[2] watched about templateYValue[max] percent of the templateYLabel[1] templateYLabel[2] to templateXValue[0] .
generated: This statistic gives information on the Share Twitter users of selected countries market 2018 in the United States as of titleErr . During the survey period it was found that 5.8 percent of the Twitter users watched about 18.9 percent of the Twitter users to United_States .


Example 347:
data: Airline_Brand|Indigo|x|bar_chart Domestic_market_share|39.7|y|bar_chart Airline_Brand|Jet_Airways|x|bar_chart Domestic_market_share|15|y|bar_chart Airline_Brand|Spicejet|x|bar_chart Domestic_market_share|13.1|y|bar_chart Airline_Brand|Air_India|x|bar_chart Domestic_market_share|12|y|bar_chart Airline_Brand|Go_Air|x|bar_chart Domestic_market_share|8.8|y|bar_chart Airline_Brand|Air_Asia|x|bar_chart Domestic_market_share|4|y|bar_chart Airline_Brand|Vistara|x|bar_chart Domestic_market_share|3.6|y|bar_chart Airline_Brand|Jetlite|x|bar_chart Domestic_market_share|2.2|y|bar_chart Airline_Brand|Alliance_Air|x|bar_chart Domestic_market_share|1|y|bar_chart Airline_Brand|Truejet|x|bar_chart Domestic_market_share|0.4|y|bar_chart Airline_Brand|Air_India_Express|x|bar_chart Domestic_market_share|0.1|y|bar_chart Airline_Brand|Others|x|bar_chart Domestic_market_share|0.02|y|bar_chart 
title: Market share of passengers carried in India 2018 by domestic airlines

gold: India 's aviation sector had increasingly emerged as a fast-growing industry . The sector had established itself as an affordable and credible alternative to the tedious and long journeys via road or rail . With a visible growth trend , it was estimated that by 2034 , India would become one of the largest aviation markets in the world .
gold_template: templateXValue[3] 's aviation sector had increasingly emerged as a fast-growing industry . The sector had established itself as an affordable and credible alternative to the tedious and long journeys via road or rail . With a visible growth trend , it was estimated that templateTitle[6] 2034 , templateXValue[3] would become templateYValue[8] of the largest aviation markets in the world .

generated_template: This statistic shows the percentage of templateTitle[0] 's templateYLabel[1] templateYLabel[2] in templateTitle[4] , templateTitle[5] templateXLabel[0] . During the last reported period , templateYValue[max] percent of the total templateYLabel[2] in the United States .
generated: This statistic shows the percentage of Market 's market share in India , 2018 Airline . During the last reported period , 39.7 percent of the total share in the United States .


Example 348:
data: Province|Tibet|x|bar_chart Share_of_ethnic_minorities|90.05|y|bar_chart Province|Hunan|x|bar_chart Share_of_ethnic_minorities|83.5|y|bar_chart Province|Chongqing|x|bar_chart Share_of_ethnic_minorities|74.39|y|bar_chart Province|Qinghai|x|bar_chart Share_of_ethnic_minorities|67.57|y|bar_chart Province|Hebei|x|bar_chart Share_of_ethnic_minorities|63.75|y|bar_chart Province|Sichuan|x|bar_chart Share_of_ethnic_minorities|63.03|y|bar_chart Province|Gansu|x|bar_chart Share_of_ethnic_minorities|62.69|y|bar_chart Province|Xinjiang|x|bar_chart Share_of_ethnic_minorities|60.22|y|bar_chart Province|Guizhou|x|bar_chart Share_of_ethnic_minorities|60.14|y|bar_chart Province|Yunnan|x|bar_chart Share_of_ethnic_minorities|58.87|y|bar_chart Province|Hubei|x|bar_chart Share_of_ethnic_minorities|56.78|y|bar_chart Province|Liaoning|x|bar_chart Share_of_ethnic_minorities|54.49|y|bar_chart Province|Hainan|x|bar_chart Share_of_ethnic_minorities|51.69|y|bar_chart Province|National_total|x|bar_chart Share_of_ethnic_minorities|51.07|y|bar_chart Province|Guangxi|x|bar_chart Share_of_ethnic_minorities|44.75|y|bar_chart Province|Guangdong|x|bar_chart Share_of_ethnic_minorities|38.7|y|bar_chart Province|Ningxia|x|bar_chart Share_of_ethnic_minorities|37.39|y|bar_chart Province|Jilin|x|bar_chart Share_of_ethnic_minorities|34.49|y|bar_chart Province|Inner_Mongolia|x|bar_chart Share_of_ethnic_minorities|22.16|y|bar_chart Province|Heilongjiang|x|bar_chart Share_of_ethnic_minorities|21.87|y|bar_chart Province|Zhejiang|x|bar_chart Share_of_ethnic_minorities|11.81|y|bar_chart 
title: Share of ethnic minorities in the China 's minority autonomous regions 2018

gold: The graph shows the share of ethnic minorities in the population of China 's minority autonomous regions by province . In 2018 , about 60.22 percent of the population in minority areas in Xinjiang belonged to ethnic minorities .
gold_template: The graph shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the population of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] by templateXLabel[0] . In templateTitle[8] , about templateYValue[7] percent of the population in templateTitle[5] areas in templateXValue[7] belonged to templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] of templateTitle[4] templateTitle[5] in templateTitle[7] templateTitle[8] in templateYLabel[1] templateTitle[8] . In templateTitle[8] , templateXValue[0] had the highest rate of templateYValue[max] percent of the templateYLabel[1] fashion region with its U.S. dollars .
generated: This statistic shows the Share ethnic of minorities yLabelErr of 's minority in regions 2018 in ethnic 2018 . In 2018 , Tibet had the highest rate of 90.05 percent of the ethnic fashion region with its U.S. dollars .


Example 349:
data: Country|Syria|x|bar_chart Number_of_refugees_admitted|33266|y|bar_chart Country|Eritrea|x|bar_chart Number_of_refugees_admitted|3934|y|bar_chart Country|Iraq|x|bar_chart Number_of_refugees_admitted|1650|y|bar_chart Country|Congo|x|bar_chart Number_of_refugees_admitted|1644|y|bar_chart Country|Afghanistan|x|bar_chart Number_of_refugees_admitted|1354|y|bar_chart 
title: Top 5 origin countries of refugees admitted to Canada in 2016

gold: This statistic shows the top five origin counties of refugees that were admitted to Canada in 2016 . Syria topped the list in 2016 with 33,266 refugees from the country admitted into Canada .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] counties of templateYLabel[1] that were templateYLabel[2] to templateTitle[6] in templateTitle[7] . templateXValue[0] topped the list in templateTitle[7] with templateYValue[max] templateYLabel[1] from the templateXLabel[0] templateYLabel[2] into templateTitle[6] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] in templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] . The survey revealed that templateYValue[max] percent of the templateYLabel[1] were living in templateXValue[0] .
generated: This statistic shows the Number refugees of admitted in origin countries refugees in admitted . The survey revealed that 33266 percent of the refugees were living in Syria .


Example 350:
data: Month|Tesco|x|bar_chart Volume_in_tonnes|59400|y|bar_chart Month|Sainsbury's|x|bar_chart Volume_in_tonnes|35832|y|bar_chart Month|Asda|x|bar_chart Volume_in_tonnes|32020|y|bar_chart Month|Aldi|x|bar_chart Volume_in_tonnes|13377|y|bar_chart Month|Waitrose|x|bar_chart Volume_in_tonnes|12529|y|bar_chart Month|Co-op|x|bar_chart Volume_in_tonnes|12411|y|bar_chart Month|Marks_and_Spencer|x|bar_chart Volume_in_tonnes|10152|y|bar_chart Month|Iceland|x|bar_chart Volume_in_tonnes|2080|y|bar_chart 
title: Estimated food waste from major supermarkets in the United Kingdom ( UK ) 2016

gold: This statistic shows estimates of wasted food from major supermarkets in the United Kingdom ( UK ) in 2016 . In this year Tesco was found to generate the highest volume of food waste at 59.4 thousand tonnes . This was followed by Sainsbury 's with a waste generation of approximately 35.8 thousand tonnes and Asda with 32 thousand tonnes of food waste generated .
gold_template: This statistic shows estimates of wasted templateTitle[1] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] templateTitle[7] ( templateTitle[8] ) in templateTitle[9] . In this year templateXValue[0] was found to generate the highest templateYLabel[0] of templateTitle[1] templateTitle[2] at templateYValue[max] thousand templateYLabel[1] . This was followed by Sainsbury 's with a templateTitle[2] generation of approximately templateYValue[1] thousand templateYLabel[1] and templateXValue[2] with templateYValue[2] thousand templateYLabel[1] of templateTitle[1] templateTitle[2] generated .

generated_template: templateXValue[0] templateXValue[0] topped the number of templateTitle[1] templateTitle[2] templateYLabel[1] ( UK ) in templateTitle[4] templateTitle[5] in September templateTitle[7] . More than templateYValue[max] percent of the templateTitle[3] templateTitle[4] templateTitle[5] the United States had been exported from templateYValue[1] percent in templateXValue[1] . More than 100 times , a templateYLabel[1] in templateTitle[3] can be accessed here .
generated: Tesco topped the number of food waste tonnes ( UK ) in major supermarkets in September Kingdom . More than 59400 percent of the from major supermarkets the United States had been exported from 35832 percent in Sainsbury's . More than 100 times , a tonnes in from can be accessed here .


Example 351:
data: Year|2018|x|line_chart Household_income_in_current_U.S._dollars|53369|y|line_chart Year|2017|x|line_chart Household_income_in_current_U.S._dollars|50343|y|line_chart Year|2016|x|line_chart Household_income_in_current_U.S._dollars|53764|y|line_chart Year|2015|x|line_chart Household_income_in_current_U.S._dollars|50797|y|line_chart Year|2014|x|line_chart Household_income_in_current_U.S._dollars|46784|y|line_chart Year|2013|x|line_chart Household_income_in_current_U.S._dollars|41208|y|line_chart Year|2012|x|line_chart Household_income_in_current_U.S._dollars|41553|y|line_chart Year|2011|x|line_chart Household_income_in_current_U.S._dollars|45206|y|line_chart Year|2010|x|line_chart Household_income_in_current_U.S._dollars|43830|y|line_chart Year|2009|x|line_chart Household_income_in_current_U.S._dollars|41906|y|line_chart Year|2008|x|line_chart Household_income_in_current_U.S._dollars|42930|y|line_chart Year|2007|x|line_chart Household_income_in_current_U.S._dollars|43513|y|line_chart Year|2006|x|line_chart Household_income_in_current_U.S._dollars|39797|y|line_chart Year|2005|x|line_chart Household_income_in_current_U.S._dollars|42056|y|line_chart Year|2004|x|line_chart Household_income_in_current_U.S._dollars|40238|y|line_chart Year|2003|x|line_chart Household_income_in_current_U.S._dollars|37279|y|line_chart Year|2002|x|line_chart Household_income_in_current_U.S._dollars|36515|y|line_chart Year|2001|x|line_chart Household_income_in_current_U.S._dollars|38162|y|line_chart Year|2000|x|line_chart Household_income_in_current_U.S._dollars|38317|y|line_chart Year|1999|x|line_chart Household_income_in_current_U.S._dollars|37254|y|line_chart Year|1998|x|line_chart Household_income_in_current_U.S._dollars|35838|y|line_chart Year|1997|x|line_chart Household_income_in_current_U.S._dollars|35840|y|line_chart Year|1996|x|line_chart Household_income_in_current_U.S._dollars|35601|y|line_chart Year|1995|x|line_chart Household_income_in_current_U.S._dollars|31979|y|line_chart Year|1994|x|line_chart Household_income_in_current_U.S._dollars|30114|y|line_chart Year|1993|x|line_chart Household_income_in_current_U.S._dollars|28820|y|line_chart Year|1992|x|line_chart Household_income_in_current_U.S._dollars|27771|y|line_chart Year|1991|x|line_chart Household_income_in_current_U.S._dollars|26853|y|line_chart Year|1990|x|line_chart Household_income_in_current_U.S._dollars|26329|y|line_chart 
title: North Carolina - Median household income 1990 - 2018

gold: This statistic shows the median household income in North Carolina from 1990 to 2018 . In 2018 , the median household income in North Carolina amounted to 53,369 U.S. dollars .
gold_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] templateTitle[1] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] at templateYValue[0] templateYLabel[3] .
generated: The statistic shows the North Carolina Household income household from 1990 to 2018 . In 2018 , the North Carolina Household income at 53369 U.S. .


Example 352:
data: Region|Europe|x|bar_chart Number_of_employees|70599|y|bar_chart Region|North_America|x|bar_chart Number_of_employees|21541|y|bar_chart Region|Asia_(excl._India)|x|bar_chart Number_of_employees|15259|y|bar_chart Region|South_America|x|bar_chart Number_of_employees|8166|y|bar_chart Region|Africa_India_Middle-East|x|bar_chart Number_of_employees|1848|y|bar_chart 
title: Michelin - worldwide number of employees by region 2018

gold: The statistic shows the worldwide number of Michelin 's employees in 2018 , by region . Michelin had 21,541 employees in North America in 2018 . In that same year , some 17.5 percent of their employees worldwide were women .
gold_template: The statistic shows the templateTitle[1] templateYLabel[0] of templateTitle[0] 's templateYLabel[1] in templateTitle[6] , templateTitle[4] templateXLabel[0] . templateTitle[0] had templateYValue[1] templateYLabel[1] in templateXValue[1] templateXValue[1] in templateTitle[6] . In that same year , some 17.5 percent of their templateYLabel[1] templateTitle[1] were women .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] at templateTitle[0] agricultural company in templateTitle[3] , templateTitle[4] templateXLabel[0] . In templateXValue[2] templateXValue[1] , the company employed approximately templateYValue[2] percent of the templateYLabel[1] working for the templateXValue[0] templateXValue[0] .
generated: This statistic shows the Number of employees at Michelin agricultural company in employees , by Region . In Asia_(excl._India) North_America , the company employed approximately 15259 percent of the employees working for the Europe .


Example 353:
data: Year|2023/24|x|line_chart U.S._dollars_per_barrel|64.5|y|line_chart Year|2022/23|x|line_chart U.S._dollars_per_barrel|63.3|y|line_chart Year|2021/22|x|line_chart U.S._dollars_per_barrel|62.0|y|line_chart Year|2020/21|x|line_chart U.S._dollars_per_barrel|61.6|y|line_chart Year|2019/20|x|line_chart U.S._dollars_per_barrel|62.1|y|line_chart Year|2018/19|x|line_chart U.S._dollars_per_barrel|71.3|y|line_chart Year|2017/18|x|line_chart U.S._dollars_per_barrel|54.6|y|line_chart 
title: United Kingdom ( UK ) oil price forecast in U.S. dollars 2017 - 2024

gold: This statistic shows the forecasted price of oil in the United Kingdom ( UK ) from 2017 to 2024 , in U.S. dollars per barrel . The price of oil is expected to increase to 64.5 U.S. dollars in 2023/24 .
gold_template: This statistic shows the forecasted templateTitle[4] of templateTitle[3] in the templateTitle[0] templateTitle[1] ( templateTitle[2] ) from templateTitle[8] to templateTitle[9] , in templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . The templateTitle[4] of templateTitle[3] is expected to increase to templateYValue[0] templateYLabel[0] templateYLabel[1] in templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[0] million templateYLabel[1] living in templateTitle[2] templateTitle[3] templateTitle[4] in the United States .
generated: This statistic shows the U.S. dollars of price forecast in the United States from 2023/24 to 2023/24 . In 2023/24 , there were approximately 64.5 million dollars living in UK oil price in the United States .


Example 354:
data: Year|2018|x|line_chart Number_of_arrivals_in_millions|5.49|y|line_chart Year|2017|x|line_chart Number_of_arrivals_in_millions|5.29|y|line_chart Year|2016|x|line_chart Number_of_arrivals_in_millions|4.94|y|line_chart Year|2015|x|line_chart Number_of_arrivals_in_millions|4.27|y|line_chart Year|2014|x|line_chart Number_of_arrivals_in_millions|3.69|y|line_chart Year|2013|x|line_chart Number_of_arrivals_in_millions|4.0|y|line_chart Year|2012|x|line_chart Number_of_arrivals_in_millions|3.73|y|line_chart Year|2011|x|line_chart Number_of_arrivals_in_millions|3.54|y|line_chart Year|2010|x|line_chart Number_of_arrivals_in_millions|3.36|y|line_chart Year|2009|x|line_chart Number_of_arrivals_in_millions|3.34|y|line_chart Year|2008|x|line_chart Number_of_arrivals_in_millions|4.03|y|line_chart Year|2007|x|line_chart Number_of_arrivals_in_millions|3.74|y|line_chart Year|2006|x|line_chart Number_of_arrivals_in_millions|3.55|y|line_chart 
title: Number of arrivals in tourist accommodation Slovakia 2006 - 2018

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Slovakia from 2006 to 2018 . In 2018 there were around 5.49 million arrivals at accommodation establishments in Slovakia .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] there were around templateYValue[max] million templateYLabel[1] at templateTitle[3] establishments in templateTitle[4] .

generated_template: This statistic shows templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] of templateTitle[0] tourists in templateTitle[2] was templateYValue[min] million . This figure was forecasted to increase to templateYValue[max] million by templateXValue[max] .
generated: This statistic shows Number arrivals in tourist accommodation 2006 to 2018 . In 2006 , the Number of Number tourists in tourist was 3.34 million . This figure was forecasted to increase to 5.49 million by 2018 .


Example 355:
data: Year|2018|x|line_chart Production_level_in_million_bricks|2025|y|line_chart Year|2017|x|line_chart Production_level_in_million_bricks|1877|y|line_chart Year|2016|x|line_chart Production_level_in_million_bricks|1800|y|line_chart Year|2015|x|line_chart Production_level_in_million_bricks|1915|y|line_chart Year|2014|x|line_chart Production_level_in_million_bricks|1824|y|line_chart Year|2013|x|line_chart Production_level_in_million_bricks|1555|y|line_chart 
title: Annual levels of brick production in Great Britain ( GB ) 2013 - 2018

gold: British producers had manufactured nearly 2.03 billion bricks in 2018 . This was the peak since the beginning of the reporting period in 2013 and the first time figures exceeded two billion units . Following increased demand , the Brick Development Association expects production volumes to increase further in the coming years , with companies within the industry seeking to invest in greater production capacity .
gold_template: British producers had manufactured nearly templateYValue[max] billion templateYLabel[3] in templateXValue[max] . This was the peak since the beginning of the reporting period in templateXValue[min] and the first time figures exceeded templateYValue[max] billion units . Following increased demand , the templateTitle[2] Development Association expects templateYLabel[0] volumes to increase further in the coming years , with companies within the industry seeking to invest in greater templateYLabel[0] capacity .

generated_template: In templateXValue[max] , over templateYValue[0] thousand templateYLabel[1] were committed in templateTitle[3] templateTitle[4] . The value of templateYLabel[1] templateYLabel[2] in templateTitle[3] templateTitle[4] can be an increase of about templateYValue[max] percent compared to the previous templateXLabel[0] . The share of alcoholic percent in the country 's population can be seen that they had between many years .
generated: In 2018 , over 2025 thousand level were committed in production Great . The value of level million in production Great can be an increase of about 2025 percent compared to the previous Year . The share of alcoholic percent in the country 's population can be seen that they had between many years .


Example 356:
data: Year|2017|x|line_chart Percentage_of_population|8.7|y|line_chart Year|2016|x|line_chart Percentage_of_population|9.4|y|line_chart Year|2015|x|line_chart Percentage_of_population|9.4|y|line_chart Year|2014|x|line_chart Percentage_of_population|8.6|y|line_chart Year|2013|x|line_chart Percentage_of_population|10.1|y|line_chart Year|2012|x|line_chart Percentage_of_population|11.4|y|line_chart Year|2011|x|line_chart Percentage_of_population|12.2|y|line_chart Year|2010|x|line_chart Percentage_of_population|14.7|y|line_chart Year|2009|x|line_chart Percentage_of_population|17.2|y|line_chart Year|2008|x|line_chart Percentage_of_population|18.1|y|line_chart Year|2007|x|line_chart Percentage_of_population|19.2|y|line_chart Year|2006|x|line_chart Percentage_of_population|19.8|y|line_chart Year|2005|x|line_chart Percentage_of_population|25.7|y|line_chart 
title: Ecuador : poverty headcount ratio at 3.20 U.S. dollars a day 2005 - 2017

gold: The poverty rate in Ecuador has been decreasing lately . In 2017 , approximately 8.7 percent of the Ecuadorian population was living on less than 3.20 U.S. dollars per day , down from 25.7 percent in 2005.Still , social inequality remains a challenge in Ecuador and Latin America as a whole .
gold_template: The templateTitle[1] rate in templateTitle[0] has been decreasing lately . In templateXValue[max] , approximately templateYValue[0] percent of the Ecuadorian templateYLabel[1] was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] percent in 2005.Still , social inequality remains a challenge in templateTitle[0] and Latin America as a whole .

generated_template: templateTitle[0] computing has come a long way in the past decade , with increased power , battery life , portability and display quality all enhancing the attractiveness of a portable computer over a desktop computer . The templateTitle[1] rate of the templateTitle[0] in the templateTitle[2] templateTitle[3] reflects this , increasing from templateYValue[min] percent in templateXValue[min] , to templateYValue[0] percent in templateXValue[max] . Laptops templateTitle[1] highest among 35 - 54 templateXLabel[0] olds templateTitle[0] templateTitle[1] rates are above 50 percent across each of the main age groups , and highest among those aged 35 - 54 years .
generated: Ecuador computing has come a long way in the past decade , with increased power , battery life , portability and display quality all enhancing the attractiveness of a portable computer over a desktop computer . The poverty rate of the Ecuador in the headcount ratio reflects this , increasing from 8.6 percent in 2005 , to 8.7 percent in 2017 . Laptops poverty highest among 35 - 54 Year olds Ecuador poverty rates are above 50 percent across each of the main age groups , and highest among those aged 35 - 54 years .


Example 357:
data: Year|2050|x|line_chart Percentage_of_total_population|22|y|line_chart Year|2040|x|line_chart Percentage_of_total_population|21.6|y|line_chart Year|2030|x|line_chart Percentage_of_total_population|20.6|y|line_chart Year|2020|x|line_chart Percentage_of_total_population|16.9|y|line_chart Year|2018|x|line_chart Percentage_of_total_population|16|y|line_chart Year|2010|x|line_chart Percentage_of_total_population|13.1|y|line_chart Year|2000|x|line_chart Percentage_of_total_population|12.4|y|line_chart Year|1990|x|line_chart Percentage_of_total_population|12.5|y|line_chart Year|1980|x|line_chart Percentage_of_total_population|11.3|y|line_chart Year|1970|x|line_chart Percentage_of_total_population|10|y|line_chart Year|1960|x|line_chart Percentage_of_total_population|9|y|line_chart Year|1950|x|line_chart Percentage_of_total_population|8|y|line_chart 
title: U.S. - seniors as a percentage of the population 1950 - 2050

gold: In 2017 , about 16 percent of the American population was 65 years old or over ; a figure which is expected to reach 22 percent by 2050 . This is a significant increase from 1950 , when only eight percent of the population was 65 or over . A rapidly aging population In recent years , the aging population of the United States has come into focus as a cause for concern , as the nature of work and retirement is expected to change in order to keep up .
gold_template: In 2017 , about templateYValue[4] percent of the American templateYLabel[2] was 65 years old or over ; a figure which is expected to reach templateYValue[max] percent by templateXValue[max] . This is a significant increase from templateXValue[min] , when only templateYValue[min] percent of the templateYLabel[2] was 65 or over . A rapidly aging templateYLabel[2] In recent years , the aging templateYLabel[2] of the templateTitle[0] has come into focus as a cause for concern , as the nature of work and retirement is expected to change in order to keep up .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] templateTitle[5] in the United States amounted to templateYValue[0] percent of the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] is an increase from templateYValue[1] percent in templateXValue[min] .
generated: In 2050 , the Percentage total of the population 1950 2050 in the United States amounted to 22 percent of the previous Year . The Percentage total population 1950 2050 is an increase from 21.6 percent in 1950 .


Example 358:
data: Year|2019|x|line_chart Franchise_value_in_million_U.S._dollars|1900|y|line_chart Year|2018|x|line_chart Franchise_value_in_million_U.S._dollars|1600|y|line_chart Year|2017|x|line_chart Franchise_value_in_million_U.S._dollars|1600|y|line_chart Year|2016|x|line_chart Franchise_value_in_million_U.S._dollars|1500|y|line_chart Year|2015|x|line_chart Franchise_value_in_million_U.S._dollars|1400|y|line_chart Year|2014|x|line_chart Franchise_value_in_million_U.S._dollars|935|y|line_chart Year|2013|x|line_chart Franchise_value_in_million_U.S._dollars|870|y|line_chart Year|2012|x|line_chart Franchise_value_in_million_U.S._dollars|805|y|line_chart Year|2011|x|line_chart Franchise_value_in_million_U.S._dollars|792|y|line_chart Year|2010|x|line_chart Franchise_value_in_million_U.S._dollars|799|y|line_chart Year|2009|x|line_chart Franchise_value_in_million_U.S._dollars|909|y|line_chart Year|2008|x|line_chart Franchise_value_in_million_U.S._dollars|885|y|line_chart Year|2007|x|line_chart Franchise_value_in_million_U.S._dollars|821|y|line_chart Year|2006|x|line_chart Franchise_value_in_million_U.S._dollars|756|y|line_chart Year|2005|x|line_chart Franchise_value_in_million_U.S._dollars|708|y|line_chart Year|2004|x|line_chart Franchise_value_in_million_U.S._dollars|637|y|line_chart Year|2003|x|line_chart Franchise_value_in_million_U.S._dollars|564|y|line_chart Year|2002|x|line_chart Franchise_value_in_million_U.S._dollars|458|y|line_chart 
title: Franchise value of the Buffalo Bills ( NFL ) 2002 - 2019

gold: This graph depicts the franchise value of the Buffalo Bills of the National Football League from 2002 to 2019 . In 2019 , the franchise value came to 1.9 billion U.S. dollars . The Buffalo Bills are owned by Terry and Kim Pegula .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] are owned by Terry and Kim Pegula .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[4] are owned by the Steinbrenner Family , who bought them in 1973 for 8.8 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2000 .
generated: This graph depicts the value of the Buffalo Bills NFL Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 1900 billion U.S. dollars . The Buffalo Bills NFL are owned by the Steinbrenner Family , who bought them in 1973 for 8.8 million U.S. dollars in 2000 .


Example 359:
data: Year|2017|x|line_chart Percentage_of_population|0.4|y|line_chart Year|2016|x|line_chart Percentage_of_population|0.5|y|line_chart Year|2015|x|line_chart Percentage_of_population|0.6|y|line_chart Year|2014|x|line_chart Percentage_of_population|0.7|y|line_chart Year|2013|x|line_chart Percentage_of_population|0.8|y|line_chart Year|2012|x|line_chart Percentage_of_population|1.1|y|line_chart Year|2011|x|line_chart Percentage_of_population|1|y|line_chart Year|2010|x|line_chart Percentage_of_population|1.3|y|line_chart Year|2009|x|line_chart Percentage_of_population|1.8|y|line_chart Year|2008|x|line_chart Percentage_of_population|1.8|y|line_chart Year|2007|x|line_chart Percentage_of_population|2.9|y|line_chart Year|2006|x|line_chart Percentage_of_population|3.7|y|line_chart 
title: Uruguay : poverty headcount ratio at 3.20 U.S. dollars a day 2006 - 2017

gold: The poverty rate in Uruguay has been decreasing recently . In 2017 , approximately 0.4 percent of Uruguayans was living on less than 3.20 U.S. dollars per day , down from 3.7 percent of the country 's population in 2006.Still , social inequality remains a challenge in Latin America as a whole .
gold_template: The templateTitle[1] rate in templateTitle[0] has been decreasing recently . In templateXValue[max] , approximately templateYValue[min] percent of Uruguayans was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] percent of the country 's templateYLabel[1] in 2006.Still , social inequality remains a challenge in Latin America as a whole .

generated_template: The templateTitle[1] rate in templateTitle[0] has been decreasing lately . In templateXValue[max] , approximately templateYValue[min] percent of the templateYLabel[1] of the South American country was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] percent observed in 2006.Furthermore , the percentages of Chileans with credit card have increased throughout recent years .
generated: The poverty rate in Uruguay has been decreasing lately . In 2017 , approximately 0.4 percent of the population of the South American country was living on less than 3.20 U.S. dollars per day , down from 3.7 percent observed in 2006.Furthermore , the percentages of Chileans with credit card have increased throughout recent years .


Example 360:
data: Response|3_to_4|x|bar_chart Share_of_respondents|14|y|bar_chart Response|4_to_5|x|bar_chart Share_of_respondents|20|y|bar_chart Response|5_to_6|x|bar_chart Share_of_respondents|21|y|bar_chart Response|6_to_8|x|bar_chart Share_of_respondents|21|y|bar_chart Response|8_to_10|x|bar_chart Share_of_respondents|10|y|bar_chart Response|More_than_10|x|bar_chart Share_of_respondents|14|y|bar_chart 
title: U.S. consumer business cyber security budget share 2017

gold: This statistic illustrates the share of cyber security budget as percentage of annual IT budget of consumer businesses in the United States . During the February 2017 survey period , 14 percent of C-level respondents stated that cyber security accounted for more than 10 percent of their annual IT budget .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[3] templateTitle[4] templateTitle[5] as percentage of annual IT templateTitle[5] of templateTitle[1] businesses in the templateTitle[0] . During the February templateTitle[7] survey period , templateYValue[0] percent of C-level templateYLabel[1] stated that templateTitle[3] templateTitle[4] accounted for templateXValue[last] templateXValue[last] templateXValue[4] percent of their annual IT templateTitle[5] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . During the survey , templateYValue[max] percent of templateYLabel[1] said that they would be willing to templateXValue[1] templateXValue[0] for templateXValue[0] templateXValue[0] and templateXValue[0] templateXValue[0] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the U.S. consumer business cyber security budget share . During the survey , 21 percent of respondents said that they would be willing to 4_to_5 3_to_4 for 3_to_4 and 3_to_4 .


Example 361:
data: Year|2019|x|line_chart Home_attendance|607497|y|line_chart Year|2018|x|line_chart Home_attendance|611571|y|line_chart Year|2017|x|line_chart Home_attendance|610846|y|line_chart Year|2016|x|line_chart Home_attendance|614193|y|line_chart Year|2015|x|line_chart Home_attendance|615381|y|line_chart Year|2014|x|line_chart Home_attendance|615517|y|line_chart Year|2013|x|line_chart Home_attendance|614977|y|line_chart Year|2012|x|line_chart Home_attendance|613062|y|line_chart Year|2011|x|line_chart Home_attendance|602618|y|line_chart Year|2010|x|line_chart Home_attendance|599264|y|line_chart Year|2009|x|line_chart Home_attendance|600928|y|line_chart Year|2008|x|line_chart Home_attendance|604074|y|line_chart Year|2007|x|line_chart Home_attendance|612888|y|line_chart Year|2006|x|line_chart Home_attendance|610776|y|line_chart 
title: Regular season home attendance of the Denver Broncos 2006 - 2019

gold: This graph depicts the total regular season home attendance of the Denver Broncos franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 607,497 .
gold_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] .

generated_template: This graph depicts the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] .
generated: This graph depicts the Regular season Home attendance of the Denver Broncos franchise of the National Football League from 2006 to 2019 . In 2019 , the Regular season home Home attendance of the franchise was 607497 .


Example 362:
data: Country|Malta|x|bar_chart GDP_growth_compared_to_previous_year|4.44|y|bar_chart Country|Slovak_Republic|x|bar_chart GDP_growth_compared_to_previous_year|3.46|y|bar_chart Country|Ireland|x|bar_chart GDP_growth_compared_to_previous_year|3.45|y|bar_chart Country|Cyprus|x|bar_chart GDP_growth_compared_to_previous_year|3.34|y|bar_chart Country|Latvia|x|bar_chart GDP_growth_compared_to_previous_year|3.15|y|bar_chart Country|Estonia|x|bar_chart GDP_growth_compared_to_previous_year|2.9|y|bar_chart Country|Slovenia|x|bar_chart GDP_growth_compared_to_previous_year|2.83|y|bar_chart Country|Luxembourg|x|bar_chart GDP_growth_compared_to_previous_year|2.75|y|bar_chart Country|Lithuania|x|bar_chart GDP_growth_compared_to_previous_year|2.63|y|bar_chart Country|Greece|x|bar_chart GDP_growth_compared_to_previous_year|2.16|y|bar_chart Country|Spain|x|bar_chart GDP_growth_compared_to_previous_year|1.88|y|bar_chart Country|Finland|x|bar_chart GDP_growth_compared_to_previous_year|1.73|y|bar_chart Country|Austria|x|bar_chart GDP_growth_compared_to_previous_year|1.7|y|bar_chart Country|Netherlands|x|bar_chart GDP_growth_compared_to_previous_year|1.69|y|bar_chart Country|Portugal|x|bar_chart GDP_growth_compared_to_previous_year|1.5|y|bar_chart Country|Germany|x|bar_chart GDP_growth_compared_to_previous_year|1.44|y|bar_chart Country|France|x|bar_chart GDP_growth_compared_to_previous_year|1.41|y|bar_chart Country|Belgium|x|bar_chart GDP_growth_compared_to_previous_year|1.39|y|bar_chart Country|Italy|x|bar_chart GDP_growth_compared_to_previous_year|0.91|y|bar_chart 
title: Forecast of the gross domestic product ( GDP ) growth in the euro countries 2020

gold: This statistic shows a forecast of the gross domestic product ( GDP ) growth in the euro countries in 2020 . In 2020 , the gross domestic product in Germany is forecasted to grow by 1.44 percent over the previous year .
gold_template: This statistic shows a templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) templateYLabel[1] in the templateTitle[6] templateTitle[7] in templateTitle[8] . In templateTitle[8] , the templateTitle[1] templateTitle[2] templateTitle[3] in templateXValue[15] is forecasted to grow by templateYValue[15] percent over the templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateTitle[7] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in euro from Malta to countries . In Slovenia , euro 's real Forecast gross domestic increased by around 2.83 percent compared to the previous Country .


Example 363:
data: Year|2024|x|line_chart Ratio_of_government_expenditure_to_GDP|53.94|y|line_chart Year|2023|x|line_chart Ratio_of_government_expenditure_to_GDP|53.96|y|line_chart Year|2022|x|line_chart Ratio_of_government_expenditure_to_GDP|54|y|line_chart Year|2021|x|line_chart Ratio_of_government_expenditure_to_GDP|54.19|y|line_chart Year|2020|x|line_chart Ratio_of_government_expenditure_to_GDP|54.5|y|line_chart Year|2019|x|line_chart Ratio_of_government_expenditure_to_GDP|55.65|y|line_chart Year|2018|x|line_chart Ratio_of_government_expenditure_to_GDP|56.04|y|line_chart Year|2017|x|line_chart Ratio_of_government_expenditure_to_GDP|56.38|y|line_chart Year|2016|x|line_chart Ratio_of_government_expenditure_to_GDP|56.59|y|line_chart Year|2015|x|line_chart Ratio_of_government_expenditure_to_GDP|56.8|y|line_chart Year|2014|x|line_chart Ratio_of_government_expenditure_to_GDP|57.21|y|line_chart 
title: Ratio of government expenditure to gross domestic product ( GDP ) in France 2024

gold: The statistic shows the ratio of government expenditure to the gross domestic product ( GDP ) in France from 2014 to 2018 , with projections up until 2024 . In 2018 , the government expenditure in France amounted to about 56.04 percent of the gross domestic product .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to the templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[1] templateYLabel[2] in templateTitle[7] amounted to about templateYValue[6] percent of the templateTitle[3] templateTitle[4] templateTitle[5] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to the templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitle[7] amounted to about templateYValue[6] percent of the templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to the gross domestic product ( GDP ) in France from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in France amounted to about 56.04 percent of the gross domestic product .


Example 364:
data: Year|2024|x|line_chart Inhabitants_in_millions|5.2|y|line_chart Year|2023|x|line_chart Inhabitants_in_millions|5.15|y|line_chart Year|2022|x|line_chart Inhabitants_in_millions|5.11|y|line_chart Year|2021|x|line_chart Inhabitants_in_millions|5.06|y|line_chart Year|2020|x|line_chart Inhabitants_in_millions|5.01|y|line_chart Year|2019|x|line_chart Inhabitants_in_millions|4.95|y|line_chart Year|2018|x|line_chart Inhabitants_in_millions|4.89|y|line_chart Year|2017|x|line_chart Inhabitants_in_millions|4.83|y|line_chart Year|2016|x|line_chart Inhabitants_in_millions|4.77|y|line_chart Year|2015|x|line_chart Inhabitants_in_millions|4.71|y|line_chart Year|2014|x|line_chart Inhabitants_in_millions|4.67|y|line_chart 
title: Total population of Ireland 2024

gold: This statistic shows the total population of Ireland from 2014 to 2018 , with projections up to 2024 . In 2018 , the total population of Ireland was at approximately 4.89 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] was at approximately templateYValue[6] million templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] amounted to approximately templateYValue[6] million templateYLabel[0] . The templateTitle[0] templateTitle[1] of templateTitle[2] is the sixth most populated country in the EU , and the ninth most populated one in Europe .
generated: This statistic shows the Total population of Ireland from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Ireland amounted to approximately 4.89 million Inhabitants . The Total population of Ireland is the sixth most populated country in the EU , and the ninth most populated one in Europe .


Example 365:
data: Platform|Samsung|x|bar_chart Buzz_score|47.7|y|bar_chart Platform|Albert_Heijn|x|bar_chart Buzz_score|46.8|y|bar_chart Platform|Philips|x|bar_chart Buzz_score|38.6|y|bar_chart Platform|Google|x|bar_chart Buzz_score|37.9|y|bar_chart Platform|Lidl|x|bar_chart Buzz_score|34.8|y|bar_chart Platform|Jumbo|x|bar_chart Buzz_score|33.8|y|bar_chart Platform|YouTube|x|bar_chart Buzz_score|33.5|y|bar_chart Platform|Sony|x|bar_chart Buzz_score|28.9|y|bar_chart Platform|Wikipedia|x|bar_chart Buzz_score|25.4|y|bar_chart Platform|Bose|x|bar_chart Buzz_score|21.9|y|bar_chart 
title: Leading brands in the Netherlands 2018 , ranked by Buzz score

gold: In 2018 , Samsung was the brand with the highest Buzz score in the Netherlands , followed by two Dutch brands : food retailer Albert Heijn and Philips . A brand 's Buzz score indicates how positive a brand is perceived in a certain country . The survey tracks how many people have heard anything about a certain brand in the last two weeks , through advertising , news or word of mouth .
gold_template: In templateTitle[3] , templateXValue[0] was the brand with the highest templateYLabel[0] templateYLabel[1] in the templateTitle[2] , followed templateTitle[5] two Dutch templateTitle[1] : food retailer templateXValue[1] templateXValue[1] and templateXValue[2] . A brand 's templateYLabel[0] templateYLabel[1] indicates how positive a brand is perceived in a certain country . The survey tracks how many people have heard anything about a certain brand in the last two weeks , through advertising , news or word of mouth .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the United States templateTitle[3] 2018 in templateTitle[4] . In that year , templateXValue[0] had a templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[max] percent .
generated: This statistic shows the Buzz of score of the United States 2018 2018 in ranked . In that year , Samsung had a Buzz score yLabelErr of 47.7 percent .


Example 366:
data: Gross_leasable_area_in_square_feet|Less_than_100001|x|bar_chart Total_retail_sales_in_billion_U.S._dollars|443.8|y|bar_chart Gross_leasable_area_in_square_feet|100001_to_200000|x|bar_chart Total_retail_sales_in_billion_U.S._dollars|388.6|y|bar_chart Gross_leasable_area_in_square_feet|200001_to_400000|x|bar_chart Total_retail_sales_in_billion_U.S._dollars|234.2|y|bar_chart Gross_leasable_area_in_square_feet|400001_to_800000|x|bar_chart Total_retail_sales_in_billion_U.S._dollars|197.6|y|bar_chart Gross_leasable_area_in_square_feet|800001_to_1000000|x|bar_chart Total_retail_sales_in_billion_U.S._dollars|97.3|y|bar_chart Gross_leasable_area_in_square_feet|More_than_one_million|x|bar_chart Total_retail_sales_in_billion_U.S._dollars|168.9|y|bar_chart 
title: Total retail sales of U.S. shopping malls 2005 , by size

gold: This statistic shows of the total retail sales of all retail shopping malls in the United States , sorted by mall size in square feet of gross leasable area . In 2005 , shopping malls sized between 200,001 and 400,000 square feet made a total of 234.2 billion U.S. dollars of retail sales .
gold_template: This statistic shows of the templateYLabel[0] templateYLabel[1] templateYLabel[2] of all templateYLabel[1] templateTitle[4] templateTitle[5] in the templateTitle[3] , sorted templateTitle[7] mall templateTitle[8] in templateXLabel[3] templateXLabel[4] of templateXLabel[0] templateXLabel[1] templateXLabel[2] . In templateTitle[6] , templateTitle[4] templateTitle[5] sized between templateXValue[2] and templateXValue[2] templateXLabel[3] templateXLabel[4] made a templateYLabel[0] of templateYValue[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] templateXLabel[1] of templateXLabel[1] within the United States in templateTitle[8] . There were templateYValue[1] templateYLabel[1] templateYLabel[2] of templateXValue[1] templateYLabel[2] operating within the United States .
generated: This statistic shows the Total retail sales Gross leasable of leasable within the United States in size . There were 388.6 retail sales of 100001_to_200000 sales operating within the United States .


Example 367:
data: Year|2024|x|line_chart Inhabitants_in_millions|145.74|y|line_chart Year|2023|x|line_chart Inhabitants_in_millions|146.02|y|line_chart Year|2022|x|line_chart Inhabitants_in_millions|146.27|y|line_chart Year|2021|x|line_chart Inhabitants_in_millions|146.47|y|line_chart Year|2020|x|line_chart Inhabitants_in_millions|146.62|y|line_chart Year|2019|x|line_chart Inhabitants_in_millions|146.73|y|line_chart Year|2018|x|line_chart Inhabitants_in_millions|146.8|y|line_chart Year|2017|x|line_chart Inhabitants_in_millions|146.9|y|line_chart Year|2016|x|line_chart Inhabitants_in_millions|146.8|y|line_chart Year|2015|x|line_chart Inhabitants_in_millions|146.5|y|line_chart Year|2014|x|line_chart Inhabitants_in_millions|146.3|y|line_chart 
title: Total population of Russia 2024

gold: This statistic shows the total population of Russia from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population of Russia was around 146.8 million people . Only a fraction of them live in the major Russian cities .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] was around templateYValue[6] million people . Only a fraction of them live in the major Russian cities .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] amounted to around templateYValue[6] million templateYLabel[0] . See figures for Switzerland 's templateTitle[1] of the templateTitle[1] of Italy for comparison .
generated: This statistic shows the Total population of Russia from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Russia amounted to around 146.8 million Inhabitants . See figures for Switzerland 's population of the population of Italy for comparison .


Example 368:
data: Response|Total|x|bar_chart Share_of_respondents|16|y|bar_chart Response|Millennials|x|bar_chart Share_of_respondents|27|y|bar_chart Response|Gen_X|x|bar_chart Share_of_respondents|17|y|bar_chart Response|Boomers|x|bar_chart Share_of_respondents|6|y|bar_chart Response|Retirees|x|bar_chart Share_of_respondents|3|y|bar_chart 
title: Consumers with a YouTube or YouTube Red subscription in the U.S. 2017 , by age group

gold: This statistic provides information on the share of consumers with an active YouTube or YouTube Red subscription in the United States as of January 2017 , sorted by age . According to the source , 27 percent of Millennials who subscribe to online video or music subscriptions had a YouTube or YouTube Red subscription as of January 2017 .
gold_template: This statistic provides information on the templateYLabel[0] of templateTitle[0] templateTitle[1] an active templateTitle[2] or templateTitle[2] templateTitle[4] templateTitle[5] in the templateTitle[6] as of January templateTitle[7] , sorted templateTitle[8] templateTitle[9] . According to the source , templateYValue[max] percent of templateXValue[1] who subscribe to online video or music subscriptions had a templateTitle[2] or templateTitle[2] templateTitle[4] templateTitle[5] as of January templateTitle[7] .

generated_template: At the beginning of templateTitle[5] , templateYValue[0] percent of templateTitle[0] templateTitle[1] an online video or music templateTitle[3] confirmed that they had an active templateTitle[2] templateTitle[3] at the time of survey . templateXValue[1] and Gen-Xers were more likely to have a templateTitle[2] templateTitle[3] than their older peers , which comes as no surprise given that templateTitle[2] is considered the most indispensable TV and video source among templateTitle[0] aged between 16 to 34 years old . One of the most appealing aspects of templateTitle[2] for viewers is the availability of original content , which has fast become one of the keys to the company 's success .
generated: At the beginning of U.S. , 16 percent of Consumers YouTube an online video or music Red confirmed that they had an active YouTube Red at the time of survey . Millennials and Gen-Xers were more likely to have a YouTube Red than their older peers , which comes as no surprise given that YouTube is considered the most indispensable TV and video source among Consumers aged between 16 to 34 years old . One of the most appealing aspects of YouTube for viewers is the availability of original content , which has fast become one of the keys to the company 's success .


Example 369:
data: Year|2004|x|line_chart Unemployment_rate|23.5|y|line_chart Year|2005|x|line_chart Unemployment_rate|24.1|y|line_chart Year|2006|x|line_chart Unemployment_rate|21.8|y|line_chart Year|2007|x|line_chart Unemployment_rate|20.4|y|line_chart Year|2008|x|line_chart Unemployment_rate|21.2|y|line_chart Year|2009|x|line_chart Unemployment_rate|25.3|y|line_chart Year|2010|x|line_chart Unemployment_rate|27.9|y|line_chart Year|2011|x|line_chart Unemployment_rate|29.2|y|line_chart Year|2012|x|line_chart Unemployment_rate|35.3|y|line_chart Year|2013|x|line_chart Unemployment_rate|40|y|line_chart Year|2014|x|line_chart Unemployment_rate|42.7|y|line_chart Year|2015|x|line_chart Unemployment_rate|40.3|y|line_chart Year|2016|x|line_chart Unemployment_rate|37.8|y|line_chart Year|2017|x|line_chart Unemployment_rate|34.7|y|line_chart Year|2018|x|line_chart Unemployment_rate|32.2|y|line_chart Year|2019|x|line_chart Unemployment_rate|28.9|y|line_chart 
title: Youth unemployment rate in Italy 2004 - 2019

gold: The problem of unemployment in Italy became critical in the first years of the financial crisis which started in 2008 . Although the labor market crisis seriously affected the entire Italian working population , it particularly impacted the youngest part of the labor force . Between 2008 and 2014 alone , the share of unemployed individuals aged between 15 and 24 years increased by more than 15 percent .
gold_template: The problem of templateYLabel[0] in templateTitle[3] became critical in the first years of the financial crisis which started in templateXValue[4] . Although the labor market crisis seriously affected the entire Italian working population , it particularly impacted the youngest part of the labor force . Between templateXValue[4] and templateXValue[10] alone , the share of unemployed individuals aged between 15 and templateYValue[0] years increased by more than 15 percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at templateYValue[min] percent .
generated: This statistic shows the Unemployment rate in rate from 2004 to 2019 . In 2019 , the Unemployment rate in rate was at 20.4 percent .


Example 370:
data: Country|Sweden|x|bar_chart Surface_area_in_square_kilometers|447420|y|bar_chart Country|Greenland|x|bar_chart Surface_area_in_square_kilometers|410450|y|bar_chart Country|Norway|x|bar_chart Surface_area_in_square_kilometers|385178|y|bar_chart Country|Finland|x|bar_chart Surface_area_in_square_kilometers|338420|y|bar_chart Country|Iceland|x|bar_chart Surface_area_in_square_kilometers|103000|y|bar_chart Country|Denmark|x|bar_chart Surface_area_in_square_kilometers|42922|y|bar_chart Country|Faroe_Islands|x|bar_chart Surface_area_in_square_kilometers|1396|y|bar_chart 
title: Surface area of the Nordic countries 2017

gold: This statistic shows the surface area of the Nordic countries in 2017 . The largest of all Nordic countries is Sweden , with a surface of roughly 447 thousand square kilometers . Its neighboring country Norway has a size of approximately 385 thousand square kilometers , which includes the arctic islands of Svalbard and Jan Mayen .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] in templateTitle[4] . The largest of all templateTitle[2] templateTitle[3] is templateXValue[0] , with a templateYLabel[0] of roughly templateYValue[max] thousand templateYLabel[2] templateYLabel[3] . Its neighboring templateXLabel[0] templateXValue[2] has a size of approximately templateYValue[2] thousand templateYLabel[2] templateYLabel[3] , which includes the arctic templateXValue[last] of Svalbard and Jan Mayen .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of different templateTitle[5] templateTitle[6] in templateTitle[7] templateTitle[8] . templateXValue[0] templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] at templateYValue[max] percent in the world 's highest templateYLabel[0] templateYLabel[1] since its region with 100 countries .
generated: The statistic shows the Surface area square of different titleErr titleErr in titleErr titleErr . Sweden had the highest Surface area at 447420 percent in the world 's highest Surface area since its region with 100 countries .


Example 371:
data: Year|2023|x|line_chart Share_of_population|51|y|line_chart Year|2022|x|line_chart Share_of_population|50|y|line_chart Year|2021|x|line_chart Share_of_population|49|y|line_chart Year|2020|x|line_chart Share_of_population|47|y|line_chart Year|2019|x|line_chart Share_of_population|45|y|line_chart Year|2018|x|line_chart Share_of_population|43|y|line_chart Year|2017|x|line_chart Share_of_population|41|y|line_chart 
title: Brazil : mobile phone internet user penetration 2017 - 2023

gold: This statistic gives information on the mobile internet penetration in Brazil from 2017 to 2023 . In 2017 , 41 percent of the Brazilian population accessed internet from their mobile device . This figure is expected to grow to 51 percent in 2023 .
gold_template: This statistic gives information on the templateTitle[1] templateTitle[3] templateTitle[5] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the Brazilian templateYLabel[1] accessed templateTitle[3] from their templateTitle[1] device . This figure is expected to grow to templateYValue[max] percent in templateXValue[max] .

generated_template: This statistic presents the templateTitle[0] networking reach in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the local templateYLabel[1] accessed the social network . This templateYLabel[0] is projected to grow to templateYValue[max] percent in templateXValue[max] .
generated: This statistic presents the Brazil networking reach in user from 2017 to 2023 . In 2017 , 41 percent of the local population accessed the social network . This Share is projected to grow to 51 percent in 2023 .


Example 372:
data: Year|2018|x|line_chart Per_capita_consumption_in_pounds|2.2|y|line_chart Year|2017|x|line_chart Per_capita_consumption_in_pounds|2.66|y|line_chart Year|2016|x|line_chart Per_capita_consumption_in_pounds|2.73|y|line_chart Year|2015|x|line_chart Per_capita_consumption_in_pounds|2.91|y|line_chart Year|2014|x|line_chart Per_capita_consumption_in_pounds|3.15|y|line_chart Year|2013|x|line_chart Per_capita_consumption_in_pounds|3.02|y|line_chart Year|2012|x|line_chart Per_capita_consumption_in_pounds|3.86|y|line_chart Year|2011|x|line_chart Per_capita_consumption_in_pounds|4.47|y|line_chart Year|2010|x|line_chart Per_capita_consumption_in_pounds|4.73|y|line_chart Year|2009|x|line_chart Per_capita_consumption_in_pounds|4.41|y|line_chart Year|2008|x|line_chart Per_capita_consumption_in_pounds|5.08|y|line_chart Year|2007|x|line_chart Per_capita_consumption_in_pounds|4.46|y|line_chart Year|2006|x|line_chart Per_capita_consumption_in_pounds|4.58|y|line_chart Year|2005|x|line_chart Per_capita_consumption_in_pounds|4.83|y|line_chart Year|2004|x|line_chart Per_capita_consumption_in_pounds|5.15|y|line_chart Year|2003|x|line_chart Per_capita_consumption_in_pounds|5.17|y|line_chart Year|2002|x|line_chart Per_capita_consumption_in_pounds|5.23|y|line_chart Year|2001|x|line_chart Per_capita_consumption_in_pounds|5.16|y|line_chart Year|2000|x|line_chart Per_capita_consumption_in_pounds|5.3|y|line_chart 
title: U.S. per capita consumption of fresh peaches and nectarines 2000 - 2018

gold: This statistic depicts the per capita consumption of fresh peaches in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh peaches amounted to approximately 2.2 pounds in 2018 .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[min] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of fresh peaches in the United States from 2000 to 2018 . According to the report , the U.S. Per capita consumption of fresh peaches amounted to approximately 2.2 pounds in 2018 .


Example 373:
data: Year|2018|x|line_chart Per_capita_consumption_in_pounds|8.5|y|line_chart Year|2017|x|line_chart Per_capita_consumption_in_pounds|7.4|y|line_chart Year|2016|x|line_chart Per_capita_consumption_in_pounds|7.8|y|line_chart Year|2015|x|line_chart Per_capita_consumption_in_pounds|8.8|y|line_chart Year|2014|x|line_chart Per_capita_consumption_in_pounds|8.5|y|line_chart Year|2013|x|line_chart Per_capita_consumption_in_pounds|8.0|y|line_chart Year|2012|x|line_chart Per_capita_consumption_in_pounds|7.9|y|line_chart Year|2011|x|line_chart Per_capita_consumption_in_pounds|7.5|y|line_chart Year|2010|x|line_chart Per_capita_consumption_in_pounds|7.8|y|line_chart Year|2009|x|line_chart Per_capita_consumption_in_pounds|7.4|y|line_chart Year|2008|x|line_chart Per_capita_consumption_in_pounds|8.1|y|line_chart Year|2007|x|line_chart Per_capita_consumption_in_pounds|8.0|y|line_chart Year|2006|x|line_chart Per_capita_consumption_in_pounds|8.1|y|line_chart Year|2005|x|line_chart Per_capita_consumption_in_pounds|8.7|y|line_chart Year|2004|x|line_chart Per_capita_consumption_in_pounds|8.7|y|line_chart Year|2003|x|line_chart Per_capita_consumption_in_pounds|8.8|y|line_chart Year|2002|x|line_chart Per_capita_consumption_in_pounds|8.4|y|line_chart Year|2001|x|line_chart Per_capita_consumption_in_pounds|9.4|y|line_chart Year|2000|x|line_chart Per_capita_consumption_in_pounds|9.2|y|line_chart 
title: U.S. per capita consumption of fresh carrots 2000 - 2018

gold: The timeline shows the per capita consumption of fresh carrots in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh carrots amounted to approximately 8.5 pounds in 2018 .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] ( green beans ) in the United States from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] amounted to about templateYValue[0] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of fresh ( green beans ) in the United States from 2000 to 2018 . According to the report , the U.S. Per capita consumption of fresh amounted to about 8.5 pounds in 2018 .


Example 374:
data: Country|Romania|x|bar_chart Debt_in_billion_euros|11.8|y|bar_chart Country|Ukraine|x|bar_chart Debt_in_billion_euros|10.3|y|bar_chart Country|Greece|x|bar_chart Debt_in_billion_euros|10.2|y|bar_chart Country|Hungary|x|bar_chart Debt_in_billion_euros|8.5|y|bar_chart Country|Pakistan|x|bar_chart Debt_in_billion_euros|6.3|y|bar_chart Country|Ireland|x|bar_chart Debt_in_billion_euros|5.6|y|bar_chart Country|Turkey|x|bar_chart Debt_in_billion_euros|4.1|y|bar_chart Country|Belarus|x|bar_chart Debt_in_billion_euros|2.5|y|bar_chart 
title: IMF - biggest debtor nations 2011

gold: The statistic shows IMF 's biggest debtor states in May 2011 . Belarus reported a debt of 2.5 billion euros .
gold_template: The statistic shows templateTitle[0] 's templateTitle[1] templateTitle[2] states in May templateTitle[4] . templateXValue[last] reported a templateYLabel[0] of templateYValue[min] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] in the United States in templateTitle[5] . In templateTitle[6] , templateYValue[2] percent of templateYLabel[1] were living in templateXValue[0] .
generated: This statistic shows the Debt billion in debtor nations 2011 in the United States in titleErr . In titleErr , 10.2 percent of billion were living in Romania .


Example 375:
data: Year|2010|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|60.04|y|line_chart Year|2009|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|53.94|y|line_chart Year|2008|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|52.63|y|line_chart Year|2007|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|40.49|y|line_chart Year|2006|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|33.82|y|line_chart Year|2005|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|28.88|y|line_chart Year|2004|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|25.2|y|line_chart Year|2003|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|21.7|y|line_chart Year|2002|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|22.76|y|line_chart Year|2001|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|20.98|y|line_chart Year|2000|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|19.86|y|line_chart Year|1999|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|16.79|y|line_chart Year|1998|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|16.14|y|line_chart Year|1997|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|16.57|y|line_chart Year|1996|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|17.76|y|line_chart Year|1995|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|16.56|y|line_chart Year|1994|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|15.11|y|line_chart Year|1993|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|13.8|y|line_chart Year|1992|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|13.26|y|line_chart Year|1991|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|12.74|y|line_chart Year|1990|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|12.3|y|line_chart Year|1989|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|9.85|y|line_chart Year|1988|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|16.54|y|line_chart Year|1987|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|32.5|y|line_chart Year|1986|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|25.43|y|line_chart Year|1985|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|21.18|y|line_chart Year|1984|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|19.17|y|line_chart 
title: Gross domestic product ( GDP ) in Syria 2010

gold: The statistic shows gross domestic product ( GDP ) in Syria from 1984 to 2010 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Syria from 1984 to 2004 , with projections up until 2010 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year . GDP is an important indicator of a country 's economic power .


Example 376:
data: Year|2007|x|line_chart Price_in_euros_per_square_meter_built|2246|y|line_chart Year|2008|x|line_chart Price_in_euros_per_square_meter_built|2285|y|line_chart Year|2009|x|line_chart Price_in_euros_per_square_meter_built|2185|y|line_chart Year|2010|x|line_chart Price_in_euros_per_square_meter_built|2060|y|line_chart Year|2011|x|line_chart Price_in_euros_per_square_meter_built|1907|y|line_chart Year|2012|x|line_chart Price_in_euros_per_square_meter_built|1768|y|line_chart Year|2013|x|line_chart Price_in_euros_per_square_meter_built|1602|y|line_chart Year|2014|x|line_chart Price_in_euros_per_square_meter_built|1477|y|line_chart Year|2015|x|line_chart Price_in_euros_per_square_meter_built|1431|y|line_chart Year|2016|x|line_chart Price_in_euros_per_square_meter_built|1447|y|line_chart Year|2017|x|line_chart Price_in_euros_per_square_meter_built|1532|y|line_chart Year|2018|x|line_chart Price_in_euros_per_square_meter_built|1613|y|line_chart 
title: Annual average housing prices Spain 2007 - 2018

gold: In December 2018 , a house in Spain would cost around 1.699 thousand euros per square meter built . After a long period of time in which Spain 's real estate prices increased sharply , the market was hit by the global financial crisis of 2007 , making the Spanish property bubble collapse and damaging home value . House prices have picked up ever since in the Mediterranean country .
gold_template: In December templateXValue[max] , a house in templateTitle[4] would cost around 1.699 thousand templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . After a long period of time in which templateTitle[4] 's real estate templateTitle[3] increased sharply , the market was hit by the global financial crisis of templateXValue[min] , making the Spanish property bubble collapse and damaging home value . House templateTitle[3] have picked up ever since in the Mediterranean country .

generated_template: In templateXValue[max] , there were templateYValue[0] templateTitle[1] templateTitle[2] templateYLabel[1] in the United States . This was a slight decrease from the previous templateXLabel[0] 's history with the highest level of templateTitle[4] templateTitle[5] in the United States since templateXValue[10] . The templateYLabel[0] templateYLabel[1] can be seen in the given period shown in the United States has gradually risen dramatically in the past two percent in the past years .
generated: In 2018 , there were 2246 average housing euros in the United States . This was a slight decrease from the previous Year 's history with the highest level of Spain 2007 in the United States since 2017 . The Price euros can be seen in the given period shown in the United States has gradually risen dramatically in the past two percent in the past years .


Example 377:
data: Year|2019|x|line_chart Revenue_in_million_U.S._dollars|4108.4|y|line_chart Year|2018|x|line_chart Revenue_in_million_U.S._dollars|4120.9|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|3802.2|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|3508.1|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|3311.1|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|3838.7|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|3599.7|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|3416.8|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|3367.0|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|3039.6|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|2776.1|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|4708.7|y|line_chart Year|2007|x|line_chart Revenue_in_million_U.S._dollars|5671.2|y|line_chart 
title: Global revenue of the Brunswick Corporation 2007 - 2019

gold: The statistic depicts the net sales of the Brunswick Corporation worldwide from 2007 to 2019 . In 2019 , Brunswick 's net sales was at about 4.11 billion U.S. dollars.The Brunswick Corporation is a U.S.-based company that manufactures and sells boats and marine engines . The company previously made and sold sporting and fitness equipment and bowling & billiards equipment .
gold_template: The statistic depicts the net sales of the templateTitle[2] templateTitle[3] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] 's net sales was at about templateYValue[0] billion templateYLabel[2] dollars.The templateTitle[2] templateTitle[3] is a U.S.-based company that manufactures and sells boats and marine engines . The company previously made and sold sporting and fitness equipment and bowling & billiards equipment .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the women templateTitle[1] templateTitle[2] was templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitle[1] templateTitle[2] - additional information The templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] peaked in templateXValue[1] , accounting for templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in annual templateYLabel[0] .
generated: The statistic shows the Global revenue Brunswick Revenue in the United States from 2007 to 2019 . In 2018 , the women revenue Brunswick was 4108.4 million U.S. dollars . revenue Brunswick - additional information The Global revenue Brunswick Revenue peaked in 2018 , accounting for 4108.4 million U.S. dollars in annual Revenue .


Example 378:
data: Year|2018/19|x|line_chart Number_of_players|112236|y|line_chart Year|2017/18|x|line_chart Number_of_players|110624|y|line_chart Year|2016/17|x|line_chart Number_of_players|105059|y|line_chart Year|2015/16|x|line_chart Number_of_players|102179|y|line_chart Year|2014/15|x|line_chart Number_of_players|99172|y|line_chart Year|2013/14|x|line_chart Number_of_players|84270|y|line_chart Year|2012/13|x|line_chart Number_of_players|66551|y|line_chart Year|2011/12|x|line_chart Number_of_players|64326|y|line_chart Year|2010/11|x|line_chart Number_of_players|63580|y|line_chart 
title: Ice hockey players in Russia 2010 - 2019

gold: The statistics depicts the number of registered ice hockey players in Russia from 2010/11 to 2018/19 . In the 2018/19 season , there were a total of 112,236 registered ice hockey players in Russia according to the International Ice Hockey Federation .
gold_template: The statistics depicts the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitle[3] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitle[3] according to the International templateTitle[0] templateTitle[1] Federation .

generated_template: The graph depicts the templateTitle[3] templateTitle[4] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] templateTitle[4] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a templateYLabel[1] of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] templateTitle[4] according to the International templateTitle[0] templateTitle[1] Federation .
generated: The graph depicts the Russia 2010 of Ice hockey players in the Russia 2010 from 2010/11 to 2018/19 . In the 2018/19 season , there were a players of 112236 registered Ice hockey players in the Russia 2010 according to the International Ice hockey Federation .


Example 379:
data: Year|2019|x|line_chart Net_income_in_million_U.S._dollars|2300|y|line_chart Year|2018|x|line_chart Net_income_in_million_U.S._dollars|2465|y|line_chart Year|2017|x|line_chart Net_income_in_million_U.S._dollars|3357|y|line_chart Year|2016|x|line_chart Net_income_in_million_U.S._dollars|2183|y|line_chart Year|2015|x|line_chart Net_income_in_million_U.S._dollars|2181|y|line_chart Year|2014|x|line_chart Net_income_in_million_U.S._dollars|1136|y|line_chart Year|2013|x|line_chart Net_income_in_million_U.S._dollars|754|y|line_chart Year|2012|x|line_chart Net_income_in_million_U.S._dollars|421|y|line_chart Year|2011|x|line_chart Net_income_in_million_U.S._dollars|178|y|line_chart Year|2010|x|line_chart Net_income_in_million_U.S._dollars|459|y|line_chart 
title: Net income of Southwest Airlines 2010 - 2019

gold: This statistic shows Southwest Airlines Co. 's net income from the fiscal year of 2010 to the fiscal year of 2019 . In the fiscal year of 2019 , the low-cost carrier 's net income amounted to 2.3 billion U.S. dollars .
gold_template: This statistic shows templateTitle[2] templateTitle[3] Co. 's templateYLabel[0] templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the low-cost carrier 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] billion templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net income of Southwest Airlines 2010 worldwide from 2010 to 2019 . In 2019 , Net income Southwest Airlines 2010 amounted to 2300 million U.S. dollars .


Example 380:
data: Year|2018|x|line_chart Number_of_births|117800|y|line_chart Year|2017|x|line_chart Number_of_births|119102|y|line_chart Year|2016|x|line_chart Number_of_births|121713|y|line_chart Year|2015|x|line_chart Number_of_births|121713|y|line_chart Year|2014|x|line_chart Number_of_births|124415|y|line_chart Year|2013|x|line_chart Number_of_births|124862|y|line_chart Year|2012|x|line_chart Number_of_births|126993|y|line_chart Year|2011|x|line_chart Number_of_births|127655|y|line_chart Year|2010|x|line_chart Number_of_births|129173|y|line_chart Year|2009|x|line_chart Number_of_births|127297|y|line_chart Year|2008|x|line_chart Number_of_births|128049|y|line_chart 
title: Number of births in Belgium 2008 - 2018

gold: In 2018 , nearly 118,000 babies were born in Belgium . This was the lowest number of births in the last decade . The number of children born in the country peaked in 2010 , at just over 129,000 .
gold_template: In templateXValue[max] , nearly 118,000 babies were born in templateTitle[2] . This was the lowest templateYLabel[0] of templateYLabel[1] in the last decade . The templateYLabel[0] of children born in the country peaked in templateXValue[8] , at just over 129,000 .

generated_template: There were templateYValue[0] thousand templateYLabel[1] in templateTitle[3] templateTitle[4] in templateXValue[max] , a slight decrease from the templateXLabel[0] of templateXValue[1] . The highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] decreased from templateYValue[max] percent in templateXValue[9] , when there were almost templateYValue[0] thousand templateYLabel[1] templateYLabel[2] . The templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitle[3] peaked in templateXValue[1] , at almost templateYValue[0] percent .
generated: There were 117800 thousand births in 2008 2018 in 2018 , a slight decrease from the Year of 2017 . The highest Number of births yLabelErr decreased from 129173 percent in 2009 , when there were almost 117800 thousand births yLabelErr . The Number of births yLabelErr in 2008 peaked in 2017 , at almost 117800 percent .


Example 381:
data: Year|1993|x|line_chart FIFA_World_Ranking_position|115|y|line_chart Year|1994|x|line_chart FIFA_World_Ranking_position|133|y|line_chart Year|1995|x|line_chart FIFA_World_Ranking_position|120|y|line_chart Year|1996|x|line_chart FIFA_World_Ranking_position|135|y|line_chart Year|1997|x|line_chart FIFA_World_Ranking_position|117|y|line_chart Year|1998|x|line_chart FIFA_World_Ranking_position|125|y|line_chart Year|1999|x|line_chart FIFA_World_Ranking_position|112|y|line_chart Year|2000|x|line_chart FIFA_World_Ranking_position|117|y|line_chart Year|2001|x|line_chart FIFA_World_Ranking_position|117|y|line_chart Year|2002|x|line_chart FIFA_World_Ranking_position|114|y|line_chart Year|2003|x|line_chart FIFA_World_Ranking_position|126|y|line_chart Year|2004|x|line_chart FIFA_World_Ranking_position|131|y|line_chart Year|2005|x|line_chart FIFA_World_Ranking_position|132|y|line_chart Year|2006|x|line_chart FIFA_World_Ranking_position|181|y|line_chart Year|2007|x|line_chart FIFA_World_Ranking_position|194|y|line_chart Year|2008|x|line_chart FIFA_World_Ranking_position|184|y|line_chart Year|2009|x|line_chart FIFA_World_Ranking_position|117|y|line_chart Year|2010|x|line_chart FIFA_World_Ranking_position|136|y|line_chart Year|2011|x|line_chart FIFA_World_Ranking_position|116|y|line_chart Year|2012|x|line_chart FIFA_World_Ranking_position|153|y|line_chart Year|2013|x|line_chart FIFA_World_Ranking_position|170|y|line_chart Year|2014|x|line_chart FIFA_World_Ranking_position|104|y|line_chart Year|2015|x|line_chart FIFA_World_Ranking_position|97|y|line_chart Year|2016|x|line_chart FIFA_World_Ranking_position|83|y|line_chart Year|2017|x|line_chart FIFA_World_Ranking_position|95|y|line_chart Year|2018|x|line_chart FIFA_World_Ranking_position|98|y|line_chart Year|2019|x|line_chart FIFA_World_Ranking_position|102|y|line_chart 
title: World ranking of Faroe Islands ' national football team 1993 - 2019

gold: In 2016 , the Faroe Island 's national football team , controlled by the Football Association of the Faroe Islands , reached its highest position in the FIFA World Ranking . The team took part in the qualifying for the UEFA European Championship 2016 . Out of the ten qualifying matches , the Faroe Island 's national football team won both matches against Greece .
gold_template: In templateXValue[23] , the templateTitle[2] Island 's templateTitle[5] templateTitle[6] templateTitle[7] , controlled by the templateTitle[6] Association of the templateTitle[2] templateTitle[3] , reached its highest templateYLabel[3] in the templateYLabel[0] templateYLabel[1] templateYLabel[2] . The templateTitle[7] took part in the qualifying for the UEFA European Championship templateXValue[23] . Out of the ten qualifying matches , the templateTitle[2] Island 's templateTitle[5] templateTitle[6] templateTitle[7] won both matches against Greece .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . The highest templateYLabel[3] ever reached was templateYValue[min] in templateXValue[14] . Rank templateYValue[max] was the lowest result of the templateTitle[6] , which was reached in templateXValue[23] .
generated: This statistic shows the FIFA World Ranking of the Faroe Islands ' national from 1993 to 2019 . The highest position ever reached was 83 in 2007 . Rank 194 was the lowest result of the football , which was reached in 2016 .


Example 382:
data: Year|2018|x|line_chart Consumption_in_million_hectoliters|20.0|y|line_chart Year|2017|x|line_chart Consumption_in_million_hectoliters|19.7|y|line_chart Year|2016|x|line_chart Consumption_in_million_hectoliters|20.1|y|line_chart Year|2015|x|line_chart Consumption_in_million_hectoliters|19.6|y|line_chart Year|2014|x|line_chart Consumption_in_million_hectoliters|20.2|y|line_chart Year|2013|x|line_chart Consumption_in_million_hectoliters|20.4|y|line_chart Year|2012|x|line_chart Consumption_in_million_hectoliters|20.3|y|line_chart Year|2011|x|line_chart Consumption_in_million_hectoliters|19.7|y|line_chart Year|2010|x|line_chart Consumption_in_million_hectoliters|20.2|y|line_chart Year|2009|x|line_chart Consumption_in_million_hectoliters|20.2|y|line_chart Year|2008|x|line_chart Consumption_in_million_hectoliters|20.7|y|line_chart Year|2007|x|line_chart Consumption_in_million_hectoliters|20.8|y|line_chart Year|2004|x|line_chart Consumption_in_million_hectoliters|19.6|y|line_chart Year|2003|x|line_chart Consumption_in_million_hectoliters|20.2|y|line_chart Year|2001|x|line_chart Consumption_in_million_hectoliters|20.0|y|line_chart Year|2000|x|line_chart Consumption_in_million_hectoliters|20.2|y|line_chart 
title: Consumption of wine in Germany 2000 - 2018

gold: Over 20 million hectoliters of wine a year are consumed on average in Germany . Consumption levels have so far mostly been steady during the last decade . Meanwhile , per capita wine drinking has also remained largely unchanged during the same time .
gold_template: Over templateYValue[0] templateYLabel[1] templateYLabel[2] of templateTitle[1] a templateXLabel[0] are consumed on average in templateTitle[2] . templateYLabel[0] levels have so far mostly been steady during the last decade . Meanwhile , per capita templateTitle[1] drinking has also remained largely unchanged during the same time .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] million people were living in templateTitle[3] templateTitle[4] .
generated: This statistic shows the Consumption wine million in Germany from 2000 to 2018 . In 2018 , approximately 20.0 million people were living in 2000 2018 .


Example 383:
data: Response|Total|x|bar_chart Share_of_respondents|41|y|bar_chart Response|Millennials|x|bar_chart Share_of_respondents|33|y|bar_chart Response|Gen_X|x|bar_chart Share_of_respondents|35|y|bar_chart Response|Boomers|x|bar_chart Share_of_respondents|45|y|bar_chart Response|Retirees|x|bar_chart Share_of_respondents|54|y|bar_chart 
title: Consumers with a newspaper or magazine subscriptions in the U.S. 2017 , by age group

gold: This statistic provides information on the share of consumers with an active newspaper or magazine subscription in the United States as of January 2017 , sorted by age . According to the source , 54 percent of Retirees who subscribe to service subscriptions had a newspaper or magazine subscription as of January 2017 .
gold_template: This statistic provides information on the templateYLabel[0] of templateTitle[0] templateTitle[1] an active templateTitle[2] or templateTitle[3] subscription in the templateTitle[5] as of January templateTitle[6] , sorted templateTitle[7] templateTitle[8] . According to the source , templateYValue[max] percent of templateXValue[last] who subscribe to service templateTitle[4] had a templateTitle[2] or templateTitle[3] subscription as of January templateTitle[6] .

generated_template: At the beginning of templateTitle[5] , templateYValue[0] percent of templateTitle[0] templateTitle[1] an online video or music templateTitle[3] confirmed that they had an active templateTitle[2] templateTitle[3] at the time of survey . templateXValue[1] and Gen-Xers were more likely to have a templateTitle[2] templateTitle[3] than their older peers , which comes as no surprise given that templateTitle[2] is considered the most indispensable TV and video source among templateTitle[0] aged between 16 to 34 years old . One of the most appealing aspects of templateTitle[2] for viewers is the availability of original content , which has fast become one of the keys to the company 's success .
generated: At the beginning of 2017 , 41 percent of Consumers newspaper an online video or music subscriptions confirmed that they had an active magazine subscriptions at the time of survey . Millennials and Gen-Xers were more likely to have a magazine subscriptions than their older peers , which comes as no surprise given that magazine is considered the most indispensable TV and video source among Consumers aged between 16 to 34 years old . One of the most appealing aspects of magazine for viewers is the availability of original content , which has fast become one of the keys to the company 's success .


Example 384:
data: Year|2035|x|line_chart Share_of_population_older_than_65_years_old|22.8|y|line_chart Year|2030|x|line_chart Share_of_population_older_than_65_years_old|19.4|y|line_chart Year|2025|x|line_chart Share_of_population_older_than_65_years_old|16|y|line_chart Year|2020|x|line_chart Share_of_population_older_than_65_years_old|12.9|y|line_chart Year|2015|x|line_chart Share_of_population_older_than_65_years_old|10.6|y|line_chart 
title: Share of aging population Thailand 2015 - 2035

gold: The statistic shows the share of population older than 65 in Thailand in 2015 , with a projection from 2020 to 2035 . In 2015 , the share of population older than 65 amounted to about 10.6 percent . In 2035 , the percentage of the population above the age of 65 was forecasted to reach 22.8 percent .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitle[3] in templateXValue[min] , with a projection from templateXValue[3] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] amounted to about templateYValue[min] percent . In templateXValue[max] , the percentage of the templateYLabel[1] above the age of templateYLabel[4] was forecasted to reach templateYValue[max] percent .

generated_template: In templateXValue[2] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States amounted to templateYValue[max] percent of the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] refers to the population market value of three percent since templateXValue[10] .
generated: In 2025 , the Share Share population of Thailand 2015 2035 in the United States amounted to 22.8 percent of the previous Year . The Share population refers to the population market value of three percent since xValErr .


Example 385:
data: Year|2002|x|line_chart Number_of_new_books_/_editions|25102|y|line_chart Year|2003|x|line_chart Number_of_new_books_/_editions|24666|y|line_chart Year|2004|x|line_chart Number_of_new_books_/_editions|38832|y|line_chart Year|2005|x|line_chart Number_of_new_books_/_editions|34927|y|line_chart Year|2006|x|line_chart Number_of_new_books_/_editions|42777|y|line_chart Year|2007|x|line_chart Number_of_new_books_/_editions|53590|y|line_chart Year|2008|x|line_chart Number_of_new_books_/_editions|53058|y|line_chart Year|2009|x|line_chart Number_of_new_books_/_editions|48738|y|line_chart Year|2010|x|line_chart Number_of_new_books_/_editions|46641|y|line_chart Year|2011|x|line_chart Number_of_new_books_/_editions|43016|y|line_chart Year|2012|x|line_chart Number_of_new_books_/_editions|49853|y|line_chart Year|2013_(projected)|x|line_chart Number_of_new_books_/_editions|50498|y|line_chart 
title: Number of books published in the U.S. in the category 'fiction ' 2002 - 2013

gold: This statistic contains data on the U.S. book publishing in the category 'fiction ' from 2002 to 2013 . In 2006 , 42,777 books of fiction were published in the United States .
gold_template: This statistic contains data on the templateTitle[3] book publishing in the templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[0] to templateXValue[last] . In templateXValue[4] , templateYValue[4] templateYLabel[2] of fiction were templateTitle[2] in the templateTitle[3] .

generated_template: U.S. fashion fashion retailer templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] United States between templateXValue[min] and templateXValue[max] , templateYValue[0] percent of the United States were between the highest and templateYValue[0] percent . The highest level was valued at least once again in templateXValue[1] when compared with templateYValue[max] percent of templateYLabel[1] 100,000 of the templateXLabel[0] . templateYLabel[0] templateYLabel[1] observed in templateTitle[3] templateTitle[4] templateTitle[5] - additional information The best friend of people among one of 96 percent in the United States has seen in the templateXLabel[0] .
generated: U.S. fashion retailer U.S. category 'fiction in books United States between 2002 and 2012 , 25102 percent of the United States were between the highest and 25102 percent . The highest level was valued at least once again in 2003 when compared with 53590 percent of new 100,000 of the Year . Number new observed in U.S. category 'fiction - additional information The best friend of people among one of 96 percent in the United States has seen in the Year .


Example 386:
data: Month|Luis_Fonsi_-_Despacito_ft._Daddy_Yankee|x|bar_chart Number_of_views_in_billions|6.55|y|bar_chart Month|Ed_Sheeran_-_Shape_of_You|x|bar_chart Number_of_views_in_billions|4.51|y|bar_chart Month|Wiz_Khalifa_-_See_You_Again_ft._Charlie_Puth_[Official_Video]_Furious_7_Soundtrack|x|bar_chart Number_of_views_in_billions|4.31|y|bar_chart Month|Masha_and_the_Bear:_Recipe_for_Disaster|x|bar_chart Number_of_views_in_billions|4.18|y|bar_chart Month|Pinkfong_Kids'_Songs_&_Stories_-_Baby_Shark_Dance|x|bar_chart Number_of_views_in_billions|4.06|y|bar_chart Month|Mark_Ronson_ft._Bruno_Mars_-_Uptown_Funk|x|bar_chart Number_of_views_in_billions|3.73|y|bar_chart Month|PSY_-_GANGNAM_STYLE|x|bar_chart Number_of_views_in_billions|3.47|y|bar_chart Month|Justin_Bieber_-_Sorry|x|bar_chart Number_of_views_in_billions|3.22|y|bar_chart Month|Maroon_5_-_Sugar|x|bar_chart Number_of_views_in_billions|3.08|y|bar_chart Month|Katy_Perry_-_Roar|x|bar_chart Number_of_views_in_billions|2.97|y|bar_chart 
title: Most viewed YouTube videos of all time 2019

gold: On January 12 , 2017 , Puerto Rican singer Luis Fonsi released his Spanish-language music video `` Despacito '' featuring Daddy Yankee , and the rest is history . In August of the same year , the video became the most-viewed YouTube video of all time and as of December 2019 , the video still holds the top spot with over 6.55 billion lifetime views on the video platform . Music videos on YouTube `` Descpacito '' might be the current record-holder in terms of total views , but Korean artist Psy 's `` Gangnam Style '' video remained on the top spot for longest ( 1,689 days or 4.6 years ) before ceding its spot to its successor .
gold_template: On January 12 , 2017 , Puerto Rican singer templateXValue[0] templateXValue[0] released his Spanish-language music video `` templateXValue[0] '' featuring templateXValue[0] templateXValue[0] , and the rest is history . In August of the same year , the video became the most-viewed templateTitle[2] video of templateTitle[4] templateTitle[5] and as of December templateTitle[6] , the video still holds the top spot with over templateYValue[max] billion lifetime templateYLabel[1] on the video platform . Music templateTitle[3] on templateTitle[2] `` Descpacito '' might be the current record-holder in terms of total templateYLabel[1] , but Korean artist templateXValue[6] 's `` templateXValue[6] templateXValue[6] '' video remained on the top spot templateXValue[3] longest ( 1,689 days or 4.6 years ) before ceding its spot to its successor .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] ( templateTitle[5] ) in the United States from July templateTitle[6] to templateTitle[7] . Over the last reported period , templateYValue[max] percent of the German templateTitle[2] templateTitle[3] templateTitle[4] were generated by the United States .
generated: This statistic shows the Number views of viewed YouTube videos all ( time ) in the United States from July 2019 to titleErr . Over the last reported period , 6.55 percent of the German YouTube videos all were generated by the United States .


Example 387:
data: Industry|Real_estate_and_rental_and_leasing|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|254294|y|bar_chart Industry|Manufacturing|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|199234|y|bar_chart Industry|Mining_quarrying_and_oil_and_gas_extraction|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|145909|y|bar_chart Industry|Construction|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|142530|y|bar_chart Industry|Health_care_and_social_assistance|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|142028|y|bar_chart Industry|Public_administration|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|134370|y|bar_chart Industry|Finance_and_insurance|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|133599|y|bar_chart Industry|Professional_scientific_and_technical_services|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|120820|y|bar_chart Industry|Educational_services|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|104771|y|bar_chart Industry|Wholesale_trade|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|103082|y|bar_chart Industry|Retail_trade|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|102619|y|bar_chart Industry|Transportation_and_warehousing|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|89599|y|bar_chart Industry|Information_and_cultural_industries|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|64323|y|bar_chart Industry|Administrative_and_support_waste_management_and_remediation_services|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|52649|y|bar_chart Industry|Accommodation_and_food_services|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|45160|y|bar_chart Industry|Utilities|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|43668|y|bar_chart Industry|Agriculture_forestry_fishing_and_hunting|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|40058|y|bar_chart Industry|Other_services_(except_public_administration)|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|38127|y|bar_chart Industry|Arts_entertainment_and_recreation|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|15699|y|bar_chart Industry|Management_of_companies_and_enterprises|x|bar_chart GDP_in_million_chained_2012_Canadian_dollars|9303|y|bar_chart 
title: Canada : Gross Domestic Product ( GDP ) by industry December 2019

gold: This statistic shows the Gross Domestic Product ( GDP ) of Canada in December 2019 , distinguished by major industry . In December 2019 , the construction industry of Canada contributed about 142.5 billion Canadian dollars to the total Canadian GDP .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) of templateTitle[0] in templateTitle[7] templateTitle[8] , distinguished templateTitle[5] major templateXLabel[0] . In templateTitle[7] templateTitle[8] , the templateXValue[3] templateXLabel[0] of templateTitle[0] contributed about templateYValue[3] billion templateYLabel[4] templateYLabel[5] to the total templateYLabel[4] templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in the United States in templateTitle[6] , templateTitle[7] templateXLabel[0] . In templateTitle[6] , the templateXValue[6] templateXLabel[0] with an estimated percent of the templateXLabel[0] .
generated: This statistic shows the Canada Gross Domestic ( GDP ) million chained in the United States in industry , December Industry . In industry , the Finance_and_insurance Industry with an estimated percent of the Industry .


Example 388:
data: Year|2024|x|line_chart Budget_balance_in_trillion_yen|-11.67|y|line_chart Year|2023|x|line_chart Budget_balance_in_trillion_yen|-10.46|y|line_chart Year|2022|x|line_chart Budget_balance_in_trillion_yen|-10.24|y|line_chart Year|2021|x|line_chart Budget_balance_in_trillion_yen|-11.12|y|line_chart Year|2020|x|line_chart Budget_balance_in_trillion_yen|-12.25|y|line_chart Year|2019|x|line_chart Budget_balance_in_trillion_yen|-16.48|y|line_chart Year|2018|x|line_chart Budget_balance_in_trillion_yen|-17.64|y|line_chart Year|2017|x|line_chart Budget_balance_in_trillion_yen|-17.27|y|line_chart Year|2016|x|line_chart Budget_balance_in_trillion_yen|-19.8|y|line_chart Year|2015|x|line_chart Budget_balance_in_trillion_yen|-20.23|y|line_chart Year|2014|x|line_chart Budget_balance_in_trillion_yen|-28.96|y|line_chart 
title: Budget balance in Japan 2024

gold: The statistic shows the budget balance of Japan from 2014 to 2017 , with projections up until 2024 . In 2017 , the state deficit of Japan was at about 17.27 trillion yen .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the state deficit of templateTitle[2] was at about 17.27 templateYLabel[2] templateYLabel[3] .

generated_template: U.S. than templateYValue[0] percent of the templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] in the United States . This figure is expected to increase by around templateYValue[0] percent in templateXValue[max] . The results of the decline of templateTitle[3] templateTitle[4] templateTitle[5] reason for the growth rate in the templateTitle[3] templateTitle[4] templateTitle[5] - additional information , a house prices increased throughout the templateXLabel[0] .
generated: U.S. than -11.67 percent of the 2024 titleErr titleErr in trillion in the United States . This figure is expected to increase by around -11.67 percent in 2024 . The results of the decline of 2024 titleErr titleErr reason for the growth rate in the 2024 titleErr titleErr - additional information , a house prices increased throughout the Year .


Example 389:
data: Year|2016|x|line_chart Spending_in_billion_U.S._dollars|1.82|y|line_chart Year|2015|x|line_chart Spending_in_billion_U.S._dollars|1.73|y|line_chart Year|2014|x|line_chart Spending_in_billion_U.S._dollars|1.65|y|line_chart Year|2013|x|line_chart Spending_in_billion_U.S._dollars|1.6|y|line_chart Year|2012|x|line_chart Spending_in_billion_U.S._dollars|1.51|y|line_chart Year|2011|x|line_chart Spending_in_billion_U.S._dollars|1.44|y|line_chart Year|2010|x|line_chart Spending_in_billion_U.S._dollars|1.36|y|line_chart 
title: Global spending on golf sponsorships 2010 - 2016

gold: This statistic shows the worldwide spending for golf sponsorship from 2010 to 2016 . In 2013 , global spendings on golf sponsorships amounted to 1.6 billion U.S. dollars .
gold_template: This statistic shows the worldwide templateYLabel[0] for templateTitle[2] sponsorship from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitle[0] spendings on templateTitle[2] templateTitle[3] amounted to templateYValue[3] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the total templateTitle[0] templateYLabel[0] in the United States from templateXValue[min] to templateXValue[max] . According to the report , templateTitle[0] templateYLabel[0] amounted to around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: The statistic shows the total Global Spending in the United States from 2010 to 2016 . According to the report , Global Spending amounted to around 1.82 billion U.S. dollars in 2016 .


Example 390:
data: Year|2024|x|line_chart GDP_growth_compared_to_previous_year|3.78|y|line_chart Year|2023|x|line_chart GDP_growth_compared_to_previous_year|4.13|y|line_chart Year|2022|x|line_chart GDP_growth_compared_to_previous_year|3.33|y|line_chart Year|2021|x|line_chart GDP_growth_compared_to_previous_year|2.87|y|line_chart Year|2020|x|line_chart GDP_growth_compared_to_previous_year|1.15|y|line_chart Year|2019|x|line_chart GDP_growth_compared_to_previous_year|-0.27|y|line_chart Year|2018|x|line_chart GDP_growth_compared_to_previous_year|-1.2|y|line_chart Year|2017|x|line_chart GDP_growth_compared_to_previous_year|-0.15|y|line_chart Year|2016|x|line_chart GDP_growth_compared_to_previous_year|-2.58|y|line_chart Year|2015|x|line_chart GDP_growth_compared_to_previous_year|0.94|y|line_chart Year|2014|x|line_chart GDP_growth_compared_to_previous_year|4.82|y|line_chart 
title: Gross domestic product ( GDP ) growth rate in Angola 2024*

gold: The statistic shows the growth in real GDP in Angola from 2014 to 2018 , with projections up until 2024 . In 2018 , Angola 's real gross domestic product fell by around 1.2 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] fell by around templateYValue[4] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Angola from 2014 to 2018 , with projections up until 2024 . In 2018 , Angola 's real Gross domestic product grew by around -1.2 percent compared to the previous Year .


Example 391:
data: Year|2016|x|line_chart Expenditures_per_pupil_in_U.S._dollars|12617|y|line_chart Year|2015|x|line_chart Expenditures_per_pupil_in_U.S._dollars|12224|y|line_chart Year|2014|x|line_chart Expenditures_per_pupil_in_U.S._dollars|11819|y|line_chart Year|2013|x|line_chart Expenditures_per_pupil_in_U.S._dollars|11509|y|line_chart Year|2012|x|line_chart Expenditures_per_pupil_in_U.S._dollars|11362|y|line_chart Year|2011|x|line_chart Expenditures_per_pupil_in_U.S._dollars|11433|y|line_chart Year|2010|x|line_chart Expenditures_per_pupil_in_U.S._dollars|11427|y|line_chart Year|2009|x|line_chart Expenditures_per_pupil_in_U.S._dollars|11239|y|line_chart Year|2008|x|line_chart Expenditures_per_pupil_in_U.S._dollars|10982|y|line_chart Year|2007|x|line_chart Expenditures_per_pupil_in_U.S._dollars|10336|y|line_chart Year|2006|x|line_chart Expenditures_per_pupil_in_U.S._dollars|9778|y|line_chart Year|2005|x|line_chart Expenditures_per_pupil_in_U.S._dollars|9316|y|line_chart Year|2004|x|line_chart Expenditures_per_pupil_in_U.S._dollars|8900|y|line_chart Year|2003|x|line_chart Expenditures_per_pupil_in_U.S._dollars|8610|y|line_chart Year|2002|x|line_chart Expenditures_per_pupil_in_U.S._dollars|8259|y|line_chart Year|2001|x|line_chart Expenditures_per_pupil_in_U.S._dollars|7904|y|line_chart Year|2000|x|line_chart Expenditures_per_pupil_in_U.S._dollars|7394|y|line_chart Year|1999|x|line_chart Expenditures_per_pupil_in_U.S._dollars|7013|y|line_chart Year|1998|x|line_chart Expenditures_per_pupil_in_U.S._dollars|6676|y|line_chart Year|1997|x|line_chart Expenditures_per_pupil_in_U.S._dollars|6393|y|line_chart Year|1996|x|line_chart Expenditures_per_pupil_in_U.S._dollars|6147|y|line_chart Year|1995|x|line_chart Expenditures_per_pupil_in_U.S._dollars|5989|y|line_chart Year|1994|x|line_chart Expenditures_per_pupil_in_U.S._dollars|5767|y|line_chart Year|1993|x|line_chart Expenditures_per_pupil_in_U.S._dollars|5584|y|line_chart Year|1992|x|line_chart Expenditures_per_pupil_in_U.S._dollars|5421|y|line_chart Year|1991|x|line_chart Expenditures_per_pupil_in_U.S._dollars|5258|y|line_chart Year|1990|x|line_chart Expenditures_per_pupil_in_U.S._dollars|4980|y|line_chart Year|1989|x|line_chart Expenditures_per_pupil_in_U.S._dollars|4645|y|line_chart Year|1988|x|line_chart Expenditures_per_pupil_in_U.S._dollars|4240|y|line_chart Year|1987|x|line_chart Expenditures_per_pupil_in_U.S._dollars|3970|y|line_chart Year|1986|x|line_chart Expenditures_per_pupil_in_U.S._dollars|3756|y|line_chart Year|1985|x|line_chart Expenditures_per_pupil_in_U.S._dollars|3470|y|line_chart Year|1980|x|line_chart Expenditures_per_pupil_in_U.S._dollars|2272|y|line_chart 
title: U.S. public schools - average expenditure per pupil 1980 - 2016

gold: This graph shows the average expenditure per pupil in daily attendance in public elementary and secondary schools in the United States from the academic year of 1980 to 2016 . An average of 12,617 U.S. dollars was spent on each pupil in public elementary and secondary schools in the academic year of 2016 .
gold_template: This graph shows the templateTitle[3] templateTitle[4] templateYLabel[1] templateYLabel[2] in daily attendance in templateTitle[1] elementary and secondary templateTitle[2] in the templateTitle[0] from the academic templateXLabel[0] of templateXValue[min] to templateXValue[max] . An templateTitle[3] of templateYValue[max] templateYLabel[3] templateYLabel[4] was spent on each templateYLabel[2] in templateTitle[1] elementary and secondary templateTitle[2] in the academic templateXLabel[0] of templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the templateYLabel[1] were living in the United States .
generated: This statistic shows the Expenditures per of schools average in the United States from 1980 to 2016 . In 2016 , 12617 percent of the per were living in the United States .


Example 392:
data: Year|2019|x|line_chart Exports_in_billion_U.S._dollars|1645.53|y|line_chart Year|2018|x|line_chart Exports_in_billion_U.S._dollars|1665.99|y|line_chart Year|2017|x|line_chart Exports_in_billion_U.S._dollars|1546.47|y|line_chart Year|2016|x|line_chart Exports_in_billion_U.S._dollars|1451.46|y|line_chart Year|2015|x|line_chart Exports_in_billion_U.S._dollars|1503.33|y|line_chart Year|2014|x|line_chart Exports_in_billion_U.S._dollars|1621.87|y|line_chart Year|2013|x|line_chart Exports_in_billion_U.S._dollars|1578.52|y|line_chart Year|2012|x|line_chart Exports_in_billion_U.S._dollars|1545.82|y|line_chart Year|2011|x|line_chart Exports_in_billion_U.S._dollars|1482.51|y|line_chart Year|2010|x|line_chart Exports_in_billion_U.S._dollars|1278.49|y|line_chart Year|2009|x|line_chart Exports_in_billion_U.S._dollars|1056.04|y|line_chart Year|2008|x|line_chart Exports_in_billion_U.S._dollars|1287.44|y|line_chart Year|2007|x|line_chart Exports_in_billion_U.S._dollars|1148.2|y|line_chart Year|2006|x|line_chart Exports_in_billion_U.S._dollars|1025.97|y|line_chart Year|2005|x|line_chart Exports_in_billion_U.S._dollars|901.08|y|line_chart Year|2004|x|line_chart Exports_in_billion_U.S._dollars|814.87|y|line_chart Year|2003|x|line_chart Exports_in_billion_U.S._dollars|724.77|y|line_chart Year|2002|x|line_chart Exports_in_billion_U.S._dollars|693.1|y|line_chart Year|2001|x|line_chart Exports_in_billion_U.S._dollars|729.1|y|line_chart Year|2000|x|line_chart Exports_in_billion_U.S._dollars|781.92|y|line_chart Year|1999|x|line_chart Exports_in_billion_U.S._dollars|695.8|y|line_chart Year|1998|x|line_chart Exports_in_billion_U.S._dollars|682.14|y|line_chart Year|1997|x|line_chart Exports_in_billion_U.S._dollars|689.18|y|line_chart Year|1996|x|line_chart Exports_in_billion_U.S._dollars|625.07|y|line_chart Year|1995|x|line_chart Exports_in_billion_U.S._dollars|584.74|y|line_chart Year|1994|x|line_chart Exports_in_billion_U.S._dollars|512.63|y|line_chart Year|1993|x|line_chart Exports_in_billion_U.S._dollars|465.09|y|line_chart Year|1992|x|line_chart Exports_in_billion_U.S._dollars|448.16|y|line_chart Year|1991|x|line_chart Exports_in_billion_U.S._dollars|421.73|y|line_chart Year|1990|x|line_chart Exports_in_billion_U.S._dollars|393.59|y|line_chart Year|1989|x|line_chart Exports_in_billion_U.S._dollars|363.81|y|line_chart Year|1988|x|line_chart Exports_in_billion_U.S._dollars|322.43|y|line_chart Year|1987|x|line_chart Exports_in_billion_U.S._dollars|254.12|y|line_chart 
title: U.S. exports of trade goods to the world 1987 - 2019

gold: This statistic shows the growth in the U.S. export volume of trade goods to the world from 1987 to 2019 . In 2019 , U.S. exports amounted to about 1.65 trillion U.S. dollars .
gold_template: This statistic shows the growth in the templateYLabel[2] export volume of templateTitle[2] templateTitle[3] to the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[2] templateYLabel[0] amounted to about templateYValue[0] trillion templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] amounted to approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the U.S. Exports in trade from 1987 to 2019 . In 2019 , the U.S. of trade Exports billion U.S. dollars amounted to approximately 1645.53 billion U.S. dollars .


Example 393:
data: Year|2016_France|x|line_chart Revenue_in_million_euros|1916.0|y|line_chart Year|2012_Poland_&_Ukraine|x|line_chart Revenue_in_million_euros|1390.9|y|line_chart Year|2008_Switzerland_&_Austria|x|line_chart Revenue_in_million_euros|1350.9|y|line_chart Year|2004_Portugal|x|line_chart Revenue_in_million_euros|855.2|y|line_chart Year|2000_Belgium_&_the_Netherlands|x|line_chart Revenue_in_million_euros|229.9|y|line_chart Year|1996_England|x|line_chart Revenue_in_million_euros|147.3|y|line_chart Year|1992_Sweden|x|line_chart Revenue_in_million_euros|40.9|y|line_chart 
title: Revenue of the UEFA European Championships 1992 - 2016

gold: This statistic shows the total revenue of the four UEFA European Championships held since 1992 . The EURO 2008 generated around 1.35 billion euros in revenue .
gold_template: This statistic shows the total templateYLabel[0] of the four templateTitle[1] templateTitle[2] templateTitle[3] held since templateXValue[last] . The EURO templateXValue[2] generated around templateYValue[2] billion templateYLabel[2] in templateYLabel[0] .

generated_template: There were templateYValue[0] billion templateYLabel[2] recorded in the United States in templateXValue[max] , a slight increase from templateYValue[1] percent in templateXValue[1] . templateYLabel[0] results can be observed within the highest level of some templateYValue[max] billion templateYLabel[2] . Within the past few years , templateTitle[0] templateTitle[1] templateYLabel[0] increased again in templateXValue[6] .
generated: There were 1916.0 billion euros recorded in the United States in 2016_France , a slight increase from 1390.9 percent in 2012_Poland_&_Ukraine . Revenue results can be observed within the highest level of some 1916.0 billion euros . Within the past few years , Revenue UEFA Revenue increased again in 1992_Sweden .


Example 394:
data: Year|2015|x|line_chart Share_of_respondents|26.3|y|line_chart Year|2014|x|line_chart Share_of_respondents|28.6|y|line_chart Year|2013|x|line_chart Share_of_respondents|27.3|y|line_chart Year|2012|x|line_chart Share_of_respondents|25.1|y|line_chart Year|2011|x|line_chart Share_of_respondents|27.9|y|line_chart Year|2010|x|line_chart Share_of_respondents|26.8|y|line_chart Year|2005|x|line_chart Share_of_respondents|50.6|y|line_chart 
title: Comic book reading by young people in the United Kingdom ( UK ) 2005 - 2015

gold: This statistic illustrates the proportion of young people who read comics outside of school in the United Kingdom from 2005 to 2015 . In 2015 , 26.3 percent of school children aged eight to 18 years reported reading comic books , which was a considerable decline from 2005 . Reading comics was less common than reading magazines , fiction and newspapers in 2014 .
gold_template: This statistic illustrates the proportion of templateTitle[4] templateTitle[5] who read comics outside of school in the templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of school children aged eight to 18 years reported templateTitle[2] templateTitle[0] books , which was a considerable decline from templateXValue[min] . templateTitle[2] comics was less common than templateTitle[2] magazines , fiction and newspapers in templateXValue[1] .

generated_template: In templateXValue[max] , approximately templateYValue[min] percent of the population shop templateTitle[1] templateTitle[2] in the United States , three percent of the previous templateXLabel[0] . The majority of household templateTitle[2] was currently smoke templateTitle[4] and magazines in the United States , and was currently smoke smoke smoke smoke smoke . Only templateYValue[min] percent of household templateTitle[2] was founded in 1946 .
generated: In 2015 , approximately 25.1 percent of the population shop book reading in the United States , three percent of the previous Year . The majority of household reading was currently smoke young and magazines in the United States , and was currently smoke smoke smoke . Only 25.1 percent of household reading was founded in 1946 .


Example 395:
data: Year|1999|x|line_chart Expenditure_in_billion_U.S._dollars|3.93|y|line_chart Year|2000|x|line_chart Expenditure_in_billion_U.S._dollars|4.75|y|line_chart Year|2001|x|line_chart Expenditure_in_billion_U.S._dollars|4.93|y|line_chart Year|2002|x|line_chart Expenditure_in_billion_U.S._dollars|5.18|y|line_chart Year|2003|x|line_chart Expenditure_in_billion_U.S._dollars|5.06|y|line_chart Year|2004|x|line_chart Expenditure_in_billion_U.S._dollars|5.33|y|line_chart Year|2005|x|line_chart Expenditure_in_billion_U.S._dollars|5.32|y|line_chart Year|2006|x|line_chart Expenditure_in_billion_U.S._dollars|5.52|y|line_chart Year|2007|x|line_chart Expenditure_in_billion_U.S._dollars|5.39|y|line_chart Year|2008|x|line_chart Expenditure_in_billion_U.S._dollars|5.13|y|line_chart Year|2009|x|line_chart Expenditure_in_billion_U.S._dollars|4.57|y|line_chart Year|2010|x|line_chart Expenditure_in_billion_U.S._dollars|4.58|y|line_chart Year|2011|x|line_chart Expenditure_in_billion_U.S._dollars|4.67|y|line_chart Year|2012|x|line_chart Expenditure_in_billion_U.S._dollars|5.14|y|line_chart Year|2013|x|line_chart Expenditure_in_billion_U.S._dollars|5.2|y|line_chart 
title: Consumer expenditure on musical instruments in the U.S. 1999 - 2013

gold: This statistic shows consumer expenditure on musical instruments in the United States from 1999 to 2013 . In 2013 , consumer expenditure on musical instruments reached approximately 5.2 billion U.S. dollars .
gold_template: This statistic shows templateTitle[0] templateYLabel[0] on templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateYLabel[0] on templateTitle[2] templateTitle[3] reached approximately templateYValue[3] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] from templateTitle[5] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Consumer billion of musical instruments U.S. from 1999 to 2013 . In 2013 , the Consumer expenditure musical instruments U.S. was at approximately 5.52 U.S. dollars .


Example 396:
data: Year|2019|x|line_chart Retail_revenue_in_billion_U.S._dollars|53.2|y|line_chart Year|2014|x|line_chart Retail_revenue_in_billion_U.S._dollars|4.5|y|line_chart 
title: Retail revenue from smart wearable devices worldwide 2014 and 2019

gold: The statistic depicts the expected retail revenue from smart wearable devices worldwide in 2014 and 2019 . For 2019 , the global retail revenue from smart wearable devices is expected to reach 53.2 billion U.S. dollars .
gold_template: The statistic depicts the expected templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateXValue[min] and templateXValue[max] . For templateXValue[max] , the global templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] is expected to reach templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The figure shows the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] of templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , it was estimated that there will be around templateYValue[2] percent of the templateTitle[3] templateTitle[4] 's population in templateXValue[max] .
generated: The figure shows the Retail revenue of revenue from of smart from 2014 to 2019 . In xValErr , it was estimated that there will be around yValErr percent of the smart wearable 's population in 2019 .


Example 397:
data: Province|Guangdong|x|bar_chart Population_in_million_inhabitants|113.46|y|bar_chart Province|Shandong|x|bar_chart Population_in_million_inhabitants|100.47|y|bar_chart Province|Henan|x|bar_chart Population_in_million_inhabitants|96.05|y|bar_chart Province|Sichuan|x|bar_chart Population_in_million_inhabitants|83.41|y|bar_chart Province|Jiangsu|x|bar_chart Population_in_million_inhabitants|80.51|y|bar_chart Province|Hebei|x|bar_chart Population_in_million_inhabitants|75.56|y|bar_chart Province|Hunan|x|bar_chart Population_in_million_inhabitants|68.99|y|bar_chart Province|Anhui|x|bar_chart Population_in_million_inhabitants|63.24|y|bar_chart Province|Hubei|x|bar_chart Population_in_million_inhabitants|59.17|y|bar_chart Province|Zhejiang|x|bar_chart Population_in_million_inhabitants|57.37|y|bar_chart Province|Guangxi|x|bar_chart Population_in_million_inhabitants|49.26|y|bar_chart Province|Yunnan|x|bar_chart Population_in_million_inhabitants|48.3|y|bar_chart Province|Jiangxi|x|bar_chart Population_in_million_inhabitants|46.48|y|bar_chart Province|Liaoning|x|bar_chart Population_in_million_inhabitants|43.59|y|bar_chart Province|Fujian|x|bar_chart Population_in_million_inhabitants|39.41|y|bar_chart Province|Shaanxi|x|bar_chart Population_in_million_inhabitants|38.64|y|bar_chart Province|Heilongjiang|x|bar_chart Population_in_million_inhabitants|37.73|y|bar_chart Province|Shanxi|x|bar_chart Population_in_million_inhabitants|37.18|y|bar_chart Province|Guizhou|x|bar_chart Population_in_million_inhabitants|36.0|y|bar_chart Province|Chongqing|x|bar_chart Population_in_million_inhabitants|31.02|y|bar_chart Province|Jilin|x|bar_chart Population_in_million_inhabitants|27.04|y|bar_chart Province|Gansu|x|bar_chart Population_in_million_inhabitants|26.37|y|bar_chart Province|Inner_Mongolia|x|bar_chart Population_in_million_inhabitants|25.34|y|bar_chart Province|Xinjiang|x|bar_chart Population_in_million_inhabitants|24.87|y|bar_chart Province|Shanghai|x|bar_chart Population_in_million_inhabitants|24.24|y|bar_chart Province|Beijing|x|bar_chart Population_in_million_inhabitants|21.54|y|bar_chart Province|Tianjin|x|bar_chart Population_in_million_inhabitants|15.6|y|bar_chart Province|Hainan|x|bar_chart Population_in_million_inhabitants|9.34|y|bar_chart Province|Ningxia|x|bar_chart Population_in_million_inhabitants|6.88|y|bar_chart Province|Qinghai|x|bar_chart Population_in_million_inhabitants|6.03|y|bar_chart Province|Tibet|x|bar_chart Population_in_million_inhabitants|3.44|y|bar_chart 
title: Population in China in 2018 , by region

gold: This statistic shows the regional distribution of the population in China in 2018 . That year , approximately 75.6 million people lived in Hebei province in China . Regional differences in China China is the world 's most populous country , with an exceptional economic growth momentum .
gold_template: This statistic shows the regional distribution of the templateYLabel[0] in templateTitle[1] in templateTitle[2] . That year , approximately templateYValue[5] templateYLabel[1] people lived in templateXValue[5] templateXLabel[0] in templateTitle[1] . Regional differences in templateTitle[1] is the world 's most populous country , with an exceptional economic growth momentum .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in templateTitle[4] templateTitle[5] in templateTitle[7] templateTitle[8] . In that year , templateYValue[max] percent of the United States had an annual templateYLabel[0] templateYLabel[1] at some time time of templateXValue[0] dollars .
generated: This statistic shows the Population million of 2018 by in region titleErr in titleErr titleErr . In that year , 113.46 percent of the United States had an annual Population million at some time of Guangdong dollars .


Example 398:
data: Country|China_mainland|x|bar_chart Production_value_in_million_U.S._dollars|3434.02|y|bar_chart Country|United_States_of_America|x|bar_chart Production_value_in_million_U.S._dollars|1108.88|y|bar_chart Country|Brazil|x|bar_chart Production_value_in_million_U.S._dollars|873.93|y|bar_chart Country|India|x|bar_chart Production_value_in_million_U.S._dollars|616.44|y|bar_chart Country|Japan|x|bar_chart Production_value_in_million_U.S._dollars|304.35|y|bar_chart Country|Indonesia|x|bar_chart Production_value_in_million_U.S._dollars|264.49|y|bar_chart Country|Turkey|x|bar_chart Production_value_in_million_U.S._dollars|232.77|y|bar_chart Country|Republic_of_Korea|x|bar_chart Production_value_in_million_U.S._dollars|212.25|y|bar_chart Country|Italy|x|bar_chart Production_value_in_million_U.S._dollars|177.31|y|bar_chart Country|Mozambique|x|bar_chart Production_value_in_million_U.S._dollars|146.08|y|bar_chart 
title: Global tobacco production value 2016 , by country

gold: This statistic shows the leading tobacco producing countries worldwide in 2016 , based on gross production value . In that year , China 's produced tobacco was worth approximately 3.43 billion U.S. dollars .
gold_template: This statistic shows the leading templateTitle[1] producing countries worldwide in templateTitle[4] , based on gross templateYLabel[0] templateYLabel[1] . In that year , templateXValue[0] 's produced templateTitle[1] was worth approximately templateYValue[max] billion templateYLabel[3] templateYLabel[4] .

generated_template: templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] of U.S. dollars in templateTitle[4] templateTitle[5] in templateTitle[7] . templateYValue[max] percent of the templateXLabel[0] consumed in templateTitle[3] . The templateXValue[0] templateXValue[0] had the highest level , with the highest templateYLabel[1] templateTitle[1] at 100 percent of templateYLabel[1] in templateTitle[7] .
generated: China_mainland had the highest Production value million of U.S. dollars in 2016 by in titleErr . 3434.02 percent of the Country consumed in value . The China_mainland had the highest level , with the highest value tobacco at 100 percent of value in titleErr .


Example 399:
data: Year|2019|x|line_chart Revenue_in_billion_U.S._dollars|21.55|y|line_chart Year|2018|x|line_chart Revenue_in_billion_U.S._dollars|20.01|y|line_chart Year|2017|x|line_chart Revenue_in_billion_U.S._dollars|17.62|y|line_chart Year|2016|x|line_chart Revenue_in_billion_U.S._dollars|15.6|y|line_chart Year|2015|x|line_chart Revenue_in_billion_U.S._dollars|15.03|y|line_chart Year|2014|x|line_chart Revenue_in_billion_U.S._dollars|14.54|y|line_chart Year|2013|x|line_chart Revenue_in_billion_U.S._dollars|13.65|y|line_chart Year|2012|x|line_chart Revenue_in_billion_U.S._dollars|12.47|y|line_chart Year|2011|x|line_chart Revenue_in_billion_U.S._dollars|10.39|y|line_chart Year|2010|x|line_chart Revenue_in_billion_U.S._dollars|8.79|y|line_chart Year|2009|x|line_chart Revenue_in_billion_U.S._dollars|7.17|y|line_chart Year|2008|x|line_chart Revenue_in_billion_U.S._dollars|6.71|y|line_chart Year|2007|x|line_chart Revenue_in_billion_U.S._dollars|5.79|y|line_chart Year|2006|x|line_chart Revenue_in_billion_U.S._dollars|4.16|y|line_chart Year|2005|x|line_chart Revenue_in_billion_U.S._dollars|2.69|y|line_chart Year|2004|x|line_chart Revenue_in_billion_U.S._dollars|1.55|y|line_chart Year|2003|x|line_chart Revenue_in_billion_U.S._dollars|0.63|y|line_chart Year|2002|x|line_chart Revenue_in_billion_U.S._dollars|0.1|y|line_chart Year|2001|x|line_chart Revenue_in_billion_U.S._dollars|0.0|y|line_chart 
title: Google network sites : advertising revenue 2001 - 2019

gold: This statistic gives information on the advertising revenue of Google network websites from 2002 to 2019 . As of the most recently reported period , the advertising revenue of Google network sites amounted to 21.54 billion U.S. dollars . That year , Alphabet 's total Google segment revenue amounted to over 160.74 billion US dollars .
gold_template: This statistic gives information on the templateTitle[3] templateYLabel[0] of templateTitle[0] templateTitle[1] websites from templateXValue[17] to templateXValue[max] . As of the most recently reported period , the templateTitle[3] templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] amounted to 21.54 templateYLabel[1] templateYLabel[2] templateYLabel[3] . That templateXLabel[0] , Alphabet 's total templateTitle[0] segment templateYLabel[0] amounted to over 160.74 templateYLabel[1] US templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] generated approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Google Revenue of sites from 2001 to 2019 . In 2019 , Google network sites generated approximately 21.55 billion U.S. dollars .


Example 400:
data: Year|2019/20_(Fly_Emirates)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|40.0|y|line_chart Year|2018/19_(Fly_Emirates)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|40.0|y|line_chart Year|2017/18_(Fly_Emirates)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|30.0|y|line_chart Year|2016/17_(Fly_Emirates)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|30.0|y|line_chart Year|2015/16_(Fly_Emirates)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|30.0|y|line_chart Year|2014/15_(Fly_Emirates)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|30.0|y|line_chart Year|2013/14_(Fly_Emirates)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|30.0|y|line_chart Year|2012/13_(Fly_Emirates)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|5.5|y|line_chart Year|2011/12_(Fly_Emirates)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|5.5|y|line_chart Year|2010/11_(Fly_Emirates)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|5.5|y|line_chart Year|2009/10_(Fly_Emirates)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|5.5|y|line_chart 
title: Value of Arsenal FC 's jersey sponsorship 2009 - 2020

gold: The statistic shows the revenue Arsenal FC generated from its jersey sponsorship deal from the 2009/10 season to the 2019/20 season . In the 2019/20 season , Arsenal FC received 40 million GBP from its jersey sponsor Fly Emirates .
gold_template: The statistic shows the templateYLabel[2] templateTitle[1] templateTitle[2] generated from its templateYLabel[0] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , templateTitle[1] templateTitle[2] received templateYValue[max] templateYLabel[3] templateYLabel[4] from its templateYLabel[0] sponsor Fly Emirates .

generated_template: This statistic shows the templateYLabel[2] templateTitle[1] templateTitle[2] generated from its templateYLabel[0] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , templateTitle[1] templateTitle[2] received templateYValue[max] templateYLabel[3] templateYLabel[4] from its templateYLabel[0] sponsor SportPesa .
generated: This statistic shows the revenue Arsenal FC generated from its Jersey sponsorship deal from the 2009/10_(Fly_Emirates) season to the 2019/20_(Fly_Emirates) season . In the 2019/20_(Fly_Emirates) season , Arsenal FC received 40.0 million GBP from its Jersey sponsor SportPesa .


Example 401:
data: Year|2006|x|line_chart Revenue_in_billion_U.S._dollars|340.0|y|line_chart Year|2007|x|line_chart Revenue_in_billion_U.S._dollars|331.37|y|line_chart Year|2008|x|line_chart Revenue_in_billion_U.S._dollars|353.55|y|line_chart Year|2009|x|line_chart Revenue_in_billion_U.S._dollars|293.19|y|line_chart Year|2010|x|line_chart Revenue_in_billion_U.S._dollars|316.59|y|line_chart Year|2011|x|line_chart Revenue_in_billion_U.S._dollars|347.39|y|line_chart Year|2012|x|line_chart Revenue_in_billion_U.S._dollars|394.2|y|line_chart Year|2013|x|line_chart Revenue_in_billion_U.S._dollars|370.79|y|line_chart Year|2014|x|line_chart Revenue_in_billion_U.S._dollars|380.65|y|line_chart Year|2015|x|line_chart Revenue_in_billion_U.S._dollars|423.76|y|line_chart Year|2016|x|line_chart Revenue_in_billion_U.S._dollars|390.5|y|line_chart Year|2017|x|line_chart Revenue_in_billion_U.S._dollars|401.59|y|line_chart 
title: Mechanical engineering in the United States - market size 2017

gold: The statistic portrays the revenue of the mechanical engineering industry in the United States from 2006 through 2017 . In 2017 , the U.S. market for mechanical engineering was sized at around 401.6 billion U.S. dollars ( or about 326 billion euros ) .
gold_template: The statistic portrays the templateYLabel[0] of the templateTitle[0] templateTitle[1] industry in the templateTitle[2] templateTitle[3] from templateXValue[min] through templateXValue[max] . In templateXValue[max] , the templateYLabel[2] templateTitle[4] for templateTitle[0] templateTitle[1] was sized at around templateYValue[11] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( or about 326 templateYLabel[1] euros ) .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company generated by templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] . templateTitle[2] - additional information In templateXValue[11] , templateTitle[0] templateTitle[1] templateTitle[2] is one of the largest construction firms in the world .
generated: This statistic shows the Revenue of U.S. dollars in the United States from 2006 to 2017 . In 2017 , the company generated by 340.0 billion U.S. dollars in Revenue . United - additional information In 2017 , Mechanical engineering United is one of the largest construction firms in the world .


Example 402:
data: Year|2011|x|line_chart Occupancy_rate|67|y|line_chart Year|2012|x|line_chart Occupancy_rate|66|y|line_chart Year|2013|x|line_chart Occupancy_rate|67|y|line_chart Year|2014|x|line_chart Occupancy_rate|69|y|line_chart Year|2015|x|line_chart Occupancy_rate|69|y|line_chart Year|2016|x|line_chart Occupancy_rate|70|y|line_chart Year|2017|x|line_chart Occupancy_rate|70|y|line_chart Year|2018|x|line_chart Occupancy_rate|70|y|line_chart Year|2019|x|line_chart Occupancy_rate|71|y|line_chart 
title: Hotel occupancy rate in Rome 2011 - 2019

gold: This statistic illustrates the hotel occupancy rate in Rome from 2011 to 2019 . The occupancy rate of hotels in the city was measured at 70 percent in 2017 . Rates are forecast to remain stable in 2018 and rise by one percentage point in 2019 .
gold_template: This statistic illustrates the templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of hotels in the city was measured at templateYValue[5] percent in templateXValue[6] . Rates are forecast to remain stable in templateXValue[7] and rise by one percentage point in templateXValue[max] .

generated_template: The statistic illustrates the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[0] percent . The figure is projected to grow to templateYValue[max] percent in templateXValue[max] .
generated: The statistic illustrates the Occupancy rate of rate Rome 2011 in 2019 from 2011 to 2019 . In 2019 , the Occupancy rate of rate Rome 2011 was 67 percent . The figure is projected to grow to 71 percent in 2019 .


Example 403:
data: Industry|Music_creators|x|bar_chart Number_of_workers|139352|y|bar_chart Industry|Music_retail|x|bar_chart Number_of_workers|11688|y|bar_chart Industry|Recorded_music|x|bar_chart Number_of_workers|5379|y|bar_chart Industry|Music_representatives|x|bar_chart Number_of_workers|2624|y|bar_chart Industry|Music_publishing|x|bar_chart Number_of_workers|1363|y|bar_chart Industry|Live_music|x|bar_chart Number_of_workers|30529|y|bar_chart 
title: Music industry employment in the United Kingdom ( UK ) 2018 , by sector

gold: This statistic shows employment in the UK music industry in 2018 , by thematic grouping . In 2018 , it was estimated that there were over 30 thousand workers in live music . In the same year , there were 139 thousand people working as music creators .
gold_template: This statistic shows templateTitle[2] in the templateTitle[5] templateXValue[0] templateXLabel[0] in templateTitle[6] , templateTitle[7] thematic grouping . In templateTitle[6] , it was estimated that there were over 30 thousand templateYLabel[1] in templateXValue[last] templateXValue[0] . In the same year , there were templateYValue[max] thousand people working as templateXValue[0] templateXValue[0] .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[7] templateTitle[8] . In templateTitle[5] , about templateYValue[max] percent of templateYLabel[1] remain exported by the U.S. dollars in the United States .
generated: This statistic shows the results of a survey conducted in the United States in by sector . In UK , about 139352 percent of workers remain exported by the U.S. dollars in the United States .


Example 404:
data: Quarter|Q4_'19|x|bar_chart Number_of_available_apps|2570520|y|bar_chart Quarter|Q3_'19|x|bar_chart Number_of_available_apps|2469894|y|bar_chart Quarter|Q2_'19|x|bar_chart Number_of_available_apps|2327628|y|bar_chart Quarter|Q1_'19|x|bar_chart Number_of_available_apps|2134302|y|bar_chart Quarter|Q4_'18|x|bar_chart Number_of_available_apps|1977776|y|bar_chart Quarter|Q3_'18|x|bar_chart Number_of_available_apps|2108450|y|bar_chart Quarter|Q2_'18|x|bar_chart Number_of_available_apps|2977833|y|bar_chart Quarter|Q1_'18|x|bar_chart Number_of_available_apps|3849865|y|bar_chart Quarter|Q4_'17|x|bar_chart Number_of_available_apps|3662276|y|bar_chart Quarter|Q3_'17|x|bar_chart Number_of_available_apps|3364880|y|bar_chart Quarter|Q2_'17|x|bar_chart Number_of_available_apps|3172310|y|bar_chart Quarter|Q1_'17|x|bar_chart Number_of_available_apps|2956763|y|bar_chart Quarter|Q4_'16|x|bar_chart Number_of_available_apps|2811106|y|bar_chart Quarter|Q3_'16|x|bar_chart Number_of_available_apps|2781508|y|bar_chart Quarter|Q2_'16|x|bar_chart Number_of_available_apps|2539526|y|bar_chart Quarter|Q1_'16|x|bar_chart Number_of_available_apps|2294798|y|bar_chart Quarter|Q4_'15|x|bar_chart Number_of_available_apps|2012040|y|bar_chart Quarter|Q3_'15|x|bar_chart Number_of_available_apps|1811532|y|bar_chart Quarter|Q2_'15|x|bar_chart Number_of_available_apps|1670113|y|bar_chart Quarter|Q1_'15|x|bar_chart Number_of_available_apps|1605359|y|bar_chart 
title: Google Play : number of available apps as of Q4 2019

gold: This statistic gives information on the number of available apps in the Google Play app store . As of the fourth quarter of 2019 , over 2.57 million mobile apps were available , representing a 4.07 percent increase compared to the previous quarter .
gold_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[0] templateTitle[1] app store . As of the fourth templateXLabel[0] of templateTitle[6] , over templateYValue[0] million mobile templateYLabel[2] were templateYLabel[1] , representing a 4.07 percent increase compared to the previous templateXLabel[0] .

generated_template: This statistic gives information on the templateYLabel[1] templateYLabel[2] of templateYLabel[1] templateYLabel[2] in the templateTitle[0] templateTitle[1] app store . As of the third templateXLabel[0] of templateTitle[7] , templateYValue[max] percent of templateYLabel[1] templateYLabel[2] were reported templateXLabel[0] .
generated: This statistic gives information on the available apps of available apps in the Google Play app store . As of the third Quarter of titleErr , 3849865 percent of available apps were reported Quarter .


Example 405:
data: Year|2024|x|line_chart GDP_growth_compared_to_previous_year|2.29|y|line_chart Year|2023|x|line_chart GDP_growth_compared_to_previous_year|2.3|y|line_chart Year|2022|x|line_chart GDP_growth_compared_to_previous_year|2.39|y|line_chart Year|2021|x|line_chart GDP_growth_compared_to_previous_year|2.46|y|line_chart Year|2020|x|line_chart GDP_growth_compared_to_previous_year|2.72|y|line_chart Year|2019|x|line_chart GDP_growth_compared_to_previous_year|3.41|y|line_chart Year|2018|x|line_chart GDP_growth_compared_to_previous_year|3.49|y|line_chart Year|2017|x|line_chart GDP_growth_compared_to_previous_year|4.14|y|line_chart Year|2016|x|line_chart GDP_growth_compared_to_previous_year|2.35|y|line_chart Year|2015|x|line_chart GDP_growth_compared_to_previous_year|2.02|y|line_chart Year|2014|x|line_chart GDP_growth_compared_to_previous_year|3.54|y|line_chart 
title: Gross domestic product ( GDP ) growth rate in Lithuania 2024

gold: The statistic shows the growth in real GDP in Lithuania from 2014 to 2018 , with projections up until 2024 . In 2018 , Lithuania 's real gross domestic product grew by around 3.49 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Lithuania from 2014 to 2018 , with projections up until 2024 . In 2018 , Lithuania 's real Gross domestic product grew by around 3.49 percent compared to the previous Year .


Example 406:
data: Year|2018|x|line_chart Household_income_in_current_U.S._dollars|62629|y|line_chart Year|2017|x|line_chart Household_income_in_current_U.S._dollars|63451|y|line_chart Year|2016|x|line_chart Household_income_in_current_U.S._dollars|59817|y|line_chart Year|2015|x|line_chart Household_income_in_current_U.S._dollars|55425|y|line_chart Year|2014|x|line_chart Household_income_in_current_U.S._dollars|58080|y|line_chart Year|2013|x|line_chart Household_income_in_current_U.S._dollars|55258|y|line_chart Year|2012|x|line_chart Household_income_in_current_U.S._dollars|53079|y|line_chart Year|2011|x|line_chart Household_income_in_current_U.S._dollars|52058|y|line_chart Year|2010|x|line_chart Household_income_in_current_U.S._dollars|50351|y|line_chart Year|2009|x|line_chart Household_income_in_current_U.S._dollars|51237|y|line_chart Year|2008|x|line_chart Household_income_in_current_U.S._dollars|51200|y|line_chart Year|2007|x|line_chart Household_income_in_current_U.S._dollars|51277|y|line_chart Year|2006|x|line_chart Household_income_in_current_U.S._dollars|51692|y|line_chart Year|2005|x|line_chart Household_income_in_current_U.S._dollars|44650|y|line_chart Year|2004|x|line_chart Household_income_in_current_U.S._dollars|45732|y|line_chart Year|2003|x|line_chart Household_income_in_current_U.S._dollars|46269|y|line_chart Year|2002|x|line_chart Household_income_in_current_U.S._dollars|45903|y|line_chart Year|2001|x|line_chart Household_income_in_current_U.S._dollars|45346|y|line_chart Year|2000|x|line_chart Household_income_in_current_U.S._dollars|45088|y|line_chart Year|1999|x|line_chart Household_income_in_current_U.S._dollars|45667|y|line_chart Year|1998|x|line_chart Household_income_in_current_U.S._dollars|41327|y|line_chart Year|1997|x|line_chart Household_income_in_current_U.S._dollars|39595|y|line_chart Year|1996|x|line_chart Household_income_in_current_U.S._dollars|40001|y|line_chart Year|1995|x|line_chart Household_income_in_current_U.S._dollars|40955|y|line_chart Year|1994|x|line_chart Household_income_in_current_U.S._dollars|35388|y|line_chart Year|1993|x|line_chart Household_income_in_current_U.S._dollars|31766|y|line_chart Year|1992|x|line_chart Household_income_in_current_U.S._dollars|33308|y|line_chart Year|1991|x|line_chart Household_income_in_current_U.S._dollars|31133|y|line_chart Year|1990|x|line_chart Household_income_in_current_U.S._dollars|30711|y|line_chart 
title: Wisconsin - Median household income 1990 - 2018

gold: This statistic shows the median household income in Wisconsin from 1990 to 2018 . In 2018 , the median household income in Wisconsin amounted to 62,629 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] .

generated_template: The graph shows the annual templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: The graph shows the annual Household income in Wisconsin from 1990 to 2018 . In 2018 , the Median Household income in Wisconsin amounted to 63451 U.S. dollars .


Example 407:
data: Year|2018|x|line_chart Costs_in_million_U.S._dollars|208|y|line_chart Year|2017|x|line_chart Costs_in_million_U.S._dollars|141|y|line_chart Year|2016|x|line_chart Costs_in_million_U.S._dollars|494|y|line_chart Year|2015|x|line_chart Costs_in_million_U.S._dollars|1290|y|line_chart Year|2014|x|line_chart Costs_in_million_U.S._dollars|1439|y|line_chart Year|2013|x|line_chart Costs_in_million_U.S._dollars|5278|y|line_chart Year|2012|x|line_chart Costs_in_million_U.S._dollars|3104|y|line_chart Year|2011|x|line_chart Costs_in_million_U.S._dollars|2266|y|line_chart Year|2010|x|line_chart Costs_in_million_U.S._dollars|2036|y|line_chart 
title: Royal Dutch Shell 's exploration costs 2010 - 2018

gold: This statistic shows Royal Dutch Shell 's exploration costs from 2010 through to 2018 . In 2018 , the company spent some 208 million U.S. dollars for such purposes . Royal Dutch Shell is one of the top oil and gas companies worldwide , operating in every segment of the oil and gas industry .
gold_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] from templateXValue[min] through to templateXValue[max] . In templateXValue[max] , the company spent some templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] for such purposes . templateTitle[0] templateTitle[1] templateTitle[2] is one of the top oil and gas companies worldwide , operating in every segment of the oil and gas industry .

generated_template: templateTitle[0] templateTitle[1] research and development templateYLabel[0] amounted to templateYValue[max] billion templateYLabel[2] templateYLabel[3] in templateXValue[max] . This is a significant increase from templateYValue[1] percent in templateXValue[1] . templateTitle[1] research and development templateYLabel[0] – additional information The company 's research and gas corporation headquartered in Irving , Texas .
generated: Royal Dutch research and development Costs amounted to 5278 billion U.S. dollars in 2018 . This is a significant increase from 141 percent in 2017 . Dutch research and development Costs – additional information The company 's research and gas corporation headquartered in Irving , Texas .


Example 408:
data: Year|2024|x|line_chart Inhabitants_in_millions|224.66|y|line_chart Year|2023|x|line_chart Inhabitants_in_millions|220.53|y|line_chart Year|2022|x|line_chart Inhabitants_in_millions|216.47|y|line_chart Year|2021|x|line_chart Inhabitants_in_millions|212.48|y|line_chart Year|2020|x|line_chart Inhabitants_in_millions|208.57|y|line_chart Year|2019|x|line_chart Inhabitants_in_millions|204.73|y|line_chart Year|2018|x|line_chart Inhabitants_in_millions|200.96|y|line_chart Year|2017|x|line_chart Inhabitants_in_millions|197.26|y|line_chart Year|2016|x|line_chart Inhabitants_in_millions|193.56|y|line_chart Year|2015|x|line_chart Inhabitants_in_millions|189.87|y|line_chart Year|2014|x|line_chart Inhabitants_in_millions|186.19|y|line_chart 
title: Total population of Pakistan 2024

gold: This statistic shows the total population of Pakistan from 2014 to 2018 with forecasts up to 2024 . In 2018 , the total population of Pakistan amounted to approximately 200.96 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] with forecasts up to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] amounted to approximately templateYValue[6] million templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] amounted to approximately templateYValue[6] million templateYLabel[0] . templateTitle[1] of templateTitle[2] is the second largest countries in the world 's templateTitle[0] templateTitle[1] of area size , reporting period .
generated: This statistic shows the Total population of Pakistan from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Pakistan amounted to approximately 200.96 million Inhabitants . population of Pakistan is the second largest countries in the world 's Total population of area size , reporting period .


Example 409:
data: Year|2023|x|line_chart Market_size_in_billion_U.S._dollars|37.19|y|line_chart Year|2022|x|line_chart Market_size_in_billion_U.S._dollars|35.07|y|line_chart Year|2021|x|line_chart Market_size_in_billion_U.S._dollars|33.08|y|line_chart Year|2020|x|line_chart Market_size_in_billion_U.S._dollars|31.2|y|line_chart Year|2019|x|line_chart Market_size_in_billion_U.S._dollars|29.42|y|line_chart Year|2018|x|line_chart Market_size_in_billion_U.S._dollars|27.75|y|line_chart Year|2017|x|line_chart Market_size_in_billion_U.S._dollars|26.17|y|line_chart 
title: Global sexual wellness market size 2017 - 2022

gold: This timeline depicts the size of the sexual wellness market worldwide from 2017 to 2022 . In 2017 , the size of the global sexual wellness market was over 26 billion U.S. dollars , and is forecasted to reach to about 37.2 billion U.S. dollars by 2025 .
gold_template: This timeline depicts the templateYLabel[1] of the templateTitle[1] templateTitle[2] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[1] . In templateXValue[min] , the templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] was over templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] , and is forecasted to reach to about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] by 2025 .

generated_template: The statistic shows the templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] templateYLabel[0] is expected to be worth templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the size of the Global sexual wellness market size worldwide from 2017 to 2023 . In 2017 , the Global Market size of size 2017 Market is expected to be worth 26.17 billion U.S. dollars .


Example 410:
data: Month|Ontario|x|bar_chart Number_of_firearms_licenses|616489|y|bar_chart Month|Quebec|x|bar_chart Number_of_firearms_licenses|500058|y|bar_chart Month|Alberta|x|bar_chart Number_of_firearms_licenses|316791|y|bar_chart Month|British_Columbia|x|bar_chart Number_of_firearms_licenses|301775|y|bar_chart Month|Saskatchewan|x|bar_chart Number_of_firearms_licenses|110573|y|bar_chart Month|Manitoba|x|bar_chart Number_of_firearms_licenses|91107|y|bar_chart Month|Newfoundland_and_Labrador|x|bar_chart Number_of_firearms_licenses|76802|y|bar_chart Month|Nova_Scotia|x|bar_chart Number_of_firearms_licenses|76180|y|bar_chart Month|New_Brunswick|x|bar_chart Number_of_firearms_licenses|70111|y|bar_chart Month|Yukon|x|bar_chart Number_of_firearms_licenses|7711|y|bar_chart Month|Prince_Edward_Island|x|bar_chart Number_of_firearms_licenses|6363|y|bar_chart Month|Northwest_Territories|x|bar_chart Number_of_firearms_licenses|5955|y|bar_chart Month|Nunavut|x|bar_chart Number_of_firearms_licenses|3912|y|bar_chart 
title: Canada : number of individual firearms licenses held , by province or territory 2018

gold: This graph shows the number of individual firearms licenses held in Canada in 2018 , by province or territory . In Ontario , 616,489 firearms licenses were held in 2018 .
gold_template: This graph shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] templateYLabel[2] templateTitle[5] in templateTitle[0] in templateTitle[9] , templateTitle[6] templateTitle[7] or templateTitle[8] . In templateXValue[0] , templateYValue[max] templateYLabel[1] templateYLabel[2] were templateTitle[5] in templateTitle[9] .

generated_template: After Canadian templateXValue[6] , templateYValue[6] percent of templateYLabel[1] had the highest level of templateYLabel[0] templateYLabel[1] in the United States in templateTitle[9] . More than templateYValue[max] percent of templateYLabel[1] throughout the templateXLabel[0] . templateXValue[1] , which reported due to the highest templateYLabel[0] templateYLabel[1] at the share of people .
generated: After Canadian Newfoundland_and_Labrador , 76802 percent of firearms had the highest level of Number firearms in the United States in 2018 . More than 616489 percent of firearms throughout the Month . Quebec , which reported due to the highest Number firearms at the share of people .


Example 411:
data: Year|2018|x|line_chart Unemployment_rate|6|y|line_chart Year|2017|x|line_chart Unemployment_rate|7.1|y|line_chart Year|2016|x|line_chart Unemployment_rate|7.9|y|line_chart Year|2015|x|line_chart Unemployment_rate|8.6|y|line_chart Year|2014|x|line_chart Unemployment_rate|8.6|y|line_chart Year|2013|x|line_chart Unemployment_rate|8.5|y|line_chart Year|2012|x|line_chart Unemployment_rate|7.6|y|line_chart Year|2011|x|line_chart Unemployment_rate|7.2|y|line_chart Year|2010|x|line_chart Unemployment_rate|8.4|y|line_chart Year|2009|x|line_chart Unemployment_rate|8|y|line_chart Year|2008|x|line_chart Unemployment_rate|7|y|line_chart 
title: Unemployment rate in Belgium 2008 - 2018

gold: In 2019 , the unemployment rate in Belgium was six percent . This is a decrease in comparison to the previous year , and the lowest unemployment rate in the last ten years . Unemployment peaked in 2014 and 2015 , at 8.6 percent .
gold_template: In 2019 , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was templateYValue[min] percent . This is a decrease in comparison to the previous templateXLabel[0] , and the lowest templateYLabel[0] templateYLabel[1] in the last ten years . templateYLabel[0] peaked in templateXValue[4] and templateXValue[3] , at templateYValue[max] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] was templateYValue[0] percent . The templateYLabel[0] templateYLabel[1] of templateTitle[2] 's labor force without jobs but available and actively seeking employment .
generated: The statistic shows the Unemployment rate of Belgium from 2008 to 2018 . In 2018 , the Unemployment rate of Belgium was 6 percent . The Unemployment rate of Belgium 's labor force without jobs but available and actively seeking employment .


Example 412:
data: Response|To_meet_people_who_share_my_interests_or_hobbies|x|bar_chart Share_of_respondents|61|y|bar_chart Response|To_meet_people_who_share_my_beliefs_or_values|x|bar_chart Share_of_respondents|44|y|bar_chart Response|To_find_someone_for_a_long-term_relationship_or_marriage|x|bar_chart Share_of_respondents|42|y|bar_chart Response|I_have_a_schedule_that_makes_it_hard_to_meet_interesting_people_in_other_ways|x|bar_chart Share_of_respondents|21|y|bar_chart Response|To_meet_people_who_just_want_to_have_fun_without_being_in_a_serious_relationship|x|bar_chart Share_of_respondents|26|y|bar_chart Response|None_of_the_above|x|bar_chart Share_of_respondents|7|y|bar_chart 
title: U.S. user reasons for using online dating sites or apps 2017

gold: This statistic presents the reasons why users in the United States use online dating sites or apps . During the April 2017 survey , 61 percent of responding current or former dating website or app users said they used dating websites and apps to meet people who share their interests or hobbies .
gold_template: This statistic presents the templateTitle[2] why users in the templateTitle[0] use templateTitle[5] templateTitle[6] templateTitle[7] or templateTitle[8] . During the April templateTitle[9] survey , templateYValue[max] percent of responding current or former templateTitle[6] website or app users said they used templateTitle[6] websites and templateTitle[8] to templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] their templateXValue[0] or templateXValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the United States as of July templateTitle[7] . During the survey period , templateYValue[1] percent of templateYLabel[1] stated they used templateXValue[0] templateXValue[0] or templateXValue[1] .
generated: This statistic shows the U.S. user reasons for using online dating in the United States as of July sites . During the survey period , 44 percent of respondents stated they used To_meet_people_who_share_my_interests_or_hobbies or To_meet_people_who_share_my_beliefs_or_values .


Example 413:
data: Country|United_States|x|bar_chart Market_value_share|26.71|y|bar_chart Country|China|x|bar_chart Market_value_share|22.15|y|bar_chart Country|France|x|bar_chart Market_value_share|6.23|y|bar_chart Country|Brazil|x|bar_chart Market_value_share|5.84|y|bar_chart Country|Canada|x|bar_chart Market_value_share|4.72|y|bar_chart Country|India|x|bar_chart Market_value_share|4.45|y|bar_chart Country|Japan|x|bar_chart Market_value_share|3.01|y|bar_chart Country|Germany|x|bar_chart Market_value_share|2.6|y|bar_chart Country|Argentina|x|bar_chart Market_value_share|2.2|y|bar_chart Country|Italy|x|bar_chart Market_value_share|1.71|y|bar_chart 
title: Share of global seeds market value by country 2012

gold: This graph depicts the shares of the global seeds market value in 2012 , by country . The United States and China both held more than 20 percent of the market value worldwide in that year .
gold_template: This graph depicts the shares of the templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[7] , templateTitle[5] templateXLabel[0] . The templateXValue[0] templateXValue[0] and templateXValue[1] both held more than 20 percent of the templateYLabel[0] templateYLabel[1] worldwide in that year .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the United States as of templateTitle[4] . During the last reported period , it was found that templateYValue[1] percent of templateYLabel[1] templateYLabel[2] were located in templateXValue[0] .
generated: This statistic gives information on the Market of value share yLabelErr in the United States as of value . During the last reported period , it was found that 22.15 percent of value share were located in United_States .


Example 414:
data: Year|2018|x|line_chart Expenditure_in_million_GBP|5631|y|line_chart Year|2017|x|line_chart Expenditure_in_million_GBP|5091|y|line_chart Year|2016|x|line_chart Expenditure_in_million_GBP|4640|y|line_chart Year|2015|x|line_chart Expenditure_in_million_GBP|4571|y|line_chart Year|2014|x|line_chart Expenditure_in_million_GBP|4573|y|line_chart Year|2013|x|line_chart Expenditure_in_million_GBP|4435|y|line_chart Year|2012|x|line_chart Expenditure_in_million_GBP|4188|y|line_chart Year|2011|x|line_chart Expenditure_in_million_GBP|4119|y|line_chart Year|2010|x|line_chart Expenditure_in_million_GBP|3994|y|line_chart Year|2009|x|line_chart Expenditure_in_million_GBP|4142|y|line_chart Year|2008|x|line_chart Expenditure_in_million_GBP|3677|y|line_chart Year|2007|x|line_chart Expenditure_in_million_GBP|3802|y|line_chart Year|2006|x|line_chart Expenditure_in_million_GBP|3976|y|line_chart Year|2005|x|line_chart Expenditure_in_million_GBP|3714|y|line_chart 
title: Expenditure on beer in the United Kingdom 2005 - 2018

gold: In 2018 , consumers spent 5.6 billion British pounds on beer in the United Kingdom ( UK ) . This is the highest consumer spending recorded in the past thirteen years and the second time spending surpassed five billion pounds . Spending has generally grown since 2010 .
gold_template: In templateXValue[max] , consumers spent templateYValue[max] billion British pounds on templateTitle[1] in the templateTitle[2] templateTitle[3] ( UK ) . This is the highest consumer spending recorded in the past thirteen years and the second time spending surpassed templateYValue[1] billion pounds . Spending has generally grown since templateXValue[8] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] in the templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] , measured in templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . In templateXValue[max] , households purchased templateYValue[0] templateYLabel[1] templateYLabel[2] British pounds .
generated: This statistic shows the Expenditure of beer million GBP in the 2005 2018 from 2005 to 2018 , measured in million GBP yLabelErr yLabelErr yLabelErr . In 2018 , households purchased 5631 million GBP British pounds .


Example 415:
data: Year|2018|x|line_chart Total_number_of_ATMs|406532|y|line_chart Year|2017|x|line_chart Total_number_of_ATMs|413414|y|line_chart Year|2016|x|line_chart Total_number_of_ATMs|420200|y|line_chart Year|2015|x|line_chart Total_number_of_ATMs|411243|y|line_chart Year|2014|x|line_chart Total_number_of_ATMs|409136|y|line_chart Year|2013|x|line_chart Total_number_of_ATMs|407001|y|line_chart Year|2012|x|line_chart Total_number_of_ATMs|412799|y|line_chart Year|2011|x|line_chart Total_number_of_ATMs|403996|y|line_chart Year|2010|x|line_chart Total_number_of_ATMs|398040|y|line_chart Year|2009|x|line_chart Total_number_of_ATMs|391175|y|line_chart Year|2008|x|line_chart Total_number_of_ATMs|383951|y|line_chart Year|2007|x|line_chart Total_number_of_ATMs|362244|y|line_chart Year|2006|x|line_chart Total_number_of_ATMs|335083|y|line_chart Year|2005|x|line_chart Total_number_of_ATMs|324797|y|line_chart 
title: European ATM numbers 2005 - 2018

gold: This statistic presents the development of ATM numbers ( automated teller machines ) for self-operated cash withdrawals in European countries from 2005 to 2018 . In 2005 , there were approximately 325 thousand ATMs in Europe and the number grew up to more than 420 thousand as of 2016 . By 2018 , the number of ATMs in Europe had decreased to approximately 406.5 thousand .
gold_template: This statistic presents the development of templateTitle[1] templateTitle[2] ( automated teller machines ) for self-operated cash withdrawals in templateTitle[0] countries from templateXValue[min] to templateXValue[max] . In templateXValue[min] , there were approximately templateYValue[min] thousand templateYLabel[2] in Europe and the templateYLabel[1] grew up to more than templateYValue[max] thousand as of templateXValue[2] . By templateXValue[max] , the templateYLabel[1] of templateYLabel[2] in Europe had decreased to approximately templateYValue[0] thousand .

generated_template: U.S. fashion fashion retailer templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] United States between templateXValue[min] and templateXValue[max] . In templateYValue[max] , an increase of templateYValue[0] percent compared with the previous templateXLabel[0] . The highest level was valued at templateYValue[0] percent in templateXValue[max] .
generated: U.S. fashion retailer 2005 2018 titleErr in ATMs United States between 2005 and 2018 . In 420200 , an increase of 406532 percent compared with the previous Year . The highest level was valued at 406532 percent in 2018 .


Example 416:
data: Year|2019|x|line_chart Unemployment_rate|21.22|y|line_chart Year|2018|x|line_chart Unemployment_rate|20.84|y|line_chart Year|2017|x|line_chart Unemployment_rate|20.47|y|line_chart Year|2016|x|line_chart Unemployment_rate|25.41|y|line_chart Year|2015|x|line_chart Unemployment_rate|27.65|y|line_chart Year|2014|x|line_chart Unemployment_rate|27.52|y|line_chart Year|2013|x|line_chart Unemployment_rate|27.45|y|line_chart Year|2012|x|line_chart Unemployment_rate|28.01|y|line_chart Year|2011|x|line_chart Unemployment_rate|27.58|y|line_chart Year|2010|x|line_chart Unemployment_rate|27.31|y|line_chart Year|2009|x|line_chart Unemployment_rate|24.07|y|line_chart Year|2008|x|line_chart Unemployment_rate|23.41|y|line_chart Year|2007|x|line_chart Unemployment_rate|28.98|y|line_chart Year|2006|x|line_chart Unemployment_rate|31.11|y|line_chart Year|2005|x|line_chart Unemployment_rate|30.49|y|line_chart Year|2004|x|line_chart Unemployment_rate|29.87|y|line_chart Year|2003|x|line_chart Unemployment_rate|29.03|y|line_chart Year|2002|x|line_chart Unemployment_rate|28.22|y|line_chart Year|2001|x|line_chart Unemployment_rate|27.13|y|line_chart Year|2000|x|line_chart Unemployment_rate|26.19|y|line_chart Year|1999|x|line_chart Unemployment_rate|25.31|y|line_chart 
title: Unemployment rate in Bosnia-Herzegovina 2019

gold: This statistic shows the unemployment rate in Bosnia & Herzegovina from 1999 to 2019 . In 2019 , the unemployment rate in Bosnia & Herzegovina was at 21.22 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in Bosnia & Herzegovina from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in Bosnia & Herzegovina was at templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Bosnia-Herzegovina from 1999 to 2019 . In 2019 , the Unemployment rate in Bosnia-Herzegovina was at approximately 21.22 percent .


Example 417:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|2.2|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|2.2|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|2.2|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|2.2|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|1.7|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|1.26|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|1.46|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|1.35|y|line_chart Year|2016|x|line_chart Inflation_rate_compared_to_previous_year|-0.24|y|line_chart Year|2015|x|line_chart Inflation_rate_compared_to_previous_year|-0.3|y|line_chart Year|2014|x|line_chart Inflation_rate_compared_to_previous_year|-0.28|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|2.79|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|3.32|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|3.91|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|1.51|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|-0.74|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|7.52|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|2.79|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|3.27|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|-0.57|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|-0.66|y|line_chart Year|2003|x|line_chart Inflation_rate_compared_to_previous_year|1.11|y|line_chart Year|2002|x|line_chart Inflation_rate_compared_to_previous_year|2.31|y|line_chart Year|2001|x|line_chart Inflation_rate_compared_to_previous_year|5.2|y|line_chart Year|2000|x|line_chart Inflation_rate_compared_to_previous_year|6.61|y|line_chart Year|1999|x|line_chart Inflation_rate_compared_to_previous_year|-1.28|y|line_chart Year|1998|x|line_chart Inflation_rate_compared_to_previous_year|0.54|y|line_chart Year|1997|x|line_chart Inflation_rate_compared_to_previous_year|1.29|y|line_chart Year|1996|x|line_chart Inflation_rate_compared_to_previous_year|2.47|y|line_chart Year|1995|x|line_chart Inflation_rate_compared_to_previous_year|16.37|y|line_chart Year|1994|x|line_chart Inflation_rate_compared_to_previous_year|126.58|y|line_chart 
title: Inflation rate in North Macedonia 2024

gold: This statistic shows the average inflation rate in North Macedonia from 1994 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in North Macedonia amounted to about 1.46 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in North from 1994 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in North amounted to about 1.46 percent compared to the previous Year .


Example 418:
data: Country|Worldwide|x|bar_chart Share_of_search_traffic|9|y|bar_chart Country|United_States|x|bar_chart Share_of_search_traffic|33|y|bar_chart Country|Brazil|x|bar_chart Share_of_search_traffic|3|y|bar_chart Country|Canada|x|bar_chart Share_of_search_traffic|17|y|bar_chart Country|Latin_America|x|bar_chart Share_of_search_traffic|5|y|bar_chart Country|Asia_Pacific|x|bar_chart Share_of_search_traffic|3|y|bar_chart Country|Australia|x|bar_chart Share_of_search_traffic|12|y|bar_chart Country|Hong_Kong|x|bar_chart Share_of_search_traffic|19|y|bar_chart Country|India|x|bar_chart Share_of_search_traffic|7|y|bar_chart Country|Indonesia|x|bar_chart Share_of_search_traffic|7|y|bar_chart Country|Malaysia|x|bar_chart Share_of_search_traffic|8|y|bar_chart Country|New_Zealand|x|bar_chart Share_of_search_traffic|6|y|bar_chart Country|Philippines|x|bar_chart Share_of_search_traffic|5|y|bar_chart Country|Singapore|x|bar_chart Share_of_search_traffic|8|y|bar_chart Country|Taiwan|x|bar_chart Share_of_search_traffic|24|y|bar_chart Country|Vietnam|x|bar_chart Share_of_search_traffic|8|y|bar_chart Country|Europe|x|bar_chart Share_of_search_traffic|9|y|bar_chart Country|Austria|x|bar_chart Share_of_search_traffic|12|y|bar_chart Country|Belgium|x|bar_chart Share_of_search_traffic|12|y|bar_chart Country|Denmark|x|bar_chart Share_of_search_traffic|9|y|bar_chart Country|Finland|x|bar_chart Share_of_search_traffic|7|y|bar_chart Country|France|x|bar_chart Share_of_search_traffic|19|y|bar_chart Country|Germany|x|bar_chart Share_of_search_traffic|12|y|bar_chart Country|Ireland|x|bar_chart Share_of_search_traffic|8|y|bar_chart Country|Italy|x|bar_chart Share_of_search_traffic|9|y|bar_chart Country|Netherlands|x|bar_chart Share_of_search_traffic|9|y|bar_chart Country|Norway|x|bar_chart Share_of_search_traffic|17|y|bar_chart Country|Spain|x|bar_chart Share_of_search_traffic|9|y|bar_chart Country|Sweden|x|bar_chart Share_of_search_traffic|12|y|bar_chart Country|Switzerland|x|bar_chart Share_of_search_traffic|12|y|bar_chart Country|United_Kingdom|x|bar_chart Share_of_search_traffic|26|y|bar_chart 
title: Bing global search market share 2017 , by country

gold: This statistic shows the worldwide search market share of Bing as of August 2017 in leading online markets . During the measured period , Bing accounted for 17 percent of search traffic in Canada . The Microsoft-owned platform accounted for nine percent of search traffic worldwide .
gold_template: This statistic shows the templateXValue[0] templateYLabel[1] templateTitle[3] templateYLabel[0] of templateTitle[0] as of August templateTitle[5] in leading online markets . During the measured period , templateTitle[0] accounted for templateYValue[3] percent of templateYLabel[1] templateYLabel[2] in templateXValue[3] . The Microsoft-owned platform accounted for templateYValue[0] percent of templateYLabel[1] templateYLabel[2] templateXValue[0] .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitle[0] templateTitle[1] templateTitle[3] in templateTitle[4] templateTitle[5] . In this year , there were some templateYValue[max] percent of the templateYLabel[1] throughout the templateXValue[0] templateXValue[0] .
generated: This statistic displays the Share of search traffic in Bing global market in share 2017 . In this year , there were some 33 percent of the search throughout the Worldwide .


Example 419:
data: Year|2024|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|46.4|y|line_chart Year|2023|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|44.28|y|line_chart Year|2022|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|42.4|y|line_chart Year|2021|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|40.76|y|line_chart Year|2020|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|39.31|y|line_chart Year|2019|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|38.18|y|line_chart Year|2018|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|37.75|y|line_chart Year|2017|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|35.43|y|line_chart Year|2016|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|32.25|y|line_chart Year|2015|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|31.13|y|line_chart Year|2014|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|33.39|y|line_chart Year|2013|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|32.54|y|line_chart Year|2012|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|30.75|y|line_chart Year|2011|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|28.78|y|line_chart Year|2010|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|25.71|y|line_chart Year|2009|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|22.94|y|line_chart Year|2008|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|25.71|y|line_chart Year|2007|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|21.73|y|line_chart Year|2006|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|18.51|y|line_chart Year|2005|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|15.97|y|line_chart Year|2004|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|13.15|y|line_chart Year|2003|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|11.08|y|line_chart Year|2002|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|9.59|y|line_chart Year|2001|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|9.19|y|line_chart Year|2000|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|9.06|y|line_chart Year|1999|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|7.58|y|line_chart Year|1998|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|7.0|y|line_chart Year|1997|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|7.32|y|line_chart Year|1996|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|7.06|y|line_chart Year|1995|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|6.79|y|line_chart Year|1994|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|6.41|y|line_chart Year|1993|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|5.99|y|line_chart Year|1992|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|5.44|y|line_chart Year|1991|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|5.21|y|line_chart Year|1990|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|4.97|y|line_chart Year|1989|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|4.68|y|line_chart Year|1988|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|4.48|y|line_chart Year|1987|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.63|y|line_chart Year|1986|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|3.35|y|line_chart Year|1985|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|4.28|y|line_chart Year|1984|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|4.53|y|line_chart 
title: Gross domestic product ( GDP ) in Bahrain 2024

gold: The statistic shows gross domestic product ( GDP ) in Bahrain from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Bahrain from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year . GDP is an important indicator of a country 's economic power .


Example 420:
data: Year|2025|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|2475|y|line_chart Year|2024|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|2470|y|line_chart Year|2023|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|2465|y|line_chart Year|2022|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|2460|y|line_chart Year|2021|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|2455|y|line_chart Year|2020|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|2450|y|line_chart Year|2019|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|2570|y|line_chart Year|2018|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|2922|y|line_chart Year|2017|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|2891|y|line_chart Year|2016|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|2090|y|line_chart Year|2015|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|1932|y|line_chart Year|2014|x|line_chart Price_in_nominal_U.S._dollars_per_metric_ton|2161|y|line_chart 
title: Average prices for zinc worldwide from 2014 to 2025

gold: This statistic depicts the average annual prices for zinc from 2014 through 2025* . In 2018 , the average price for zinc stood at 2,922 nominal U.S. dollars per metric ton .
gold_template: This statistic depicts the templateTitle[0] annual templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[5] templateXValue[min] through 2025* . In templateXValue[7] , the templateTitle[0] templateYLabel[0] templateTitle[2] templateTitle[3] stood at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: This statistic depicts the templateTitle[0] annual templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[6] templateXValue[min] through 2025* . In templateXValue[7] , the templateTitle[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] stood at templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: This statistic depicts the Average annual prices for zinc worldwide 2014 2014 through 2025* . In 2018 , the Average Price for zinc worldwide stood at 2922 nominal U.S. dollars per metric ton .


Example 421:
data: Year|'18|x|line_chart Unemployment_rate|3.9|y|line_chart Year|'17|x|line_chart Unemployment_rate|4.7|y|line_chart Year|'16|x|line_chart Unemployment_rate|5.4|y|line_chart Year|'15|x|line_chart Unemployment_rate|6|y|line_chart Year|'14|x|line_chart Unemployment_rate|7.1|y|line_chart Year|'13|x|line_chart Unemployment_rate|8.2|y|line_chart Year|'12|x|line_chart Unemployment_rate|9.2|y|line_chart Year|'11|x|line_chart Unemployment_rate|10.2|y|line_chart Year|'10|x|line_chart Unemployment_rate|10.5|y|line_chart Year|'09|x|line_chart Unemployment_rate|9.9|y|line_chart Year|'08|x|line_chart Unemployment_rate|6.2|y|line_chart Year|'07|x|line_chart Unemployment_rate|4.5|y|line_chart Year|'06|x|line_chart Unemployment_rate|4.7|y|line_chart Year|'05|x|line_chart Unemployment_rate|5.3|y|line_chart Year|'04|x|line_chart Unemployment_rate|4.8|y|line_chart Year|'03|x|line_chart Unemployment_rate|4.8|y|line_chart Year|'02|x|line_chart Unemployment_rate|5|y|line_chart Year|'01|x|line_chart Unemployment_rate|4|y|line_chart Year|'00|x|line_chart Unemployment_rate|3.6|y|line_chart Year|'99|x|line_chart Unemployment_rate|3.9|y|line_chart Year|'98|x|line_chart Unemployment_rate|4.3|y|line_chart Year|'97|x|line_chart Unemployment_rate|4.6|y|line_chart Year|'96|x|line_chart Unemployment_rate|4.7|y|line_chart Year|'95|x|line_chart Unemployment_rate|4.8|y|line_chart Year|'94|x|line_chart Unemployment_rate|5.2|y|line_chart Year|'93|x|line_chart Unemployment_rate|6|y|line_chart Year|'92|x|line_chart Unemployment_rate|6.9|y|line_chart 
title: Georgia - unemployment rate 1992 - 2018

gold: This statistic displays the unemployment rate in Georgia from 1992 to 2018 . In 2018 , the unemployment rate in Georgia was at 3.9 percent .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateTitle[3] to templateTitle[4] . In templateTitle[4] , the templateYLabel[0] templateYLabel[1] in templateTitle[0] was at templateYValue[0] percent .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] of the templateTitle[2] from templateTitle[3] to templateTitle[4] . In templateTitle[4] , the templateYLabel[0] templateYLabel[1] in templateTitle[0] was templateYValue[min] percent .
generated: This statistic displays the Unemployment rate of the rate from 1992 to 2018 . In 2018 , the Unemployment rate in Georgia was 3.6 percent .


Example 422:
data: Year|2019/20|x|line_chart Revenue_in_billion_euros|17.95|y|line_chart Year|2018/19|x|line_chart Revenue_in_billion_euros|17.0|y|line_chart Year|2017/18|x|line_chart Revenue_in_billion_euros|15.59|y|line_chart Year|2016/17|x|line_chart Revenue_in_billion_euros|14.66|y|line_chart Year|2015/16|x|line_chart Revenue_in_billion_euros|13.42|y|line_chart Year|2014/15|x|line_chart Revenue_in_billion_euros|12.1|y|line_chart Year|2013/14|x|line_chart Revenue_in_billion_euros|11.3|y|line_chart Year|2012/13|x|line_chart Revenue_in_billion_euros|9.8|y|line_chart Year|2011/12|x|line_chart Revenue_in_billion_euros|9.3|y|line_chart Year|2010/11|x|line_chart Revenue_in_billion_euros|8.6|y|line_chart Year|2009/10|x|line_chart Revenue_in_billion_euros|8.4|y|line_chart Year|2008/09|x|line_chart Revenue_in_billion_euros|7.9|y|line_chart Year|2007/08|x|line_chart Revenue_in_billion_euros|7.7|y|line_chart Year|2006/07|x|line_chart Revenue_in_billion_euros|7.16|y|line_chart 
title: Revenue of the top European soccer leagues ( Big Five ) 2006 - 2020

gold: The statistic shows the total revenue of the top European professional soccer leagues ( Big Five ) from 2006/07 to 2019/20 . In the 2017/18 season , the total revenue of the `` Big Five '' leagues was estimated at 15.59 billion euros .
gold_template: The statistic shows the total templateYLabel[0] of the templateTitle[1] templateTitle[2] professional templateTitle[3] templateTitle[4] ( templateTitle[5] Five ) from templateXValue[last] to templateXValue[0] . In the templateXValue[2] season , the total templateYLabel[0] of the `` templateTitle[5] Five '' templateTitle[4] was estimated at templateYValue[2] templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the total templateYLabel[0] of the European templateTitle[2] soccer templateTitle[0] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , the total templateYLabel[0] of the European templateTitle[2] soccer templateTitle[0] was estimated at templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the total Revenue of the European European soccer Revenue from 2006/07 to 2019/20 . In the 2019/20 season , the total Revenue of the European European soccer Revenue was estimated at 17.95 billion euros .


Example 423:
data: Year|2050|x|line_chart Median_age_in_years|34.9|y|line_chart Year|2045|x|line_chart Median_age_in_years|33.1|y|line_chart Year|2040|x|line_chart Median_age_in_years|31.2|y|line_chart Year|2035|x|line_chart Median_age_in_years|29.3|y|line_chart Year|2030|x|line_chart Median_age_in_years|27.5|y|line_chart Year|2025|x|line_chart Median_age_in_years|25.6|y|line_chart Year|2020|x|line_chart Median_age_in_years|23.8|y|line_chart Year|2015|x|line_chart Median_age_in_years|22.1|y|line_chart Year|2010|x|line_chart Median_age_in_years|21.3|y|line_chart Year|2005|x|line_chart Median_age_in_years|20.6|y|line_chart Year|2000|x|line_chart Median_age_in_years|19.6|y|line_chart Year|1995|x|line_chart Median_age_in_years|19.1|y|line_chart Year|1990|x|line_chart Median_age_in_years|16.8|y|line_chart Year|1985|x|line_chart Median_age_in_years|16.3|y|line_chart Year|1980|x|line_chart Median_age_in_years|15.5|y|line_chart Year|1975|x|line_chart Median_age_in_years|16.4|y|line_chart Year|1970|x|line_chart Median_age_in_years|17.1|y|line_chart Year|1965|x|line_chart Median_age_in_years|17.4|y|line_chart Year|1960|x|line_chart Median_age_in_years|18.0|y|line_chart Year|1955|x|line_chart Median_age_in_years|17.6|y|line_chart Year|1950|x|line_chart Median_age_in_years|17.2|y|line_chart 
title: Median age of the population in Jordan 2015

gold: This statistic shows the median age of the population in Jordan from 1950 to 2050 . The median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .
generated: This statistic shows the Median age of the population in Jordan 2015 from 1950 to 2050 . The Median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population .


Example 424:
data: Year|2028|x|line_chart Value_in_billion_Saudi_Riyal|573.1|y|line_chart Year|2018|x|line_chart Value_in_billion_Saudi_Riyal|258.1|y|line_chart Year|2017|x|line_chart Value_in_billion_Saudi_Riyal|240.9|y|line_chart Year|2016|x|line_chart Value_in_billion_Saudi_Riyal|228.1|y|line_chart Year|2015|x|line_chart Value_in_billion_Saudi_Riyal|232.3|y|line_chart Year|2014|x|line_chart Value_in_billion_Saudi_Riyal|215.4|y|line_chart Year|2013|x|line_chart Value_in_billion_Saudi_Riyal|21.7|y|line_chart Year|2012|x|line_chart Value_in_billion_Saudi_Riyal|209.2|y|line_chart 
title: Total contribution of travel and tourism to GDP in Saudi Arabia 2012 - 2028

gold: This statistic describes the total economic contribution of tourism and travel to Saudi Arabia from 2012 to 2017 , with an estimate for 2018 and a forecast for 2028 . According to forecasts , the total economic contribution of tourism and travel to the GDP of Saudi Arabia in 2028 will be around 573 billion Saudi Riyal in 2028 .
gold_template: This statistic describes the templateTitle[0] economic templateTitle[1] of templateTitle[3] and templateTitle[2] to templateYLabel[2] templateTitle[6] from templateXValue[min] to templateXValue[2] , with an estimate for templateXValue[1] and a forecast for templateXValue[max] . According to forecasts , the templateTitle[0] economic templateTitle[1] of templateTitle[3] and templateTitle[2] to the templateTitle[4] of templateYLabel[2] templateTitle[6] in templateXValue[max] will be around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in United States from templateXValue[min] to templateXValue[max] . In templateXValue[2] , there were some templateYValue[2] percent of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Value billion Saudi of GDP Saudi in United States from 2012 to 2028 . In 2017 , there were some 240.9 percent of the travel tourism GDP Saudi in the United States .


Example 425:
data: Company|Netizen|x|bar_chart Revenue_in_million_U.S._dollars|6.3|y|bar_chart Company|Cisoshare|x|bar_chart Revenue_in_million_U.S._dollars|3.9|y|bar_chart Company|Exabeam|x|bar_chart Revenue_in_million_U.S._dollars|38.8|y|bar_chart Company|KnowBe4|x|bar_chart Revenue_in_million_U.S._dollars|72.3|y|bar_chart Company|Transcend_Security_Solutions|x|bar_chart Revenue_in_million_U.S._dollars|8.3|y|bar_chart Company|Perimeter_Security_Partners|x|bar_chart Revenue_in_million_U.S._dollars|15.3|y|bar_chart Company|Tomahawk_Strategic_Solutions|x|bar_chart Revenue_in_million_U.S._dollars|2.8|y|bar_chart Company|Kisi_Security|x|bar_chart Revenue_in_million_U.S._dollars|2.8|y|bar_chart Company|Aysco_Technology_Integration|x|bar_chart Revenue_in_million_U.S._dollars|20.4|y|bar_chart Company|Kenna_Security|x|bar_chart Revenue_in_million_U.S._dollars|13.2|y|bar_chart Company|Point3_Security|x|bar_chart Revenue_in_million_U.S._dollars|5.8|y|bar_chart Company|BOS_Security|x|bar_chart Revenue_in_million_U.S._dollars|7.3|y|bar_chart Company|Satelles|x|bar_chart Revenue_in_million_U.S._dollars|5.0|y|bar_chart Company|Skynet_Integrations|x|bar_chart Revenue_in_million_U.S._dollars|2.0|y|bar_chart Company|Home_View_Technologies|x|bar_chart Revenue_in_million_U.S._dollars|16.5|y|bar_chart 
title: Revenue of the fastest-growing private security companies in the U.S. 2018

gold: This statistic shows the revenue of the fastest-growing private security companies in the United States in 2018 . The fastest growing security company in the United States was Netizen , which generated revenue of 6.3 million U.S. dollars in 2018 .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateXValue[4] templateTitle[4] in the templateTitle[5] in templateTitle[6] . The fastest growing templateXValue[4] templateXLabel[0] in the templateTitle[5] was templateXValue[0] , which generated templateYLabel[0] of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[6] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] worldwide in templateTitle[5] . In that year , templateXValue[0] had a templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Revenue of the Revenue fastest-growing private security companies worldwide in U.S. . In that year , Netizen had a Revenue of 72.3 million U.S. dollars .


Example 426:
data: Year|2024|x|line_chart Budget_balance_in_relation_to_GDP|8.6|y|line_chart Year|2023|x|line_chart Budget_balance_in_relation_to_GDP|8.21|y|line_chart Year|2022|x|line_chart Budget_balance_in_relation_to_GDP|7.84|y|line_chart Year|2021|x|line_chart Budget_balance_in_relation_to_GDP|7.75|y|line_chart Year|2020|x|line_chart Budget_balance_in_relation_to_GDP|7.82|y|line_chart Year|2019|x|line_chart Budget_balance_in_relation_to_GDP|7.57|y|line_chart Year|2018|x|line_chart Budget_balance_in_relation_to_GDP|7.25|y|line_chart Year|2017|x|line_chart Budget_balance_in_relation_to_GDP|4.92|y|line_chart Year|2016|x|line_chart Budget_balance_in_relation_to_GDP|4.04|y|line_chart Year|2015|x|line_chart Budget_balance_in_relation_to_GDP|6.07|y|line_chart Year|2014|x|line_chart Budget_balance_in_relation_to_GDP|8.77|y|line_chart 
title: Norway 's budget balance in relation to GDP 2024

gold: The statistic shows Norway 's budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 . A positive value indicates a budget surplus , a negative value indicates a deficit . In 2018 , Norway 's surplus amounted to around 7.25 percent of GDP .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[6] , templateTitle[0] templateTitle[1] surplus amounted to around templateYValue[6] percent of templateYLabel[3] .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[6] , templateTitle[0] templateTitle[1] templateYLabel[0] surplus amounted to around templateYValue[max] percent of templateYLabel[3] .
generated: This statistic shows Norway 's Budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 . A positive value indicates a Budget surplus , a negative value indicates a deficit . In 2018 , Norway 's Budget surplus amounted to around 8.77 percent of GDP .


Example 427:
data: Year|2010|x|line_chart Number_of_suicides|289|y|line_chart Year|2009|x|line_chart Number_of_suicides|302|y|line_chart Year|2008|x|line_chart Number_of_suicides|259|y|line_chart Year|2007|x|line_chart Number_of_suicides|211|y|line_chart Year|2006|x|line_chart Number_of_suicides|213|y|line_chart Year|2005|x|line_chart Number_of_suicides|182|y|line_chart Year|2004|x|line_chart Number_of_suicides|197|y|line_chart Year|2003|x|line_chart Number_of_suicides|190|y|line_chart Year|2002|x|line_chart Number_of_suicides|174|y|line_chart Year|2001|x|line_chart Number_of_suicides|153|y|line_chart Year|2000|x|line_chart Number_of_suicides|153|y|line_chart Year|1999|x|line_chart Number_of_suicides|150|y|line_chart Year|1998|x|line_chart Number_of_suicides|165|y|line_chart Year|1997|x|line_chart Number_of_suicides|159|y|line_chart Year|1996|x|line_chart Number_of_suicides|188|y|line_chart Year|1995|x|line_chart Number_of_suicides|250|y|line_chart Year|1994|x|line_chart Number_of_suicides|232|y|line_chart Year|1993|x|line_chart Number_of_suicides|236|y|line_chart Year|1992|x|line_chart Number_of_suicides|238|y|line_chart Year|1991|x|line_chart Number_of_suicides|256|y|line_chart Year|1990|x|line_chart Number_of_suicides|232|y|line_chart 
title: U.S active duty military suicides 1990 - 2010

gold: This graph shows the total number of self-inflicted deaths of active military personnel in the United States from 1990 to 2010 . In 2010 , 289 active duty U.S. military personnel committed suicide .
gold_template: This graph shows the total templateYLabel[0] of self-inflicted deaths of templateTitle[1] templateTitle[3] personnel in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] templateTitle[1] templateTitle[2] U.S. templateTitle[3] personnel committed suicide .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[min] thousand people committed suicide in templateTitle[3] . The numbers peaked in templateXValue[min] with around templateYValue[max] million suicide victims in templateTitle[0] .
generated: The statistic shows the Number of suicides in the United States from 1990 to 2010 . In 2010 , around 150 thousand people committed suicide in military . The numbers peaked in 1990 with around 302 million suicide victims in U.S .


Example 428:
data: Year|2014|x|line_chart Revenue_in_million_U.S._dollars|66.5|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|54.1|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|44.1|y|line_chart 
title: PC online games revenue in Malaysia 2012 - 2014

gold: The statistic presents a forecast of the PC online games revenue in Malaysia from 2012 to 2014 . It was estimated that the 2014 PC online games revenue for Malaysia would be 66.5 million U.S. dollars .
gold_template: The statistic presents a forecast of the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitle[4] from templateXValue[min] to templateXValue[max] . It was estimated that the templateXValue[max] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] for templateTitle[4] would be templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the PC online games from 2012 to 2014 . In 2013 , the PC online games Revenue amounted to 54.1 million U.S. dollars .


Example 429:
data: Quarter|Q4_'16|x|bar_chart Number_of_monthly_users_in_millions|217.0|y|bar_chart Quarter|Q3_'16|x|bar_chart Number_of_monthly_users_in_millions|220.0|y|bar_chart Quarter|Q2_'16|x|bar_chart Number_of_monthly_users_in_millions|220.0|y|bar_chart Quarter|Q1_'16|x|bar_chart Number_of_monthly_users_in_millions|218.4|y|bar_chart Quarter|Q4_'15|x|bar_chart Number_of_monthly_users_in_millions|215.0|y|bar_chart Quarter|Q3_'15|x|bar_chart Number_of_monthly_users_in_millions|212.0|y|bar_chart Quarter|Q2_'15|x|bar_chart Number_of_monthly_users_in_millions|211.0|y|bar_chart Quarter|Q1_'15|x|bar_chart Number_of_monthly_users_in_millions|205.0|y|bar_chart Quarter|Q4_'14|x|bar_chart Number_of_monthly_users_in_millions|190.0|y|bar_chart Quarter|Q3_'14|x|bar_chart Number_of_monthly_users_in_millions|179.0|y|bar_chart Quarter|Q2_'14|x|bar_chart Number_of_monthly_users_in_millions|170.0|y|bar_chart 
title: LINE : number of monthly active users 2014 - 2016

gold: This statistic shows a timeline with the amount of monthly active LINE users worldwide as of the fourth quarter of 2016 . As of that period , the mobile messaging app announced more than 217 million monthly active users . In October 2014 , LINE had also reported 560 million registered users worldwide .
gold_template: This statistic shows a timeline with the amount of templateYLabel[1] templateTitle[3] templateTitle[0] templateYLabel[2] worldwide as of the fourth templateXLabel[0] of templateTitle[6] . As of that period , the mobile messaging app announced more than templateYValue[0] million templateYLabel[1] templateTitle[3] templateYLabel[2] . In October templateTitle[5] , templateTitle[0] had also reported 560 million registered templateYLabel[2] worldwide .

generated_template: Messaging app templateTitle[0] is templateTitle[5] 's most popular online communication tool , peaking at approximately templateYValue[max] million templateYLabel[1] actives templateYLabel[2] in templateTitle[5] during the fourth templateXLabel[0] of templateTitle[7] . templateTitle[0] 's main target group are young adults aged 15 to 34 years , representing more than 50 percent of the company 's Japanese user base in 2018 . The rise of templateTitle[0] in templateTitle[5] The success story of messaging service templateTitle[0] , a Japanese subsidiary of South Korean internet powerhouse Naver , started back in 2011 during the Great East templateTitle[5] Earthquake .
generated: Messaging app LINE is 2014 's most popular online communication tool , peaking at approximately 220.0 million monthly actives users in 2014 during the fourth Quarter of titleErr . LINE 's main target group are young adults aged 15 to 34 years , representing more than 50 percent of the company 's Japanese user base in 2018 . The rise of LINE in 2014 The success story of messaging service LINE , a Japanese subsidiary of South Korean internet powerhouse Naver , started back in 2011 during the Great East 2014 Earthquake .


Example 430:
data: Year|2019|x|line_chart Unemployment_rate|2.03|y|line_chart Year|2018|x|line_chart Unemployment_rate|2.03|y|line_chart Year|2017|x|line_chart Unemployment_rate|2.03|y|line_chart Year|2016|x|line_chart Unemployment_rate|2.15|y|line_chart Year|2015|x|line_chart Unemployment_rate|2.18|y|line_chart Year|2014|x|line_chart Unemployment_rate|2.21|y|line_chart Year|2013|x|line_chart Unemployment_rate|2.29|y|line_chart Year|2012|x|line_chart Unemployment_rate|2.26|y|line_chart Year|2011|x|line_chart Unemployment_rate|2.28|y|line_chart Year|2010|x|line_chart Unemployment_rate|2.27|y|line_chart Year|2009|x|line_chart Unemployment_rate|2.25|y|line_chart Year|2008|x|line_chart Unemployment_rate|1.96|y|line_chart Year|2007|x|line_chart Unemployment_rate|2.03|y|line_chart Year|2006|x|line_chart Unemployment_rate|2.23|y|line_chart Year|2005|x|line_chart Unemployment_rate|2.42|y|line_chart Year|2004|x|line_chart Unemployment_rate|2.53|y|line_chart Year|2003|x|line_chart Unemployment_rate|2.61|y|line_chart Year|2002|x|line_chart Unemployment_rate|2.66|y|line_chart Year|2001|x|line_chart Unemployment_rate|2.61|y|line_chart Year|2000|x|line_chart Unemployment_rate|2.77|y|line_chart Year|1999|x|line_chart Unemployment_rate|2.78|y|line_chart 
title: Unemployment rate in Liberia 2019

gold: This statistic shows the unemployment rate in Liberia from 1999 to 2019 . In 2019 , the unemployment rate in Liberia was at 2.03 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Liberia from 1999 to 2019 . In 2019 , the Unemployment rate in Liberia was at 2.03 percent .


Example 431:
data: Year|2019|x|line_chart Youth_unemployment_rate|8.13|y|line_chart Year|2018|x|line_chart Youth_unemployment_rate|8.16|y|line_chart Year|2017|x|line_chart Youth_unemployment_rate|8.18|y|line_chart Year|2016|x|line_chart Youth_unemployment_rate|8.56|y|line_chart Year|2015|x|line_chart Youth_unemployment_rate|8.79|y|line_chart Year|2014|x|line_chart Youth_unemployment_rate|8.86|y|line_chart Year|2013|x|line_chart Youth_unemployment_rate|8.96|y|line_chart Year|2012|x|line_chart Youth_unemployment_rate|8.86|y|line_chart Year|2011|x|line_chart Youth_unemployment_rate|8.47|y|line_chart Year|2010|x|line_chart Youth_unemployment_rate|8.15|y|line_chart Year|2009|x|line_chart Youth_unemployment_rate|8.06|y|line_chart Year|2008|x|line_chart Youth_unemployment_rate|7.39|y|line_chart Year|2007|x|line_chart Youth_unemployment_rate|6.96|y|line_chart Year|2006|x|line_chart Youth_unemployment_rate|6.85|y|line_chart Year|2005|x|line_chart Youth_unemployment_rate|7.21|y|line_chart Year|2004|x|line_chart Youth_unemployment_rate|7.41|y|line_chart Year|2003|x|line_chart Youth_unemployment_rate|8.54|y|line_chart Year|2002|x|line_chart Youth_unemployment_rate|9.5|y|line_chart Year|2001|x|line_chart Youth_unemployment_rate|10.11|y|line_chart Year|2000|x|line_chart Youth_unemployment_rate|11.02|y|line_chart Year|1999|x|line_chart Youth_unemployment_rate|11.95|y|line_chart 
title: Youth unemployment rate in Zimbabwe in 2019

gold: The statistic shows the youth unemployment rate in Zimbabwe from 1999 and 2019 . The data are ILO estimates . In 2019 , the estimated youth unemployment rate in Zimbabwe was at 8.13 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] and templateXValue[max] . The data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] was at templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Zimbabwe from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Zimbabwe was at 8.13 percent .


Example 432:
data: Month|July_2017-June_2018|x|bar_chart Circulation|163759|y|bar_chart Month|2016|x|bar_chart Circulation|194005|y|bar_chart Month|2015|x|bar_chart Circulation|221390|y|bar_chart Month|2014|x|bar_chart Circulation|259775|y|bar_chart Month|2013|x|bar_chart Circulation|292227|y|bar_chart Month|2012|x|bar_chart Circulation|324814|y|bar_chart Month|2011|x|bar_chart Circulation|365118|y|bar_chart Month|2010|x|bar_chart Circulation|370080|y|bar_chart Month|2009|x|bar_chart Circulation|391816|y|bar_chart Month|2008|x|bar_chart Circulation|431034|y|bar_chart Month|2007|x|bar_chart Circulation|435083|y|bar_chart Month|2006|x|bar_chart Circulation|432204|y|bar_chart Month|2005|x|bar_chart Circulation|453602|y|bar_chart Month|2004|x|bar_chart Circulation|469183|y|bar_chart Month|2003|x|bar_chart Circulation|440226|y|bar_chart Month|2002|x|bar_chart Circulation|435299|y|bar_chart Month|2001|x|bar_chart Circulation|433617|y|bar_chart Month|2000|x|bar_chart Circulation|436302|y|bar_chart Month|1999|x|bar_chart Circulation|435433|y|bar_chart 
title: El Pais ( Spain ) : circulation 1999 - 2018

gold: This timeline shows the average circulation of the Spanish daily El Pais from 1999 to 2018 . In the period between July 2017 and June 2018 , the Spanish newspaper sold on average 163.8 thousand copies daily .
gold_template: This timeline shows the average templateYLabel[0] of the Spanish daily templateTitle[0] templateTitle[1] from templateXValue[last] to templateXValue[0] . In the period between templateXValue[0] 2017 and June templateXValue[0] , the Spanish newspaper sold on average templateYValue[min] thousand copies daily .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the newspaper sold on average templateYValue[0] thousand copies daily , up from templateYValue[2] thousand in the previous year . The newspaper launched its paywall in March templateXValue[8] .
generated: The statistic shows the Circulation of Pais Spain in the United States from July_2017-June_2018 to July_2017-June_2018 . In 2015 , the newspaper sold on average 163759 thousand copies daily , up from 221390 thousand in the previous year . The newspaper launched its paywall in March 2009 .


Example 433:
data: Car_Model|Tesla_Model_S_MkI|x|bar_chart Percentage|97.19|y|bar_chart Car_Model|Land_Rover_Discovery_MkIV|x|bar_chart Percentage|94.63|y|bar_chart Car_Model|Renault_Kadjar_MkI|x|bar_chart Percentage|93.72|y|bar_chart Car_Model|Lexus_IS_MkIII|x|bar_chart Percentage|93.3|y|bar_chart Car_Model|Renault_ZOE_MkI|x|bar_chart Percentage|92.99|y|bar_chart Car_Model|Lexus_GS_MkIV|x|bar_chart Percentage|92.8|y|bar_chart Car_Model|Jaguar_XF_MkI|x|bar_chart Percentage|92.68|y|bar_chart Car_Model|Citroen_C5_MkII|x|bar_chart Percentage|92.62|y|bar_chart Car_Model|Skoda_Citigo_MkI|x|bar_chart Percentage|92.41|y|bar_chart Car_Model|Jeep_Grand_Cherokee_MKIV|x|bar_chart Percentage|92.38|y|bar_chart Car_Model|Toyota_Land_Cruiser_MkVII|x|bar_chart Percentage|92.26|y|bar_chart Car_Model|Lexus_RX_MkII|x|bar_chart Percentage|92.07|y|bar_chart Car_Model|MG_MG6_MkI|x|bar_chart Percentage|91.7|y|bar_chart Car_Model|Lexus_RX_MkIII|x|bar_chart Percentage|91.35|y|bar_chart Car_Model|Subaru_Forester_MkIV|x|bar_chart Percentage|91.32|y|bar_chart 
title: Best cars to own based on ride quality in Great Britain ( UK ) 2016

gold: This statistic shows the leading 15 car models according to the Auto Express Driver Power 2016 survey responses based on ride quality . The survey was carried out by the British automotive magazine online between 2015 and 2016 . Lexus had four models in the top 15 based on ride quality .
gold_template: This statistic shows the leading 15 templateXLabel[0] models according to the Auto Express Driver Power templateTitle[9] survey responses templateTitle[3] on templateTitle[4] templateTitle[5] . The survey was carried out by the British automotive magazine online between 2015 and templateTitle[9] . templateXValue[3] had four models in the top 15 templateTitle[3] on templateTitle[4] templateTitle[5] .

generated_template: This statistic shows the templateTitle[0] of templateYLabel[0] of templateYLabel[1] templateYLabel[2] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States in templateTitle[8] . In templateTitle[7] , the templateXValue[0] templateXValue[0] had the highest level of templateYLabel[0] of templateYLabel[1] .
generated: This statistic shows the Best of Percentage of yLabelErr yLabelErr of own based ride quality in the United States in UK . In Britain , the Tesla_Model_S_MkI had the highest level of Percentage of yLabelErr .


Example 434:
data: Year|2018|x|line_chart Number_of_illegal_entries_in_thousands|150.1|y|line_chart Year|2017|x|line_chart Number_of_illegal_entries_in_thousands|204.72|y|line_chart Year|2016|x|line_chart Number_of_illegal_entries_in_thousands|511.05|y|line_chart Year|2015|x|line_chart Number_of_illegal_entries_in_thousands|1822.18|y|line_chart Year|2014|x|line_chart Number_of_illegal_entries_in_thousands|282.93|y|line_chart Year|2013|x|line_chart Number_of_illegal_entries_in_thousands|107.37|y|line_chart Year|2012|x|line_chart Number_of_illegal_entries_in_thousands|72.44|y|line_chart Year|2011|x|line_chart Number_of_illegal_entries_in_thousands|141.05|y|line_chart Year|2010|x|line_chart Number_of_illegal_entries_in_thousands|104.06|y|line_chart Year|2009|x|line_chart Number_of_illegal_entries_in_thousands|104.6|y|line_chart 
title: Number of illegal entries between BCPs to the EU 2009 - 2018

gold: This statistic shows the total number of individuals detected entering the European Union ( EU ) illegally between border-crossing points ( BCPs ) from 2009 to 2018 . In 2013 , there was a total of approximately 107 thousand illegal entries between BCPs , making it a 48 percent increase on the previous year . By 2015 the number of individuals had increased to almost two million illegal entries .
gold_template: This statistic shows the total templateYLabel[0] of individuals detected entering the European Union ( templateTitle[5] ) illegally templateTitle[3] border-crossing points ( templateTitle[4] ) from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there was a total of approximately templateYValue[5] thousand templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] , making it a 48 percent increase on the previous templateXLabel[0] . By templateXValue[3] the templateYLabel[0] of individuals had increased to almost templateYValue[max] million templateYLabel[1] templateYLabel[2] .

generated_template: Over templateYValue[max] percent of the United States had been steadily throughout the templateTitle[3] templateTitle[4] templateTitle[5] in templateXValue[max] , an increase of templateYValue[0] percent compared with the previous templateXLabel[0] . The highest level since templateXValue[min] , templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] can be observed within the given time period , reaching templateYValue[2] percent of the templateXLabel[0] .
generated: Over 1822.18 percent of the United States had been steadily throughout the between BCPs EU in 2018 , an increase of 150.1 percent compared with the previous Year . The highest level since 2009 , illegal entries between BCPs can be observed within the given time period , reaching 511.05 percent of the Year .


Example 435:
data: Year|2021|x|line_chart Revenue_in_billion_U.S._dollars|15.86|y|line_chart Year|2020|x|line_chart Revenue_in_billion_U.S._dollars|17.35|y|line_chart Year|2019|x|line_chart Revenue_in_billion_U.S._dollars|18.57|y|line_chart Year|2018|x|line_chart Revenue_in_billion_U.S._dollars|19.53|y|line_chart Year|2017|x|line_chart Revenue_in_billion_U.S._dollars|20.33|y|line_chart Year|2016|x|line_chart Revenue_in_billion_U.S._dollars|20.85|y|line_chart 
title: Self-paced e-learning industry revenue in the U.S. 2016 - 2021

gold: This statistic presents the self-paced e-learning market revenue in the United States from 2016 to 2021 . The self-paced online learning market , in the U.S. , reached $ 20.85 billion in revenues in 2016 . The figure is estimated to decrease to $ 15.86 billion by 2021 .
gold_template: This statistic presents the templateTitle[0] templateTitle[1] market templateYLabel[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitle[0] online learning market , in the templateYLabel[2] , reached $ templateYValue[max] templateYLabel[1] in revenues in templateXValue[min] . The figure is estimated to decrease to $ templateYValue[min] templateYLabel[1] by templateXValue[max] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of the templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] amounted to templateYValue[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Self-paced Revenue of the industry revenue from 2016 to 2021 . In 2016 , Self-paced e-learning industry revenue Revenue amounted to 20.85 billion U.S. dollars .


Example 436:
data: Month|AMC_Theatres|x|bar_chart Number_of_screens|8218|y|bar_chart Month|Regal_Entertainment_Group|x|bar_chart Number_of_screens|7350|y|bar_chart Month|Cinemark_USA_Inc.|x|bar_chart Number_of_screens|4544|y|bar_chart Month|Cineplex_Entertainment_LP|x|bar_chart Number_of_screens|1683|y|bar_chart Month|Marcus_Theatres_Corp.|x|bar_chart Number_of_screens|895|y|bar_chart Month|Harkins_Theatres|x|bar_chart Number_of_screens|515|y|bar_chart Month|Southern_Theatres_LLC|x|bar_chart Number_of_screens|499|y|bar_chart Month|B_&_B_Theatres|x|bar_chart Number_of_screens|400|y|bar_chart Month|National_Amusements_Inc.|x|bar_chart Number_of_screens|392|y|bar_chart Month|Malco_Theatres_Inc.|x|bar_chart Number_of_screens|353|y|bar_chart 
title: Leading cinema circuits in North America in 2018 , by number of screens

gold: The graph shows leading cinema circuits in North America as of July 2018 , ranked by number of screens . AMC Theatres ranked first with 8,218 screens . Total attendance at AMC Theatres worldwide reached record levels in 2017 , with over 346 million attendees .
gold_template: The graph shows templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[3] templateTitle[4] as of July templateTitle[5] , ranked templateTitle[6] templateYLabel[0] of templateYLabel[1] . templateXValue[0] templateXValue[0] ranked first with templateYValue[max] templateYLabel[1] . Total attendance at templateXValue[0] templateXValue[0] worldwide reached record levels in 2017 , with over 346 million attendees .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States between July templateTitle[7] and templateTitle[8] . In the survey , templateYValue[max] people were killed by templateYLabel[1] , a templateXValue[0] templateXValue[0] .
generated: This statistic shows the Number screens of circuits North America 2018 in the United States between July number and screens . In the survey , 8218 people were killed by screens , a AMC_Theatres .


Example 437:
data: Response|Price_-_too_expensive|x|bar_chart Share_of_respondents|86.7|y|bar_chart Response|I_use_an_internet_streaming_service_such_as_Netflix_Hulu_Amazon_Video_etc.|x|bar_chart Share_of_respondents|39.7|y|bar_chart Response|I_use_an_antenna_to_get_the_basic_channels_on_my_TV|x|bar_chart Share_of_respondents|23|y|bar_chart Response|I_like_to_binge_watch_an_entire_season_of_a_TV_series_through_my_streaming_service|x|bar_chart Share_of_respondents|15.9|y|bar_chart Response|I_moved/relocated_and_I_do_not_plan_to_sign-up_for_cable/satellite_service_again|x|bar_chart Share_of_respondents|13|y|bar_chart Response|The_bulk_of_my_TV_viewing_was_the_original_series_on_streaming_services|x|bar_chart Share_of_respondents|7.7|y|bar_chart Response|I_share_a_friend/family_member's_login_to_watch_shows_on_their_cable/satellite_provider's_app|x|bar_chart Share_of_respondents|0.9|y|bar_chart 
title: Reasons for cutting the cord in North America 2017

gold: The graph shows reasons for cutting the cord named by respondents from North America in the fourth quarter of 2017 . During a a survey , it was found that 86.7 percent of respondents cut off their cable or satellite service because it was too expensive .
gold_template: The graph templateXValue[last] templateTitle[0] templateXValue[4] templateTitle[2] the templateTitle[3] named by templateYLabel[1] from templateTitle[4] templateTitle[5] in the fourth quarter of templateTitle[6] . During a survey , it templateXValue[5] found that templateYValue[max] percent of templateYLabel[1] cut off templateXValue[last] cable or satellite templateXValue[1] because it templateXValue[5] templateXValue[0] templateXValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] in the United States as of templateTitle[5] templateTitle[6] . During the survey period , it was found that templateYValue[max] percent of templateYLabel[1] claimed to be templateXValue[0] templateXValue[0] in a templateXValue[0] .
generated: This statistic shows the Reasons for of cutting cord in the United States as of America 2017 . During the survey period , it was found that 86.7 percent of respondents claimed to be Price_-_too_expensive in a Price_-_too_expensive .


Example 438:
data: Sex|Female|x|bar_chart Percentage_of_U.S._adults|77|y|bar_chart Sex|Male|x|bar_chart Percentage_of_U.S._adults|73|y|bar_chart 
title: Dietary supplement usage in U.S. adults by gender 2018

gold: This statistic indicates the percentage of U.S. adults that take dietary supplements , distributed by gender . The statistic is based on a survey conducted in August 2018 . Among U.S. adult males , some 73 percent reported taking dietary supplements .
gold_template: This statistic indicates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] that take templateTitle[0] supplements , distributed templateTitle[5] templateTitle[6] . The statistic is based on a survey conducted in August templateTitle[7] . Among templateYLabel[1] adult males , some templateYValue[min] percent reported taking templateTitle[0] supplements .

generated_template: In templateTitle[4] , templateYValue[max] percent of the social network in the United States , up from templateYValue[1] percent of the previous templateXLabel[0] . The majority of templateTitle[0] templateTitle[1] templateYLabel[1] stated they used the rise in which both templateXValue[0] and templateXValue[1] . The templateYLabel[1] rates are women in the U.S .
generated: In adults , 77 percent of the social network in the United States , up from 73 percent of the previous Sex . The majority of Dietary supplement U.S. stated they used the rise in which both Female and Male . The U.S. rates are women in the U.S .


Example 439:
data: Year|2018/19|x|line_chart Revenue_in_million_U.S._dollars|228|y|line_chart Year|2017/18|x|line_chart Revenue_in_million_U.S._dollars|191|y|line_chart Year|2016/17|x|line_chart Revenue_in_million_U.S._dollars|176|y|line_chart Year|2015/16|x|line_chart Revenue_in_million_U.S._dollars|169|y|line_chart Year|2014/15|x|line_chart Revenue_in_million_U.S._dollars|158|y|line_chart Year|2013/14|x|line_chart Revenue_in_million_U.S._dollars|164|y|line_chart Year|2012/13|x|line_chart Revenue_in_million_U.S._dollars|114|y|line_chart Year|2011/12|x|line_chart Revenue_in_million_U.S._dollars|129|y|line_chart Year|2010/11|x|line_chart Revenue_in_million_U.S._dollars|125|y|line_chart Year|2009/10|x|line_chart Revenue_in_million_U.S._dollars|110|y|line_chart Year|2008/09|x|line_chart Revenue_in_million_U.S._dollars|108|y|line_chart Year|2007/08|x|line_chart Revenue_in_million_U.S._dollars|97|y|line_chart Year|2006/07|x|line_chart Revenue_in_million_U.S._dollars|87|y|line_chart Year|2005/06|x|line_chart Revenue_in_million_U.S._dollars|86|y|line_chart 
title: Boston Bruins ' revenue 2005 - 2019

gold: The statistic shows the revenue of the Boston Bruins from the 2005/06 season to the 2018/19 season . The revenue of the Boston Bruins amounted to 228 million U.S. dollars in the 2018/19 season .
gold_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitle[0] templateTitle[1] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the templateXValue[last] season to the templateXValue[0] season . In templateXValue[0] , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Boston Bruins franchise from the 2005/06 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 228 million U.S. dollars .


Example 440:
data: Year|2018|x|line_chart Share_of_urban_population_in_total_population|17.21|y|line_chart Year|2017|x|line_chart Share_of_urban_population_in_total_population|17.13|y|line_chart Year|2016|x|line_chart Share_of_urban_population_in_total_population|17.06|y|line_chart Year|2015|x|line_chart Share_of_urban_population_in_total_population|17|y|line_chart Year|2014|x|line_chart Share_of_urban_population_in_total_population|16.97|y|line_chart Year|2013|x|line_chart Share_of_urban_population_in_total_population|16.95|y|line_chart Year|2012|x|line_chart Share_of_urban_population_in_total_population|16.94|y|line_chart Year|2011|x|line_chart Share_of_urban_population_in_total_population|16.94|y|line_chart Year|2010|x|line_chart Share_of_urban_population_in_total_population|16.93|y|line_chart Year|2009|x|line_chart Share_of_urban_population_in_total_population|16.93|y|line_chart Year|2008|x|line_chart Share_of_urban_population_in_total_population|16.93|y|line_chart 
title: Urbanization in Rwanda 2018

gold: This statistic shows the degree of urbanization in Rwanda from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 17.21 percent of Rwanda 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Rwanda from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 17.21 percent of Rwanda 's total population lived in urban areas and cities .


Example 441:
data: Year|2020|x|line_chart Thousand_tons_carcass_weight|96.38|y|line_chart Year|2019|x|line_chart Thousand_tons_carcass_weight|106.0|y|line_chart Year|2018|x|line_chart Thousand_tons_carcass_weight|112.94|y|line_chart Year|2017|x|line_chart Thousand_tons_carcass_weight|112.07|y|line_chart Year|2016|x|line_chart Thousand_tons_carcass_weight|111.39|y|line_chart Year|2015|x|line_chart Thousand_tons_carcass_weight|112.01|y|line_chart Year|2014|x|line_chart Thousand_tons_carcass_weight|110.65|y|line_chart Year|2013|x|line_chart Thousand_tons_carcass_weight|108.85|y|line_chart 
title: Production of pork worldwide 2013 - 2020

gold: This statistic depicts the production of pork worldwide from 2013 to 2020 . The net production of pork worldwide amounted to about 113 million tons carcass weight in 2018 , and forecasted to decrease to 96.4 million metric tones by 2020 .
gold_template: This statistic depicts the templateTitle[0] of templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . The net templateTitle[0] of templateTitle[1] templateTitle[2] amounted to about templateYValue[max] million templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[2] , and forecasted to decrease to templateYValue[min] million metric tones by templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of the templateYLabel[1] at the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: The statistic shows the Thousand tons carcass of 2013 2020 titleErr in the United States from 2013 to 2020 . In 2020 , about 96.38 percent of the tons at the worldwide 2013 2020 titleErr in the United States .


Example 442:
data: Year|2017/18|x|line_chart Bonus_payments_in_million_euros|1412.6|y|line_chart Year|2016/17|x|line_chart Bonus_payments_in_million_euros|1396.13|y|line_chart Year|2015/16|x|line_chart Bonus_payments_in_million_euros|1349.43|y|line_chart Year|2014/15|x|line_chart Bonus_payments_in_million_euros|1033.43|y|line_chart Year|2013/14|x|line_chart Bonus_payments_in_million_euros|904.6|y|line_chart Year|2012/13|x|line_chart Bonus_payments_in_million_euros|910.0|y|line_chart Year|2011/12|x|line_chart Bonus_payments_in_million_euros|754.1|y|line_chart Year|2010/11|x|line_chart Bonus_payments_in_million_euros|786.3|y|line_chart Year|2009/10|x|line_chart Bonus_payments_in_million_euros|757.5|y|line_chart Year|2008/09|x|line_chart Bonus_payments_in_million_euros|583.4|y|line_chart Year|2007/08|x|line_chart Bonus_payments_in_million_euros|585.6|y|line_chart Year|2006/07|x|line_chart Bonus_payments_in_million_euros|584.9|y|line_chart Year|2005/06|x|line_chart Bonus_payments_in_million_euros|437.1|y|line_chart 
title: UEFA Champions League total performance and bonus payments to clubs 2005 - 2018

gold: The statistic shows the total amount of bonus payments to the participating clubs in the UEFA Champions League from the 2005/06 season to the 2017/18 season . In the 2017/18 season , the total bonus payments to the participating clubs amounted to 1,412.6 million euros .
gold_template: The statistic shows the templateTitle[3] amount of templateYLabel[0] templateYLabel[1] to the participating templateTitle[7] in the templateTitle[0] templateTitle[1] templateTitle[2] from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , the templateTitle[3] templateYLabel[0] templateYLabel[1] to the participating templateTitle[7] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: There were templateYValue[0] templateYLabel[1] recorded in templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[7] , an increase of templateYValue[0] people when compared with the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] had since its highest templateTitle[3] templateTitle[4] templateTitle[5] in templateXValue[1] , reaching templateYValue[max] percent of templateYLabel[1] recorded in templateTitle[3] at just templateYValue[min] percent in templateYLabel[1] between templateXValue[0] .
generated: There were 1412.6 payments recorded in total performance bonus in clubs , an increase of 1412.6 people when compared with the previous Year . The Bonus payments had since its highest total performance bonus in 2016/17 , reaching 1412.6 percent of payments recorded in total at just 437.1 percent in payments between 2017/18 .


Example 443:
data: Year|2018|x|line_chart Expenditure_in_million_U.S._dollars|-1300|y|line_chart Year|2017|x|line_chart Expenditure_in_million_U.S._dollars|-1273|y|line_chart Year|2016|x|line_chart Expenditure_in_million_U.S._dollars|-1299|y|line_chart Year|2015|x|line_chart Expenditure_in_million_U.S._dollars|1362|y|line_chart Year|2014|x|line_chart Expenditure_in_million_U.S._dollars|1430|y|line_chart Year|2013|x|line_chart Expenditure_in_million_U.S._dollars|1376|y|line_chart Year|2012|x|line_chart Expenditure_in_million_U.S._dollars|1257|y|line_chart Year|2011|x|line_chart Expenditure_in_million_U.S._dollars|1191|y|line_chart Year|2010|x|line_chart Expenditure_in_million_U.S._dollars|1032|y|line_chart Year|2009|x|line_chart Expenditure_in_million_U.S._dollars|952|y|line_chart 
title: Syngenta 's R & D expenditure worldwide 2009 - 2018

gold: The statistic shows Syngenta AG 's expenditure on research and development ( R & D ) worldwide from 2009 to 2018 . Syngenta is one of the largest global agribusiness companies . It is based in Basel , Switzerland , and notably markets seeds and pesticides .
gold_template: The statistic shows templateTitle[0] AG templateTitle[1] templateYLabel[0] on research and development ( templateTitle[2] templateTitle[3] templateTitle[4] ) templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitle[0] is one of the largest global agribusiness companies . It is based in Basel , Switzerland , and notably markets seeds and pesticides .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] of the templateTitle[2] templateTitle[3] and templateTitle[4] from templateXValue[min] to templateXValue[max] . According to the report , the company 's global templateTitle[2] templateTitle[3] and templateTitle[4] amounted to approximately templateYValue[max] billion templateYLabel[2] templateYLabel[3] in templateXValue[max] . templateTitle[2] templateTitle[3] - additional information In recent times , the company 's net sales amounted to about templateYValue[1] billion templateYLabel[2] templateYLabel[3] in templateXValue[1] .
generated: The statistic shows the Expenditure of million U.S. of the R & and D from 2009 to 2018 . According to the report , the company 's global R & and D amounted to approximately 1430 billion U.S. dollars in 2018 . R & - additional information In recent times , the company 's net sales amounted to about -1273 billion U.S. dollars in 2017 .


Example 444:
data: Year|2018|x|line_chart Number_of_restaurants|201|y|line_chart Year|2017|x|line_chart Number_of_restaurants|199|y|line_chart Year|2016|x|line_chart Number_of_restaurants|194|y|line_chart Year|2015|x|line_chart Number_of_restaurants|188|y|line_chart Year|2014|x|line_chart Number_of_restaurants|177|y|line_chart Year|2013|x|line_chart Number_of_restaurants|168|y|line_chart Year|2012|x|line_chart Number_of_restaurants|162|y|line_chart Year|2011|x|line_chart Number_of_restaurants|156|y|line_chart Year|2010|x|line_chart Number_of_restaurants|149|y|line_chart Year|2009|x|line_chart Number_of_restaurants|160|y|line_chart 
title: The Cheesecake Factory 's number of establishments 2009 - 2018

gold: This statistic shows the number of The Cheesecake Factory restaurants from 2009 to 2018 . In 2018 , U.S.-based restaurant chain The Cheesecake Factory had 201 restaurants , up from 199 the previous year .
gold_template: This statistic shows the templateYLabel[0] of The templateTitle[0] templateTitle[1] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , U.S.-based restaurant chain The templateTitle[0] templateTitle[1] had templateYValue[max] templateYLabel[1] , up from templateYValue[1] the previous templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitle[4] templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateYLabel[1] in templateTitle[4] templateTitle[5] .
generated: The statistic shows the Number of Cheesecake Factory 's restaurants in establishments 2009 2009 to 2018 . In 2018 , there were 201 restaurants in establishments 2009 .


Example 445:
data: Year|2017|x|line_chart Life_expectancy_at_birth_in_years|75.24|y|line_chart Year|2016|x|line_chart Life_expectancy_at_birth_in_years|75.17|y|line_chart Year|2015|x|line_chart Life_expectancy_at_birth_in_years|75.11|y|line_chart Year|2014|x|line_chart Life_expectancy_at_birth_in_years|75.06|y|line_chart Year|2013|x|line_chart Life_expectancy_at_birth_in_years|75.01|y|line_chart Year|2012|x|line_chart Life_expectancy_at_birth_in_years|74.96|y|line_chart Year|2011|x|line_chart Life_expectancy_at_birth_in_years|74.9|y|line_chart Year|2010|x|line_chart Life_expectancy_at_birth_in_years|74.84|y|line_chart Year|2009|x|line_chart Life_expectancy_at_birth_in_years|74.75|y|line_chart Year|2008|x|line_chart Life_expectancy_at_birth_in_years|74.63|y|line_chart Year|2007|x|line_chart Life_expectancy_at_birth_in_years|74.47|y|line_chart 
title: Life expectancy at birth in Vietnam 2017

gold: This statistic shows the life expectancy at birth in Vietnam from 2007 to 2017 . In 2017 , the average life expectancy at birth in Vietnam was 75.24 years .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitle[3] was templateYValue[max] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] at templateYLabel[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] among the templateYLabel[3] . templateTitle[3] was about templateYValue[max] templateYLabel[3] .
generated: The statistic shows the Life expectancy of birth at birth in Vietnam from 2007 to 2017 . In 2017 , the average Life expectancy at birth among the years . Vietnam was about 75.24 years .


Example 446:
data: Team_Name|Atlanta_United|x|bar_chart Operating_income/loss_in_million_U.S._dollars|7|y|bar_chart Team_Name|LA_Galaxy|x|bar_chart Operating_income/loss_in_million_U.S._dollars|5|y|bar_chart Team_Name|Portland_Timbers|x|bar_chart Operating_income/loss_in_million_U.S._dollars|4|y|bar_chart Team_Name|Real_Salt_Lake|x|bar_chart Operating_income/loss_in_million_U.S._dollars|2|y|bar_chart Team_Name|Seattle_Sounders|x|bar_chart Operating_income/loss_in_million_U.S._dollars|1|y|bar_chart Team_Name|D.C._United|x|bar_chart Operating_income/loss_in_million_U.S._dollars|1|y|bar_chart Team_Name|Sporting_Kansas_City|x|bar_chart Operating_income/loss_in_million_U.S._dollars|1|y|bar_chart Team_Name|Orlando_City_SC|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-1|y|bar_chart Team_Name|New_England_Revolution|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-2|y|bar_chart Team_Name|Philadelphia_Union|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-5|y|bar_chart Team_Name|Los_Angeles_FC|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-5|y|bar_chart Team_Name|Vancouver_Whitecaps|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-5|y|bar_chart Team_Name|Colorado_Rapids|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-5|y|bar_chart Team_Name|San_Jose_Earthquakes|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-5|y|bar_chart Team_Name|New_York_Red_Bulls|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-6|y|bar_chart Team_Name|Houston_Dynamo|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-6|y|bar_chart Team_Name|FC_Dallas|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-7|y|bar_chart Team_Name|Columbus_Crew|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-8|y|bar_chart Team_Name|Minnesota_United|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-8|y|bar_chart Team_Name|Montreal_Impact|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-12|y|bar_chart Team_Name|Chicago_Fire|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-16|y|bar_chart Team_Name|New_York_City_FC|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-16|y|bar_chart Team_Name|Toronto_FC|x|bar_chart Operating_income/loss_in_million_U.S._dollars|-19|y|bar_chart 
title: Major League Soccer teams ranked by operating income 2019

gold: The statistic shows a ranking of Major League Soccer teams according to their operating income/loss . Atlanta United had an operating income of seven million U.S. dollars in the 2019 MLS season .
gold_template: The statistic shows a ranking of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] according to their templateYLabel[0] templateYLabel[1] . templateXValue[0] templateXValue[0] had an templateYLabel[0] templateTitle[7] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the templateTitle[8] MLS season .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] templateYLabel[1] . In templateTitle[4] , templateYValue[max] percent of the United States were killed by consumed in the United States .
generated: This statistic shows the Operating League income/loss in the United States in ranked , by Team income/loss . In ranked , 7 percent of the United States were killed by consumed in the United States .


Example 447:
data: Year|2024|x|line_chart Inhabitants_in_millions|6.68|y|line_chart Year|2023|x|line_chart Inhabitants_in_millions|6.58|y|line_chart Year|2022|x|line_chart Inhabitants_in_millions|6.48|y|line_chart Year|2021|x|line_chart Inhabitants_in_millions|6.38|y|line_chart Year|2020|x|line_chart Inhabitants_in_millions|6.27|y|line_chart Year|2019|x|line_chart Inhabitants_in_millions|6.16|y|line_chart Year|2018|x|line_chart Inhabitants_in_millions|6.05|y|line_chart Year|2017|x|line_chart Inhabitants_in_millions|5.93|y|line_chart Year|2016|x|line_chart Inhabitants_in_millions|5.82|y|line_chart Year|2015|x|line_chart Inhabitants_in_millions|5.7|y|line_chart Year|2014|x|line_chart Inhabitants_in_millions|5.58|y|line_chart 
title: Total population of Eritrea 2024

gold: This statistic shows the total population of Eritrea from 2014 to 2024 . All figures are estimates . In 2018 , the total population of Eritrea was estimated to amount to approximately 6.05 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[max] . All figures are estimates . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] was estimated to amount to approximately templateYValue[6] million templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] amounted to approximately templateYValue[6] million templateYLabel[0] . See the figures for the templateTitle[1] of Italy for comparison .
generated: The statistic shows the Total population of Eritrea from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Eritrea amounted to approximately 6.05 million Inhabitants . See the figures for the population of Italy for comparison .


Example 448:
data: Year|20_or_younger|x|line_chart Share_of_average_increase|6|y|line_chart Year|20_to_40_years|x|line_chart Share_of_average_increase|11|y|line_chart Year|40_to_50_years|x|line_chart Share_of_average_increase|18|y|line_chart Year|50_to_60_years|x|line_chart Share_of_average_increase|24|y|line_chart Year|60_or_older|x|line_chart Share_of_average_increase|42|y|line_chart 
title: Europe : forecasted distribution of golfers in 2020 , by age group

gold: The statistic displays the forecast of a golf player distribution in Europe in 2020 , by age group . With data from five European countries it was forecasted that in 2020 approximately 24 percent of golf players will be between 50 and 60 years old .
gold_template: The statistic displays the forecast of a golf player templateTitle[2] in templateTitle[0] in templateTitle[4] , templateTitle[5] templateTitle[6] templateTitle[7] . With data from five European countries it was templateTitle[1] that in templateTitle[4] approximately templateYValue[3] percent of golf players will be between templateXValue[2] and templateXValue[3] templateXValue[1] old .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Share of forecasted distribution golfers 2020 by in the United States from 20_or_younger to 20_or_younger . In 20_or_younger , there were 6 Europe forecasted distribution golfers 2020 by in the United States .


Example 449:
data: Country|Global|x|bar_chart Estimated_number_of_people_with_hearing_loss|328.0|y|bar_chart Country|Europe|x|bar_chart Estimated_number_of_people_with_hearing_loss|119.0|y|bar_chart Country|European_Union|x|bar_chart Estimated_number_of_people_with_hearing_loss|51.0|y|bar_chart Country|United_Kingdom|x|bar_chart Estimated_number_of_people_with_hearing_loss|10.0|y|bar_chart Country|France|x|bar_chart Estimated_number_of_people_with_hearing_loss|6.0|y|bar_chart Country|Spain|x|bar_chart Estimated_number_of_people_with_hearing_loss|3.5|y|bar_chart Country|Netherlands|x|bar_chart Estimated_number_of_people_with_hearing_loss|1.6|y|bar_chart Country|Austria|x|bar_chart Estimated_number_of_people_with_hearing_loss|1.6|y|bar_chart Country|Sweden|x|bar_chart Estimated_number_of_people_with_hearing_loss|1.4|y|bar_chart Country|Belgium|x|bar_chart Estimated_number_of_people_with_hearing_loss|1.3|y|bar_chart Country|Poland|x|bar_chart Estimated_number_of_people_with_hearing_loss|1.0|y|bar_chart Country|Denmark|x|bar_chart Estimated_number_of_people_with_hearing_loss|0.8|y|bar_chart Country|Ireland|x|bar_chart Estimated_number_of_people_with_hearing_loss|0.8|y|bar_chart 
title: Number of people with hearing loss global vs European countries 2015

gold: This statistic shows the estimated number of people with hearing loss worldwide and in Europe as of 2015 , by country , in millions . As of this time an estimated 119 million people in the whole of Europe were hard of hearing , with 3.5 million of these people located in Spain .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] worldwide and in templateXValue[1] as of templateTitle[9] , by templateXLabel[0] , in millions . As of this time an templateYLabel[0] templateYValue[1] million templateYLabel[2] in the whole of templateXValue[1] were hard of templateYLabel[4] , templateYLabel[3] templateYValue[5] million of these templateYLabel[2] located in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] in the world templateTitle[5] templateTitle[6] to templateTitle[7] . In this year , there were about templateYValue[max] million people living in templateXValue[0] .
generated: This statistic shows the Estimated number of the hearing loss global in the world vs European to countries . In this year , there were about 328.0 million people living in Global .


Example 450:
data: Year|2010|x|line_chart Price_in_U.S._dollars_per_kilogram|41|y|line_chart Year|2011|x|line_chart Price_in_U.S._dollars_per_kilogram|303|y|line_chart Year|2012|x|line_chart Price_in_U.S._dollars_per_kilogram|107|y|line_chart Year|2013|x|line_chart Price_in_U.S._dollars_per_kilogram|66|y|line_chart Year|2014|x|line_chart Price_in_U.S._dollars_per_kilogram|63|y|line_chart Year|2015|x|line_chart Price_in_U.S._dollars_per_kilogram|55|y|line_chart Year|2016|x|line_chart Price_in_U.S._dollars_per_kilogram|51|y|line_chart Year|2017|x|line_chart Price_in_U.S._dollars_per_kilogram|49|y|line_chart Year|2018|x|line_chart Price_in_U.S._dollars_per_kilogram|46|y|line_chart Year|2019|x|line_chart Price_in_U.S._dollars_per_kilogram|38|y|line_chart Year|2020|x|line_chart Price_in_U.S._dollars_per_kilogram|20|y|line_chart Year|2021|x|line_chart Price_in_U.S._dollars_per_kilogram|20|y|line_chart Year|2022|x|line_chart Price_in_U.S._dollars_per_kilogram|20|y|line_chart Year|2023|x|line_chart Price_in_U.S._dollars_per_kilogram|21|y|line_chart Year|2024|x|line_chart Price_in_U.S._dollars_per_kilogram|21|y|line_chart Year|2025|x|line_chart Price_in_U.S._dollars_per_kilogram|22|y|line_chart 
title: Forecast of rare earth oxide holmium oxide price globally 2010 - 2025

gold: This statistic displays the price development of rare earth oxide holmium oxide globally from 2009 to 2025 . It expected that the price of holmium oxide will reach some 49 U.S. dollars per kilogram in 2017 .
gold_template: This statistic displays the templateYLabel[0] development of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[3] templateTitle[7] from 2009 to templateXValue[max] . It expected that the templateYLabel[0] of templateTitle[4] templateTitle[3] will reach some templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] million people employed by the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Price U.S. of earth oxide holmium oxide price from 2010 to 2025 . In 2025 , there were 41 million people employed by the earth oxide holmium oxide in the United States .


Example 451:
data: Platform|Facebook|x|bar_chart Number_of_followers_in_millions|17.91|y|bar_chart Platform|Instagram|x|bar_chart Number_of_followers_in_millions|16.0|y|bar_chart Platform|Twitter|x|bar_chart Number_of_followers_in_millions|3.5|y|bar_chart 
title: Number of followers of Michael Kors on social media 2020

gold: This statistic depicts the number of followers of Michael Kors on social media as of January 2020 . During the measured period , the largest social media presence of the brand was on Facebook with 17.91 million followers , as opposed to its 3.5 million follower base on Twitter .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[1] of templateTitle[2] templateTitle[3] on templateTitle[4] templateTitle[5] as of January templateTitle[6] . During the measured period , the largest templateTitle[4] templateTitle[5] presence of the brand was on templateXValue[0] with templateYValue[max] million templateYLabel[1] , as opposed to its templateYValue[min] million follower base on templateXValue[last] .

generated_template: templateXValue[0] was the most popular templateTitle[1] templateTitle[2] templateXLabel[0] among templateTitle[0] templateTitle[1] templateTitle[2] users in templateTitle[4] as of October templateTitle[5] . During the survey period it was found that templateYValue[5] million templateYLabel[1] templateYLabel[2] templateTitle[3] users had a templateXValue[0] account .
generated: Facebook was the most popular followers Michael Platform among Number followers Michael users in social as of October media . During the survey period it was found that yValErr million followers millions Kors users had a Facebook account .


Example 452:
data: Month|Dec_'10|x|bar_chart Number_of_registered_members_in_millions|5|y|bar_chart Month|Jan_'11|x|bar_chart Number_of_registered_members_in_millions|6|y|bar_chart Month|Mar_'12|x|bar_chart Number_of_registered_members_in_millions|15|y|bar_chart Month|May_'12|x|bar_chart Number_of_registered_members_in_millions|20|y|bar_chart Month|Jan_'13|x|bar_chart Number_of_registered_members_in_millions|30|y|bar_chart Month|Jan_'14|x|bar_chart Number_of_registered_members_in_millions|45|y|bar_chart Month|May_'14|x|bar_chart Number_of_registered_members_in_millions|50|y|bar_chart Month|Oct_'14|x|bar_chart Number_of_registered_members_in_millions|55|y|bar_chart 
title: Number of registered members on Foursquare 2010 - 2014

gold: This statistic gives information on the number of registered members on Foursquare between December 2010 and October 2014 . As of that month , the social check-in app community had accumulated over 55 million members worldwide .
gold_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] on templateTitle[3] between December templateTitle[4] and October templateTitle[5] . As of that templateXLabel[0] , the social check-in app community had accumulated over templateYValue[max] million templateYLabel[2] worldwide .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] of mobile templateYLabel[2] worldwide as of January templateTitle[4] , sorted templateTitle[5] templateTitle[6] . As of the last reported period , it was found that templateYValue[max] percent of the social network had been victim of the previous year .
generated: This statistic presents the Number registered of mobile members worldwide as of January 2010 , sorted 2014 titleErr . As of the last reported period , it was found that 55 percent of the social network had been victim of the previous year .


Example 453:
data: Year|2018|x|line_chart Production_in_billion_cubic_meters|831.8|y|line_chart Year|2017|x|line_chart Production_in_billion_cubic_meters|745.8|y|line_chart Year|2016|x|line_chart Production_in_billion_cubic_meters|727.4|y|line_chart Year|2015|x|line_chart Production_in_billion_cubic_meters|740.3|y|line_chart Year|2014|x|line_chart Production_in_billion_cubic_meters|704.7|y|line_chart Year|2013|x|line_chart Production_in_billion_cubic_meters|655.7|y|line_chart Year|2012|x|line_chart Production_in_billion_cubic_meters|649.1|y|line_chart Year|2011|x|line_chart Production_in_billion_cubic_meters|617.4|y|line_chart Year|2010|x|line_chart Production_in_billion_cubic_meters|575.2|y|line_chart Year|2009|x|line_chart Production_in_billion_cubic_meters|557.6|y|line_chart Year|2008|x|line_chart Production_in_billion_cubic_meters|546.1|y|line_chart Year|2007|x|line_chart Production_in_billion_cubic_meters|521.9|y|line_chart Year|2006|x|line_chart Production_in_billion_cubic_meters|524.0|y|line_chart Year|2005|x|line_chart Production_in_billion_cubic_meters|511.1|y|line_chart Year|2004|x|line_chart Production_in_billion_cubic_meters|526.4|y|line_chart Year|2003|x|line_chart Production_in_billion_cubic_meters|540.8|y|line_chart Year|2002|x|line_chart Production_in_billion_cubic_meters|536.0|y|line_chart Year|2001|x|line_chart Production_in_billion_cubic_meters|555.5|y|line_chart Year|2000|x|line_chart Production_in_billion_cubic_meters|543.2|y|line_chart Year|1998|x|line_chart Production_in_billion_cubic_meters|538.7|y|line_chart 
title: Natural gas production - United States 1998 - 2018

gold: Production of natural gas in the United States has been increasing for the past decade and amounted to 831.8 billion cubic meters in 2018 . An increase in production corresponded with rising demand for natural gas in the United States , particularly after the 2008 Recession . Natural gas becomes competitive Since the early 2000s , the price of coal had been going up , and increased more rapidly following the 2008 Recession , which affected the cost of crude oil to an even greater degree .
gold_template: templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitle[3] templateTitle[4] has been increasing for the past decade and amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . An increase in templateYLabel[0] corresponded with rising demand for templateTitle[0] templateTitle[1] in the templateTitle[3] templateTitle[4] , particularly after the templateXValue[10] Recession . templateTitle[0] templateTitle[1] becomes competitive Since the early 2000s , the price of coal had been going up , and increased more rapidly following the templateXValue[10] Recession , which affected the cost of crude oil to an even greater degree .

generated_template: There were templateYValue[0] in templateTitle[3] templateTitle[4] in templateXValue[max] United States in the United States in templateXValue[max] . The templateYLabel[0] templateYLabel[1] results since templateXValue[min] and templateXValue[6] , there were templateYValue[0] percent in the United States . The templateYLabel[0] templateYLabel[1] in templateYLabel[3] in templateYLabel[1] commonly used the number of people .
generated: There were 831.8 in United States in 2018 United States in the United States in 2018 . The Production billion results since 1998 and 2012 , there were 831.8 percent in the United States . The Production billion in meters in billion commonly used the number of people .


Example 454:
data: Response|Yes_I'm_doing_so_currently|x|bar_chart Share_of_respondents|7|y|bar_chart Response|Yes_I've_done_so_in_the_past|x|bar_chart Share_of_respondents|24|y|bar_chart Response|No_never|x|bar_chart Share_of_respondents|65|y|bar_chart Response|Don't_know|x|bar_chart Share_of_respondents|3|y|bar_chart 
title: Dating website or app usage among U.S. online users 2019

gold: This statistic presents the percentage of adult online users in the United States who have used a dating website or app as of January 2019 . According to the findings , only seven percent of respondents stated that they were currently using a dating website or app , while in comparison 65 percent of respondents reported to have never used a dating app or website before .
gold_template: This statistic presents the percentage of adult templateTitle[6] templateTitle[7] in the templateTitle[5] who have used a templateTitle[0] templateTitle[1] or templateTitle[2] as of January templateTitle[8] . According to the findings , only templateYValue[0] percent of templateYLabel[1] stated that they were templateXValue[0] using a templateTitle[0] templateTitle[1] or templateTitle[2] , while in comparison templateYValue[max] percent of templateYLabel[1] reported to have templateXValue[2] used a templateTitle[0] templateTitle[2] or templateTitle[1] before .

generated_template: This statistic shows the results of a survey conducted in the templateTitle[0] templateTitle[1] in templateTitle[3] templateTitle[4] on templateTitle[5] templateTitle[6] survey conducted in templateTitle[7] templateTitle[8] . During the survey period of templateTitle[5] survey , templateYValue[max] percent of templateYLabel[1] stated that they used templateXValue[0] templateXValue[0] to templateXValue[1] templateXValue[1] .
generated: This statistic shows the results of a survey conducted in the Dating website in usage among on U.S. online survey conducted in users 2019 . During the survey period of U.S. survey , 65 percent of respondents stated that they used Yes_I'm_doing_so_currently to Yes_I've_done_so_in_the_past .


Example 455:
data: Year|2019|x|line_chart Home_attendance|572811|y|line_chart Year|2018|x|line_chart Home_attendance|583184|y|line_chart Year|2017|x|line_chart Home_attendance|575681|y|line_chart Year|2016|x|line_chart Home_attendance|559998|y|line_chart Year|2015|x|line_chart Home_attendance|562845|y|line_chart Year|2014|x|line_chart Home_attendance|493515|y|line_chart Year|2013|x|line_chart Home_attendance|561795|y|line_chart Year|2012|x|line_chart Home_attendance|560773|y|line_chart Year|2011|x|line_chart Home_attendance|551892|y|line_chart Year|2010|x|line_chart Home_attendance|542800|y|line_chart Year|2009|x|line_chart Home_attendance|545384|y|line_chart Year|2008|x|line_chart Home_attendance|512520|y|line_chart Year|2007|x|line_chart Home_attendance|547610|y|line_chart Year|2006|x|line_chart Home_attendance|563456|y|line_chart 
title: Regular season home attendance of the Atlanta Falcons 2006 - 2019

gold: This graph depicts the total regular season home attendance of the Atlanta Falcons franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 572,811 .
gold_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] .

generated_template: This graph depicts the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] .
generated: This graph depicts the Regular season Home attendance of the Atlanta Falcons franchise of the National Football League from 2006 to 2019 . In 2019 , the Regular season home Home attendance of the franchise was 572811 .


Example 456:
data: Country|Russia|x|bar_chart Inflation_rate_compared_to_previous_year|4.68|y|bar_chart Country|Brazil|x|bar_chart Inflation_rate_compared_to_previous_year|3.79|y|bar_chart Country|India|x|bar_chart Inflation_rate_compared_to_previous_year|3.44|y|bar_chart Country|China|x|bar_chart Inflation_rate_compared_to_previous_year|2.31|y|bar_chart Country|USA|x|bar_chart Inflation_rate_compared_to_previous_year|1.82|y|bar_chart Country|United_Kingdom|x|bar_chart Inflation_rate_compared_to_previous_year|1.81|y|bar_chart Country|Germany|x|bar_chart Inflation_rate_compared_to_previous_year|1.49|y|bar_chart Country|France|x|bar_chart Inflation_rate_compared_to_previous_year|1.17|y|bar_chart Country|Japan|x|bar_chart Inflation_rate_compared_to_previous_year|0.99|y|bar_chart 
title: Inflation rate of the main industrialized and emerging countries 2019

gold: This statistic shows the inflation rate of the main industrialized and emerging countries in 2019 . In 2019 , the inflation rate in China was estimated to amount to approximately 2.31 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] and templateTitle[4] templateTitle[5] in templateTitle[6] . In templateTitle[6] , the templateYLabel[0] templateYLabel[1] in templateXValue[3] was estimated to amount to approximately templateYValue[3] percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[5] . In templateTitle[5] , templateXValue[0] ranked 1st templateTitle[1] a negative templateYLabel[0] templateYLabel[1] of about templateYValue[1] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the 20 Inflation rate the main Inflation rate in countries . In countries , Russia ranked 1st rate a negative Inflation rate of about 3.79 percent compared to the previous Country .


Example 457:
data: Month|Jan_20|x|bar_chart Interest_rate|4.56|y|bar_chart Month|Dec_19|x|bar_chart Interest_rate|4.61|y|bar_chart Month|Nov_19|x|bar_chart Interest_rate|4.59|y|bar_chart Month|Oct_19|x|bar_chart Interest_rate|4.61|y|bar_chart Month|Sep_19|x|bar_chart Interest_rate|4.61|y|bar_chart Month|Aug_19|x|bar_chart Interest_rate|4.63|y|bar_chart Month|Jul_19|x|bar_chart Interest_rate|4.66|y|bar_chart Month|Jun_19|x|bar_chart Interest_rate|4.74|y|bar_chart Month|May_19|x|bar_chart Interest_rate|4.72|y|bar_chart Month|Apr_19|x|bar_chart Interest_rate|4.77|y|bar_chart Month|Mar_19|x|bar_chart Interest_rate|4.77|y|bar_chart Month|Feb_19|x|bar_chart Interest_rate|4.78|y|bar_chart Month|Jan_19|x|bar_chart Interest_rate|4.77|y|bar_chart Month|Dec_18|x|bar_chart Interest_rate|4.96|y|bar_chart Month|Nov_18|x|bar_chart Interest_rate|4.93|y|bar_chart Month|Oct_18|x|bar_chart Interest_rate|4.93|y|bar_chart Month|Sep_18|x|bar_chart Interest_rate|4.79|y|bar_chart Month|Aug_18|x|bar_chart Interest_rate|4.8|y|bar_chart Month|Jul_18|x|bar_chart Interest_rate|4.83|y|bar_chart Month|Jun_18|x|bar_chart Interest_rate|4.82|y|bar_chart Month|May_18|x|bar_chart Interest_rate|4.64|y|bar_chart Month|Apr_18|x|bar_chart Interest_rate|4.59|y|bar_chart Month|Mar_18|x|bar_chart Interest_rate|4.52|y|bar_chart Month|Feb_18|x|bar_chart Interest_rate|4.53|y|bar_chart Month|Jan_18|x|bar_chart Interest_rate|4.51|y|bar_chart Month|Dec_17|x|bar_chart Interest_rate|4.43|y|bar_chart Month|Nov_17|x|bar_chart Interest_rate|4.29|y|bar_chart Month|Oct_17|x|bar_chart Interest_rate|4.3|y|bar_chart Month|Sep_17|x|bar_chart Interest_rate|4.29|y|bar_chart Month|Aug_17|x|bar_chart Interest_rate|4.25|y|bar_chart Month|Jul_17|x|bar_chart Interest_rate|4.51|y|bar_chart Month|Jun_17|x|bar_chart Interest_rate|4.44|y|bar_chart Month|May_17|x|bar_chart Interest_rate|4.38|y|bar_chart Month|Apr_17|x|bar_chart Interest_rate|4.35|y|bar_chart Month|Mar_17|x|bar_chart Interest_rate|4.38|y|bar_chart Month|Feb_17|x|bar_chart Interest_rate|4.36|y|bar_chart Month|Jan_17|x|bar_chart Interest_rate|4.35|y|bar_chart Month|Dec_16|x|bar_chart Interest_rate|4.32|y|bar_chart Month|Nov_16|x|bar_chart Interest_rate|4.27|y|bar_chart Month|Oct_16|x|bar_chart Interest_rate|4.26|y|bar_chart Month|Sep_16|x|bar_chart Interest_rate|4.23|y|bar_chart Month|Aug_16|x|bar_chart Interest_rate|4.18|y|bar_chart Month|Jul_16|x|bar_chart Interest_rate|4.17|y|bar_chart Month|Jun_16|x|bar_chart Interest_rate|4.17|y|bar_chart Month|May_16|x|bar_chart Interest_rate|4.25|y|bar_chart Month|Apr_16|x|bar_chart Interest_rate|4.28|y|bar_chart Month|Mar_16|x|bar_chart Interest_rate|4.33|y|bar_chart Month|Feb_16|x|bar_chart Interest_rate|4.29|y|bar_chart Month|Jan_16|x|bar_chart Interest_rate|4.33|y|bar_chart Month|Dec_15|x|bar_chart Interest_rate|4.4|y|bar_chart Month|Nov_15|x|bar_chart Interest_rate|4.33|y|bar_chart Month|Oct_15|x|bar_chart Interest_rate|4.3|y|bar_chart Month|Sep_15|x|bar_chart Interest_rate|4.32|y|bar_chart Month|Aug_15|x|bar_chart Interest_rate|4.38|y|bar_chart Month|Jul_15|x|bar_chart Interest_rate|4.37|y|bar_chart Month|Jun_15|x|bar_chart Interest_rate|4.39|y|bar_chart Month|May_15|x|bar_chart Interest_rate|4.37|y|bar_chart Month|Apr_15|x|bar_chart Interest_rate|4.37|y|bar_chart Month|Mar_15|x|bar_chart Interest_rate|4.31|y|bar_chart Month|Feb_15|x|bar_chart Interest_rate|4.07|y|bar_chart Month|Jan_15|x|bar_chart Interest_rate|4.07|y|bar_chart Month|Dec_14|x|bar_chart Interest_rate|4.12|y|bar_chart Month|Nov_14|x|bar_chart Interest_rate|4.06|y|bar_chart Month|Oct_14|x|bar_chart Interest_rate|4.04|y|bar_chart Month|Sep_14|x|bar_chart Interest_rate|4.02|y|bar_chart Month|Aug_14|x|bar_chart Interest_rate|4.03|y|bar_chart Month|Jul_14|x|bar_chart Interest_rate|4.03|y|bar_chart Month|Jun_14|x|bar_chart Interest_rate|4.13|y|bar_chart Month|May_14|x|bar_chart Interest_rate|4.13|y|bar_chart Month|Apr_14|x|bar_chart Interest_rate|4.18|y|bar_chart Month|Mar_14|x|bar_chart Interest_rate|4.23|y|bar_chart Month|Feb_14|x|bar_chart Interest_rate|4.21|y|bar_chart Month|Jan_14|x|bar_chart Interest_rate|4.25|y|bar_chart 
title: Monthly car loan rates in the U.S. 2017 - 2020

gold: This statistic presents the average interest rate on 60-month new car loans in the United States from January 2014 to January 2020 . Car loan interest rates amounted to 4.56 percent as of January 30 , 2020 . The smaller the car loan interest rates , the cheaper the loan is .
gold_template: This statistic presents the average templateYLabel[0] templateYLabel[1] on 60-month new templateTitle[1] loans in the templateTitle[4] from January 2014 to January templateTitle[6] . templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[3] amounted to templateYValue[0] percent as of January 30 , templateTitle[6] . The smaller the templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[3] , the cheaper the templateTitle[2] is .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the United States in the templateTitle[4] templateTitle[5] ( templateTitle[6] ) from January templateTitle[7] to December templateTitle[8] . In December templateTitle[8] , there were approximately templateYValue[0] million templateYLabel[1] 100,000 of the United States .
generated: This statistic shows the Interest rate of the United States in the U.S. 2017 ( 2020 ) from January titleErr to December titleErr . In December titleErr , there were approximately 4.56 million rate 100,000 of the United States .


Example 458:
data: Country|Germany|x|bar_chart Litres_consumed_per_capita|821|y|bar_chart Country|Italy|x|bar_chart Litres_consumed_per_capita|322|y|bar_chart Country|Hungary|x|bar_chart Litres_consumed_per_capita|214|y|bar_chart Country|Spain|x|bar_chart Litres_consumed_per_capita|165|y|bar_chart Country|Poland|x|bar_chart Litres_consumed_per_capita|119|y|bar_chart Country|France|x|bar_chart Litres_consumed_per_capita|90|y|bar_chart Country|Romania|x|bar_chart Litres_consumed_per_capita|69|y|bar_chart Country|United_Kingdom|x|bar_chart Litres_consumed_per_capita|67|y|bar_chart Country|Greece|x|bar_chart Litres_consumed_per_capita|44|y|bar_chart Country|Austria|x|bar_chart Litres_consumed_per_capita|33|y|bar_chart Country|Belgium|x|bar_chart Litres_consumed_per_capita|27|y|bar_chart Country|Bulgaria|x|bar_chart Litres_consumed_per_capita|22|y|bar_chart Country|Portugal|x|bar_chart Litres_consumed_per_capita|21|y|bar_chart Country|Slovakia|x|bar_chart Litres_consumed_per_capita|20|y|bar_chart Country|Lithuania|x|bar_chart Litres_consumed_per_capita|17|y|bar_chart Country|Netherlands|x|bar_chart Litres_consumed_per_capita|13|y|bar_chart Country|Czech_Republic|x|bar_chart Litres_consumed_per_capita|11|y|bar_chart Country|Denmark|x|bar_chart Litres_consumed_per_capita|11|y|bar_chart Country|Sweden|x|bar_chart Litres_consumed_per_capita|11|y|bar_chart Country|Slovenia|x|bar_chart Litres_consumed_per_capita|9|y|bar_chart Country|Latvia|x|bar_chart Litres_consumed_per_capita|5|y|bar_chart Country|Estonia|x|bar_chart Litres_consumed_per_capita|4|y|bar_chart Country|Croatia|x|bar_chart Litres_consumed_per_capita|4|y|bar_chart Country|Ireland|x|bar_chart Litres_consumed_per_capita|2|y|bar_chart Country|Finland|x|bar_chart Litres_consumed_per_capita|1|y|bar_chart 
title: Number of natural mineral waters in Europe 2016 , by country

gold: This statistic represents the number of natural mineral waters in Europe in 2016 . Germany had the highest number of natural mineral waters with 821 certified natural mineral water sources .
gold_template: This statistic represents the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[4] in templateTitle[5] . templateXValue[0] had the highest templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] with templateYValue[max] certified templateTitle[1] templateTitle[2] water sources .

generated_template: This statistic shows the templateYLabel[2] templateYLabel[3] templateTitle[2] of templateTitle[0] templateTitle[1] in templateTitle[5] in templateTitle[6] . In this year , templateXValue[0] was the leading market for the templateTitle[2] of templateTitle[0] templateTitle[1] with templateYValue[max] liters of templateTitle[0] templateTitle[1] templateYLabel[1] templateYLabel[2] person and templateXValue[1] was the second largest consumer of templateTitle[0] templateTitle[1] with templateYValue[1] liters templateYLabel[1] templateYLabel[2] person . templateTitle[0] templateTitle[1] in the European Union is predominantly made up of the natural mineral templateTitle[1] category.Germany is the market with the largest amount of different mineral templateTitle[1] brands .
generated: This statistic shows the per capita mineral of Number natural in 2016 in by . In this year , Germany was the leading market for the mineral of Number natural with 821 liters of Number natural consumed per person and Italy was the second largest consumer of Number natural with 322 liters consumed per person . Number natural in the European Union is predominantly made up of the natural mineral natural category.Germany is the market with the largest amount of different mineral natural brands .


Example 459:
data: Country|Kenya|x|bar_chart Year-over-year_travel_growth|59|y|bar_chart Country|Iceland|x|bar_chart Year-over-year_travel_growth|56|y|bar_chart Country|Saint_Martin|x|bar_chart Year-over-year_travel_growth|39|y|bar_chart Country|China|x|bar_chart Year-over-year_travel_growth|35|y|bar_chart Country|Ecuador|x|bar_chart Year-over-year_travel_growth|34|y|bar_chart Country|Japan|x|bar_chart Year-over-year_travel_growth|32|y|bar_chart Country|South_Africa|x|bar_chart Year-over-year_travel_growth|28|y|bar_chart Country|Tanzania|x|bar_chart Year-over-year_travel_growth|27|y|bar_chart Country|Croatia|x|bar_chart Year-over-year_travel_growth|25|y|bar_chart Country|Jamaica|x|bar_chart Year-over-year_travel_growth|23|y|bar_chart 
title: Luxury destinations with the largest growth in travel worldwide 2016

gold: This statistic shows the luxury travel destinations with the largest growth in travel worldwide as of August 2016 . Luxury travel to Kenya grew by 59 percent in 2016 compared with the previous year . In 2017 , the Caribbean was the leading international destination for a spring vacation according to U.S. respondents .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] templateTitle[1] templateTitle[2] the templateTitle[3] templateYLabel[2] in templateYLabel[1] templateTitle[6] as of August templateTitle[7] . templateTitle[0] templateYLabel[1] to templateXValue[0] grew by templateYValue[max] percent in templateTitle[7] compared templateTitle[2] the previous year . In 2017 , the Caribbean was the leading international destination for a spring vacation according to U.S. respondents .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States as of April templateTitle[6] . During the survey , templateYValue[3] percent of templateYLabel[1] aged between templateYValue[2] and templateXValue[3] templateYLabel[1] were located in templateTitle[3] templateTitle[4] templateTitle[5] .
generated: This statistic shows the Year-over-year destinations travel of growth travel worldwide in the United States as of April 2016 . During the survey , 35 percent of travel aged between 39 and China travel were located in growth travel worldwide .


Example 460:
data: Year|2016|x|line_chart Number_of_hotel_rooms|34060|y|line_chart Year|2015|x|line_chart Number_of_hotel_rooms|34451|y|line_chart Year|2014|x|line_chart Number_of_hotel_rooms|39178|y|line_chart Year|2013|x|line_chart Number_of_hotel_rooms|30982|y|line_chart Year|2012|x|line_chart Number_of_hotel_rooms|37818|y|line_chart 
title: Forecast for the number of new hotel rooms opening in Europe from 2012 to 2016

gold: This statistic shows a forecast for the number of new hotel rooms opening in Europe from 2012 to 2016 . In 2013 , 30,982 new hotel rooms opened in the European hotel market . It was forecasted that 34,060 new hotel rooms would open in 2016 .
gold_template: This statistic shows a templateTitle[0] templateTitle[1] the templateYLabel[0] of templateTitle[3] templateYLabel[1] templateYLabel[2] templateTitle[6] in templateTitle[7] templateTitle[8] templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateYValue[min] templateTitle[3] templateYLabel[1] templateYLabel[2] opened in the European templateYLabel[1] market . It was forecasted that templateYValue[0] templateTitle[3] templateYLabel[1] templateYLabel[2] would open in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[5] - additional information templateTitle[1] templateYLabel[2] templateYLabel[3] in templateTitle[3] from templateXValue[min] to templateXValue[max] . There were templateYValue[max] million templateYLabel[1] templateYLabel[2] templateTitle[5] in templateXValue[max] .
generated: This statistic shows the Number of hotel rooms rooms - additional information for rooms yLabelErr in new from 2012 to 2016 . There were 39178 million hotel rooms rooms in 2016 .


Example 461:
data: Year|2024|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|330.94|y|line_chart Year|2023|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|317.53|y|line_chart Year|2022|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|305.24|y|line_chart Year|2021|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|292.81|y|line_chart Year|2020|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|280.71|y|line_chart Year|2019|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|269.65|y|line_chart Year|2018|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|274.21|y|line_chart Year|2017|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|252.87|y|line_chart Year|2016|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|239.11|y|line_chart Year|2015|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|232.97|y|line_chart Year|2014|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|273.04|y|line_chart Year|2013|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|270.07|y|line_chart Year|2012|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|256.85|y|line_chart Year|2011|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|273.93|y|line_chart Year|2010|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|248.26|y|line_chart Year|2009|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|252.14|y|line_chart Year|2008|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|285.09|y|line_chart Year|2007|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|255.74|y|line_chart Year|2006|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|216.73|y|line_chart Year|2005|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|204.77|y|line_chart Year|2004|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|196.98|y|line_chart Year|2003|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|171.37|y|line_chart Year|2002|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|139.98|y|line_chart Year|2001|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|129.34|y|line_chart Year|2000|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|125.88|y|line_chart Year|1999|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|135.4|y|line_chart Year|1998|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|134.11|y|line_chart Year|1997|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|127.0|y|line_chart Year|1996|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|132.15|y|line_chart Year|1995|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|134.35|y|line_chart Year|1994|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|103.76|y|line_chart Year|1993|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|89.32|y|line_chart Year|1992|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|113.23|y|line_chart Year|1991|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|128.29|y|line_chart Year|1990|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|141.8|y|line_chart Year|1989|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|119.11|y|line_chart Year|1988|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|109.26|y|line_chart Year|1987|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|91.78|y|line_chart Year|1986|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|73.65|y|line_chart Year|1985|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|56.22|y|line_chart Year|1984|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|53.03|y|line_chart 
title: Gross domestic product ( GDP ) in Finland 2024

gold: The statistic shows gross domestic product ( GDP ) in Finland from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Finland 2024 from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year . GDP is an important indicator of a country 's economic power .


Example 462:
data: Year|2024|x|line_chart National_debt_in_billion_U.S._dollars|258.2|y|line_chart Year|2023|x|line_chart National_debt_in_billion_U.S._dollars|261.41|y|line_chart Year|2022|x|line_chart National_debt_in_billion_U.S._dollars|264.78|y|line_chart Year|2021|x|line_chart National_debt_in_billion_U.S._dollars|267.72|y|line_chart Year|2020|x|line_chart National_debt_in_billion_U.S._dollars|270.63|y|line_chart Year|2019|x|line_chart National_debt_in_billion_U.S._dollars|273.8|y|line_chart Year|2018|x|line_chart National_debt_in_billion_U.S._dollars|280.14|y|line_chart Year|2017|x|line_chart National_debt_in_billion_U.S._dollars|286.05|y|line_chart Year|2016|x|line_chart National_debt_in_billion_U.S._dollars|276.79|y|line_chart Year|2015|x|line_chart National_debt_in_billion_U.S._dollars|281.84|y|line_chart Year|2014|x|line_chart National_debt_in_billion_U.S._dollars|279.83|y|line_chart 
title: National debt of Switzerland 2024

gold: The statistic shows the national debt of Switzerland from 2014 to 2017 , with projections up until 2024 . In 2017 , the national debt of Switzerland amounted to around 286.05 billion U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] amounted to around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] was around templateYValue[6] percent templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] of the templateYLabel[1] of the templateTitle[2] templateTitle[3] The templateTitle[2] templateTitle[3] , which amounted to around 1,600 templateYLabel[2] pounds in templateXValue[6] .
generated: This statistic shows the National debt of the Switzerland 2024 from 2014 to 2018 , with projections up until 2024 . In 2018 , the National debt of the Switzerland 2024 was around 280.14 percent billion U.S. dollars . National debt of the Switzerland 2024 National debt of the debt of the Switzerland 2024 The Switzerland 2024 , which amounted to around 1,600 billion pounds in 2018 .


Example 463:
data: Year|2023|x|line_chart Share_of_population|59|y|line_chart Year|2022|x|line_chart Share_of_population|58|y|line_chart Year|2021|x|line_chart Share_of_population|56|y|line_chart Year|2020|x|line_chart Share_of_population|53|y|line_chart Year|2019|x|line_chart Share_of_population|50|y|line_chart Year|2018|x|line_chart Share_of_population|47|y|line_chart Year|2017|x|line_chart Share_of_population|43|y|line_chart 
title: Mexico : mobile phone internet user penetration 2017 - 2023

gold: The statistic shows the mobile phone internet user penetration in Mexico from 2017 to 2023 . In 2017 , 43 percent of the population users accessed the internet through their mobile device . This figure is projected to grow to 59percent in 2023 .
gold_template: The statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[1] users accessed the templateTitle[3] through their templateTitle[1] device . This figure is projected to grow to 59percent in templateXValue[max] .

generated_template: This statistic presents the templateTitle[0] networking reach in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateYValue[5] percent of the total templateYLabel[1] accessed the social network . This templateYLabel[0] is projected to grow to templateYValue[max] percent in templateXValue[max] .
generated: This statistic presents the Mexico networking reach in user from 2017 to 2023 . In 2018 , 47 percent of the total population accessed the social network . This Share is projected to grow to 59 percent in 2023 .


Example 464:
data: Year|2019|x|line_chart Expenses_in_million_U.S._dollars|971|y|line_chart Year|2018|x|line_chart Expenses_in_million_U.S._dollars|862|y|line_chart Year|2017|x|line_chart Expenses_in_million_U.S._dollars|787|y|line_chart Year|2016|x|line_chart Expenses_in_million_U.S._dollars|715|y|line_chart Year|2015|x|line_chart Expenses_in_million_U.S._dollars|625|y|line_chart Year|2014|x|line_chart Expenses_in_million_U.S._dollars|614|y|line_chart Year|2013|x|line_chart Expenses_in_million_U.S._dollars|536|y|line_chart Year|2012|x|line_chart Expenses_in_million_U.S._dollars|471|y|line_chart Year|2011|x|line_chart Expenses_in_million_U.S._dollars|462|y|line_chart 
title: Stryker 's annual research , development and engineering expenses 2011 - 2019

gold: The statistic shows the annual research , development and engineering expenses of Stryker from 2011 to 2019 . Stryker 's research , development and engineering expenses have gradually increased since 2011 , reaching 971 million U.S. dollars in 2019 . The Stryker Corporation is a U.S. medical technology company headquartered in Kalamazoo , Michigan .
gold_template: The statistic shows the templateTitle[2] templateTitle[3] , templateTitle[4] and templateTitle[5] templateYLabel[0] of templateTitle[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] templateTitle[1] templateTitle[3] , templateTitle[4] and templateTitle[5] templateYLabel[0] have gradually increased since templateXValue[min] , reaching templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . The templateTitle[0] Corporation is a templateYLabel[2] medical technology company headquartered in Kalamazoo , Michigan .

generated_template: This statistic shows the templateTitle[3] templateTitle[4] of the NRA in the United States from templateXValue[min] to templateXValue[max] . As of July templateXValue[max] , the NRA spent about templateYValue[min] templateYLabel[2] templateYLabel[3] on templateTitle[3] . This is a significant decrease from templateXValue[1] , when the NRA spent about templateYValue[1] templateYLabel[1] on templateTitle[3] templateYLabel[0] .
generated: This statistic shows the research development of the NRA in the United States from 2011 to 2019 . As of July 2019 , the NRA spent about 462 U.S. dollars on research . This is a significant decrease from 2018 , when the NRA spent about 862 million on research Expenses .


Example 465:
data: Country|Spain|x|bar_chart Volume_in_1,000_tons|3731|y|bar_chart Country|Italy|x|bar_chart Volume_in_1,000_tons|1500|y|bar_chart Country|Greece|x|bar_chart Volume_in_1,000_tons|920|y|bar_chart Country|Portugal|x|bar_chart Volume_in_1,000_tons|344|y|bar_chart Country|Cyprus|x|bar_chart Volume_in_1,000_tons|30|y|bar_chart 
title: Fresh orange production volume in the European Union 2016/17 , by country

gold: In 2018/2019 , Spain was the leading producer of fresh oranges in the European Union ( EU28 ) , with over 3.7 million tons of fresh oranges produced . The Spanish production was more than two times the production of Italy , the second largest producer of oranges . The other three producers in the EU produced less than one million tons during this year .
gold_template: In 2018/2019 , templateXValue[0] was the leading producer of templateTitle[0] oranges in the templateTitle[4] templateTitle[5] ( EU28 ) , with over 3.7 million templateYLabel[2] of templateTitle[0] oranges produced . The Spanish templateTitle[2] was more than two times the templateTitle[2] of templateXValue[1] , the second largest producer of oranges . The other three producers in the EU produced less than one million templateYLabel[2] during this year .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States in templateTitle[7] . In templateTitle[6] , templateXValue[0] had a total of templateYValue[max] people throughout the European Union .
generated: This statistic shows the Volume 1,000 of volume European Union in the United States in by . In 2016/17 , Spain had a total of 3731 people throughout the European Union .


Example 466:
data: Year|2018|x|line_chart Number_of_persons|3.03|y|line_chart Year|2017|x|line_chart Number_of_persons|3.17|y|line_chart Year|2016|x|line_chart Number_of_persons|3.11|y|line_chart Year|2015|x|line_chart Number_of_persons|3.1|y|line_chart Year|2014|x|line_chart Number_of_persons|2.97|y|line_chart Year|2013|x|line_chart Number_of_persons|2.98|y|line_chart Year|2012|x|line_chart Number_of_persons|3.02|y|line_chart Year|2011|x|line_chart Number_of_persons|2.87|y|line_chart Year|2010|x|line_chart Number_of_persons|2.88|y|line_chart Year|2009|x|line_chart Number_of_persons|2.89|y|line_chart Year|2000|x|line_chart Number_of_persons|3.13|y|line_chart Year|1995|x|line_chart Number_of_persons|3.23|y|line_chart Year|1990|x|line_chart Number_of_persons|3.5|y|line_chart 
title: Average size of households in China 1990 - 2018

gold: This graph shows the average size of households in China from 1990 to 2018 . That year , approximately three people were living in an average Chinese household.Average number of people per household in China – additional information A household is commonly defined as one person living alone or a group of people living together and sharing certain living accommodations . The average number of people living in one household in China dropped from 3.5 in 1990 to 2.87 in 2011 .
gold_template: This graph shows the templateTitle[0] templateTitle[1] of templateTitle[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . That templateXLabel[0] , approximately templateYValue[0] people were living in an templateTitle[0] Chinese household.Average templateYLabel[0] of people per household in templateTitle[3] – additional information A household is commonly defined as one person living alone or a group of people living together and sharing certain living accommodations . The templateTitle[0] templateYLabel[0] of people living in one household in templateTitle[3] dropped from templateYValue[max] in templateXValue[min] to templateYValue[min] in templateXValue[7] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] refugees templateYLabel[1] from the United States .
generated: This statistic shows the Number of persons of the United States from 1990 to 2018 . In 2018 , there were 3.03 refugees persons from the United States .


Example 467:
data: Year|18/19|x|line_chart Revenue_in_million_U.S._dollars|246|y|line_chart Year|17/18|x|line_chart Revenue_in_million_U.S._dollars|235|y|line_chart Year|16/17|x|line_chart Revenue_in_million_U.S._dollars|218|y|line_chart Year|15/16|x|line_chart Revenue_in_million_U.S._dollars|173|y|line_chart Year|14/15|x|line_chart Revenue_in_million_U.S._dollars|154|y|line_chart Year|13/14|x|line_chart Revenue_in_million_U.S._dollars|145|y|line_chart Year|12/13|x|line_chart Revenue_in_million_U.S._dollars|137|y|line_chart Year|11/12|x|line_chart Revenue_in_million_U.S._dollars|121|y|line_chart Year|10/11|x|line_chart Revenue_in_million_U.S._dollars|136|y|line_chart Year|09/10|x|line_chart Revenue_in_million_U.S._dollars|147|y|line_chart Year|08/09|x|line_chart Revenue_in_million_U.S._dollars|148|y|line_chart Year|07/08|x|line_chart Revenue_in_million_U.S._dollars|148|y|line_chart Year|06/07|x|line_chart Revenue_in_million_U.S._dollars|145|y|line_chart Year|05/06|x|line_chart Revenue_in_million_U.S._dollars|132|y|line_chart Year|04/05|x|line_chart Revenue_in_million_U.S._dollars|132|y|line_chart Year|03/04|x|line_chart Revenue_in_million_U.S._dollars|111|y|line_chart Year|02/03|x|line_chart Revenue_in_million_U.S._dollars|109|y|line_chart Year|01/02|x|line_chart Revenue_in_million_U.S._dollars|107|y|line_chart 
title: Phoenix Suns ' revenue 2001 - 2019

gold: The statistic shows the revenue of the Phoenix Suns franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 246 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Phoenix Suns franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 246 million U.S. dollars .


Example 468:
data: Country|China|x|bar_chart Share_in_total_export|21.8|y|bar_chart Country|United_States|x|bar_chart Share_in_total_export|12.5|y|bar_chart Country|Argentina|x|bar_chart Share_in_total_export|8.1|y|bar_chart Country|Netherlands|x|bar_chart Share_in_total_export|4.3|y|bar_chart 
title: Most important export partner countries for Brazil in 2017

gold: This statistic shows the most important export partner countries for Brazil in 2017 . In 20167 the main export partner country of Brazil was China with a share of 21.8 percent in exports .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . In 20167 the main templateYLabel[2] templateTitle[3] templateXLabel[0] of templateTitle[6] was templateXValue[0] with a templateYLabel[0] of templateYValue[max] percent in exports .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] templateTitle[5] templateTitle[6] templateXLabel[0] . In that year , there were templateYValue[max] percent of the templateXValue[3] templateYLabel[1] throughout the templateXValue[0] templateXValue[0] .
generated: This statistic shows the Share total of the export partner countries total for Brazil Country . In that year , there were 21.8 percent of the Netherlands total throughout the China .


Example 469:
data: Country|Iran|x|bar_chart Percentage_of_infected_hosts|58.31|y|bar_chart Country|Indonesia|x|bar_chart Percentage_of_infected_hosts|17.83|y|bar_chart Country|India|x|bar_chart Percentage_of_infected_hosts|9.96|y|bar_chart Country|Azerbaijan|x|bar_chart Percentage_of_infected_hosts|3.4|y|bar_chart Country|Pakistan|x|bar_chart Percentage_of_infected_hosts|1.4|y|bar_chart Country|Malaysia|x|bar_chart Percentage_of_infected_hosts|1.16|y|bar_chart Country|U.S.|x|bar_chart Percentage_of_infected_hosts|0.89|y|bar_chart Country|Uzbekistan|x|bar_chart Percentage_of_infected_hosts|0.71|y|bar_chart Country|Russia|x|bar_chart Percentage_of_infected_hosts|0.61|y|bar_chart Country|Great_Britain|x|bar_chart Percentage_of_infected_hosts|0.57|y|bar_chart Country|Other|x|bar_chart Percentage_of_infected_hosts|5.15|y|bar_chart 
title: Stuxnet - percentage of infected hosts by country

gold: The statistic shows the percentage of Stuxnet infected hosts by country in 2010 . 58.31 percent of infected hosts were located in Iran .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] templateYLabel[2] templateTitle[4] templateXLabel[0] in 2010 . templateYValue[max] percent of templateYLabel[1] templateYLabel[2] were located in templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[7] . In that year , templateXValue[0] had the most templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] the United States . In templateXValue[2] , about templateYValue[2] percent of the templateYLabel[1] templateYLabel[2] in the United States .
generated: This statistic shows the Percentage infected in infected hosts by country in titleErr . In that year , Iran had the most Percentage infected hosts of by country the United States . In India , about 9.96 percent of the infected hosts in the United States .


Example 470:
data: Year|1917|x|line_chart Spending_in_million_U.S._dollars|10.41|y|line_chart Year|Dolittle|x|line_chart Spending_in_million_U.S._dollars|5.77|y|line_chart Year|Bad_Boys_for_Life|x|line_chart Spending_in_million_U.S._dollars|4.18|y|line_chart Year|Like_a_Boss|x|line_chart Spending_in_million_U.S._dollars|3.9|y|line_chart Year|Just_Mercy|x|line_chart Spending_in_million_U.S._dollars|3.72|y|line_chart 
title: Leading trailers in the U.S. January 2020 , by weekly TV ad spend

gold: The leading movie commercial in the United States based on weekly television advertising spending for the week ending January 5 , 2020 was for war drama film ' 1917 ' _ , with a 10.41 million U.S. dollar spend by studio Universal Pictures . Universal also spent 5.77 million U.S. dollars on TV promotion of 'Dolittle ' _ .
gold_template: The templateTitle[0] movie commercial in the templateTitle[2] based on templateTitle[6] television advertising templateYLabel[0] templateXValue[2] the week ending templateTitle[3] 5 , templateTitle[4] was templateXValue[2] war drama film ' templateXValue[0] ' _ , with a templateYValue[max] templateYLabel[1] templateYLabel[2] dollar templateTitle[9] templateTitle[5] studio Universal Pictures . Universal also spent templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] on templateTitle[7] promotion of 'Dolittle ' _ .

generated_template: This statistic shows the templateTitle[0] templateTitle[2] templateTitle[3] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the source predicts some templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[2] were produced in the United States .
generated: This statistic shows the Leading U.S. January in the United States from 1917 to 1917 . In 1917 , the source predicts some 10.41 million U.S. dollars of U.S. were produced in the United States .


Example 471:
data: Sex|Female|x|bar_chart Percentage_of_users|43|y|bar_chart Sex|Male|x|bar_chart Percentage_of_users|57|y|bar_chart 
title: LinkedIn : distribution of global audiences 2020 , by gender

gold: This statistic gives information on the distribution of LinkedIn users worldwide as of January 2020 , sorted by gender . During the survey period , 43 percent of LinkedIn audiences were female and 57 percent were male .
gold_template: This statistic gives information on the templateTitle[1] of templateTitle[0] templateYLabel[1] worldwide as of January templateTitle[4] , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[min] percent of templateTitle[0] templateTitle[3] were templateXValue[0] and templateYValue[max] percent were templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[1] of templateTitle[0] templateYLabel[1] worldwide as of January templateTitle[4] , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[max] percent of templateTitle[0] templateTitle[3] were templateXValue[0] and templateYValue[min] percent were templateXValue[last] .
generated: This statistic gives information on the distribution of LinkedIn users worldwide as of January 2020 , sorted by gender . During the survey period , 57 percent of LinkedIn audiences were Female and 43 percent were Male .


Example 472:
data: Year|2018/19|x|line_chart Broadcasting_payments_in_million_GBP|2456.01|y|line_chart Year|2017/18|x|line_chart Broadcasting_payments_in_million_GBP|2419.6|y|line_chart Year|2016/17|x|line_chart Broadcasting_payments_in_million_GBP|2398.5|y|line_chart Year|2015/16|x|line_chart Broadcasting_payments_in_million_GBP|1633.9|y|line_chart Year|2014/15|x|line_chart Broadcasting_payments_in_million_GBP|1605.3|y|line_chart Year|2013/14|x|line_chart Broadcasting_payments_in_million_GBP|1563.0|y|line_chart Year|2012/13|x|line_chart Broadcasting_payments_in_million_GBP|1061.0|y|line_chart Year|2011/12|x|line_chart Broadcasting_payments_in_million_GBP|1055.0|y|line_chart Year|2010/11|x|line_chart Broadcasting_payments_in_million_GBP|953.0|y|line_chart 
title: Premier League total broadcasting payments to clubs 2010 - 2019

gold: The statistic depicts the broadcasting payments to Premier League clubs from 2010/11 to 2018/19 . In the 2018/19 season , all Premier League clubs combined received a total of 2.46 billion British Pounds in broadcasting payments .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] to templateTitle[0] templateTitle[1] templateTitle[5] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , all templateTitle[0] templateTitle[1] templateTitle[5] combined received a templateTitle[2] of templateYValue[max] billion British Pounds in templateYLabel[0] templateYLabel[1] .

generated_template: There were templateYValue[0] templateTitle[1] templateTitle[2] at the U.S. dollars in templateTitle[3] templateTitle[4] templateTitle[5] in the United States between January 1 and 34 years . The templateYLabel[0] templateYLabel[1] had the highest templateTitle[3] templateTitle[4] at templateYValue[max] percent of templateYLabel[1] with its highest templateYLabel[0] templateYLabel[1] recorded in the U.S. dollars .
generated: There were 2456.01 League total at the U.S. dollars in broadcasting payments clubs in the United States between January 1 and 34 years . The Broadcasting payments had the highest broadcasting payments at 2456.01 percent of payments with its highest Broadcasting payments recorded in the U.S. dollars .


Example 473:
data: Year|2000|x|line_chart Rate_of_arson_per_100,000_residents|44.5|y|line_chart Year|2001|x|line_chart Rate_of_arson_per_100,000_residents|46.27|y|line_chart Year|2002|x|line_chart Rate_of_arson_per_100,000_residents|41.36|y|line_chart Year|2003|x|line_chart Rate_of_arson_per_100,000_residents|43.12|y|line_chart Year|2004|x|line_chart Rate_of_arson_per_100,000_residents|40.22|y|line_chart Year|2005|x|line_chart Rate_of_arson_per_100,000_residents|40.48|y|line_chart Year|2006|x|line_chart Rate_of_arson_per_100,000_residents|40.54|y|line_chart Year|2007|x|line_chart Rate_of_arson_per_100,000_residents|39.57|y|line_chart Year|2008|x|line_chart Rate_of_arson_per_100,000_residents|40.22|y|line_chart Year|2009|x|line_chart Rate_of_arson_per_100,000_residents|39.86|y|line_chart Year|2010|x|line_chart Rate_of_arson_per_100,000_residents|35.98|y|line_chart Year|2011|x|line_chart Rate_of_arson_per_100,000_residents|30.29|y|line_chart Year|2012|x|line_chart Rate_of_arson_per_100,000_residents|31.96|y|line_chart Year|2013|x|line_chart Rate_of_arson_per_100,000_residents|25.41|y|line_chart Year|2014|x|line_chart Rate_of_arson_per_100,000_residents|24.06|y|line_chart Year|2015|x|line_chart Rate_of_arson_per_100,000_residents|25.12|y|line_chart Year|2016|x|line_chart Rate_of_arson_per_100,000_residents|23.67|y|line_chart Year|2017|x|line_chart Rate_of_arson_per_100,000_residents|23.4|y|line_chart Year|2018|x|line_chart Rate_of_arson_per_100,000_residents|21.59|y|line_chart 
title: Canada : reported arson rate 2000 - 2018

gold: This statistic shows the reported arson rate in Canada from 2000 to 2018 . There were about 21.59 arsons per 100,000 residents in Canada in 2018 .
gold_template: This statistic shows the templateTitle[1] templateYLabel[1] templateYLabel[0] in templateTitle[0] from templateXValue[min] to templateXValue[max] . There were about templateYValue[min] arsons templateYLabel[2] 100,000 templateYLabel[4] in templateTitle[0] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people living in the templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Rate arson of arson rate 2000 2018 in the United States from 2000 to 2018 . In 2018 , there were 44.5 people living in the 2000 2018 in the United States .


Example 474:
data: Year|2018|x|line_chart Number_of_employees|2400|y|line_chart Year|2017|x|line_chart Number_of_employees|2300|y|line_chart Year|2016|x|line_chart Number_of_employees|2117|y|line_chart Year|2015|x|line_chart Number_of_employees|2611|y|line_chart Year|2014|x|line_chart Number_of_employees|3330|y|line_chart Year|2013|x|line_chart Number_of_employees|3359|y|line_chart Year|2012|x|line_chart Number_of_employees|3367|y|line_chart Year|2011|x|line_chart Number_of_employees|3322|y|line_chart Year|2010|x|line_chart Number_of_employees|29677|y|line_chart 
title: Marathon Oil 's number of employees 2010 - 2018

gold: This statistic outlines Marathon Oil 's number of employees from 2010 to 2018 . Marathon Oil Corporation is an internationally leading United States-based oil and natural gas exploration and production company . In 2018 , the company had 2,400 employees .
gold_template: This statistic outlines templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] of templateYLabel[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] templateTitle[1] Corporation is an internationally leading United States-based templateTitle[1] and natural gas exploration and production company . In templateXValue[max] , the company had templateYValue[0] templateYLabel[1] .

generated_template: This statistic represents the templateYLabel[0] of templateYLabel[1] of people employed worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people employed by templateTitle[0] . The templateTitle[2] templateTitle[3] – additional information templateTitle[1] templateTitle[2] templateTitle[3] had been awarded with the highest templateYLabel[0] of templateYLabel[1] in the United States between templateXValue[min] and templateXValue[6] .
generated: This statistic represents the Number of employees of people employed worldwide from 2010 to 2018 . In 2018 , there were 2400 people employed by Marathon . The 's number – additional information Oil 's number had been awarded with the highest Number of employees in the United States between 2010 and 2012 .


Example 475:
data: Platform|YouTube|x|bar_chart Percentage_of_teenagers|91|y|bar_chart Platform|Gmail|x|bar_chart Percentage_of_teenagers|75|y|bar_chart Platform|Snapchat|x|bar_chart Percentage_of_teenagers|66|y|bar_chart Platform|Instagram|x|bar_chart Percentage_of_teenagers|65|y|bar_chart Platform|Facebook|x|bar_chart Percentage_of_teenagers|61|y|bar_chart Platform|Kik_Messenger|x|bar_chart Percentage_of_teenagers|52|y|bar_chart Platform|Skype|x|bar_chart Percentage_of_teenagers|43|y|bar_chart Platform|Twitter|x|bar_chart Percentage_of_teenagers|40|y|bar_chart Platform|Vine|x|bar_chart Percentage_of_teenagers|31|y|bar_chart Platform|Tumblr|x|bar_chart Percentage_of_teenagers|24|y|bar_chart 
title: Most popular social networks of U.S. teens 2016

gold: This statistic provides information about the most popular websites visited by teenagers in the United States as of June 2016 . During the survey period , video sharing platform YouTube was most popular among U.S. teens with a 91 percent usage rate . Snapchat was ranked third with 66 percent reporting that they accessed the photo sharing app .
gold_template: This statistic provides information about the templateTitle[0] templateTitle[1] websites visited by templateYLabel[1] in the templateTitle[4] as of June templateTitle[6] . During the survey period , video sharing templateXLabel[0] templateXValue[0] was templateTitle[0] templateTitle[1] among templateTitle[4] templateTitle[5] with a templateYValue[max] percent usage rate . templateXValue[2] was ranked third with templateYValue[2] percent reporting that they accessed the photo sharing app .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[2] internet users in the United States as of January templateTitle[5] . During the survey period it was found that templateYValue[max] percent of templateTitle[2] templateTitle[3] users had a templateXValue[0] account .
generated: This statistic gives information on the Percentage of yLabelErr internet users in the United States as of January teens . During the survey period it was found that 91 percent of social networks users had a YouTube account .


Example 476:
data: Year|16_to_24_years|x|line_chart Percentage_of_population_volunteering|21.8|y|line_chart Year|25_to_34_years|x|line_chart Percentage_of_population_volunteering|22.3|y|line_chart Year|35_to_44_years|x|line_chart Percentage_of_population_volunteering|28.9|y|line_chart Year|45_to_54_years|x|line_chart Percentage_of_population_volunteering|28|y|line_chart Year|55_to_64_years|x|line_chart Percentage_of_population_volunteering|25.1|y|line_chart Year|65_years_and_over|x|line_chart Percentage_of_population_volunteering|23.5|y|line_chart 
title: Percentage of population volunteering in the U.S. in 2015 , by age

gold: This statistic displays the percentage of population volunteering in the U.S. in 2015 , by age . In 2015 , 21.8 percent of Americans 16 to 24 years old volunteered at least once during the year .
gold_template: This statistic displays the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[3] in templateTitle[4] , templateTitle[5] templateTitle[6] . In templateTitle[4] , templateYValue[min] percent of Americans templateXValue[0] to templateXValue[0] templateXValue[0] old volunteered at least once during the templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] in the United States from templateXValue[last] to templateXValue[0] . In templateTitle[6] , about templateYValue[max] million people were living in the templateTitle[3] templateTitle[4] .
generated: The statistic shows the Percentage population of the U.S. 2015 in the United States from 65_years_and_over to 16_to_24_years . In age , about 28.9 million people were living in the U.S. 2015 .


Example 477:
data: Year|2018|x|line_chart Production_in_thousand_tons|344.4|y|line_chart Year|2017|x|line_chart Production_in_thousand_tons|437.6|y|line_chart Year|2016|x|line_chart Production_in_thousand_tons|350.2|y|line_chart Year|2015|x|line_chart Production_in_thousand_tons|335.5|y|line_chart Year|2014|x|line_chart Production_in_thousand_tons|363.6|y|line_chart Year|2013|x|line_chart Production_in_thousand_tons|332.1|y|line_chart Year|2012|x|line_chart Production_in_thousand_tons|424.0|y|line_chart Year|2011|x|line_chart Production_in_thousand_tons|334.4|y|line_chart Year|2010|x|line_chart Production_in_thousand_tons|313.2|y|line_chart Year|2009|x|line_chart Production_in_thousand_tons|442.9|y|line_chart Year|2008|x|line_chart Production_in_thousand_tons|248.1|y|line_chart Year|2007|x|line_chart Production_in_thousand_tons|310.7|y|line_chart Year|2006|x|line_chart Production_in_thousand_tons|294.2|y|line_chart Year|2005|x|line_chart Production_in_thousand_tons|250.8|y|line_chart Year|2004|x|line_chart Production_in_thousand_tons|283.1|y|line_chart Year|2003|x|line_chart Production_in_thousand_tons|245.7|y|line_chart Year|2002|x|line_chart Production_in_thousand_tons|181.4|y|line_chart Year|2001|x|line_chart Production_in_thousand_tons|230.4|y|line_chart Year|2000|x|line_chart Production_in_thousand_tons|207.9|y|line_chart 
title: U.S. sweet cherry production 2000 - 2018

gold: This statistic shows the total amount of sweet cherries produced in the United States from 2000 to 2018 . In 2018 , around 344 thousand tons of sweet cherries were produced in the U.S .
gold_template: This statistic shows the total amount of templateTitle[1] cherries produced in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[0] thousand templateYLabel[2] of templateTitle[1] cherries were produced in the templateTitle[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] from templateXValue[min] to templateXValue[max] . According to the report , approximately templateYValue[0] million templateYLabel[2] of templateTitle[4] were produced in the United States in templateXValue[max] .
generated: This statistic shows the U.S. sweet Production thousand tons of 2000 from 2000 to 2018 . According to the report , approximately 344.4 million tons of 2000 were produced in the United States in 2018 .


Example 478:
data: Company|Private_label|x|bar_chart Million_U.S._dollars|494.4|y|bar_chart Company|Kimberly_Clark_Corp.|x|bar_chart Million_U.S._dollars|416.2|y|bar_chart Company|Procter_&_Gamble|x|bar_chart Million_U.S._dollars|276.6|y|bar_chart Company|Seventh_Generation|x|bar_chart Million_U.S._dollars|9.6|y|bar_chart Company|The_Honest_Co.|x|bar_chart Million_U.S._dollars|8.6|y|bar_chart Company|Johnson_&_Johnson|x|bar_chart Million_U.S._dollars|7.1|y|bar_chart Company|Nice-Pak_Products|x|bar_chart Million_U.S._dollars|6.3|y|bar_chart Company|Paper_Partners|x|bar_chart Million_U.S._dollars|6.1|y|bar_chart Company|Kas_Direct|x|bar_chart Million_U.S._dollars|5.7|y|bar_chart Company|Irish_Breeze|x|bar_chart Million_U.S._dollars|4.4|y|bar_chart 
title: Leading baby wipes vendors in the U.S. 2016 , based on sales

gold: The statistic shows the leading baby wipes vendors in the United States in 2016 , based on sales . In that year , Kimberly Clark was the second largest U.S. baby wipes vendor with sales of 416.2 million U.S. dollars . Total sales of U.S. baby wipes vendors amounted to about 1.25 billion U.S. dollars in 2016 .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitle[5] , templateTitle[6] on templateTitle[7] . In that year , templateXValue[1] templateXValue[1] was the second largest templateYLabel[1] templateTitle[1] templateTitle[2] vendor with templateTitle[7] of templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] . Total templateTitle[7] of templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] amounted to about 1.25 billion templateYLabel[1] templateYLabel[2] in templateTitle[5] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the United States in templateTitle[7] . During this year , templateXValue[0] templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] templateTitle[4] templateXLabel[0] templateTitle[4] templateXLabel[0] with a templateYLabel[0] of templateYValue[0] percent .
generated: The statistic shows the Leading baby wipes vendors U.S. 2016 based in the United States in sales . During this year , Private_label was the Leading baby wipes Company U.S. Company U.S. Company with a Million of 494.4 percent .


Example 479:
data: Year|2019|x|line_chart Home_attendance|498605|y|line_chart Year|2018|x|line_chart Home_attendance|579439|y|line_chart Year|2017|x|line_chart Home_attendance|507136|y|line_chart Year|2016|x|line_chart Home_attendance|665318|y|line_chart Year|2015|x|line_chart Home_attendance|419220|y|line_chart Year|2014|x|line_chart Home_attendance|456146|y|line_chart Year|2013|x|line_chart Home_attendance|455657|y|line_chart Year|2012|x|line_chart Home_attendance|396925|y|line_chart Year|2011|x|line_chart Home_attendance|451153|y|line_chart Year|2010|x|line_chart Home_attendance|423376|y|line_chart Year|2009|x|line_chart Home_attendance|441896|y|line_chart Year|2008|x|line_chart Home_attendance|479840|y|line_chart Year|2007|x|line_chart Home_attendance|514352|y|line_chart Year|2006|x|line_chart Home_attendance|522608|y|line_chart 
title: Regular season home attendance of the Los Angeles Rams 2006 - 2019

gold: This graph depicts the total regular season home attendance of the St. Louis / Los Angeles Rams franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 498,605 . The franchise moved from St. Louis to Los Angeles before the 2016 season .
gold_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the St. Louis / templateTitle[4] templateTitle[5] templateTitle[6] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] . The franchise moved from St. Louis to templateTitle[4] templateTitle[5] before the templateXValue[3] templateTitle[1] .

generated_template: This graph depicts the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] .
generated: This graph depicts the Regular season Home attendance of the Los Angeles franchise of the National Football League from 2006 to 2019 . In 2019 , the Regular season home Home attendance of the franchise was 498605 .


Example 480:
data: Year|2017|x|line_chart Per_capita_sales_in_U.S._dollars|795|y|line_chart Year|2016|x|line_chart Per_capita_sales_in_U.S._dollars|804|y|line_chart Year|2015|x|line_chart Per_capita_sales_in_U.S._dollars|797|y|line_chart Year|2014|x|line_chart Per_capita_sales_in_U.S._dollars|786|y|line_chart Year|2013|x|line_chart Per_capita_sales_in_U.S._dollars|774|y|line_chart Year|2012|x|line_chart Per_capita_sales_in_U.S._dollars|763|y|line_chart Year|2011|x|line_chart Per_capita_sales_in_U.S._dollars|734|y|line_chart Year|2010|x|line_chart Per_capita_sales_in_U.S._dollars|689|y|line_chart Year|2009|x|line_chart Per_capita_sales_in_U.S._dollars|667|y|line_chart Year|2008|x|line_chart Per_capita_sales_in_U.S._dollars|709|y|line_chart Year|2007|x|line_chart Per_capita_sales_in_U.S._dollars|734|y|line_chart Year|2006|x|line_chart Per_capita_sales_in_U.S._dollars|714|y|line_chart Year|2005|x|line_chart Per_capita_sales_in_U.S._dollars|680|y|line_chart Year|2004|x|line_chart Per_capita_sales_in_U.S._dollars|650|y|line_chart Year|2003|x|line_chart Per_capita_sales_in_U.S._dollars|616|y|line_chart Year|2002|x|line_chart Per_capita_sales_in_U.S._dollars|599|y|line_chart Year|2001|x|line_chart Per_capita_sales_in_U.S._dollars|587|y|line_chart Year|2000|x|line_chart Per_capita_sales_in_U.S._dollars|594|y|line_chart 
title: Estimated U.S. clothing and clothing accessories per capita sales 2000 - 2017

gold: The timeline shows the estimated clothing and clothing accessories per capita sales in the United States from 2000 to 2017 . In 2017 , clothing and clothing accessories sales amounted to 795 U.S. dollars per capita .
gold_template: The timeline shows the templateTitle[0] templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[2] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were about templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] in the United States .
generated: This statistic shows the Estimated U.S. Per capita sales in the United States from 2000 to 2017 . In 2017 , there were about 804 U.S. capita sales in the United States .


Example 481:
data: State|California|x|bar_chart Number_of_aggravated_assaults|105412|y|bar_chart State|Texas|x|bar_chart Number_of_aggravated_assaults|73656|y|bar_chart State|Florida|x|bar_chart Number_of_aggravated_assaults|55551|y|bar_chart State|New_York|x|bar_chart Number_of_aggravated_assaults|43171|y|bar_chart State|Tennessee|x|bar_chart Number_of_aggravated_assaults|31717|y|bar_chart State|Michigan|x|bar_chart Number_of_aggravated_assaults|31021|y|bar_chart State|Illinois|x|bar_chart Number_of_aggravated_assaults|30539|y|bar_chart State|North_Carolina|x|bar_chart Number_of_aggravated_assaults|27526|y|bar_chart State|Pennsylvania|x|bar_chart Number_of_aggravated_assaults|24077|y|bar_chart State|Arizona|x|bar_chart Number_of_aggravated_assaults|23528|y|bar_chart State|Georgia|x|bar_chart Number_of_aggravated_assaults|22783|y|bar_chart State|Missouri|x|bar_chart Number_of_aggravated_assaults|22042|y|bar_chart State|Alabama|x|bar_chart Number_of_aggravated_assaults|18944|y|bar_chart State|South_Carolina|x|bar_chart Number_of_aggravated_assaults|18446|y|bar_chart State|Louisiana|x|bar_chart Number_of_aggravated_assaults|17866|y|bar_chart State|Ohio|x|bar_chart Number_of_aggravated_assaults|17674|y|bar_chart State|Indiana|x|bar_chart Number_of_aggravated_assaults|16834|y|bar_chart State|Massachusetts|x|bar_chart Number_of_aggravated_assaults|16648|y|bar_chart State|Maryland|x|bar_chart Number_of_aggravated_assaults|16135|y|bar_chart State|Colorado|x|bar_chart Number_of_aggravated_assaults|14547|y|bar_chart State|Washington|x|bar_chart Number_of_aggravated_assaults|14251|y|bar_chart State|New_Mexico|x|bar_chart Number_of_aggravated_assaults|13598|y|bar_chart State|Oklahoma|x|bar_chart Number_of_aggravated_assaults|13084|y|bar_chart State|Arkansas|x|bar_chart Number_of_aggravated_assaults|12378|y|bar_chart State|Wisconsin|x|bar_chart Number_of_aggravated_assaults|11263|y|bar_chart State|New_Jersey|x|bar_chart Number_of_aggravated_assaults|10463|y|bar_chart State|Virginia|x|bar_chart Number_of_aggravated_assaults|10113|y|bar_chart State|Nevada|x|bar_chart Number_of_aggravated_assaults|10027|y|bar_chart State|Kansas|x|bar_chart Number_of_aggravated_assaults|9559|y|bar_chart State|Oregon|x|bar_chart Number_of_aggravated_assaults|7360|y|bar_chart State|Minnesota|x|bar_chart Number_of_aggravated_assaults|6857|y|bar_chart State|Iowa|x|bar_chart Number_of_aggravated_assaults|5931|y|bar_chart State|Kentucky|x|bar_chart Number_of_aggravated_assaults|5059|y|bar_chart State|Mississippi|x|bar_chart Number_of_aggravated_assaults|4696|y|bar_chart State|Alaska|x|bar_chart Number_of_aggravated_assaults|4391|y|bar_chart State|Utah|x|bar_chart Number_of_aggravated_assaults|4319|y|bar_chart State|Connecticut|x|bar_chart Number_of_aggravated_assaults|4294|y|bar_chart State|District_of_Columbia|x|bar_chart Number_of_aggravated_assaults|3971|y|bar_chart State|West_Virginia|x|bar_chart Number_of_aggravated_assaults|3945|y|bar_chart State|Nebraska|x|bar_chart Number_of_aggravated_assaults|3461|y|bar_chart State|Montana|x|bar_chart Number_of_aggravated_assaults|3120|y|bar_chart State|Idaho|x|bar_chart Number_of_aggravated_assaults|2957|y|bar_chart State|Delaware|x|bar_chart Number_of_aggravated_assaults|2845|y|bar_chart State|South_Dakota|x|bar_chart Number_of_aggravated_assaults|2682|y|bar_chart State|Hawaii|x|bar_chart Number_of_aggravated_assaults|1925|y|bar_chart State|North_Dakota|x|bar_chart Number_of_aggravated_assaults|1560|y|bar_chart State|New_Hampshire|x|bar_chart Number_of_aggravated_assaults|1435|y|bar_chart State|Rhode_Island|x|bar_chart Number_of_aggravated_assaults|1366|y|bar_chart State|Wyoming|x|bar_chart Number_of_aggravated_assaults|870|y|bar_chart State|Maine|x|bar_chart Number_of_aggravated_assaults|803|y|bar_chart State|Vermont|x|bar_chart Number_of_aggravated_assaults|710|y|bar_chart 
title: Number of aggravated assaults in the U.S. in 2018 , by state

gold: This statistic shows the total number of aggravated assaults reported in the United States in 2018 , by state . In 2018 , the federal state of California was ranked first with 105,412 cases of aggravated assaults , followed by Texas with 73,656 reported cases of aggravated assaults .
gold_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] reported in the templateTitle[3] in templateTitle[4] , templateTitle[5] templateXLabel[0] . In templateTitle[4] , the federal templateXLabel[0] of templateXValue[0] was ranked first with templateYValue[max] cases of templateYLabel[1] templateYLabel[2] , followed templateTitle[5] templateXValue[1] with templateYValue[1] reported cases of templateYLabel[1] templateYLabel[2] .

generated_template: templateXValue[0] had the most templateYLabel[0] templateTitle[1] templateYLabel[1] in the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] . In templateTitle[4] , templateYValue[max] percent of the United States had the highest templateYLabel[0] templateYLabel[1] throughout the templateXLabel[0] at templateYValue[1] percent in templateXValue[1] . templateYLabel[0] templateYLabel[1] 100,000 templateTitle[5] population in the United States The templateTitle[2] is defined as of templateTitle[6] one of the United States .
generated: California had the most Number aggravated aggravated in the United States in 2018 , by State . In 2018 , 105412 percent of the United States had the highest Number aggravated throughout the State at 73656 percent in Texas . Number aggravated 100,000 by population in the United States The assaults is defined as of state one of the United States .


Example 482:
data: Month|March_2015|x|bar_chart Percentage_of_respondents|84|y|bar_chart Month|March_2014|x|bar_chart Percentage_of_respondents|79|y|bar_chart Month|March_2013|x|bar_chart Percentage_of_respondents|76|y|bar_chart Month|March_2012|x|bar_chart Percentage_of_respondents|68|y|bar_chart 
title: Smartphone use for professional reasons among U.S. physicians 2012 - 2015

gold: This survey indicates the percentage of physicians in the United States who use smartphones for professional purposes from March 2012 to March 2015 . In March 2014 , 79 percent of surveyed physicians used smartphones for their medical practice . Use of digital health has grown in the medical industry as consumers are expressing increasing interests in mobile health technologies .
gold_template: This survey indicates the templateYLabel[0] of templateTitle[7] in the templateTitle[6] who templateTitle[1] smartphones templateTitle[2] templateTitle[3] purposes from templateXValue[0] templateXValue[last] to templateXValue[0] templateXValue[0] . In templateXValue[0] templateXValue[1] , templateYValue[1] percent of surveyed templateTitle[7] used smartphones templateTitle[2] their medical practice . templateTitle[1] of digital health has grown in the medical industry as consumers are expressing increasing interests in mobile health technologies .

generated_template: The statistic shows the percent of templateYLabel[1] in the United Kingdom ( templateTitle[0] ) that own a templateTitle[3] telephone . In templateXValue[last] , templateYValue[max] percent of templateXValue[0] used a templateTitle[3] templateTitle[4] .
generated: The statistic shows the percent of respondents in the United Kingdom ( Smartphone ) that own a professional telephone . In March_2012 , 84 percent of March_2015 used a professional reasons .


Example 483:
data: Year|2020|x|line_chart Total_demand_in_million_metric_tons|69.0|y|line_chart Year|2019|x|line_chart Total_demand_in_million_metric_tons|67.7|y|line_chart Year|2018|x|line_chart Total_demand_in_million_metric_tons|70.38|y|line_chart Year|2017|x|line_chart Total_demand_in_million_metric_tons|68.08|y|line_chart Year|2016|x|line_chart Total_demand_in_million_metric_tons|65.65|y|line_chart Year|2015|x|line_chart Total_demand_in_million_metric_tons|63.47|y|line_chart Year|2014|x|line_chart Total_demand_in_million_metric_tons|61.44|y|line_chart 
title: Forecast of sulfur fertilizer demand worldwide 2014 - 2020

gold: This statistic displays a forecast of total global demand for sulfur fertilizer from 2014 to 2020 . By 2020 , the annual demand for sulfur fertilizer is expected to reach some 69 million metric tons . Increasing crop prices lead to increased fertilizer demands and has been especially noted in recent years in South Asia .
gold_template: This statistic displays a templateTitle[0] of templateYLabel[0] global templateYLabel[1] for templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . By templateXValue[max] , the annual templateYLabel[1] for templateTitle[1] templateTitle[2] is expected to reach some templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Increasing crop prices lead to increased templateTitle[2] demands and has been especially noted in recent years in South Asia .

generated_template: The statistic shows the templateTitle[0] of the global templateYLabel[1] for templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] to templateTitle[6] . In templateXValue[max] , the global templateYLabel[1] for templateTitle[1] fertilizers is expected to reach some templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Forecast of the global demand for sulfur fertilizer demand worldwide 2014 to 2020 . In 2020 , the global demand for sulfur fertilizers is expected to reach some 69.0 million metric tons .


Example 484:
data: Year|2021|x|line_chart Smartphone_users_in_billions|3.8|y|line_chart Year|2020|x|line_chart Smartphone_users_in_billions|3.5|y|line_chart Year|2019|x|line_chart Smartphone_users_in_billions|3.2|y|line_chart Year|2018|x|line_chart Smartphone_users_in_billions|2.9|y|line_chart Year|2017|x|line_chart Smartphone_users_in_billions|2.7|y|line_chart Year|2016|x|line_chart Smartphone_users_in_billions|2.5|y|line_chart 
title: Smartphone users worldwide 2016 - 2021

gold: How many people have smartphones worldwide ? The number of smartphone users worldwide today surpasses three billion and is forecast to further grow by several hundred million in the next few years . China , India , and the United States are the countries with the highest number of smartphone users , with each country easily surpassing the 100 million user mark . Smartphone unit sales levelling off In the past five years , about 1.4 billion smartphones were sold worldwide annually , reflecting stagnation in the smartphone market during the last few years .
gold_template: How many people have smartphones templateTitle[2] ? The number of templateYLabel[0] templateYLabel[1] templateTitle[2] today surpasses templateYValue[2] billion and is forecast to further grow by several hundred million in the next few years . China , India , and the United States are the countries with the highest number of templateYLabel[0] templateYLabel[1] , with each country easily surpassing the 100 million user mark . templateYLabel[0] unit sales levelling off In the past five years , about 1.4 billion smartphones were sold templateTitle[2] annually , reflecting stagnation in the templateYLabel[0] market during the last few years .

generated_template: In templateXValue[2] , the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[5] in the United States amounted to an increase from templateYValue[min] percent in templateXValue[min] to templateYValue[max] percent in templateXValue[max] . The distribution of the templateTitle[3] templateTitle[4] can be an indicator for the previous templateXLabel[0] .
generated: In 2019 , the Smartphone users of 2021 titleErr in the Smartphone users worldwide titleErr in the United States amounted to an increase from 2.5 percent in 2016 to 3.8 percent in 2021 . The distribution of the 2016 2021 can be an indicator for the previous Year .


Example 485:
data: Month|Jan_'19|x|bar_chart Number_of_DAU_in_millions|500|y|bar_chart Month|Jun_'18|x|bar_chart Number_of_DAU_in_millions|400|y|bar_chart Month|Oct_'17|x|bar_chart Number_of_DAU_in_millions|300|y|bar_chart Month|Jun_'17|x|bar_chart Number_of_DAU_in_millions|250|y|bar_chart Month|Apr_'17|x|bar_chart Number_of_DAU_in_millions|200|y|bar_chart Month|Jan_'17|x|bar_chart Number_of_DAU_in_millions|150|y|bar_chart Month|Oct_'16|x|bar_chart Number_of_DAU_in_millions|100|y|bar_chart 
title: Daily active users of Instagram Stories 2019

gold: In January 2019 , photo sharing platform Instagram reported 500 million daily active Stories users worldwide , up from 400 million global DAU in June 2018 . Stories is a feature of the app allowing users post photo and video sequences that disappear 24 hours after being posted . Instagram usageInstagram has over one billion monthly active users and is one of the most popular social networks worldwide .
gold_template: In January templateTitle[5] , photo sharing platform templateTitle[3] reported templateYValue[max] million templateTitle[0] templateTitle[1] templateTitle[4] templateTitle[2] worldwide , up from templateYValue[1] million global templateYLabel[1] in June 2018 . templateTitle[4] is a feature of the app allowing templateTitle[2] post photo and video sequences that disappear 24 hours after being posted . templateTitle[3] usageInstagram has over templateYValue[max] billion monthly templateTitle[1] templateTitle[2] and is one of the most popular social networks worldwide .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] of templateYLabel[1] from January templateTitle[4] to January templateTitle[5] . As of the last measured period , templateTitle[3] had templateYValue[max] million users worldwide , up from templateYValue[1] million in the preceding year .
generated: This statistic presents the Daily active users of DAU from January Stories to January 2019 . As of the last measured period , Instagram had 500 million users worldwide , up from 400 million in the preceding year .


Example 486:
data: Year|2024|x|line_chart National_debt_in_billion_U.S._dollars|575.52|y|line_chart Year|2023|x|line_chart National_debt_in_billion_U.S._dollars|436.78|y|line_chart Year|2022|x|line_chart National_debt_in_billion_U.S._dollars|344.33|y|line_chart Year|2021|x|line_chart National_debt_in_billion_U.S._dollars|256.69|y|line_chart Year|2020|x|line_chart National_debt_in_billion_U.S._dollars|205.66|y|line_chart Year|2019|x|line_chart National_debt_in_billion_U.S._dollars|167.57|y|line_chart Year|2018|x|line_chart National_debt_in_billion_U.S._dollars|142.95|y|line_chart Year|2017|x|line_chart National_debt_in_billion_U.S._dollars|139.13|y|line_chart Year|2016|x|line_chart National_debt_in_billion_U.S._dollars|143.56|y|line_chart Year|2015|x|line_chart National_debt_in_billion_U.S._dollars|101.62|y|line_chart Year|2014|x|line_chart National_debt_in_billion_U.S._dollars|31.64|y|line_chart 
title: National debt of Iran 2014 - 2024

gold: The statistic shows the national debt of Iran from 2014 to 2018 , with projections up until 2024 . In 2018 , the national debt of Iran amounted to around 142.95 billion U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] amounted to around templateYValue[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[1] of templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] was around templateYValue[6] percent templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] templateTitle[4] - additional information amounts of the templateTitle[2] templateTitle[3] templateTitle[4] in templateXValue[6] , including it causing political turmoil between Democrats and Republicans in templateTitle[3] , the second largest projected to keep rising .
generated: The statistic shows the National debt of Iran debt of 2014 from 2014 to 2018 , with projections up until 2024 . In 2018 , the National debt of the Iran 2014 was around 142.95 percent billion U.S. dollars . National debt of the Iran 2014 National debt 2024 - additional information amounts of the Iran 2014 2024 in 2018 , including it causing political turmoil between Democrats and Republicans in 2014 , the second largest projected to keep rising .


Example 487:
data: Country|El_Salvador|x|bar_chart Homicides_per_100,000_inhabitants|61.8|y|bar_chart Country|Jamaica|x|bar_chart Homicides_per_100,000_inhabitants|57.0|y|bar_chart Country|Honduras|x|bar_chart Homicides_per_100,000_inhabitants|41.7|y|bar_chart Country|Belize|x|bar_chart Homicides_per_100,000_inhabitants|37.9|y|bar_chart Country|Bahamas|x|bar_chart Homicides_per_100,000_inhabitants|30.9|y|bar_chart Country|Brazil|x|bar_chart Homicides_per_100,000_inhabitants|30.5|y|bar_chart Country|Guatemala|x|bar_chart Homicides_per_100,000_inhabitants|26.1|y|bar_chart Country|Colombia|x|bar_chart Homicides_per_100,000_inhabitants|24.9|y|bar_chart Country|Mexico|x|bar_chart Homicides_per_100,000_inhabitants|24.8|y|bar_chart Country|Puerto_Rico|x|bar_chart Homicides_per_100,000_inhabitants|18.5|y|bar_chart Country|Guyana|x|bar_chart Homicides_per_100,000_inhabitants|14.8|y|bar_chart Country|Costa_Rica|x|bar_chart Homicides_per_100,000_inhabitants|12.3|y|bar_chart Country|Dominican_Republic|x|bar_chart Homicides_per_100,000_inhabitants|11.3|y|bar_chart Country|Grenada|x|bar_chart Homicides_per_100,000_inhabitants|11.1|y|bar_chart Country|Panama|x|bar_chart Homicides_per_100,000_inhabitants|9.7|y|bar_chart Country|Uruguay|x|bar_chart Homicides_per_100,000_inhabitants|8.2|y|bar_chart Country|Peru|x|bar_chart Homicides_per_100,000_inhabitants|7.7|y|bar_chart Country|Nicaragua|x|bar_chart Homicides_per_100,000_inhabitants|7.4|y|bar_chart Country|Ecuador|x|bar_chart Homicides_per_100,000_inhabitants|5.8|y|bar_chart Country|Suriname|x|bar_chart Homicides_per_100,000_inhabitants|5.5|y|bar_chart Country|Argentina|x|bar_chart Homicides_per_100,000_inhabitants|5.2|y|bar_chart Country|Chile|x|bar_chart Homicides_per_100,000_inhabitants|4.3|y|bar_chart 
title: Latin America & the Caribbean : homicide rates 2017 , by country

gold: Countries in Central America and the Caribbean registered some of the highest homicide rates in the Latin American region in 2017 . El Salvador ranked first , with nearly 62 homicides committed per 100,000 inhabitants . Jamaica came in second , with 57 homicides per 100,000 people .
gold_template: Countries in Central templateTitle[1] and the templateTitle[3] registered some of the highest templateTitle[4] templateTitle[5] in the templateTitle[0] American region in templateTitle[6] . templateXValue[0] templateXValue[0] ranked first , with nearly templateYValue[max] templateYLabel[0] committed templateYLabel[1] 100,000 templateYLabel[3] . templateXValue[1] came in second , with templateYValue[1] templateYLabel[0] templateYLabel[1] 100,000 people .

generated_template: In the United States , templateYValue[max] million people died in templateTitle[4] and the United States . This figure represented an increase from the previous year . The templateYLabel[0] templateYLabel[1] rate rate rate has fluctuated between templateYValue[min] and templateYValue[max] million people in the world .
generated: In the United States , 61.8 million people died in homicide and the United States . This figure represented an increase from the previous year . The Homicides per rate rate has fluctuated between 4.3 and 61.8 million people in the world .


Example 488:
data: Quarter|Q1_'15|x|bar_chart Cost_per_square_meter_in_euros|760|y|bar_chart Quarter|Q2_'15|x|bar_chart Cost_per_square_meter_in_euros|697|y|bar_chart Quarter|Q3_'15|x|bar_chart Cost_per_square_meter_in_euros|692|y|bar_chart Quarter|Q4_'15|x|bar_chart Cost_per_square_meter_in_euros|670|y|bar_chart Quarter|Q1_'16|x|bar_chart Cost_per_square_meter_in_euros|670|y|bar_chart Quarter|Q2_'16|x|bar_chart Cost_per_square_meter_in_euros|720|y|bar_chart Quarter|Q3_'16|x|bar_chart Cost_per_square_meter_in_euros|613|y|bar_chart Quarter|Q4_'16|x|bar_chart Cost_per_square_meter_in_euros|760|y|bar_chart Quarter|Q1_'17|x|bar_chart Cost_per_square_meter_in_euros|726|y|bar_chart Quarter|Q2_'17|x|bar_chart Cost_per_square_meter_in_euros|684|y|bar_chart Quarter|Q3_'17|x|bar_chart Cost_per_square_meter_in_euros|669|y|bar_chart Quarter|Q4_'17|x|bar_chart Cost_per_square_meter_in_euros|654|y|bar_chart Quarter|Q2_'18|x|bar_chart Cost_per_square_meter_in_euros|693|y|bar_chart Quarter|Q3_'18|x|bar_chart Cost_per_square_meter_in_euros|693|y|bar_chart Quarter|Q1_'19|x|bar_chart Cost_per_square_meter_in_euros|703|y|bar_chart Quarter|Q2_'19|x|bar_chart Cost_per_square_meter_in_euros|704|y|bar_chart 
title: Prime office rental prices in Moscow Q1 2015-Q2 2019

gold: The statistic displays the rental costs per square meter of prime office spaces in Moscow , Russia , from the first quarter 2015 to the second quarter 2019 . It can be seen that the price of prime office properties in Moscow fluctuated , reaching the lowest price in the third quarter of 2016 at 613 euros per square meter per year . As of the second quarter of 2019 , rental costs per square meter of prime office spaces in Moscow amounted to 703 .
gold_template: The statistic displays the templateTitle[2] costs templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] spaces in templateTitle[4] , Russia , from the first templateXLabel[0] 2015 to the second templateXLabel[0] templateTitle[7] . It can be seen that the price of templateTitle[0] templateTitle[1] properties in templateTitle[4] fluctuated , reaching the lowest price in the third templateXLabel[0] of 2016 at templateYValue[min] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year . As of the second templateXLabel[0] of templateTitle[7] , templateTitle[2] costs templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] spaces in templateTitle[4] amounted to templateYValue[14] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[4] templateTitle[5] templateTitle[6] worldwide templateTitle[7] templateTitle[8] templateTitle[9] to templateTitle[10] , templateTitle[7] templateXLabel[0] . In the first templateXLabel[0] of templateTitle[7] , templateTitle[4] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] amounted to templateYValue[max] templateYLabel[4] . The first templateXLabel[0] of 2013 , templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] was founded in 1997 .
generated: The statistic shows the Cost per square meter of Moscow Q1 2015-Q2 worldwide 2019 titleErr titleErr to titleErr , 2019 Quarter . In the first Quarter of 2019 , Moscow 's Cost per square meter amounted to 760 euros . The first Quarter of 2013 , Cost per square meter was founded in 1997 .


Example 489:
data: Month|Utilities|x|bar_chart Average_response_rate|18|y|bar_chart Month|Retail|x|bar_chart Average_response_rate|16|y|bar_chart Month|Consumer_Goods|x|bar_chart Average_response_rate|14|y|bar_chart Month|Banking/Finance|x|bar_chart Average_response_rate|13|y|bar_chart Month|Travel/Hospitality|x|bar_chart Average_response_rate|12|y|bar_chart Month|Internet/Technology|x|bar_chart Average_response_rate|11|y|bar_chart Month|Marketing/Advertising|x|bar_chart Average_response_rate|11|y|bar_chart Month|Automotive|x|bar_chart Average_response_rate|11|y|bar_chart Month|Real_Estate|x|bar_chart Average_response_rate|10|y|bar_chart Month|Healthcare|x|bar_chart Average_response_rate|9|y|bar_chart Month|Professional_Services|x|bar_chart Average_response_rate|9|y|bar_chart Month|Government|x|bar_chart Average_response_rate|8|y|bar_chart Month|Education|x|bar_chart Average_response_rate|7|y|bar_chart Month|Nonprofit|x|bar_chart Average_response_rate|7|y|bar_chart Month|Media/Entertainment|x|bar_chart Average_response_rate|6|y|bar_chart 
title: Average U.S. brand response rate on social media 2017 , by vertical

gold: This statistic presents the average brand response rate on social media in the United States as of the third quarter of 2017 , by vertical . According to the findings , the retail industry had an average response rate of 16 percent to communicating back to their consumers on social media , while the consumer goods industry reported in 14 percent .
gold_template: This statistic presents the templateYLabel[0] templateTitle[2] templateYLabel[1] templateYLabel[2] on templateTitle[5] templateTitle[6] in the templateTitle[1] as of the third quarter of templateTitle[7] , templateTitle[8] templateTitle[9] . According to the findings , the templateXValue[1] industry had an templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[1] percent to communicating back to their consumers on templateTitle[5] templateTitle[6] , while the templateXValue[2] templateXValue[2] industry reported in templateYValue[2] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] in the United States from the first quarter of templateTitle[5] to templateTitle[6] . During the survey period , it was found that templateYValue[0] percent of U.S. dollars in the United States .
generated: This statistic shows the Average response of rate in the United States from the first quarter of social to media . During the survey period , it was found that 18 percent of U.S. dollars in the United States .


Example 490:
data: Year|2013/14|x|line_chart Imports_in_thousand_metric_tons|755|y|line_chart Year|2014/15|x|line_chart Imports_in_thousand_metric_tons|757|y|line_chart Year|2015/16|x|line_chart Imports_in_thousand_metric_tons|768|y|line_chart Year|2016/17|x|line_chart Imports_in_thousand_metric_tons|787|y|line_chart Year|2017/18|x|line_chart Imports_in_thousand_metric_tons|775|y|line_chart 
title: U.S. rice import volume 2013/14 - 2017/18

gold: This statistic shows the volume of rice imports to the United States from 2013/2014 to 2017/2018 , measured in thousand metric tons . During the trade year 2016/17 , rice imports to the U.S. amounted to about 787 thousand metric tons .
gold_template: This statistic shows the templateTitle[3] of templateTitle[1] templateYLabel[0] to the templateTitle[0] from 2013/2014 to 2017/2018 , measured in thousand templateYLabel[2] templateYLabel[3] . During the trade templateXLabel[0] templateXValue[3] , templateTitle[1] templateYLabel[0] to the templateTitle[0] amounted to about templateYValue[max] thousand templateYLabel[2] templateYLabel[3] .

generated_template: How many templateYLabel[1] are registered in the templateTitle[3] ? In the United States , there were an increase of templateYValue[0] million people living in the templateTitle[3] templateTitle[4] templateTitle[5] , an increase from the previous templateXLabel[0] when compared to templateXValue[1] . However , this figure had the peak of templateYLabel[1] As of templateXValue[0] , approximately templateYValue[max] million people who had an increase .
generated: How many thousand are registered in the volume ? In the United States , there were an increase of 755 million people living in the volume 2013/14 2017/18 , an increase from the previous Year when compared to 2014/15 . However , this figure had the peak of thousand As of 2013/14 , approximately 787 million people who had an increase .


Example 491:
data: Platform|Youtube|x|bar_chart Share_of_respondents|80|y|bar_chart Platform|Facebook|x|bar_chart Share_of_respondents|78|y|bar_chart Platform|FB_Messenger|x|bar_chart Share_of_respondents|60|y|bar_chart Platform|Whatsapp|x|bar_chart Share_of_respondents|58|y|bar_chart Platform|Instagram|x|bar_chart Share_of_respondents|47|y|bar_chart Platform|Twitter|x|bar_chart Share_of_respondents|46|y|bar_chart Platform|Snapchat|x|bar_chart Share_of_respondents|27|y|bar_chart Platform|LinkedIn|x|bar_chart Share_of_respondents|27|y|bar_chart Platform|Pinterest|x|bar_chart Share_of_respondents|27|y|bar_chart Platform|Skype|x|bar_chart Share_of_respondents|22|y|bar_chart Platform|Reddit|x|bar_chart Share_of_respondents|14|y|bar_chart Platform|Tumblr|x|bar_chart Share_of_respondents|13|y|bar_chart Platform|Twitch|x|bar_chart Share_of_respondents|12|y|bar_chart Platform|WeChat|x|bar_chart Share_of_respondents|8|y|bar_chart Platform|Viber|x|bar_chart Share_of_respondents|8|y|bar_chart Platform|Imgur|x|bar_chart Share_of_respondents|7|y|bar_chart 
title: UK : reach of top active social media platforms in Q3 2018

gold: This statistic illustrates the results of a survey about the leading active social media platforms in the UK in 2018 . During the survey period , it was found that 80 percent of the respondents reported that they used Facebook . Facebook is a popular free social networking website that allows registered users to create profiles , upload photos and video , send messages and keep in touch with friends , family and colleagues .
gold_template: This statistic illustrates the results of a survey about the leading templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[0] in templateTitle[8] . During the survey period , it was found that templateYValue[max] percent of the templateYLabel[1] reported that they used templateXValue[1] . templateXValue[1] is a popular free templateTitle[4] networking website that allows registered users to create profiles , upload photos and video , send messages and keep in touch with friends , family and colleagues .

generated_template: templateXValue[0] was the most popular templateTitle[1] templateTitle[2] templateXLabel[0] in templateTitle[4] templateTitle[5] among templateTitle[0] templateTitle[1] templateTitle[2] , with templateYValue[max] percent of templateYLabel[1] saying they templateTitle[3] templateTitle[4] . templateXValue[1] ( templateYValue[1] percent of templateYLabel[1] stated they knew about templateXValue[1] . templateXValue[2] ranked second and third with templateYValue[1] percent of templateYLabel[1] indicated templateTitle[2] templateTitle[3] templateTitle[4] in the United States .
generated: Youtube was the most popular reach top Platform in social media among UK reach top , with 80 percent of respondents saying they active social . Facebook ( 78 percent of respondents stated they knew about Facebook . FB_Messenger ranked second and third with 78 percent of respondents indicated top active social in the United States .


Example 492:
data: Date,_Location|January_17_1994_Los_Angeles|x|bar_chart Damage_in_million_U.S._dollars|30000|y|bar_chart Date,_Location|October_18_1989_San_Francisco|x|bar_chart Damage_in_million_U.S._dollars|5600|y|bar_chart Date,_Location|February_28_2001_Seattle|x|bar_chart Damage_in_million_U.S._dollars|2000|y|bar_chart Date,_Location|March_28_1964_Prince_William_Sound|x|bar_chart Damage_in_million_U.S._dollars|1020|y|bar_chart Date,_Location|August_24_2014_San_Francisco_California|x|bar_chart Damage_in_million_U.S._dollars|700|y|bar_chart Date,_Location|February_9_1971_Los_Angeles|x|bar_chart Damage_in_million_U.S._dollars|535|y|bar_chart Date,_Location|April_18_1906_San_Francisco|x|bar_chart Damage_in_million_U.S._dollars|524|y|bar_chart Date,_Location|October_1_1987_Los_Angeles|x|bar_chart Damage_in_million_U.S._dollars|213|y|bar_chart Date,_Location|December_22_2003_San_Robbles_(California)|x|bar_chart Damage_in_million_U.S._dollars|200|y|bar_chart Date,_Location|October_15_2006_Hawai_Island|x|bar_chart Damage_in_million_U.S._dollars|150|y|bar_chart Date,_Location|June_28_1992_Landers_California|x|bar_chart Damage_in_million_U.S._dollars|100|y|bar_chart Date,_Location|April_22_1992_South_California|x|bar_chart Damage_in_million_U.S._dollars|100|y|bar_chart 
title: Earthquakes that caused the most economic damage in the U.S. 1900 - 2016

gold: The statistic shows the earthquakes that resulted in the most economic damage in the United States from 1900 to 2016 . The earthquake that occurred on January 17 , 1994 in Los Angeles caused approximately 30 billion U.S. dollars worth of damage and is the costliest earthquake on record .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] resulted in the templateTitle[3] templateTitle[4] templateYLabel[0] in the templateTitle[6] from templateTitle[7] to templateTitle[8] . The earthquake templateTitle[1] occurred on templateXValue[0] templateXValue[0] , templateXValue[0] in templateXValue[0] templateXValue[0] templateTitle[2] approximately templateYValue[max] billion templateYLabel[2] templateYLabel[3] worth of templateYLabel[0] and is the costliest earthquake on record .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States templateTitle[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the United States . According to templateXValue[1] , there were templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateXValue[0] templateXValue[0] in the United States .
generated: This statistic shows the Earthquakes that caused most economic in the United States damage Damage million U.S. dollars in the United States . According to October_18_1989_San_Francisco , there were 30000 million U.S. dollars of January_17_1994_Los_Angeles in the United States .


Example 493:
data: Year|2018|x|line_chart Inhabitants_per_square_kilometer|205.45|y|line_chart Year|2017|x|line_chart Inhabitants_per_square_kilometer|205.81|y|line_chart Year|2016|x|line_chart Inhabitants_per_square_kilometer|206.12|y|line_chart Year|2015|x|line_chart Inhabitants_per_square_kilometer|206.47|y|line_chart Year|2014|x|line_chart Inhabitants_per_square_kilometer|206.67|y|line_chart Year|2013|x|line_chart Inhabitants_per_square_kilometer|204.78|y|line_chart Year|2012|x|line_chart Inhabitants_per_square_kilometer|202.42|y|line_chart Year|2011|x|line_chart Inhabitants_per_square_kilometer|201.87|y|line_chart Year|2010|x|line_chart Inhabitants_per_square_kilometer|201.53|y|line_chart Year|2009|x|line_chart Inhabitants_per_square_kilometer|200.91|y|line_chart Year|2008|x|line_chart Inhabitants_per_square_kilometer|200.0|y|line_chart 
title: Population density in Italy 2018

gold: The statistic shows the population density in Italy from 2008 to 2018 . In 2018 , the population density in Italy amounted to about 205.45 inhabitants per square kilometer . See the population of Italy for comparison .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[2] amounted to about templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . See the templateTitle[0] of templateTitle[2] for comparison .

generated_template: The templateTitle[0] templateTitle[1] in templateTitle[2] was templateYValue[max] people templateYLabel[1] templateYLabel[2] templateYLabel[3] ( 47.24 templateYLabel[1] templateYLabel[2] mile ) in templateXValue[max] . This number has been slowly increasing for the past ten years . Higher templateTitle[0] templateTitle[1] is associated with urbanization , but not necessarily economic growth .
generated: The Population density in Italy was 206.67 people per square kilometer ( 47.24 per square mile ) in 2018 . This number has been slowly increasing for the past ten years . Higher Population density is associated with urbanization , but not necessarily economic growth .


Example 494:
data: Response|"Big_Box"_retail_store_(e.g._Walmart_Target)|x|bar_chart Share_of_respondents|62|y|bar_chart Response|Grocery_store/Supermarket|x|bar_chart Share_of_respondents|36|y|bar_chart Response|Pharmacy_(e.g._CVS_Walgreens)|x|bar_chart Share_of_respondents|31|y|bar_chart Response|Online_(Net)|x|bar_chart Share_of_respondents|12|y|bar_chart Response|Online_mass_merchandiser_(e.g._Amazon_drugstore.com)|x|bar_chart Share_of_respondents|8|y|bar_chart Response|Department_Stores_(e.g._Macy's_Nordstrom)|x|bar_chart Share_of_respondents|5|y|bar_chart Response|In-person_at_a_specialty_beauty_products_merchant_(e.g._Sephora)|x|bar_chart Share_of_respondents|4|y|bar_chart Response|Online_specialty_beauty_products_merchant_(e.g._Sephora_Ultra)|x|bar_chart Share_of_respondents|3|y|bar_chart Response|In-person/not_in_a_store_(e.g._Avon_Mary_Kay)|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Online_through_a_"sampling"_membership_program_(e.g._Ipsy_Birchbox)|x|bar_chart Share_of_respondents|1|y|bar_chart Response|Online_through_a_specific_brand's_website_(e.g._Clairol_CoverGirl)|x|bar_chart Share_of_respondents|1|y|bar_chart Response|Somewhere_else|x|bar_chart Share_of_respondents|10|y|bar_chart 
title: U.S. consumers ' purchase location of shampoos and conditioners 2014

gold: This statistic presents the results of a survey among U.S. adult consumers . The survey was fielded online by Harris Interactive in June 2014 , asking the respondents where they usually purchase their shampoo and/or conditioners . Some 12 percent of U.S. adults indicated that they buy their shampoo/conditioner online .
gold_template: This statistic presents the results of a survey among templateTitle[0] adult templateTitle[1] . The survey was fielded templateXValue[3] by Harris Interactive in June templateTitle[7] , asking the templateYLabel[1] where they usually templateTitle[3] their shampoo and/or templateTitle[6] . Some templateYValue[3] percent of templateTitle[0] adults indicated that they buy their shampoo/conditioner templateXValue[3] .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[10] on templateXValue[0] templateTitle[2] templateTitle[3] in templateTitle[4] templateTitle[5] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated they could be both templateXValue[0] and templateYValue[1] percent of templateYLabel[1] stated they did not templateXValue[1] templateXValue[1] templateXValue[1] a templateXValue[1] templateXValue[1] .
generated: This statistic shows the results of a survey conducted in the United States in titleErr on "Big_Box"_retail_store_(e.g._Walmart_Target) ' purchase in location shampoos . During the survey period , 62 percent of respondents stated they could be both "Big_Box"_retail_store_(e.g._Walmart_Target) and 36 percent of respondents stated they did not Grocery_store/Supermarket Grocery_store/Supermarket a Grocery_store/Supermarket .


Example 495:
data: State|South_Dakota|x|bar_chart Production_in_thousand_pounds|831600|y|bar_chart State|North_Dakota|x|bar_chart Production_in_thousand_pounds|740700|y|bar_chart State|Minnesota|x|bar_chart Production_in_thousand_pounds|102630|y|bar_chart State|California|x|bar_chart Production_in_thousand_pounds|70680|y|bar_chart State|Colorado|x|bar_chart Production_in_thousand_pounds|59400|y|bar_chart State|Kansas|x|bar_chart Production_in_thousand_pounds|53925|y|bar_chart State|Nebraska|x|bar_chart Production_in_thousand_pounds|44850|y|bar_chart State|Texas|x|bar_chart Production_in_thousand_pounds|39650|y|bar_chart 
title: Leading U.S. states in sunflower production 2019

gold: The U.S. state with the highest production volume of sunflowers is South Dakota at 831.6 million pounds in 2019 . North Dakota came in second at 740.7 million pounds of sunflowers . Sunflower products There are several products that are derived from sunflowers .
gold_template: The templateTitle[1] templateXLabel[0] with the highest templateYLabel[0] volume of sunflowers is templateXValue[0] templateXValue[0] at templateYValue[max] million templateYLabel[2] in templateTitle[5] . templateXValue[1] templateXValue[0] came in second at templateYValue[1] million templateYLabel[2] of sunflowers . templateTitle[3] products There are several products that are derived from sunflowers .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateYLabel[2] in the United States in templateTitle[6] , templateTitle[7] templateXLabel[0] . In templateXValue[1] , templateYValue[1] percent of templateYLabel[1] templateYLabel[2] of templateTitle[3] .
generated: This statistic shows the Leading U.S. states of sunflower production pounds in the United States in titleErr , titleErr State . In North_Dakota , 740700 percent of thousand pounds of sunflower .


Example 496:
data: Month|Søren_Bjerg_(Bjergsen)|x|bar_chart Number_of_Twitter_followers_in_thousands|604|y|bar_chart Month|Enrique_Cedeño_Martínez_(xPeke)|x|bar_chart Number_of_Twitter_followers_in_thousands|596|y|bar_chart Month|Yiliang_Peng_(Doublelift)|x|bar_chart Number_of_Twitter_followers_in_thousands|393|y|bar_chart Month|Jason_Tran_(WildTurtle)|x|bar_chart Number_of_Twitter_followers_in_thousands|376|y|bar_chart Month|Danil_Ishutin_(Dendi)|x|bar_chart Number_of_Twitter_followers_in_thousands|334|y|bar_chart Month|Hai_Du_Lam_(Hai)|x|bar_chart Number_of_Twitter_followers_in_thousands|297|y|bar_chart Month|Henrik_Hansen_(Froggen)|x|bar_chart Number_of_Twitter_followers_in_thousands|270|y|bar_chart Month|Martin_Larsson_(Rekkles)|x|bar_chart Number_of_Twitter_followers_in_thousands|258|y|bar_chart Month|Bora_Kim_(Yell0wStaR)|x|bar_chart Number_of_Twitter_followers_in_thousands|256|y|bar_chart Month|Zachary_Scuderi_(Sneaky)|x|bar_chart Number_of_Twitter_followers_in_thousands|244|y|bar_chart 
title: Leading eSports pro players on Twitter worldwide 2016 , by number of followers

gold: The graph shows the leading eSports professional players on Twitter worldwide as of January 2016 , ranked by the number of fans . As of the measured period , Søren Bjerg , a player from Denmark also known as Bjergsen , was the most famous on Twitter , with 604 thousand followers . He was followed by Enrique Martínez , aka xPeke , who gathered 596 thousand followers on Twitter .
gold_template: The graph shows the templateTitle[0] templateTitle[1] professional templateTitle[3] on templateYLabel[1] templateTitle[5] as of January templateTitle[6] , ranked templateTitle[7] the templateYLabel[0] of fans . As of the measured period , templateXValue[0] templateXValue[0] , a player from Denmark also known as Bjergsen , was the most famous on templateYLabel[1] , with templateYValue[max] thousand templateYLabel[2] . He was followed templateTitle[7] templateXValue[1] templateXValue[1] , aka xPeke , who gathered templateYValue[1] thousand templateYLabel[2] on templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in the United States from the templateXValue[last] season to the templateXValue[0] season . Over the last reported period , templateYValue[max] percent of people in the templateTitle[3] templateTitle[4] according to the United States .
generated: This statistic shows the Number Twitter of Twitter worldwide in the United States from the Zachary_Scuderi_(Sneaky) season to the Søren_Bjerg_(Bjergsen) season . Over the last reported period , 604 percent of people in the players Twitter according to the United States .


Example 497:
data: Year|2019|x|line_chart Inhabitants_in_millions|10.33|y|line_chart Year|2018|x|line_chart Inhabitants_in_millions|10.23|y|line_chart Year|2017|x|line_chart Inhabitants_in_millions|10.12|y|line_chart Year|2016|x|line_chart Inhabitants_in_millions|10.0|y|line_chart Year|2015|x|line_chart Inhabitants_in_millions|9.85|y|line_chart Year|2014|x|line_chart Inhabitants_in_millions|9.75|y|line_chart Year|2013|x|line_chart Inhabitants_in_millions|9.64|y|line_chart Year|2012|x|line_chart Inhabitants_in_millions|9.56|y|line_chart Year|2011|x|line_chart Inhabitants_in_millions|9.48|y|line_chart Year|2010|x|line_chart Inhabitants_in_millions|9.42|y|line_chart Year|2009|x|line_chart Inhabitants_in_millions|9.34|y|line_chart 
title: Population in Sweden 2009 - 2019

gold: This statistic shows the total population in Sweden from 2009 to 2019 . The number of inhabitants in Sweden has increased by nearly one million in this time period . In 2009 , there were approximately 9.34 million inhabitants in Sweden and by the end of 2019 the Swedish population reached 10.33 million people .
gold_template: This statistic shows the total templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . The number of templateYLabel[0] in templateTitle[1] has increased by nearly one million in this time period . In templateXValue[min] , there were approximately templateYValue[min] million templateYLabel[0] in templateTitle[1] and by the end of templateXValue[max] the Swedish templateTitle[0] reached templateYValue[max] million people .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[2] was around templateYValue[max] million people . templateTitle[0] templateTitle[1] of templateTitle[2] – additional information templateTitle[0] templateTitle[1] of templateTitle[2] has a surprisingly low ( and decreasing ) templateTitle[1] growth rate ; despite it being home to the largest number of Catholics .
generated: The statistic shows the Population Sweden of 2009 from 2009 to 2019 . In 2019 , the Population Sweden of 2009 was around 10.33 million people . Population Sweden of 2009 – additional information Population Sweden of 2009 has a surprisingly low ( and decreasing ) Sweden growth rate ; despite it being home to the largest number of Catholics .


Example 498:
data: Year|2026-2027|x|line_chart Annual_funding_increase_in_million_Canadian_dollars|2300|y|line_chart Year|2025-2026|x|line_chart Annual_funding_increase_in_million_Canadian_dollars|2020|y|line_chart Year|2024-2025|x|line_chart Annual_funding_increase_in_million_Canadian_dollars|1751|y|line_chart Year|2023-2024|x|line_chart Annual_funding_increase_in_million_Canadian_dollars|1495|y|line_chart Year|2022-2023|x|line_chart Annual_funding_increase_in_million_Canadian_dollars|1250|y|line_chart Year|2021-2022|x|line_chart Annual_funding_increase_in_million_Canadian_dollars|1016|y|line_chart Year|2020-2021|x|line_chart Annual_funding_increase_in_million_Canadian_dollars|793|y|line_chart Year|2019-2020|x|line_chart Annual_funding_increase_in_million_Canadian_dollars|580|y|line_chart Year|2018-2019|x|line_chart Annual_funding_increase_in_million_Canadian_dollars|377|y|line_chart Year|2017-2018|x|line_chart Annual_funding_increase_in_million_Canadian_dollars|184|y|line_chart 
title: Projected increase of the national defense budget in Canada fiscal years 2018 - 2027

gold: This statistic shows the projected increase over baseline spending for the national defense budget of Canada between the fiscal years 2018 to 2027 . In fiscal year 2027 , the defense budget for Canada is projected to be 2.3 billion Canadian dollars greater than 2026 budget .
gold_template: This statistic shows the templateTitle[0] templateYLabel[2] over baseline spending for the templateTitle[2] templateTitle[3] templateTitle[4] of templateTitle[5] between the templateTitle[6] templateTitle[7] templateTitle[8] to templateTitle[9] . In templateTitle[6] templateXLabel[0] templateTitle[9] , the templateTitle[3] templateTitle[4] for templateTitle[5] is templateTitle[0] to be templateYValue[max] billion templateYLabel[4] templateYLabel[5] greater than 2026 templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people employed by the templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Annual funding of national defense budget Canada in the United States from 2026-2027 to 2026-2027 . In 2026-2027 , there were 2300 people employed by the budget Canada in the United States .


Example 499:
data: Year|2020|x|line_chart Year_on_year_percentage_change|1.3|y|line_chart Year|2019|x|line_chart Year_on_year_percentage_change|1.6|y|line_chart Year|2018|x|line_chart Year_on_year_percentage_change|0.3|y|line_chart Year|2017|x|line_chart Year_on_year_percentage_change|0.3|y|line_chart Year|2016|x|line_chart Year_on_year_percentage_change|2.6|y|line_chart Year|2015|x|line_chart Year_on_year_percentage_change|1|y|line_chart 
title: Purchasing power change in the Netherlands 2015 - 2020

gold: In 2018 , the purchasing power in the Netherlands increased for the fourth year in a row . Purchasing power growth slowed down considerably though in 2017 and 2018 in comparison to 2015 and especially 2016 . In the next two years , the purchasing power was forecast to increase further .
gold_template: In templateXValue[2] , the templateTitle[0] templateTitle[1] in the templateTitle[3] increased for the fourth templateXLabel[0] in a row . templateTitle[0] templateTitle[1] growth slowed down considerably though in templateXValue[3] and templateXValue[2] in comparison to templateXValue[min] and especially templateXValue[4] . In the next templateYValue[1] years , the templateTitle[0] templateTitle[1] was forecast to increase further .

generated_template: This statistic shows the templateYLabel[2] templateYLabel[3] on the previous templateXLabel[0] of templateTitle[0] prices in templateTitle[4] in templateXValue[min] with a templateTitle[3] for templateXValue[1] and templateXValue[max] . In templateXValue[1] , templateTitle[0] prices in templateTitle[4] are expected to increase with 2.3 percent compared to the previous templateXLabel[0] . In recent years , the templateTitle[0] market in templateTitle[4] remained stable .
generated: This statistic shows the percentage change on the previous Year of Purchasing prices in 2015 in 2015 with a Netherlands for 2019 and 2020 . In 2019 , Purchasing prices in 2015 are expected to increase with 2.3 percent compared to the previous Year . In recent years , the Purchasing market in 2015 remained stable .


Example 500:
data: Year|2014|x|line_chart Passenger_cars_produced_(in_millions)|6.64|y|line_chart Year|2013|x|line_chart Passenger_cars_produced_(in_millions)|6.73|y|line_chart Year|2012|x|line_chart Passenger_cars_produced_(in_millions)|6.61|y|line_chart Year|2011|x|line_chart Passenger_cars_produced_(in_millions)|6.87|y|line_chart Year|2010|x|line_chart Passenger_cars_produced_(in_millions)|6.27|y|line_chart Year|2009|x|line_chart Passenger_cars_produced_(in_millions)|5.0|y|line_chart Year|2008|x|line_chart Passenger_cars_produced_(in_millions)|6.02|y|line_chart Year|2007|x|line_chart Passenger_cars_produced_(in_millions)|6.26|y|line_chart Year|2006|x|line_chart Passenger_cars_produced_(in_millions)|5.78|y|line_chart Year|2005|x|line_chart Passenger_cars_produced_(in_millions)|5.66|y|line_chart Year|2004|x|line_chart Passenger_cars_produced_(in_millions)|4.5|y|line_chart Year|2003|x|line_chart Passenger_cars_produced_(in_millions)|4.68|y|line_chart Year|2002|x|line_chart Passenger_cars_produced_(in_millions)|4.9|y|line_chart Year|2001|x|line_chart Passenger_cars_produced_(in_millions)|4.66|y|line_chart Year|2000|x|line_chart Passenger_cars_produced_(in_millions)|5.27|y|line_chart Year|1999|x|line_chart Passenger_cars_produced_(in_millions)|5.34|y|line_chart 
title: General Motors - passenger cars produced worldwide 1999 - 2014

gold: The timeline shows the passenger car production of General Motors worldwide from 1999 to 2014 . In 2013 , GM produced 6.7 million passenger cars worldwide . The U.S. automaker is world 's fourth largest manufacturer of passenger cars in terms of production .
gold_template: The timeline shows the templateYLabel[0] car production of templateTitle[0] templateTitle[1] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , GM templateYLabel[2] templateYValue[1] million templateYLabel[0] templateYLabel[1] templateTitle[5] . The U.S. automaker is world 's fourth largest manufacturer of templateYLabel[0] templateYLabel[1] in terms of production .

generated_template: This timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateYLabel[2] around templateYValue[max] templateYLabel[3] templateYLabel[0] templateYLabel[1] templateTitle[4] .
generated: This timeline shows the Passenger cars produced of General produced from 1999 to 2014 . In 2014 , General produced around 6.87 (in Passenger cars produced .


Example 501:
data: Platform|Pinterest|x|bar_chart ACSI_score_(100-point_scale)|80|y|bar_chart Platform|YouTube|x|bar_chart ACSI_score_(100-point_scale)|78|y|bar_chart Platform|Wikipedia|x|bar_chart ACSI_score_(100-point_scale)|74|y|bar_chart Platform|Instagram|x|bar_chart ACSI_score_(100-point_scale)|72|y|bar_chart Platform|Snapchat|x|bar_chart ACSI_score_(100-point_scale)|71|y|bar_chart Platform|Twitter|x|bar_chart ACSI_score_(100-point_scale)|69|y|bar_chart Platform|LinkedIn|x|bar_chart ACSI_score_(100-point_scale)|69|y|bar_chart Platform|Tumblr|x|bar_chart ACSI_score_(100-point_scale)|64|y|bar_chart Platform|Facebook|x|bar_chart ACSI_score_(100-point_scale)|63|y|bar_chart 
title: ACSI - U.S. customer satisfaction with social media 2019

gold: This graph shows the American Customer Satisfaction Index ( ACSI ) of customer satisfaction with social media websites in 2019 . Overall , Pinterest scored the highest level of customer satisfaction with 80 index points . Facebook was ranked last with an index score rating of 63 / 100 index points .
gold_template: This graph shows the American templateTitle[2] templateTitle[3] Index ( templateYLabel[0] ) of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] websites in templateTitle[7] . Overall , templateXValue[0] scored the highest level of templateTitle[2] templateTitle[3] templateTitle[4] templateYValue[max] index points . templateXValue[last] was ranked last templateTitle[4] an index templateYLabel[1] rating of templateYValue[min] / 100 index points .

generated_template: This statistic gives information on the most popular templateTitle[1] templateTitle[2] templateTitle[3] rate as of January templateTitle[4] . During the measured period , it was found that templateYValue[1] percent of templateYLabel[1] had a monthly active users in the world .
generated: This statistic gives information on the most popular U.S. customer satisfaction rate as of January social . During the measured period , it was found that 78 percent of score had a monthly active users in the world .


Example 502:
data: Response|Teens_(13-17)|x|bar_chart Share_of_respondents|83|y|bar_chart Response|Millennials_(18-34)|x|bar_chart Share_of_respondents|74|y|bar_chart Response|Gen_X_(35-54)|x|bar_chart Share_of_respondents|59|y|bar_chart Response|Boomers_(55-64)|x|bar_chart Share_of_respondents|39|y|bar_chart 
title: U.S. mobile device owner monthly app download rate 2018 , by age group

gold: This statistic gives information on the percentage of mobile device owners in the United States who download apps at least once a month or more as of April 2018 , sorted by age group . During the survey period , it was found that 74 percent of responding Millennial app users downloaded apps to their mobile device on a monthly basis .
gold_template: This statistic gives information on the percentage of templateTitle[1] templateTitle[2] owners in the templateTitle[0] who templateTitle[6] apps at least once a month or more as of April templateTitle[8] , sorted templateTitle[9] templateTitle[10] templateTitle[11] . During the survey period , it was found that templateYValue[1] percent of responding Millennial templateTitle[5] users downloaded apps to their templateTitle[1] templateTitle[2] on a templateTitle[4] basis .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitle[3] templateTitle[4] who were using templateTitle[0] as of February templateTitle[5] , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] percent of templateYLabel[1] indicated templateTitle[2] templateTitle[3] .
generated: This statistic shows the Share of adults in the owner monthly who were using U.S. as of February app , sorted download rate 2018 . During that period of time , 83 percent of respondents indicated device owner .


Example 503:
data: Year|2019|x|line_chart Number_of_aircraft|69|y|line_chart Year|2018|x|line_chart Number_of_aircraft|837|y|line_chart Year|2017|x|line_chart Number_of_aircraft|865|y|line_chart Year|2016|x|line_chart Number_of_aircraft|701|y|line_chart Year|2015|x|line_chart Number_of_aircraft|666|y|line_chart Year|2014|x|line_chart Number_of_aircraft|1196|y|line_chart Year|2013|x|line_chart Number_of_aircraft|1208|y|line_chart Year|2012|x|line_chart Number_of_aircraft|1184|y|line_chart Year|2011|x|line_chart Number_of_aircraft|625|y|line_chart Year|2010|x|line_chart Number_of_aircraft|508|y|line_chart Year|2009|x|line_chart Number_of_aircraft|197|y|line_chart Year|2008|x|line_chart Number_of_aircraft|488|y|line_chart Year|2007|x|line_chart Number_of_aircraft|850|y|line_chart Year|2006|x|line_chart Number_of_aircraft|733|y|line_chart Year|2005|x|line_chart Number_of_aircraft|574|y|line_chart Year|2004|x|line_chart Number_of_aircraft|152|y|line_chart 
title: Boeing 737 - orders 2004 - 2019

gold: In 2019 , Boeing received gross orders for 69 units of its 737 narrow-body jet airliner series , but net orders after cancellations and changes ( under the ASC 606 revenue recognition standard ) came to a devastating negative 183 units . The slump in net orders came as a result of the jet maker 's 737 MAX crisis . Boeing delivered some 18 units of its 737 aircraft to Delta Air Lines in 2019 .
gold_template: In templateXValue[max] , templateTitle[0] received gross templateTitle[2] for templateYValue[min] units of its templateTitle[1] narrow-body jet airliner series , but net templateTitle[2] after cancellations and changes ( under the ASC 606 revenue recognition standard ) came to a devastating negative 183 units . The slump in net templateTitle[2] came as a result of the jet maker 's templateTitle[1] MAX crisis . templateTitle[0] delivered some 18 units of its templateTitle[1] templateYLabel[1] to Delta Air Lines in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[0] thousand templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] and templateTitle[5] in the United States .
generated: This statistic shows the Number aircraft of 2019 titleErr in the United States from 2004 to 2019 . In 2019 , there were approximately 69 thousand 737 orders 2004 2019 and titleErr in the United States .


Example 504:
data: Country|Brazil|x|bar_chart Area_in_thousand_hectares|1800.4|y|bar_chart Country|Indonesia|x|bar_chart Area_in_thousand_hectares|1253.8|y|bar_chart Country|Côte_d’Ivoire|x|bar_chart Area_in_thousand_hectares|925.44|y|bar_chart Country|Colombia|x|bar_chart Area_in_thousand_hectares|798.36|y|bar_chart Country|Ethiopia|x|bar_chart Area_in_thousand_hectares|694.33|y|bar_chart Country|Mexico|x|bar_chart Area_in_thousand_hectares|638.6|y|bar_chart Country|Viet_Nam|x|bar_chart Area_in_thousand_hectares|605.18|y|bar_chart Country|Honduras|x|bar_chart Area_in_thousand_hectares|505.12|y|bar_chart Country|India|x|bar_chart Area_in_thousand_hectares|449.36|y|bar_chart Country|Peru|x|bar_chart Area_in_thousand_hectares|423.55|y|bar_chart 
title: Leading countries worldwide based on coffee area harvested 2017

gold: This statistic illustrates the global leading 10 countries based on coffee area harvested in 2017 . In that year , Mexico harvested an area of 638.6 thousand hectares of green coffee and was ranked sixth among coffee-growing countries worldwide .
gold_template: This statistic illustrates the global templateTitle[0] 10 templateTitle[1] templateTitle[3] on templateTitle[4] templateYLabel[0] templateTitle[6] in templateTitle[7] . In that year , templateXValue[5] templateTitle[6] an templateYLabel[0] of templateYValue[5] thousand templateYLabel[2] of green templateTitle[4] and was ranked sixth among coffee-growing templateTitle[1] templateTitle[2] .

generated_template: templateXValue[0] had the most templateYLabel[1] in templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[7] at templateYValue[max] percent of templateYLabel[1] templateYLabel[2] in the United States since templateXValue[6] . The source defined as China with templateYValue[1] percent of templateYLabel[1] with templateYValue[2] percent of templateYLabel[1] in templateXValue[2] U.S. templateYLabel[2] in templateTitle[7] .
generated: Brazil had the most thousand in based coffee area in 2017 at 1800.4 percent of thousand hectares in the United States since Viet_Nam . The source defined as China with 1253.8 percent of thousand with 925.44 percent of thousand in Côte_d’Ivoire U.S. hectares in 2017 .


Example 505:
data: Quarter|Q4_'19|x|bar_chart Revenue_in_million_U.S._dollars|21082|y|bar_chart Quarter|Q3_'19|x|bar_chart Revenue_in_million_U.S._dollars|17652|y|bar_chart Quarter|Q2_'19|x|bar_chart Revenue_in_million_U.S._dollars|16886|y|bar_chart Quarter|Q1_'19|x|bar_chart Revenue_in_million_U.S._dollars|15077|y|bar_chart Quarter|Q4_'18|x|bar_chart Revenue_in_million_U.S._dollars|16914|y|bar_chart Quarter|Q3_'18|x|bar_chart Revenue_in_million_U.S._dollars|13727|y|bar_chart Quarter|Q2_'18|x|bar_chart Revenue_in_million_U.S._dollars|13231|y|bar_chart Quarter|Q1_'18|x|bar_chart Revenue_in_million_U.S._dollars|11966|y|bar_chart Quarter|Q4_'17|x|bar_chart Revenue_in_million_U.S._dollars|12972|y|bar_chart Quarter|Q3_'17|x|bar_chart Revenue_in_million_U.S._dollars|10328|y|bar_chart Quarter|Q2_'17|x|bar_chart Revenue_in_million_U.S._dollars|9321|y|bar_chart Quarter|Q1_'17|x|bar_chart Revenue_in_million_U.S._dollars|8032|y|bar_chart Quarter|Q4_'16|x|bar_chart Revenue_in_million_U.S._dollars|8809|y|bar_chart Quarter|Q3_'16|x|bar_chart Revenue_in_million_U.S._dollars|7011|y|bar_chart Quarter|Q2_'16|x|bar_chart Revenue_in_million_U.S._dollars|6436|y|bar_chart Quarter|Q1_'16|x|bar_chart Revenue_in_million_U.S._dollars|5382|y|bar_chart Quarter|Q4_'15|x|bar_chart Revenue_in_million_U.S._dollars|5841|y|bar_chart Quarter|Q3_'15|x|bar_chart Revenue_in_million_U.S._dollars|4501|y|bar_chart Quarter|Q2_'15|x|bar_chart Revenue_in_million_U.S._dollars|4042|y|bar_chart Quarter|Q1_'15|x|bar_chart Revenue_in_million_U.S._dollars|3543|y|bar_chart Quarter|Q4_'14|x|bar_chart Revenue_in_million_U.S._dollars|3851|y|bar_chart Quarter|Q3_'14|x|bar_chart Revenue_in_million_U.S._dollars|3203|y|bar_chart Quarter|Q2_'14|x|bar_chart Revenue_in_million_U.S._dollars|2910|y|bar_chart Quarter|Q1_'14|x|bar_chart Revenue_in_million_U.S._dollars|2502|y|bar_chart Quarter|Q4_'13|x|bar_chart Revenue_in_million_U.S._dollars|2585|y|bar_chart Quarter|Q3_'13|x|bar_chart Revenue_in_million_U.S._dollars|2016|y|bar_chart Quarter|Q2_'13|x|bar_chart Revenue_in_million_U.S._dollars|1813|y|bar_chart Quarter|Q1_'13|x|bar_chart Revenue_in_million_U.S._dollars|1458|y|bar_chart Quarter|Q4_'12|x|bar_chart Revenue_in_million_U.S._dollars|1585|y|bar_chart Quarter|Q3_'12|x|bar_chart Revenue_in_million_U.S._dollars|1262|y|bar_chart Quarter|Q2_'12|x|bar_chart Revenue_in_million_U.S._dollars|1184|y|bar_chart Quarter|Q1_'12|x|bar_chart Revenue_in_million_U.S._dollars|1058|y|bar_chart Quarter|Q4_'11|x|bar_chart Revenue_in_million_U.S._dollars|1131|y|bar_chart 
title: Facebook : worldwide quarterly revenue 2011 - 2019

gold: In the fourth quarter of 2019 , social network Facebook 's total revenues amounted to 21.08 billion U.S. dollars , the majority of which were generated through advertising . The company announced over seven million active advertisers on Facebook during the third quarter of 2019 . During that fiscal period , the company 's net income was 7.35 billion U.S. dollars .
gold_template: In the fourth templateXLabel[0] of templateTitle[5] , social network templateTitle[0] 's total revenues amounted to templateYValue[max] billion templateYLabel[2] templateYLabel[3] , the majority of which were generated through advertising . The company announced over seven templateYLabel[1] active advertisers on templateTitle[0] during the third templateXLabel[0] of templateTitle[5] . During that fiscal period , the company 's net income was 7.35 billion templateYLabel[2] templateYLabel[3] .

generated_template: templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] worldwide reached templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the final templateXLabel[0] of 2012 to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the fourth templateXLabel[0] of templateXValue[max] . The company 's main business recommendation site derives most of its templateYLabel[0] from advertising .
generated: Facebook worldwide quarterly Revenue worldwide reached 21082 million U.S. dollars in the final Quarter of 2012 to 21082 million U.S. dollars in the fourth Quarter of Q4_'19 . The company 's main business recommendation site derives most of its Revenue from advertising .


Example 506:
data: Response|Credit_card|x|bar_chart Share_of_respondents|18|y|bar_chart Response|Debit_card|x|bar_chart Share_of_respondents|44|y|bar_chart Response|Cash|x|bar_chart Share_of_respondents|32|y|bar_chart 
title: Payment type preference when shopping at fast food restaurants in the U.S. 2018

gold: This statistic shows the preferred form of payment when shopping at fast food restaurants among consumers in the United States in 2018 . In the study it was found that 32 percent of consumers preferred to use cash when making purchases at fast food restaurants .
gold_template: This statistic shows the preferred form of templateTitle[0] templateTitle[3] templateTitle[4] at templateTitle[5] templateTitle[6] templateTitle[7] among consumers in the templateTitle[8] in templateTitle[9] . In the study it was found that templateYValue[2] percent of consumers preferred to use templateXValue[last] templateTitle[3] making purchases at templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the preferred templateTitle[3] templateTitle[4] for templateTitle[1] templateTitle[2] according to internet users in the United States in templateTitle[6] . During the survey period , it was found that templateYValue[1] percent of templateYLabel[1] most frequently used a templateXValue[1] templateXValue[0] to pay for templateTitle[1] purchases .
generated: This statistic shows the preferred when shopping for type preference according to internet users in the United States in food . During the survey period , it was found that 44 percent of respondents most frequently used a Debit_card Credit_card to pay for type purchases .


Example 507:
data: Month|Food|x|bar_chart Average_expenditure_in_U.S._dollars|47.97$|y|bar_chart Month|Clothing|x|bar_chart Average_expenditure_in_U.S._dollars|27.29$|y|bar_chart Month|Gifts|x|bar_chart Average_expenditure_in_U.S._dollars|24.01$|y|bar_chart Month|Candy|x|bar_chart Average_expenditure_in_U.S._dollars|20.78$|y|bar_chart Month|Flowers|x|bar_chart Average_expenditure_in_U.S._dollars|10.79$|y|bar_chart Month|Decorations|x|bar_chart Average_expenditure_in_U.S._dollars|8.73$|y|bar_chart Month|Greeting_cards|x|bar_chart Average_expenditure_in_U.S._dollars|6.52$|y|bar_chart Month|Other|x|bar_chart Average_expenditure_in_U.S._dollars|5.15$|y|bar_chart 
title: Planned Easter expenditure per capita in the U.S. by item 2019

gold: This statistic shows the results of a survey among people in the United States on the amount of money they are planning to spend on the following items for the 2019 Easter holidays . Respondents stated that they are planning to spend an average of 20.78 U.S. dollars on candy for the upcoming Easter holidays .
gold_template: This statistic shows the results of a survey among people in the templateTitle[5] on the amount of money they are planning to spend on the following items for the templateTitle[8] templateTitle[1] holidays . Respondents stated that they are planning to spend an templateYLabel[0] of 20.78 templateYLabel[2] templateYLabel[3] on templateXValue[3] for the upcoming templateTitle[1] holidays .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States templateTitle[3] templateTitle[4] templateTitle[5] templateXValue[min] to templateXValue[max] . area area area with the highest number of templateYLabel[1] templateYLabel[2] in the United States . templateYValue[max] percent of templateYLabel[1] were reported in favor of reported in the United States .
generated: This statistic shows the Average expenditure U.S. in the United States per capita U.S. Food to Food . area area with the highest number of expenditure U.S. in the United States . 47.97$ percent of expenditure were reported in favor of reported in the United States .


Example 508:
data: Year|2023|x|line_chart Share_of_population|94|y|line_chart Year|2022|x|line_chart Share_of_population|93|y|line_chart Year|2021|x|line_chart Share_of_population|91|y|line_chart Year|2020|x|line_chart Share_of_population|88|y|line_chart Year|2019|x|line_chart Share_of_population|85|y|line_chart Year|2018|x|line_chart Share_of_population|82|y|line_chart Year|2017|x|line_chart Share_of_population|77|y|line_chart 
title: Argentina : internet user penetration 2017 - 2023

gold: This statistic provides information on internet user penetration in Argentina from 2017 to 2023 . In 2017 , 77 percent of the population in Argentina were accessing the internet . This figure is projected to grow to 94 percent by 2023 .
gold_template: This statistic provides information on templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[1] in templateTitle[0] were accessing the templateTitle[1] . This figure is projected to grow to templateYValue[max] percent by templateXValue[max] .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[3] rate in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the Singaporean templateYLabel[1] were using the templateTitle[1] . In templateXValue[max] , this figure is projected to grow to templateYValue[max] percent .
generated: This statistic gives information on the internet penetration rate in Argentina from 2017 to 2023 . In 2017 , 77 percent of the Singaporean population were using the internet . In 2023 , this figure is projected to grow to 94 percent .


Example 509:
data: Year|2018|x|line_chart Migration_balance|50180|y|line_chart Year|2017|x|line_chart Migration_balance|44536|y|line_chart Year|2016|x|line_chart Migration_balance|42239|y|line_chart Year|2015|x|line_chart Migration_balance|47682|y|line_chart Year|2014|x|line_chart Migration_balance|39954|y|line_chart Year|2013|x|line_chart Migration_balance|34843|y|line_chart Year|2012|x|line_chart Migration_balance|44365|y|line_chart Year|2011|x|line_chart Migration_balance|62157|y|line_chart Year|2010|x|line_chart Migration_balance|79446|y|line_chart 
title: Migration balance in Belgium 2010 - 2018

gold: In 2018 , the migration balance in Belgium was roughly 50,000 , meaning that the number of immigrants moving to Belgium outnumbered the number of people leaving the country by about 50,000 . This was an increase in comparison to the previous year , but significantly lower than for example in 2010 and 2011 , when the migration balance was 79,446 and 62,157 respectively . It was also considerably lower than in neighboring country the Netherlands , which in 2018 had a positive migration balance of over 86,000 .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was roughly 50,000 , meaning that the number of immigrants moving to templateTitle[2] outnumbered the number of people leaving the country by about 50,000 . This was an increase in comparison to the previous templateXLabel[0] , but significantly lower than for example in templateXValue[min] and templateXValue[7] , when the templateYLabel[0] templateYLabel[1] was templateYValue[max] and templateYValue[7] respectively . It was also considerably lower than in neighboring country the Netherlands , which in templateXValue[max] had a positive templateYLabel[0] templateYLabel[1] of over 86,000 .

generated_template: The graph shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the templateYLabel[1] were recorded at the templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The graph shows the Migration balance yLabelErr of 2010 2018 worldwide from 2010 to 2018 . In 2018 , 50180 percent of the balance were recorded at the 2010 2018 titleErr .


Example 510:
data: Year|2019|x|line_chart Employment_in_1,000s|733.7|y|line_chart Year|2018|x|line_chart Employment_in_1,000s|730.5|y|line_chart Year|2017|x|line_chart Employment_in_1,000s|730.8|y|line_chart Year|2016|x|line_chart Employment_in_1,000s|728.7|y|line_chart Year|2015|x|line_chart Employment_in_1,000s|725.5|y|line_chart Year|2014|x|line_chart Employment_in_1,000s|727.4|y|line_chart Year|2013|x|line_chart Employment_in_1,000s|735.7|y|line_chart Year|2012|x|line_chart Employment_in_1,000s|741.1|y|line_chart Year|2011|x|line_chart Employment_in_1,000s|751.1|y|line_chart Year|2010|x|line_chart Employment_in_1,000s|768.6|y|line_chart Year|2009|x|line_chart Employment_in_1,000s|837.8|y|line_chart Year|2008|x|line_chart Employment_in_1,000s|897.4|y|line_chart Year|2007|x|line_chart Employment_in_1,000s|902.8|y|line_chart Year|2006|x|line_chart Employment_in_1,000s|901.2|y|line_chart Year|2005|x|line_chart Employment_in_1,000s|901.5|y|line_chart Year|2004|x|line_chart Employment_in_1,000s|913.8|y|line_chart Year|2003|x|line_chart Employment_in_1,000s|942.2|y|line_chart Year|2002|x|line_chart Employment_in_1,000s|986.6|y|line_chart Year|2001|x|line_chart Employment_in_1,000s|1045.7|y|line_chart 
title: Employment in U.S. publishing industries 2001 - 2019

gold: The statistic above presents employment data for the U.S. publishing industries from 2001 to 2019 . In January 2019 , over 733 thousand people were estimated to be working in print or software publishing companies , down from the 730.5 thousand people recorded in January of the previous year .
gold_template: The statistic above presents templateYLabel[0] data for the templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In January templateXValue[max] , over 733 thousand people were estimated to be working in print or software templateTitle[2] companies , down from the templateYValue[1] thousand people recorded in January of the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in templateTitle[2] was templateYValue[0] percent . The first time of templateYLabel[1] templateTitle[2] templateTitle[3] were recorded between templateXValue[2] and templateXValue[1] , worldwide .
generated: This statistic shows the Employment of 1,000s in publishing from 2001 to 2019 . In 2019 , the Employment of 1,000s in publishing was 733.7 percent . The first time of 1,000s publishing industries were recorded between 2017 and 2018 , worldwide .


Example 511:
data: Year|2020|x|line_chart Employed_persons_in_millions|5.02|y|line_chart Year|2019|x|line_chart Employed_persons_in_millions|4.97|y|line_chart Year|2018|x|line_chart Employed_persons_in_millions|5.06|y|line_chart Year|2017|x|line_chart Employed_persons_in_millions|5.01|y|line_chart Year|2016|x|line_chart Employed_persons_in_millions|4.96|y|line_chart Year|2015|x|line_chart Employed_persons_in_millions|4.9|y|line_chart Year|2014|x|line_chart Employed_persons_in_millions|4.82|y|line_chart Year|2013|x|line_chart Employed_persons_in_millions|4.73|y|line_chart Year|2012|x|line_chart Employed_persons_in_millions|4.67|y|line_chart Year|2011|x|line_chart Employed_persons_in_millions|4.66|y|line_chart Year|2010|x|line_chart Employed_persons_in_millions|4.56|y|line_chart 
title: Number of employed persons in Switzerland 2020

gold: The statistic shows the number of employed persons in Switzerland from 2010 to 2018 , with projections up until 2020 . In 2018 , the amount of gainfully employed persons in Switzerland amounted to 5.06 million .
gold_template: The statistic shows the templateTitle[0] of templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[2] , with projections up until templateXValue[max] . In templateXValue[2] , the amount of gainfully templateYLabel[0] templateYLabel[1] in templateTitle[3] amounted to templateYValue[max] million .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[2] million . The templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States are expected to increase to templateYValue[max] million in templateXValue[max] .
generated: This statistic shows the Employed persons in persons from 2010 to 2020 . In 2018 , the Employed persons in persons was at approximately 5.06 million . The persons Switzerland 2020 titleErr in the United States are expected to increase to 5.06 million in 2020 .


Example 512:
data: Year|2017|x|line_chart Number_of_children_born_per_woman|4.63|y|line_chart Year|2016|x|line_chart Number_of_children_born_per_woman|4.8|y|line_chart Year|2015|x|line_chart Number_of_children_born_per_woman|4.98|y|line_chart Year|2014|x|line_chart Number_of_children_born_per_woman|5.16|y|line_chart Year|2013|x|line_chart Number_of_children_born_per_woman|5.36|y|line_chart Year|2012|x|line_chart Number_of_children_born_per_woman|5.56|y|line_chart Year|2011|x|line_chart Number_of_children_born_per_woman|5.77|y|line_chart Year|2010|x|line_chart Number_of_children_born_per_woman|5.98|y|line_chart Year|2009|x|line_chart Number_of_children_born_per_woman|6.18|y|line_chart Year|2008|x|line_chart Number_of_children_born_per_woman|6.37|y|line_chart Year|2007|x|line_chart Number_of_children_born_per_woman|6.56|y|line_chart 
title: Fertility rate in Afghanistan 2017

gold: This timeline shows the fertility rate in Afghanistan from 2007 to 2017 . In 2017 , Afghanistan 's fertility rate amounted to 4.63 children born per woman . Today , Afghanistan is among the countries with the highest fertility rate on the world fertility rate ranking .
gold_template: This timeline shows the templateTitle[0] templateTitle[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Today , templateTitle[2] is among the countries with the highest templateTitle[0] templateTitle[1] on the world templateTitle[0] templateTitle[1] ranking .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] to one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , templateTitle[2] templateTitle[3] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate in Afghanistan 2017 from 2007 to 2017 . The Fertility rate is the average Number of children born to one woman while being of child-bearing age . In 2017 , Afghanistan 2017 's Fertility rate amounted to 4.63 children born per woman .


Example 513:
data: Country|Japan|x|bar_chart National_debt_in_relation_to_gross_domestic_product_(GDP)|237.69|y|bar_chart Country|United_States|x|bar_chart National_debt_in_relation_to_gross_domestic_product_(GDP)|106.22|y|bar_chart Country|France|x|bar_chart National_debt_in_relation_to_gross_domestic_product_(GDP)|99.31|y|bar_chart Country|Brazil|x|bar_chart National_debt_in_relation_to_gross_domestic_product_(GDP)|91.57|y|bar_chart Country|United_Kingdom|x|bar_chart National_debt_in_relation_to_gross_domestic_product_(GDP)|85.55|y|bar_chart Country|India|x|bar_chart National_debt_in_relation_to_gross_domestic_product_(GDP)|69.04|y|bar_chart Country|Germany|x|bar_chart National_debt_in_relation_to_gross_domestic_product_(GDP)|58.58|y|bar_chart Country|China|x|bar_chart National_debt_in_relation_to_gross_domestic_product_(GDP)|55.57|y|bar_chart Country|Russia|x|bar_chart National_debt_in_relation_to_gross_domestic_product_(GDP)|16.49|y|bar_chart 
title: National debt of selected countries in relation to gross domestic product ( GDP ) 2018

gold: This statistic shows the national debt of important industrial and emerging countries in 2019 in relation to the gross domestic product ( GDP ) . In 2019 , the national debt of China was at about 55.57 percent of the gross domestic product .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of important industrial and emerging templateTitle[3] in 2019 in templateYLabel[2] to the templateYLabel[3] templateYLabel[4] templateYLabel[5] ( templateTitle[8] ) . In 2019 , the templateYLabel[0] templateYLabel[1] of templateXValue[7] was at about templateYValue[7] percent of the templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the United States templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] . In templateXValue[2] , the templateYLabel[1] of the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] people . In templateYValue[2] percent of the templateTitle[3] templateTitle[4] 's total .
generated: This statistic shows the National debt of the United States countries relation gross in domestic . In France , the debt of the National debt of relation gross people . In 99.31 percent of the countries relation 's total .


Example 514:
data: Country_of_residence|England|x|bar_chart Number_of_transplants|779|y|bar_chart Country_of_residence|Scotland|x|bar_chart Number_of_transplants|114|y|bar_chart Country_of_residence|Wales|x|bar_chart Number_of_transplants|35|y|bar_chart Country_of_residence|Northern_Ireland|x|bar_chart Number_of_transplants|34|y|bar_chart 
title: Liver transplants in the United Kingdom ( UK ) 2018/19

gold: In the period 2018/19 , 779 liver transplants were carried out in England , followed by 114 conducted in Scotland . England has by far the largest population of the countries in the United Kingdom , so it is unsurprising it has the highest number of transplants performed in a year . State of liver transplants in the UK The number of liver transplants in the United Kingdom in 2018/19 was an five percent increase from the number that took place in the preceding year .
gold_template: In the period templateTitle[5] , templateYValue[max] templateTitle[0] templateYLabel[1] were carried out in templateXValue[0] , followed by templateYValue[1] conducted in templateXValue[1] . templateXValue[0] has by far the largest population of the countries in the templateTitle[2] templateTitle[3] , so it is unsurprising it has the highest templateYLabel[0] of templateYLabel[1] performed in a year . State of templateTitle[0] templateYLabel[1] in the templateTitle[4] The templateYLabel[0] of templateTitle[0] templateYLabel[1] in the templateTitle[2] templateTitle[3] in templateTitle[5] was an five percent increase from the templateYLabel[0] that took place in the preceding year .

generated_template: This statistic shows the number of people in the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] . In templateTitle[4] , about templateYValue[max] percent of templateYLabel[1] stated that they used used templateXValue[0] templateXValue[0] .
generated: This statistic shows the number of people in the United States in UK , 2018/19 Country . In UK , about 779 percent of transplants stated that they used England .


Example 515:
data: Country|Afghanistan|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|110.6|y|bar_chart Country|Somalia|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|94.8|y|bar_chart Country|Central_African_Republic|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|86.3|y|bar_chart Country|Guinea-Bissau|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|85.7|y|bar_chart Country|Chad|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|85.4|y|bar_chart Country|Niger|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|81.1|y|bar_chart Country|Burkina_Faso|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|72.2|y|bar_chart Country|Nigeria|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|69.8|y|bar_chart Country|Mali|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|69.5|y|bar_chart Country|Sierra_Leone|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|68.4|y|bar_chart Country|Democratic_Republic_of_Congo|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|68.2|y|bar_chart Country|Angola|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|67.6|y|bar_chart Country|Mozambique|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|65.9|y|bar_chart Country|Equatorial_Guinea|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|65.2|y|bar_chart Country|South_Sudan|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|62.8|y|bar_chart Country|Zambia|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|61.1|y|bar_chart Country|Gambia|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|60.2|y|bar_chart Country|Comoros|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|60.0|y|bar_chart Country|Burundi|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|58.8|y|bar_chart Country|Uganda|x|bar_chart Child_deaths_in_the_first_year_of_life_per_1,000_live_births|56.1|y|bar_chart 
title: Countries with the highest infant mortality rate 2017

gold: This statistic shows the 20 countries* with the highest infant mortality rate in 2017 . An estimated 110.6 infants per 1,000 live births died in the first year of life in Afghanistan in 2017 . Infant and child mortality Infant mortality usually refers to the death of children younger than one year .
gold_template: This statistic shows the 20 countries* templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] . An estimated templateYValue[max] infants templateYLabel[5] 1,000 templateYLabel[7] templateYLabel[8] died in the templateYLabel[2] templateYLabel[3] of templateYLabel[4] in templateXValue[0] in templateTitle[6] . templateTitle[3] and templateYLabel[0] templateTitle[4] templateTitle[3] templateTitle[4] usually refers to the death of children younger than one templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the United States from templateTitle[4] to templateTitle[5] . In templateTitle[5] , there were approximately templateYValue[max] million people living on the templateTitle[2] templateTitle[3] templateTitle[4] in the United States .
generated: This statistic shows the Child highest deaths in the United States from rate to 2017 . In 2017 , there were approximately 110.6 million people living on the infant mortality rate in the United States .


Example 516:
data: Response|Do_a_good_job_of_portraying_racial_minorities|x|bar_chart Share_of_respondents|38|y|bar_chart Response|Give_into_stereotypes_when_portraying_racial_minorities|x|bar_chart Share_of_respondents|37|y|bar_chart Response|Not_sure|x|bar_chart Share_of_respondents|24|y|bar_chart 
title: Stereotyping of ethnic minorities in Hollywood movies 2016

gold: The survey shows result of survey on stereotyping of racial minorities in Hollywood movies in the United States as of February 2016 . Durign the survey , 38 of respondents stated Hollywood movies did a good job of potraying racial minorities .
gold_template: The survey shows result of survey on templateTitle[0] of templateXValue[0] templateXValue[0] in templateTitle[3] templateTitle[4] in the United States as of February templateTitle[5] . Durign the survey , templateYValue[max] of templateYLabel[1] stated templateTitle[3] templateTitle[4] did a templateXValue[0] templateXValue[0] of potraying templateXValue[0] templateXValue[0] .

generated_template: The statistic shows the results of a survey conducted in the United States in templateTitle[5] , sorted by professional chefs for templateXValue[1] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated that they thought would be willing to spend templateXValue[0] in the U.S .
generated: The statistic shows the results of a survey conducted in the United States in 2016 , sorted by professional chefs for Give_into_stereotypes_when_portraying_racial_minorities . During the survey period , 38 percent of respondents stated that they thought would be willing to spend Do_a_good_job_of_portraying_racial_minorities in the U.S .


Example 517:
data: Year|2017|x|line_chart Number_of_children_born_per_woman|1.76|y|line_chart Year|2016|x|line_chart Number_of_children_born_per_woman|1.76|y|line_chart Year|2015|x|line_chart Number_of_children_born_per_woman|1.78|y|line_chart Year|2014|x|line_chart Number_of_children_born_per_woman|1.75|y|line_chart Year|2013|x|line_chart Number_of_children_born_per_woman|1.71|y|line_chart Year|2012|x|line_chart Number_of_children_born_per_woman|1.69|y|line_chart Year|2011|x|line_chart Number_of_children_born_per_woman|1.58|y|line_chart Year|2010|x|line_chart Number_of_children_born_per_woman|1.57|y|line_chart Year|2009|x|line_chart Number_of_children_born_per_woman|1.54|y|line_chart Year|2008|x|line_chart Number_of_children_born_per_woman|1.5|y|line_chart Year|2007|x|line_chart Number_of_children_born_per_woman|1.42|y|line_chart 
title: Fertility rate in Russia 2017

gold: This statistic shows the fertility rate of Russia from 2007 to 2017 . The fertility rate is the average number of children a woman will have during her child-bearing years . In 2017 , the fertility rate of Russia 's population was 1.76 children per woman .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] a templateYLabel[4] will have during her child-bearing years . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[2] 's population was templateYValue[0] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[2] amounted to templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate in Russia from 2007 to 2017 . The Fertility rate is the average Number of children born by one woman while being of child-bearing age . In 2017 , the Fertility rate in Russia amounted to 1.42 children born per woman .


Example 518:
data: Year|2018|x|line_chart Operating_income_in_million_U.S._dollars|750.0|y|line_chart Year|2017|x|line_chart Operating_income_in_million_U.S._dollars|700.0|y|line_chart Year|2016|x|line_chart Operating_income_in_million_U.S._dollars|537.0|y|line_chart Year|2015|x|line_chart Operating_income_in_million_U.S._dollars|516.78|y|line_chart Year|2014|x|line_chart Operating_income_in_million_U.S._dollars|574.85|y|line_chart Year|2013|x|line_chart Operating_income_in_million_U.S._dollars|567.59|y|line_chart Year|2012|x|line_chart Operating_income_in_million_U.S._dollars|7.85|y|line_chart Year|2011|x|line_chart Operating_income_in_million_U.S._dollars|-1.32|y|line_chart Year|2010|x|line_chart Operating_income_in_million_U.S._dollars|1.74|y|line_chart 
title: King annual income 2010 - 2018

gold: This statistic shows a timeline with the global annual operating income of King.com from 2010 to 2018 . In 2018 , the company reported an income of 750 million U.S. dollars . Popular King titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga .
gold_template: This statistic shows a timeline with the global templateTitle[1] templateYLabel[0] templateYLabel[1] of King.com from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company reported an templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Popular templateTitle[0] titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga .

generated_template: The graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] franchise amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The graph depicts the Operating income of the King annual income from 2010 to 2018 . In 2018 , the Operating income of the King annual income franchise amounted to 750.0 million U.S. dollars .


Example 519:
data: State|United_States_|x|bar_chart Number_of_children_enrolled|1565168|y|bar_chart State|California|x|bar_chart Number_of_children_enrolled|241859|y|bar_chart State|Texas|x|bar_chart Number_of_children_enrolled|231485|y|bar_chart State|Florida|x|bar_chart Number_of_children_enrolled|173645|y|bar_chart State|New_York|x|bar_chart Number_of_children_enrolled|121572|y|bar_chart State|Georgia|x|bar_chart Number_of_children_enrolled|80536|y|bar_chart State|Illinois|x|bar_chart Number_of_children_enrolled|74940|y|bar_chart State|New_Jersey|x|bar_chart Number_of_children_enrolled|50684|y|bar_chart State|Wisconsin|x|bar_chart Number_of_children_enrolled|46736|y|bar_chart State|Oklahoma|x|bar_chart Number_of_children_enrolled|39807|y|bar_chart State|Michigan|x|bar_chart Number_of_children_enrolled|37325|y|bar_chart State|Massachusetts|x|bar_chart Number_of_children_enrolled|34130|y|bar_chart State|Maryland|x|bar_chart Number_of_children_enrolled|31162|y|bar_chart State|Pennsylvania|x|bar_chart Number_of_children_enrolled|29710|y|bar_chart State|North_Carolina|x|bar_chart Number_of_children_enrolled|28385|y|bar_chart State|South_Carolina|x|bar_chart Number_of_children_enrolled|27443|y|bar_chart State|Iowa|x|bar_chart Number_of_children_enrolled|27195|y|bar_chart State|Kentucky|x|bar_chart Number_of_children_enrolled|21270|y|bar_chart State|Colorado|x|bar_chart Number_of_children_enrolled|21037|y|bar_chart State|Arkansas|x|bar_chart Number_of_children_enrolled|19498|y|bar_chart State|Louisiana|x|bar_chart Number_of_children_enrolled|18911|y|bar_chart State|Tennessee|x|bar_chart Number_of_children_enrolled|18354|y|bar_chart State|Virginia|x|bar_chart Number_of_children_enrolled|17959|y|bar_chart State|Ohio|x|bar_chart Number_of_children_enrolled|17913|y|bar_chart State|Alabama|x|bar_chart Number_of_children_enrolled|16051|y|bar_chart State|West_Virginia|x|bar_chart Number_of_children_enrolled|14629|y|bar_chart State|Connecticut|x|bar_chart Number_of_children_enrolled|14449|y|bar_chart State|Kansas|x|bar_chart Number_of_children_enrolled|14022|y|bar_chart State|District_of_Columbia|x|bar_chart Number_of_children_enrolled|13332|y|bar_chart State|Nebraska|x|bar_chart Number_of_children_enrolled|12950|y|bar_chart State|Washington|x|bar_chart Number_of_children_enrolled|12491|y|bar_chart State|Oregon|x|bar_chart Number_of_children_enrolled|9464|y|bar_chart State|New_Mexico|x|bar_chart Number_of_children_enrolled|9119|y|bar_chart State|Vermont|x|bar_chart Number_of_children_enrolled|8449|y|bar_chart State|Minnesota|x|bar_chart Number_of_children_enrolled|7672|y|bar_chart State|Maine|x|bar_chart Number_of_children_enrolled|5551|y|bar_chart State|Arizona|x|bar_chart Number_of_children_enrolled|5256|y|bar_chart State|Missouri|x|bar_chart Number_of_children_enrolled|2378|y|bar_chart State|Nevada|x|bar_chart Number_of_children_enrolled|2102|y|bar_chart State|Mississippi|x|bar_chart Number_of_children_enrolled|1840|y|bar_chart State|Rhode_Island|x|bar_chart Number_of_children_enrolled|1080|y|bar_chart State|North_Dakota|x|bar_chart Number_of_children_enrolled|965|y|bar_chart State|Delaware|x|bar_chart Number_of_children_enrolled|845|y|bar_chart State|Hawaii|x|bar_chart Number_of_children_enrolled|373|y|bar_chart State|Alaska|x|bar_chart Number_of_children_enrolled|315|y|bar_chart State|Montana|x|bar_chart Number_of_children_enrolled|279|y|bar_chart State|Guam|x|bar_chart Number_of_children_enrolled|71|y|bar_chart 
title: Total number of U.S. children enrolled in pre-K , by state 2017 - 2018

gold: The statistic above provides information on the number of the 3- and 4-year-old children enrolled in pre-kindergarten programs in the United States for the 2017/2018 school year , by state . Between 2017 and 2018 , about 50,684 children in New Jersey were enrolled in pre-K programs .
gold_template: The statistic above provides information on the templateYLabel[0] of the 3- and 4-year-old templateYLabel[1] templateYLabel[2] in pre-kindergarten programs in the templateXValue[0] templateXValue[0] for the 2017/2018 school year , templateTitle[6] templateXLabel[0] . Between templateTitle[8] and templateTitle[9] , about templateYValue[7] templateYLabel[1] in templateXValue[4] templateXValue[7] were templateYLabel[2] in templateTitle[5] programs .

generated_template: templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] in templateTitle[2] in the United States in templateTitle[6] at templateYValue[max] percent of the templateXLabel[0] . templateXValue[1] , about templateYValue[1] percent of templateYLabel[1] between the templateXLabel[0] templateTitle[3] and templateYValue[2] percent of the templateXLabel[0] with the highest templateYLabel[0] templateYLabel[1] in templateTitle[2] at segment in templateTitle[4] . The United States can be found here .
generated: United_States_ had the highest Number children in U.S. in the United States in by at 1565168 percent of the State . California , about 241859 percent of children between the State children and 231485 percent of the State with the highest Number children in U.S. at segment in enrolled . The United States can be found here .


Example 520:
data: City|Reading|x|bar_chart Price_per_square_meter_in_euros|468|y|bar_chart City|Manchester|x|bar_chart Price_per_square_meter_in_euros|444|y|bar_chart City|Bristol|x|bar_chart Price_per_square_meter_in_euros|438|y|bar_chart City|Edinburgh|x|bar_chart Price_per_square_meter_in_euros|425|y|bar_chart City|Birmingham|x|bar_chart Price_per_square_meter_in_euros|413|y|bar_chart City|Glasgow|x|bar_chart Price_per_square_meter_in_euros|389|y|bar_chart City|Leeds|x|bar_chart Price_per_square_meter_in_euros|365|y|bar_chart City|Cardiff|x|bar_chart Price_per_square_meter_in_euros|304|y|bar_chart City|Newcastle|x|bar_chart Price_per_square_meter_in_euros|298|y|bar_chart 
title: UK : real estate prime office rent prices in selected cities Q3 2019

gold: This statistic displays the most expensive cities for prime office rents in the United Kingdom ( UK ) as of September 2019 , excluding London . As of September 2019 , it can be seen that Reading was the most expensive location within the UK for prime office rents outside of London , with an average price reaching 468 euros per square meter per year . This was followed by Manchester , Bristol and Edinburgh .
gold_template: This statistic displays the most expensive templateTitle[8] for templateTitle[3] templateTitle[4] rents in the United Kingdom ( templateTitle[0] ) as of September templateTitle[10] , excluding London . As of September templateTitle[10] , it can be seen that templateXValue[0] was the most expensive location within the templateTitle[0] for templateTitle[3] templateTitle[4] rents outside of London , with an average templateYLabel[0] reaching templateYValue[max] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year . This was followed by templateXValue[1] , templateXValue[2] and templateXValue[3] .

generated_template: How many templateYLabel[1] does templateTitle[2] ? In templateTitle[4] , there were templateYValue[max] million people in the United States . The only one of the most popular templateXLabel[0] in the United States .
generated: How many per does estate ? In office , there were 468 million people in the United States . The only one of the most popular City in the United States .


Example 521:
data: Year|2019|x|line_chart Number_of_drive-in_cinema_sites|321|y|line_chart Year|2018|x|line_chart Number_of_drive-in_cinema_sites|321|y|line_chart Year|2017|x|line_chart Number_of_drive-in_cinema_sites|349|y|line_chart Year|2016|x|line_chart Number_of_drive-in_cinema_sites|349|y|line_chart Year|2015|x|line_chart Number_of_drive-in_cinema_sites|349|y|line_chart Year|2014|x|line_chart Number_of_drive-in_cinema_sites|393|y|line_chart Year|2013|x|line_chart Number_of_drive-in_cinema_sites|393|y|line_chart Year|2012|x|line_chart Number_of_drive-in_cinema_sites|366|y|line_chart Year|2011|x|line_chart Number_of_drive-in_cinema_sites|366|y|line_chart Year|2010|x|line_chart Number_of_drive-in_cinema_sites|374|y|line_chart Year|2009|x|line_chart Number_of_drive-in_cinema_sites|381|y|line_chart Year|2008|x|line_chart Number_of_drive-in_cinema_sites|383|y|line_chart Year|2007|x|line_chart Number_of_drive-in_cinema_sites|383|y|line_chart Year|2006|x|line_chart Number_of_drive-in_cinema_sites|396|y|line_chart Year|2005|x|line_chart Number_of_drive-in_cinema_sites|401|y|line_chart Year|2004|x|line_chart Number_of_drive-in_cinema_sites|402|y|line_chart Year|2003|x|line_chart Number_of_drive-in_cinema_sites|400|y|line_chart Year|2002|x|line_chart Number_of_drive-in_cinema_sites|432|y|line_chart Year|2001|x|line_chart Number_of_drive-in_cinema_sites|440|y|line_chart Year|2000|x|line_chart Number_of_drive-in_cinema_sites|442|y|line_chart Year|1999|x|line_chart Number_of_drive-in_cinema_sites|446|y|line_chart Year|1998|x|line_chart Number_of_drive-in_cinema_sites|524|y|line_chart Year|1997|x|line_chart Number_of_drive-in_cinema_sites|577|y|line_chart Year|1996|x|line_chart Number_of_drive-in_cinema_sites|583|y|line_chart Year|1995|x|line_chart Number_of_drive-in_cinema_sites|593|y|line_chart 
title: Number of drive-in cinema sites in the U.S. 1995 - 2019

gold: The number of drive-in cinema sites in the United States remained at 321 in 2019 , the same as in the previous year . The figure tends to remain the same for years at a time , and is always far lower than the number of indoor sites , which make up the vast majority of cinemas in the country .
gold_template: The templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[4] remained at templateYValue[min] in templateXValue[max] , the same as in the previous templateXLabel[0] . The figure tends to remain the same for years at a time , and is always far lower than the templateYLabel[0] of indoor templateYLabel[3] , which make up the vast majority of cinemas in the country .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people died than the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Number drive-in of cinema sites U.S. 1995 2019 from 1995 to 2019 . In 2019 , there were 321 people died than the cinema sites U.S. 1995 in the United States .


Example 522:
data: Year|2018|x|line_chart Births_per_1,000_of_Hispanic_population|14.8|y|line_chart Year|2017|x|line_chart Births_per_1,000_of_Hispanic_population|15.2|y|line_chart Year|2016|x|line_chart Births_per_1,000_of_Hispanic_population|16.0|y|line_chart Year|2015|x|line_chart Births_per_1,000_of_Hispanic_population|16.3|y|line_chart Year|2014|x|line_chart Births_per_1,000_of_Hispanic_population|16.5|y|line_chart Year|2013|x|line_chart Births_per_1,000_of_Hispanic_population|16.7|y|line_chart Year|2012|x|line_chart Births_per_1,000_of_Hispanic_population|17.1|y|line_chart Year|2011|x|line_chart Births_per_1,000_of_Hispanic_population|17.6|y|line_chart Year|2010|x|line_chart Births_per_1,000_of_Hispanic_population|18.7|y|line_chart Year|2009|x|line_chart Births_per_1,000_of_Hispanic_population|20.3|y|line_chart Year|2008|x|line_chart Births_per_1,000_of_Hispanic_population|21.8|y|line_chart Year|2007|x|line_chart Births_per_1,000_of_Hispanic_population|23.0|y|line_chart Year|2006|x|line_chart Births_per_1,000_of_Hispanic_population|23.3|y|line_chart Year|2005|x|line_chart Births_per_1,000_of_Hispanic_population|22.9|y|line_chart Year|2004|x|line_chart Births_per_1,000_of_Hispanic_population|22.8|y|line_chart Year|2003|x|line_chart Births_per_1,000_of_Hispanic_population|22.8|y|line_chart Year|2002|x|line_chart Births_per_1,000_of_Hispanic_population|22.7|y|line_chart Year|2001|x|line_chart Births_per_1,000_of_Hispanic_population|22.9|y|line_chart Year|2000|x|line_chart Births_per_1,000_of_Hispanic_population|23.1|y|line_chart Year|1999|x|line_chart Births_per_1,000_of_Hispanic_population|22.5|y|line_chart Year|1998|x|line_chart Births_per_1,000_of_Hispanic_population|22.7|y|line_chart Year|1997|x|line_chart Births_per_1,000_of_Hispanic_population|23.0|y|line_chart Year|1996|x|line_chart Births_per_1,000_of_Hispanic_population|23.8|y|line_chart Year|1995|x|line_chart Births_per_1,000_of_Hispanic_population|24.1|y|line_chart Year|1994|x|line_chart Births_per_1,000_of_Hispanic_population|24.7|y|line_chart Year|1993|x|line_chart Births_per_1,000_of_Hispanic_population|25.4|y|line_chart Year|1992|x|line_chart Births_per_1,000_of_Hispanic_population|26.1|y|line_chart Year|1991|x|line_chart Births_per_1,000_of_Hispanic_population|26.5|y|line_chart Year|1990|x|line_chart Births_per_1,000_of_Hispanic_population|26.7|y|line_chart 
title: Birth rate of Hispanics in the U.S. 1990 - 2018

gold: This graph displays the birth rate of Hispanics in the United States from 1990 to 2018 . In 2018 , about 14.8 children were born per 1,000 of Hispanic population .
gold_template: This graph displays the templateTitle[0] templateTitle[1] of templateTitle[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[min] children were born templateYLabel[1] 1,000 of templateYLabel[3] templateYLabel[4] .

generated_template: As of templateXValue[max] , templateYValue[max] million people were born in templateTitle[3] in the United States . This figure is an increase from the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] has remained relatively steady throughout the years later in the past 30 years . templateYLabel[3] ? The templateYLabel[0] templateYLabel[1] rate is an increase from a steady growth since templateXValue[9] .
generated: As of 2018 , 26.7 million people were born in U.S. in the United States . This figure is an increase from the previous Year . The Births per has remained relatively steady throughout the years later in the past 30 years . Hispanic ? The Births per rate is an increase from a steady growth since 2009 .


Example 523:
data: Year|2018|x|line_chart Share_of_households|76|y|line_chart Year|2017|x|line_chart Share_of_households|71|y|line_chart Year|2016|x|line_chart Share_of_households|69|y|line_chart Year|2015|x|line_chart Share_of_households|68|y|line_chart Year|2014|x|line_chart Share_of_households|66|y|line_chart Year|2013|x|line_chart Share_of_households|56|y|line_chart Year|2012|x|line_chart Share_of_households|54|y|line_chart Year|2011|x|line_chart Share_of_households|50|y|line_chart Year|2010|x|line_chart Share_of_households|46|y|line_chart Year|2009|x|line_chart Share_of_households|38|y|line_chart Year|2008|x|line_chart Share_of_households|31|y|line_chart Year|2007|x|line_chart Share_of_households|25|y|line_chart 
title: Household internet access in Greece 2007 - 2018

gold: This statistic shows the share of households in Greece that had access to the internet from 2007 to 2018 . Internet penetration grew in Greece during this period . In 2018 , 76 percent of Greek households had internet access .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[3] that had templateTitle[2] to the templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[1] penetration grew in templateTitle[3] during this period . In templateXValue[max] , templateYValue[max] percent of Greek templateYLabel[1] had templateTitle[1] templateTitle[2] .

generated_template: Following two consecutive years at templateYValue[1] percent , the templateYLabel[0] of templateYLabel[1] with templateTitle[1] access in the templateTitle[3] templateTitle[4] ( templateTitle[5] ) increased in templateXValue[max] , reaching templateYValue[max] percent . This marked a new record , and continued the streak of either stability or growth . More users gaining templateTitle[1] access Worldwide , the number of templateTitle[1] users rose from 1.02 billion users in templateXValue[14] , to 3.9 billion users in templateXValue[1] .
generated: Following two consecutive years at 71 percent , the Share of households with internet access in the Greece 2007 ( 2018 ) increased in 2018 , reaching 76 percent . This marked a new record , and continued the streak of either stability or growth . More users gaining internet access Worldwide , the number of internet users rose from 1.02 billion users in xValErr , to 3.9 billion users in 2017 .


Example 524:
data: Year|2018|x|line_chart Enrolled_university_students_in_millions|2.03|y|line_chart Year|2017|x|line_chart Enrolled_university_students_in_millions|2.05|y|line_chart Year|2016|x|line_chart Enrolled_university_students_in_millions|2.08|y|line_chart Year|2015|x|line_chart Enrolled_university_students_in_millions|2.11|y|line_chart Year|2014|x|line_chart Enrolled_university_students_in_millions|2.13|y|line_chart Year|2013|x|line_chart Enrolled_university_students_in_millions|2.13|y|line_chart Year|2012|x|line_chart Enrolled_university_students_in_millions|2.1|y|line_chart Year|2011|x|line_chart Enrolled_university_students_in_millions|2.07|y|line_chart 
title: Number of enrolled university students in South Korea 2011 - 2018

gold: This statistic illustrates the number of students enrolled in universities in South Korea from 2011 to 2018 . In 2018 , there were approximately 2.03 million students enrolled in universities in South Korea .
gold_template: This statistic illustrates the templateTitle[0] of templateYLabel[2] templateYLabel[0] in universities in templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[min] million templateYLabel[2] templateYLabel[0] in universities in templateTitle[4] templateTitle[5] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] million people were sold in templateTitle[3] templateTitle[4] .
generated: This statistic shows the Enrolled of university in students from 2011 to 2018 . In 2018 , about 2.03 million people were sold in students South .


Example 525:
data: City|Tijuana_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|138.26|y|bar_chart City|Acapulco_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|110.5|y|bar_chart City|Caracas_-_Venezuela|x|bar_chart Murder_rate_per_100,000_inhabitants|99.98|y|bar_chart City|Ciudad_Victoria_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|86.01|y|bar_chart City|Ciudad_Juarez_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|85.56|y|bar_chart City|Irapuato_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|81.44|y|bar_chart City|Ciudad_Guayana_-_Venezuela|x|bar_chart Murder_rate_per_100,000_inhabitants|78.3|y|bar_chart City|Natal_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|74.67|y|bar_chart City|Fortaleza_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|69.15|y|bar_chart City|Ciudad_Bolivar_-_Venezuela|x|bar_chart Murder_rate_per_100,000_inhabitants|69.09|y|bar_chart City|Cape_Town_-_South_Africa|x|bar_chart Murder_rate_per_100,000_inhabitants|66.36|y|bar_chart City|Belem_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|65.31|y|bar_chart City|Cancun_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|64.46|y|bar_chart City|Feira_de_Santana_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|63.29|y|bar_chart City|St._Louis_Missouri_-_U.S.|x|bar_chart Murder_rate_per_100,000_inhabitants|60.59|y|bar_chart City|Culiacan_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|60.52|y|bar_chart City|Barquisimeto_-_Venezuela|x|bar_chart Murder_rate_per_100,000_inhabitants|56.67|y|bar_chart City|Uruapan_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|54.52|y|bar_chart City|Kingston_-_Jamaica|x|bar_chart Murder_rate_per_100,000_inhabitants|54.12|y|bar_chart City|Ciudad_Obregón_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|52.09|y|bar_chart City|Maceio_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|51.46|y|bar_chart City|Vitoria_da_Conquista_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|50.75|y|bar_chart City|Baltimore_Maryland_-_U.S.|x|bar_chart Murder_rate_per_100,000_inhabitants|50.52|y|bar_chart City|San_Salvador_-_El_Salvador|x|bar_chart Murder_rate_per_100,000_inhabitants|50.32|y|bar_chart City|Aracaju_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|48.77|y|bar_chart City|Coatzacoalcos_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|48.35|y|bar_chart City|Palmira_-_Colombia|x|bar_chart Murder_rate_per_100,000_inhabitants|47.97|y|bar_chart City|Maturin_-_Venezuela|x|bar_chart Murder_rate_per_100,000_inhabitants|47.24|y|bar_chart City|Salvador_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|47.23|y|bar_chart City|Macapa_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|47.2|y|bar_chart City|Cali_-_Colombia|x|bar_chart Murder_rate_per_100,000_inhabitants|47.03|y|bar_chart City|Celaya_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|46.99|y|bar_chart City|San_Pedro_Sula_-_Honduras|x|bar_chart Murder_rate_per_100,000_inhabitants|46.67|y|bar_chart City|Ensenada_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|46.6|y|bar_chart City|Campos_dos_Goytacazes_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|46.28|y|bar_chart City|Tepic_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|44.89|y|bar_chart City|Manaus_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|44.0|y|bar_chart City|Guatemala_City_-_Guatemala|x|bar_chart Murder_rate_per_100,000_inhabitants|43.73|y|bar_chart City|Recife_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|43.72|y|bar_chart City|Distrito_Central_-_Honduras|x|bar_chart Murder_rate_per_100,000_inhabitants|43.3|y|bar_chart City|San_Juan_-_Puerto_Rico|x|bar_chart Murder_rate_per_100,000_inhabitants|42.4|y|bar_chart City|Valencia_-_Venezuela|x|bar_chart Murder_rate_per_100,000_inhabitants|42.36|y|bar_chart City|Reynosa_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|41.48|y|bar_chart City|João_Pessoa_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|41.36|y|bar_chart City|Nelson_Mandela_Bay_-_South_Africa|x|bar_chart Murder_rate_per_100,000_inhabitants|39.16|y|bar_chart City|Detroit_Michigan_-_U.S.|x|bar_chart Murder_rate_per_100,000_inhabitants|38.78|y|bar_chart City|Durban_-_South_Africa|x|bar_chart Murder_rate_per_100,000_inhabitants|38.51|y|bar_chart City|Teresina_-_Brazil|x|bar_chart Murder_rate_per_100,000_inhabitants|37.61|y|bar_chart City|Chihuahua_-_Mexico|x|bar_chart Murder_rate_per_100,000_inhabitants|37.5|y|bar_chart City|New_Orleans_Louisiana_-_U.S.|x|bar_chart Murder_rate_per_100,000_inhabitants|36.87|y|bar_chart 
title: Worlds ' most dangerous cities , by murder rate 2018

gold: This statistic ranks the 50 most dangerous cities of 2018 , by murder rate per 100,000 inhabitants . Tijuana 's murder rate was 138.26 for every 100,000 people living in the city . The world 's most dangerous cities The Citizens ' Council for Public Security and Criminal Justice published a ranking of the world 's most dangerous cities in 2018 , ranking cities according to the number of murders per 100,000 inhabitants that year .
gold_template: This statistic ranks the templateYValue[23] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitle[8] , templateTitle[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] 100,000 templateYLabel[4] . templateXValue[0] 's templateYLabel[0] templateYLabel[1] was templateYValue[max] for every 100,000 people living in the templateXValue[37] . The world 's templateTitle[2] templateTitle[3] templateTitle[4] The Citizens templateTitle[1] Council for Public Security and Criminal Justice published a ranking of the world 's templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[8] , ranking templateTitle[4] according to the number of murders templateYLabel[2] 100,000 templateYLabel[4] that year .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States in templateTitle[5] . In templateTitle[4] , a templateYLabel[1] of templateYValue[0] templateYLabel[2] 100,000 templateYLabel[4] in the United States .
generated: This statistic shows the Murder rate of ' most dangerous cities in the United States in by . In cities , a rate of 138.26 per 100,000 inhabitants in the United States .


Example 526:
data: Category|Video|x|bar_chart Share_of_downstream_internet_traffic|57.7|y|bar_chart Category|Web|x|bar_chart Share_of_downstream_internet_traffic|17|y|bar_chart Category|Gaming|x|bar_chart Share_of_downstream_internet_traffic|7.8|y|bar_chart Category|Social_media|x|bar_chart Share_of_downstream_internet_traffic|5.1|y|bar_chart Category|Content_marketplaces|x|bar_chart Share_of_downstream_internet_traffic|4.6|y|bar_chart Category|File_sharing|x|bar_chart Share_of_downstream_internet_traffic|2.8|y|bar_chart Category|Audio_streaming|x|bar_chart Share_of_downstream_internet_traffic|1|y|bar_chart 
title: Leading internet traffic categories worldwide 2018

gold: This statistic presents the distribution of global downstream internet traffic as of October 2018 , by category . During the measured period , video accounted for over half of downstream internet traffic volume . Within that category , Netflix was by far the market leader in terms of global video traffic .
gold_template: This statistic presents the distribution of global templateYLabel[1] templateYLabel[2] templateYLabel[3] as of October templateTitle[5] , by templateXLabel[0] . During the measured period , templateXValue[0] accounted for over half of templateYLabel[1] templateYLabel[2] templateYLabel[3] volume . Within that templateXLabel[0] , Netflix was by far the market leader in terms of global templateXValue[0] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] in the United States in templateTitle[4] templateXValue[0] . In templateTitle[4] , there were templateYValue[0] templateYLabel[1] templateYLabel[2] by templateXValue[0] .
generated: This statistic shows the Share of internet downstream internet in the United States in worldwide Video . In worldwide , there were 57.7 downstream internet by Video .


Example 527:
data: Month|Tim_Finchem_(PGA_Tour_commissioner_&_CEO)_2013|x|bar_chart Compensations_in_million_U.S._dollars|4.58|y|bar_chart Month|Joe_Steranka_(Former_of_America_CEO)_2013|x|bar_chart Compensations_in_million_U.S._dollars|2.59|y|bar_chart Month|Dick_Rugge_(Former_USGA_senior_director_equipment_standards)_2012|x|bar_chart Compensations_in_million_U.S._dollars|1.8|y|bar_chart Month|Tom_Wade_(PGA_Tour_global_commercial_officer)_2013|x|bar_chart Compensations_in_million_U.S._dollars|1.17|y|bar_chart Month|Charles_Zink_(PGA_Tour_co-chief_operating_officer)_2013|x|bar_chart Compensations_in_million_U.S._dollars|1.16|y|bar_chart Month|Ed_Moorhouse_(PGA_Tour_co-chief_operating_officer)_2013|x|bar_chart Compensations_in_million_U.S._dollars|1.13|y|bar_chart Month|Ron_Price_(PGA_Tour_executive_VP_CFO)_2013|x|bar_chart Compensations_in_million_U.S._dollars|1.06|y|bar_chart Month|David_Pillsbury_(PGA_Tour_executive_VP_championship_managment_&_tournament_business_affairs)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.97|y|bar_chart Month|Mike_Whan_(LPGA_Tour_commissioner)_2012|x|bar_chart Compensations_in_million_U.S._dollars|0.89|y|bar_chart Month|Ty_Votaw_(PGA_Tour_executive_VP_&_chief_of_global_communications)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.79|y|bar_chart Month|Mike_Davis_(USGA_executive_director)_2012|x|bar_chart Compensations_in_million_U.S._dollars|0.77|y|bar_chart Month|Joseph_Monahan_(PGA_Tour_executive_VP_&_chief_marketing_officer)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.73|y|bar_chart Month|David_Fay_(Former_USGA_executive_director)_2012|x|bar_chart Compensations_in_million_U.S._dollars|0.65|y|bar_chart Month|Michael_Butz_(USGA_senior_managing_director_Open_championships_&_association_relations)|x|bar_chart Compensations_in_million_U.S._dollars|0.64|y|bar_chart Month|Joe_Louis_Barrow_Jr._(World_Golf_Foundation_executive_VP_The_First_Tee_CEO)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.62|y|bar_chart Month|Bill_Calfee_(PGA_Tour_president_Web.com_Tour)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.55|y|bar_chart Month|Darrell_Crall_(PGA_of_America_COO)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.54|y|bar_chart Month|Kerry_Haigh_(PGA_of_America_chief_championships_officer)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.54|y|bar_chart Month|Rick_Anderson_(PGA_Tour_executive_VP_television_and_digital)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.54|y|bar_chart Month|James_Pazder_(PGA_Tour_executive_VP_&_chief_of_operations)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.51|y|bar_chart Month|Mike_Stevens_(PGA_Tour_president_Champions_Tour)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.49|y|bar_chart Month|Stephen_Mona_(World_Golf_Foundation_CEO)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.45|y|bar_chart Month|Mark_Russell_(PGA_Tour_VP_rules_and_competitions)_2013|x|bar_chart Compensations_in_million_U.S._dollars|0.45|y|bar_chart Month|Stephen_Hamblin_(American_Junior_Golf_Assosiation_executive_director)_2012|x|bar_chart Compensations_in_million_U.S._dollars|0.43|y|bar_chart Month|Joseph_Beditz_(National_Golf_Foundation_president/CEO)_2012|x|bar_chart Compensations_in_million_U.S._dollars|0.25|y|bar_chart 
title: Golf-Association executives ' compensation 2012 - 2013

gold: The graph depicts the earnings of 25 golf association executives in 2012 and 2013 . Tim Finchem , PGA Tour commissioner and CEO , tops the earnings with an amount of 4.58 million U.S. dollars .
gold_template: The graph depicts the earnings of 25 templateXValue[14] templateXValue[13] templateTitle[1] in templateXValue[2] and templateXValue[0] . templateXValue[0] templateXValue[0] , PGA templateXValue[0] templateXValue[0] and CEO , tops the earnings with an amount of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] . In templateTitle[4] , templateYValue[max] percent of the United States stated that they had an increase from the previous year .
generated: This statistic shows the results of a survey conducted in the United States in 2012 , 2013 Month . In 2012 , 4.58 percent of the United States stated that they had an increase from the previous year .


Example 528:
data: Year|2029|x|line_chart Number_of_inhabitants_(in_millions)|6.09|y|line_chart Year|2028|x|line_chart Number_of_inhabitants_(in_millions)|6.07|y|line_chart Year|2027|x|line_chart Number_of_inhabitants_(in_millions)|6.04|y|line_chart Year|2026|x|line_chart Number_of_inhabitants_(in_millions)|6.02|y|line_chart Year|2025|x|line_chart Number_of_inhabitants_(in_millions)|6.0|y|line_chart Year|2024|x|line_chart Number_of_inhabitants_(in_millions)|5.97|y|line_chart Year|2023|x|line_chart Number_of_inhabitants_(in_millions)|5.95|y|line_chart Year|2022|x|line_chart Number_of_inhabitants_(in_millions)|5.92|y|line_chart Year|2021|x|line_chart Number_of_inhabitants_(in_millions)|5.89|y|line_chart Year|2020|x|line_chart Number_of_inhabitants_(in_millions)|5.87|y|line_chart Year|2019|x|line_chart Number_of_inhabitants_(in_millions)|5.83|y|line_chart 
title: Forecast of population growth in Denmark 2019 - 2029

gold: The statistic shows a forecast of the Danish population growth from 2019 to 2029 . The total number of inhabitants will keep on increasing . According to the forecast there will be roughly over 6 million of people living in Denmark by 2029 .
gold_template: The statistic shows a templateTitle[0] of the Danish templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . The total templateYLabel[0] of templateYLabel[1] will keep on increasing . According to the templateTitle[0] there will be roughly over templateYValue[max] million of people living in templateTitle[3] by templateXValue[max] .

generated_template: In templateXValue[2] , it was estimated that there were approximately templateYValue[0] thousand templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] in the United States . This figure is an increase from the previous templateXLabel[0] , when compared with the previous templateXLabel[0] . However , in the United States has been steadily decreasing .
generated: In 2027 , it was estimated that there were approximately 6.09 thousand population growth Denmark inhabitants in the United States . This figure is an increase from the previous Year , when compared with the previous Year . However , in the United States has been steadily decreasing .


Example 529:
data: Country|Nigeria|x|bar_chart Number_of_incidents|35|y|bar_chart Country|Indonesia|x|bar_chart Number_of_incidents|25|y|bar_chart Country|Singapore_Staits|x|bar_chart Number_of_incidents|12|y|bar_chart Country|Malaysia|x|bar_chart Number_of_incidents|11|y|bar_chart Country|Peru|x|bar_chart Number_of_incidents|10|y|bar_chart Country|Venezuela|x|bar_chart Number_of_incidents|6|y|bar_chart Country|Cameroon|x|bar_chart Number_of_incidents|6|y|bar_chart 
title: Piracy - actual and attempted attacks worldwide by country 2019

gold: The statistic represents the total number of actual and attempted piracy attacks in the world 's most perilous territorial waters in 2019 . That year , there were six actual and attempted piracy attacks off the Venezuelan coast .
gold_template: The statistic represents the total templateYLabel[0] of templateTitle[1] and templateTitle[2] templateTitle[0] templateTitle[3] in the world 's most perilous territorial waters in templateTitle[7] . That year , there were templateYValue[min] templateTitle[1] and templateTitle[2] templateTitle[0] templateTitle[3] off the Venezuelan coast .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States . In that year , there were a total of templateYValue[max] templateYLabel[1] templateTitle[3] templateTitle[4] in templateXValue[0] .
generated: The statistic shows the Number of Piracy actual attempted attacks worldwide in the United States . In that year , there were a total of 35 incidents attacks worldwide in Nigeria .


Example 530:
data: Year|2018|x|line_chart Production_in_million_gallons|50.2|y|line_chart Year|2017|x|line_chart Production_in_million_gallons|62.5|y|line_chart Year|2016|x|line_chart Production_in_million_gallons|66.93|y|line_chart Year|2015|x|line_chart Production_in_million_gallons|74.23|y|line_chart Year|2014|x|line_chart Production_in_million_gallons|66.76|y|line_chart Year|2013|x|line_chart Production_in_million_gallons|74.48|y|line_chart Year|2012|x|line_chart Production_in_million_gallons|74.0|y|line_chart Year|2011|x|line_chart Production_in_million_gallons|62.7|y|line_chart Year|2010|x|line_chart Production_in_million_gallons|50.1|y|line_chart Year|2009|x|line_chart Production_in_million_gallons|46.0|y|line_chart Year|2008|x|line_chart Production_in_million_gallons|78.6|y|line_chart Year|2007|x|line_chart Production_in_million_gallons|74.7|y|line_chart Year|2006|x|line_chart Production_in_million_gallons|66.0|y|line_chart 
title: Frozen yogurt production in the U.S. 2006 - 2018

gold: This statistic shows the frozen yogurt production in the United States from 2006 to 2018 . In 2018 , about 50.2 million gallons of frozen yogurt were produced . Frozen yogurt is a frozen , low-calorie dessert , which is often served in a large variety of flavors .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] were produced . templateTitle[0] templateTitle[1] is a templateTitle[0] , low-calorie dessert , which is often served in a large variety of flavors .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] of templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] in templateTitle[3] was templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Frozen Production of million gallons of U.S. from 2006 to 2018 . In 2018 , the Frozen yogurt Production in U.S. was 78.6 million gallons .


Example 531:
data: Year|1999|x|line_chart Turnout_rate|46.4|y|line_chart Year|2003|x|line_chart Turnout_rate|38.2|y|line_chart Year|2007|x|line_chart Turnout_rate|43.5|y|line_chart Year|2011|x|line_chart Turnout_rate|41.8|y|line_chart 
title: Welsh Assembly elections : turnout rates 1999 - 2011

gold: This statistic shows the voter turnout rates for constituency votes in the Welsh Assembly elections from 1999 to 2011 . Over the last four elections there was a variation in voter turnout of 8.2 percent . The peak , in 1999 , was followed in 2003 by the lowest turnout rate of this period .
gold_template: This statistic shows the voter templateYLabel[0] templateTitle[4] for constituency votes in the templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . Over the last four templateTitle[2] there was a variation in voter templateYLabel[0] of 8.2 percent . The peak , in templateXValue[min] , was followed in templateXValue[1] by the lowest templateYLabel[0] templateYLabel[1] of this period .

generated_template: In templateXValue[max] , there were templateYValue[0] million templateYLabel[1] templateYLabel[2] in the United States . Over the last five years templateYLabel[1] templateYLabel[2] , there were just templateYValue[min] thousand templateTitle[1] templateYLabel[1] in the United States . What are templateTitle[1] templateTitle[2] ? The templateYLabel[0] templateYLabel[1] is templateTitle[1] in the United States .
generated: In 2011 , there were 46.4 million rate yLabelErr in the United States . Over the last five years rate yLabelErr , there were just 38.2 thousand Assembly rate in the United States . What are Assembly elections ? The Turnout rate is Assembly in the United States .


Example 532:
data: Age_group|14-15|x|bar_chart Share_of_respondents|13|y|bar_chart Age_group|16-17|x|bar_chart Share_of_respondents|29|y|bar_chart Age_group|18-19|x|bar_chart Share_of_respondents|61|y|bar_chart Age_group|20-24|x|bar_chart Share_of_respondents|78|y|bar_chart Age_group|25-29|x|bar_chart Share_of_respondents|89|y|bar_chart Age_group|30-39|x|bar_chart Share_of_respondents|80|y|bar_chart Age_group|40-49|x|bar_chart Share_of_respondents|83|y|bar_chart Age_group|50-59|x|bar_chart Share_of_respondents|80|y|bar_chart Age_group|60-69|x|bar_chart Share_of_respondents|73|y|bar_chart Age_group|70_and_older|x|bar_chart Share_of_respondents|43|y|bar_chart 
title: U.S. women who have given oral sex to a male in their lifetime , by age group

gold: This statistic shows the share of American women who have ever given oral sex to a male in their lifetime , sorted by age group . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the United States , in 2010 . 89 percent of respondents aged 25 to 29 stated they have given oral sex to a male at some time during their life .
gold_template: This statistic shows the templateYLabel[0] of American templateTitle[1] templateTitle[2] templateTitle[3] ever templateTitle[4] templateTitle[5] templateTitle[6] to a templateTitle[7] in templateTitle[8] templateTitle[9] , sorted templateTitle[10] templateXLabel[0] templateXLabel[1] . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the templateTitle[0] , in 2010 . templateYValue[max] percent of templateYLabel[1] aged 25 to templateYValue[1] stated they templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] to a templateTitle[7] at some time during templateTitle[8] life .

generated_template: This statistic shows the templateYLabel[0] of American templateTitle[1] templateTitle[2] templateTitle[3] ever templateTitle[4] templateTitle[5] in templateTitle[6] templateTitle[7] , sorted templateTitle[8] templateXLabel[0] templateXLabel[1] . The findings were acquired in early templateTitle[11] and published in the Journal of Sexual Medicine , a publication on sexual behavior in the United States , in 2010 . templateYValue[max] percent of templateYLabel[1] aged 25 to 29 stated they templateTitle[3] templateTitle[4] templateTitle[5] at some time during templateTitle[6] life .
generated: This statistic shows the Share of American women who have ever given oral in sex male , sorted their Age group . The findings were acquired in early age and published in the Journal of Sexual Medicine , a publication on sexual behavior in the United States , in 2010 . 89 percent of respondents aged 25 to 29 stated they have given oral at some time during sex life .


Example 533:
data: Year|2018|x|line_chart Share_of_internet_users|84|y|line_chart Year|2017|x|line_chart Share_of_internet_users|81|y|line_chart Year|2016|x|line_chart Share_of_internet_users|79|y|line_chart Year|2015|x|line_chart Share_of_internet_users|77.6|y|line_chart Year|2014|x|line_chart Share_of_internet_users|76.8|y|line_chart Year|2013|x|line_chart Share_of_internet_users|76.5|y|line_chart Year|2012|x|line_chart Share_of_internet_users|75.6|y|line_chart Year|2011|x|line_chart Share_of_internet_users|74.7|y|line_chart Year|2010|x|line_chart Share_of_internet_users|72|y|line_chart Year|2009|x|line_chart Share_of_internet_users|69.1|y|line_chart Year|2008|x|line_chart Share_of_internet_users|65.1|y|line_chart Year|2007|x|line_chart Share_of_internet_users|60.2|y|line_chart Year|2006|x|line_chart Share_of_internet_users|58.2|y|line_chart Year|2005|x|line_chart Share_of_internet_users|55.1|y|line_chart Year|2004|x|line_chart Share_of_internet_users|52.7|y|line_chart Year|2003|x|line_chart Share_of_internet_users|50.1|y|line_chart Year|2002|x|line_chart Share_of_internet_users|41.7|y|line_chart Year|2001|x|line_chart Share_of_internet_users|37|y|line_chart 
title: Share of internet users in Germany 2001 - 2018

gold: In 2018 , the share of German internet users amounted to 84 percent , an increase compared to the previous year at 81 percent . This share has only been growing in recent years . Considering current German population numbers stand at almost 83 million , such a high share of internet users is significant in itself and also for predicting future trends on digitalization and online connectivity in the country .
gold_template: In templateXValue[max] , the templateYLabel[0] of German templateYLabel[1] templateYLabel[2] amounted to templateYValue[max] percent , an increase compared to the previous templateXLabel[0] at templateYValue[1] percent . This templateYLabel[0] has only been growing in recent years . Considering current German population numbers stand at almost 83 million , such a high templateYLabel[0] of templateYLabel[1] templateYLabel[2] is significant in itself and also for predicting future trends on digitalization and online connectivity in the country .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the United States templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of U.S. templateYLabel[1] templateYLabel[2] were between 18 and templateYValue[0] percent compared to the previous templateXLabel[0] .
generated: This statistic shows the Share Share of internet users in the United States 2001 2001 to 2018 . In 2018 , 84 percent of U.S. internet users were between 18 and 84 percent compared to the previous Year .


Example 534:
data: Year|2017|x|line_chart Exports_as_a_percentage_of_GDP|12.06|y|line_chart Year|2016|x|line_chart Exports_as_a_percentage_of_GDP|11.85|y|line_chart Year|2015|x|line_chart Exports_as_a_percentage_of_GDP|12.43|y|line_chart Year|2014|x|line_chart Exports_as_a_percentage_of_GDP|13.53|y|line_chart Year|2013|x|line_chart Exports_as_a_percentage_of_GDP|13.54|y|line_chart Year|2012|x|line_chart Exports_as_a_percentage_of_GDP|13.53|y|line_chart Year|2011|x|line_chart Exports_as_a_percentage_of_GDP|13.53|y|line_chart Year|2010|x|line_chart Exports_as_a_percentage_of_GDP|12.32|y|line_chart Year|2009|x|line_chart Exports_as_a_percentage_of_GDP|11.01|y|line_chart Year|2008|x|line_chart Exports_as_a_percentage_of_GDP|12.51|y|line_chart Year|2007|x|line_chart Exports_as_a_percentage_of_GDP|11.5|y|line_chart Year|2006|x|line_chart Exports_as_a_percentage_of_GDP|10.65|y|line_chart Year|2005|x|line_chart Exports_as_a_percentage_of_GDP|10|y|line_chart Year|2004|x|line_chart Exports_as_a_percentage_of_GDP|9.63|y|line_chart Year|2003|x|line_chart Exports_as_a_percentage_of_GDP|9.04|y|line_chart Year|2002|x|line_chart Exports_as_a_percentage_of_GDP|9.13|y|line_chart Year|2001|x|line_chart Exports_as_a_percentage_of_GDP|9.67|y|line_chart Year|2000|x|line_chart Exports_as_a_percentage_of_GDP|10.66|y|line_chart Year|1999|x|line_chart Exports_as_a_percentage_of_GDP|10.27|y|line_chart Year|1998|x|line_chart Exports_as_a_percentage_of_GDP|10.48|y|line_chart Year|1997|x|line_chart Exports_as_a_percentage_of_GDP|11.08|y|line_chart Year|1996|x|line_chart Exports_as_a_percentage_of_GDP|10.71|y|line_chart Year|1995|x|line_chart Exports_as_a_percentage_of_GDP|10.61|y|line_chart Year|1994|x|line_chart Exports_as_a_percentage_of_GDP|9.86|y|line_chart Year|1993|x|line_chart Exports_as_a_percentage_of_GDP|9.52|y|line_chart Year|1992|x|line_chart Exports_as_a_percentage_of_GDP|9.68|y|line_chart Year|1991|x|line_chart Exports_as_a_percentage_of_GDP|9.64|y|line_chart Year|1990|x|line_chart Exports_as_a_percentage_of_GDP|9.23|y|line_chart 
title: U.S. exports , as a percentage of GDP 1990 - 2017

gold: In 2017 , exports of goods and services from the United States made up just over 12 percent of its gross domestic product ( GDP ) . This is an increase from 9.23 percent of the GDP of the United States in 1990 . Trade and foreign relations The United States ' GDP is the largest in the world , clocking in at around 18.57 trillion U.S. dollars in 2018. International trade is a huge boon to the U.S. economy , both financially and regarding foreign relations .
gold_template: In templateXValue[max] , templateYLabel[0] of goods and services from the templateTitle[0] made up just over templateYValue[0] percent of its gross domestic product ( templateYLabel[2] ) . This is an increase from templateYValue[27] percent of the templateYLabel[2] of the templateTitle[0] in templateXValue[min] . Trade and foreign relations The templateTitle[0] ' templateYLabel[2] is the largest in the world , clocking in at around 18.57 trillion templateTitle[0] dollars in 2018. International trade is a huge boon to the templateTitle[0] economy , both financially and regarding foreign relations .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] people were living in the templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Exports percentage in percentage GDP 1990 2017 in the United States from 1990 to 2017 . In 2017 , about 12.06 people were living in the 1990 2017 in the United States .


Example 535:
data: Country|Germany|x|bar_chart Number_of_millionaire_households|1433985|y|bar_chart Country|France|x|bar_chart Number_of_millionaire_households|1334066|y|bar_chart Country|Italy|x|bar_chart Number_of_millionaire_households|818538|y|bar_chart Country|United_Kingdom|x|bar_chart Number_of_millionaire_households|796646|y|bar_chart Country|Netherlands|x|bar_chart Number_of_millionaire_households|703108|y|bar_chart Country|Switzerland|x|bar_chart Number_of_millionaire_households|555483|y|bar_chart Country|Belgium|x|bar_chart Number_of_millionaire_households|415117|y|bar_chart Country|Austria|x|bar_chart Number_of_millionaire_households|200298|y|bar_chart Country|Spain|x|bar_chart Number_of_millionaire_households|168134|y|bar_chart Country|Luxembourg|x|bar_chart Number_of_millionaire_households|50612|y|bar_chart Country|Portugal|x|bar_chart Number_of_millionaire_households|46416|y|bar_chart Country|Greece|x|bar_chart Number_of_millionaire_households|34723|y|bar_chart Country|Finland|x|bar_chart Number_of_millionaire_households|25995|y|bar_chart Country|Slovakia|x|bar_chart Number_of_millionaire_households|9532|y|bar_chart Country|Cyprus|x|bar_chart Number_of_millionaire_households|7269|y|bar_chart Country|Slovenia|x|bar_chart Number_of_millionaire_households|6784|y|bar_chart 
title: Millionaire households number in Europe in 2014 , by country

gold: The statistic displays the number of households that own net private wealth of at least one million euros in Europe as of 2014 . The countries with the largest number of millionaire households include Germany ( 1.4 million of ultra-rich households ) and France ( 1.3 million households ) .
gold_template: The statistic displays the templateYLabel[0] of templateYLabel[2] that own net private wealth of at least one million euros in templateTitle[3] as of templateTitle[4] . The countries with the largest templateYLabel[0] of templateYLabel[1] templateYLabel[2] include templateXValue[0] ( 1.4 million of ultra-rich templateYLabel[2] ) and templateXValue[1] ( 1.3 million templateYLabel[2] ) .

generated_template: templateXValue[0] templateXValue[0] had the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] . templateYValue[max] percent of templateYLabel[1] in the templateXValue[0] had the highest rate of the world 's highest templateYLabel[0] recorded in this region with templateYValue[1] percent in templateXValue[1] . More than templateXValue[1] at templateYValue[1] percent of templateYLabel[1] earn percent .
generated: Germany had the Millionaire households number of Europe 2014 by in country . 1433985 percent of millionaire in the Germany had the highest rate of the world 's highest Number recorded in this region with 1334066 percent in France . More than France at 1334066 percent of millionaire earn percent .


Example 536:
data: Year|2025|x|line_chart Share_of_retail_prescriptions_filled|46.55|y|line_chart Year|2024|x|line_chart Share_of_retail_prescriptions_filled|44.55|y|line_chart Year|2023|x|line_chart Share_of_retail_prescriptions_filled|42.55|y|line_chart Year|2022|x|line_chart Share_of_retail_prescriptions_filled|40.55|y|line_chart Year|2021|x|line_chart Share_of_retail_prescriptions_filled|38.55|y|line_chart Year|2020|x|line_chart Share_of_retail_prescriptions_filled|36.55|y|line_chart Year|2019|x|line_chart Share_of_retail_prescriptions_filled|34.55|y|line_chart Year|2018|x|line_chart Share_of_retail_prescriptions_filled|33.55|y|line_chart Year|2017|x|line_chart Share_of_retail_prescriptions_filled|32.72|y|line_chart Year|2016|x|line_chart Share_of_retail_prescriptions_filled|30.1|y|line_chart Year|2015|x|line_chart Share_of_retail_prescriptions_filled|23.45|y|line_chart Year|2014|x|line_chart Share_of_retail_prescriptions_filled|21.63|y|line_chart Year|2013|x|line_chart Share_of_retail_prescriptions_filled|20.99|y|line_chart Year|2012|x|line_chart Share_of_retail_prescriptions_filled|17.25|y|line_chart 
title: CVS Health 's share of retail prescriptions filled in the U.S. 2012 - 2025

gold: This statistic depicts CVS Caremark 's share of retail prescriptions filled in the United States from 2012 to 2025 . The CVS Caremark Corporation is a U.S. drug retailing company , with three major segments : CVS/pharmacy , Caremark Pharmacy Services and MinuteClinic . CVS Caremark is headquartered in Woonsocket , Rhode Island .
gold_template: This statistic depicts templateTitle[0] Caremark templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[7] from templateXValue[min] to templateXValue[max] . The templateTitle[0] Caremark Corporation is a templateTitle[7] drug retailing company , with three major segments : CVS/pharmacy , Caremark Pharmacy Services and MinuteClinic . templateTitle[0] Caremark is headquartered in Woonsocket , Rhode Island .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , around templateYValue[6] percent of the templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Share retail of 's share in retail prescriptions filled from 2012 to 2019 , with projections up until 2025 . In 2019 , around 34.55 percent of the retail in 's share retail prescriptions in the United States .


Example 537:
data: Year|2050|x|line_chart Median_age_in_years|26.9|y|line_chart Year|2045|x|line_chart Median_age_in_years|25.7|y|line_chart Year|2040|x|line_chart Median_age_in_years|24.4|y|line_chart Year|2035|x|line_chart Median_age_in_years|22.8|y|line_chart Year|2030|x|line_chart Median_age_in_years|21.1|y|line_chart Year|2025|x|line_chart Median_age_in_years|19.6|y|line_chart Year|2020|x|line_chart Median_age_in_years|18.7|y|line_chart Year|2015|x|line_chart Median_age_in_years|18.4|y|line_chart Year|2010|x|line_chart Median_age_in_years|18.6|y|line_chart Year|2005|x|line_chart Median_age_in_years|18.3|y|line_chart Year|2000|x|line_chart Median_age_in_years|18.2|y|line_chart Year|1995|x|line_chart Median_age_in_years|17.6|y|line_chart Year|1990|x|line_chart Median_age_in_years|16.9|y|line_chart Year|1985|x|line_chart Median_age_in_years|16.0|y|line_chart Year|1980|x|line_chart Median_age_in_years|15.1|y|line_chart Year|1975|x|line_chart Median_age_in_years|15.4|y|line_chart Year|1970|x|line_chart Median_age_in_years|15.6|y|line_chart Year|1965|x|line_chart Median_age_in_years|16.0|y|line_chart Year|1960|x|line_chart Median_age_in_years|17.2|y|line_chart Year|1955|x|line_chart Median_age_in_years|18.1|y|line_chart Year|1950|x|line_chart Median_age_in_years|19.0|y|line_chart 
title: Median age of the population in Zimbabwe 2015

gold: This statistic shows the median age of the population in Zimbabwe from 1950 to 2050 . The median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .

generated_template: The templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of a templateTitle[2] is an index that divides the templateTitle[2] into two equal groups : half of the templateTitle[2] is older than the templateYLabel[0] templateYLabel[1] and the other half younger . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] 's templateTitle[2] was templateYValue[7] templateYLabel[2] .
generated: The Median age of the population in Zimbabwe from 1950 to 2050 . The Median age of a population is an index that divides the population into two equal groups : half of the population is older than the Median age and the other half younger . In 2015 , the Median age of Zimbabwe 's population was 18.4 years .


Example 538:
data: Year|2018|x|line_chart Profit_in_million_British_pounds|1376|y|line_chart Year|2017|x|line_chart Profit_in_million_British_pounds|1351|y|line_chart Year|2016|x|line_chart Profit_in_million_British_pounds|1265|y|line_chart Year|2015|x|line_chart Profit_in_million_British_pounds|1059|y|line_chart Year|2014|x|line_chart Profit_in_million_British_pounds|671|y|line_chart Year|2013|x|line_chart Profit_in_million_British_pounds|808|y|line_chart Year|2012|x|line_chart Profit_in_million_British_pounds|839|y|line_chart Year|2011|x|line_chart Profit_in_million_British_pounds|910|y|line_chart 
title: GlaxoSmithKline 's advertising spending 2011 - 2018

gold: This statistic describes the advertising spending of GlaxoSmithKline from 2011 to 2018 . In 2018 , the company reported ad spending of some 1.38 billion British pounds . GlaxoSmithKline plc is a global pharmaceutical and biotech company , headquartered in London .
gold_template: This statistic describes the templateTitle[2] templateTitle[3] of templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company reported ad templateTitle[3] of some templateYValue[max] billion templateYLabel[2] templateYLabel[3] . templateTitle[0] plc is a global pharmaceutical and biotech company , headquartered in London .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . According to the report , there were approximately templateYValue[0] million people in templateXValue[max] .
generated: This statistic shows the Profit million of spending 2011 2018 in the United States from 2011 to 2018 . According to the report , there were approximately 1376 million people in 2018 .


Example 539:
data: Year|15_to_19_years|x|line_chart Number_of_children_born_in_thousands|469|y|line_chart Year|20_to_24_years|x|line_chart Number_of_children_born_in_thousands|3268|y|line_chart Year|25_to_29_years|x|line_chart Number_of_children_born_in_thousands|9668|y|line_chart Year|30_to_34_years|x|line_chart Number_of_children_born_in_thousands|15269|y|line_chart Year|35_to_39_years|x|line_chart Number_of_children_born_in_thousands|19902|y|line_chart Year|40_to_44_years|x|line_chart Number_of_children_born_in_thousands|20145|y|line_chart Year|45_to_50_years|x|line_chart Number_of_children_born_in_thousands|25038|y|line_chart 
title: Births - number by age of mother 2018

gold: This statistic displays the total number of births in the United States as of June 2018 , by age of mother . In 2018 , women aged between 15 and 19 years gave birth to 469,000 children in the United States .
gold_template: This statistic displays the total templateYLabel[0] of templateTitle[0] in the United States as of June templateTitle[5] , templateTitle[2] templateTitle[3] of templateTitle[4] . In templateTitle[5] , women aged between templateXValue[0] and templateXValue[0] templateXValue[0] gave birth to templateYValue[min] templateYLabel[1] in the United States .

generated_template: As of January templateTitle[4] , there were approximately templateYValue[max] thousand templateYLabel[1] throughout the United States in templateTitle[4] , up from templateYValue[1] thousand people in the previous templateXLabel[0] . The number of people among those aged 15 to 100 thousand templateTitle[1] templateYLabel[1] 100,000 people in the United States in templateTitle[4] templateTitle[5] .
generated: As of January mother , there were approximately 25038 thousand children throughout the United States in mother , up from 3268 thousand people in the previous Year . The number of people among those aged 15 to 100 thousand number children 100,000 people in the United States in mother 2018 .


Example 540:
data: Year|18/19|x|line_chart Revenue_in_million_U.S._dollars|244|y|line_chart Year|17/18|x|line_chart Revenue_in_million_U.S._dollars|223|y|line_chart Year|16/17|x|line_chart Revenue_in_million_U.S._dollars|211|y|line_chart Year|15/16|x|line_chart Revenue_in_million_U.S._dollars|166|y|line_chart Year|14/15|x|line_chart Revenue_in_million_U.S._dollars|143|y|line_chart Year|13/14|x|line_chart Revenue_in_million_U.S._dollars|143|y|line_chart Year|12/13|x|line_chart Revenue_in_million_U.S._dollars|139|y|line_chart Year|11/12|x|line_chart Revenue_in_million_U.S._dollars|126|y|line_chart Year|10/11|x|line_chart Revenue_in_million_U.S._dollars|140|y|line_chart Year|09/10|x|line_chart Revenue_in_million_U.S._dollars|108|y|line_chart Year|08/09|x|line_chart Revenue_in_million_U.S._dollars|107|y|line_chart Year|07/08|x|line_chart Revenue_in_million_U.S._dollars|100|y|line_chart Year|06/07|x|line_chart Revenue_in_million_U.S._dollars|92|y|line_chart Year|05/06|x|line_chart Revenue_in_million_U.S._dollars|89|y|line_chart Year|04/05|x|line_chart Revenue_in_million_U.S._dollars|82|y|line_chart Year|03/04|x|line_chart Revenue_in_million_U.S._dollars|78|y|line_chart Year|02/03|x|line_chart Revenue_in_million_U.S._dollars|80|y|line_chart Year|01/02|x|line_chart Revenue_in_million_U.S._dollars|82|y|line_chart 
title: Orlando Magic 's revenue 2001 - 2019

gold: The statistic shows the revenue of the Orlando Magic franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 244 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitle[0] templateTitle[1] The templateTitle[0] templateTitle[1] are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .
generated: The statistic shows the Revenue of the Orlando Magic franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 244 million U.S. dollars . Orlando Magic The Orlando Magic are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .


Example 541:
data: Money_raised_(in_U.S._dollars)|Less_than_1000|x|bar_chart Number_of_projects|21945|y|bar_chart Money_raised_(in_U.S._dollars)|1000_to_9999|x|bar_chart Number_of_projects|92970|y|bar_chart Money_raised_(in_U.S._dollars)|10000_to_19999|x|bar_chart Number_of_projects|24579|y|bar_chart Money_raised_(in_U.S._dollars)|20000_to_99999|x|bar_chart Number_of_projects|24804|y|bar_chart Money_raised_(in_U.S._dollars)|100K_to_999999|x|bar_chart Number_of_projects|6063|y|bar_chart Money_raised_(in_U.S._dollars)|More_than_1M+|x|bar_chart Number_of_projects|385|y|bar_chart 
title: Distribution of Kickstarter funding amounts raised 2019

gold: The statistic shows the number of successfully funded projects on the crowdfunding platform Kickstarter as of October 2 , 2018 . It shows the number of total successfully funded projects by funds raised . As of that time , the number of successfully funded projects at Kickstarter which raised more than one million U.S. dollars amounted to 385 projects .
gold_template: The statistic shows the templateYLabel[0] of successfully funded templateYLabel[1] on the crowdfunding platform templateTitle[1] as of October 2 , 2018 . It shows the templateYLabel[0] of total successfully funded templateYLabel[1] by funds templateXLabel[1] . As of that time , the templateYLabel[0] of successfully funded templateYLabel[1] at templateTitle[1] which templateXLabel[1] templateXValue[last] templateXValue[0] one million templateXLabel[3] dollars amounted to templateYValue[min] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitle[2] templateTitle[3] in the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] . Approximately templateYValue[max] percent of templateTitle[2] were between U.S. and templateXValue[1] was observed in the U.S. and templateYValue[2] percent of U.S. dollars in the United States .
generated: This statistic shows the Number of projects of funding amounts in the United States in raised , 2019 Money . Approximately 92970 percent of funding were between U.S. and 1000_to_9999 was observed in the U.S. and 24579 percent of U.S. dollars in the United States .


Example 542:
data: Year|2018|x|line_chart Number_of_residents_per_square_mile|213.6|y|line_chart Year|2017|x|line_chart Number_of_residents_per_square_mile|211.3|y|line_chart Year|2016|x|line_chart Number_of_residents_per_square_mile|208.7|y|line_chart Year|2015|x|line_chart Number_of_residents_per_square_mile|206.6|y|line_chart Year|2014|x|line_chart Number_of_residents_per_square_mile|204.5|y|line_chart Year|2013|x|line_chart Number_of_residents_per_square_mile|202.6|y|line_chart Year|2012|x|line_chart Number_of_residents_per_square_mile|200.6|y|line_chart Year|2010|x|line_chart Number_of_residents_per_square_mile|196.1|y|line_chart Year|2000|x|line_chart Number_of_residents_per_square_mile|165.6|y|line_chart Year|1990|x|line_chart Number_of_residents_per_square_mile|136.4|y|line_chart Year|1980|x|line_chart Number_of_residents_per_square_mile|120.9|y|line_chart Year|1970|x|line_chart Number_of_residents_per_square_mile|104.6|y|line_chart Year|1960|x|line_chart Number_of_residents_per_square_mile|93.5|y|line_chart 
title: Population density in North Carolina 1960 - 2018

gold: This graph shows the population density in the federal state of North Carolina from 1960 to 2018 . In 2018 , the population density of North Carolina stood at 213.6 residents per square mile of land area .
gold_template: This graph shows the templateTitle[0] templateTitle[1] in the federal state of templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] stood at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in the federal state of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[2] stood at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .
generated: This statistic shows the Population density in the federal state of North from 1960 to 2018 . In 2018 , the Population density of North stood at 213.6 residents per square mile of land area .


Example 543:
data: Year|2016|x|line_chart Consumption_in_kilowatt_hours_per_capita|348.3|y|line_chart Year|2015|x|line_chart Consumption_in_kilowatt_hours_per_capita|333.3|y|line_chart Year|2014|x|line_chart Consumption_in_kilowatt_hours_per_capita|319.9|y|line_chart Year|2013|x|line_chart Consumption_in_kilowatt_hours_per_capita|296.5|y|line_chart Year|2012|x|line_chart Consumption_in_kilowatt_hours_per_capita|281.9|y|line_chart Year|2011|x|line_chart Consumption_in_kilowatt_hours_per_capita|258.8|y|line_chart Year|2010|x|line_chart Consumption_in_kilowatt_hours_per_capita|238.2|y|line_chart Year|2009|x|line_chart Consumption_in_kilowatt_hours_per_capita|222.7|y|line_chart Year|2008|x|line_chart Consumption_in_kilowatt_hours_per_capita|203.3|y|line_chart Year|2007|x|line_chart Consumption_in_kilowatt_hours_per_capita|195.2|y|line_chart Year|2006|x|line_chart Consumption_in_kilowatt_hours_per_capita|183.5|y|line_chart Year|2005|x|line_chart Consumption_in_kilowatt_hours_per_capita|174.5|y|line_chart Year|2004|x|line_chart Consumption_in_kilowatt_hours_per_capita|171.9|y|line_chart Year|2003|x|line_chart Consumption_in_kilowatt_hours_per_capita|153.9|y|line_chart Year|2002|x|line_chart Consumption_in_kilowatt_hours_per_capita|148.0|y|line_chart Year|2001|x|line_chart Consumption_in_kilowatt_hours_per_capita|147.2|y|line_chart Year|2000|x|line_chart Consumption_in_kilowatt_hours_per_capita|135.9|y|line_chart 
title: Household electricity consumption per capita in Indonesia 2000 - 2016

gold: This statistic represents the household consumption of electricity per capita in Indonesia from the year 2000 to 2016 , in kilowatt hours . In the year 2016 , household consumption of electricity per capita in Indonesia was about 348 kilowatts per hour .
gold_template: This statistic represents the templateTitle[0] templateYLabel[0] of templateTitle[1] templateYLabel[3] templateYLabel[4] in templateTitle[5] from the templateXLabel[0] templateXValue[min] to templateXValue[max] , in templateYLabel[1] templateYLabel[2] . In the templateXLabel[0] templateXValue[max] , templateTitle[0] templateYLabel[0] of templateTitle[1] templateYLabel[3] templateYLabel[4] in templateTitle[5] was about templateYValue[max] kilowatts templateYLabel[3] hour .

generated_template: There were templateYValue[0] templateTitle[1] templateYLabel[0] in templateTitle[3] templateTitle[4] in templateXValue[max] , a slight decrease from the templateXLabel[0] templateXValue[min] to templateXValue[1] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] was at about templateYValue[1] percent . templateYLabel[1] templateYLabel[2] in templateTitle[2] templateTitle[3] templateTitle[4] - additional information In 2016 , most templateXValue[1] , templateTitle[0] templateTitle[1] templateYLabel[0] of of the templateTitle[2] templateTitle[3] templateTitle[4] came in North America .
generated: There were 348.3 electricity Consumption in per capita in 2016 , a slight decrease from the Year 2000 to 2015 . In 2016 , the Consumption of kilowatt hours was at about 333.3 percent . kilowatt hours in consumption per capita - additional information In 2016 , most 2015 , Household electricity Consumption of the consumption per capita came in North America .


Example 544:
data: Designer_Brand|Louis_Vuitton|x|bar_chart Followers_in_millions|23.28|y|bar_chart Designer_Brand|Chanel|x|bar_chart Followers_in_millions|21.96|y|bar_chart Designer_Brand|Gucci|x|bar_chart Followers_in_millions|18.2|y|bar_chart Designer_Brand|Michael_Kors|x|bar_chart Followers_in_millions|17.94|y|bar_chart Designer_Brand|Burberry|x|bar_chart Followers_in_millions|17.31|y|bar_chart Designer_Brand|Dior|x|bar_chart Followers_in_millions|16.65|y|bar_chart Designer_Brand|Dolce_&_Gabbana|x|bar_chart Followers_in_millions|11.74|y|bar_chart Designer_Brand|Ralph_Lauren|x|bar_chart Followers_in_millions|9.16|y|bar_chart Designer_Brand|Armani|x|bar_chart Followers_in_millions|8.63|y|bar_chart Designer_Brand|Coach|x|bar_chart Followers_in_millions|7.36|y|bar_chart Designer_Brand|Prada|x|bar_chart Followers_in_millions|6.6|y|bar_chart Designer_Brand|Versace|x|bar_chart Followers_in_millions|5.37|y|bar_chart Designer_Brand|Jimmy_Choo|x|bar_chart Followers_in_millions|3.71|y|bar_chart Designer_Brand|Christian_Louboutin|x|bar_chart Followers_in_millions|3.35|y|bar_chart Designer_Brand|Hermès|x|bar_chart Followers_in_millions|3.13|y|bar_chart 
title: Facebook : number of followers of popular luxury brands 2019

gold: This statistic provides information on the leading luxury brands with the most followers on Facebook as of May 2019 , ranked by number of followers . According to the findings , the luxury brand Louis Vuitton had recorded in a total of 23.28 million likes on Facebook , and ranking second was Chanel with 21.96 million page likes .
gold_template: This statistic provides information on the leading templateTitle[4] templateTitle[5] with the most templateYLabel[0] on templateTitle[0] as of May templateTitle[6] , ranked by templateTitle[1] of templateYLabel[0] . According to the findings , the templateTitle[4] templateXLabel[1] templateXValue[0] templateXValue[0] had recorded in a total of templateYValue[max] million likes on templateTitle[0] , and ranking second was templateXValue[1] with templateYValue[1] million page likes .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] in the United States from templateTitle[6] to templateTitle[7] . In templateTitle[6] , about templateYValue[max] templateYLabel[3] of the world 's templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Followers millions yLabelErr of popular luxury in the United States from 2019 to titleErr . In 2019 , about 23.28 yLabelErr of the world 's Followers millions yLabelErr yLabelErr yLabelErr yLabelErr .


Example 545:
data: Year|2017|x|line_chart Sales_in_million_GBP|1720|y|line_chart Year|2016|x|line_chart Sales_in_million_GBP|1608|y|line_chart Year|2015|x|line_chart Sales_in_million_GBP|1572|y|line_chart Year|2014|x|line_chart Sales_in_million_GBP|1612|y|line_chart Year|2013|x|line_chart Sales_in_million_GBP|1710|y|line_chart Year|2012|x|line_chart Sales_in_million_GBP|1553|y|line_chart Year|2011|x|line_chart Sales_in_million_GBP|1253|y|line_chart Year|2010|x|line_chart Sales_in_million_GBP|1064|y|line_chart Year|2009|x|line_chart Sales_in_million_GBP|749|y|line_chart Year|2008|x|line_chart Sales_in_million_GBP|635|y|line_chart Year|2007|x|line_chart Sales_in_million_GBP|458|y|line_chart Year|2006|x|line_chart Sales_in_million_GBP|285|y|line_chart Year|2005|x|line_chart Sales_in_million_GBP|195|y|line_chart Year|2004|x|line_chart Sales_in_million_GBP|141|y|line_chart Year|2003|x|line_chart Sales_in_million_GBP|92|y|line_chart Year|2002|x|line_chart Sales_in_million_GBP|63|y|line_chart Year|2001|x|line_chart Sales_in_million_GBP|51|y|line_chart Year|2000|x|line_chart Sales_in_million_GBP|33|y|line_chart Year|1999|x|line_chart Sales_in_million_GBP|22|y|line_chart 
title: Fairtrade food and drink sales revenue in the United Kingdom 1999 - 2017

gold: This statistic illustrates the sales of Fairtrade food and drink products in the United Kingdom ( UK ) from 1999 to 2017 . In 2005 , 195 million British pounds was spent on Fairtrade food and drink products . Sales rose during the period under consideration to approximately 1.72 billion British pounds in sales in 2017 .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[0] templateTitle[1] and templateTitle[2] products in the templateTitle[5] templateTitle[6] ( UK ) from templateXValue[min] to templateXValue[max] . In templateXValue[12] , templateYValue[12] templateYLabel[1] British pounds was spent on templateTitle[0] templateTitle[1] and templateTitle[2] products . templateYLabel[0] rose during the period under consideration to approximately templateYValue[max] billion British pounds in templateYLabel[0] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] of the templateTitle[3] templateTitle[4] ( templateTitle[5] ) in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[3] templateTitle[4] amounted to approximately templateYValue[0] million .
generated: This statistic shows the Sales of million GBP of the sales revenue ( United ) in the United States from 1999 to 2017 . In 2017 , the Sales of Fairtrade food sales revenue amounted to approximately 1720 million .


Example 546:
data: Medicine|Metformin_HCI|x|bar_chart Rx_dispensed_in_million_units|59.2|y|bar_chart Medicine|Glimepiride|x|bar_chart Rx_dispensed_in_million_units|12.7|y|bar_chart Medicine|Metformin_ER_(G)|x|bar_chart Rx_dispensed_in_million_units|12.5|y|bar_chart Medicine|Glipizide|x|bar_chart Rx_dispensed_in_million_units|10.4|y|bar_chart Medicine|Lantus_(long-acting_insulin)|x|bar_chart Rx_dispensed_in_million_units|9.6|y|bar_chart Medicine|Lantus_SoloStar_(long-acting_insulin)|x|bar_chart Rx_dispensed_in_million_units|9.5|y|bar_chart Medicine|Januvia_(sitagliptin)|x|bar_chart Rx_dispensed_in_million_units|8.8|y|bar_chart Medicine|Glipizide_ER|x|bar_chart Rx_dispensed_in_million_units|7.1|y|bar_chart Medicine|Glyburide|x|bar_chart Rx_dispensed_in_million_units|6.5|y|bar_chart Medicine|Pioglitazone|x|bar_chart Rx_dispensed_in_million_units|5.5|y|bar_chart 
title: Leading prescriptions dispensed in the U.S. diabetes market 2014

gold: The statistic shows the leading prescriptions dispensed in the U.S. diabetes market in 2014 . In that year , Metformin HCI was the leading diabetes prescription dispensed in the United States at 59.2 million units .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] . In that year , templateXValue[0] templateXValue[0] was the templateTitle[0] templateTitle[4] prescription templateYLabel[1] in the templateTitle[3] at templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] templateYLabel[2] in the United States from templateTitle[6] to templateTitle[7] . In templateTitle[8] , templateXValue[0] had a templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[max] percent .
generated: This statistic shows the Rx prescriptions dispensed million in the United States from 2014 to titleErr . In titleErr , Metformin_HCI had a Rx dispensed million of 59.2 percent .


Example 547:
data: Year|2018|x|line_chart Direct_investments_in_trillion_U.S._dollars|3.61|y|line_chart Year|2017|x|line_chart Direct_investments_in_trillion_U.S._dollars|3.55|y|line_chart Year|2016|x|line_chart Direct_investments_in_trillion_U.S._dollars|3.31|y|line_chart Year|2015|x|line_chart Direct_investments_in_trillion_U.S._dollars|3.08|y|line_chart Year|2014|x|line_chart Direct_investments_in_trillion_U.S._dollars|2.9|y|line_chart Year|2013|x|line_chart Direct_investments_in_trillion_U.S._dollars|2.6|y|line_chart Year|2012|x|line_chart Direct_investments_in_trillion_U.S._dollars|2.45|y|line_chart Year|2011|x|line_chart Direct_investments_in_trillion_U.S._dollars|2.25|y|line_chart Year|2010|x|line_chart Direct_investments_in_trillion_U.S._dollars|2.03|y|line_chart Year|2009|x|line_chart Direct_investments_in_trillion_U.S._dollars|1.99|y|line_chart Year|2008|x|line_chart Direct_investments_in_trillion_U.S._dollars|1.84|y|line_chart Year|2007|x|line_chart Direct_investments_in_trillion_U.S._dollars|1.68|y|line_chart Year|2006|x|line_chart Direct_investments_in_trillion_U.S._dollars|1.4|y|line_chart Year|2005|x|line_chart Direct_investments_in_trillion_U.S._dollars|1.21|y|line_chart Year|2004|x|line_chart Direct_investments_in_trillion_U.S._dollars|1.18|y|line_chart Year|2003|x|line_chart Direct_investments_in_trillion_U.S._dollars|0.98|y|line_chart Year|2002|x|line_chart Direct_investments_in_trillion_U.S._dollars|0.86|y|line_chart Year|2001|x|line_chart Direct_investments_in_trillion_U.S._dollars|0.77|y|line_chart Year|2000|x|line_chart Direct_investments_in_trillion_U.S._dollars|0.69|y|line_chart 
title: Direct investment position of the U.S. in Europe 2000 - 2018

gold: This statistic shows the direct investment position of the United States in Europe from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in Europe were valued at approximately 3.61 trillion U.S. dollars . The total direct position of the United States abroad amounted to 5.95 trillion U.S. dollars in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitle[4] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitle[4] were valued at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The total templateYLabel[0] templateTitle[2] of the templateTitle[3] abroad amounted to 5.95 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: In templateXValue[max] , foreign templateYLabel[0] templateTitle[1] ( FDI ) from the United States to other countries amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Foreign templateYLabel[0] templateTitle[1] reflects the ownership of business from templateYValue[17] country in another country . It differs from a traditional templateTitle[1] in companies located templateTitle[4] by the ownership factor in case of FDI .
generated: In 2018 , foreign Direct investment ( FDI ) from the United States to other countries amounted to 3.61 trillion U.S. dollars . Foreign Direct investment reflects the ownership of business from 0.77 country in another country . It differs from a traditional investment in companies located Europe by the ownership factor in case of FDI .


Example 548:
data: Year|2019|x|line_chart Franchise_value_in_million_U.S._dollars|2400|y|line_chart Year|2018|x|line_chart Franchise_value_in_million_U.S._dollars|2300|y|line_chart Year|2017|x|line_chart Franchise_value_in_million_U.S._dollars|2300|y|line_chart Year|2016|x|line_chart Franchise_value_in_million_U.S._dollars|2075|y|line_chart Year|2015|x|line_chart Franchise_value_in_million_U.S._dollars|1560|y|line_chart Year|2014|x|line_chart Franchise_value_in_million_U.S._dollars|1250|y|line_chart Year|2013|x|line_chart Franchise_value_in_million_U.S._dollars|1057|y|line_chart Year|2012|x|line_chart Franchise_value_in_million_U.S._dollars|1048|y|line_chart Year|2011|x|line_chart Franchise_value_in_million_U.S._dollars|1002|y|line_chart Year|2010|x|line_chart Franchise_value_in_million_U.S._dollars|1037|y|line_chart Year|2009|x|line_chart Franchise_value_in_million_U.S._dollars|1049|y|line_chart Year|2008|x|line_chart Franchise_value_in_million_U.S._dollars|1040|y|line_chart Year|2007|x|line_chart Franchise_value_in_million_U.S._dollars|956|y|line_chart Year|2006|x|line_chart Franchise_value_in_million_U.S._dollars|936|y|line_chart Year|2005|x|line_chart Franchise_value_in_million_U.S._dollars|878|y|line_chart Year|2004|x|line_chart Franchise_value_in_million_U.S._dollars|760|y|line_chart Year|2003|x|line_chart Franchise_value_in_million_U.S._dollars|642|y|line_chart Year|2002|x|line_chart Franchise_value_in_million_U.S._dollars|609|y|line_chart 
title: Franchise value of the Carolina Panthers ( NFL ) 2002 - 2019

gold: This graph depicts the franchise value of the Carolina Panthers from the National Football League from 2002 to 2019 . In 2019 , the franchise value came to 2.4 billion U.S. dollars.The Carolina Panthers are owned by David Tepper , who bought the franchise for about 2.3 billion U.S. dollars in 2018 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] billion templateYLabel[3] dollars.The templateTitle[2] templateTitle[3] are owned by David Tepper , who bought the templateYLabel[0] for about templateYValue[1] billion templateYLabel[3] templateYLabel[4] in templateXValue[1] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[4] are owned by the Steinbrenner Family , who bought them in 1973 for 8.8 templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the value of the Carolina Panthers NFL Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 2400 billion U.S. dollars . The Carolina Panthers NFL are owned by the Steinbrenner Family , who bought them in 1973 for 8.8 million U.S. dollars .


Example 549:
data: Year|2050|x|line_chart Production_in_quadrillion_Btu|5.54|y|line_chart Year|2045|x|line_chart Production_in_quadrillion_Btu|5.39|y|line_chart Year|2040|x|line_chart Production_in_quadrillion_Btu|5.27|y|line_chart Year|2035|x|line_chart Production_in_quadrillion_Btu|5.2|y|line_chart Year|2030|x|line_chart Production_in_quadrillion_Btu|5.13|y|line_chart Year|2025|x|line_chart Production_in_quadrillion_Btu|4.96|y|line_chart Year|2020|x|line_chart Production_in_quadrillion_Btu|4.74|y|line_chart Year|2019|x|line_chart Production_in_quadrillion_Btu|4.82|y|line_chart 
title: U.S. production of energy from biomass forecast 2019 - 2050

gold: This statistic gives outlook figures on the production of biomass energy between 2019 and 2050 . In 2050 , U.S. biomass energy production is forecast to increase to around 5.54 quadrillion British thermal units .
gold_template: This statistic gives outlook figures on the templateYLabel[0] of templateTitle[4] templateTitle[2] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[4] templateTitle[2] templateYLabel[0] is templateTitle[5] to increase to around templateYValue[max] templateYLabel[1] British thermal units .

generated_template: This statistic shows the number of people employed in the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , about templateYValue[1] million people were born in templateTitle[4] .
generated: This statistic shows the number of people employed in the U.S. production energy from biomass from 2019 to 2050 . In 2045 , about 5.39 million people were born in biomass .


Example 550:
data: Year|2015|x|line_chart Market_value_in_million_U.S._dollars|553.6|y|line_chart Year|2011|x|line_chart Market_value_in_million_U.S._dollars|419.6|y|line_chart Year|2010|x|line_chart Market_value_in_million_U.S._dollars|380.8|y|line_chart Year|2009|x|line_chart Market_value_in_million_U.S._dollars|348.2|y|line_chart Year|2008|x|line_chart Market_value_in_million_U.S._dollars|328.7|y|line_chart 
title: Market value of honey in China based on sale price 2008 - 2015

gold: The statistic shows the market value of honey in China between 2008 and 2010 , including a forecast for 2015 , based on sales price .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] in templateTitle[3] between templateXValue[min] and templateXValue[2] , including a forecast for templateXValue[max] , templateTitle[4] on sales templateTitle[6] .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] in templateTitle[3] templateTitle[4] templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitle[3] templateTitle[4] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . The templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of years .
generated: The statistic shows the Market of value honey in China based sale 2008 to 2015 . In 2015 , the value of China based was 553.6 million U.S. dollars yLabelErr . The value million U.S. Market value million U.S. in Market value million U.S. dollars yLabelErr of years .


Example 551:
data: Platform|Snapchat|x|bar_chart Share_of_uploads|49|y|bar_chart Platform|Facebook|x|bar_chart Share_of_uploads|43|y|bar_chart Platform|Instagram|x|bar_chart Share_of_uploads|7|y|bar_chart Platform|Flickr|x|bar_chart Share_of_uploads|1|y|bar_chart 
title: Photo sharing sites : daily upload market share 2013

gold: This statistic presents the four most popular photo sharing sites as of November 2013 , sorted by share of daily photo uploads . During that month , Instagram accounted for seven percent of daily photo uploads .
gold_template: This statistic presents the four most popular templateTitle[0] templateTitle[1] templateTitle[2] as of November templateTitle[7] , sorted by templateYLabel[0] of templateTitle[3] templateTitle[0] templateYLabel[1] . During that month , templateXValue[2] accounted for templateYValue[2] percent of templateTitle[3] templateTitle[0] templateYLabel[1] .

generated_template: In templateTitle[4] , there were roughly templateYValue[0] percent of the United States stated that they had the most popular templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[4] . During the survey period , templateYValue[2] percent of templateYLabel[1] stated that they had been steadily falling throughout the photo sharing app in recent years .
generated: In upload , there were roughly 49 percent of the United States stated that they had the most popular sharing sites daily upload in upload . During the survey period , 7 percent of uploads stated that they had been steadily falling throughout the photo sharing app in recent years .


Example 552:
data: Year|2019|x|line_chart Net_sales_in_billion_euros|23.32|y|line_chart Year|2018|x|line_chart Net_sales_in_billion_euros|22.56|y|line_chart Year|2017|x|line_chart Net_sales_in_billion_euros|23.15|y|line_chart Year|2016|x|line_chart Net_sales_in_billion_euros|23.64|y|line_chart Year|2015|x|line_chart Net_sales_in_billion_euros|12.5|y|line_chart Year|2014|x|line_chart Net_sales_in_billion_euros|11.76|y|line_chart Year|2013|x|line_chart Net_sales_in_billion_euros|12.71|y|line_chart Year|2012|x|line_chart Net_sales_in_billion_euros|30.18|y|line_chart Year|2011|x|line_chart Net_sales_in_billion_euros|38.66|y|line_chart Year|2010|x|line_chart Net_sales_in_billion_euros|42.45|y|line_chart Year|2009|x|line_chart Net_sales_in_billion_euros|40.98|y|line_chart Year|2008|x|line_chart Net_sales_in_billion_euros|50.71|y|line_chart Year|2007|x|line_chart Net_sales_in_billion_euros|51.06|y|line_chart Year|2006|x|line_chart Net_sales_in_billion_euros|41.12|y|line_chart Year|2005|x|line_chart Net_sales_in_billion_euros|34.19|y|line_chart Year|2004|x|line_chart Net_sales_in_billion_euros|29.37|y|line_chart Year|2003|x|line_chart Net_sales_in_billion_euros|29.53|y|line_chart Year|2002|x|line_chart Net_sales_in_billion_euros|30.02|y|line_chart Year|2001|x|line_chart Net_sales_in_billion_euros|31.19|y|line_chart Year|2000|x|line_chart Net_sales_in_billion_euros|30.38|y|line_chart Year|1999|x|line_chart Net_sales_in_billion_euros|19.77|y|line_chart 
title: Nokia 's net sales 1999 - 2019

gold: In 2018 , Nokia had 22.5 billion euros in net sales , which is a small decrease from the year before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in 2014 , Nokia has focused on its network infrastructure business .
gold_template: In templateXValue[1] , templateTitle[0] had 22.5 templateYLabel[2] templateYLabel[3] in templateYLabel[0] templateYLabel[1] , which is a small decrease from the templateXLabel[0] before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in templateXValue[5] , templateTitle[0] has focused on its network infrastructure business .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people living in templateTitle[2] in the United States .
generated: This statistic shows the Net sales in net from 1999 to 2019 . In 2019 , there were 23.32 people living in net in the United States .


Example 553:
data: Company_(Country_of_origin)|Schwarz_Unternehmenstreuhand_KG_(Germany)|x|bar_chart Billion_U.S._dollars|111.77|y|bar_chart Company_(Country_of_origin)|Aldi_Einkauf_GmbH_&_Co._oHG_(Germany)|x|bar_chart Billion_U.S._dollars|98.29|y|bar_chart Company_(Country_of_origin)|Tesco_PLC_(UK)|x|bar_chart Billion_U.S._dollars|73.96|y|bar_chart Company_(Country_of_origin)|Ahold_Delhaize_(formerly_Koninklijke_Ahold_N.V._and_Delhaize_Group_SA_[Netherlands])|x|bar_chart Billion_U.S._dollars|72.31|y|bar_chart Company_(Country_of_origin)|Auchan_Holding_SA_(France)|x|bar_chart Billion_U.S._dollars|58.61|y|bar_chart Company_(Country_of_origin)|Edeka_Group_(Germany)|x|bar_chart Billion_U.S._dollars|57.48|y|bar_chart Company_(Country_of_origin)|Rewe_Combine_(Germany)|x|bar_chart Billion_U.S._dollars|49.71|y|bar_chart Company_(Country_of_origin)|Casino_Guichard-Perrachon_S.A._(France)|x|bar_chart Billion_U.S._dollars|42.6|y|bar_chart Company_(Country_of_origin)|Centres_Distributeurs_E._Leclerc_(France)_|x|bar_chart Billion_U.S._dollars|41.54|y|bar_chart Company_(Country_of_origin)|Metro_AG_(Germany)|x|bar_chart Billion_U.S._dollars|40.96|y|bar_chart Company_(Country_of_origin)|The_IKEA_Group_(INGKA_Holding_B.V.)_(Netherlands)|x|bar_chart Billion_U.S._dollars|37.43|y|bar_chart Company_(Country_of_origin)|J_Sainsbury_plc_(UK)|x|bar_chart Billion_U.S._dollars|36.6|y|bar_chart Company_(Country_of_origin)|LVMH_Moët_Hennessy-Louis_Vuitton_S.A._(France)|x|bar_chart Billion_U.S._dollars|33.29|y|bar_chart Company_(Country_of_origin)|ITM_Developpement_International_(Intermarche;_France)_|x|bar_chart Billion_U.S._dollars|31.85|y|bar_chart Company_(Country_of_origin)|Inditex_S.A._(Spain)|x|bar_chart Billion_U.S._dollars|28.89|y|bar_chart Company_(Country_of_origin)|Migros-Genossenschafts_Bund_(Switzerland)_|x|bar_chart Billion_U.S._dollars|24.53|y|bar_chart Company_(Country_of_origin)|Ceconomy_AG_(Germany)|x|bar_chart Billion_U.S._dollars|24.43|y|bar_chart Company_(Country_of_origin)|Mercadona_SA_(Spain)|x|bar_chart Billion_U.S._dollars|23.68|y|bar_chart Company_(Country_of_origin)|Coop_Group_(Switzerland)_|x|bar_chart Billion_U.S._dollars|22.52|y|bar_chart Company_(Country_of_origin)|Wm_Morrison_Supermarkets_PLC_(UK)|x|bar_chart Billion_U.S._dollars|22.43|y|bar_chart 
title: Leading food and beverage retailers of Europe 2017 , based on revenue

gold: In 2018 , the German based Schwarz Gruppe was the leading food and beverage retailer from Europe and generated 111.77 billion U.S. dollars in revenue . The second largest retailer was also German . Aldi Einkauf GmbH & Ko .
gold_template: In 2018 , the German templateTitle[6] templateXValue[0] Gruppe was the templateTitle[0] templateTitle[1] and templateTitle[2] retailer from templateTitle[4] and generated templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[7] . The second largest retailer was also German . templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] Ko .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] in templateTitle[4] , templateTitle[5] on retail templateTitle[6] . In that year , templateXValue[0] was about templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Leading food of beverage retailers in Europe , 2017 on retail based . In that year , Schwarz_Unternehmenstreuhand_KG_(Germany) was about 111.77 Billion U.S. dollars .


Example 554:
data: Year|2018|x|line_chart Number_of_children_enrolled_(in_millions)|8.74|y|line_chart Year|2017|x|line_chart Number_of_children_enrolled_(in_millions)|8.64|y|line_chart Year|2016|x|line_chart Number_of_children_enrolled_(in_millions)|8.76|y|line_chart Year|2015|x|line_chart Number_of_children_enrolled_(in_millions)|8.61|y|line_chart Year|2014|x|line_chart Number_of_children_enrolled_(in_millions)|8.76|y|line_chart Year|2013|x|line_chart Number_of_children_enrolled_(in_millions)|8.83|y|line_chart Year|2012|x|line_chart Number_of_children_enrolled_(in_millions)|8.77|y|line_chart Year|2011|x|line_chart Number_of_children_enrolled_(in_millions)|9.16|y|line_chart Year|2010|x|line_chart Number_of_children_enrolled_(in_millions)|9.01|y|line_chart Year|2009|x|line_chart Number_of_children_enrolled_(in_millions)|8.84|y|line_chart Year|2008|x|line_chart Number_of_children_enrolled_(in_millions)|8.66|y|line_chart Year|2007|x|line_chart Number_of_children_enrolled_(in_millions)|8.76|y|line_chart Year|2006|x|line_chart Number_of_children_enrolled_(in_millions)|8.73|y|line_chart Year|2005|x|line_chart Number_of_children_enrolled_(in_millions)|8.52|y|line_chart Year|2004|x|line_chart Number_of_children_enrolled_(in_millions)|8.73|y|line_chart Year|2000|x|line_chart Number_of_children_enrolled_(in_millions)|8.65|y|line_chart Year|1995|x|line_chart Number_of_children_enrolled_(in_millions)|8.04|y|line_chart Year|1990|x|line_chart Number_of_children_enrolled_(in_millions)|8.03|y|line_chart Year|1985|x|line_chart Number_of_children_enrolled_(in_millions)|8.23|y|line_chart Year|1980|x|line_chart Number_of_children_enrolled_(in_millions)|5.16|y|line_chart Year|1975|x|line_chart Number_of_children_enrolled_(in_millions)|5.14|y|line_chart Year|1970|x|line_chart Number_of_children_enrolled_(in_millions)|4.28|y|line_chart 
title: Pre-primary school enrollment numbers in the U.S. 1970 - 2018

gold: This graph shows the number of children enrolled in pre-primary school institutions ( kindergarten or nursery ) in the United States from 1970 to 2018 . In 2018 , around 8.74 million children were enrolled in nursery or kindergarten programs in the United States .
gold_template: This graph shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitle[0] templateTitle[1] institutions ( kindergarten or nursery ) in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[0] million templateYLabel[1] were templateYLabel[2] in nursery or kindergarten programs in the templateTitle[4] .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in the United States amounted to an increase from the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] of the most recently reported period was one of the most likely by the previous templateXLabel[0] . The number of people held in the world .
generated: In 2018 , the Number children of U.S. 1970 in the United States amounted to an increase from the previous Year . The Number children of the most recently reported period was one of the most likely by the previous Year . The number of people held in the world .


Example 555:
data: Year|'18|x|line_chart Unemployment_rate|3.9|y|line_chart Year|'17|x|line_chart Unemployment_rate|4.5|y|line_chart Year|'16|x|line_chart Unemployment_rate|5.1|y|line_chart Year|'15|x|line_chart Unemployment_rate|5.7|y|line_chart Year|'14|x|line_chart Unemployment_rate|6.3|y|line_chart Year|'13|x|line_chart Unemployment_rate|8|y|line_chart Year|'12|x|line_chart Unemployment_rate|9.3|y|line_chart Year|'11|x|line_chart Unemployment_rate|10.3|y|line_chart Year|'10|x|line_chart Unemployment_rate|10.9|y|line_chart Year|'09|x|line_chart Unemployment_rate|10.6|y|line_chart Year|'08|x|line_chart Unemployment_rate|6.1|y|line_chart Year|'07|x|line_chart Unemployment_rate|4.7|y|line_chart Year|'06|x|line_chart Unemployment_rate|4.7|y|line_chart Year|'05|x|line_chart Unemployment_rate|5.2|y|line_chart Year|'04|x|line_chart Unemployment_rate|5.5|y|line_chart Year|'03|x|line_chart Unemployment_rate|6.4|y|line_chart Year|'02|x|line_chart Unemployment_rate|6.6|y|line_chart Year|'01|x|line_chart Unemployment_rate|5.5|y|line_chart Year|'00|x|line_chart Unemployment_rate|3.7|y|line_chart Year|'99|x|line_chart Unemployment_rate|3.2|y|line_chart Year|'98|x|line_chart Unemployment_rate|3.5|y|line_chart Year|'97|x|line_chart Unemployment_rate|3.7|y|line_chart Year|'96|x|line_chart Unemployment_rate|4.3|y|line_chart Year|'95|x|line_chart Unemployment_rate|4.3|y|line_chart Year|'94|x|line_chart Unemployment_rate|4.4|y|line_chart Year|'93|x|line_chart Unemployment_rate|5|y|line_chart Year|'92|x|line_chart Unemployment_rate|6|y|line_chart 
title: North Carolina - Unemployment rate 1992 - 2018

gold: This statistic displays the unemployment rate in North Carolina from 1992 to 2018 . In 2018 , unemployment in North Carolina was 3.9 percent .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitle[0] templateTitle[1] from templateTitle[4] to templateTitle[5] . In templateTitle[5] , templateYLabel[0] in templateTitle[0] templateTitle[1] was templateYValue[0] percent .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] of templateTitle[0] or Latin templateTitle[1] from templateTitle[5] to templateTitle[6] . In templateTitle[6] , the templateYLabel[0] templateYLabel[1] of templateTitle[0] or Latin templateTitle[1] decreased to templateYValue[min] percent from templateYValue[max] percent in 2010 . The overall national templateYLabel[0] templateYLabel[1] was 3.7 percent in templateTitle[6] and can be found here .
generated: This statistic displays the Unemployment rate of North or Latin Carolina from 2018 to titleErr . In titleErr , the Unemployment rate of North or Latin Carolina decreased to 3.2 percent from 10.9 percent in 2010 . The overall national Unemployment rate was 3.7 percent in titleErr and can be found here .


Example 556:
data: Year|'19|x|line_chart Unemployed_in_millions|6.0|y|line_chart Year|'18|x|line_chart Unemployed_in_millions|6.31|y|line_chart Year|'17|x|line_chart Unemployed_in_millions|6.98|y|line_chart Year|'16|x|line_chart Unemployed_in_millions|7.75|y|line_chart Year|'15|x|line_chart Unemployed_in_millions|8.3|y|line_chart Year|'14|x|line_chart Unemployed_in_millions|9.62|y|line_chart Year|'13|x|line_chart Unemployed_in_millions|11.46|y|line_chart Year|'12|x|line_chart Unemployed_in_millions|12.51|y|line_chart Year|'11|x|line_chart Unemployed_in_millions|13.75|y|line_chart Year|'10|x|line_chart Unemployed_in_millions|14.83|y|line_chart Year|'09|x|line_chart Unemployed_in_millions|14.27|y|line_chart Year|'08|x|line_chart Unemployed_in_millions|8.92|y|line_chart Year|'07|x|line_chart Unemployed_in_millions|7.08|y|line_chart Year|'06|x|line_chart Unemployed_in_millions|7.0|y|line_chart Year|'05|x|line_chart Unemployed_in_millions|7.59|y|line_chart Year|'04|x|line_chart Unemployed_in_millions|8.15|y|line_chart Year|'03|x|line_chart Unemployed_in_millions|8.77|y|line_chart Year|'02|x|line_chart Unemployed_in_millions|8.38|y|line_chart Year|'01|x|line_chart Unemployed_in_millions|6.8|y|line_chart Year|'00|x|line_chart Unemployed_in_millions|5.69|y|line_chart Year|'99|x|line_chart Unemployed_in_millions|5.88|y|line_chart Year|'98|x|line_chart Unemployed_in_millions|6.21|y|line_chart Year|'97|x|line_chart Unemployed_in_millions|6.74|y|line_chart Year|'96|x|line_chart Unemployed_in_millions|7.24|y|line_chart Year|'95|x|line_chart Unemployed_in_millions|7.4|y|line_chart Year|'94|x|line_chart Unemployed_in_millions|8.0|y|line_chart Year|'93|x|line_chart Unemployed_in_millions|8.94|y|line_chart Year|'92|x|line_chart Unemployed_in_millions|9.61|y|line_chart Year|'91|x|line_chart Unemployed_in_millions|8.63|y|line_chart Year|'90|x|line_chart Unemployed_in_millions|7.05|y|line_chart 
title: U.S. unemployment level 1990 - 2019

gold: This statistic shows the unemployment level in the United States from 1990 to 2019 . National unemployment level decreased to an average of six million people looking for work in 2019 . See the United States unemployment rate and the monthly unemployment rate for further information .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in the templateTitle[0] from templateTitle[3] to templateTitle[4] . National templateTitle[1] templateTitle[2] decreased to an average of templateYValue[0] million people looking for work in templateTitle[4] . See the templateTitle[0] templateTitle[1] rate and the monthly templateTitle[1] rate for further information .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[0] in the United States from templateTitle[4] to templateTitle[5] . In templateTitle[5] , about templateYValue[max] percent of the templateYLabel[1] had an increase from templateYValue[max] percent in the previous templateXLabel[0] .
generated: This statistic shows the Unemployed millions of U.S. in the United States from 2019 to titleErr . In titleErr , about 14.83 percent of the millions had an increase from 14.83 percent in the previous Year .


Example 557:
data: Year|2018/19|x|line_chart Revenue_in_million_U.S._dollars|102|y|line_chart Year|2017/18|x|line_chart Revenue_in_million_U.S._dollars|96|y|line_chart Year|2016/17|x|line_chart Revenue_in_million_U.S._dollars|98|y|line_chart Year|2015/16|x|line_chart Revenue_in_million_U.S._dollars|101|y|line_chart Year|2014/15|x|line_chart Revenue_in_million_U.S._dollars|92|y|line_chart Year|2013/14|x|line_chart Revenue_in_million_U.S._dollars|80|y|line_chart Year|2012/13|x|line_chart Revenue_in_million_U.S._dollars|67|y|line_chart Year|2011/12|x|line_chart Revenue_in_million_U.S._dollars|83|y|line_chart Year|2010/11|x|line_chart Revenue_in_million_U.S._dollars|70|y|line_chart Year|2009/10|x|line_chart Revenue_in_million_U.S._dollars|67|y|line_chart Year|2008/09|x|line_chart Revenue_in_million_U.S._dollars|66|y|line_chart Year|2007/08|x|line_chart Revenue_in_million_U.S._dollars|68|y|line_chart Year|2006/07|x|line_chart Revenue_in_million_U.S._dollars|67|y|line_chart Year|2005/06|x|line_chart Revenue_in_million_U.S._dollars|63|y|line_chart 
title: Arizona Coyotes ' revenue 2005 - 2019

gold: This graph depicts the annual National Hockey League revenue of the Arizona Coyotes from the 2005/06 season to the 2018/19 season . The revenue of the Arizona Coyotes amounted to 102 million U.S. dollars in the 2018/19 season .
gold_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitle[0] templateTitle[1] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitle[0] templateTitle[1] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the templateXValue[last] season to the templateXValue[0] season . In templateXValue[0] , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitle[0] templateTitle[1] The templateTitle[0] templateTitle[1] are a professional basketball team of the National Basketball Association ( NBA ) .
generated: The statistic shows the Revenue of the Arizona Coyotes franchise from the 2005/06 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 102 million U.S. dollars . Arizona Coyotes The Arizona Coyotes are a professional basketball team of the National Basketball Association ( NBA ) .


Example 558:
data: Country|Luxembourg|x|bar_chart Average_wealth_per_adult|432221|y|bar_chart Country|Switzerland|x|bar_chart Average_wealth_per_adult|394917|y|bar_chart Country|Belgium|x|bar_chart Average_wealth_per_adult|240928|y|bar_chart Country|Netherlands|x|bar_chart Average_wealth_per_adult|213365|y|bar_chart Country|Austria|x|bar_chart Average_wealth_per_adult|188552|y|bar_chart Country|Germany|x|bar_chart Average_wealth_per_adult|185857|y|bar_chart Country|United_Kingdom|x|bar_chart Average_wealth_per_adult|183325|y|bar_chart Country|France|x|bar_chart Average_wealth_per_adult|178862|y|bar_chart Country|Italy|x|bar_chart Average_wealth_per_adult|163493|y|bar_chart Country|Cyprus|x|bar_chart Average_wealth_per_adult|137298|y|bar_chart Country|Finland|x|bar_chart Average_wealth_per_adult|124285|y|bar_chart Country|Spain|x|bar_chart Average_wealth_per_adult|92341|y|bar_chart Country|Portugal|x|bar_chart Average_wealth_per_adult|84847|y|bar_chart Country|Slovenia|x|bar_chart Average_wealth_per_adult|67878|y|bar_chart Country|Greece|x|bar_chart Average_wealth_per_adult|58877|y|bar_chart Country|Slovakia|x|bar_chart Average_wealth_per_adult|33295|y|bar_chart 
title: Wealth per adult on average in Europe in 2014 , by country

gold: The statistic displays the average value of wealth per adult in selected European countries as of 2014 . The average value of wealth per adult in Luxembourg amounted to 432.2 thousand euros , while in the United Kingdom ( UK ) it reached approximately 188.6 thousand euros .
gold_template: The statistic displays the templateYLabel[0] value of templateYLabel[1] templateYLabel[2] templateYLabel[3] in selected European countries as of templateTitle[5] . The templateYLabel[0] value of templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[0] amounted to templateYValue[max] thousand euros , while in the templateXValue[6] templateXValue[6] ( UK ) it reached approximately templateYValue[4] thousand euros .

generated_template: templateXValue[0] templateXValue[0] had the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] . templateYValue[max] percent of templateYLabel[1] in the templateXValue[0] had the highest rate of the world 's highest templateYLabel[0] recorded in this region with templateYValue[1] percent in templateXValue[1] . More than templateXValue[1] at templateYValue[1] percent of templateYLabel[1] earn percent .
generated: Luxembourg had the Wealth per adult of average Europe 2014 in by . 432221 percent of wealth in the Luxembourg had the highest rate of the world 's highest Average recorded in this region with 394917 percent in Switzerland . More than Switzerland at 394917 percent of wealth earn percent .


Example 559:
data: Year|2019|x|line_chart Revenue_in_million_GBP|1129.7|y|line_chart Year|2018|x|line_chart Revenue_in_million_GBP|1072.8|y|line_chart Year|2017|x|line_chart Revenue_in_million_GBP|954.6|y|line_chart Year|2016|x|line_chart Revenue_in_million_GBP|810.3|y|line_chart Year|2015|x|line_chart Revenue_in_million_GBP|764.2|y|line_chart Year|2014|x|line_chart Revenue_in_million_GBP|724.9|y|line_chart Year|2013|x|line_chart Revenue_in_million_GBP|719.0|y|line_chart Year|2012|x|line_chart Revenue_in_million_GBP|734.0|y|line_chart Year|2011|x|line_chart Revenue_in_million_GBP|672.1|y|line_chart Year|2010|x|line_chart Revenue_in_million_GBP|557.7|y|line_chart Year|2009|x|line_chart Revenue_in_million_GBP|670.8|y|line_chart Year|2008|x|line_chart Revenue_in_million_GBP|786.8|y|line_chart Year|2007|x|line_chart Revenue_in_million_GBP|633.6|y|line_chart 
title: Revenue of Hays worldwide 2007 - 2019

gold: This statistic shows the revenue of Hays worldwide from 2007 to 2019 . In 2019 , the UK-based recruitment specialist Hays generated over 1.1 billion British pounds in revenue worldwide , up from one billion the previous year .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the UK-based recruitment specialist templateTitle[1] generated over templateYValue[max] billion British pounds in templateYLabel[0] templateTitle[2] , up from templateYValue[max] billion the previous templateXLabel[0] .

generated_template: templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] rose in templateXValue[1] to around templateYValue[0] percent in the United Kingdom ( templateTitle[5] ) from templateXValue[min] to templateXValue[max] . templateTitle[0] templateTitle[1] templateTitle[2] – additional information According to the source , templateTitle[0] templateTitle[1] templateTitle[2] fashion retailer made in the United Kingdom ( UK ) and reached its expansion expansion in templateXValue[1] . The templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] streams can be observed observed observed in templateXValue[1] .
generated: Revenue Hays worldwide 2007 rose in 2018 to around 1129.7 percent in the United Kingdom ( titleErr ) from 2007 to 2019 . Revenue Hays worldwide – additional information According to the source , Revenue Hays worldwide fashion retailer made in the United Kingdom ( UK ) and reached its expansion in 2018 . The Revenue Hays worldwide Revenue streams can be observed observed in 2018 .


Example 560:
data: Country|France|x|bar_chart Revenue_in_billion_euros|24.98|y|bar_chart Country|Other_EU_countries|x|bar_chart Revenue_in_billion_euros|15.45|y|bar_chart Country|Belgium|x|bar_chart Revenue_in_billion_euros|5.96|y|bar_chart Country|Asia_Middle_East_and_Oceania|x|bar_chart Revenue_in_billion_euros|4.94|y|bar_chart Country|South_America|x|bar_chart Revenue_in_billion_euros|4.2|y|bar_chart Country|North_America|x|bar_chart Revenue_in_billion_euros|3.87|y|bar_chart Country|Other_European_countries|x|bar_chart Revenue_in_billion_euros|0.82|y|bar_chart Country|Africa|x|bar_chart Revenue_in_billion_euros|0.39|y|bar_chart 
title: Engie - revenue by region 2018

gold: This statistic represents Engie 's revenue in the fiscal year of 2018 , by region . The French multinational energy company generated a revenue of around six billion euros in its Belgium segment . The company was formed by the merger of Gaz de France and Suez to GDF Suez and officially changed its name to Engie in April 2015 .
gold_template: This statistic represents templateTitle[0] 's templateYLabel[0] in the fiscal year of templateTitle[4] , templateTitle[2] templateTitle[3] . The French multinational energy company generated a templateYLabel[0] of around templateYValue[2] templateYLabel[1] templateYLabel[2] in its templateXValue[2] segment . The company was formed templateTitle[2] the merger of Gaz de templateXValue[0] and Suez to GDF Suez and officially changed its name to templateTitle[0] in April 2015 .

generated_template: This statistic displays the templateTitle[0] templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] worldwide in templateTitle[5] . In that year , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] generated around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[2] . When it comes to sustainable templateYLabel[1] templateYLabel[2] ( or about templateYValue[1] templateYLabel[1] templateYLabel[2] . The total templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] .
generated: This statistic displays the Engie Revenue of the revenue by region 2018 worldwide in titleErr . In that year , the Engie revenue by region 2018 generated around 24.98 billion euros . When it comes to sustainable billion euros ( or about 15.45 billion euros . The total Revenue of Engie revenue by region 2018 .


Example 561:
data: Year|1982|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|1983|x|line_chart Total_number_of_mass_shootings|0|y|line_chart Year|1984|x|line_chart Total_number_of_mass_shootings|2|y|line_chart Year|1985|x|line_chart Total_number_of_mass_shootings|0|y|line_chart Year|1986|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|1987|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|1988|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|1989|x|line_chart Total_number_of_mass_shootings|2|y|line_chart Year|1990|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|1991|x|line_chart Total_number_of_mass_shootings|3|y|line_chart Year|1992|x|line_chart Total_number_of_mass_shootings|2|y|line_chart Year|1993|x|line_chart Total_number_of_mass_shootings|4|y|line_chart Year|1994|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|1995|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|1996|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|1997|x|line_chart Total_number_of_mass_shootings|2|y|line_chart Year|1998|x|line_chart Total_number_of_mass_shootings|3|y|line_chart Year|1999|x|line_chart Total_number_of_mass_shootings|5|y|line_chart Year|2000|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|2001|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|2002|x|line_chart Total_number_of_mass_shootings|0|y|line_chart Year|2003|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|2004|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|2005|x|line_chart Total_number_of_mass_shootings|2|y|line_chart Year|2006|x|line_chart Total_number_of_mass_shootings|3|y|line_chart Year|2007|x|line_chart Total_number_of_mass_shootings|4|y|line_chart Year|2008|x|line_chart Total_number_of_mass_shootings|3|y|line_chart Year|2009|x|line_chart Total_number_of_mass_shootings|4|y|line_chart Year|2010|x|line_chart Total_number_of_mass_shootings|1|y|line_chart Year|2011|x|line_chart Total_number_of_mass_shootings|3|y|line_chart Year|2012|x|line_chart Total_number_of_mass_shootings|7|y|line_chart Year|2013|x|line_chart Total_number_of_mass_shootings|5|y|line_chart Year|2014|x|line_chart Total_number_of_mass_shootings|4|y|line_chart Year|2015|x|line_chart Total_number_of_mass_shootings|7|y|line_chart Year|2016|x|line_chart Total_number_of_mass_shootings|6|y|line_chart Year|2017|x|line_chart Total_number_of_mass_shootings|11|y|line_chart Year|2018|x|line_chart Total_number_of_mass_shootings|12|y|line_chart Year|2019|x|line_chart Total_number_of_mass_shootings|10|y|line_chart Year|2020|x|line_chart Total_number_of_mass_shootings|1|y|line_chart 
title: Mass shootings in the U.S. 1982 - 2020

gold: As of February 26 , there was one mass shootings in the United States in 2020 . This is compared to one mass shooting in 1982 , one in 2000 , and 12 mass shootings in 2018 . School shootings The United States sees the most school shootings in the world .
gold_template: As of February 26 , there was templateYValue[0] templateYLabel[2] templateYLabel[3] in the templateTitle[2] in templateXValue[max] . This is compared to templateYValue[0] templateYLabel[2] shooting in templateXValue[min] , templateYValue[0] in templateXValue[18] , and templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[36] . School templateYLabel[3] The templateTitle[2] sees the most school templateYLabel[3] in the world .

generated_template: This statistic shows the number of units in the United States from templateXValue[min] to templateXValue[max] . As of templateXValue[max] , templateYValue[3] templateYLabel[1] were templateYLabel[3] in the United States . .
generated: This statistic shows the number of units in the United States from 1982 to 2020 . As of 2020 , 0 number were shootings in the United States .


Example 562:
data: Year|2018|x|line_chart Attendance_in_millions|69.27|y|line_chart Year|2017|x|line_chart Attendance_in_millions|70.4|y|line_chart Year|2016|x|line_chart Attendance_in_millions|74.6|y|line_chart Year|2015|x|line_chart Attendance_in_millions|77.0|y|line_chart Year|2014|x|line_chart Attendance_in_millions|73.6|y|line_chart Year|2013|x|line_chart Attendance_in_millions|72.7|y|line_chart Year|2012|x|line_chart Attendance_in_millions|71.2|y|line_chart Year|2011|x|line_chart Attendance_in_millions|66.1|y|line_chart Year|2010|x|line_chart Attendance_in_millions|67.0|y|line_chart 
title: Attendance at Cineplex cinemas 2010 - 2018

gold: The timeline presents the attendance figures at Cineplex from 2010 to 2018 . In 2018 , 69.27 million people attended movies at the Canadian movie theater chain , down from 70.4 million visitors a year earlier .
gold_template: The timeline presents the templateYLabel[0] figures at templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] million people attended movies at the Canadian movie theater chain , down from templateYValue[1] million visitors a templateXLabel[0] earlier .

generated_template: In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] generated approximately templateYValue[max] billion templateYLabel[1] templateYLabel[2] in the United States . This figure has been steadily since the past two years as well as most recently reported period . The company 's concert industry – additional information In Beyonce and Guns ' N ' Rose were among the most successful templateTitle[1] tours in North America , generating 169.4 million templateTitle[5] dollars and 130.8 million templateTitle[5] dollars , respectively in gross revenue .
generated: In 2018 , Attendance Cineplex cinemas generated approximately 77.0 billion millions yLabelErr in the United States . This figure has been steadily since the past two years as well as most recently reported period . The company 's concert industry – additional information In Beyonce and Guns ' N ' Rose were among the most successful Cineplex tours in North America , generating 169.4 million titleErr dollars and 130.8 million titleErr dollars , respectively in gross revenue .


Example 563:
data: Year|2019|x|line_chart Unemployment_rate|7.25|y|line_chart Year|2018|x|line_chart Unemployment_rate|7.25|y|line_chart Year|2017|x|line_chart Unemployment_rate|7.14|y|line_chart Year|2016|x|line_chart Unemployment_rate|7.28|y|line_chart Year|2015|x|line_chart Unemployment_rate|7.28|y|line_chart Year|2014|x|line_chart Unemployment_rate|7.43|y|line_chart Year|2013|x|line_chart Unemployment_rate|7.45|y|line_chart Year|2012|x|line_chart Unemployment_rate|7.36|y|line_chart Year|2011|x|line_chart Unemployment_rate|7.36|y|line_chart Year|2010|x|line_chart Unemployment_rate|9.09|y|line_chart Year|2009|x|line_chart Unemployment_rate|10.61|y|line_chart Year|2008|x|line_chart Unemployment_rate|12.04|y|line_chart Year|2007|x|line_chart Unemployment_rate|14.63|y|line_chart Year|2006|x|line_chart Unemployment_rate|17.67|y|line_chart Year|2005|x|line_chart Unemployment_rate|20.53|y|line_chart Year|2004|x|line_chart Unemployment_rate|23.64|y|line_chart Year|2003|x|line_chart Unemployment_rate|23.93|y|line_chart Year|2002|x|line_chart Unemployment_rate|23.9|y|line_chart Year|2001|x|line_chart Unemployment_rate|23.12|y|line_chart Year|2000|x|line_chart Unemployment_rate|22.89|y|line_chart Year|1999|x|line_chart Unemployment_rate|20.9|y|line_chart 
title: Unemployment rate in Angola 2019

gold: This statistic shows the unemployment rate in Angola from 1999 to 2019 . In 2019 , the unemployment rate in Angola was 7.25 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Angola from 1999 to 2019 . In 2019 , the Unemployment rate in Angola was at approximately 7.25 percent .


Example 564:
data: Year|2015/16_(Intuit_Quickbooks)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|5|y|line_chart Year|2014/15_(Dafabet.com)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|5|y|line_chart Year|2013/14_(Dafabet.com)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|5|y|line_chart Year|2012/13_(Genting)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|8|y|line_chart Year|2011/12_(Genting)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|8|y|line_chart Year|2010/11_(FxPro)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|5|y|line_chart Year|2009/10_(Acorns)|x|line_chart Jersey_sponsorship_revenue_in_million_GBP|0|y|line_chart 
title: Value of Aston Villa 's jersey sponsorship 2016

gold: The statistic shows the revenue Aston Villa generated from its jersey sponsorship deal from the 2009/10 season to the 2015/16 season . In the 2012/13 season Aston Villa received 8 million GBP from its jersey sponsor Genting .
gold_template: The statistic shows the templateYLabel[2] templateTitle[1] templateTitle[2] generated from its templateYLabel[0] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[3] season templateTitle[1] templateTitle[2] received templateYValue[max] templateYLabel[3] templateYLabel[4] from its templateYLabel[0] sponsor Genting .

generated_template: The statistic shows the templateYLabel[2] templateTitle[1] templateTitle[2] generated from its templateYLabel[0] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , templateTitle[1] templateTitle[2] received templateYValue[max] templateYLabel[3] templateYLabel[4] from its templateYLabel[0] sponsor SportPesa .
generated: The statistic shows the revenue Aston Villa generated from its Jersey sponsorship deal from the 2009/10_(Acorns) season to the 2015/16_(Intuit_Quickbooks) season . In the 2015/16_(Intuit_Quickbooks) season , Aston Villa received 8 million GBP from its Jersey sponsor SportPesa .


Example 565:
data: Response|Trivial/no_loss|x|bar_chart Share_of_respondents|43|y|bar_chart Response|Lack_of_police_engagement|x|bar_chart Share_of_respondents|40|y|bar_chart Response|Private/dealt_with_ourselves|x|bar_chart Share_of_respondents|13|y|bar_chart Response|Lack_of_evidence|x|bar_chart Share_of_respondents|3|y|bar_chart Response|Reported_to_other_authorities|x|bar_chart Share_of_respondents|3|y|bar_chart Response|Inconvenient_to_report|x|bar_chart Share_of_respondents|3|y|bar_chart Response|Police_came|x|bar_chart Share_of_respondents|3|y|bar_chart Response|Common_occurrence|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Fear_of_reprisal|x|bar_chart Share_of_respondents|1|y|bar_chart Response|Other|x|bar_chart Share_of_respondents|6|y|bar_chart 
title: Reasons for unreported vandalism against businesses in England and Wales 2014

gold: This survey shows the reasons businesses gave for not reporting cases of vandalism on their premises to the police in England and Wales in 2014 . Of respondents , 43 percent claimed they did not report the incident as there was no loss or damage or the crime was too trivial to report to the police .
gold_template: This survey shows the templateTitle[0] templateTitle[5] gave templateTitle[1] not reporting cases of templateTitle[3] on their premises to the templateXValue[1] in templateTitle[6] and templateTitle[7] in templateTitle[8] . Of templateYLabel[1] , templateYValue[max] percent claimed they did not templateXValue[5] the incident as there was no templateXValue[0] or damage or the crime was too trivial to templateXValue[5] to the templateXValue[1] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] nation in templateTitle[2] templateTitle[3] as of templateTitle[5] . templateYValue[max] percent of templateYLabel[1] stated they purchased goods and at templateXValue[0] templateXValue[0] a templateXValue[0] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Reasons nation in unreported vandalism as of businesses . 43 percent of respondents stated they purchased goods and at Trivial/no_loss a Trivial/no_loss .


Example 566:
data: Year|2018/19|x|line_chart Fatailities|30|y|line_chart Year|2017/18|x|line_chart Fatailities|17|y|line_chart Year|2016/17|x|line_chart Fatailities|28|y|line_chart Year|2015/16|x|line_chart Fatailities|13|y|line_chart Year|2014/15|x|line_chart Fatailities|7|y|line_chart Year|2013/14|x|line_chart Fatailities|10|y|line_chart Year|2012/13|x|line_chart Fatailities|27|y|line_chart Year|2011/12|x|line_chart Fatailities|12|y|line_chart Year|2010/11|x|line_chart Fatailities|13|y|line_chart Year|2009/10|x|line_chart Fatailities|19|y|line_chart Year|2008/09|x|line_chart Fatailities|22|y|line_chart Year|2007/08|x|line_chart Fatailities|17|y|line_chart Year|2006/07|x|line_chart Fatailities|19|y|line_chart Year|2005/06|x|line_chart Fatailities|32|y|line_chart Year|2004/05|x|line_chart Fatailities|23|y|line_chart 
title: Road deaths involving police pursuit in England and Wales from 2005 to 2019

gold: This statistic shows the number of road traffic fatalities related to police pursuits in England and Wales from 2004/05 to 2018/19 . During the period concerned , the number of road traffic fatalities related to police pursuits fluctuated , peaking in 2005/06 at 32 deaths .
gold_template: This statistic shows the number of templateTitle[0] traffic fatalities related to templateTitle[3] pursuits in templateTitle[5] and templateTitle[6] templateTitle[7] templateXValue[last] to templateXValue[0] . During the period concerned , the number of templateTitle[0] traffic fatalities related to templateTitle[3] pursuits fluctuated , peaking in templateXValue[13] at templateYValue[max] templateTitle[1] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in templateTitle[3] from templateXValue[last] to templateXValue[0] . In templateXValue[2] , templateTitle[0] templateTitle[1] in templateTitle[0] was at approximately templateYValue[2] million templateYLabel[2] .
generated: This statistic shows the Road of deaths in police from 2004/05 to 2018/19 . In 2016/17 , Road deaths in Road was at approximately 28 million yLabelErr .


Example 567:
data: Year|2014|x|line_chart EBITDA_margin|16.71|y|line_chart Year|2013|x|line_chart EBITDA_margin|51.3|y|line_chart Year|2012|x|line_chart EBITDA_margin|27|y|line_chart Year|2011|x|line_chart EBITDA_margin|21.3|y|line_chart 
title: Burger King 's EBITDA margin worldwide 2011 - 2014

gold: This statistic shows Burger King 's EBITDA margin worldwide from 2011 to 2014 . Between 2012 and 2013 fast food chain Burger King 's earnings before interest , taxes , depreciation and amortization increased by 51.3 percent .
gold_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] from templateXValue[min] to templateXValue[max] . Between templateXValue[2] and templateXValue[1] fast food chain templateTitle[0] templateTitle[1] templateTitle[2] earnings before interest , taxes , depreciation and amortization increased by templateYValue[max] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] million people died as of templateTitle[3] templateTitle[4] and templateTitle[5] , up from templateYValue[1] million in the previous templateXLabel[0] .
generated: The statistic shows the EBITDA margin of 's EBITDA margin in the United States from 2011 to 2014 . In 2014 , 51.3 million people died as of EBITDA margin and worldwide , up from 51.3 million in the previous Year .


Example 568:
data: Year|18/19|x|line_chart Gate_receipts_in_million_U.S._dollars|178|y|line_chart Year|17/18|x|line_chart Gate_receipts_in_million_U.S._dollars|164|y|line_chart Year|16/17|x|line_chart Gate_receipts_in_million_U.S._dollars|143|y|line_chart Year|15/16|x|line_chart Gate_receipts_in_million_U.S._dollars|134|y|line_chart Year|14/15|x|line_chart Gate_receipts_in_million_U.S._dollars|77|y|line_chart Year|13/14|x|line_chart Gate_receipts_in_million_U.S._dollars|55|y|line_chart Year|12/13|x|line_chart Gate_receipts_in_million_U.S._dollars|50|y|line_chart Year|11/12|x|line_chart Gate_receipts_in_million_U.S._dollars|31|y|line_chart Year|10/11|x|line_chart Gate_receipts_in_million_U.S._dollars|41|y|line_chart 
title: Gate receipts of the Golden State Warriors ( NBA ) 2018/19

gold: The statistic depicts the gate receipts/ticket sales of the Golden State Warriors , franchise of the National Basketball Association , from 2010/11 to 2018/19 . In the 2018/19 season , the gate receipts of the Golden State Warriors were at 178 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] receipts/ticket sales of the templateTitle[2] templateTitle[3] templateTitle[4] , franchise of the National Basketball Association , from 2010/11 to templateTitle[6] . In the templateTitle[6] season , the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] were at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the National Basketball Association from the first season to the 2018/19 season . In 2018/19 , the templateYLabel[0] templateYLabel[1] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Gate receipts of the National Basketball Association from the first season to the 2018/19 season . In 2018/19 , the Gate receipts of the National Basketball Association franchise amounted to 178 million U.S. dollars .


Example 569:
data: Month|Dec_19|x|bar_chart Units_sold|708|y|bar_chart Month|Nov_19|x|bar_chart Units_sold|157|y|bar_chart Month|Oct_19|x|bar_chart Units_sold|88|y|bar_chart Month|Sep_19|x|bar_chart Units_sold|526|y|bar_chart Month|Aug_19|x|bar_chart Units_sold|52|y|bar_chart Month|Jul_19|x|bar_chart Units_sold|103|y|bar_chart Month|Jun_19|x|bar_chart Units_sold|244|y|bar_chart Month|May_19|x|bar_chart Units_sold|138|y|bar_chart Month|Apr_19|x|bar_chart Units_sold|101|y|bar_chart Month|Mar_19|x|bar_chart Units_sold|632|y|bar_chart Month|Feb_19|x|bar_chart Units_sold|74|y|bar_chart Month|Jan_19|x|bar_chart Units_sold|174|y|bar_chart Month|Dec_18|x|bar_chart Units_sold|193|y|bar_chart Month|Nov_18|x|bar_chart Units_sold|145|y|bar_chart Month|Oct_18|x|bar_chart Units_sold|135|y|bar_chart Month|Sep_18|x|bar_chart Units_sold|829|y|bar_chart Month|Aug_18|x|bar_chart Units_sold|100|y|bar_chart Month|Jul_18|x|bar_chart Units_sold|112|y|bar_chart Month|Jun_18|x|bar_chart Units_sold|265|y|bar_chart Month|May_18|x|bar_chart Units_sold|231|y|bar_chart Month|Apr_18|x|bar_chart Units_sold|153|y|bar_chart Month|Mar_18|x|bar_chart Units_sold|761|y|bar_chart Month|Feb_18|x|bar_chart Units_sold|62|y|bar_chart Month|Jan_18|x|bar_chart Units_sold|155|y|bar_chart Month|Dec_17|x|bar_chart Units_sold|246|y|bar_chart Month|Nov_17|x|bar_chart Units_sold|216|y|bar_chart Month|Oct_17|x|bar_chart Units_sold|99|y|bar_chart Month|Sep_17|x|bar_chart Units_sold|510|y|bar_chart Month|Aug_17|x|bar_chart Units_sold|44|y|bar_chart Month|Jul_17|x|bar_chart Units_sold|152|y|bar_chart Month|Jun_17|x|bar_chart Units_sold|202|y|bar_chart Month|May_17|x|bar_chart Units_sold|155|y|bar_chart Month|Apr_17|x|bar_chart Units_sold|123|y|bar_chart Month|Mar_17|x|bar_chart Units_sold|706|y|bar_chart Month|Feb_17|x|bar_chart Units_sold|48|y|bar_chart Month|Jan_17|x|bar_chart Units_sold|178|y|bar_chart Month|Dec_16|x|bar_chart Units_sold|330|y|bar_chart Month|Nov_16|x|bar_chart Units_sold|219|y|bar_chart Month|Oct_16|x|bar_chart Units_sold|256|y|bar_chart Month|Sep_16|x|bar_chart Units_sold|762|y|bar_chart Month|Aug_16|x|bar_chart Units_sold|69|y|bar_chart Month|Jul_16|x|bar_chart Units_sold|148|y|bar_chart 
title: Subaru car sales in the United Kingdom ( UK ) 2016 - 2019

gold: This statistic shows the monthly amount of cars sold by Subaru in the United Kingdom ( UK ) between July 2016 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December 2019 , 708 new Subaru cars had been sold .
gold_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitle[0] in the templateTitle[3] templateTitle[4] ( templateTitle[5] ) between July templateTitle[6] and December templateTitle[7] . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December templateTitle[7] , templateYValue[0] new templateTitle[0] cars had been templateYLabel[1] .

generated_template: In December templateTitle[7] , templateTitle[0] templateYLabel[1] templateYValue[0] vehicles in the templateTitle[3] templateTitle[4] market . templateTitle[2] of the British luxury templateTitle[1] usually peaked in March and September of each year , as those were the months when the Driver & Vehicle Licensing Agency ( DVLA ) issued new registration plates . September 2018 was the notable exception , as there had only been templateYValue[15] templateTitle[2] recorded .
generated: In December 2019 , Subaru sold 708 vehicles in the United Kingdom market . sales of the British luxury car usually peaked in March and September of each year , as those were the months when the Driver & Vehicle Licensing Agency ( DVLA ) issued new registration plates . September 2018 was the notable exception , as there had only been 829 sales recorded .


Example 570:
data: Year|2018|x|line_chart Number_of_enterprises|5690|y|line_chart Year|2017|x|line_chart Number_of_enterprises|5864|y|line_chart Year|2016|x|line_chart Number_of_enterprises|5929|y|line_chart Year|2015|x|line_chart Number_of_enterprises|5874|y|line_chart Year|2014|x|line_chart Number_of_enterprises|5940|y|line_chart Year|2013|x|line_chart Number_of_enterprises|6056|y|line_chart Year|2012|x|line_chart Number_of_enterprises|6134|y|line_chart Year|2011|x|line_chart Number_of_enterprises|6220|y|line_chart Year|2010|x|line_chart Number_of_enterprises|6283|y|line_chart Year|2009|x|line_chart Number_of_enterprises|6399|y|line_chart Year|2008|x|line_chart Number_of_enterprises|6633|y|line_chart 
title: Number of butcher shops and meat retailers in the United Kingdom ( UK ) 2008 - 2018

gold: Between 2008 and 2018 , the number of stores that specialize in the sales of meat has been shrinking In the United Kingdom . During this period , the number of meat specialty stores has seen a decrease of almost 943 . Of these stores , approximately 1.75 thousand generated a turnover between one hundred thousand and 250 thousand British pounds in 2018 .
gold_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] of stores that specialize in the sales of templateTitle[3] has been shrinking In the templateTitle[5] templateTitle[6] . During this period , the templateYLabel[0] of templateTitle[3] specialty stores has seen a decrease of almost 943 . Of these stores , approximately 1.75 thousand generated a turnover between one hundred thousand and 250 thousand British pounds in templateXValue[max] .

generated_template: In templateXValue[max] , the templateYLabel[1] in templateTitle[2] in the United States was at approximately templateYValue[max] percent . After a slight decrease compared to the previous templateXLabel[0] , where the templateYLabel[0] of templateYLabel[1] in templateTitle[3] has increased each templateXLabel[0] . The templateYLabel[1] in recent years , templateTitle[3] only in templateXValue[5] , at approximately templateYValue[6] percent .
generated: In 2018 , the enterprises in shops in the United States was at approximately 6633 percent . After a slight decrease compared to the previous Year , where the Number of enterprises in meat has increased each Year . The enterprises in recent years , meat only in 2013 , at approximately 6134 percent .


Example 571:
data: Year|2018|x|line_chart Average_annual_wages_in_euros|27946|y|line_chart Year|2017|x|line_chart Average_annual_wages_in_euros|28171|y|line_chart Year|2016|x|line_chart Average_annual_wages_in_euros|28738|y|line_chart Year|2015|x|line_chart Average_annual_wages_in_euros|28902|y|line_chart Year|2014|x|line_chart Average_annual_wages_in_euros|28405|y|line_chart Year|2013|x|line_chart Average_annual_wages_in_euros|28400|y|line_chart Year|2012|x|line_chart Average_annual_wages_in_euros|28336|y|line_chart Year|2011|x|line_chart Average_annual_wages_in_euros|29166|y|line_chart Year|2010|x|line_chart Average_annual_wages_in_euros|29585|y|line_chart Year|2009|x|line_chart Average_annual_wages_in_euros|30101|y|line_chart Year|2008|x|line_chart Average_annual_wages_in_euros|28198|y|line_chart Year|2007|x|line_chart Average_annual_wages_in_euros|27101|y|line_chart Year|2006|x|line_chart Average_annual_wages_in_euros|26751|y|line_chart Year|2005|x|line_chart Average_annual_wages_in_euros|26853|y|line_chart Year|2004|x|line_chart Average_annual_wages_in_euros|26697|y|line_chart Year|2003|x|line_chart Average_annual_wages_in_euros|26976|y|line_chart Year|2002|x|line_chart Average_annual_wages_in_euros|27049|y|line_chart Year|2001|x|line_chart Average_annual_wages_in_euros|26851|y|line_chart Year|2000|x|line_chart Average_annual_wages_in_euros|26856|y|line_chart 
title: Average annual wages in Spain 2000 - 2018

gold: This statistic shows the average annual wages in Spain from 2000 to 2018 . Over this 18-year period , annual wages in Spain have fluctuated greatly , peaking at approximately 30 thousand euros in 2009 and decreasing to approximately 28 thousand euros yearly in 2012 . The average annual wage stood at approximately 28 thousand euros in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . Over this 18-year period , templateYLabel[1] templateYLabel[2] in templateTitle[3] have fluctuated greatly , peaking at approximately templateYValue[8] thousand templateYLabel[3] in templateXValue[9] and decreasing to approximately templateYValue[0] thousand templateYLabel[3] yearly in templateXValue[6] . The templateYLabel[0] templateYLabel[1] wage stood at approximately templateYValue[0] thousand templateYLabel[3] in templateXValue[max] .

generated_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] was at templateYValue[max] thousand templateYLabel[3] in templateTitle[3] . From templateXValue[10] to templateXValue[11] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[8] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] rose by templateYValue[0] percent . templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] rose by templateXValue[1] , which was recorded in templateXValue[8] at templateYValue[max] percent .
generated: Between 2000 and 2018 , the Average annual wages of Spain was at 30101 thousand euros in Spain . From 2008 to 2007 , the Average annual wages in 2010 , the Average annual wages rose by 27946 percent . Average annual wages in Spain rose by 2017 , which was recorded in 2010 at 30101 percent .


Example 572:
data: Country|Canada|x|bar_chart Number_of_players|621026|y|bar_chart Country|United_States|x|bar_chart Number_of_players|567908|y|bar_chart Country|Czech_Republic|x|bar_chart Number_of_players|121613|y|bar_chart Country|Russia|x|bar_chart Number_of_players|112236|y|bar_chart Country|Finland|x|bar_chart Number_of_players|64641|y|bar_chart Country|Sweden|x|bar_chart Number_of_players|55431|y|bar_chart Country|Switzerland|x|bar_chart Number_of_players|27867|y|bar_chart Country|France|x|bar_chart Number_of_players|21667|y|bar_chart Country|Germany|x|bar_chart Number_of_players|21340|y|bar_chart Country|Japan|x|bar_chart Number_of_players|18837|y|bar_chart Country|Slovakia|x|bar_chart Number_of_players|11394|y|bar_chart Country|Norway|x|bar_chart Number_of_players|10353|y|bar_chart Country|Great_Britain|x|bar_chart Number_of_players|8162|y|bar_chart Country|Austria|x|bar_chart Number_of_players|7670|y|bar_chart Country|Hungary|x|bar_chart Number_of_players|7106|y|bar_chart Country|Latvia|x|bar_chart Number_of_players|7000|y|bar_chart Country|Kazakhstan|x|bar_chart Number_of_players|6915|y|bar_chart Country|Ukraine|x|bar_chart Number_of_players|5384|y|bar_chart Country|Italy|x|bar_chart Number_of_players|5210|y|bar_chart Country|Belarus|x|bar_chart Number_of_players|4580|y|bar_chart 
title: Countries ranked by number of ice hockey players 2018/19

gold: The statistics ranks countries by the number of registered ice hockey players in 2018/19 . In the 2018/19 season , Canada had the most registered ice hockey players with 621 thousand according to the International Ice Hockey Federation .
gold_template: The statistics ranks templateTitle[0] templateTitle[2] the templateYLabel[0] of registered templateTitle[4] templateTitle[5] templateYLabel[1] in templateTitle[7] . In the templateTitle[7] season , templateXValue[0] had the most registered templateTitle[4] templateTitle[5] templateYLabel[1] with templateYValue[max] thousand according to the International templateTitle[4] templateTitle[5] Federation .

generated_template: The templateXValue[0] templateXValue[0] was the leading templateXLabel[0] with the most templateYLabel[0] of templateYLabel[1] in templateTitle[7] . templateYValue[max] percent of the respondents with the most U.S. templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitle[6] .
generated: The Canada was the leading Country with the most Number of players in 2018/19 . 621026 percent of the respondents with the most U.S. Countries ranked players in players .


Example 573:
data: Year|2019|x|line_chart Unemployment_rate|12.22|y|line_chart Year|2018|x|line_chart Unemployment_rate|12.15|y|line_chart Year|2017|x|line_chart Unemployment_rate|12.12|y|line_chart Year|2016|x|line_chart Unemployment_rate|12.34|y|line_chart Year|2015|x|line_chart Unemployment_rate|12.55|y|line_chart Year|2014|x|line_chart Unemployment_rate|12.37|y|line_chart Year|2013|x|line_chart Unemployment_rate|12.28|y|line_chart Year|2012|x|line_chart Unemployment_rate|11.93|y|line_chart Year|2011|x|line_chart Unemployment_rate|11.74|y|line_chart Year|2010|x|line_chart Unemployment_rate|11.66|y|line_chart Year|2009|x|line_chart Unemployment_rate|11.4|y|line_chart Year|2008|x|line_chart Unemployment_rate|10.47|y|line_chart Year|2007|x|line_chart Unemployment_rate|10.48|y|line_chart Year|2006|x|line_chart Unemployment_rate|10.7|y|line_chart Year|2005|x|line_chart Unemployment_rate|11.09|y|line_chart Year|2004|x|line_chart Unemployment_rate|11.58|y|line_chart Year|2003|x|line_chart Unemployment_rate|11.76|y|line_chart Year|2002|x|line_chart Unemployment_rate|11.81|y|line_chart Year|2001|x|line_chart Unemployment_rate|11.76|y|line_chart Year|2000|x|line_chart Unemployment_rate|11.86|y|line_chart Year|1999|x|line_chart Unemployment_rate|12.06|y|line_chart 
title: Unemployment rate in Guyana 2019

gold: This statistic shows the unemployment rate in Guyana from 1999 to 2019 . In 2019 , the unemployment rate in Guyana was 12.22 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Guyana from 1999 to 2019 . In 2019 , the Unemployment rate in Guyana was at approximately 12.22 percent .


Example 574:
data: Year|2018|x|line_chart Production_volume_in_million_metric_tons|1088.9|y|line_chart Year|2017|x|line_chart Production_volume_in_million_metric_tons|1094.34|y|line_chart Year|2016|x|line_chart Production_volume_in_million_metric_tons|1075.2|y|line_chart Year|2015|x|line_chart Production_volume_in_million_metric_tons|1051.52|y|line_chart Year|2014|x|line_chart Production_volume_in_million_metric_tons|1030.32|y|line_chart Year|2013|x|line_chart Production_volume_in_million_metric_tons|997.84|y|line_chart Year|2012|x|line_chart Production_volume_in_million_metric_tons|978.52|y|line_chart Year|2011|x|line_chart Production_volume_in_million_metric_tons|954.89|y|line_chart Year|2010|x|line_chart Production_volume_in_million_metric_tons|921.52|y|line_chart Year|2009|x|line_chart Production_volume_in_million_metric_tons|900.66|y|line_chart Year|2008|x|line_chart Production_volume_in_million_metric_tons|876.15|y|line_chart Year|2007|x|line_chart Production_volume_in_million_metric_tons|843.23|y|line_chart Year|2006|x|line_chart Production_volume_in_million_metric_tons|809.33|y|line_chart Year|2005|x|line_chart Production_volume_in_million_metric_tons|779.82|y|line_chart Year|2004|x|line_chart Production_volume_in_million_metric_tons|760.29|y|line_chart Year|2003|x|line_chart Production_volume_in_million_metric_tons|750.86|y|line_chart Year|2002|x|line_chart Production_volume_in_million_metric_tons|721.42|y|line_chart Year|2001|x|line_chart Production_volume_in_million_metric_tons|700.09|y|line_chart Year|2000|x|line_chart Production_volume_in_million_metric_tons|682.43|y|line_chart 
title: Vegetables : global production volume 2000 - 2018

gold: This statistic depicts the total production volume of vegetables ( including melons ) worldwide from 1990 to 2018 . In 2014 , some 1169.45 million metric tons of vegetables and melons were produced worldwide .
gold_template: This statistic depicts the total templateYLabel[0] templateYLabel[1] of templateTitle[0] ( including melons ) worldwide from 1990 to templateXValue[max] . In templateXValue[4] , some 1169.45 templateYLabel[2] templateYLabel[3] templateYLabel[4] of templateTitle[0] and melons were produced worldwide .

generated_template: The templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States has increased each templateXLabel[0] since templateXValue[min] , from around templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[2] templateTitle[3] was among the highest level . The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[2] templateTitle[3] was recorded in templateXValue[1] .
generated: The Production volume of volume 2000 2018 in the United States has increased each Year since 2000 , from around 1088.9 volume million metric in 2018 . The Production volume million metric in the production volume was among the highest level . The Production volume million metric in production volume was recorded in 2017 .


Example 575:
data: Year|2018|x|line_chart Number_of_arrivals_in_millions|2.8|y|line_chart Year|2017|x|line_chart Number_of_arrivals_in_millions|2.58|y|line_chart Year|2016|x|line_chart Number_of_arrivals_in_millions|2.3|y|line_chart Year|2015|x|line_chart Number_of_arrivals_in_millions|2.14|y|line_chart Year|2014|x|line_chart Number_of_arrivals_in_millions|2.1|y|line_chart Year|2013|x|line_chart Number_of_arrivals_in_millions|1.84|y|line_chart Year|2012|x|line_chart Number_of_arrivals_in_millions|1.64|y|line_chart Year|2011|x|line_chart Number_of_arrivals_in_millions|1.58|y|line_chart Year|2010|x|line_chart Number_of_arrivals_in_millions|1.31|y|line_chart Year|2009|x|line_chart Number_of_arrivals_in_millions|1.11|y|line_chart Year|2008|x|line_chart Number_of_arrivals_in_millions|1.56|y|line_chart Year|2007|x|line_chart Number_of_arrivals_in_millions|1.49|y|line_chart Year|2006|x|line_chart Number_of_arrivals_in_millions|1.33|y|line_chart 
title: Number of arrivals in tourist accommodation Latvia 2006 - 2018

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Latvia from 2006 to 2018 . Since 2009 there has been an increasing trend in arrivals . In 2018 , the number of arrivals ( including both foreign and domestic ) at accommodation in Latvia amounted to approximately 2.8 million .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitle[4] from templateXValue[min] to templateXValue[max] . Since templateXValue[9] there has been an increasing trend in templateYLabel[1] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( including both foreign and domestic ) at templateTitle[3] in templateTitle[4] amounted to approximately templateYValue[max] million .

generated_template: This statistic shows templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] of templateTitle[0] tourists in templateTitle[2] was templateYValue[min] million . This figure was forecasted to increase to templateYValue[max] million by templateXValue[max] .
generated: This statistic shows Number arrivals in tourist accommodation 2006 to 2018 . In 2006 , the Number of Number tourists in tourist was 1.11 million . This figure was forecasted to increase to 2.8 million by 2018 .


Example 576:
data: Year|18-24|x|line_chart Percentage_of_respondents|7|y|line_chart Year|25-34|x|line_chart Percentage_of_respondents|17|y|line_chart Year|35-44|x|line_chart Percentage_of_respondents|19|y|line_chart Year|45-54|x|line_chart Percentage_of_respondents|22|y|line_chart Year|55-64|x|line_chart Percentage_of_respondents|24|y|line_chart Year|65+|x|line_chart Percentage_of_respondents|11|y|line_chart 
title: Age distribution of mobile gamers in the U.S. 2013

gold: This statistic gives information on the age distribution of mobile gamers in the United States as of May 2013 . During the survey period , it was found that 17 percent of mobile games were 25 to 34 years old . The average age of a mobile gamer was 46.5 years .
gold_template: This statistic gives information on the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] as of May templateTitle[5] . During the survey period , it was found that templateYValue[1] percent of templateTitle[2] games were 25 to 34 years old . The average templateTitle[0] of a templateTitle[2] gamer was 46.5 years .

generated_template: The statistic shows the percent of templateTitle[1] in the United States who were using templateTitle[0] as of January templateTitle[4] , sorted templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] percent of templateTitle[1] 's templateYLabel[1] aged between 18 and 34 years or older .
generated: The statistic shows the percent of distribution in the United States who were using Age as of January U.S. , sorted 2013 titleErr titleErr titleErr . During that period of time , 24 percent of distribution 's respondents aged between 18 and 34 years or older .


Example 577:
data: Year|2024|x|line_chart Budget_in_billion_U.S._dollars|21.87|y|line_chart Year|2023|x|line_chart Budget_in_billion_U.S._dollars|21.66|y|line_chart Year|2022|x|line_chart Budget_in_billion_U.S._dollars|21.44|y|line_chart Year|2021|x|line_chart Budget_in_billion_U.S._dollars|21.23|y|line_chart Year|2020|x|line_chart Budget_in_billion_U.S._dollars|21.02|y|line_chart Year|2019|x|line_chart Budget_in_billion_U.S._dollars|21.5|y|line_chart Year|2018|x|line_chart Budget_in_billion_U.S._dollars|20.74|y|line_chart Year|2017|x|line_chart Budget_in_billion_U.S._dollars|19.65|y|line_chart Year|2016|x|line_chart Budget_in_billion_U.S._dollars|19.29|y|line_chart Year|2015|x|line_chart Budget_in_billion_U.S._dollars|18.01|y|line_chart Year|2014|x|line_chart Budget_in_billion_U.S._dollars|17.65|y|line_chart 
title: NASA - budget 2014 - 2024

gold: This graph show NASA 's projected budget from 2014 to 2024 . NASA 's budget is projected to be at around 21 billion U.S. dollars in 2020 . The National Aeronautics and Space Administration ( NASA ) is the U.S. agency responsible for aeronautics and aerospace research .
gold_template: This graph show templateTitle[0] 's projected templateYLabel[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] 's templateYLabel[0] is projected to be at around templateYValue[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[4] . The National Aeronautics and Space Administration ( templateTitle[0] ) is the templateYLabel[2] agency responsible for aeronautics and aerospace research .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[6] , about templateYValue[6] percent of the templateYLabel[1] in templateTitle[3] templateTitle[4] were expected to increase to templateYValue[0] percent in templateXValue[max] .
generated: This statistic shows the Budget billion of 2014 2024 titleErr titleErr in the United States from 2014 to 2024 . In 2018 , about 20.74 percent of the billion in 2024 titleErr were expected to increase to 21.87 percent in 2024 .


Example 578:
data: Response|Yes_many_times|x|bar_chart Share_of_respondents|9.6|y|bar_chart Response|Yes_once_or_twice|x|bar_chart Share_of_respondents|37.6|y|bar_chart Response|Never|x|bar_chart Share_of_respondents|52.8|y|bar_chart 
title: Frequency of making online restaurant reservations in the U.S. as of June 2014

gold: This statistic shows the frequency with which consumers made online reservations when dining out in restaurants in the United States as of June 2014 . During the survey , 37.6 percent of respondents said they had made online reservations once or twice .
gold_template: This statistic shows the templateTitle[0] with which consumers made templateTitle[2] templateTitle[4] when dining out in restaurants in the templateTitle[5] as of templateTitle[6] templateTitle[7] . During the survey , templateYValue[1] percent of templateYLabel[1] said they had made templateTitle[2] templateTitle[4] templateXValue[1] or templateXValue[1] .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[4] , sorted templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated that they had a templateXValue[0] templateXValue[0] a templateXValue[0] .
generated: This statistic shows the results of a survey conducted in the United States in reservations , sorted U.S. June 2014 titleErr . During the survey period , 52.8 percent of respondents stated that they had a Yes_many_times a Yes_many_times .


Example 579:
data: Month|May_2018|x|bar_chart Number_of_players_in_millions|40|y|bar_chart Month|October_2017|x|bar_chart Number_of_players_in_millions|35|y|bar_chart Month|April_2017|x|bar_chart Number_of_players_in_millions|30|y|bar_chart Month|January_2017|x|bar_chart Number_of_players_in_millions|25|y|bar_chart Month|October_2016|x|bar_chart Number_of_players_in_millions|20|y|bar_chart Month|August_2016|x|bar_chart Number_of_players_in_millions|15|y|bar_chart Month|May_2016|x|bar_chart Number_of_players_in_millions|7|y|bar_chart 
title: Number of Overwatch players worldwide 2018

gold: How many people play Overwatch ? Overwatch , a team-based first-person shooter video game , launched in May 2016 and already a week later it was reported to have had seven million players . As of May 2018 , Overwatch had 40 million players worldwide . Overwatch 's eSports success While the number of gamers playing Overwatch has increased dramatically , so has the appeal of the game as an eSport .
gold_template: How many people play templateTitle[1] ? templateTitle[1] , a team-based first-person shooter video game , launched in templateXValue[0] templateXValue[4] and already a week later it was reported to have had templateYValue[min] million templateYLabel[1] . As of templateXValue[0] templateXValue[0] , templateTitle[1] had templateYValue[max] million templateYLabel[1] templateTitle[3] . templateTitle[1] 's eSports success While the templateYLabel[0] of gamers playing templateTitle[1] has increased dramatically , so has the appeal of the game as an eSport .

generated_template: The statistic presents the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] as of March templateTitle[6] , templateTitle[7] templateXLabel[1] . In June 2018 , templateTitle[0] had a templateTitle[1] of over templateYValue[max] million templateYLabel[1] from which came from templateYValue[5] percent in templateXValue[5] . templateYLabel[0] – additional information One of the templateYLabel[0] of templateTitle[0] 's extremely popular templateTitle[3] templateTitle[4] is one of the biggest games and 36 36 percent of the biggest games on Steam in the crown of Blizzard Entertainment .
generated: The statistic presents the Number of players players worldwide 2018 titleErr as of March titleErr , titleErr xLabelErr . In June 2018 , Number had a Overwatch of over 40 million players from which came from 15 percent in August_2016 . Number – additional information One of the Number of Number 's extremely popular worldwide 2018 is one of the biggest games and 36 percent of the biggest games on Steam in the crown of Blizzard Entertainment .


Example 580:
data: Year|2018/19|x|line_chart Number_of_students|369548|y|line_chart Year|2017/18|x|line_chart Number_of_students|363341|y|line_chart Year|2016/17|x|line_chart Number_of_students|350755|y|line_chart Year|2015/16|x|line_chart Number_of_students|328547|y|line_chart Year|2014/15|x|line_chart Number_of_students|304040|y|line_chart Year|2013/14|x|line_chart Number_of_students|274439|y|line_chart Year|2012/13|x|line_chart Number_of_students|235597|y|line_chart Year|2011/12|x|line_chart Number_of_students|194029|y|line_chart Year|2010/11|x|line_chart Number_of_students|157558|y|line_chart Year|2009/10|x|line_chart Number_of_students|127628|y|line_chart Year|2008/09|x|line_chart Number_of_students|98235|y|line_chart 
title: Number of Chinese students in the U.S. 2008/09 - 2018/19

gold: Colleges and universities in the United States are becoming an increasingly popular study destination for Chinese students , with over 369 thousand choosing to take courses there in the 2018/19 academic year . This made China the leading source of international students in the U.S. education market . The education exodus Business and management courses led the field in terms of what Chinese students were studying in the United States , followed closely by engineering , mathematics and computer science programs .
gold_template: Colleges and universities in the templateTitle[3] are becoming an increasingly popular study destination for templateTitle[1] templateYLabel[1] , with over 369 thousand choosing to take courses there in the templateXValue[0] academic templateXLabel[0] . This made China the leading source of international templateYLabel[1] in the templateTitle[3] education market . The education exodus Business and management courses led the field in terms of what templateTitle[1] templateYLabel[1] were studying in the templateTitle[3] , followed closely by engineering , mathematics and computer science programs .

generated_template: In the academic templateXLabel[0] templateXValue[0] , templateYValue[0] templateYLabel[1] were templateTitle[1] worldwide in the templateTitle[3] templateTitle[4] . This was a slight decrease from the academic templateXLabel[0] with the highest in the templateYLabel[0] of templateYLabel[1] enrolled in the academic templateXLabel[0] . The templateYLabel[0] of templateYLabel[1] enrolled at over time period under its templateTitle[5] academic templateXLabel[0] at almost templateYValue[max] templateYLabel[1] .
generated: In the academic Year 2018/19 , 369548 students were Chinese worldwide in the U.S. 2008/09 . This was a slight decrease from the academic Year with the highest in the Number of students enrolled in the academic Year . The Number of students enrolled at over time period under its 2018/19 academic Year at almost 369548 students .


Example 581:
data: Year|2018|x|line_chart Number_of_employees|70270|y|line_chart Year|2017|x|line_chart Number_of_employees|73596|y|line_chart Year|2016|x|line_chart Number_of_employees|73062|y|line_chart Year|2015|x|line_chart Number_of_employees|74098|y|line_chart Year|2014|x|line_chart Number_of_employees|76531|y|line_chart Year|2013|x|line_chart Number_of_employees|83286|y|line_chart Year|2012|x|line_chart Number_of_employees|85305|y|line_chart Year|2011|x|line_chart Number_of_employees|79646|y|line_chart Year|2010|x|line_chart Number_of_employees|70785|y|line_chart Year|2009|x|line_chart Number_of_employees|60036|y|line_chart 
title: Vale 's employee number 2009 - 2018

gold: This statistic shows mining company Vale 's number of employees worldwide from 2009 to 2018 . In 2018 , the company employed some 70,300 people . Vale S.A. , formerly called by the full name Companhia Vale do Rio Doce , is a Brazilian multinational metals and mining company , also specializing in logistic operations .
gold_template: This statistic shows mining company templateTitle[0] templateTitle[1] templateYLabel[0] of templateYLabel[1] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company employed some 70,300 people . templateTitle[0] S.A. , formerly called by the full name Companhia templateTitle[0] do Rio Doce , is a Brazilian multinational metals and mining company , also specializing in logistic operations .

generated_template: templateTitle[2] templateYLabel[1] templateYValue[0] people in the United States in templateXValue[max] , the highest number of templateYLabel[1] between templateXValue[min] and templateXValue[max] fiscal templateXLabel[0] . The company operates globally . Between templateXValue[min] and templateXValue[1] , the highest templateYLabel[0] of templateYLabel[1] . templateTitle[2] of templateYLabel[1] at its value of templateYLabel[1] .
generated: employee employees 70270 people in the United States in 2018 , the highest number of employees between 2009 and 2018 fiscal Year . The company operates globally . Between 2009 and 2017 , the highest Number of employees . employee of employees at its value of employees .


Example 582:
data: Month|Newfoundland_and_Labrador|x|bar_chart Production_in_metric_tons|27456|y|bar_chart Month|Prince_Edward_Island|x|bar_chart Production_in_metric_tons|0|y|bar_chart Month|Nova_Scotia|x|bar_chart Production_in_metric_tons|0|y|bar_chart Month|New_Brunswick|x|bar_chart Production_in_metric_tons|487|y|bar_chart Month|Quebec|x|bar_chart Production_in_metric_tons|35912|y|bar_chart Month|Ontario|x|bar_chart Production_in_metric_tons|135297|y|bar_chart Month|Manitoba|x|bar_chart Production_in_metric_tons|33608|y|bar_chart Month|Saskatchewan|x|bar_chart Production_in_metric_tons|0|y|bar_chart Month|Alberta|x|bar_chart Production_in_metric_tons|0|y|bar_chart Month|British_Columbia|x|bar_chart Production_in_metric_tons|293468|y|bar_chart Month|Yukon|x|bar_chart Production_in_metric_tons|9282|y|bar_chart Month|Northwest_Territories|x|bar_chart Production_in_metric_tons|0|y|bar_chart Month|Nunavut|x|bar_chart Production_in_metric_tons|0|y|bar_chart 
title: Production of copper in Canada by province 2018

gold: This statistic displays preliminary estimates of the copper production in Canada , distributed by province , in 2018 . During that year , Quebec produced some 35,912 metric tons of this mineral . Copper is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .
gold_template: This statistic displays preliminary estimates of the templateTitle[1] templateYLabel[0] in templateTitle[2] , distributed templateTitle[3] templateTitle[4] , in templateTitle[5] . During that year , templateXValue[4] produced some templateYValue[4] templateYLabel[1] templateYLabel[2] of this mineral . templateTitle[1] is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . In that year , templateXValue[0] templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] of templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Production copper of Canada by province 2018 titleErr in titleErr . In that year , Newfoundland_and_Labrador had the highest Production of metric tons of 293468 metric tons .


Example 583:
data: Country|Europe|x|bar_chart Number_of_CFPs|600|y|bar_chart Country|North_America|x|bar_chart Number_of_CFPs|375|y|bar_chart Country|Asia|x|bar_chart Number_of_CFPs|169|y|bar_chart Country|South_America|x|bar_chart Number_of_CFPs|50|y|bar_chart Country|Oceania|x|bar_chart Number_of_CFPs|37|y|bar_chart Country|Africa|x|bar_chart Number_of_CFPs|19|y|bar_chart 
title: Number of crowdfunding platforms worldwide 2014 , by region

gold: The statistic shows the number of crowdfunding platforms worldwide in 2014 , by region . In that year , there were 375 crowdfunding platforms in North America . Crowdfunding is a way of collecting money from various individuals interested in a given project .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[4] , templateTitle[5] templateTitle[6] . In that year , there were templateYValue[1] templateTitle[1] templateTitle[2] in templateXValue[1] templateXValue[1] . templateTitle[1] is a way of collecting money from various individuals interested in a given project .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[3] templateTitle[4] templateTitle[5] as of templateTitle[6] . In templateXValue[2] , there were some templateYValue[max] million templateYLabel[1] living in templateXValue[0] .
generated: The statistic shows the Number of CFPs in worldwide 2014 by as of region . In Asia , there were some 600 million CFPs living in Europe .


Example 584:
data: Year|2018|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|62862.5|y|line_chart Year|2017|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|61932.6|y|line_chart Year|2016|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|59966.8|y|line_chart Year|2015|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|59082.5|y|line_chart Year|2014|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|58276.3|y|line_chart Year|2013|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|57169.9|y|line_chart Year|2012|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|55676.4|y|line_chart Year|2011|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|54057.9|y|line_chart Year|2010|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|52841.8|y|line_chart Year|2009|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|51522.1|y|line_chart Year|2008|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|51668.8|y|line_chart Year|2007|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|50017.3|y|line_chart Year|2006|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|48918.3|y|line_chart Year|2005|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|47127.8|y|line_chart Year|2004|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|45727.8|y|line_chart Year|2003|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|44494.6|y|line_chart Year|2002|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|44031.4|y|line_chart Year|2001|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|43301.5|y|line_chart Year|2000|x|line_chart GDP_in_million_chained_2012_Canadian_dollars|42734.1|y|line_chart 
title: GDP of Manitoba , Canada 2000 - 2018

gold: This statistic shows the gross domestic product ( GDP ) of Manitoba from 2000 to 2018 . In 2018 , Manitoba 's GDP was 62.86 billion chained 2012 Canadian dollars .
gold_template: This statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[1] 's templateYLabel[0] was templateYValue[max] billion templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] 's templateYLabel[0] was templateYValue[0] templateYLabel[1] templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the GDP Manitoba Canada ( GDP ) million United States from 2000 to 2018 . In 2018 , 2000 's GDP was 62862.5 million chained 2012 Canadian dollars .


Example 585:
data: Response|0$_no_expenses|x|bar_chart Share_of_respondents|1|y|bar_chart Response|Up_to_25$|x|bar_chart Share_of_respondents|26|y|bar_chart Response|Up_to_50$|x|bar_chart Share_of_respondents|34|y|bar_chart Response|Up_to_75$|x|bar_chart Share_of_respondents|12|y|bar_chart Response|Up_to_100$|x|bar_chart Share_of_respondents|14|y|bar_chart Response|Up_to_150$|x|bar_chart Share_of_respondents|6|y|bar_chart Response|Up_to_300$|x|bar_chart Share_of_respondents|6|y|bar_chart Response|More_than_300$|x|bar_chart Share_of_respondents|0|y|bar_chart 
title: Average order value of online food orders in the U.S. 2017

gold: This statistic displays the average order value of online food orders in the United States as of April 2017 . During the survey period , 26 percent of responding online food shoppers stated that their usual online food order amounted to up to 25 U.S. dollars .
gold_template: This statistic displays the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of April templateTitle[7] . During the survey period , templateYValue[1] percent of responding templateTitle[3] templateTitle[4] shoppers stated that their usual templateTitle[3] templateTitle[4] templateTitle[1] amounted to templateXValue[1] to 25 templateTitle[6] dollars .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[5] according to a survey conducted in templateTitle[7] templateTitle[8] . During the survey , templateYValue[max] percent of templateYLabel[1] stated they could be both templateXValue[1] and templateYValue[1] percent of templateYLabel[1] stated they did so .
generated: This statistic shows the results of a survey conducted in the United States in orders according to a survey conducted in 2017 titleErr . During the survey , 34 percent of respondents stated they could be both Up_to_25$ and 26 percent of respondents stated they did so .


Example 586:
data: Month|Rob_Gronkowski|x|bar_chart Touchdowns_scored|80|y|bar_chart Month|Stanley_Morgan|x|bar_chart Touchdowns_scored|68|y|bar_chart Month|Ben_Coates|x|bar_chart Touchdowns_scored|50|y|bar_chart Month|Randy_Moss|x|bar_chart Touchdowns_scored|50|y|bar_chart Month|Sam_Cunningham|x|bar_chart Touchdowns_scored|49|y|bar_chart Month|Jim_Nance|x|bar_chart Touchdowns_scored|46|y|bar_chart Month|Tony_Collins|x|bar_chart Touchdowns_scored|44|y|bar_chart Month|Gino_Cappelletti|x|bar_chart Touchdowns_scored|42|y|bar_chart Month|Irving_Fryar|x|bar_chart Touchdowns_scored|42|y|bar_chart Month|Larry_Garron|x|bar_chart Touchdowns_scored|42|y|bar_chart Month|Julian_Edelman|x|bar_chart Touchdowns_scored|41|y|bar_chart Month|Jim_Colclough|x|bar_chart Touchdowns_scored|39|y|bar_chart Month|Corey_Dillon|x|bar_chart Touchdowns_scored|39|y|bar_chart Month|Curtis_Martin|x|bar_chart Touchdowns_scored|37|y|bar_chart Month|Wes_Welker|x|bar_chart Touchdowns_scored|37|y|bar_chart Month|Steve_Grogan|x|bar_chart Touchdowns_scored|36|y|bar_chart Month|Troy_Brown|x|bar_chart Touchdowns_scored|35|y|bar_chart Month|LeGarrette_Blount|x|bar_chart Touchdowns_scored|35|y|bar_chart Month|Kevin_Faulk|x|bar_chart Touchdowns_scored|33|y|bar_chart Month|James_White|x|bar_chart Touchdowns_scored|32|y|bar_chart 
title: Career touchdown leaders - New England Patriots 1960 - 2020

gold: The statistic shows New England Patriots players with the most touchdowns scored in franchise history . Rob Gronkowski is the career touchdown leader of the New England Patriots with 80 touchdowns .
gold_template: The statistic shows templateTitle[3] templateTitle[4] templateTitle[5] players with the most templateYLabel[0] templateYLabel[1] in franchise history . templateXValue[0] templateXValue[0] is the templateTitle[0] templateTitle[1] leader of the templateTitle[3] templateTitle[4] templateTitle[5] with templateYValue[max] templateYLabel[0] .

generated_template: The statistic shows templateTitle[3] templateTitle[4] players with the most templateYLabel[0] templateYLabel[1] in franchise history . templateXValue[0] templateXValue[0] is the templateTitle[0] templateTitle[1] leader of the templateTitle[3] templateTitle[4] with templateYValue[max] templateYLabel[0] .
generated: The statistic shows New England players with the most Touchdowns scored in franchise history . Rob_Gronkowski is the Career touchdown leader of the New England with 80 Touchdowns .


Example 587:
data: Year|2018/2019|x|line_chart Domestic_consumption_in_metric_tons|205564|y|line_chart Year|2017/2018|x|line_chart Domestic_consumption_in_metric_tons|205000|y|line_chart Year|2016/2017|x|line_chart Domestic_consumption_in_metric_tons|220909|y|line_chart Year|2015/2016|x|line_chart Domestic_consumption_in_metric_tons|235136|y|line_chart Year|2014/2015|x|line_chart Domestic_consumption_in_metric_tons|238039|y|line_chart Year|2013/2014|x|line_chart Domestic_consumption_in_metric_tons|215636|y|line_chart Year|2012/2013|x|line_chart Domestic_consumption_in_metric_tons|205122|y|line_chart Year|2011/2012|x|line_chart Domestic_consumption_in_metric_tons|215579|y|line_chart Year|2010/2011|x|line_chart Domestic_consumption_in_metric_tons|208646|y|line_chart 
title: Total U.S. domestic raisin consumption 2010/11 - 2018/19

gold: This statistic shows the total United States domestic raisin consumption from 2010/2011 to 2017/2018 , and provides a projection for 2018/2019 . In crop year 2015/2016 , the domestic raisin consumption in the United States amounted to 235,136 metric tons .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateYLabel[1] from templateXValue[last] to templateXValue[1] , and provides a projection for templateXValue[0] . In crop templateXLabel[0] templateXValue[3] , the templateYLabel[0] templateTitle[3] templateYLabel[1] in the templateTitle[1] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[7] United States from templateXValue[last] to templateXValue[0] . In templateXValue[0] , about templateYValue[0] percent of templateYLabel[1] throughout the United States were reported in templateTitle[3] templateTitle[4] .
generated: This statistic shows the Domestic consumption metric of raisin consumption 2010/11 in titleErr United States from 2010/2011 to 2018/2019 . In 2018/2019 , about 205564 percent of consumption throughout the United States were reported in raisin consumption .


Example 588:
data: Year|2018|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|59674|y|line_chart Year|2017|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|58125|y|line_chart Year|2016|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|57280|y|line_chart Year|2015|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|58077|y|line_chart Year|2014|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|56457|y|line_chart Year|2013|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|55573|y|line_chart Year|2012|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|54097|y|line_chart Year|2011|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|52397|y|line_chart Year|2010|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|51568|y|line_chart Year|2009|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|51264|y|line_chart Year|2008|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|52481|y|line_chart Year|2007|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|53470|y|line_chart Year|2006|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|51811|y|line_chart Year|2005|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|49732|y|line_chart Year|2004|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|49241|y|line_chart Year|2003|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|47583|y|line_chart Year|2002|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|48031|y|line_chart Year|2001|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|47932|y|line_chart Year|2000|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|47664|y|line_chart 
title: Per capita real GDP of Texas 2000 - 2018

gold: This statistic shows the per capita real Gross Domestic Product of Texas from 2000 to 2018 . In 2018 , the per capita real GDP of Texas stood at 59,674 chained 2012 U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[4] stood at templateYValue[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[4] templateTitle[5] stood at templateYValue[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Per capita real Gross Domestic Product of Texas 2000 from 2000 to 2018 . In 2018 , the Per capita real GDP of Texas 2000 stood at 59674 chained 2012 U.S. dollars .


Example 589:
data: Year|2016|x|line_chart Average_spend_in_euros_(excluding_VAT)|54.0|y|line_chart Year|2015|x|line_chart Average_spend_in_euros_(excluding_VAT)|53.7|y|line_chart Year|2014|x|line_chart Average_spend_in_euros_(excluding_VAT)|50.7|y|line_chart Year|2013|x|line_chart Average_spend_in_euros_(excluding_VAT)|48.1|y|line_chart Year|2012|x|line_chart Average_spend_in_euros_(excluding_VAT)|46.4|y|line_chart Year|2011|x|line_chart Average_spend_in_euros_(excluding_VAT)|46.2|y|line_chart Year|2010|x|line_chart Average_spend_in_euros_(excluding_VAT)|45.3|y|line_chart 
title: Disneyland Paris visitors spending per day 2006 - 2016

gold: This statistic displays daily expenditure per person at Disneyland Paris theme parks in France between 2006 and 2016 . Visitors spending includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal year 2016 , the average spending dipped to 54 euros ( before VAT ) .
gold_template: This statistic displays daily expenditure templateTitle[4] person at templateTitle[0] templateTitle[1] theme parks in France between templateTitle[6] and templateXValue[max] . templateTitle[2] templateTitle[3] includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal templateXLabel[0] templateXValue[max] , the templateYLabel[0] templateTitle[3] dipped to templateYValue[max] templateYLabel[2] ( before VAT ) .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States amounted to templateYValue[0] million . The templateYLabel[0] templateYLabel[1] generally increased from templateYValue[min] million in templateXValue[min] to templateYValue[max] million in templateXValue[max] .
generated: In 2016 , the Average spend of visitors spending per day in the United States amounted to 54.0 million . The Average spend generally increased from 45.3 million in 2010 to 54.0 million in 2016 .


Example 590:
data: Winter_of|2019/20|x|bar_chart Price_in_U.S._dollars_per_gallon|3.02|y|bar_chart Winter_of|2018/19|x|bar_chart Price_in_U.S._dollars_per_gallon|3.07|y|bar_chart Winter_of|2017/18|x|bar_chart Price_in_U.S._dollars_per_gallon|2.78|y|bar_chart Winter_of|2016/17|x|bar_chart Price_in_U.S._dollars_per_gallon|2.41|y|bar_chart Winter_of|2015/16|x|bar_chart Price_in_U.S._dollars_per_gallon|2.06|y|bar_chart Winter_of|2014/15|x|bar_chart Price_in_U.S._dollars_per_gallon|3.04|y|bar_chart Winter_of|2013/14|x|bar_chart Price_in_U.S._dollars_per_gallon|3.88|y|bar_chart Winter_of|2012/13|x|bar_chart Price_in_U.S._dollars_per_gallon|3.87|y|bar_chart Winter_of|2011/12|x|bar_chart Price_in_U.S._dollars_per_gallon|3.73|y|bar_chart Winter_of|2010/11|x|bar_chart Price_in_U.S._dollars_per_gallon|3.38|y|bar_chart Winter_of|2009/10|x|bar_chart Price_in_U.S._dollars_per_gallon|2.85|y|bar_chart Winter_of|2008/09|x|bar_chart Price_in_U.S._dollars_per_gallon|2.65|y|bar_chart Winter_of|2007/08|x|bar_chart Price_in_U.S._dollars_per_gallon|3.33|y|bar_chart Winter_of|2006/07|x|bar_chart Price_in_U.S._dollars_per_gallon|2.42|y|bar_chart Winter_of|2005/06|x|bar_chart Price_in_U.S._dollars_per_gallon|2.44|y|bar_chart 
title: U.S. winter heating oil prices 2005/06 - 2019/20

gold: The average price of heating oil in the United States in the winter between 2019 and 2020 is expected to reach 3.02 U.S. dollars per gallon . The number of heating degree days , days the average temperature is below 18° Celsius ( 65° Fahrenheit ) , helps quantify the energy demand required to heat a building . Heating oil basics Heating oil is a liquid petroleum product that is , among other things , used to heat residential buildings as a fuel oil in furnaces or boilers .
gold_template: The average templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitle[0] in the templateXLabel[0] between 2019 and 2020 is expected to reach templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The number of templateTitle[2] degree days , days the average temperature is below 18° Celsius ( 65° Fahrenheit ) , helps quantify the energy demand required to heat a building . templateTitle[2] templateTitle[3] basics templateTitle[2] templateTitle[3] is a liquid petroleum product that is , among other things , used to heat residential buildings as a fuel templateTitle[3] in furnaces or boilers .

generated_template: This statistic shows the number of templateYLabel[1] templateYLabel[2] in the United States in templateTitle[6] , templateTitle[3] templateXLabel[0] . In templateTitle[4] , there were an estimated templateYValue[0] thousand templateYLabel[1] templateYLabel[2] throughout the templateXValue[0] templateXValue[0] .
generated: This statistic shows the number of U.S. dollars in the United States in 2019/20 , oil Winter . In prices , there were an estimated 3.02 thousand U.S. dollars throughout the 2019/20 .


Example 591:
data: Country_of_origin|Other|x|bar_chart Share_of_nickel_imports|32|y|bar_chart Country_of_origin|Finland|x|bar_chart Share_of_nickel_imports|8|y|bar_chart Country_of_origin|Australia|x|bar_chart Share_of_nickel_imports|8|y|bar_chart Country_of_origin|Norway|x|bar_chart Share_of_nickel_imports|11|y|bar_chart Country_of_origin|Canada|x|bar_chart Share_of_nickel_imports|41|y|bar_chart 
title: Share of U.S. nickel imports by country 2015 - 2018

gold: This statistic shows the percentage of nickel imports to the United States over the period between 2014 and 2018 , by country of origin . In that period , some 41 percent of all nickel imports into the United States came from Canada .
gold_template: This statistic shows the percentage of templateYLabel[1] templateYLabel[2] to the templateTitle[1] over the period between 2014 and templateTitle[7] , templateTitle[4] templateXLabel[0] of templateXLabel[1] . In that period , some templateYValue[max] percent of all templateYLabel[1] templateYLabel[2] into the templateTitle[1] came from templateXValue[last] .

generated_template: In templateTitle[4] , templateXValue[0] was the largest templateTitle[1] templateXLabel[0] in the United States , templateTitle[4] templateXLabel[0] . In that year , it was found that templateYValue[max] percent of the country 's total people lived in templateXValue[0] , up from templateYValue[1] percent in the previous year . The United States can be accessed here .
generated: In by , Other was the largest U.S. Country in the United States , by Country . In that year , it was found that 41 percent of the country 's total people lived in Other , up from 8 percent in the previous year . The United States can be accessed here .


Example 592:
data: Year|2019|x|line_chart Revenue_growth|1|y|line_chart Year|2018|x|line_chart Revenue_growth|4|y|line_chart Year|2017|x|line_chart Revenue_growth|3|y|line_chart Year|2016|x|line_chart Revenue_growth|22|y|line_chart Year|2015|x|line_chart Revenue_growth|28|y|line_chart Year|2014|x|line_chart Revenue_growth|32|y|line_chart Year|2013|x|line_chart Revenue_growth|27|y|line_chart Year|2012|x|line_chart Revenue_growth|25|y|line_chart Year|2011|x|line_chart Revenue_growth|38|y|line_chart Year|2010|x|line_chart Revenue_growth|24|y|line_chart Year|2009|x|line_chart Revenue_growth|18|y|line_chart 
title: Global revenue growth of Under Armour 2009 - 2019

gold: This statistic depicts the growth of Under Armour 's revenue worldwide from 2009 to 2019 . In 2019 , Under Armour 's net revenue increased by one percent . Under Armour is an American sporting goods manufacturer , based in Baltimore , Maryland .
gold_template: This statistic depicts the templateYLabel[1] of templateTitle[3] templateTitle[4] 's templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] templateTitle[4] 's net templateYLabel[0] increased by templateYValue[min] percent . templateTitle[3] templateTitle[4] is an American sporting goods manufacturer , based in Baltimore , Maryland .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the templateYLabel[1] were committed templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Revenue of growth of Under Armour 2009 in the United States from 2009 to 2019 . In 2019 , 38 percent of the growth were committed growth Under Armour 2009 in the United States .


Example 593:
data: Year|2018|x|line_chart Number_of_reported_cases|282061|y|line_chart Year|2017|x|line_chart Number_of_reported_cases|320596|y|line_chart Year|2016|x|line_chart Number_of_reported_cases|332797|y|line_chart Year|2015|x|line_chart Number_of_reported_cases|328109|y|line_chart Year|2014|x|line_chart Number_of_reported_cases|322905|y|line_chart Year|2013|x|line_chart Number_of_reported_cases|345093|y|line_chart Year|2012|x|line_chart Number_of_reported_cases|355051|y|line_chart Year|2011|x|line_chart Number_of_reported_cases|354746|y|line_chart Year|2010|x|line_chart Number_of_reported_cases|369089|y|line_chart Year|2009|x|line_chart Number_of_reported_cases|408742|y|line_chart Year|2008|x|line_chart Number_of_reported_cases|443563|y|line_chart Year|2007|x|line_chart Number_of_reported_cases|447324|y|line_chart Year|2006|x|line_chart Number_of_reported_cases|449246|y|line_chart Year|2005|x|line_chart Number_of_reported_cases|417438|y|line_chart Year|2004|x|line_chart Number_of_reported_cases|401470|y|line_chart Year|2003|x|line_chart Number_of_reported_cases|414235|y|line_chart Year|2002|x|line_chart Number_of_reported_cases|420806|y|line_chart Year|2001|x|line_chart Number_of_reported_cases|422921|y|line_chart Year|2000|x|line_chart Number_of_reported_cases|408016|y|line_chart Year|1999|x|line_chart Number_of_reported_cases|409371|y|line_chart Year|1998|x|line_chart Number_of_reported_cases|447186|y|line_chart Year|1997|x|line_chart Number_of_reported_cases|497950|y|line_chart Year|1996|x|line_chart Number_of_reported_cases|535590|y|line_chart Year|1995|x|line_chart Number_of_reported_cases|580510|y|line_chart Year|1994|x|line_chart Number_of_reported_cases|618950|y|line_chart Year|1993|x|line_chart Number_of_reported_cases|659870|y|line_chart Year|1992|x|line_chart Number_of_reported_cases|672480|y|line_chart Year|1991|x|line_chart Number_of_reported_cases|687730|y|line_chart Year|1990|x|line_chart Number_of_reported_cases|639270|y|line_chart 
title: U.S. : reported robbery cases 1990 - 2018

gold: This graph shows the reported number of robbery cases in the United States from 1990 to 2018 . In 2018 an estimated 282,061 cases occurred nationwide .
gold_template: This graph shows the templateYLabel[1] templateYLabel[0] of templateTitle[2] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] an estimated templateYValue[min] templateYLabel[2] occurred nationwide .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[2] between templateXValue[min] and templateXValue[max] .
generated: This statistic shows the U.S. reported robbery cases between 1990 and 2018 .


Example 594:
data: Year|2018|x|line_chart Revenue_in_million_U.S._dollars|380|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|359|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|355|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|329|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|296|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|258|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|250|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|235|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|236|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|232|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|222|y|line_chart Year|2007|x|line_chart Revenue_in_million_U.S._dollars|205|y|line_chart Year|2006|x|line_chart Revenue_in_million_U.S._dollars|194|y|line_chart Year|2005|x|line_chart Revenue_in_million_U.S._dollars|175|y|line_chart Year|2004|x|line_chart Revenue_in_million_U.S._dollars|171|y|line_chart Year|2003|x|line_chart Revenue_in_million_U.S._dollars|150|y|line_chart Year|2002|x|line_chart Revenue_in_million_U.S._dollars|141|y|line_chart Year|2001|x|line_chart Revenue_in_million_U.S._dollars|130|y|line_chart 
title: Revenue of the Cincinnati Bengals ( NFL ) 2001 - 2018

gold: The statistic depicts the revenue of the Cincinnati Bengals , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Cincinnati Bengals was 380 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitle[1] templateTitle[2] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[1] templateTitle[2] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[1] templateTitle[2] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[1] templateTitle[2] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Cincinnati Bengals , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the Revenue of the Cincinnati Bengals was 380 million U.S. dollars .


Example 595:
data: Movie_Name|War|x|bar_chart Box_office_gross_in_billion_Indian_rupees|2.92|y|bar_chart Movie_Name|Kabir_Singh|x|bar_chart Box_office_gross_in_billion_Indian_rupees|2.76|y|bar_chart Movie_Name|Uri-_The_Surgical_Strike|x|bar_chart Box_office_gross_in_billion_Indian_rupees|2.44|y|bar_chart Movie_Name|Bharat|x|bar_chart Box_office_gross_in_billion_Indian_rupees|1.97|y|bar_chart Movie_Name|Mission_Mangal|x|bar_chart Box_office_gross_in_billion_Indian_rupees|1.93|y|bar_chart Movie_Name|Kesari|x|bar_chart Box_office_gross_in_billion_Indian_rupees|1.52|y|bar_chart Movie_Name|Total_Dhamaal|x|bar_chart Box_office_gross_in_billion_Indian_rupees|1.5|y|bar_chart Movie_Name|Saaho|x|bar_chart Box_office_gross_in_billion_Indian_rupees|1.49|y|bar_chart Movie_Name|Chhichhore|x|bar_chart Box_office_gross_in_billion_Indian_rupees|1.47|y|bar_chart Movie_Name|Super_30|x|bar_chart Box_office_gross_in_billion_Indian_rupees|1.47|y|bar_chart 
title: Highest grossing domestic movies India 2019

gold: The Bollywood movie 'War ' was the highest grossing domestic movie produced in India in 2019 with an all India net collection of almost three billion Indian rupees . This was followed by 'Kabir Singh ' at around 2.8 billion rupees worth box office collection that year .
gold_template: The Bollywood templateXLabel[0] 'War ' was the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] produced in templateTitle[4] in templateTitle[5] with an all templateTitle[4] net collection of almost templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] . This was followed by 'Kabir templateXValue[1] ' at around templateYValue[1] templateYLabel[3] templateYLabel[5] worth templateYLabel[0] templateYLabel[1] collection that year .

generated_template: This statistic shows the number of people in the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] . In templateTitle[4] , about templateYValue[max] percent of templateYLabel[1] in the United States .
generated: This statistic shows the number of people in the United States in India , 2019 Movie . In India , about 2.92 percent of office in the United States .


Example 596:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|1.93|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|1.88|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|1.95|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|1.87|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|1.74|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|1.73|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|2.02|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|2.11|y|line_chart Year|2016|x|line_chart Inflation_rate_compared_to_previous_year|0.04|y|line_chart Year|2015|x|line_chart Inflation_rate_compared_to_previous_year|0.06|y|line_chart Year|2014|x|line_chart Inflation_rate_compared_to_previous_year|0.7|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|1.7|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|2.89|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|3.73|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|2.8|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|0.01|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|4.09|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|2.66|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|2.96|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|3.76|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|3.24|y|line_chart Year|2003|x|line_chart Inflation_rate_compared_to_previous_year|2.53|y|line_chart Year|2002|x|line_chart Inflation_rate_compared_to_previous_year|2.06|y|line_chart Year|2001|x|line_chart Inflation_rate_compared_to_previous_year|2.4|y|line_chart Year|2000|x|line_chart Inflation_rate_compared_to_previous_year|3.78|y|line_chart Year|1999|x|line_chart Inflation_rate_compared_to_previous_year|1.02|y|line_chart Year|1998|x|line_chart Inflation_rate_compared_to_previous_year|0.97|y|line_chart Year|1997|x|line_chart Inflation_rate_compared_to_previous_year|1.37|y|line_chart Year|1996|x|line_chart Inflation_rate_compared_to_previous_year|1.56|y|line_chart Year|1995|x|line_chart Inflation_rate_compared_to_previous_year|1.9|y|line_chart Year|1994|x|line_chart Inflation_rate_compared_to_previous_year|2.2|y|line_chart Year|1993|x|line_chart Inflation_rate_compared_to_previous_year|3.6|y|line_chart Year|1992|x|line_chart Inflation_rate_compared_to_previous_year|3.2|y|line_chart Year|1991|x|line_chart Inflation_rate_compared_to_previous_year|3.1|y|line_chart Year|1990|x|line_chart Inflation_rate_compared_to_previous_year|3.7|y|line_chart Year|1989|x|line_chart Inflation_rate_compared_to_previous_year|3.4|y|line_chart Year|1988|x|line_chart Inflation_rate_compared_to_previous_year|1.4|y|line_chart Year|1987|x|line_chart Inflation_rate_compared_to_previous_year|-0.1|y|line_chart Year|1986|x|line_chart Inflation_rate_compared_to_previous_year|0.3|y|line_chart Year|1985|x|line_chart Inflation_rate_compared_to_previous_year|4.09|y|line_chart Year|1984|x|line_chart Inflation_rate_compared_to_previous_year|5.64|y|line_chart 
title: Inflation rate in Luxembourg 2024

gold: This statistic shows the average inflation rate in Luxembourg from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Luxembourg amounted to about 2.02 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Luxembourg from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Luxembourg amounted to about 2.02 percent compared to the previous Year .


Example 597:
data: Country|United_Kingdom|x|bar_chart Median_amount_in_euros|420|y|bar_chart Country|Luxembourg|x|bar_chart Median_amount_in_euros|300|y|bar_chart Country|France|x|bar_chart Median_amount_in_euros|250|y|bar_chart Country|Austria|x|bar_chart Median_amount_in_euros|250|y|bar_chart Country|Spain|x|bar_chart Median_amount_in_euros|200|y|bar_chart Country|Italy|x|bar_chart Median_amount_in_euros|200|y|bar_chart Country|Germany|x|bar_chart Median_amount_in_euros|200|y|bar_chart Country|Belgium|x|bar_chart Median_amount_in_euros|150|y|bar_chart Country|Czech_Republic|x|bar_chart Median_amount_in_euros|150|y|bar_chart Country|Romania|x|bar_chart Median_amount_in_euros|110|y|bar_chart Country|Poland|x|bar_chart Median_amount_in_euros|70|y|bar_chart Country|Netherlands|x|bar_chart Median_amount_in_euros|40|y|bar_chart 
title: Average planned spend on Christmas presents in selected European countries 2015

gold: This statistic displays the average amount consumers plan to spend on Christmas presents in 2015 in selected European countries . The United Kingdom ( UK ) had the highest spend , with consumers expecting to budget 420 euros for Christmas gifts .
gold_template: This statistic displays the templateTitle[0] templateYLabel[1] consumers plan to templateTitle[2] on templateTitle[3] templateTitle[4] in templateTitle[8] in templateTitle[5] templateTitle[6] templateTitle[7] . The templateXValue[0] templateXValue[0] ( UK ) had the highest templateTitle[2] , with consumers expecting to budget templateYValue[max] templateYLabel[2] for templateTitle[3] gifts .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] . The templateXValue[0] templateXValue[0] had the highest proportion of templateYLabel[1] with templateYValue[max] percent of templateYLabel[1] templateTitle[6] .
generated: This statistic gives information on the Median of amount euros yLabelErr in Average planned spend Christmas presents in selected . The United_Kingdom had the highest proportion of amount with 420 percent of amount European .


Example 598:
data: Year|2018|x|line_chart Estimated_revenue_in_billion_U.S._dollars|69.91|y|line_chart Year|2017|x|line_chart Estimated_revenue_in_billion_U.S._dollars|64.41|y|line_chart Year|2016|x|line_chart Estimated_revenue_in_billion_U.S._dollars|66.86|y|line_chart Year|2015|x|line_chart Estimated_revenue_in_billion_U.S._dollars|64.43|y|line_chart Year|2014|x|line_chart Estimated_revenue_in_billion_U.S._dollars|62.83|y|line_chart Year|2013|x|line_chart Estimated_revenue_in_billion_U.S._dollars|64.5|y|line_chart Year|2012|x|line_chart Estimated_revenue_in_billion_U.S._dollars|61.89|y|line_chart Year|2011|x|line_chart Estimated_revenue_in_billion_U.S._dollars|59.63|y|line_chart Year|2010|x|line_chart Estimated_revenue_in_billion_U.S._dollars|59.41|y|line_chart Year|2009|x|line_chart Estimated_revenue_in_billion_U.S._dollars|55.83|y|line_chart Year|2008|x|line_chart Estimated_revenue_in_billion_U.S._dollars|61.14|y|line_chart Year|2007|x|line_chart Estimated_revenue_in_billion_U.S._dollars|61.91|y|line_chart Year|2006|x|line_chart Estimated_revenue_in_billion_U.S._dollars|59.17|y|line_chart Year|2005|x|line_chart Estimated_revenue_in_billion_U.S._dollars|56.83|y|line_chart 
title: U.S. motion picture/video production and distribution - revenue 2005 - 2018

gold: The statistic above presents estimates of the annual aggregate revenue of the U.S. motion picture and video production and distribution industry from 2005 to 2018 . In 2018 , the industry generated an estimated total revenue of 69.91 billion U.S. dollars .
gold_template: The statistic above presents estimates of the annual aggregate templateYLabel[1] of the templateYLabel[3] templateTitle[1] picture and video templateTitle[3] and templateTitle[4] industry from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the industry generated an templateYLabel[0] total templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: According to templateYLabel[3] Census data , the templateTitle[2] and public relations templateTitle[3] saw slightly increasing revenues in since templateXValue[2] . At approximately 1118 templateYLabel[2] templateYLabel[3] templateYLabel[4] , the templateTitle[3] reached a record high in templateXValue[max] . The significant drops in earnings in templateXValue[min] and templateXValue[9] can be tied to the general economic situation in the country at that time .
generated: According to U.S. Census data , the picture/video and public relations production saw slightly increasing revenues in since 2016 . At approximately 1118 billion U.S. dollars , the production reached a record high in 2018 . The significant drops in earnings in 2005 and 2009 can be tied to the general economic situation in the country at that time .


Example 599:
data: Year|2018|x|line_chart Gross_profit_in_million_U.S._dollars|2437|y|line_chart Year|2017|x|line_chart Gross_profit_in_million_U.S._dollars|2489|y|line_chart Year|2016|x|line_chart Gross_profit_in_million_U.S._dollars|2366|y|line_chart Year|2015|x|line_chart Gross_profit_in_million_U.S._dollars|2183|y|line_chart Year|2014|x|line_chart Gross_profit_in_million_U.S._dollars|2087|y|line_chart Year|2013|x|line_chart Gross_profit_in_million_U.S._dollars|1944|y|line_chart Year|2012|x|line_chart Gross_profit_in_million_U.S._dollars|1837|y|line_chart Year|2011|x|line_chart Gross_profit_in_million_U.S._dollars|1595|y|line_chart Year|2010|x|line_chart Gross_profit_in_million_U.S._dollars|1449|y|line_chart Year|2009|x|line_chart Gross_profit_in_million_U.S._dollars|1217|y|line_chart Year|2008|x|line_chart Gross_profit_in_million_U.S._dollars|1184|y|line_chart Year|2007|x|line_chart Gross_profit_in_million_U.S._dollars|1158|y|line_chart Year|2006|x|line_chart Gross_profit_in_million_U.S._dollars|897|y|line_chart 
title: Dick 's Sporting Goods : gross profit 2006 - 2018

gold: The timeline depicts the gross profit of Dick 's Sporting Goods from 2006 to 2018 . The gross profit of Dick 's Sporting Goods amounted to 2,437 million U.S. dollars in 2018 .
gold_template: The timeline depicts the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: In templateXValue[max] , there were templateYValue[0] such templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitle[4] and templateTitle[5] in the United States . After a decrease from the previous templateXLabel[0] 's history , the templateYLabel[0] templateYLabel[1] has increased again in the past few years .
generated: In 2018 , there were 2437 such 's Sporting profit in gross and profit in the United States . After a decrease from the previous Year 's history , the Gross profit has increased again in the past few years .


Example 600:
data: Year|2018|x|line_chart Household_income_in_current_U.S._dollars|60449|y|line_chart Year|2017|x|line_chart Household_income_in_current_U.S._dollars|57700|y|line_chart Year|2016|x|line_chart Household_income_in_current_U.S._dollars|57091|y|line_chart Year|2015|x|line_chart Household_income_in_current_U.S._dollars|54203|y|line_chart Year|2014|x|line_chart Household_income_in_current_U.S._dollars|52005|y|line_chart Year|2013|x|line_chart Household_income_in_current_U.S._dollars|48801|y|line_chart Year|2012|x|line_chart Household_income_in_current_U.S._dollars|50015|y|line_chart Year|2011|x|line_chart Household_income_in_current_U.S._dollars|48879|y|line_chart Year|2010|x|line_chart Household_income_in_current_U.S._dollars|46276|y|line_chart Year|2009|x|line_chart Household_income_in_current_U.S._dollars|45994|y|line_chart Year|2008|x|line_chart Household_income_in_current_U.S._dollars|49788|y|line_chart Year|2007|x|line_chart Household_income_in_current_U.S._dollars|49370|y|line_chart Year|2006|x|line_chart Household_income_in_current_U.S._dollars|48647|y|line_chart Year|2005|x|line_chart Household_income_in_current_U.S._dollars|45933|y|line_chart Year|2004|x|line_chart Household_income_in_current_U.S._dollars|42256|y|line_chart Year|2003|x|line_chart Household_income_in_current_U.S._dollars|45022|y|line_chart Year|2002|x|line_chart Household_income_in_current_U.S._dollars|42715|y|line_chart Year|2001|x|line_chart Household_income_in_current_U.S._dollars|45047|y|line_chart Year|2000|x|line_chart Household_income_in_current_U.S._dollars|45512|y|line_chart Year|1999|x|line_chart Household_income_in_current_U.S._dollars|46089|y|line_chart Year|1998|x|line_chart Household_income_in_current_U.S._dollars|41821|y|line_chart Year|1997|x|line_chart Household_income_in_current_U.S._dollars|38742|y|line_chart Year|1996|x|line_chart Household_income_in_current_U.S._dollars|39225|y|line_chart Year|1995|x|line_chart Household_income_in_current_U.S._dollars|36426|y|line_chart Year|1994|x|line_chart Household_income_in_current_U.S._dollars|35284|y|line_chart Year|1993|x|line_chart Household_income_in_current_U.S._dollars|32662|y|line_chart Year|1992|x|line_chart Household_income_in_current_U.S._dollars|32267|y|line_chart Year|1991|x|line_chart Household_income_in_current_U.S._dollars|32117|y|line_chart Year|1990|x|line_chart Household_income_in_current_U.S._dollars|29937|y|line_chart 
title: Michigan - median household income 1990 - 2018

gold: This statistic shows the median household income in Michigan from 1990 to 2018 . In 2018 , the median household income in Michigan amounted to 60,449 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] was at templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Household income current in household from 1990 to 2018 . In 2018 , the Household income current of income was at 60449 U.S. dollars .


Example 601:
data: Year|2019|x|line_chart Fan_Cost_Index_in_U.S._dollars|540.52|y|line_chart Year|2018|x|line_chart Fan_Cost_Index_in_U.S._dollars|536.04|y|line_chart Year|2016|x|line_chart Fan_Cost_Index_in_U.S._dollars|502.84|y|line_chart Year|2015|x|line_chart Fan_Cost_Index_in_U.S._dollars|480.89|y|line_chart Year|2014|x|line_chart Fan_Cost_Index_in_U.S._dollars|479.11|y|line_chart Year|2013|x|line_chart Fan_Cost_Index_in_U.S._dollars|459.73|y|line_chart Year|2012|x|line_chart Fan_Cost_Index_in_U.S._dollars|443.93|y|line_chart Year|2011|x|line_chart Fan_Cost_Index_in_U.S._dollars|427.42|y|line_chart Year|2010|x|line_chart Fan_Cost_Index_in_U.S._dollars|420.54|y|line_chart Year|2009|x|line_chart Fan_Cost_Index_in_U.S._dollars|412.64|y|line_chart Year|2008|x|line_chart Fan_Cost_Index_in_U.S._dollars|396.36|y|line_chart Year|2007|x|line_chart Fan_Cost_Index_in_U.S._dollars|367.31|y|line_chart Year|2006|x|line_chart Fan_Cost_Index_in_U.S._dollars|346.16|y|line_chart 
title: Average Fan Cost Index of NFL teams 2006 - 2019

gold: The statistic shows the average Fan Cost Index in the National Football League from 2006 to 2019 . The average Fan Cost Index was at 540.52 U.S. dollars in 2019 .
gold_template: The statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the National Football League from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[max] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: U.S. fashion fashion retailer templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] history between templateXValue[min] and templateXValue[max] , almost templateYValue[0] templateYLabel[1] templateYLabel[2] British pounds when compared with the highest rates rose since around templateYValue[0] percent in templateXValue[max] . templateTitle[4] templateTitle[5] The templateTitle[2] templateTitle[3] templateTitle[4] category in the United States has increased again in recent years , reaching in templateXValue[1] , which reached since many of templateYValue[0] percent of the templateXLabel[0] .
generated: U.S. fashion retailer Index NFL teams in Index history between 2006 and 2019 , almost 540.52 Cost Index British pounds when compared with the highest rates rose since around 540.52 percent in 2019 . NFL teams The Cost Index NFL category in the United States has increased again in recent years , reaching in 2018 , which reached since many of 540.52 percent of the Year .


Example 602:
data: Platform|Macintosh|x|bar_chart Order_value_in_U.S._dollars|132.6|y|bar_chart Platform|Windows|x|bar_chart Order_value_in_U.S._dollars|127.77|y|bar_chart Platform|iOS|x|bar_chart Order_value_in_U.S._dollars|93.52|y|bar_chart Platform|Chrome_OS|x|bar_chart Order_value_in_U.S._dollars|87.98|y|bar_chart Platform|Linux|x|bar_chart Order_value_in_U.S._dollars|85.72|y|bar_chart Platform|Android|x|bar_chart Order_value_in_U.S._dollars|76.21|y|bar_chart Platform|Windows_Phone|x|bar_chart Order_value_in_U.S._dollars|66.06|y|bar_chart 
title: Global online shopping order value 2019 , by platform

gold: This statistic provides information on the average order value of online shopping orders worldwide in the second quarter of 2019 , differentiated by platform . During that period , online orders which were placed through Android devices had an average value of 76.21 U.S. dollars .
gold_template: This statistic provides information on the average templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] orders worldwide in the second quarter of templateTitle[5] , differentiated templateTitle[6] templateXLabel[0] . During that period , templateTitle[1] orders which were placed through templateXValue[5] devices had an average templateYLabel[1] of templateYValue[5] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in the United States in templateTitle[5] templateTitle[6] . During the most recent survey period it was found that templateYValue[max] percent of people an increase of templateYValue[1] percent in templateXValue[1] .
generated: This statistic shows the Order value of shopping order value in the United States in 2019 by . During the most recent survey period it was found that 132.6 percent of people an increase of 127.77 percent in Windows .


Example 603:
data: Month|Kholop|x|bar_chart Revenue_in_thousand_U.S._dollars|12530.82|y|bar_chart Month|Perfect_Man|x|bar_chart Revenue_in_thousand_U.S._dollars|6603.13|y|bar_chart Month|Spies_in_Disguise|x|bar_chart Revenue_in_thousand_U.S._dollars|5899.09|y|bar_chart Month|Bad_Boys_for_Life|x|bar_chart Revenue_in_thousand_U.S._dollars|5092.75|y|bar_chart Month|Invasion|x|bar_chart Revenue_in_thousand_U.S._dollars|3106.25|y|bar_chart Month|Marafon_Zhelaniy|x|bar_chart Revenue_in_thousand_U.S._dollars|1927.94|y|bar_chart Month|Soyuz_Spaseniya|x|bar_chart Revenue_in_thousand_U.S._dollars|1856.38|y|bar_chart Month|The_Grudge|x|bar_chart Revenue_in_thousand_U.S._dollars|1376.52|y|bar_chart Month|Jumanji:_The_Next_Level|x|bar_chart Revenue_in_thousand_U.S._dollars|1098.84|y|bar_chart Month|Richard_Jewell|x|bar_chart Revenue_in_thousand_U.S._dollars|828.02|y|bar_chart 
title: Weekend box office revenue in Russia and CIS January 2020 , by film

gold: Over three weekends of January 2020 , the Russian comedy film `` Kholop , '' translated as `` Serf , '' had the largest aggregate gross box office in Armenia , Belarus , Kazakhstan , Moldova , and Russia , measuring at approximately 12.5 million U.S. dollars , which made it the leading movie of the month by revenue . The romantic comedy `` Perfect Man , '' where the main character was played by a popular Russian singer Egor Kreed , ranked second with the box office of over 6.6 million U.S. dollars .
gold_template: Over three weekends of templateTitle[6] templateTitle[7] , the Russian comedy templateTitle[9] `` templateXValue[0] , '' translated as `` Serf , '' had the largest aggregate gross templateTitle[1] templateTitle[2] in Armenia , Belarus , Kazakhstan , Moldova , and templateTitle[4] , measuring at approximately templateYValue[max] million templateYLabel[2] templateYLabel[3] , which made it the leading movie of the templateXLabel[0] templateTitle[8] templateYLabel[0] . The romantic comedy `` templateXValue[1] templateXValue[1] , '' where the main character was played templateTitle[8] a popular Russian singer Egor Kreed , ranked second with the templateTitle[1] templateTitle[2] of over templateYValue[1] million templateYLabel[2] templateYLabel[3] .

generated_template: U.S. fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion U.S. fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion for many years . U.S. fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion
generated: U.S. fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion U.S. fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion for many years . U.S. fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion


Example 604:
data: State|Alabama|x|bar_chart COPD_prevalence|10.1|y|bar_chart State|Alaska|x|bar_chart COPD_prevalence|6.3|y|bar_chart State|Arizona|x|bar_chart COPD_prevalence|5.9|y|bar_chart State|Arkansas|x|bar_chart COPD_prevalence|9.3|y|bar_chart State|California|x|bar_chart COPD_prevalence|4.4|y|bar_chart State|Colorado|x|bar_chart COPD_prevalence|4.2|y|bar_chart State|Connecticut|x|bar_chart COPD_prevalence|5.3|y|bar_chart State|Delaware|x|bar_chart COPD_prevalence|7.3|y|bar_chart State|District_of_Columbia|x|bar_chart COPD_prevalence|5.8|y|bar_chart State|Florida|x|bar_chart COPD_prevalence|7.1|y|bar_chart State|Georgia|x|bar_chart COPD_prevalence|6.8|y|bar_chart State|Hawaii|x|bar_chart COPD_prevalence|3.4|y|bar_chart State|Idaho|x|bar_chart COPD_prevalence|4.7|y|bar_chart State|Illinois|x|bar_chart COPD_prevalence|6.4|y|bar_chart State|Indiana|x|bar_chart COPD_prevalence|8|y|bar_chart State|Iowa|x|bar_chart COPD_prevalence|5.9|y|bar_chart State|Kansas|x|bar_chart COPD_prevalence|6.2|y|bar_chart State|Kentucky|x|bar_chart COPD_prevalence|11.3|y|bar_chart State|Louisiana|x|bar_chart COPD_prevalence|8.4|y|bar_chart State|Maine|x|bar_chart COPD_prevalence|6.5|y|bar_chart State|Maryland|x|bar_chart COPD_prevalence|5.4|y|bar_chart State|Massachusetts|x|bar_chart COPD_prevalence|5|y|bar_chart State|Michigan|x|bar_chart COPD_prevalence|8|y|bar_chart State|Minnesota|x|bar_chart COPD_prevalence|4|y|bar_chart State|Mississippi|x|bar_chart COPD_prevalence|7.5|y|bar_chart State|Missouri|x|bar_chart COPD_prevalence|7.9|y|bar_chart State|Montana|x|bar_chart COPD_prevalence|5.7|y|bar_chart State|Nebraska|x|bar_chart COPD_prevalence|5.3|y|bar_chart State|Nevada|x|bar_chart COPD_prevalence|6.5|y|bar_chart State|New_Hampshire|x|bar_chart COPD_prevalence|6|y|bar_chart State|New_Jersey|x|bar_chart COPD_prevalence|5.8|y|bar_chart State|New_Mexico|x|bar_chart COPD_prevalence|5.6|y|bar_chart State|New_York|x|bar_chart COPD_prevalence|5|y|bar_chart State|North_Carolina|x|bar_chart COPD_prevalence|7.3|y|bar_chart State|North_Dakota|x|bar_chart COPD_prevalence|4.8|y|bar_chart State|Ohio|x|bar_chart COPD_prevalence|7.6|y|bar_chart State|Oklahoma|x|bar_chart COPD_prevalence|8.1|y|bar_chart State|Oregon|x|bar_chart COPD_prevalence|4.9|y|bar_chart State|Pennsylvania|x|bar_chart COPD_prevalence|5.9|y|bar_chart State|Rhode_Island|x|bar_chart COPD_prevalence|7|y|bar_chart State|South_Carolina|x|bar_chart COPD_prevalence|7.2|y|bar_chart State|South_Dakota|x|bar_chart COPD_prevalence|4.4|y|bar_chart State|Tennessee|x|bar_chart COPD_prevalence|8.9|y|bar_chart State|Texas|x|bar_chart COPD_prevalence|4.8|y|bar_chart State|Total|x|bar_chart COPD_prevalence|6.2|y|bar_chart State|Utah|x|bar_chart COPD_prevalence|4.1|y|bar_chart State|Vermont|x|bar_chart COPD_prevalence|5.7|y|bar_chart State|Virginia|x|bar_chart COPD_prevalence|6.6|y|bar_chart State|Washington|x|bar_chart COPD_prevalence|5.4|y|bar_chart State|West_Virginia|x|bar_chart COPD_prevalence|13.8|y|bar_chart State|Wisconsin|x|bar_chart COPD_prevalence|4.7|y|bar_chart State|Wyoming|x|bar_chart COPD_prevalence|6.1|y|bar_chart 
title: COPD prevalence in the U.S. 2017 , by state

gold: This statistic shows the prevalence of Chronic Obstructive Pulmonary Disease ( COPD ) in the U.S. in 2017 , by state . As of that year , around 11.3 percent of adults in Kentucky suffered from COPD .
gold_template: This statistic shows the templateYLabel[1] of Chronic Obstructive Pulmonary Disease ( templateYLabel[0] ) in the templateTitle[2] in templateTitle[3] , templateTitle[4] templateXLabel[0] . As of that year , around templateYValue[17] percent of adults in templateXValue[17] suffered from templateYLabel[0] .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] . In templateXValue[2] , templateYValue[3] percent of the United States had the highest templateYLabel[0] templateYLabel[1] , followed by templateXValue[1] with templateYValue[1] percent .
generated: This statistic shows the COPD prevalence prevalence in the United States in by , state State . In Arizona , 9.3 percent of the United States had the highest COPD prevalence , followed by Alaska with 6.3 percent .


Example 605:
data: Year|2017|x|line_chart Number_of_households_in_millions|34.07|y|line_chart Year|2016|x|line_chart Number_of_households_in_millions|32.9|y|line_chart Year|2015|x|line_chart Number_of_households_in_millions|31.95|y|line_chart Year|2010|x|line_chart Number_of_households_in_millions|28.16|y|line_chart Year|2005|x|line_chart Number_of_households_in_millions|24.8|y|line_chart 
title: Mexico : number of households 2005 - 2017

gold: The statistic presents a timeline with the number of households in Mexico between 2005 and 2017 . In 2017 , there were more than 34 million households in Mexico , up from nearly 33 million households a year earlier .
gold_template: The statistic presents a timeline with the templateYLabel[0] of templateYLabel[1] in templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were more than templateYValue[max] million templateYLabel[1] in templateTitle[0] , up from nearly templateYValue[1] million templateYLabel[1] a templateXLabel[0] earlier .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were about templateYValue[max] million templateYLabel[1] in the United States .
generated: This statistic shows the Mexico of number households households in the United States from 2005 to 2017 . In 2017 , there were about 34.07 million households in the United States .


Example 606:
data: Year|2019|x|line_chart Conversion_rate|13.5|y|line_chart Year|2018|x|line_chart Conversion_rate|11.6|y|line_chart Year|2017|x|line_chart Conversion_rate|11.2|y|line_chart 
title: U.S. Amazon Prime Day conversion rate 2017 - 2019

gold: During the Amazon Prime Day shopping event in July 2019 , the desktop conversion rate amounted to 13.5 percent , which represented a 16 percent growth from the previous year . Prime Day does not only drive conversion on Amazon but also on other retail platforms .
gold_template: During the templateTitle[1] templateTitle[2] templateTitle[3] shopping event in July templateXValue[max] , the desktop templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] percent , which represented a 16 percent growth from the previous templateXLabel[0] . templateTitle[2] templateTitle[3] does not only drive templateYLabel[0] on templateTitle[1] but also on other retail platforms .

generated_template: templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in the United States has expected to reach templateYValue[max] percent in templateXValue[max] , up from templateYValue[1] percent in the previous templateXLabel[0] . This figure represents U.S. dollars in templateXValue[min] , and templateYValue[min] percent on the previous templateXLabel[0] . As of templateXValue[max] , the company 's templateYLabel[0] templateYLabel[1] amounted to 2.57 percent .
generated: Amazon Prime Conversion rate in the United States has expected to reach 13.5 percent in 2019 , up from 11.6 percent in the previous Year . This figure represents U.S. dollars in 2017 , and 11.2 percent on the previous Year . As of 2019 , the company 's Conversion rate amounted to 2.57 percent .


Example 607:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|2.09|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|2.09|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|2.12|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|2.09|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|2.23|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|-1.05|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|2.48|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|-0.85|y|line_chart Year|2016|x|line_chart Inflation_rate_compared_to_previous_year|2.03|y|line_chart Year|2015|x|line_chart Inflation_rate_compared_to_previous_year|1.27|y|line_chart Year|2014|x|line_chart Inflation_rate_compared_to_previous_year|2.2|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|3.52|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|2.88|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|3.76|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|3.78|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|4.18|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|6.08|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|5.06|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|1.9|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|0.55|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|0.27|y|line_chart Year|2003|x|line_chart Inflation_rate_compared_to_previous_year|0.55|y|line_chart Year|2002|x|line_chart Inflation_rate_compared_to_previous_year|0.14|y|line_chart Year|2001|x|line_chart Inflation_rate_compared_to_previous_year|-1.22|y|line_chart Year|2000|x|line_chart Inflation_rate_compared_to_previous_year|-1.08|y|line_chart Year|1999|x|line_chart Inflation_rate_compared_to_previous_year|-2.11|y|line_chart Year|1998|x|line_chart Inflation_rate_compared_to_previous_year|-0.39|y|line_chart Year|1997|x|line_chart Inflation_rate_compared_to_previous_year|-0.26|y|line_chart Year|1996|x|line_chart Inflation_rate_compared_to_previous_year|0.26|y|line_chart Year|1995|x|line_chart Inflation_rate_compared_to_previous_year|5.25|y|line_chart Year|1994|x|line_chart Inflation_rate_compared_to_previous_year|1.26|y|line_chart Year|1993|x|line_chart Inflation_rate_compared_to_previous_year|1.28|y|line_chart Year|1992|x|line_chart Inflation_rate_compared_to_previous_year|-0.98|y|line_chart Year|1991|x|line_chart Inflation_rate_compared_to_previous_year|3.79|y|line_chart Year|1990|x|line_chart Inflation_rate_compared_to_previous_year|-1.01|y|line_chart Year|1989|x|line_chart Inflation_rate_compared_to_previous_year|1.17|y|line_chart Year|1988|x|line_chart Inflation_rate_compared_to_previous_year|-0.35|y|line_chart Year|1987|x|line_chart Inflation_rate_compared_to_previous_year|-2.39|y|line_chart Year|1986|x|line_chart Inflation_rate_compared_to_previous_year|-3.12|y|line_chart Year|1985|x|line_chart Inflation_rate_compared_to_previous_year|-2.31|y|line_chart Year|1984|x|line_chart Inflation_rate_compared_to_previous_year|-0.68|y|line_chart 
title: Inflation in Saudi Arabia since 1984

gold: The statistic shows the inflation rate in Saudi Arabia from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate amounted to 2.48 percent compared to the previous year . Oil production in Saudi Arabia Saudi Arabia 's economy relies heavily on production and export of oil and petroleum .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] amounted to templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . Oil production in templateTitle[1] templateTitle[2] templateTitle[1] templateTitle[2] 's economy relies heavily on production and export of oil and petroleum .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Arabia from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Arabia amounted to about 2.48 percent compared to the previous Year .


Example 608:
data: Response|Morally_acceptable|x|bar_chart Share_of_respondents|43|y|bar_chart Response|Morally_wrong|x|bar_chart Share_of_respondents|55|y|bar_chart Response|Depends|x|bar_chart Share_of_respondents|1|y|bar_chart Response|No_opinion|x|bar_chart Share_of_respondents|1|y|bar_chart 
title: Americans ' moral stance towards pornography in 2018

gold: This statistic shows the moral stance of Americans regarding pornography in 2018 . During the survey , 43 percent of respondents stated they think pornography is morally acceptable , while 1 percent stated it depends on the situation .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitle[0] regarding templateTitle[5] in templateTitle[6] . During the survey , templateYValue[0] percent of templateYLabel[1] stated they think templateTitle[5] is templateXValue[0] templateXValue[0] , while templateYValue[min] percent stated it templateXValue[2] on the situation .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitle[0] regarding templateTitle[5] or templateTitle[6] templateTitle[7] in templateTitle[8] . During this survey , templateYValue[max] percent of templateYLabel[1] stated they think templateTitle[5] or templateTitle[6] templateTitle[7] are templateXValue[0] templateXValue[0] , while templateYValue[min] percent said it templateXValue[2] on the situation .
generated: This statistic shows the moral stance of Americans regarding pornography or 2018 titleErr in titleErr . During this survey , 55 percent of respondents stated they think pornography or 2018 titleErr are Morally_acceptable , while 1 percent said it Depends on the situation .


Example 609:
data: Country|Singapore|x|bar_chart Brand_Strength_Index_Score|90.5|y|bar_chart Country|Switzerland|x|bar_chart Brand_Strength_Index_Score|89.9|y|bar_chart Country|Netherlands|x|bar_chart Brand_Strength_Index_Score|89.6|y|bar_chart Country|Germany|x|bar_chart Brand_Strength_Index_Score|88.2|y|bar_chart Country|Luxembourg|x|bar_chart Brand_Strength_Index_Score|86.9|y|bar_chart Country|United_Arab_Emirates|x|bar_chart Brand_Strength_Index_Score|86.6|y|bar_chart Country|Finland|x|bar_chart Brand_Strength_Index_Score|86.4|y|bar_chart Country|Japan|x|bar_chart Brand_Strength_Index_Score|85.8|y|bar_chart Country|United_States|x|bar_chart Brand_Strength_Index_Score|85.7|y|bar_chart Country|Denmark|x|bar_chart Brand_Strength_Index_Score|85.6|y|bar_chart 
title: Top 10 strongest nation brands by BSI score 2019

gold: The statistic depicts the top ten strongest nation brands of 2019 as measured by the Brand Strength Index ( BSI ) . In 2019 , Singapore received the highest BSI score of any nation with a score of 90.5 .
gold_template: The statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitle[8] as measured templateTitle[5] the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[6] ) . In templateTitle[8] , templateXValue[0] received the highest templateTitle[6] templateYLabel[3] of any templateTitle[3] with a templateYLabel[3] of templateYValue[max] .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitle[4] templateTitle[5] as of templateTitle[6] . In templateTitle[7] , there were an templateYLabel[0] of templateYValue[max] million people living in templateXValue[0] .
generated: This statistic gives information on the Brand of Strength Index in brands by as of BSI . In score , there were an Brand of 90.5 million people living in Singapore .


Example 610:
data: Year|2024|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|469.04|y|line_chart Year|2023|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|455.73|y|line_chart Year|2022|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|443.84|y|line_chart Year|2021|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|432.97|y|line_chart Year|2020|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|422.06|y|line_chart Year|2019|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|417.63|y|line_chart Year|2018|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|434.17|y|line_chart Year|2017|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|398.39|y|line_chart Year|2016|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|368.83|y|line_chart Year|2015|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|385.8|y|line_chart Year|2014|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|498.41|y|line_chart Year|2013|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|523.5|y|line_chart Year|2012|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|510.23|y|line_chart Year|2011|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|498.83|y|line_chart Year|2010|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|429.13|y|line_chart Year|2009|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|386.62|y|line_chart Year|2008|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|462.55|y|line_chart Year|2007|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|401.09|y|line_chart Year|2006|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|345.42|y|line_chart Year|2005|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|308.72|y|line_chart Year|2004|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|264.36|y|line_chart Year|2003|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|228.75|y|line_chart Year|2002|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|195.42|y|line_chart Year|2001|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|174.0|y|line_chart Year|2000|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|171.32|y|line_chart Year|1999|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|162.29|y|line_chart Year|1998|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|154.17|y|line_chart Year|1997|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|161.35|y|line_chart Year|1996|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|163.52|y|line_chart Year|1995|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|152.03|y|line_chart Year|1994|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|127.13|y|line_chart Year|1993|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|120.58|y|line_chart Year|1992|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|130.84|y|line_chart Year|1991|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|121.87|y|line_chart Year|1990|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|119.79|y|line_chart Year|1989|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|102.63|y|line_chart Year|1988|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|101.9|y|line_chart Year|1987|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|94.23|y|line_chart Year|1986|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|78.69|y|line_chart Year|1985|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|65.42|y|line_chart Year|1984|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|62.06|y|line_chart 
title: Gross domestic product ( GDP ) in Norway 2024

gold: The statistic shows gross domestic product ( GDP ) in Norway from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Norway 2024 from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year . GDP is an important indicator of a country 's economic power .


Example 611:
data: Specialty_area|Psychiatry|x|bar_chart Number_of_physicians|304|y|bar_chart Specialty_area|Surgery|x|bar_chart Number_of_physicians|316|y|bar_chart Specialty_area|Anesthesiologists|x|bar_chart Number_of_physicians|439|y|bar_chart Specialty_area|Emergency_medicine|x|bar_chart Number_of_physicians|426|y|bar_chart Specialty_area|Radiology|x|bar_chart Number_of_physicians|311|y|bar_chart Specialty_area|Cardiology|x|bar_chart Number_of_physicians|174|y|bar_chart Specialty_area|Oncology_(cancer)|x|bar_chart Number_of_physicians|106|y|bar_chart Specialty_area|Endocrinology_diabetes_&_metabolism|x|bar_chart Number_of_physicians|33|y|bar_chart Specialty_area|All_other_specialities|x|bar_chart Number_of_physicians|1587|y|bar_chart Specialty_area|Total_specialty|x|bar_chart Number_of_physicians|3696|y|bar_chart 
title: Number of active physicians in Utah 2019 , by specialty area

gold: This statistic depicts the number of active physicians in Utah as of March 2019 , ordered by specialty area . At that time , there were 439 anesthesiologists active in Utah . In total , there were almost 4,000 physicians in the state .
gold_template: This statistic depicts the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitle[3] as of March templateTitle[4] , ordered templateTitle[5] templateXValue[last] templateXLabel[1] . At that time , there were templateYValue[2] templateXValue[2] templateTitle[1] in templateTitle[3] . In templateXValue[last] , there were almost 4,000 templateYLabel[1] in the state .

generated_template: This statistic depicts the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitle[3] as of March templateTitle[4] , ordered templateTitle[5] templateXValue[last] templateXLabel[1] . At that time , there were templateYValue[2] templateXValue[2] templateTitle[1] in templateTitle[3] . There are approximately 21,400 templateXValue[last] templateYLabel[1] templateTitle[1] in the state .
generated: This statistic depicts the Number of active physicians in Utah as of March 2019 , ordered by Total_specialty area . At that time , there were 439 Anesthesiologists active in Utah . There are approximately 21,400 Total_specialty physicians active in the state .


Example 612:
data: Country|France|x|bar_chart Number_of_employees|60214|y|bar_chart Country|Germany|x|bar_chart Number_of_employees|58170|y|bar_chart Country|United_Kingdom|x|bar_chart Number_of_employees|49824|y|bar_chart Country|Italy|x|bar_chart Number_of_employees|43731|y|bar_chart Country|Spain|x|bar_chart Number_of_employees|35378|y|bar_chart Country|Portugal|x|bar_chart Number_of_employees|24248|y|bar_chart Country|Netherlands|x|bar_chart Number_of_employees|14641|y|bar_chart Country|Belgium|x|bar_chart Number_of_employees|12992|y|bar_chart Country|Poland|x|bar_chart Number_of_employees|8418|y|bar_chart Country|Austria|x|bar_chart Number_of_employees|6637|y|bar_chart Country|Ireland|x|bar_chart Number_of_employees|3942|y|bar_chart Country|Greece|x|bar_chart Number_of_employees|3647|y|bar_chart Country|Slovenia|x|bar_chart Number_of_employees|1237|y|bar_chart Country|Estonia|x|bar_chart Number_of_employees|937|y|bar_chart Country|Luxembourg|x|bar_chart Number_of_employees|534|y|bar_chart 
title: General practitioners practicing in Europe in 2017 , by country

gold: In 2017 , there were over 60 thousand general practitioners ( GP ) practicing in France , the highest number recorded in Europe , followed by Germany with approximately 58.1 thousand GPs and the United Kingdom with almost 49.8 thousand . These three countries having the highest number of GPs goes in direct correlation with their population sizes being the highest in Europe . Visiting the doctor In 2018 , a survey of French individuals revealed that 83 percent of respondents consult with a physician at least once a year .
gold_template: In templateTitle[4] , there were over templateYValue[max] thousand templateTitle[0] templateTitle[1] ( GP ) templateTitle[2] in templateXValue[0] , the highest templateYLabel[0] recorded in templateTitle[3] , followed templateTitle[5] templateXValue[1] with approximately 58.1 thousand GPs and the templateXValue[2] templateXValue[2] with almost templateYValue[2] thousand . These three countries having the highest templateYLabel[0] of GPs goes in direct correlation with their population sizes being the highest in templateTitle[3] . Visiting the doctor In 2018 , a survey of French individuals revealed that 83 percent of respondents consult with a physician at least once a year .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] of templateYLabel[2] in selected countries in templateTitle[3] templateTitle[4] ( templateTitle[5] ) as of January templateTitle[6] . As of January templateTitle[7] , templateXValue[0] was the highest templateYLabel[0] of templateYValue[max] people . The company in templateXValue[1] , and templateXValue[2] templateXValue[2] with around templateYValue[2] templateYLabel[1] .
generated: This statistic displays the Number of employees of yLabelErr in selected countries in Europe 2017 ( by ) as of January country . As of January titleErr , France was the highest Number of 60214 people . The company in Germany , and United_Kingdom with around 49824 employees .


Example 613:
data: Month|Helse_Sør-Øst_RHF|x|bar_chart Number_of_employees|60368|y|bar_chart Month|Telenor_ASA|x|bar_chart Number_of_employees|31000|y|bar_chart Month|Aker_ASA|x|bar_chart Number_of_employees|20753|y|bar_chart Month|Equinor_ASA_/_Statoil|x|bar_chart Number_of_employees|20245|y|bar_chart Month|Posten_Norge_AS|x|bar_chart Number_of_employees|18327|y|bar_chart Month|Orkla_ASA|x|bar_chart Number_of_employees|18154|y|bar_chart Month|Yara_International_ASA|x|bar_chart Number_of_employees|14736|y|bar_chart Month|Aker_Solutions_ASA|x|bar_chart Number_of_employees|14300|y|bar_chart Month|Tallyman_AS|x|bar_chart Number_of_employees|13760|y|bar_chart Month|Norges_Statsbaner_AS|x|bar_chart Number_of_employees|13006|y|bar_chart Month|Norsk_Hydro_ASA|x|bar_chart Number_of_employees|12911|y|bar_chart Month|Marine_Harvest_ASA|x|bar_chart Number_of_employees|12717|y|bar_chart Month|Strawberry_Holding_AS|x|bar_chart Number_of_employees|10412|y|bar_chart Month|Nordic_Choice_Hospitality_Group_AS|x|bar_chart Number_of_employees|10320|y|bar_chart Month|Kongsberg_Automotive_ASA|x|bar_chart Number_of_employees|9791|y|bar_chart Month|DNB_ASA|x|bar_chart Number_of_employees|9561|y|bar_chart Month|Hfn_Group_AS|x|bar_chart Number_of_employees|9172|y|bar_chart Month|Evry_ASA|x|bar_chart Number_of_employees|9100|y|bar_chart Month|Hospitality_Invest_AS|x|bar_chart Number_of_employees|9001|y|bar_chart Month|Nokas_AS|x|bar_chart Number_of_employees|8273|y|bar_chart 
title: Leading companies in Norway 2019 , by number of employees

gold: This statistic shows the 20 biggest companies in Norway as of March 2019 , by number of employees . Helse Sør-Øst RHF was ranked first with over 60 thousand employees , while Telenor ASA was ranked second with 31 thousand employees .
gold_template: This statistic shows the 20 biggest templateTitle[1] in templateTitle[2] as of March templateTitle[3] , templateTitle[4] templateYLabel[0] of templateYLabel[1] . templateXValue[0] templateXValue[0] templateXValue[0] was ranked first with over templateYValue[max] thousand templateYLabel[1] , while templateXValue[1] templateXValue[1] was ranked second with templateYValue[1] thousand templateYLabel[1] .

generated_template: templateTitle[2] Canadian fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion recorded in templateTitle[10] , templateXValue[0] , was templateYValue[0] thousand templateYLabel[1] . The highest templateYLabel[0] of templateYLabel[1] compared to templateXValue[1] templateXValue[1] , which recorded in the U.S. recorded recorded in at templateYValue[max] people killed in templateTitle[7] .
generated: Norway Canadian fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion fashion recorded in titleErr , Helse_Sør-Øst_RHF , was 60368 thousand employees . The highest Number of employees compared to Telenor_ASA , which recorded in the U.S. recorded in at 60368 people killed in titleErr .


Example 614:
data: State|California|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|15.1|y|bar_chart State|Florida|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|13.8|y|bar_chart State|Texas|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|7.4|y|bar_chart State|New_York|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|5.3|y|bar_chart State|North_Carolina|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|5.3|y|bar_chart State|Georgia|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|5.1|y|bar_chart State|Ohio|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|4.8|y|bar_chart State|Illinois|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|4.8|y|bar_chart State|Michigan|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|4.2|y|bar_chart State|Arizona|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|3.4|y|bar_chart State|Virginia|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|3.1|y|bar_chart State|New_Jersey|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|2.8|y|bar_chart State|Massachusetts|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|2.8|y|bar_chart State|Oregon|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|2.5|y|bar_chart State|Hawaii|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|2.5|y|bar_chart State|Washington|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|2.5|y|bar_chart State|Wisconsin|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|2.4|y|bar_chart State|Minnesota|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|2.4|y|bar_chart State|Pennsylvania|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|2.3|y|bar_chart State|South_Carolina|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|2.3|y|bar_chart State|Colorado|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|1.7|y|bar_chart State|Indiana|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|1.7|y|bar_chart State|Connecticut|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|1.1|y|bar_chart State|New_Mexico|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|0.99|y|bar_chart State|Louisiana|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|0.81|y|bar_chart State|Iowa|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|0.77|y|bar_chart State|Kentucky|x|bar_chart Total_economic_output_(in_billion_U.S._dollars)|0.71|y|bar_chart 
title: Golf industry in the U.S. : total economic output by state 2009

gold: This graph depicts the total economic output of the golf industry in the U.S. by state as of 2009 . In New Mexico , the total economic output was at 985 million U.S. dollars in 2006 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[0] templateTitle[1] in the templateYLabel[5] templateTitle[6] templateXLabel[0] as of templateTitle[8] . In templateXValue[3] templateXValue[23] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at 985 million templateYLabel[5] dollars in 2006 .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] . In templateTitle[4] , about templateYValue[max] percent of the United States 's templateYLabel[1] were born in the United States .
generated: This statistic shows the Total industry economic in the United States in economic , output State . In economic , about 15.1 percent of the United States 's economic were born in the United States .


Example 615:
data: Year|2015|x|line_chart Revenue_in_million_U.S._dollars|23616|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|23628|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|23886|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|23695|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|23348|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|23081|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|22992|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|23767|y|line_chart Year|2007|x|line_chart Revenue_in_million_U.S._dollars|23813|y|line_chart 
title: Forecast : printer cartridge revenue in North America 2007 - 2015

gold: The statistic shows a forecast for revenue from printer cartridges in North America between 2007 and 2015 . In 2012 , revenue of about 23.7 billion U.S. dollars are expected .
gold_template: The statistic shows a templateTitle[0] for templateYLabel[0] from templateTitle[1] cartridges in templateTitle[4] templateTitle[5] between templateXValue[min] and templateXValue[max] . In templateXValue[3] , templateYLabel[0] of about templateYValue[3] billion templateYLabel[2] templateYLabel[3] are expected .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the nationwide templateTitle[3] templateTitle[4] was templateYValue[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitle[1] templateTitle[2] - additional information The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] is a large hotel company with high level .
generated: The statistic shows the Forecast printer cartridge Revenue in the United States from 2007 to 2015 . In 2013 , the nationwide revenue North was 23886 million U.S. dollars . printer cartridge - additional information The Forecast printer cartridge revenue North is a large hotel company with high level .


Example 616:
data: Year|2018/19|x|line_chart Inheritance_tax_receipts_in_billion_GBP|5.36|y|line_chart Year|2017/18|x|line_chart Inheritance_tax_receipts_in_billion_GBP|5.2|y|line_chart Year|2016/17|x|line_chart Inheritance_tax_receipts_in_billion_GBP|4.8|y|line_chart Year|2015/16|x|line_chart Inheritance_tax_receipts_in_billion_GBP|4.7|y|line_chart Year|2014/15|x|line_chart Inheritance_tax_receipts_in_billion_GBP|3.8|y|line_chart Year|2013/14|x|line_chart Inheritance_tax_receipts_in_billion_GBP|3.4|y|line_chart Year|2012/13|x|line_chart Inheritance_tax_receipts_in_billion_GBP|3.1|y|line_chart Year|2011/12|x|line_chart Inheritance_tax_receipts_in_billion_GBP|2.9|y|line_chart Year|2010/11|x|line_chart Inheritance_tax_receipts_in_billion_GBP|2.7|y|line_chart Year|2009/10|x|line_chart Inheritance_tax_receipts_in_billion_GBP|2.4|y|line_chart Year|2008/09|x|line_chart Inheritance_tax_receipts_in_billion_GBP|2.8|y|line_chart Year|2007/08|x|line_chart Inheritance_tax_receipts_in_billion_GBP|3.8|y|line_chart Year|2006/07|x|line_chart Inheritance_tax_receipts_in_billion_GBP|3.5|y|line_chart Year|2005/06|x|line_chart Inheritance_tax_receipts_in_billion_GBP|3.3|y|line_chart Year|2004/05|x|line_chart Inheritance_tax_receipts_in_billion_GBP|2.9|y|line_chart Year|2003/04|x|line_chart Inheritance_tax_receipts_in_billion_GBP|2.5|y|line_chart Year|2002/03|x|line_chart Inheritance_tax_receipts_in_billion_GBP|2.4|y|line_chart Year|2001/02|x|line_chart Inheritance_tax_receipts_in_billion_GBP|2.4|y|line_chart Year|2000/01|x|line_chart Inheritance_tax_receipts_in_billion_GBP|2.2|y|line_chart 
title: Inheritance tax : United Kingdom HMRC tax receipts 2000 - 2019

gold: This statistic shows the total United Kingdom ( UK ) HMRC inheritance tax receipts from fiscal year 2000/01 to fiscal year 2018/19 . Despite a dip in 2008/09 and 2009/10 the overall trend was one of increase . The peak was in 2018/19 at 5.36 billion British pounds ( GBP ) .
gold_template: This statistic shows the total templateTitle[2] templateTitle[3] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . Despite a dip in templateXValue[10] and templateXValue[9] the overall trend was one of increase . The peak was in templateXValue[0] at templateYValue[max] templateYLabel[3] British pounds ( templateYLabel[4] ) .

generated_template: In templateXValue[0] , the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateYLabel[1] ( templateTitle[4] ) in the United States amounted to templateYValue[0] in templateTitle[6] , an increase from the previous templateXLabel[0] . The highest level of templateTitle[4] in templateXValue[5] and templateXValue[3] , there were just templateYValue[min] percent in the previous templateXLabel[0] .
generated: In 2018/19 , the Inheritance tax of Inheritance tax ( HMRC ) in the United States amounted to 5.36 in receipts , an increase from the previous Year . The highest level of HMRC in 2013/14 and 2015/16 , there were just 2.2 percent in the previous Year .


Example 617:
data: Year|2019|x|line_chart R&D_expenditure_in_million_euros|140|y|line_chart Year|2018|x|line_chart R&D_expenditure_in_million_euros|130|y|line_chart Year|2017|x|line_chart R&D_expenditure_in_million_euros|130|y|line_chart Year|2016|x|line_chart R&D_expenditure_in_million_euros|111|y|line_chart Year|2015|x|line_chart R&D_expenditure_in_million_euros|97|y|line_chart Year|2014|x|line_chart R&D_expenditure_in_million_euros|79|y|line_chart Year|2013|x|line_chart R&D_expenditure_in_million_euros|71|y|line_chart Year|2012|x|line_chart R&D_expenditure_in_million_euros|68|y|line_chart Year|2011|x|line_chart R&D_expenditure_in_million_euros|63|y|line_chart Year|2010|x|line_chart R&D_expenditure_in_million_euros|46|y|line_chart Year|2009|x|line_chart R&D_expenditure_in_million_euros|45|y|line_chart Year|2008|x|line_chart R&D_expenditure_in_million_euros|43|y|line_chart 
title: LVMH Group 's R & D expenditure worldwide 2008 - 2019

gold: This statistic highlights the trend in research and development ( R & D ) expenditure of the LVMH Group worldwide from 2008 to 2019 . In 2019 , LVMH Group 's global R & D expenditure amounted to about 140 million euros .
gold_template: This statistic highlights the trend in research and development ( templateTitle[3] templateTitle[4] templateTitle[5] ) templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] global templateTitle[3] templateTitle[4] templateTitle[5] templateYLabel[1] amounted to about templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] million people employed by the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the R&D of expenditure million in the United States from 2008 to 2019 . In 2019 , there were 140 million people employed by the 's R & D in the United States .


Example 618:
data: Age_of_offender_in_years|Infant_(<1)|x|bar_chart Number_of_offenders|0|y|bar_chart Age_of_offender_in_years|1_to_4|x|bar_chart Number_of_offenders|1|y|bar_chart Age_of_offender_in_years|5_to_8|x|bar_chart Number_of_offenders|1|y|bar_chart Age_of_offender_in_years|9_to_12|x|bar_chart Number_of_offenders|8|y|bar_chart Age_of_offender_in_years|13_to_16|x|bar_chart Number_of_offenders|496|y|bar_chart Age_of_offender_in_years|17_to_19|x|bar_chart Number_of_offenders|1479|y|bar_chart Age_of_offender_in_years|20_to_24|x|bar_chart Number_of_offenders|2254|y|bar_chart Age_of_offender_in_years|25_to_29|x|bar_chart Number_of_offenders|1998|y|bar_chart Age_of_offender_in_years|30_to_34|x|bar_chart Number_of_offenders|1440|y|bar_chart Age_of_offender_in_years|35_to_39|x|bar_chart Number_of_offenders|1161|y|bar_chart Age_of_offender_in_years|40_to_44|x|bar_chart Number_of_offenders|651|y|bar_chart Age_of_offender_in_years|45_to_49|x|bar_chart Number_of_offenders|495|y|bar_chart Age_of_offender_in_years|50_to_54|x|bar_chart Number_of_offenders|439|y|bar_chart Age_of_offender_in_years|55_to_59|x|bar_chart Number_of_offenders|346|y|bar_chart Age_of_offender_in_years|60_to_64|x|bar_chart Number_of_offenders|186|y|bar_chart Age_of_offender_in_years|65_to_69|x|bar_chart Number_of_offenders|106|y|bar_chart Age_of_offender_in_years|70_to_74|x|bar_chart Number_of_offenders|82|y|bar_chart Age_of_offender_in_years|75+|x|bar_chart Number_of_offenders|93|y|bar_chart Age_of_offender_in_years|Unknown|x|bar_chart Number_of_offenders|5099|y|bar_chart 
title: Murder in the U.S. : number of offenders by age 2018

gold: 2,254 murderers in the United States in 2018 were individuals between the ages of 20 and 24 . In the same year , the youngest murder offender was between the ages of one and four , and there were 93 murder offenders over the age of 75 . Murder rate in the United States Despite some feeling that violent crime in the United States is on the rise , perhaps due to sensationalized media coverage , the murder and nonnegligent manslaughter rate has declined steeply since 1990 .
gold_template: templateYValue[6] murderers in the templateTitle[1] in templateTitle[6] were individuals between the ages of templateXValue[6] and templateXValue[6] . In the same year , the youngest templateTitle[0] templateXLabel[1] was between the ages of templateXValue[1] and templateXValue[1] , and there were templateYValue[17] templateTitle[0] templateYLabel[1] over the templateXLabel[0] of 75 . templateTitle[0] rate in the templateTitle[1] Despite some feeling that violent crime in the templateTitle[1] is on the rise , perhaps due to sensationalized media coverage , the templateTitle[0] and nonnegligent manslaughter rate has declined steeply since 1990 .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the United States in templateTitle[6] , templateTitle[4] templateXLabel[0] . In templateTitle[6] , about templateYValue[max] percent of the United States were killed by households in the United States .
generated: This statistic shows the Number of offenders yLabelErr in the United States in 2018 , by Age . In 2018 , about 5099 percent of the United States were killed by households in the United States .


Example 619:
data: Year|2024|x|line_chart Unemployment_rate|5.77|y|line_chart Year|2023|x|line_chart Unemployment_rate|5.77|y|line_chart Year|2022|x|line_chart Unemployment_rate|5.77|y|line_chart Year|2021|x|line_chart Unemployment_rate|5.8|y|line_chart Year|2020|x|line_chart Unemployment_rate|5.91|y|line_chart Year|2019|x|line_chart Unemployment_rate|6.11|y|line_chart Year|2018|x|line_chart Unemployment_rate|5.96|y|line_chart Year|2017|x|line_chart Unemployment_rate|6.13|y|line_chart Year|2016|x|line_chart Unemployment_rate|5.49|y|line_chart Year|2015|x|line_chart Unemployment_rate|5.05|y|line_chart Year|2014|x|line_chart Unemployment_rate|4.82|y|line_chart 
title: Unemployment rate in Panama 2024

gold: This statistic shows the unemployment rate in Panama from 2014 to 2018 , with projections up until 2024 . In 2018 , the unemployment rate in Panama was 5.96 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was templateYValue[6] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] was at approximately templateYValue[6] percent .
generated: The statistic shows the Unemployment rate in Panama 2024 from 2014 to 2018 , with projections up until 2024 . In 2018 , the Unemployment rate in Panama 2024 was at approximately 5.96 percent .


Example 620:
data: Year|2018|x|line_chart Number_of_students|148551|y|line_chart Year|2017|x|line_chart Number_of_students|150608|y|line_chart Year|2016|x|line_chart Number_of_students|149788|y|line_chart Year|2015|x|line_chart Number_of_students|148144|y|line_chart Year|2014|x|line_chart Number_of_students|147760|y|line_chart Year|2013|x|line_chart Number_of_students|148051|y|line_chart Year|2012|x|line_chart Number_of_students|144791|y|line_chart Year|2011|x|line_chart Number_of_students|140259|y|line_chart Year|2010|x|line_chart Number_of_students|132619|y|line_chart Year|2009|x|line_chart Number_of_students|122837|y|line_chart Year|2008|x|line_chart Number_of_students|118217|y|line_chart 
title: Number of students in upper secondary education in Denmark 2008 - 2018

gold: The statistic shows the number of students in upper secondary education in Denmark from 2008 to 2018 . The number increased from about 118 thousand upper secondary education students in 2008 to about 149 thousand students in 2018 .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] increased from about templateYValue[min] thousand templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] in templateXValue[min] to about templateYValue[0] thousand templateYLabel[1] in templateXValue[max] .

generated_template: In templateXValue[max] , almost templateYValue[0] templateYLabel[1] enrolled in the United States . This was a slight decrease from templateXValue[1] , the previous templateXLabel[0] 's highest templateYLabel[0] of templateYLabel[1] since templateXValue[6] , when it reached into templateYValue[min] thousand templateYLabel[1] . The templateYLabel[0] of templateYLabel[1] enrolled in citizens between templateXValue[5] and templateXValue[1] , which rose , with templateYValue[max] percent of female householder and 24.9 percent of the male population in the U.S. dollars per hour , and templateXValue[6] .
generated: In 2018 , almost 148551 students enrolled in the United States . This was a slight decrease from 2017 , the previous Year 's highest Number of students since 2012 , when it reached into 118217 thousand students . The Number of students enrolled in citizens between 2013 and 2017 , which rose , with 150608 percent of female householder and 24.9 percent of the male population in the U.S. dollars per hour , and 2012 .


Example 621:
data: Month|Dec_'19|x|bar_chart Number_of_hours|46.2|y|bar_chart Month|Nov_'19|x|bar_chart Number_of_hours|48.1|y|bar_chart Month|Oct_'19|x|bar_chart Number_of_hours|87.9|y|bar_chart Month|Sep_'19|x|bar_chart Number_of_hours|144.0|y|bar_chart Month|Aug_'19|x|bar_chart Number_of_hours|173.6|y|bar_chart Month|Jul_'19|x|bar_chart Number_of_hours|173.2|y|bar_chart Month|Jun_'19|x|bar_chart Number_of_hours|160.8|y|bar_chart Month|May_'19|x|bar_chart Number_of_hours|188.5|y|bar_chart Month|Apr_'19|x|bar_chart Number_of_hours|168.9|y|bar_chart Month|Mar_'19|x|bar_chart Number_of_hours|115.6|y|bar_chart Month|Feb_'19|x|bar_chart Number_of_hours|100.6|y|bar_chart Month|Jan_'19|x|bar_chart Number_of_hours|47.9|y|bar_chart Month|Dec_'18|x|bar_chart Number_of_hours|37.6|y|bar_chart Month|Nov_'18|x|bar_chart Number_of_hours|63.0|y|bar_chart Month|Oct_'18|x|bar_chart Number_of_hours|113.2|y|bar_chart Month|Sep_'18|x|bar_chart Number_of_hours|134.1|y|bar_chart Month|Aug_'18|x|bar_chart Number_of_hours|147.4|y|bar_chart Month|Jul_'18|x|bar_chart Number_of_hours|237.6|y|bar_chart Month|Jun_'18|x|bar_chart Number_of_hours|239.9|y|bar_chart Month|May_'18|x|bar_chart Number_of_hours|246.0|y|bar_chart Month|Apr_'18|x|bar_chart Number_of_hours|132.6|y|bar_chart Month|Mar_'18|x|bar_chart Number_of_hours|85.0|y|bar_chart Month|Feb_'18|x|bar_chart Number_of_hours|95.6|y|bar_chart Month|Jan_'18|x|bar_chart Number_of_hours|48.7|y|bar_chart Month|Dec_'17|x|bar_chart Number_of_hours|45.3|y|bar_chart Month|Nov_'17|x|bar_chart Number_of_hours|71.1|y|bar_chart Month|Oct_'17|x|bar_chart Number_of_hours|72.7|y|bar_chart Month|Sep_'17|x|bar_chart Number_of_hours|109.0|y|bar_chart Month|Aug_'17|x|bar_chart Number_of_hours|155.5|y|bar_chart Month|Jul_'17|x|bar_chart Number_of_hours|168.7|y|bar_chart Month|Jun_'17|x|bar_chart Number_of_hours|155.7|y|bar_chart Month|May_'17|x|bar_chart Number_of_hours|208.3|y|bar_chart Month|Apr_'17|x|bar_chart Number_of_hours|158.0|y|bar_chart Month|Mar_'17|x|bar_chart Number_of_hours|119.7|y|bar_chart Month|Feb_'17|x|bar_chart Number_of_hours|55.0|y|bar_chart Month|Jan_'17|x|bar_chart Number_of_hours|55.1|y|bar_chart Month|Dec_'16|x|bar_chart Number_of_hours|40.7|y|bar_chart Month|Nov_'16|x|bar_chart Number_of_hours|74.7|y|bar_chart Month|Oct_'16|x|bar_chart Number_of_hours|105.3|y|bar_chart Month|Sep_'16|x|bar_chart Number_of_hours|119.9|y|bar_chart Month|Aug_'16|x|bar_chart Number_of_hours|181.7|y|bar_chart Month|Jul_'16|x|bar_chart Number_of_hours|156.4|y|bar_chart Month|Jun_'16|x|bar_chart Number_of_hours|136.5|y|bar_chart Month|May_'16|x|bar_chart Number_of_hours|209.6|y|bar_chart Month|Apr_'16|x|bar_chart Number_of_hours|160.8|y|bar_chart Month|Mar_'16|x|bar_chart Number_of_hours|117.3|y|bar_chart Month|Feb_'16|x|bar_chart Number_of_hours|84.9|y|bar_chart Month|Jan_'16|x|bar_chart Number_of_hours|37.1|y|bar_chart Month|Dec_'15|x|bar_chart Number_of_hours|29.2|y|bar_chart Month|Nov_'15|x|bar_chart Number_of_hours|35.6|y|bar_chart Month|Oct_'15|x|bar_chart Number_of_hours|91.2|y|bar_chart Month|Sep_'15|x|bar_chart Number_of_hours|157.8|y|bar_chart Month|Aug_'15|x|bar_chart Number_of_hours|148.8|y|bar_chart Month|Jul_'15|x|bar_chart Number_of_hours|160.6|y|bar_chart Month|Jun_'15|x|bar_chart Number_of_hours|189.7|y|bar_chart Month|May_'15|x|bar_chart Number_of_hours|174.4|y|bar_chart Month|Apr_'15|x|bar_chart Number_of_hours|212.9|y|bar_chart Month|Mar_'15|x|bar_chart Number_of_hours|121.9|y|bar_chart Month|Feb_'15|x|bar_chart Number_of_hours|76.0|y|bar_chart Month|Jan_'15|x|bar_chart Number_of_hours|58.5|y|bar_chart Month|Dec_'14|x|bar_chart Number_of_hours|57.1|y|bar_chart Month|Nov_'14|x|bar_chart Number_of_hours|51.9|y|bar_chart Month|Oct_'14|x|bar_chart Number_of_hours|82.8|y|bar_chart Month|Sep_'14|x|bar_chart Number_of_hours|123.3|y|bar_chart Month|Aug_'14|x|bar_chart Number_of_hours|171.0|y|bar_chart Month|Jul_'14|x|bar_chart Number_of_hours|223.0|y|bar_chart Month|Jun_'14|x|bar_chart Number_of_hours|178.4|y|bar_chart Month|May_'14|x|bar_chart Number_of_hours|149.6|y|bar_chart Month|Apr_'14|x|bar_chart Number_of_hours|144.9|y|bar_chart Month|Mar_'14|x|bar_chart Number_of_hours|126.7|y|bar_chart Month|Feb_'14|x|bar_chart Number_of_hours|75.0|y|bar_chart Month|Jan_'14|x|bar_chart Number_of_hours|42.8|y|bar_chart 
title: Monthly hours of sunlight in UK 2014 - 2019

gold: In the period of consideration , the total monthly hours of sunlight in the UK followed a similar pattern each year . The most notable change occurred in 2018 , when the hours of sunlight shot up in May , June and July to 246 , 240 and 238 hours respectively . Unsurprisingly it was the end of each year when sunlight hours were lowest .
gold_template: In the period of consideration , the total templateTitle[0] templateYLabel[1] of templateTitle[2] in the templateTitle[3] followed a similar pattern each year . The most notable change occurred in 2018 , when the templateYLabel[1] of templateTitle[2] shot up in templateXValue[7] , June and July to templateYValue[max] , templateYValue[18] and templateYValue[17] templateYLabel[1] respectively . Unsurprisingly it was the end of each year when templateTitle[2] templateYLabel[1] were lowest .

generated_template: This statistic shows the templateTitle[3] templateYLabel[0] templateYLabel[1] of templateYLabel[2] in the United States from January 2018 to December 2019 . In December 2019 , about templateYValue[max] people were killed by templateYLabel[1] throughout the last templateYValue[1] million people .
generated: This statistic shows the UK Number hours of yLabelErr in the United States from January 2018 to December 2019 . In December 2019 , about 246.0 people were killed by hours throughout the last 48.1 million people .


Example 622:
data: Country|Burundi|x|bar_chart Access_rate|9.3|y|bar_chart Country|Chad|x|bar_chart Access_rate|10.9|y|bar_chart Country|Malawi|x|bar_chart Access_rate|12.7|y|bar_chart Country|Dem._Republic_of_the_Congo|x|bar_chart Access_rate|19.1|y|bar_chart Country|Niger|x|bar_chart Access_rate|20|y|bar_chart Country|Liberia|x|bar_chart Access_rate|21.5|y|bar_chart Country|Uganda|x|bar_chart Access_rate|22|y|bar_chart Country|Sierra_Leone|x|bar_chart Access_rate|23.4|y|bar_chart Country|Madagascar|x|bar_chart Access_rate|24.1|y|bar_chart Country|South_Sudan|x|bar_chart Access_rate|25.4|y|bar_chart Country|Burkina_Faso|x|bar_chart Access_rate|25.5|y|bar_chart Country|Guinea-Bissau|x|bar_chart Access_rate|26|y|bar_chart Country|Mozambique|x|bar_chart Access_rate|27.4|y|bar_chart Country|Central_African_Republic|x|bar_chart Access_rate|30|y|bar_chart Country|Tanzania|x|bar_chart Access_rate|32.8|y|bar_chart Country|Somalia|x|bar_chart Access_rate|32.9|y|bar_chart Country|Lesotho|x|bar_chart Access_rate|33.7|y|bar_chart Country|Rwanda|x|bar_chart Access_rate|34.1|y|bar_chart Country|Guinea|x|bar_chart Access_rate|35.4|y|bar_chart Country|Zambia|x|bar_chart Access_rate|40.3|y|bar_chart Country|Global_average|x|bar_chart Access_rate|88.8|y|bar_chart 
title: Countries with the lowest access to electricity 2017

gold: This statistic shows the countries with the lowest access to electricity in 2017 based on access rate . As of that time , about 12.7 percent of the population in Malawi had access to electricity .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] to templateTitle[4] in templateTitle[5] based on templateYLabel[0] templateYLabel[1] . As of that time , about templateYValue[2] percent of the population in templateXValue[2] had templateYLabel[0] to templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] in the United States as of templateTitle[5] templateTitle[6] . The survey revealed that templateYValue[max] percent of the templateYLabel[1] templateTitle[3] templateTitle[4] in the templateXValue[0] templateXValue[0] had an increase of templateYValue[max] percent in the previous year .
generated: This statistic shows the Access rate of electricity 2017 in the United States as of titleErr titleErr . The survey revealed that 88.8 percent of the rate electricity 2017 in the Burundi had an increase of 88.8 percent in the previous year .


Example 623:
data: Quarter|Q3_'19|x|bar_chart Number_of_cash_machines|60534|y|bar_chart Quarter|Q2_'19|x|bar_chart Number_of_cash_machines|61967|y|bar_chart Quarter|Q1_'19|x|bar_chart Number_of_cash_machines|62581|y|bar_chart Quarter|Q4_'18|x|bar_chart Number_of_cash_machines|63360|y|bar_chart Quarter|Q3_'18|x|bar_chart Number_of_cash_machines|64362|y|bar_chart Quarter|Q2_'18|x|bar_chart Number_of_cash_machines|65379|y|bar_chart Quarter|Q1_'18|x|bar_chart Number_of_cash_machines|67419|y|bar_chart Quarter|Q4_'17|x|bar_chart Number_of_cash_machines|69603|y|bar_chart Quarter|Q3_'17|x|bar_chart Number_of_cash_machines|70045|y|bar_chart Quarter|Q2_'17|x|bar_chart Number_of_cash_machines|70114|y|bar_chart Quarter|Q1_'17|x|bar_chart Number_of_cash_machines|70045|y|bar_chart Quarter|Q4_'16|x|bar_chart Number_of_cash_machines|70020|y|bar_chart Quarter|Q3_'16|x|bar_chart Number_of_cash_machines|70254|y|bar_chart Quarter|Q2_'16|x|bar_chart Number_of_cash_machines|70682|y|bar_chart Quarter|Q1_'16|x|bar_chart Number_of_cash_machines|70330|y|bar_chart Quarter|Q4_'15|x|bar_chart Number_of_cash_machines|70270|y|bar_chart Quarter|Q3_'15|x|bar_chart Number_of_cash_machines|70018|y|bar_chart Quarter|Q2_'15|x|bar_chart Number_of_cash_machines|69876|y|bar_chart Quarter|Q1_'15|x|bar_chart Number_of_cash_machines|70006|y|bar_chart Quarter|Q4_'14|x|bar_chart Number_of_cash_machines|69382|y|bar_chart Quarter|Q3_'14|x|bar_chart Number_of_cash_machines|69120|y|bar_chart Quarter|Q2_'14|x|bar_chart Number_of_cash_machines|68819|y|bar_chart Quarter|Q1_'14|x|bar_chart Number_of_cash_machines|68135|y|bar_chart 
title: Number of cash machines in the United Kingdom ( UK ) Q1 2014 -Q3 2019

gold: This statistic illustrates the number of cash machines in the United Kingdom ( UK ) from the first quarter of 2014 to the third quarter of 2019 . Automated transaction machines ( ATMs ) are banking outlets designed for maximum access to banking transactions and information , outside of bank working hours and without the assistance of a clerk . It can be seen that the total number of cash machines increased between the first quarter of 2014 and the second quarter of 2016 , reaching a total of more than 70.1 thousand as of the second quarter of 2016 .
gold_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[3] templateTitle[4] ( templateTitle[5] ) from the first templateXLabel[0] of templateTitle[7] to the third templateXLabel[0] of templateTitle[9] . Automated transaction templateYLabel[2] ( ATMs ) are banking outlets designed for maximum access to banking transactions and information , outside of bank working hours and without the assistance of a clerk . It can be seen that the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] increased between the first templateXLabel[0] of templateTitle[7] and the second templateXLabel[0] of 2016 , reaching a total of more than templateYValue[9] thousand as of the second templateXLabel[0] of 2016 .

generated_template: During the fourth templateXLabel[0] of templateTitle[6] , almost templateYValue[max] people died in templateTitle[2] templateYLabel[2] in the templateTitle[4] templateTitle[5] , an increase compared with the previous templateXLabel[0] . The most templateYLabel[1] templateYLabel[2] was reported templateXLabel[0] . templateYLabel[1] rates – additional information Advances in templateYLabel[1] where the results of templateTitle[4] templateTitle[5] can be accessed here .
generated: During the fourth Quarter of Q1 , almost 70682 people died in machines machines in the Kingdom UK , an increase compared with the previous Quarter . The most cash machines was reported Quarter . cash rates – additional information Advances in cash where the results of Kingdom UK can be accessed here .


Example 624:
data: Company|Kraft_Heinz|x|bar_chart Operating_margin|21.9|y|bar_chart Company|Kimberly-Clark|x|bar_chart Operating_margin|18.2|y|bar_chart Company|General_Mills|x|bar_chart Operating_margin|15.9|y|bar_chart Company|PepsiCo|x|bar_chart Operating_margin|15.6|y|bar_chart Company|Nestlé|x|bar_chart Operating_margin|14.7|y|bar_chart 
title: Global operating margin of CPG companies 2016 , by company

gold: This statistic shows the operating margins of consumer packaged goods ( CPG ) companies worldwide in 2016 , sorted by company . In that year , Kraft Heinz had an operating margin of 21.9 percent , the highest among the referenced CPG companies .
gold_template: This statistic shows the templateYLabel[0] margins of consumer packaged goods ( templateTitle[3] ) templateTitle[4] worldwide in templateTitle[5] , sorted templateTitle[6] templateXLabel[0] . In that year , templateXValue[0] templateXValue[0] had an templateYLabel[0] templateYLabel[1] of templateYValue[max] percent , the highest among the referenced templateTitle[3] templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[2] of templateTitle[3] in the United States in templateTitle[5] . In that year , templateXValue[0] had a templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[max] percent .
generated: This statistic shows the Operating operating yLabelErr of CPG in the United States in 2016 . In that year , Kraft_Heinz had a Operating margin yLabelErr of 21.9 percent .


Example 625:
data: State|Anchorage|x|bar_chart Share_of_parkland|84.2|y|bar_chart State|Fremont|x|bar_chart Share_of_parkland|49.4|y|bar_chart State|Irvine|x|bar_chart Share_of_parkland|27.4|y|bar_chart State|Scottsdale|x|bar_chart Share_of_parkland|26.9|y|bar_chart State|North_Las_Vegas|x|bar_chart Share_of_parkland|26.2|y|bar_chart State|Chesapeake|x|bar_chart Share_of_parkland|26|y|bar_chart State|New_Orleans|x|bar_chart Share_of_parkland|25.9|y|bar_chart State|Albuquerque|x|bar_chart Share_of_parkland|23.2|y|bar_chart State|New_York|x|bar_chart Share_of_parkland|21.7|y|bar_chart State|Washington_D.C.|x|bar_chart Share_of_parkland|21.1|y|bar_chart State|San_Francisco|x|bar_chart Share_of_parkland|19.6|y|bar_chart State|Las_Vegas|x|bar_chart Share_of_parkland|19.4|y|bar_chart State|El_Paso|x|bar_chart Share_of_parkland|19.2|y|bar_chart State|San_Diego|x|bar_chart Share_of_parkland|19.1|y|bar_chart State|Jersey_City|x|bar_chart Share_of_parkland|18.1|y|bar_chart 
title: Cities with the largest parkland percentage in the U.S. 2018

gold: This statistic shows the cities with the largest parkland percentage of the city area in the United States in 2018 . In Anchorage , Alaska , 84.2 percent of the city 's area was comprised of parkland in 2018 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[1] templateTitle[4] of the templateXValue[last] area in the templateTitle[5] in templateTitle[6] . In templateXValue[0] , Alaska , templateYValue[max] percent of the templateXValue[last] 's area was comprised of templateYLabel[1] in templateTitle[6] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] as of the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] . In templateTitle[4] , there were templateYValue[max] people living in templateXValue[0] , a result of the United States .
generated: This statistic shows the Share of parkland parkland percentage as of the United States in U.S. , 2018 State . In U.S. , there were 84.2 people living in Anchorage , a result of the United States .


Example 626:
data: Year|2016|x|line_chart Number_of_sports_played|35|y|line_chart Year|2012|x|line_chart Number_of_sports_played|32|y|line_chart Year|2008|x|line_chart Number_of_sports_played|34|y|line_chart Year|2004|x|line_chart Number_of_sports_played|34|y|line_chart Year|2000|x|line_chart Number_of_sports_played|34|y|line_chart Year|1996|x|line_chart Number_of_sports_played|31|y|line_chart Year|1992|x|line_chart Number_of_sports_played|29|y|line_chart Year|1988|x|line_chart Number_of_sports_played|27|y|line_chart Year|1984|x|line_chart Number_of_sports_played|26|y|line_chart Year|1980|x|line_chart Number_of_sports_played|23|y|line_chart Year|1976|x|line_chart Number_of_sports_played|23|y|line_chart Year|1972|x|line_chart Number_of_sports_played|23|y|line_chart Year|1968|x|line_chart Number_of_sports_played|20|y|line_chart Year|1964|x|line_chart Number_of_sports_played|21|y|line_chart Year|1960|x|line_chart Number_of_sports_played|19|y|line_chart Year|1956|x|line_chart Number_of_sports_played|18|y|line_chart Year|1952|x|line_chart Number_of_sports_played|19|y|line_chart Year|1948|x|line_chart Number_of_sports_played|20|y|line_chart Year|1936|x|line_chart Number_of_sports_played|24|y|line_chart Year|1932|x|line_chart Number_of_sports_played|18|y|line_chart Year|1928|x|line_chart Number_of_sports_played|17|y|line_chart Year|1924|x|line_chart Number_of_sports_played|20|y|line_chart Year|1920|x|line_chart Number_of_sports_played|25|y|line_chart Year|1912|x|line_chart Number_of_sports_played|17|y|line_chart Year|1908|x|line_chart Number_of_sports_played|24|y|line_chart Year|1906|x|line_chart Number_of_sports_played|13|y|line_chart Year|1904|x|line_chart Number_of_sports_played|18|y|line_chart Year|1900|x|line_chart Number_of_sports_played|20|y|line_chart Year|1896|x|line_chart Number_of_sports_played|9|y|line_chart 
title: Number of sports at the Summer Olympic Games 1896 - 2016

gold: The statistic illustrates the number of sports at the Summer Olympic Games between 1896 and 2016 . In 1900 , 20 sporting events took place at the Summer Olympic Games .
gold_template: The statistic illustrates the templateYLabel[0] of templateYLabel[1] at the templateTitle[2] templateTitle[3] templateTitle[4] between templateXValue[min] and templateXValue[max] . In templateXValue[27] , templateYValue[12] sporting events took place at the templateTitle[2] templateTitle[3] templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the templateYLabel[1] were committed templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Number sports of Summer Olympic Games 1896 in the United States from 1896 to 2016 . In 2016 , 35 percent of the sports were committed Summer Olympic Games 1896 in the United States .


Example 627:
data: Year|2018|x|line_chart Share_of_urban_population_in_total_population|87.87|y|line_chart Year|2017|x|line_chart Share_of_urban_population_in_total_population|87.76|y|line_chart Year|2016|x|line_chart Share_of_urban_population_in_total_population|87.64|y|line_chart Year|2015|x|line_chart Share_of_urban_population_in_total_population|87.53|y|line_chart Year|2014|x|line_chart Share_of_urban_population_in_total_population|87.41|y|line_chart Year|2013|x|line_chart Share_of_urban_population_in_total_population|87.29|y|line_chart Year|2012|x|line_chart Share_of_urban_population_in_total_population|87.14|y|line_chart Year|2011|x|line_chart Share_of_urban_population_in_total_population|86.96|y|line_chart Year|2010|x|line_chart Share_of_urban_population_in_total_population|86.8|y|line_chart Year|2009|x|line_chart Share_of_urban_population_in_total_population|86.65|y|line_chart Year|2008|x|line_chart Share_of_urban_population_in_total_population|86.49|y|line_chart 
title: Urbanization in Denmark 2018

gold: This statistic shows the degree of urbanization in Denmark from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 87.87 percent of Denmark 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Denmark from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 87.87 percent of Denmark 's total population lived in urban areas and cities .


Example 628:
data: Year|2019|x|line_chart Number_of_births|382533|y|line_chart Year|2018|x|line_chart Number_of_births|378848|y|line_chart Year|2017|x|line_chart Number_of_births|379675|y|line_chart Year|2016|x|line_chart Number_of_births|383579|y|line_chart Year|2015|x|line_chart Number_of_births|383315|y|line_chart Year|2014|x|line_chart Number_of_births|382281|y|line_chart Year|2013|x|line_chart Number_of_births|381607|y|line_chart Year|2012|x|line_chart Number_of_births|378840|y|line_chart Year|2011|x|line_chart Number_of_births|376951|y|line_chart Year|2010|x|line_chart Number_of_births|379373|y|line_chart Year|2009|x|line_chart Number_of_births|379290|y|line_chart Year|2008|x|line_chart Number_of_births|373695|y|line_chart Year|2007|x|line_chart Number_of_births|360916|y|line_chart Year|2006|x|line_chart Number_of_births|346082|y|line_chart Year|2005|x|line_chart Number_of_births|339270|y|line_chart Year|2004|x|line_chart Number_of_births|337762|y|line_chart Year|2003|x|line_chart Number_of_births|330523|y|line_chart Year|2002|x|line_chart Number_of_births|328155|y|line_chart Year|2001|x|line_chart Number_of_births|327107|y|line_chart 
title: Number of births in Canada 2000 - 2019

gold: In 2018 , there were an estimated 382,533 babies born in Canada . This is an increase from 327,107 births in the year 2001 . Births in Canada In 2018 , there were more male babies born than female babies , and overall births have been increasing since 2000 .
gold_template: In templateXValue[1] , there were an estimated templateYValue[0] babies born in templateTitle[2] . This is an increase from templateYValue[min] templateYLabel[1] in the templateXLabel[0] templateXValue[min] . templateYLabel[1] in templateTitle[2] In templateXValue[1] , there were more male babies born than female babies , and overall templateYLabel[1] have been increasing since templateTitle[3] .

generated_template: There were templateYValue[0] thousand templateYLabel[1] in templateTitle[3] templateTitle[4] in templateXValue[max] , a slight decrease from the templateXLabel[0] . In templateXValue[1] , around templateYValue[0] thousand templateYLabel[1] templateYLabel[2] of templateXValue[0] were born in this statistic , the highest birth rate in templateTitle[2] in the templateXLabel[0] . Employment in templateYLabel[0] of children were recorded .
generated: There were 382533 thousand births in 2000 2019 in 2019 , a slight decrease from the Year . In 2018 , around 382533 thousand births yLabelErr of 2019 were born in this statistic , the highest birth rate in Canada in the Year . Employment in Number of children were recorded .


Example 629:
data: Year|2025|x|line_chart Consumption_per_capita_in_kilograms|8.39|y|line_chart Year|2019|x|line_chart Consumption_per_capita_in_kilograms|7.57|y|line_chart Year|2018|x|line_chart Consumption_per_capita_in_kilograms|7.57|y|line_chart Year|2017|x|line_chart Consumption_per_capita_in_kilograms|7.68|y|line_chart Year|2016|x|line_chart Consumption_per_capita_in_kilograms|7.91|y|line_chart Year|2015|x|line_chart Consumption_per_capita_in_kilograms|7.05|y|line_chart Year|2014|x|line_chart Consumption_per_capita_in_kilograms|6.81|y|line_chart Year|2013|x|line_chart Consumption_per_capita_in_kilograms|6.54|y|line_chart Year|2012|x|line_chart Consumption_per_capita_in_kilograms|6.25|y|line_chart Year|2011|x|line_chart Consumption_per_capita_in_kilograms|6.07|y|line_chart Year|2010|x|line_chart Consumption_per_capita_in_kilograms|5.68|y|line_chart Year|2009|x|line_chart Consumption_per_capita_in_kilograms|5.27|y|line_chart Year|2008|x|line_chart Consumption_per_capita_in_kilograms|5.17|y|line_chart Year|2007|x|line_chart Consumption_per_capita_in_kilograms|5.08|y|line_chart Year|2006|x|line_chart Consumption_per_capita_in_kilograms|4.93|y|line_chart 
title: Per capita poultry consumption in Indonesia 2006 - 2019

gold: In 2019 , Indonesians consumed around 7.6 kilograms of poultry meat per capita . In 2025 , this was expected to increase to 8.4 kilograms per capita . Indonesia 's meat consumption had been increasing in the last few years , indicating improved economic prosperity for the population .
gold_template: In templateXValue[1] , Indonesians consumed around templateYValue[1] templateYLabel[3] of templateTitle[2] meat templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this was expected to increase to templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] . templateTitle[4] 's meat templateYLabel[0] had been increasing in the last few years , indicating improved economic prosperity for the population .

generated_template: In templateXValue[1] , the templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] was approximately templateYValue[1] templateYLabel[3] . In templateXValue[max] , this was forecasted to reach around templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] . templateTitle[4] 's templateTitle[2] meat templateYLabel[0] in templateXValue[max] was above the OECD average of that templateXLabel[0] .
generated: In 2019 , the poultry Consumption per capita in Indonesia was approximately 7.57 kilograms . In 2025 , this was forecasted to reach around 8.39 kilograms per capita . Indonesia 's poultry meat Consumption in 2025 was above the OECD average of that Year .


Example 630:
data: Year|10_miles_or_less|x|line_chart Percentage_of_respondents|21|y|line_chart Year|11_-_50_miles|x|line_chart Percentage_of_respondents|17|y|line_chart Year|51_-_100_miles|x|line_chart Percentage_of_respondents|7|y|line_chart Year|101_-_150_miles|x|line_chart Percentage_of_respondents|4|y|line_chart Year|151_-_200_miles|x|line_chart Percentage_of_respondents|4|y|line_chart Year|More_than_200_miles|x|line_chart Percentage_of_respondents|43|y|line_chart Year|Only_have_grandchildren_who_live_with_me|x|line_chart Percentage_of_respondents|1|y|line_chart Year|Don't_know|x|line_chart Percentage_of_respondents|2|y|line_chart 
title: Geographic distance between grandparents and their grandchildren in the United States in 2011

gold: This statistic shows the results of a survey among grandparents in the United States in 2011 on the geographic distance between themselves and their grandchildren . In 2011 , 43 percent of the respondents stated they live more than 200 miles away from their grandchildren , whereas 21 percent said they live 10 or less miles away from their grandchildren .
gold_template: This statistic shows the results of a survey among templateTitle[3] in the templateTitle[6] templateTitle[7] in templateTitle[8] on the templateTitle[0] templateTitle[1] templateTitle[2] themselves and templateTitle[4] templateXValue[6] . In templateTitle[8] , templateYValue[max] percent of the templateYLabel[1] stated they templateXValue[6] templateXValue[5] templateXValue[5] templateXValue[4] templateXValue[0] away from templateTitle[4] templateXValue[6] , whereas templateYValue[0] percent said they templateXValue[6] templateXValue[0] or templateXValue[0] templateXValue[0] away from templateTitle[4] templateXValue[6] .

generated_template: The statistic shows the percent of templateTitle[1] in the United Kingdom ( templateTitle[0] ) that own a dishwasher from templateTitle[4] to templateTitle[5] . In templateTitle[4] , when this survey was initiated , templateYValue[min] percent of templateTitle[1] owned a dishwasher . As of templateTitle[5] , templateYValue[max] percent of templateTitle[1] in templateTitle[2] .
generated: The statistic shows the percent of distance in the United Kingdom ( Geographic ) that own a dishwasher from their to grandchildren . In their , when this survey was initiated , 1 percent of distance owned a dishwasher . As of grandchildren , 43 percent of distance in between .


Example 631:
data: Response|Extremely_positive|x|bar_chart Share_of_respondents|72|y|bar_chart Response|Somewhat_positive|x|bar_chart Share_of_respondents|22|y|bar_chart Response|Neutral|x|bar_chart Share_of_respondents|5|y|bar_chart Response|Somewhat_negative|x|bar_chart Share_of_respondents|-|y|bar_chart Response|Extremely_Negative|x|bar_chart Share_of_respondents|-|y|bar_chart 
title: Product quality rating of Under Armour footwear United States 2014

gold: This statistic shows how consumers rate the product quality of Under Armour footwear . 72 % of respondents rated Under Armour 's quality as extremely positive .
gold_template: This statistic shows how consumers rate the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitle[4] templateTitle[5] . templateYValue[0] % of templateYLabel[1] rated templateTitle[3] templateTitle[4] 's templateTitle[1] as templateXValue[0] templateXValue[0] .

generated_template: This statistic shows the results of a survey among female templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States in templateTitle[7] . According to the source , templateYValue[max] percent of templateYLabel[1] stated that they could be observed in the United States .
generated: This statistic shows the results of a survey among female quality rating Under Armour footwear in the United States in States . According to the source , 72 percent of respondents stated that they could be observed in the United States .


Example 632:
data: Response|Dissatisfaction_with_government/Poor_leadership|x|bar_chart Share_of_respondents|28|y|bar_chart Response|Immigration|x|bar_chart Share_of_respondents|6|y|bar_chart Response|Healthcare|x|bar_chart Share_of_respondents|6|y|bar_chart Response|Ethics/moral/religious/family_decline|x|bar_chart Share_of_respondents|5|y|bar_chart Response|Unifying_the_country|x|bar_chart Share_of_respondents|5|y|bar_chart Response|Poverty/Hunger/Homelessness|x|bar_chart Share_of_respondents|5|y|bar_chart Response|Lack_of_respect_for_each_other|x|bar_chart Share_of_respondents|4|y|bar_chart Response|Environment/Pollution/Climate_change|x|bar_chart Share_of_respondents|4|y|bar_chart Response|Race_relations/Racism|x|bar_chart Share_of_respondents|3|y|bar_chart Response|Situation_in_Iraq/ISIS|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Foreign_policy/Foreign_aid/Focus_overseas|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Economy_in_general|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Guns/Gun_control|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Gap_between_rich_and_poor|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Education|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Wars/War_(nonspecific)/Fear_of_war|x|bar_chart Share_of_respondents|2|y|bar_chart 
title: Public opinion on the most important problem facing the U.S. 2020

gold: This statistic represents American adults ' view of the most important problem facing the United States . In January 2020 , 28 percent of the participants stated that poor leadership and a general dissatisfaction with the government were the most important problems facing the U.S .
gold_template: This statistic represents American adults ' view of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] the templateTitle[6] . In January templateTitle[7] , templateYValue[max] percent of the participants stated that templateXValue[13] templateXValue[0] and a templateXValue[11] templateXValue[0] templateXValue[0] the government were the templateTitle[2] templateTitle[3] problems templateTitle[5] the templateTitle[6] .

generated_template: This statistic shows the results of a survey among female templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[4] in templateTitle[5] . According to the source , templateYValue[max] percent of the templateYLabel[1] cited templateXValue[1] templateXValue[1] templateXValue[2] and templateYValue[1] percent citing templateXValue[1] templateXValue[1] .
generated: This statistic shows the results of a survey among female opinion most important in problem in facing . According to the source , 28 percent of the respondents cited Immigration Healthcare and 6 percent citing Immigration .


Example 633:
data: Year|2019|x|line_chart Number_of_airstrikes|8|y|line_chart Year|2018|x|line_chart Number_of_airstrikes|35|y|line_chart Year|2017|x|line_chart Number_of_airstrikes|125|y|line_chart Year|2016|x|line_chart Number_of_airstrikes|44|y|line_chart Year|2015|x|line_chart Number_of_airstrikes|23|y|line_chart Year|2014|x|line_chart Number_of_airstrikes|23|y|line_chart Year|2013|x|line_chart Number_of_airstrikes|26|y|line_chart Year|2012|x|line_chart Number_of_airstrikes|42|y|line_chart Year|2011|x|line_chart Number_of_airstrikes|10|y|line_chart Year|2010|x|line_chart Number_of_airstrikes|4|y|line_chart Year|2009|x|line_chart Number_of_airstrikes|2|y|line_chart Year|2002|x|line_chart Number_of_airstrikes|1|y|line_chart 
title: U.S. airstrikes in Yemen 2002 - 2019

gold: This statistic shows the number of U.S. airstrikes in Yemen from 2002 to 2019 . In 2018 , there were 35 United States airstrikes in Yemen .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , there were templateYValue[1] templateTitle[0] templateYLabel[1] in templateTitle[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the United States templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] million people died at the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Number of airstrikes of the United States 2002 2019 titleErr titleErr from 2002 to 2019 . In 2019 , there were 8 million people died at the Yemen 2002 2019 titleErr in the United States .


Example 634:
data: Year|2024|x|line_chart Budget_balance_to_GDP_ratio|-2.4|y|line_chart Year|2023|x|line_chart Budget_balance_to_GDP_ratio|-2.3|y|line_chart Year|2022|x|line_chart Budget_balance_to_GDP_ratio|-2.3|y|line_chart Year|2021|x|line_chart Budget_balance_to_GDP_ratio|-2.2|y|line_chart Year|2020|x|line_chart Budget_balance_to_GDP_ratio|-2.6|y|line_chart Year|2019|x|line_chart Budget_balance_to_GDP_ratio|-2.8|y|line_chart Year|2018|x|line_chart Budget_balance_to_GDP_ratio|-2.2|y|line_chart Year|2017|x|line_chart Budget_balance_to_GDP_ratio|-1.07|y|line_chart Year|2016|x|line_chart Budget_balance_to_GDP_ratio|-2.77|y|line_chart Year|2015|x|line_chart Budget_balance_to_GDP_ratio|-4|y|line_chart Year|2014|x|line_chart Budget_balance_to_GDP_ratio|-4.54|y|line_chart 
title: Budget balance in Mexico in relation to gross domestic product ( GDP ) 2024

gold: The statistic shows the budget balance in Mexico from 2014 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . In 2018 , the state deficit of Mexico was at around 2.2 percent of the gross domestic product .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the state deficit of templateTitle[2] was at around 2.2 percent of the templateTitle[4] templateTitle[5] templateTitle[6] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the state deficit of templateTitle[2] templateTitle[3] amounted to about templateYValue[6] percent of the templateTitle[4] templateTitle[5] templateTitle[6] .
generated: The statistic shows the Budget balance in Mexico from 2014 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . In 2018 , the state deficit of Mexico relation amounted to about -2.2 percent of the gross domestic product .


Example 635:
data: Year|17|x|line_chart Gross_margin_in_billion_U.S._dollars|27.92|y|line_chart Year|16|x|line_chart Gross_margin_in_billion_U.S._dollars|26.54|y|line_chart Year|15|x|line_chart Gross_margin_in_billion_U.S._dollars|25.0|y|line_chart Year|14|x|line_chart Gross_margin_in_billion_U.S._dollars|23.93|y|line_chart Year|13|x|line_chart Gross_margin_in_billion_U.S._dollars|22.73|y|line_chart Year|12|x|line_chart Gross_margin_in_billion_U.S._dollars|21.11|y|line_chart Year|11|x|line_chart Gross_margin_in_billion_U.S._dollars|19.2|y|line_chart Year|10|x|line_chart Gross_margin_in_billion_U.S._dollars|18.73|y|line_chart Year|9|x|line_chart Gross_margin_in_billion_U.S._dollars|15.81|y|line_chart Year|8|x|line_chart Gross_margin_in_billion_U.S._dollars|20.32|y|line_chart Year|7|x|line_chart Gross_margin_in_billion_U.S._dollars|21.17|y|line_chart Year|6|x|line_chart Gross_margin_in_billion_U.S._dollars|21.19|y|line_chart Year|5|x|line_chart Gross_margin_in_billion_U.S._dollars|19.09|y|line_chart Year|4|x|line_chart Gross_margin_in_billion_U.S._dollars|18.25|y|line_chart Year|3|x|line_chart Gross_margin_in_billion_U.S._dollars|17.61|y|line_chart Year|2|x|line_chart Gross_margin_in_billion_U.S._dollars|16.94|y|line_chart Year|1|x|line_chart Gross_margin_in_billion_U.S._dollars|15.49|y|line_chart Year|0|x|line_chart Gross_margin_in_billion_U.S._dollars|15.97|y|line_chart 
title: Gross margin on furniture in U.S. wholesale 2000 - 2017

gold: This timeline depicts the U.S. merchant wholesalers ' gross margin on furniture and home furnishings from 2000 to 2017 . In 2017 , the gross margin on furniture and home furnishings in U.S. wholesale was about 27.92 billion U.S. dollars .
gold_template: This timeline depicts the templateYLabel[3] merchant wholesalers ' templateYLabel[0] templateYLabel[1] on templateTitle[2] and home furnishings from templateTitle[5] to templateTitle[6] . In templateTitle[6] , the templateYLabel[0] templateYLabel[1] on templateTitle[2] and home furnishings in templateYLabel[3] templateTitle[4] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The timeline depicts the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The timeline depicts the Gross margin of furniture U.S. wholesale 2000 2017 from 0 to 17 . In 17 , the Gross margin of furniture U.S. wholesale 2000 in the United States was 27.92 billion U.S. dollars .


Example 636:
data: Location_and_Date|Volcanic_eruption_in_the_Philippines_(June_9_1991)|x|bar_chart Number_of_victims|1036065|y|bar_chart Location_and_Date|Volcano_eruption_in_Ecuador_(August_14_2015)|x|bar_chart Number_of_victims|800000|y|bar_chart Location_and_Date|Volcanic_eruption_in_Nicaragua_(April_9_1992)|x|bar_chart Number_of_victims|300075|y|bar_chart Location_and_Date|Volcano_eruption_in_Ecuador_(August_14_2006)|x|bar_chart Number_of_victims|300013|y|bar_chart Location_and_Date|Volcano_eruption_in_Indonesia_(April_5_1982)|x|bar_chart Number_of_victims|300000|y|bar_chart Location_and_Date|Volcano_eruption_in_Indonesia_(1969)|x|bar_chart Number_of_victims|250000|y|bar_chart Location_and_Date|Volcanic_eruption_in_Comoros_(November_24_2005)|x|bar_chart Number_of_victims|245000|y|bar_chart Location_and_Date|Volcanic_eruption_in_the_Philippines_(Feb._6_1993)|x|bar_chart Number_of_victims|165009|y|bar_chart Location_and_Date|Volcanic_eruption_in_Papua_New_Guinea_(September_19_1994)|x|bar_chart Number_of_victims|152002|y|bar_chart Location_and_Date|Volcanic_eruption_in_Indonesia_(October_24_2002)|x|bar_chart Number_of_victims|137140|y|bar_chart 
title: Volcanic eruptions - people affected worldwide up to 2016

gold: The statistic shows the number of people , who were affected by the world 's most significant volcanic eruptions from 1900 to 2016* . In 1991 , total 1,036,035 were affected due to volcanic eruption in Philippines .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[2] , who were templateTitle[3] by the world 's most significant templateXValue[0] templateTitle[1] from 1900 to 2016* . In 1991 , total 1,036,035 were templateTitle[3] due to templateXValue[0] templateXValue[0] in templateXValue[0] .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[7] templateTitle[8] according to the current prices in the United States . In templateTitle[4] , templateYValue[max] percent of the templateYLabel[1] were recorded in the United States .
generated: This statistic shows the results of a survey conducted in the United States in titleErr titleErr according to the current prices in the United States . In worldwide , 1036065 percent of the victims were recorded in the United States .


Example 637:
data: Response|It_is_a_great_opportunity_to_buy_gifts_for_the_holidays|x|bar_chart Share_of_respondents|42|y|bar_chart Response|It's_a_tradition|x|bar_chart Share_of_respondents|39|y|bar_chart Response|I_like_it_even_more_now_that_I_can_shop_online|x|bar_chart Share_of_respondents|37|y|bar_chart Response|It_is_the_best_opportunity_to_buy_expensive_items_at_a_discount|x|bar_chart Share_of_respondents|33|y|bar_chart Response|It_is_when_you_find_promotions_that_are_not_available_at_any_other_time_of_year|x|bar_chart Share_of_respondents|31|y|bar_chart Response|It_is_a_good_way_to_spend_quality_time_with_friends/family|x|bar_chart Share_of_respondents|19|y|bar_chart Response|I_will_wait_until_Cyber_Monday_to_do_most_of_my_shopping|x|bar_chart Share_of_respondents|18|y|bar_chart Response|Promotions_are_never_on_products_I_am_interested_in|x|bar_chart Share_of_respondents|16|y|bar_chart Response|Retailers_just_discount_their_worst_brands|x|bar_chart Share_of_respondents|9|y|bar_chart Response|None_of_these|x|bar_chart Share_of_respondents|5|y|bar_chart 
title: U.S. consumer sentiments towards Black Friday shopping 2017

gold: This statistic shows the results of a 2017 survey in which U.S. consumers were asked about their attitude towards Black Friday shopping . According to the survey , 42 percent of respondents said that Black Friday is a great opportunity to buy gifts for the holidays .
gold_template: This statistic shows the results of a templateTitle[7] survey in which templateTitle[0] consumers were asked about templateXValue[8] attitude templateTitle[3] templateTitle[4] templateTitle[5] templateXValue[6] . According to the survey , templateYValue[max] percent of templateYLabel[1] said templateXValue[2] templateTitle[4] templateTitle[5] is a templateXValue[0] templateXValue[0] to templateXValue[0] templateXValue[0] templateXValue[0] the templateXValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States as of templateTitle[5] templateTitle[6] . During the survey , templateYValue[1] percent of templateYLabel[1] cited templateXValue[1] and templateYValue[2] percent of templateYLabel[1] said that they would be willing to spend templateXValue[0] in the U.S .
generated: This statistic shows the U.S. consumer sentiments towards Black in the United States as of Friday shopping . During the survey , 39 percent of respondents cited It's_a_tradition and 37 percent of respondents said that they would be willing to spend It_is_a_great_opportunity_to_buy_gifts_for_the_holidays in the U.S .


Example 638:
data: Month|Ryan's_World|x|bar_chart All-time_channel_views_in_billions|35.18|y|bar_chart Month|PewDiePie|x|bar_chart All-time_channel_views_in_billions|24.44|y|bar_chart Month|Like_Nastya_Vlog|x|bar_chart All-time_channel_views_in_billions|22.68|y|bar_chart Month|✿_Kids_Diana_Show|x|bar_chart All-time_channel_views_in_billions|17.01|y|bar_chart Month|DanTDM_(TheDiamondMinecart)|x|bar_chart All-time_channel_views_in_billions|16.01|y|bar_chart Month|Fun_Toys_Collector_Disney|x|bar_chart All-time_channel_views_in_billions|14.86|y|bar_chart Month|Vlad_and_Nikita|x|bar_chart All-time_channel_views_in_billions|14.07|y|bar_chart Month|FGTeeV|x|bar_chart All-time_channel_views_in_billions|13.11|y|bar_chart Month|Family_Fun_Pack|x|bar_chart All-time_channel_views_in_billions|12.66|y|bar_chart Month|CookieSwirlC|x|bar_chart All-time_channel_views_in_billions|12.42|y|bar_chart Month|Markiplier|x|bar_chart All-time_channel_views_in_billions|12.29|y|bar_chart 
title: All-time most viewed YouTube channel owners 2020

gold: As of January 2020 , Ryan from Ryan 's World ( formerly known as Ryan ToysReview ) had reached almost 35.2 billion lifetime video views , making the elementary schooler the most viewed YouTube channel owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name PewDiePie . Ryan has been uploading YouTube videos since March 2015 , and mainly features in videos where he is playing with and reviews toys `` for kids , by a kid '' .
gold_template: As of January templateTitle[6] , Ryan from Ryan 's templateXValue[0] ( formerly known as Ryan ToysReview ) had reached almost templateYValue[max] billion lifetime video templateYLabel[2] , making the elementary schooler the templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name templateXValue[1] . Ryan has been uploading templateTitle[3] videos since March 2015 , and mainly features in videos where he is playing with and reviews templateXValue[5] `` for templateXValue[3] , by a kid '' .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[3] in the United States from templateTitle[6] to templateTitle[7] . In this year , templateYValue[max] people were killed by terrorists in the United States .
generated: This statistic shows the All-time channel of most YouTube in the United States from 2020 to titleErr . In this year , 35.18 people were killed by terrorists in the United States .


Example 639:
data: Response|Pop|x|bar_chart Share_of_respondents|55.5|y|bar_chart Response|Brazilian_pop|x|bar_chart Share_of_respondents|54|y|bar_chart Response|Sertanejo|x|bar_chart Share_of_respondents|50.3|y|bar_chart Response|Rock|x|bar_chart Share_of_respondents|48.8|y|bar_chart Response|Samba/pagode|x|bar_chart Share_of_respondents|38.1|y|bar_chart Response|Electronic/dance_music|x|bar_chart Share_of_respondents|37.9|y|bar_chart Response|Dance|x|bar_chart Share_of_respondents|35.6|y|bar_chart Response|Gospel|x|bar_chart Share_of_respondents|35.1|y|bar_chart Response|Hip_hop|x|bar_chart Share_of_respondents|31.8|y|bar_chart Response|Reggae|x|bar_chart Share_of_respondents|31|y|bar_chart Response|Forró|x|bar_chart Share_of_respondents|30.2|y|bar_chart Response|Funk/soul|x|bar_chart Share_of_respondents|25.2|y|bar_chart Response|Blues|x|bar_chart Share_of_respondents|24.7|y|bar_chart Response|Latin|x|bar_chart Share_of_respondents|23.4|y|bar_chart Response|Rap|x|bar_chart Share_of_respondents|23.2|y|bar_chart Response|Country|x|bar_chart Share_of_respondents|22.6|y|bar_chart Response|Metal|x|bar_chart Share_of_respondents|17.6|y|bar_chart Response|Techno/EDM|x|bar_chart Share_of_respondents|17.4|y|bar_chart Response|R&B/soul|x|bar_chart Share_of_respondents|17.3|y|bar_chart Response|Jazz|x|bar_chart Share_of_respondents|16.8|y|bar_chart Response|Heavy_metal|x|bar_chart Share_of_respondents|14.5|y|bar_chart Response|Classical/opera|x|bar_chart Share_of_respondents|14.3|y|bar_chart Response|Reggaeton|x|bar_chart Share_of_respondents|13.2|y|bar_chart Response|Easy_listening|x|bar_chart Share_of_respondents|10.7|y|bar_chart Response|Punk|x|bar_chart Share_of_respondents|10.4|y|bar_chart Response|Folk|x|bar_chart Share_of_respondents|8.9|y|bar_chart Response|Other|x|bar_chart Share_of_respondents|4.8|y|bar_chart 
title: Brazil : most popular music genres 2018

gold: This statistic shows the results of a Deezer survey on music listening habits among adults in Brazil as of 2018 . That year , 55.5 percent of Brazilian respondents claimed to listen to pop music , whereas 54 percent said they listened to Brazilian pop .
gold_template: This statistic shows the results of a Deezer survey on templateXValue[5] templateXValue[23] habits among adults in templateTitle[0] as of templateTitle[5] . That year , templateYValue[max] percent of templateXValue[1] templateYLabel[1] claimed to listen to templateXValue[0] templateXValue[5] , whereas templateYValue[1] percent said they listened to templateXValue[1] templateXValue[0] .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[5] according to a survey conducted in templateTitle[7] . During the survey , templateYValue[1] percent of templateYLabel[1] stated that they could be observed in the United States .
generated: This statistic shows the results of a survey conducted in the United States in 2018 according to a survey conducted in titleErr . During the survey , 54 percent of respondents stated that they could be observed in the United States .


Example 640:
data: Year|2018|x|line_chart Per_capita_consumption_in_pounds|5.8|y|line_chart Year|2017|x|line_chart Per_capita_consumption_in_pounds|5.7|y|line_chart Year|2016|x|line_chart Per_capita_consumption_in_pounds|5.7|y|line_chart Year|2015|x|line_chart Per_capita_consumption_in_pounds|5.6|y|line_chart Year|2014|x|line_chart Per_capita_consumption_in_pounds|5.5|y|line_chart Year|2013|x|line_chart Per_capita_consumption_in_pounds|5.5|y|line_chart Year|2012|x|line_chart Per_capita_consumption_in_pounds|5.5|y|line_chart Year|2011|x|line_chart Per_capita_consumption_in_pounds|5.4|y|line_chart Year|2010|x|line_chart Per_capita_consumption_in_pounds|4.9|y|line_chart Year|2009|x|line_chart Per_capita_consumption_in_pounds|5.0|y|line_chart Year|2008|x|line_chart Per_capita_consumption_in_pounds|5.0|y|line_chart Year|2007|x|line_chart Per_capita_consumption_in_pounds|4.7|y|line_chart Year|2006|x|line_chart Per_capita_consumption_in_pounds|4.7|y|line_chart Year|2005|x|line_chart Per_capita_consumption_in_pounds|4.5|y|line_chart Year|2004|x|line_chart Per_capita_consumption_in_pounds|4.5|y|line_chart Year|2003|x|line_chart Per_capita_consumption_in_pounds|4.5|y|line_chart Year|2002|x|line_chart Per_capita_consumption_in_pounds|4.4|y|line_chart Year|2001|x|line_chart Per_capita_consumption_in_pounds|4.3|y|line_chart Year|2000|x|line_chart Per_capita_consumption_in_pounds|4.5|y|line_chart 
title: Per capita consumption of butter in the U.S. 2000 - 2018

gold: This statistic shows the per capita consumption of butter in the United States from 2000 to 2018 . The U.S. per capita consumption of butter amounted to 5.8 pounds in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] amounted to templateYValue[max] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . According to the report , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of U.S. 2000 in the United States from 2000 to 2018 . According to the report , the Per Per capita consumption of U.S. 2000 amounted to approximately 5.8 pounds in 2018 .


Example 641:
data: Country|Saudi_Arabia|x|bar_chart Share_of_respondents|64|y|bar_chart Country|Turkey|x|bar_chart Share_of_respondents|64|y|bar_chart Country|Brazil|x|bar_chart Share_of_respondents|62|y|bar_chart Country|New_Zealand|x|bar_chart Share_of_respondents|61|y|bar_chart Country|Australia|x|bar_chart Share_of_respondents|60|y|bar_chart Country|Mexico|x|bar_chart Share_of_respondents|56|y|bar_chart Country|Spain|x|bar_chart Share_of_respondents|53|y|bar_chart Country|Canada|x|bar_chart Share_of_respondents|50|y|bar_chart Country|United_States|x|bar_chart Share_of_respondents|50|y|bar_chart Country|South_Korea|x|bar_chart Share_of_respondents|44|y|bar_chart Country|France|x|bar_chart Share_of_respondents|42|y|bar_chart Country|Philippines|x|bar_chart Share_of_respondents|34|y|bar_chart Country|India|x|bar_chart Share_of_respondents|33|y|bar_chart Country|Germany|x|bar_chart Share_of_respondents|32|y|bar_chart Country|Japan|x|bar_chart Share_of_respondents|32|y|bar_chart Country|China|x|bar_chart Share_of_respondents|30|y|bar_chart Country|Indonesia|x|bar_chart Share_of_respondents|21|y|bar_chart Country|South_Africa|x|bar_chart Share_of_respondents|19|y|bar_chart Country|Nigeria|x|bar_chart Share_of_respondents|16|y|bar_chart 
title: Daily online video usage in selected countries 2018

gold: This statistic gives information on the share of internet users in selected countries who watch online videos every day as of January 2018 . During the survey , it was found that 50 percent of U.S. internet users watched online video content on a daily basis . Additionally , more than half of the internet users in Mexico watched online videos every day .
gold_template: This statistic gives information on the templateYLabel[0] of internet users in templateTitle[4] templateTitle[5] who watch templateTitle[1] videos every day as of January templateTitle[6] . During the survey , it was found that templateYValue[7] percent of U.S. internet users watched templateTitle[1] templateTitle[2] content on a templateTitle[0] basis . Additionally , more than half of the internet users in templateXValue[5] watched templateTitle[1] videos every day .

generated_template: This statistic presents the global templateTitle[2] of templateTitle[0] templateTitle[1] in selected countries . During the survey period , it was found that templateYValue[min] percent of the templateYLabel[1] had the highest templateTitle[0] templateTitle[1] templateTitle[3] templateTitle[4] in templateXValue[1] .
generated: This statistic presents the global video of Daily online in selected countries . During the survey period , it was found that 16 percent of the respondents had the highest Daily online usage selected in Turkey .


Example 642:
data: Year|2018|x|line_chart Youth_unemployment_rate|4.2|y|line_chart Year|2017|x|line_chart Youth_unemployment_rate|4.6|y|line_chart Year|2016|x|line_chart Youth_unemployment_rate|4.1|y|line_chart Year|2015|x|line_chart Youth_unemployment_rate|3.8|y|line_chart Year|2014|x|line_chart Youth_unemployment_rate|6.3|y|line_chart Year|2013|x|line_chart Youth_unemployment_rate|6.3|y|line_chart Year|2012|x|line_chart Youth_unemployment_rate|6.5|y|line_chart Year|2011|x|line_chart Youth_unemployment_rate|6.7|y|line_chart Year|2010|x|line_chart Youth_unemployment_rate|7.1|y|line_chart Year|2009|x|line_chart Youth_unemployment_rate|9.9|y|line_chart Year|2008|x|line_chart Youth_unemployment_rate|9.2|y|line_chart Year|2007|x|line_chart Youth_unemployment_rate|8.8|y|line_chart Year|2006|x|line_chart Youth_unemployment_rate|8.8|y|line_chart Year|2005|x|line_chart Youth_unemployment_rate|10.7|y|line_chart 
title: Youth unemployment rate in Singapore 2005 - 2018

gold: This statistic presents the unemployment rate for individuals aged 15 to 24 years in Singapore from 2005 to 2018 . In 2018 , approximately 4.2 percent of the labor force aged 15 to 24 years in Singapore were unemployed .
gold_template: This statistic presents the templateYLabel[1] templateYLabel[2] for individuals aged 15 to 24 years in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] percent of the labor force aged 15 to 24 years in templateTitle[3] were unemployed .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Singapore from 2005 and 2018 . According to the source , the data are ILO estimates . In 2018 , the estimated Youth unemployment rate in Singapore was at 4.2 percent .


Example 643:
data: Year|2017|x|line_chart Commercial_space_launches|33|y|line_chart Year|2016|x|line_chart Commercial_space_launches|21|y|line_chart Year|2015|x|line_chart Commercial_space_launches|22|y|line_chart Year|2014|x|line_chart Commercial_space_launches|23|y|line_chart Year|2013|x|line_chart Commercial_space_launches|23|y|line_chart Year|2012|x|line_chart Commercial_space_launches|20|y|line_chart Year|2011|x|line_chart Commercial_space_launches|18|y|line_chart Year|2010|x|line_chart Commercial_space_launches|23|y|line_chart Year|2009|x|line_chart Commercial_space_launches|24|y|line_chart Year|2008|x|line_chart Commercial_space_launches|28|y|line_chart Year|2007|x|line_chart Commercial_space_launches|23|y|line_chart Year|2006|x|line_chart Commercial_space_launches|21|y|line_chart Year|2005|x|line_chart Commercial_space_launches|18|y|line_chart Year|2000|x|line_chart Commercial_space_launches|35|y|line_chart Year|1995|x|line_chart Commercial_space_launches|23|y|line_chart Year|1990|x|line_chart Commercial_space_launches|15|y|line_chart 
title: Worldwide commercial space launches 1990 - 2017

gold: This statistic represents worldwide commercial space launches from 1990 to 2017 . Globally , there were 33 commercial space launches in 2017 . The major nations conducting space launches include Russia , the United States and the member states of ESA .
gold_template: This statistic represents templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] from templateXValue[min] to templateXValue[max] . Globally , there were templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The major nations conducting templateYLabel[1] templateYLabel[2] include Russia , the United States and the member states of ESA .

generated_template: As of templateXValue[max] , there were templateYValue[0] million people died in the United States in the United States . This is an increase from the templateYValue[max] million people in the previous templateXLabel[0] . Employment in the last two countries , the templateYLabel[0] rates were lived in the United States .
generated: As of 2017 , there were 33 million people died in the United States in the United States . This is an increase from the 35 million people in the previous Year . Employment in the last two countries , the Commercial rates were lived in the United States .


Example 644:
data: Year|2018|x|line_chart Number_of_divorces|14936|y|line_chart Year|2017|x|line_chart Number_of_divorces|15169|y|line_chart Year|2016|x|line_chart Number_of_divorces|17146|y|line_chart Year|2015|x|line_chart Number_of_divorces|16290|y|line_chart Year|2014|x|line_chart Number_of_divorces|19387|y|line_chart Year|2013|x|line_chart Number_of_divorces|18858|y|line_chart Year|2012|x|line_chart Number_of_divorces|15709|y|line_chart Year|2011|x|line_chart Number_of_divorces|14484|y|line_chart Year|2010|x|line_chart Number_of_divorces|14460|y|line_chart Year|2009|x|line_chart Number_of_divorces|14940|y|line_chart Year|2008|x|line_chart Number_of_divorces|14695|y|line_chart 
title: Number of divorces in Denmark 2008 - 2018

gold: In 2017 and 2018 , most Danes were never married ; the number of never married inhabitants was around 2.8 million in the fourth quarter of 2018 . By contrast , among all Danes , the fewest were divorced . In general , the number of divorces between different sexes fluctuated in recent years , peaking in 2014 at about 19 thousand divorces .
gold_template: In templateXValue[1] and templateXValue[max] , most Danes were never married ; the templateYLabel[0] of never married inhabitants was around 2.8 million in the fourth quarter of templateXValue[max] . By contrast , among all Danes , the fewest were divorced . In general , the templateYLabel[0] of templateYLabel[1] between different sexes fluctuated in recent years , peaking in templateXValue[4] at about templateYValue[max] thousand templateYLabel[1] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . The divorce divorce rate of templateTitle[3] was conducted in templateXValue[1] , the highest rate of templateYLabel[1] in templateTitle[3] was recorded in templateXValue[1] . templateYValue[0] percent of templateYLabel[1] in templateXValue[max] .
generated: The statistic shows the Number of divorces in 2008 from 2008 to 2018 . The divorce rate of 2008 was conducted in 2017 , the highest rate of divorces in 2008 was recorded in 2017 . 14936 percent of divorces in 2018 .


Example 645:
data: Year|2018|x|line_chart GDP_in_billion_euros|3386.0|y|line_chart Year|2017|x|line_chart GDP_in_billion_euros|3277.34|y|line_chart Year|2016|x|line_chart GDP_in_billion_euros|3159.75|y|line_chart Year|2015|x|line_chart GDP_in_billion_euros|3048.86|y|line_chart Year|2014|x|line_chart GDP_in_billion_euros|2938.59|y|line_chart Year|2013|x|line_chart GDP_in_billion_euros|2826.24|y|line_chart Year|2012|x|line_chart GDP_in_billion_euros|2758.26|y|line_chart Year|2011|x|line_chart GDP_in_billion_euros|2703.12|y|line_chart Year|2010|x|line_chart GDP_in_billion_euros|2580.06|y|line_chart Year|2009|x|line_chart GDP_in_billion_euros|2460.28|y|line_chart Year|2008|x|line_chart GDP_in_billion_euros|2561.74|y|line_chart Year|2007|x|line_chart GDP_in_billion_euros|2513.23|y|line_chart 
title: GDP of Germany 2018

gold: In 2018 , Germany 's gross domestic product ( GDP ) amounted to 3,386 billion euros . Germany is thus among the leading five countries in the world GDP ranking . Ze Germans are living large Germany 's economy is not only one of the largest worldwide , it is also the largest in Europe and predicted to be among the countries with the largest GDP by the year 2030 .
gold_template: In templateXValue[max] , templateTitle[1] 's gross domestic product ( templateYLabel[0] ) amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] . templateTitle[1] is thus among the leading five countries in the world templateYLabel[0] ranking . Ze Germans are living large templateTitle[1] 's economy is not only one of the largest worldwide , it is also the largest in Europe and predicted to be among the countries with the largest templateYLabel[0] by the templateXLabel[0] 2030 .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitle[3] templateTitle[4] templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] of templateTitle[3] templateTitle[4] grew by around templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . The largest templateTitle[2] was valued at approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[6] .
generated: This statistic shows the GDP Germany 2018 of the titleErr titleErr titleErr 2007 to 2018 . In 2007 , the GDP of titleErr titleErr grew by around 2460.28 billion euros yLabelErr in the previous Year . The largest 2018 was valued at approximately 3386.0 billion euros yLabelErr in 2012 .


Example 646:
data: Country|Costa_Rica|x|bar_chart Average_age_in_years|29.8|y|bar_chart Country|Argentina|x|bar_chart Average_age_in_years|29.6|y|bar_chart Country|Mexico|x|bar_chart Average_age_in_years|29.3|y|bar_chart Country|Panama|x|bar_chart Average_age_in_years|28.9|y|bar_chart Country|Brazil|x|bar_chart Average_age_in_years|28.6|y|bar_chart Country|Colombia|x|bar_chart Average_age_in_years|28.4|y|bar_chart Country|Uruguay|x|bar_chart Average_age_in_years|28.2|y|bar_chart Country|Peru|x|bar_chart Average_age_in_years|27.5|y|bar_chart 
title: 2018 FIFA World Cup : average age of Latin American soccer teams

gold: The statistic presents the average age of all Latin American soccer teams participating in the 2018 FIFA World Cup in Russia . Costa Rica was the Latin American soccer team with the oldest average age ( 29.8 years ) , followed by Argentina with team players averaging 29.6 years old .
gold_template: The statistic presents the templateYLabel[0] templateYLabel[1] of all templateTitle[6] templateTitle[7] templateTitle[8] templateTitle[9] participating in the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in Russia . templateXValue[0] templateXValue[0] was the templateTitle[6] templateTitle[7] templateTitle[8] team with the oldest templateYLabel[0] templateYLabel[1] ( templateYValue[max] templateYLabel[2] ) , followed by templateXValue[1] with team players averaging templateYValue[1] templateYLabel[2] old .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of households in the templateTitle[3] templateTitle[4] install base templateTitle[5] as of templateTitle[6] . As of January templateTitle[7] , it was found that in the templateXValue[0] templateXValue[last] had the highest templateYLabel[0] templateYLabel[1] of templateTitle[3] at templateYValue[min] templateYLabel[2] .
generated: The statistic shows the Average age of households in the Cup average install base age as of Latin . As of January American , it was found that in the Costa_Rica Peru had the highest Average age of Cup at 27.5 years .


Example 647:
data: Country|Brazil|x|bar_chart Price_in_U.S._dollars|1702.43|y|bar_chart Country|Argentina|x|bar_chart Price_in_U.S._dollars|1387.9|y|bar_chart Country|India|x|bar_chart Price_in_U.S._dollars|653.54|y|bar_chart Country|Indonesia|x|bar_chart Price_in_U.S._dollars|619.76|y|bar_chart Country|Thailand|x|bar_chart Price_in_U.S._dollars|614.77|y|bar_chart Country|South_Africa|x|bar_chart Price_in_U.S._dollars|585.79|y|bar_chart Country|United_Kingdom|x|bar_chart Price_in_U.S._dollars|580.94|y|bar_chart Country|Philippines|x|bar_chart Price_in_U.S._dollars|559.51|y|bar_chart Country|Germany|x|bar_chart Price_in_U.S._dollars|557.07|y|bar_chart Country|Malaysia|x|bar_chart Price_in_U.S._dollars|550.76|y|bar_chart Country|Russia|x|bar_chart Price_in_U.S._dollars|523.85|y|bar_chart Country|Singapore|x|bar_chart Price_in_U.S._dollars|505.7|y|bar_chart Country|South_Korea|x|bar_chart Price_in_U.S._dollars|466.82|y|bar_chart Country|Australia|x|bar_chart Price_in_U.S._dollars|492.84|y|bar_chart Country|United_Arab_Emirates|x|bar_chart Price_in_U.S._dollars|462.56|y|bar_chart Country|Canada|x|bar_chart Price_in_U.S._dollars|451.42|y|bar_chart Country|Hong_Kong|x|bar_chart Price_in_U.S._dollars|435.23|y|bar_chart Country|Taiwan|x|bar_chart Price_in_U.S._dollars|427.83|y|bar_chart Country|United_States|x|bar_chart Price_in_U.S._dollars|399.99|y|bar_chart Country|Japan|x|bar_chart Price_in_U.S._dollars|392.38|y|bar_chart 
title: Suggested retail price of a PlayStation 4 in 2014 , by country

gold: The ranking shows the suggested retail price of a PlayStation 4 in selected countries worldwide as of March 2014 . Brazil ranked first with a suggested retail price of more than 1,702 U.S. dollars , almost four times as much as the price in the United States ( 399.99 dollars ) . Global unit sales data from 2014 and 2015 shows that PlayStation 4 was the highest selling platform worldwide in those years .
gold_template: The ranking shows the templateTitle[0] templateTitle[1] templateYLabel[0] of a templateTitle[3] templateTitle[4] in selected countries worldwide as of March templateTitle[5] . templateXValue[0] ranked first with a templateTitle[0] templateTitle[1] templateYLabel[0] of more than templateYValue[max] templateYLabel[1] templateYLabel[2] , almost templateTitle[4] times as much as the templateYLabel[0] in the templateXValue[6] templateXValue[18] ( templateYValue[18] templateYLabel[2] ) . Global unit sales data from templateTitle[5] and 2015 shows that templateTitle[3] templateTitle[4] was the highest selling platform worldwide in those years .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] templateYLabel[3] in templateTitle[5] . In that year , templateTitle[3] templateTitle[4] templateTitle[5] was estimated to be some templateYValue[0] templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the Suggested retail of price PlayStation 4 2014 in dollars yLabelErr in 2014 . In that year , PlayStation 4 2014 was estimated to be some 1702.43 U.S. dollars .


Example 648:
data: Country|Mexico|x|bar_chart Manufacturing_costs_index_(U.S._=_100)|82.9|y|bar_chart Country|Canada|x|bar_chart Manufacturing_costs_index_(U.S._=_100)|88.8|y|bar_chart Country|Netherlands|x|bar_chart Manufacturing_costs_index_(U.S._=_100)|89.9|y|bar_chart Country|Italy|x|bar_chart Manufacturing_costs_index_(U.S._=_100)|90.3|y|bar_chart Country|United_Kingdom|x|bar_chart Manufacturing_costs_index_(U.S._=_100)|90.8|y|bar_chart Country|Australia|x|bar_chart Manufacturing_costs_index_(U.S._=_100)|91.3|y|bar_chart Country|France|x|bar_chart Manufacturing_costs_index_(U.S._=_100)|91.8|y|bar_chart Country|Germany|x|bar_chart Manufacturing_costs_index_(U.S._=_100)|93.4|y|bar_chart Country|Japan|x|bar_chart Manufacturing_costs_index_(U.S._=_100)|93.6|y|bar_chart Country|United_States|x|bar_chart Manufacturing_costs_index_(U.S._=_100)|100|y|bar_chart 
title: Manufacturing costs in pharmaceutical industry by country 2016

gold: This statistic compares the manufacturing costs of the pharmaceutical industry in selected countries with costs in the United States in 2016 , based on a cost index . Manufacturing costs in all selected countries were less than in the United States , with costs in Mexico being 17.1 percent less than in the United States .
gold_template: This statistic compares the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] in selected countries with templateYLabel[1] in the templateXValue[4] templateXValue[last] in templateTitle[6] , based on a cost templateYLabel[2] . templateYLabel[0] templateYLabel[1] in all selected countries were less than in the templateXValue[4] templateXValue[last] , with templateYLabel[1] in templateXValue[0] being 17.1 percent less than in the templateXValue[4] templateXValue[last] .

generated_template: This statistic gives information on the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[3] in templateTitle[4] templateTitle[5] in templateTitle[7] . In that year , there were approximately templateYValue[max] million templateYLabel[1] in the templateXValue[0] templateXValue[0] .
generated: This statistic gives information on the Manufacturing costs of Manufacturing industry in by country in titleErr . In that year , there were approximately 100 million costs in the Mexico .


Example 649:
data: Year|2028|x|line_chart Volume_in_thousand_metric_tons|12182|y|line_chart Year|2027|x|line_chart Volume_in_thousand_metric_tons|12111|y|line_chart Year|2026|x|line_chart Volume_in_thousand_metric_tons|12041|y|line_chart Year|2025|x|line_chart Volume_in_thousand_metric_tons|11976|y|line_chart Year|2024|x|line_chart Volume_in_thousand_metric_tons|11921|y|line_chart Year|2023|x|line_chart Volume_in_thousand_metric_tons|11869|y|line_chart Year|2022|x|line_chart Volume_in_thousand_metric_tons|11817|y|line_chart Year|2021|x|line_chart Volume_in_thousand_metric_tons|11751|y|line_chart Year|2020|x|line_chart Volume_in_thousand_metric_tons|11690|y|line_chart Year|2019|x|line_chart Volume_in_thousand_metric_tons|11664|y|line_chart Year|2018|x|line_chart Volume_in_thousand_metric_tons|11861|y|line_chart Year|2017|x|line_chart Volume_in_thousand_metric_tons|11606|y|line_chart Year|2016|x|line_chart Volume_in_thousand_metric_tons|11667|y|line_chart Year|2015|x|line_chart Volume_in_thousand_metric_tons|11102|y|line_chart 
title: European Union-27 : poultry meat consumption volume forecast 2015 - 2028

gold: Forecasts up until the year 2018 show that poultry meat consumption across the European Union is expected to increase to 11.86 million metric tons . In the following decade consumption will likely slow down , with the forecast up until 2028 remaining constant . By the end of the period in consideration , consumption will amount to an estimated 12.18 million metric tons .
gold_template: Forecasts up until the templateXLabel[0] templateXValue[10] show that templateTitle[2] templateTitle[3] templateTitle[4] across the templateTitle[0] Union is expected to increase to templateYValue[10] million templateYLabel[2] templateYLabel[3] . In the following decade templateTitle[4] will likely slow down , with the templateTitle[6] up until templateXValue[max] remaining constant . By the end of the period in consideration , templateTitle[4] will amount to an estimated templateYValue[max] million templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[6] , about templateYValue[6] percent of the templateYLabel[1] templateYLabel[2] in templateTitle[3] templateTitle[4] employed in the U.S. dollars .
generated: The statistic shows the Volume thousand metric of meat consumption volume in the United States from 2015 to 2028 . In 2022 , about 11817 percent of the thousand metric in meat consumption employed in the U.S. dollars .


Example 650:
data: Year|2013|x|line_chart Share_of_respondents|96|y|line_chart Year|2011|x|line_chart Share_of_respondents|80|y|line_chart Year|2009|x|line_chart Share_of_respondents|54|y|line_chart Year|2007|x|line_chart Share_of_respondents|30|y|line_chart Year|2005|x|line_chart Share_of_respondents|6|y|line_chart Year|2003|x|line_chart Share_of_respondents|1|y|line_chart 
title: Great Britain : Households that use WiFi to access the Internet 2003 - 2013

gold: This survey presents the percentage of British households that use WiFi at home to access the Internet from 2003 to 2013 . In 2009 , 54 percent of respondents reported accessing the internet via WiFi , whereas in 2013 the share of respondents increased to 96 percent .
gold_template: This survey presents the percentage of British templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] at home to templateTitle[6] the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] percent of templateYLabel[1] reported accessing the templateTitle[7] via templateTitle[5] , whereas in templateXValue[max] the templateYLabel[0] of templateYLabel[1] increased to templateYValue[max] percent .

generated_template: This statistic gives information on the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . During the survey period it was found that templateYValue[0] percent of templateYLabel[1] stated that they had an increase from templateYValue[min] percent in templateXValue[min] to templateYValue[max] percent in templateXValue[max] .
generated: This statistic gives information on the Share of Great Britain Households that use in the United States from 2003 to 2013 . During the survey period it was found that 96 percent of respondents stated that they had an increase from 1 percent in 2003 to 96 percent in 2013 .


Example 651:
data: State|District_of_Columbia|x|bar_chart Percentage_of_binge_drinkers|25.9|y|bar_chart State|North_Dakota|x|bar_chart Percentage_of_binge_drinkers|23.3|y|bar_chart State|Wisconsin|x|bar_chart Percentage_of_binge_drinkers|22.7|y|bar_chart State|Iowa|x|bar_chart Percentage_of_binge_drinkers|21.1|y|bar_chart State|Nebraska|x|bar_chart Percentage_of_binge_drinkers|20.6|y|bar_chart State|Illinois|x|bar_chart Percentage_of_binge_drinkers|20.3|y|bar_chart State|Minnesota|x|bar_chart Percentage_of_binge_drinkers|20|y|bar_chart State|Alaska|x|bar_chart Percentage_of_binge_drinkers|19.6|y|bar_chart State|Montana|x|bar_chart Percentage_of_binge_drinkers|19.5|y|bar_chart State|Hawaii|x|bar_chart Percentage_of_binge_drinkers|19.5|y|bar_chart State|Colorado|x|bar_chart Percentage_of_binge_drinkers|18.9|y|bar_chart State|Ohio|x|bar_chart Percentage_of_binge_drinkers|18.9|y|bar_chart State|Missouri|x|bar_chart Percentage_of_binge_drinkers|18.8|y|bar_chart State|Massachusetts|x|bar_chart Percentage_of_binge_drinkers|18.8|y|bar_chart State|New_Hampshire|x|bar_chart Percentage_of_binge_drinkers|18.7|y|bar_chart State|Pennsylvania|x|bar_chart Percentage_of_binge_drinkers|18.2|y|bar_chart State|Louisiana|x|bar_chart Percentage_of_binge_drinkers|18.1|y|bar_chart State|Rhode_Island|x|bar_chart Percentage_of_binge_drinkers|18.1|y|bar_chart State|Michigan|x|bar_chart Percentage_of_binge_drinkers|18.1|y|bar_chart State|Wyoming|x|bar_chart Percentage_of_binge_drinkers|18|y|bar_chart State|Maine|x|bar_chart Percentage_of_binge_drinkers|17.9|y|bar_chart State|Nevada|x|bar_chart Percentage_of_binge_drinkers|17.9|y|bar_chart State|Texas|x|bar_chart Percentage_of_binge_drinkers|17.8|y|bar_chart State|California|x|bar_chart Percentage_of_binge_drinkers|17.6|y|bar_chart State|New_York|x|bar_chart Percentage_of_binge_drinkers|17.5|y|bar_chart State|South_Dakota|x|bar_chart Percentage_of_binge_drinkers|17.4|y|bar_chart State|United_States|x|bar_chart Percentage_of_binge_drinkers|17.4|y|bar_chart State|Vermont|x|bar_chart Percentage_of_binge_drinkers|17.4|y|bar_chart State|Kansas|x|bar_chart Percentage_of_binge_drinkers|17.2|y|bar_chart State|New_Jersey|x|bar_chart Percentage_of_binge_drinkers|16.7|y|bar_chart State|Indiana|x|bar_chart Percentage_of_binge_drinkers|16.6|y|bar_chart State|Oregon|x|bar_chart Percentage_of_binge_drinkers|16.1|y|bar_chart State|Virginia|x|bar_chart Percentage_of_binge_drinkers|16|y|bar_chart State|Kentucky|x|bar_chart Percentage_of_binge_drinkers|15.8|y|bar_chart State|Washington|x|bar_chart Percentage_of_binge_drinkers|15.6|y|bar_chart State|South_Carolina|x|bar_chart Percentage_of_binge_drinkers|15.5|y|bar_chart State|North_Carolina|x|bar_chart Percentage_of_binge_drinkers|15.4|y|bar_chart State|Connecticut|x|bar_chart Percentage_of_binge_drinkers|15.4|y|bar_chart State|Maryland|x|bar_chart Percentage_of_binge_drinkers|15.3|y|bar_chart State|Arizona|x|bar_chart Percentage_of_binge_drinkers|15.2|y|bar_chart State|Florida|x|bar_chart Percentage_of_binge_drinkers|15.1|y|bar_chart State|Idaho|x|bar_chart Percentage_of_binge_drinkers|15.1|y|bar_chart State|Arkansas|x|bar_chart Percentage_of_binge_drinkers|15.1|y|bar_chart State|Delaware|x|bar_chart Percentage_of_binge_drinkers|14.8|y|bar_chart State|New_Mexico|x|bar_chart Percentage_of_binge_drinkers|14.7|y|bar_chart State|Oklahoma|x|bar_chart Percentage_of_binge_drinkers|13.4|y|bar_chart State|Tennessee|x|bar_chart Percentage_of_binge_drinkers|13.1|y|bar_chart State|Georgia|x|bar_chart Percentage_of_binge_drinkers|12.9|y|bar_chart State|Mississippi|x|bar_chart Percentage_of_binge_drinkers|12.6|y|bar_chart State|Alabama|x|bar_chart Percentage_of_binge_drinkers|12.4|y|bar_chart State|Utah|x|bar_chart Percentage_of_binge_drinkers|11.5|y|bar_chart State|West_Virginia|x|bar_chart Percentage_of_binge_drinkers|11.5|y|bar_chart 
title: U.S. binge drinking among adults by state 2018

gold: This statistic represents the percentage of binge in the United States of America as of 2018 , in the last 30 days by state . As of that year , 17.8 percent of adults in Texas consumed more than 4 ( women ) or 5 ( men ) alcoholic beverages on a single occasion within the preceding 30 days .
gold_template: This statistic represents the templateYLabel[0] of templateYLabel[1] in the templateXValue[26] templateXValue[26] of America as of templateTitle[7] , in the last 30 days templateTitle[5] templateXLabel[0] . As of that year , templateYValue[22] percent of templateTitle[4] in templateXValue[22] consumed more than 4 ( women ) or 5 ( men ) alcoholic beverages on a single occasion within the preceding 30 days .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] . In templateTitle[4] , about templateYValue[max] people were killed in the United States .
generated: This statistic shows the Percentage binge binge in the United States in adults , by State . In adults , about 25.9 people were killed in the United States .


Example 652:
data: Year|2018|x|line_chart Share_of_urban_population_in_total_population|58.3|y|line_chart Year|2017|x|line_chart Share_of_urban_population_in_total_population|58.09|y|line_chart Year|2016|x|line_chart Share_of_urban_population_in_total_population|57.91|y|line_chart Year|2015|x|line_chart Share_of_urban_population_in_total_population|57.72|y|line_chart Year|2014|x|line_chart Share_of_urban_population_in_total_population|57.53|y|line_chart Year|2013|x|line_chart Share_of_urban_population_in_total_population|57.34|y|line_chart Year|2012|x|line_chart Share_of_urban_population_in_total_population|57.15|y|line_chart Year|2011|x|line_chart Share_of_urban_population_in_total_population|57.12|y|line_chart Year|2010|x|line_chart Share_of_urban_population_in_total_population|57.4|y|line_chart Year|2009|x|line_chart Share_of_urban_population_in_total_population|57.68|y|line_chart Year|2008|x|line_chart Share_of_urban_population_in_total_population|57.97|y|line_chart 
title: Urbanization in Austria 2018

gold: This statistic shows the degree of urbanization in Austria from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 58.3 percent of Austria 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Austria from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 58.3 percent of Austria 's total population lived in urban areas and cities .


Example 653:
data: Club_Name|FC_Bayern_München|x|bar_chart Market_value_in_million_euros|933.15|y|bar_chart Club_Name|Borussia_Dortmund|x|bar_chart Market_value_in_million_euros|637.4|y|bar_chart Club_Name|RasenBallsport_Leipzig|x|bar_chart Market_value_in_million_euros|594.4|y|bar_chart Club_Name|Bayer_04_Leverkusen|x|bar_chart Market_value_in_million_euros|445.75|y|bar_chart Club_Name|Borussia_Mönchengladbach|x|bar_chart Market_value_in_million_euros|312.0|y|bar_chart Club_Name|FC_Schalke_04|x|bar_chart Market_value_in_million_euros|242.75|y|bar_chart Club_Name|TSG_1899_Hoffenheim|x|bar_chart Market_value_in_million_euros|238.23|y|bar_chart Club_Name|Hertha_BSC|x|bar_chart Market_value_in_million_euros|233.2|y|bar_chart Club_Name|VfL_Wolfsburg|x|bar_chart Market_value_in_million_euros|230.95|y|bar_chart Club_Name|Eintracht_Frankfurt|x|bar_chart Market_value_in_million_euros|215.8|y|bar_chart Club_Name|SV_Werder_Bremen|x|bar_chart Market_value_in_million_euros|189.75|y|bar_chart Club_Name|1._FSV_Mainz_05|x|bar_chart Market_value_in_million_euros|147.4|y|bar_chart Club_Name|SC_Freiburg|x|bar_chart Market_value_in_million_euros|145.4|y|bar_chart Club_Name|FC_Augsburg|x|bar_chart Market_value_in_million_euros|131.15|y|bar_chart Club_Name|1._FC_Köln|x|bar_chart Market_value_in_million_euros|102.2|y|bar_chart Club_Name|Fortuna_Düsseldorf|x|bar_chart Market_value_in_million_euros|93.15|y|bar_chart Club_Name|1._FC_Union_Berlin|x|bar_chart Market_value_in_million_euros|43.05|y|bar_chart Club_Name|SC_Paderborn|x|bar_chart Market_value_in_million_euros|31.25|y|bar_chart 
title: Market value of first Bundesliga football clubs in Germany in 2020

gold: This statistic shows the market value of the first Bundesliga football clubs in Germany as of February 11 , 2020 . The market value of FC Bayern Munich was highest at 933.15 million euros , followed by 637.4 million euros for Borussia Dortmund and 594.4 million euros for RB Leipzig .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] as of February 11 , templateTitle[7] . The templateYLabel[0] templateYLabel[1] of templateXValue[0] templateXValue[0] Munich was highest at templateYValue[max] templateYLabel[2] templateYLabel[3] , followed by templateYValue[1] templateYLabel[2] templateYLabel[3] for templateXValue[1] templateXValue[1] and templateYValue[2] templateYLabel[2] templateYLabel[3] for RB templateXValue[2] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[7] . templateXValue[0] templateXValue[0] valued at templateYValue[max] percent .
generated: The statistic shows the Market value first Bundesliga football clubs Germany Market value million euros in 2020 . FC_Bayern_München valued at 933.15 percent .


Example 654:
data: Year|18_to_24_years|x|line_chart Share_of_women_surveyed|6|y|line_chart Year|25_to_34_years|x|line_chart Share_of_women_surveyed|5|y|line_chart Year|35_to_49_years|x|line_chart Share_of_women_surveyed|9|y|line_chart Year|50_to_59_years|x|line_chart Share_of_women_surveyed|11|y|line_chart Year|60_years_and_older|x|line_chart Share_of_women_surveyed|13|y|line_chart 
title: Distribution of women practicing nudism at the beach in France 2017 , by age

gold: This statistic indicates the share of French women who have already practiced naturism on the beach or in a nudist camp in 2017 , by age group . We can see that more than 10 percent of women aged 50 to 59 had already practiced nudism at the beach or in a naturist camp . Discover also the level of interest of the French for naturism .
gold_template: This statistic indicates the templateYLabel[0] of French templateYLabel[1] who have already practiced naturism on the templateTitle[4] or in a nudist camp in templateTitle[6] , templateTitle[7] templateTitle[8] group . We can see that more than 10 percent of templateYLabel[1] aged templateXValue[3] to templateXValue[3] had already practiced templateTitle[3] at the templateTitle[4] or in a naturist camp . Discover also the level of interest of the French for naturism .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] having already practiced templateTitle[3] on the beach or in a nudist camp worldwide in templateTitle[5] , templateTitle[6] templateXLabel[0] . During the survey period , templateYValue[max] percent of respondents stated that they used to have already been entirely naked on the beach or in a naturist camp .
generated: This statistic shows the Share of women having already practiced nudism on the beach or in a nudist camp worldwide in France , 2017 Year . During the survey period , 13 percent of respondents stated that they used to have already been entirely naked on the beach or in a naturist camp .


Example 655:
data: Year|'19|x|line_chart Number_of_employees_in_thousands|157.0|y|line_chart Year|'18|x|line_chart Number_of_employees_in_thousands|145.0|y|line_chart Year|'17|x|line_chart Number_of_employees_in_thousands|138.0|y|line_chart Year|'16|x|line_chart Number_of_employees_in_thousands|102.0|y|line_chart Year|'15|x|line_chart Number_of_employees_in_thousands|98.3|y|line_chart Year|'14|x|line_chart Number_of_employees_in_thousands|108.3|y|line_chart Year|'13|x|line_chart Number_of_employees_in_thousands|111.3|y|line_chart Year|'12|x|line_chart Number_of_employees_in_thousands|109.4|y|line_chart Year|'11|x|line_chart Number_of_employees_in_thousands|103.3|y|line_chart Year|'10|x|line_chart Number_of_employees_in_thousands|96.0|y|line_chart Year|'09|x|line_chart Number_of_employees_in_thousands|78.9|y|line_chart Year|'08|x|line_chart Number_of_employees_in_thousands|88.2|y|line_chart Year|'07|x|line_chart Number_of_employees_in_thousands|90.5|y|line_chart Year|'06|x|line_chart Number_of_employees_in_thousands|65.2|y|line_chart Year|'05|x|line_chart Number_of_employees_in_thousands|55.2|y|line_chart Year|'04|x|line_chart Number_of_employees_in_thousands|46.0|y|line_chart Year|'03|x|line_chart Number_of_employees_in_thousands|39.1|y|line_chart Year|'02|x|line_chart Number_of_employees_in_thousands|34.6|y|line_chart Year|'01|x|line_chart Number_of_employees_in_thousands|40.0|y|line_chart Year|'00|x|line_chart Number_of_employees_in_thousands|36.5|y|line_chart Year|'99|x|line_chart Number_of_employees_in_thousands|24.4|y|line_chart Year|'98|x|line_chart Number_of_employees_in_thousands|16.2|y|line_chart Year|'97|x|line_chart Number_of_employees_in_thousands|10.35|y|line_chart Year|'96|x|line_chart Number_of_employees_in_thousands|8.4|y|line_chart 
title: Dell : Number of employees 1996 - 2019

gold: As of early 2019 , Dell 's employee count was 157 thousand . The majority , approximately 145 thousand , of these employees are full-time employees . 37 percent of Dell 's full-time employees are located in the company 's home market , the United States .
gold_template: As of early templateTitle[4] , templateTitle[0] 's employee count was templateYValue[max] thousand . The majority , approximately templateYValue[1] thousand , of these templateYLabel[1] are full-time templateYLabel[1] . templateYValue[19] percent of templateTitle[0] 's full-time templateYLabel[1] are located in the company 's home market , the United States .

generated_template: This statistic represents the templateYLabel[0] of templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] from templateTitle[5] to templateTitle[6] . In templateTitle[7] , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] employed an additional information templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] has risen steadily decreasing over the last decades .
generated: This statistic represents the Number of employees of Dell Number employees 1996 2019 from titleErr to titleErr . In titleErr , Dell Number employees 1996 2019 employed an additional information Number employees 1996 2019 titleErr titleErr . The Dell Number employees 1996 2019 titleErr has risen steadily decreasing over the last decades .


Example 656:
data: Year|2019|x|line_chart Youth_unemployment_rate|9.69|y|line_chart Year|2018|x|line_chart Youth_unemployment_rate|9.58|y|line_chart Year|2017|x|line_chart Youth_unemployment_rate|9.57|y|line_chart Year|2016|x|line_chart Youth_unemployment_rate|9.88|y|line_chart Year|2015|x|line_chart Youth_unemployment_rate|9.97|y|line_chart Year|2014|x|line_chart Youth_unemployment_rate|9|y|line_chart Year|2013|x|line_chart Youth_unemployment_rate|9.25|y|line_chart Year|2012|x|line_chart Youth_unemployment_rate|6.99|y|line_chart Year|2011|x|line_chart Youth_unemployment_rate|8.93|y|line_chart Year|2010|x|line_chart Youth_unemployment_rate|8.6|y|line_chart Year|2009|x|line_chart Youth_unemployment_rate|12.46|y|line_chart Year|2008|x|line_chart Youth_unemployment_rate|10.81|y|line_chart Year|2007|x|line_chart Youth_unemployment_rate|11.2|y|line_chart Year|2006|x|line_chart Youth_unemployment_rate|9.57|y|line_chart Year|2005|x|line_chart Youth_unemployment_rate|9.41|y|line_chart Year|2004|x|line_chart Youth_unemployment_rate|7.74|y|line_chart Year|2003|x|line_chart Youth_unemployment_rate|5.82|y|line_chart Year|2002|x|line_chart Youth_unemployment_rate|6.06|y|line_chart Year|2001|x|line_chart Youth_unemployment_rate|6.26|y|line_chart Year|2000|x|line_chart Youth_unemployment_rate|5.8|y|line_chart Year|1999|x|line_chart Youth_unemployment_rate|4.89|y|line_chart 
title: Youth unemployment rate in Bhutan in 2019

gold: The statistic shows the youth unemployment rate in Bhutan from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Bhutan was at 9.69 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] was at templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Bhutan from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Bhutan was at 9.69 percent .


Example 657:
data: Response|Love|x|bar_chart Share_of_reactions|41|y|bar_chart Response|Haha|x|bar_chart Share_of_reactions|28|y|bar_chart Response|Wow|x|bar_chart Share_of_reactions|15|y|bar_chart Response|Sad|x|bar_chart Share_of_reactions|12|y|bar_chart Response|Angry|x|bar_chart Share_of_reactions|5|y|bar_chart 
title: Facebook reactions on top shared content 2017

gold: This statistic presents the reaction usage in top shared posts on Facebook in September 2017 . During the measured period , Love was the most popular Facebook reaction on top shared posts on the social network .
gold_template: This statistic presents the reaction usage in templateTitle[2] templateTitle[3] posts on templateTitle[0] in September templateTitle[5] . During the measured period , templateXValue[0] was the most popular templateTitle[0] reaction on templateTitle[2] templateTitle[3] posts on the social network .

generated_template: This statistic shows the results of a survey among people in the United States in templateTitle[4] templateTitle[5] . According to the most recent survey period , templateYValue[max] percent of the templateYLabel[1] claimed that they would be observed .
generated: This statistic shows the results of a survey among people in the United States in content 2017 . According to the most recent survey period , 41 percent of the reactions claimed that they would be observed .


Example 658:
data: Year|2018|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|41361|y|line_chart Year|2017|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|40324|y|line_chart Year|2016|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|40339|y|line_chart Year|2015|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|38178|y|line_chart Year|2014|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|36689|y|line_chart Year|2013|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|37232|y|line_chart Year|2012|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|35641|y|line_chart Year|2011|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|35203|y|line_chart Year|2010|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|36195|y|line_chart Year|2009|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|37319|y|line_chart Year|2008|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|39054|y|line_chart Year|2007|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|40196|y|line_chart Year|2006|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|38963|y|line_chart Year|2005|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|38828|y|line_chart Year|2004|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|39151|y|line_chart Year|2003|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|39607|y|line_chart Year|2002|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|39661|y|line_chart Year|2001|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|40902|y|line_chart Year|2000|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|42348|y|line_chart Year|1999|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|41192|y|line_chart Year|1998|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|38212|y|line_chart Year|1997|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|38269|y|line_chart Year|1996|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|36649|y|line_chart Year|1995|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|35880|y|line_chart Year|1994|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|34503|y|line_chart Year|1993|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|32721|y|line_chart Year|1992|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|32210|y|line_chart Year|1991|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|33103|y|line_chart Year|1990|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|34068|y|line_chart 
title: Household income of black families in the U.S. 1990 - 2018

gold: This statistic shows the household income of black families in the United States from 1990 to 2018 . The median income in 2018 was at 41,361 U.S. dollars for black households .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[0] templateYLabel[5] templateYLabel[6] for templateTitle[2] households .

generated_template: U.S. fashion fashion retailer templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] United States between templateXValue[min] and templateXValue[max] , according to templateYValue[0] percent of the templateXLabel[0] . The templateYLabel[0] templateYLabel[1] represents since templateXValue[6] , this time , which Japan , with an increase of templateYValue[0] percent compared with the templateYLabel[1] in templateXValue[max] . The templateYLabel[0] templateYLabel[1] observed in the United States has since China .
generated: U.S. fashion retailer families U.S. 1990 in 2018 United States between 1990 and 2018 , according to 41361 percent of the Year . The Median income represents since 2012 , this time , which Japan , with an increase of 41361 percent compared with the income in 2018 . The Median income observed in the United States has since China .


Example 659:
data: Company|Mattel|x|bar_chart Sales_in_million_U.S._dollars|6300|y|bar_chart Company|Lego|x|bar_chart Sales_in_million_U.S._dollars|4500|y|bar_chart Company|Hasbro|x|bar_chart Sales_in_million_U.S._dollars|4000|y|bar_chart Company|MGA_Entertainment|x|bar_chart Sales_in_million_U.S._dollars|2000|y|bar_chart Company|Playmobil|x|bar_chart Sales_in_million_U.S._dollars|790|y|bar_chart Company|Jakks_Pacific|x|bar_chart Sales_in_million_U.S._dollars|700|y|bar_chart Company|LeapFrog|x|bar_chart Sales_in_million_U.S._dollars|580|y|bar_chart Company|MEGA_Bloks|x|bar_chart Sales_in_million_U.S._dollars|400|y|bar_chart Company|Melissa_&_Doug|x|bar_chart Sales_in_million_U.S._dollars|325|y|bar_chart 
title: Sales of the leading toy companies worldwide 2013

gold: This statistic shows the sales of the leading toy companies worldwide in 2013 . In that year , Mattel was the largest global toy company with estimated sales that amounted to 6.3 billion U.S. dollars . Lego and Hasbro rounded off the leading three toy companies .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] . In that year , templateXValue[0] was the largest global templateTitle[2] templateXLabel[0] with estimated templateYLabel[0] that amounted to templateYValue[max] billion templateYLabel[2] templateYLabel[3] . templateXValue[1] and templateXValue[2] rounded off the templateTitle[1] three templateTitle[2] templateTitle[3] .

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] . In that year , templateXValue[0] was the leading templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] with around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[5] .
generated: This statistic shows the Sales of the Sales leading toy companies worldwide in 2013 . In that year , Mattel was the leading Sales leading toy companies with around 6300 million U.S. dollars in 2013 .


Example 660:
data: Year|2019|x|line_chart Franchise_value_in_million_U.S._dollars|3000|y|line_chart Year|2018|x|line_chart Franchise_value_in_million_U.S._dollars|2650|y|line_chart Year|2017|x|line_chart Franchise_value_in_million_U.S._dollars|2600|y|line_chart Year|2016|x|line_chart Franchise_value_in_million_U.S._dollars|2400|y|line_chart Year|2015|x|line_chart Franchise_value_in_million_U.S._dollars|1940|y|line_chart Year|2014|x|line_chart Franchise_value_in_million_U.S._dollars|1450|y|line_chart Year|2013|x|line_chart Franchise_value_in_million_U.S._dollars|1161|y|line_chart Year|2012|x|line_chart Franchise_value_in_million_U.S._dollars|1132|y|line_chart Year|2011|x|line_chart Franchise_value_in_million_U.S._dollars|1046|y|line_chart Year|2010|x|line_chart Franchise_value_in_million_U.S._dollars|1049|y|line_chart Year|2009|x|line_chart Franchise_value_in_million_U.S._dollars|1081|y|line_chart Year|2008|x|line_chart Franchise_value_in_million_U.S._dollars|1061|y|line_chart Year|2007|x|line_chart Franchise_value_in_million_U.S._dollars|994|y|line_chart Year|2006|x|line_chart Franchise_value_in_million_U.S._dollars|975|y|line_chart Year|2005|x|line_chart Franchise_value_in_million_U.S._dollars|907|y|line_chart Year|2004|x|line_chart Franchise_value_in_million_U.S._dollars|815|y|line_chart Year|2003|x|line_chart Franchise_value_in_million_U.S._dollars|683|y|line_chart Year|2002|x|line_chart Franchise_value_in_million_U.S._dollars|604|y|line_chart 
title: Franchise value of the Denver Broncos ( NFL ) 2002 - 2019

gold: This graph depicts the franchise value of the Denver Broncos from the National Football League from 2002 to 2019 . In 2019 , the franchise value came to three billion U.S. dollars . The Denver Broncos are owned by the Pat Bowlen Trust , who bought the franchise for 78 million U.S. dollars in 1984 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] are owned by the Pat Bowlen Trust , who bought the templateYLabel[0] for 78 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1984 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] are owned by the McCaskey family , who bought the templateYLabel[0] in 1920 .
generated: This graph depicts the Franchise value of the Denver Broncos of the National Football League from 2002 to 2019 . In 2019 , the Franchise value came to 3000 billion U.S. dollars . The Denver Broncos are owned by the McCaskey family , who bought the Franchise in 1920 .


Example 661:
data: Year|2019|x|line_chart Revenue_in_million_U.S._dollars|16227.3|y|line_chart Year|2018|x|line_chart Revenue_in_million_U.S._dollars|15789.6|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|14604.4|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|14415.8|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|14329.1|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|14832.9|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|13945.7|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|13505.4|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|13082.4|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|12419.1|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|12138.1|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|13252.1|y|line_chart 
title: Facilities management industry - Aramark worldwide revenue 2008 - 2019

gold: This statistic shows the annual total worldwide revenue of Aramark from 2008 to 2019 . In 2019 , Aramark had total revenues of over 16.2 billion U.S. dollars . The Aramark Corporation is an American foodservice , facilities , and clothing provider headquartered in Philadelphia , Pennsylvania .
gold_template: This statistic shows the annual total templateTitle[4] templateYLabel[0] of templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] had total revenues of over templateYValue[max] billion templateYLabel[2] templateYLabel[3] . The templateTitle[3] Corporation is an American foodservice , templateTitle[0] , and clothing provider headquartered in Philadelphia , Pennsylvania .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the women templateTitle[1] templateTitle[2] was templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the U.S .
generated: The statistic shows the Facilities management industry Revenue in the United States from 2008 to 2019 . In 2018 , the women management industry was 16227.3 million U.S. dollars . Facilities management industry Revenue in the U.S .


Example 662:
data: Year|2019|x|line_chart Revenue_in_million_U.S._dollars|904|y|line_chart Year|2018|x|line_chart Revenue_in_million_U.S._dollars|932|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|1309|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|2160|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|3335|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|6813|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|11073|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|18423|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|19907|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|14953|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|11065|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|6009|y|line_chart Year|2007|x|line_chart Revenue_in_million_U.S._dollars|3037|y|line_chart Year|2006|x|line_chart Revenue_in_million_U.S._dollars|2066|y|line_chart Year|2005|x|line_chart Revenue_in_million_U.S._dollars|1350|y|line_chart Year|2004|x|line_chart Revenue_in_million_U.S._dollars|595|y|line_chart 
title: Revenue of RIM/Blackberry worldwide 2004 - 2019

gold: In its 2019 fiscal year , Canadian company BlackBerry recorded revenues of less than one billion U.S. dollars for only the second time in the past 15 years . The company , previously known as Research in Motion ( RIM ) , has transitioned from developing BlackBerry brand smartphones to enterprise software and services involving the internet of things . Transitioning to the Enterprise of Things In the past decade , BlackBerry has seen a significant decline in their revenue figures and share of the global and U.S. smartphone market .
gold_template: In its templateXValue[max] fiscal templateXLabel[0] , Canadian company BlackBerry recorded revenues of less than templateYValue[0] billion templateYLabel[2] templateYLabel[3] for only the second time in the past 15 years . The company , previously known as Research in Motion ( RIM ) , has transitioned from developing BlackBerry brand smartphones to enterprise software and services involving the internet of things . Transitioning to the Enterprise of Things In the past decade , BlackBerry has seen a significant decline in their templateYLabel[0] figures and share of the global and templateYLabel[2] smartphone market .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[1] templateTitle[2] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the RIM/Blackberry worldwide , a franchise of the National Football League , from 2004 to 2019 . In 2019 , the Revenue of the RIM/Blackberry worldwide was 19907 million U.S. dollars .


Example 663:
data: Decorating_service|Embroidery|x|bar_chart Share_of_respondents|45|y|bar_chart Decorating_service|Screen_printing|x|bar_chart Share_of_respondents|26|y|bar_chart Decorating_service|Heat_transfers|x|bar_chart Share_of_respondents|7|y|bar_chart Decorating_service|Vinyl_(cut)_letters/designs|x|bar_chart Share_of_respondents|6|y|bar_chart Decorating_service|Digitizing/artwork_services|x|bar_chart Share_of_respondents|5|y|bar_chart Decorating_service|Sublimation_printing|x|bar_chart Share_of_respondents|3|y|bar_chart Decorating_service|Emblems/patches|x|bar_chart Share_of_respondents|3|y|bar_chart Decorating_service|Direct-to-garment_printing|x|bar_chart Share_of_respondents|3|y|bar_chart Decorating_service|Rhinestones/crystals|x|bar_chart Share_of_respondents|1|y|bar_chart Decorating_service|Other|x|bar_chart Share_of_respondents|1|y|bar_chart 
title: Revenue share of various apparel decorating services in the U.S. 2014

gold: This statistic depicts the revenue share of various apparel decorating services in the United States in 2014 . The survey revealed that some 45 percent of the respondents felt that embroidery decorating services for apparel generated the most revenue .
gold_template: This statistic depicts the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateXLabel[0] templateXValue[4] in the templateTitle[6] in templateTitle[7] . The survey revealed that some templateYValue[max] percent of the templateYLabel[1] felt that templateXValue[0] templateXLabel[0] templateXValue[4] for templateTitle[3] generated the most templateTitle[0] .

generated_template: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the United States . templateYValue[max] percent of templateYLabel[1] stated that they were templateXValue[0] templateXValue[0] templateTitle[3] in templateTitle[4] .
generated: This statistic shows the results of a survey among 559 industrial enterprises on their opinion on the Revenue share various apparel in the United States . 45 percent of respondents stated that they were Embroidery apparel in decorating .


Example 664:
data: Year|2018|x|line_chart Number_of_arrivals_in_millions|7.8|y|line_chart Year|2017|x|line_chart Number_of_arrivals_in_millions|7.5|y|line_chart Year|2016|x|line_chart Number_of_arrivals_in_millions|7.2|y|line_chart Year|2015|x|line_chart Number_of_arrivals_in_millions|6.3|y|line_chart Year|2014|x|line_chart Number_of_arrivals_in_millions|5.9|y|line_chart Year|2013|x|line_chart Number_of_arrivals_in_millions|5.8|y|line_chart Year|2012|x|line_chart Number_of_arrivals_in_millions|5.5|y|line_chart Year|2011|x|line_chart Number_of_arrivals_in_millions|4.9|y|line_chart Year|2010|x|line_chart Number_of_arrivals_in_millions|4.3|y|line_chart Year|2009|x|line_chart Number_of_arrivals_in_millions|4.3|y|line_chart Year|2008|x|line_chart Number_of_arrivals_in_millions|5.0|y|line_chart Year|2007|x|line_chart Number_of_arrivals_in_millions|4.8|y|line_chart Year|2006|x|line_chart Number_of_arrivals_in_millions|4.3|y|line_chart 
title: Number of arrivals in tourist accommodation Bulgaria 2006 - 2018

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Bulgaria from 2006 to 2018 . In 2018 , the number of arrivals in travel accommodation ( including both international and domestic tourists ) amounted to approximately 7.8 million .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in travel templateTitle[3] ( including both international and domestic tourists ) amounted to approximately templateYValue[max] million .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitle[4] from templateXValue[min] to templateXValue[max] . Over this period templateYLabel[1] of both domestic and international tourists in templateTitle[3] establishments in templateTitle[4] has increased , reaching around templateYValue[max] million in templateXValue[max] .
generated: This statistic shows the Number of tourist arrivals at accommodation establishments in Bulgaria from 2006 to 2018 . Over this period arrivals of both domestic and international tourists in accommodation establishments in Bulgaria has increased , reaching around 7.8 million in 2018 .


Example 665:
data: Year|2018|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|43423|y|line_chart Year|2017|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|42719|y|line_chart Year|2016|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|42013|y|line_chart Year|2015|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|41491|y|line_chart Year|2014|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|40547|y|line_chart Year|2013|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|40080|y|line_chart Year|2012|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|39806|y|line_chart Year|2011|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|40001|y|line_chart Year|2010|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|40656|y|line_chart Year|2009|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|40652|y|line_chart Year|2008|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|43353|y|line_chart Year|2007|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|45507|y|line_chart Year|2006|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|45926|y|line_chart Year|2005|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|45193|y|line_chart Year|2004|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|43471|y|line_chart Year|2003|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|42074|y|line_chart Year|2002|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|41062|y|line_chart Year|2001|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|40267|y|line_chart Year|2000|x|line_chart Per_capita_real_GDP_in_chained_2012_U.S._dollars|40049|y|line_chart 
title: Per capita real GDP of Florida 2000 - 2018

gold: This statistic shows the per capita real Gross Domestic Product of Florida from 2000 to 2018 . In 2018 , the per capita real GDP of Florida stood at 43,423 chained 2012 U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[4] stood at templateYValue[0] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[4] templateTitle[5] stood at templateYValue[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Per capita real Gross Domestic Product of Florida 2000 from 2000 to 2018 . In 2018 , the Per capita real GDP of Florida 2000 stood at 45926 chained 2012 U.S. dollars .


Example 666:
data: Year|2039|x|line_chart Age|45.2|y|line_chart Year|2034|x|line_chart Age|44.3|y|line_chart Year|2029|x|line_chart Age|43.5|y|line_chart Year|2024|x|line_chart Age|42.9|y|line_chart Year|2019|x|line_chart Age|42.4|y|line_chart Year|2014|x|line_chart Age|41.9|y|line_chart 
title: Scotland : forecasted median age of population 2014 to 2039

gold: This statistic shows the forecasted median age of the population of Scotland from 2014 to 2039 . The average age of the population is predicted to rise continuously over this 25 year period , with the sharpest rise between 2034 and 2039 , of 0.9 years .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] of the templateTitle[4] of templateTitle[0] from templateXValue[min] to templateXValue[max] . The average templateYLabel[0] of the templateTitle[4] is predicted to rise continuously over this 25 templateXLabel[0] period , with the sharpest rise between templateXValue[1] and templateXValue[max] , of 0.9 years .

generated_template: In templateXValue[max] , the Italian templateYLabel[1] of templateTitle[4] templateTitle[5] in the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateYLabel[1] was at templateYValue[max] percent . The global templateYLabel[1] distribution of the European Union , followed by templateXValue[1] at templateYValue[1] percent .
generated: In 2039 , the Italian yLabelErr of population 2014 in the Scotland forecasted median age population 2014 yLabelErr was at 45.2 percent . The global yLabelErr distribution of the European Union , followed by 2034 at 44.3 percent .


Example 667:
data: Response|Training_and_awareness_programs|x|bar_chart Share_of_respondents|60|y|bar_chart Response|Expanded_use_of_encryption|x|bar_chart Share_of_respondents|55|y|bar_chart Response|Endpoint_security_solutions|x|bar_chart Share_of_respondents|49|y|bar_chart Response|Identity_and_access_management_solutions|x|bar_chart Share_of_respondents|44|y|bar_chart Response|Additional_manual_procedures_and_controls|x|bar_chart Share_of_respondents|39|y|bar_chart Response|Data_loss_prevention_(DLP)_solutions|x|bar_chart Share_of_respondents|36|y|bar_chart Response|Security_intelligence_solutions|x|bar_chart Share_of_respondents|35|y|bar_chart Response|Other_system_control_practices|x|bar_chart Share_of_respondents|26|y|bar_chart Response|Security_certification_or_audit|x|bar_chart Share_of_respondents|19|y|bar_chart Response|Strenghtening_of_perimeter_controls|x|bar_chart Share_of_respondents|16|y|bar_chart 
title: U.S. company data loss prevention methods 2017

gold: This statistic presents a ranking of common data loss prevention controls and activities of organizations in the United States in 2017 . During the survey period , it was found that 35 percent of U.S. companies had implemented security intelligence solutions .
gold_template: This statistic presents a ranking of common templateXValue[5] templateXValue[5] templateXValue[5] templateXValue[4] and activities of organizations in the templateTitle[0] in templateTitle[6] . During the survey period , it was found that templateYValue[6] percent of templateTitle[0] companies had implemented templateXValue[2] templateXValue[6] templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] on templateTitle[4] templateTitle[5] in the United States as of March templateTitle[6] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated they purchased goods templateTitle[1] at templateXValue[0] templateXValue[0] a templateXValue[0] .
generated: This statistic shows the U.S. company in data loss on prevention methods in the United States as of March 2017 . During the survey period , 60 percent of respondents stated they purchased goods company at Training_and_awareness_programs a Training_and_awareness_programs .


Example 668:
data: Country|Netherlands|x|bar_chart Share_of_customers_with_positive_experience|70.6|y|bar_chart Country|Czech_Republic|x|bar_chart Share_of_customers_with_positive_experience|67|y|bar_chart Country|Austria|x|bar_chart Share_of_customers_with_positive_experience|66.8|y|bar_chart Country|Switzerland|x|bar_chart Share_of_customers_with_positive_experience|64.8|y|bar_chart Country|Portugal|x|bar_chart Share_of_customers_with_positive_experience|63|y|bar_chart Country|Germany|x|bar_chart Share_of_customers_with_positive_experience|62.3|y|bar_chart Country|Poland|x|bar_chart Share_of_customers_with_positive_experience|61.6|y|bar_chart Country|Sweden|x|bar_chart Share_of_customers_with_positive_experience|60.7|y|bar_chart Country|Italy|x|bar_chart Share_of_customers_with_positive_experience|59.5|y|bar_chart Country|United_Kingdom|x|bar_chart Share_of_customers_with_positive_experience|58.4|y|bar_chart Country|Finland|x|bar_chart Share_of_customers_with_positive_experience|58.2|y|bar_chart Country|Belgium|x|bar_chart Share_of_customers_with_positive_experience|56.7|y|bar_chart Country|Denmark|x|bar_chart Share_of_customers_with_positive_experience|55.9|y|bar_chart Country|Norway|x|bar_chart Share_of_customers_with_positive_experience|53.9|y|bar_chart Country|France|x|bar_chart Share_of_customers_with_positive_experience|52.3|y|bar_chart Country|Spain|x|bar_chart Share_of_customers_with_positive_experience|35.7|y|bar_chart 
title: Selected European countries ranked by retail banking customer satisfaction 2016

gold: This statistic illustrates the share of customers with a positive retail banking experience in the leading selected European banking systems ( countries ) as of 2016 . Approximately 70.6 percent of surveyed bank customers in the Netherlands indicated high levels of satisfaction , ranking the country highest among European banking locations in 2016 . This was followed by the Czech Republic , with 67 percent of bank customers with a positive experience throughout the year .
gold_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] a templateYLabel[3] templateTitle[5] templateTitle[6] templateYLabel[4] in the leading templateTitle[0] templateTitle[1] templateTitle[6] systems ( templateTitle[2] ) as of templateTitle[9] . Approximately templateYValue[max] percent of surveyed bank templateYLabel[1] in the templateXValue[0] indicated high levels of templateTitle[8] , ranking the templateXLabel[0] highest among templateTitle[1] templateTitle[6] locations in templateTitle[9] . This was followed templateTitle[4] the templateXValue[1] templateXValue[1] , templateYLabel[2] templateYValue[1] percent of bank templateYLabel[1] templateYLabel[2] a templateYLabel[3] templateYLabel[4] throughout the year .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in European countries in templateTitle[4] templateTitle[5] . In this year , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] at templateYValue[max] templateYLabel[4] .
generated: This statistic shows the Share of customers positive in European countries in by retail . In this year , Netherlands had the highest Share customers positive of ranked at 70.6 yLabelErr .


Example 669:
data: Year|2019|x|line_chart Trillion_U.S._dollars|3.12|y|line_chart Year|2018|x|line_chart Trillion_U.S._dollars|3.13|y|line_chart Year|2017|x|line_chart Trillion_U.S._dollars|2.9|y|line_chart Year|2016|x|line_chart Trillion_U.S._dollars|2.72|y|line_chart Year|2015|x|line_chart Trillion_U.S._dollars|2.76|y|line_chart Year|2014|x|line_chart Trillion_U.S._dollars|2.87|y|line_chart Year|2013|x|line_chart Trillion_U.S._dollars|2.76|y|line_chart Year|2012|x|line_chart Trillion_U.S._dollars|2.76|y|line_chart Year|2011|x|line_chart Trillion_U.S._dollars|2.68|y|line_chart Year|2010|x|line_chart Trillion_U.S._dollars|2.35|y|line_chart Year|2009|x|line_chart Trillion_U.S._dollars|1.97|y|line_chart Year|2008|x|line_chart Trillion_U.S._dollars|2.55|y|line_chart Year|2007|x|line_chart Trillion_U.S._dollars|2.36|y|line_chart Year|2006|x|line_chart Trillion_U.S._dollars|2.22|y|line_chart Year|2005|x|line_chart Trillion_U.S._dollars|2.0|y|line_chart Year|2004|x|line_chart Trillion_U.S._dollars|1.77|y|line_chart Year|2003|x|line_chart Trillion_U.S._dollars|1.51|y|line_chart Year|2002|x|line_chart Trillion_U.S._dollars|1.4|y|line_chart Year|2001|x|line_chart Trillion_U.S._dollars|1.37|y|line_chart Year|2000|x|line_chart Trillion_U.S._dollars|1.45|y|line_chart 
title: Total value of international U.S. imports of goods and services 2000 - 2019

gold: The timeline shows the total value of international U.S. imports of goods and services from 2000 to 2019 . In 2019 , the total value of international U.S. imports of goods and services amounted to 3.1 trillion U.S. dollars .
gold_template: The timeline shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateYLabel[1] templateTitle[4] of templateTitle[5] and templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[2] templateYLabel[1] templateTitle[4] of templateTitle[5] and templateTitle[6] amounted to templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in the United States amounted to approximately templateYValue[max] million people living in the United States . The number of people living in this figure is an increase from templateYValue[1] million in the previous templateXLabel[0] .
generated: In 2019 , the Trillion U.S. of imports goods in the United States amounted to approximately 3.13 million people living in the United States . The number of people living in this figure is an increase from 3.13 million in the previous Year .


Example 670:
data: State|California|x|bar_chart Number_of_murder_victims|1739|y|bar_chart State|Texas|x|bar_chart Number_of_murder_victims|1322|y|bar_chart State|Florida|x|bar_chart Number_of_murder_victims|1107|y|bar_chart State|Illinois|x|bar_chart Number_of_murder_victims|884|y|bar_chart State|Pennsylvania|x|bar_chart Number_of_murder_victims|784|y|bar_chart State|Georgia|x|bar_chart Number_of_murder_victims|642|y|bar_chart State|North_Carolina|x|bar_chart Number_of_murder_victims|628|y|bar_chart State|Missouri|x|bar_chart Number_of_murder_victims|607|y|bar_chart State|Ohio|x|bar_chart Number_of_murder_victims|564|y|bar_chart State|New_York|x|bar_chart Number_of_murder_victims|562|y|bar_chart State|Michigan|x|bar_chart Number_of_murder_victims|551|y|bar_chart State|Louisiana|x|bar_chart Number_of_murder_victims|530|y|bar_chart State|Tennessee|x|bar_chart Number_of_murder_victims|498|y|bar_chart State|Maryland|x|bar_chart Number_of_murder_victims|490|y|bar_chart State|Indiana|x|bar_chart Number_of_murder_victims|438|y|bar_chart State|South_Carolina|x|bar_chart Number_of_murder_victims|392|y|bar_chart State|Virginia|x|bar_chart Number_of_murder_victims|391|y|bar_chart State|Alabama|x|bar_chart Number_of_murder_victims|383|y|bar_chart State|Arizona|x|bar_chart Number_of_murder_victims|369|y|bar_chart State|New_Jersey|x|bar_chart Number_of_murder_victims|286|y|bar_chart State|Kentucky|x|bar_chart Number_of_murder_victims|244|y|bar_chart State|Washington|x|bar_chart Number_of_murder_victims|236|y|bar_chart State|Arkansas|x|bar_chart Number_of_murder_victims|216|y|bar_chart State|Colorado|x|bar_chart Number_of_murder_victims|210|y|bar_chart State|Oklahoma|x|bar_chart Number_of_murder_victims|206|y|bar_chart State|Nevada|x|bar_chart Number_of_murder_victims|202|y|bar_chart State|Wisconsin|x|bar_chart Number_of_murder_victims|176|y|bar_chart State|Mississippi|x|bar_chart Number_of_murder_victims|171|y|bar_chart State|New_Mexico|x|bar_chart Number_of_murder_victims|167|y|bar_chart State|District_of_Columbia|x|bar_chart Number_of_murder_victims|160|y|bar_chart State|Massachusetts|x|bar_chart Number_of_murder_victims|136|y|bar_chart State|Kansas|x|bar_chart Number_of_murder_victims|113|y|bar_chart State|Minnesota|x|bar_chart Number_of_murder_victims|106|y|bar_chart State|Connecticut|x|bar_chart Number_of_murder_victims|83|y|bar_chart State|Oregon|x|bar_chart Number_of_murder_victims|82|y|bar_chart State|West_Virginia|x|bar_chart Number_of_murder_victims|67|y|bar_chart State|Utah|x|bar_chart Number_of_murder_victims|60|y|bar_chart State|Iowa|x|bar_chart Number_of_murder_victims|54|y|bar_chart State|Delaware|x|bar_chart Number_of_murder_victims|48|y|bar_chart State|Alaska|x|bar_chart Number_of_murder_victims|47|y|bar_chart State|Nebraska|x|bar_chart Number_of_murder_victims|44|y|bar_chart State|Hawaii|x|bar_chart Number_of_murder_victims|36|y|bar_chart State|Idaho|x|bar_chart Number_of_murder_victims|35|y|bar_chart State|Montana|x|bar_chart Number_of_murder_victims|34|y|bar_chart State|Maine|x|bar_chart Number_of_murder_victims|24|y|bar_chart State|New_Hampshire|x|bar_chart Number_of_murder_victims|21|y|bar_chart State|North_Dakota|x|bar_chart Number_of_murder_victims|18|y|bar_chart State|Rhode_Island|x|bar_chart Number_of_murder_victims|16|y|bar_chart State|Wyoming|x|bar_chart Number_of_murder_victims|13|y|bar_chart State|South_Dakota|x|bar_chart Number_of_murder_victims|12|y|bar_chart State|Vermont|x|bar_chart Number_of_murder_victims|10|y|bar_chart 
title: Homicide - number of murders by U.S. state in 2018

gold: This statistic displays the number of murders in the United States by state . Data includes murder and nonnegligent manslaughter . In 2018 , the number of murders in California amounted to 1,739 victims .
gold_template: This statistic displays the templateYLabel[0] of templateTitle[2] in the templateTitle[4] templateTitle[3] templateXLabel[0] . Data includes templateYLabel[1] and nonnegligent manslaughter . In templateTitle[6] , the templateYLabel[0] of templateTitle[2] in templateXValue[0] amounted to templateYValue[max] templateYLabel[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the United States as of templateTitle[6] , templateTitle[3] templateXLabel[0] . As of templateTitle[4] , around templateYValue[max] templateYLabel[1] templateYLabel[2] were counted in the country .
generated: This statistic shows the Homicide number murders in the United States as of 2018 , by State . As of U.S. , around 1739 murder victims were counted in the country .


Example 671:
data: Player|Oscar_Robertson|x|bar_chart Number_of_triple_doubles|181|y|bar_chart Player|Russell_Westbrook|x|bar_chart Number_of_triple_doubles|146|y|bar_chart Player|Magic_Johnson|x|bar_chart Number_of_triple_doubles|138|y|bar_chart Player|Jason_Kidd|x|bar_chart Number_of_triple_doubles|107|y|bar_chart Player|LeBron_James|x|bar_chart Number_of_triple_doubles|92|y|bar_chart Player|Wilt_Chamberlain|x|bar_chart Number_of_triple_doubles|78|y|bar_chart Player|Larry_Bird|x|bar_chart Number_of_triple_doubles|59|y|bar_chart Player|James_Harden|x|bar_chart Number_of_triple_doubles|45|y|bar_chart Player|Fat_Lever|x|bar_chart Number_of_triple_doubles|43|y|bar_chart Player|Nikola_Jokić|x|bar_chart Number_of_triple_doubles|39|y|bar_chart Player|Bob_Cousy|x|bar_chart Number_of_triple_doubles|33|y|bar_chart Player|Rajon_Rondo|x|bar_chart Number_of_triple_doubles|32|y|bar_chart Player|John_Havlicek|x|bar_chart Number_of_triple_doubles|31|y|bar_chart 
title: National Basketball Association all-time triple double leaders 1946 - 2020

gold: Which player has the most triple doubles ? Oscar Robertson - nicknamed ‘ The Big O ' _ , is the all-time leader in triple doubles in the National Basketball Assocation . He compiled 181 triple doubles during his playing career with the Cincinnati Royals and the Milwaukee Bucks ( 1960 to 1974 ) . The highest ranked active player is Russell Westbrook of the Oklahoma City Thunder with 144 triple doubles in second place .
gold_template: Which templateXLabel[0] has the most templateYLabel[1] templateYLabel[2] ? templateXValue[0] templateXValue[0] - nicknamed ‘ The Big O ' _ , is the templateTitle[3] leader in templateYLabel[1] templateYLabel[2] in the templateTitle[0] templateTitle[1] Assocation . He compiled templateYValue[max] templateYLabel[1] templateYLabel[2] during his playing career with the Cincinnati Royals and the Milwaukee Bucks ( 1960 to 1974 ) . The highest ranked active templateXLabel[0] is templateXValue[1] templateXValue[1] of the Oklahoma City Thunder with 144 templateYLabel[1] templateYLabel[2] in second place .

generated_template: templateXValue[0] templateXValue[0] is the most templateYLabel[1] templateYLabel[2] in the United States in templateTitle[4] , sorted templateTitle[5] templateXLabel[0] . In templateTitle[4] , about templateYValue[max] percent of the most templateYLabel[1] throughout the templateTitle[5] , up from templateYValue[1] percent in the previous year . The templateXValue[0] templateXValue[0] templateXValue[0] is also known as a result result of the United States .
generated: Oscar_Robertson is the most triple doubles in the United States in triple , sorted double Player . In triple , about 181 percent of the most triple throughout the double , up from 146 percent in the previous year . The Oscar_Robertson Oscar_Robertson is also known as a result of the United States .


Example 672:
data: Year|2024|x|line_chart GDP_per_capita_in_U.S._dollars|45934.7|y|line_chart Year|2023|x|line_chart GDP_per_capita_in_U.S._dollars|44311.93|y|line_chart Year|2022|x|line_chart GDP_per_capita_in_U.S._dollars|42842.47|y|line_chart Year|2021|x|line_chart GDP_per_capita_in_U.S._dollars|41504.89|y|line_chart Year|2020|x|line_chart GDP_per_capita_in_U.S._dollars|40391.84|y|line_chart Year|2019|x|line_chart GDP_per_capita_in_U.S._dollars|41030.23|y|line_chart Year|2018|x|line_chart GDP_per_capita_in_U.S._dollars|42579.82|y|line_chart Year|2017|x|line_chart GDP_per_capita_in_U.S._dollars|39976.78|y|line_chart Year|2016|x|line_chart GDP_per_capita_in_U.S._dollars|40657.86|y|line_chart Year|2015|x|line_chart GDP_per_capita_in_U.S._dollars|44494.86|y|line_chart Year|2014|x|line_chart GDP_per_capita_in_U.S._dollars|47003.88|y|line_chart Year|2013|x|line_chart GDP_per_capita_in_U.S._dollars|42981.25|y|line_chart Year|2012|x|line_chart GDP_per_capita_in_U.S._dollars|42023.1|y|line_chart Year|2011|x|line_chart GDP_per_capita_in_U.S._dollars|41649.66|y|line_chart Year|2010|x|line_chart GDP_per_capita_in_U.S._dollars|39122.19|y|line_chart Year|2009|x|line_chart GDP_per_capita_in_U.S._dollars|38601.32|y|line_chart Year|2008|x|line_chart GDP_per_capita_in_U.S._dollars|47469.38|y|line_chart Year|2007|x|line_chart GDP_per_capita_in_U.S._dollars|50315.56|y|line_chart Year|2006|x|line_chart GDP_per_capita_in_U.S._dollars|44403.81|y|line_chart Year|2005|x|line_chart GDP_per_capita_in_U.S._dollars|41842.7|y|line_chart Year|2004|x|line_chart GDP_per_capita_in_U.S._dollars|40111.75|y|line_chart Year|2003|x|line_chart GDP_per_capita_in_U.S._dollars|34302.42|y|line_chart Year|2002|x|line_chart GDP_per_capita_in_U.S._dollars|29912.99|y|line_chart Year|2001|x|line_chart GDP_per_capita_in_U.S._dollars|27510.33|y|line_chart Year|2000|x|line_chart GDP_per_capita_in_U.S._dollars|28043.87|y|line_chart Year|1999|x|line_chart GDP_per_capita_in_U.S._dollars|28435.06|y|line_chart Year|1998|x|line_chart GDP_per_capita_in_U.S._dollars|28077.34|y|line_chart Year|1997|x|line_chart GDP_per_capita_in_U.S._dollars|26647.95|y|line_chart Year|1996|x|line_chart GDP_per_capita_in_U.S._dollars|24256.46|y|line_chart Year|1995|x|line_chart GDP_per_capita_in_U.S._dollars|23026.71|y|line_chart Year|1994|x|line_chart GDP_per_capita_in_U.S._dollars|21344.25|y|line_chart Year|1993|x|line_chart GDP_per_capita_in_U.S._dollars|19925.66|y|line_chart Year|1992|x|line_chart GDP_per_capita_in_U.S._dollars|22305.36|y|line_chart Year|1991|x|line_chart GDP_per_capita_in_U.S._dollars|21671.88|y|line_chart Year|1990|x|line_chart GDP_per_capita_in_U.S._dollars|20808.23|y|line_chart Year|1989|x|line_chart GDP_per_capita_in_U.S._dollars|17617.85|y|line_chart Year|1988|x|line_chart GDP_per_capita_in_U.S._dollars|17364.25|y|line_chart Year|1987|x|line_chart GDP_per_capita_in_U.S._dollars|14294.99|y|line_chart Year|1986|x|line_chart GDP_per_capita_in_U.S._dollars|11551.07|y|line_chart Year|1985|x|line_chart GDP_per_capita_in_U.S._dollars|9491.99|y|line_chart Year|1984|x|line_chart GDP_per_capita_in_U.S._dollars|8943.27|y|line_chart 
title: Gross domestic product ( GDP ) per capita United Kingdom 2024 ( in U.S. dollars )

gold: The statistic shows GDP per capita in the United Kingdom from 1984 to 2018 , with projections up until 2024 . In 2018 , GDP per capita in the United Kingdom was at around 42,579.82 US dollars . The same year , the total UK population amounted to about 64.6 million people .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[6] templateTitle[7] was at around templateYValue[6] US templateYLabel[4] . The same templateXLabel[0] , the total UK population amounted to about 64.6 million people .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[6] templateTitle[7] in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[7] grew by about templateYValue[6] percent of the templateYLabel[4] templateYLabel[2] in templateTitle[7] templateXLabel[0] . templateYLabel[0] is the total value of all goods and services produced in a country and services produced in a positive change is an indicator of economic growth .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in United Kingdom in Kingdom from 1984 to 2018 , with projections up until 2024 . GDP per capita in Kingdom grew by about 42579.82 percent of the dollars capita in Kingdom Year . GDP is the total value of all goods and services produced in a country and services produced in a positive change is an indicator of economic growth .


Example 673:
data: Year|2030|x|line_chart Price_index_in_real_2010_U.S._dollars|87.2|y|line_chart Year|2025|x|line_chart Price_index_in_real_2010_U.S._dollars|79.1|y|line_chart Year|2022|x|line_chart Price_index_in_real_2010_U.S._dollars|74.7|y|line_chart Year|2021|x|line_chart Price_index_in_real_2010_U.S._dollars|73.3|y|line_chart Year|2020|x|line_chart Price_index_in_real_2010_U.S._dollars|72.0|y|line_chart Year|2019|x|line_chart Price_index_in_real_2010_U.S._dollars|74.3|y|line_chart Year|2018|x|line_chart Price_index_in_real_2010_U.S._dollars|87.0|y|line_chart Year|2017|x|line_chart Price_index_in_real_2010_U.S._dollars|68.1|y|line_chart Year|2016|x|line_chart Price_index_in_real_2010_U.S._dollars|55.1|y|line_chart Year|2015|x|line_chart Price_index_in_real_2010_U.S._dollars|65.0|y|line_chart Year|2014|x|line_chart Price_index_in_real_2010_U.S._dollars|111.7|y|line_chart Year|2013|x|line_chart Price_index_in_real_2010_U.S._dollars|120.1|y|line_chart 
title: Global energy commodity price index 2013 - 2030

gold: This statistic shows a commodity price index of energy from 2013 through 2018 , and gives projections for 2030 . In 2018 , the index stood at around 87 real US dollars . Real 2010 US dollars reflect buying power in 2010 .
gold_template: This statistic shows a templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitle[1] from templateXValue[min] through templateXValue[6] , and gives projections for templateXValue[max] . In templateXValue[6] , the templateYLabel[1] stood at around templateYValue[0] templateYLabel[2] US templateYLabel[5] . templateYLabel[2] templateYLabel[3] US templateYLabel[5] reflect buying power in templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[2] , about templateYValue[2] million people accessed the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] .
generated: This statistic shows the Price index of commodity price index 2013 in the United States from 2013 to 2030 . In 2022 , about 74.7 million people accessed the commodity price index 2013 .


Example 674:
data: Brand|Sherwin-Williams|x|bar_chart Share_of_respondents|49.5|y|bar_chart Brand|Benjamin_Moore|x|bar_chart Share_of_respondents|22.4|y|bar_chart Brand|Behr_Paint_Cooperation|x|bar_chart Share_of_respondents|12.1|y|bar_chart Brand|Kelly_Moore|x|bar_chart Share_of_respondents|2.8|y|bar_chart Brand|Valspar|x|bar_chart Share_of_respondents|1.9|y|bar_chart Brand|PPG_Pittsburgh_Paints|x|bar_chart Share_of_respondents|1.9|y|bar_chart Brand|Zar_(United_Gilsonite_Labs)|x|bar_chart Share_of_respondents|1.9|y|bar_chart Brand|Devoe_&_Raynolds|x|bar_chart Share_of_respondents|0.9|y|bar_chart Brand|Dutch_Boy|x|bar_chart Share_of_respondents|0.9|y|bar_chart Brand|Olympic|x|bar_chart Share_of_respondents|0.9|y|bar_chart Brand|None_of_these|x|bar_chart Share_of_respondents|4.7|y|bar_chart 
title: Most used paint brands in the U.S. 2018

gold: This statistic depicts paints used the most by U.S. construction firms in 2018 . The survey revealed that 49.5 percent of the respondents used Sherwin-Williams brand paints the most .
gold_template: This statistic depicts templateXValue[5] templateTitle[1] the templateTitle[0] by templateTitle[4] construction firms in templateTitle[5] . The survey revealed that templateYValue[max] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateXValue[5] the templateTitle[0] .

generated_template: This statistic displays the templateXLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] in the United States as of templateTitle[5] templateTitle[6] . During the survey period , it was found that templateYValue[max] percent of the templateYLabel[1] templateYLabel[2] by the U.S. dollars dollars .
generated: This statistic displays the Brand respondents of Most used in the United States as of 2018 titleErr . During the survey period , it was found that 49.5 percent of the respondents yLabelErr by the U.S. dollars .


Example 675:
data: Brand/Segment|Neutrogena/_makeup_remover_implements|x|bar_chart Percent_sales_change|23.7|y|bar_chart Brand/Segment|CoverGirl_Last_Blast/_mascara|x|bar_chart Percent_sales_change|-14.1|y|bar_chart Brand/Segment|Revlon_Super_Lustrous/_lipstick|x|bar_chart Percent_sales_change|9.9|y|bar_chart Brand/Segment|L'Oréal_Voluminous/_mascara|x|bar_chart Percent_sales_change|2.9|y|bar_chart Brand/Segment|Maybelline_Volum'Express_Falsies/_mascara|x|bar_chart Percent_sales_change|-17.2|y|bar_chart Brand/Segment|CoverGirl_Clean/_powder|x|bar_chart Percent_sales_change|-2.4|y|bar_chart Brand/Segment|Revlon_ColorStay/_foundation|x|bar_chart Percent_sales_change|9.9|y|bar_chart Brand/Segment|Maybelline_Great_Lash/_mascara|x|bar_chart Percent_sales_change|-9.6|y|bar_chart Brand/Segment|CoverGirl_Outlast/_lipstick|x|bar_chart Percent_sales_change|2|y|bar_chart Brand/Segment|CoverGirl_Clean/_foundation|x|bar_chart Percent_sales_change|-3.3|y|bar_chart Brand/Segment|Revlon_ColorStay/_eyeliner|x|bar_chart Percent_sales_change|-6.5|y|bar_chart Brand/Segment|L'Oréal_True_Match/_foundation|x|bar_chart Percent_sales_change|-0.6|y|bar_chart Brand/Segment|L'Oréal_Colour_Riche/_lipstick|x|bar_chart Percent_sales_change|-2.8|y|bar_chart Brand/Segment|CoverGirl_Perfect_Point_Plus/_eyeliner|x|bar_chart Percent_sales_change|-0.4|y|bar_chart Brand/Segment|Maybelline_Volum'Express_Colossal/_mascara|x|bar_chart Percent_sales_change|5.4|y|bar_chart Brand/Segment|Maybelline_Color_Sensational/_lipstick|x|bar_chart Percent_sales_change|14.9|y|bar_chart Brand/Segment|L'Oréal_True_Match/_powder|x|bar_chart Percent_sales_change|5.4|y|bar_chart Brand/Segment|Maybelline_Expert_Wear/_eyeshadow|x|bar_chart Percent_sales_change|-7.5|y|bar_chart Brand/Segment|Maybelline_Volum'Express_Rocket/_mascara|x|bar_chart Percent_sales_change|144|y|bar_chart Brand/Segment|CoverGirl_Eye_Enhancers/_eyeshadow|x|bar_chart Percent_sales_change|-8.7|y|bar_chart 
title: Sales growth of the top U.S. cosmetic brands 2014

gold: The statistic shows the sales growth of the leading cosmetic brands in 2014 . Neutrogena 's makeup remover implements saw a 23.7 percent sales increase while Maybelline 's Volum'Express Rocket mascara experienced a 144 percent increase compared to last year .
gold_template: The statistic shows the templateYLabel[1] templateTitle[1] of the leading templateTitle[4] templateTitle[5] in templateTitle[6] . Neutrogena 's templateXValue[0] templateXValue[0] templateXValue[0] saw a templateYValue[0] templateYLabel[0] templateYLabel[1] increase while templateXValue[4] 's templateXValue[4] Rocket templateXValue[1] experienced a templateYValue[max] templateYLabel[0] increase compared to templateXValue[1] year .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitle[3] templateTitle[4] in templateTitle[5] as of April templateTitle[6] . The templateYLabel[0] templateYLabel[1] templateYLabel[1] monthly templateTitle[3] templateTitle[4] was templateYValue[max] people in the templateXValue[0] templateXValue[0] templateXValue[0] .
generated: The statistic shows the Percent of sales of U.S. cosmetic in brands as of April 2014 . The Percent sales monthly U.S. cosmetic was 144 people in the Neutrogena/_makeup_remover_implements Neutrogena/_makeup_remover_implements .


Example 676:
data: Year|2012|x|line_chart Market_value_in_billion_U.S._dollars|4.2|y|line_chart Year|2022|x|line_chart Market_value_in_billion_U.S._dollars|6.6|y|line_chart 
title: Global ulcerative colitis market 2012 and 2022

gold: This statistic displays the global ulcerative colitis market value in 2012 , and a forecast for 2022 . In 2012 , the ulcerative colitis market was valued at 4.2 billion U.S. dollars . Ulcerative colitis is a type of inflammatory bowel disease ( IBD ) which is characterized by the formation of ulcers in the colon .
gold_template: This statistic displays the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in templateXValue[min] , and a forecast for templateXValue[max] . In templateXValue[min] , the templateTitle[1] templateTitle[2] templateYLabel[0] was valued at templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitle[1] templateTitle[2] is a type of inflammatory bowel disease ( IBD ) which is characterized by the formation of ulcers in the colon .

generated_template: This statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitle[4] templateTitle[5] was around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitle[4] templateTitle[5] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic depicts the Global ulcerative colitis Market in 2012 2022 from 2012 to 2022 . In 2022 , the Global ulcerative colitis of the 2012 2022 was around 6.6 billion U.S. dollars . 2012 2022 's Market value billion U.S. dollars .


Example 677:
data: Year|2024|x|line_chart GDP_growth_compared_to_previous_year|2.8|y|line_chart Year|2023|x|line_chart GDP_growth_compared_to_previous_year|2.8|y|line_chart Year|2022|x|line_chart GDP_growth_compared_to_previous_year|2.8|y|line_chart Year|2021|x|line_chart GDP_growth_compared_to_previous_year|2.8|y|line_chart Year|2020|x|line_chart GDP_growth_compared_to_previous_year|2.9|y|line_chart Year|2019|x|line_chart GDP_growth_compared_to_previous_year|3.2|y|line_chart Year|2018|x|line_chart GDP_growth_compared_to_previous_year|4.76|y|line_chart Year|2017|x|line_chart GDP_growth_compared_to_previous_year|5.75|y|line_chart Year|2016|x|line_chart GDP_growth_compared_to_previous_year|2.63|y|line_chart Year|2015|x|line_chart GDP_growth_compared_to_previous_year|1.85|y|line_chart Year|2014|x|line_chart GDP_growth_compared_to_previous_year|2.99|y|line_chart 
title: Gross domestic product ( GDP ) growth rate in Estonia 2024

gold: The statistic shows the growth in real GDP in Estonia from 2014 to 2018 , with projections up until 2024 . In 2018 , Estonia 's real gross domestic product grew by around 4.76 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[6] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Estonia from 2014 to 2018 , with projections up until 2024 . In 2018 , Estonia 's real Gross domestic product grew by around 4.76 percent compared to the previous Year .


Example 678:
data: Year|2019|x|line_chart Unemployment_rate|7.6|y|line_chart Year|2018|x|line_chart Unemployment_rate|8.4|y|line_chart Year|2017|x|line_chart Unemployment_rate|12.6|y|line_chart Year|2016|x|line_chart Unemployment_rate|13.9|y|line_chart Year|2015|x|line_chart Unemployment_rate|19.5|y|line_chart Year|2014|x|line_chart Unemployment_rate|19.4|y|line_chart Year|2013|x|line_chart Unemployment_rate|20.4|y|line_chart 
title: Youth unemployment rate in Northern Ireland ( UK ) 2013 to 2019

gold: This statistic shows the unemployment rate of young people ( aged 18 to 24 ) in Northern Ireland from 2013 to 2019 . At the start of this period the youth unemployment rate stood at over 20 percent , but by 2019 this had decreased to 7.6 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of young people ( aged 18 to 24 ) in templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . At the start of this period the templateTitle[0] templateYLabel[0] templateYLabel[1] stood at over templateYValue[4] percent , but by templateXValue[max] this had decreased to templateYValue[min] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate of rate from 2013 to 2019 . In 2019 , the Unemployment rate in rate was at approximately 7.6 percent .


Example 679:
data: Month|Mar_'12|x|bar_chart Number_of_monthly_active_users_in_millions|3.8|y|bar_chart Month|Jun_'12|x|bar_chart Number_of_monthly_active_users_in_millions|4.9|y|bar_chart Month|Sep_'12|x|bar_chart Number_of_monthly_active_users_in_millions|7.3|y|bar_chart Month|Dec_'12|x|bar_chart Number_of_monthly_active_users_in_millions|14.9|y|bar_chart Month|Mar_'13|x|bar_chart Number_of_monthly_active_users_in_millions|15.9|y|bar_chart Month|Jun_'13|x|bar_chart Number_of_monthly_active_users_in_millions|19.9|y|bar_chart Month|Sep_'13|x|bar_chart Number_of_monthly_active_users_in_millions|23.9|y|bar_chart Month|Dec_'13|x|bar_chart Number_of_monthly_active_users_in_millions|28.2|y|bar_chart Month|Mar_'14|x|bar_chart Number_of_monthly_active_users_in_millions|29.3|y|bar_chart Month|Jun_'14|x|bar_chart Number_of_monthly_active_users_in_millions|31.9|y|bar_chart Month|Sep_'14|x|bar_chart Number_of_monthly_active_users_in_millions|35.2|y|bar_chart Month|Dec_'14|x|bar_chart Number_of_monthly_active_users_in_millions|39.4|y|bar_chart Month|Mar_'15|x|bar_chart Number_of_monthly_active_users_in_millions|40.1|y|bar_chart Month|Jun_'15|x|bar_chart Number_of_monthly_active_users_in_millions|39.5|y|bar_chart 
title: Viki : number of monthly active users 2012 - 2015

gold: This statistic presents the number of monthly active Viki video platform users as of June 2015 . As of that month , the video portal had 39.5 million monthly active users worldwide . The VoD service had been acquired by Japanese e-commerce company Rakuten in September 2013 .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[0] video platform templateYLabel[3] as of June templateTitle[6] . As of that templateXLabel[0] , the video portal had templateYValue[13] million templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide . The VoD service had been acquired by Japanese e-commerce company Rakuten in September 2013 .

generated_template: Over the last two decades , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[3] templateTitle[4] templateYLabel[2] worldwide as of December templateTitle[6] . The templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] as a result of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from templateYValue[1] percent in the previous year . The templateYLabel[0] templateYLabel[1] templateYLabel[2] is a Japanese e-commerce company .
generated: Over the last two decades , the Number monthly active in the active users active worldwide as of December 2015 . The Number of monthly active active users as a result of 40.1 monthly active users , up from 4.9 percent in the previous year . The Number monthly active is a Japanese e-commerce company .


Example 680:
data: Year|2018|x|line_chart Number_of_direct_staff_in_thousands|204|y|line_chart Year|2017|x|line_chart Number_of_direct_staff_in_thousands|209|y|line_chart Year|2016|x|line_chart Number_of_direct_staff_in_thousands|219|y|line_chart Year|2015|x|line_chart Number_of_direct_staff_in_thousands|231|y|line_chart Year|2014|x|line_chart Number_of_direct_staff_in_thousands|241|y|line_chart Year|2013|x|line_chart Number_of_direct_staff_in_thousands|251|y|line_chart Year|2012|x|line_chart Number_of_direct_staff_in_thousands|259|y|line_chart Year|2011|x|line_chart Number_of_direct_staff_in_thousands|266|y|line_chart 
title: Total direct staff of Citigroup 2011 - 2018

gold: This statistic shows the number of direct employees of Citigroup from 2011 to 2018 . In 2018 , the direct staff of Citigroup amounted to approximately 204,000 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] employees of templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] templateYLabel[2] of templateTitle[3] amounted to approximately templateYValue[min] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Number direct of staff Citigroup 2011 2018 in the United States from 2011 to 2018 . In 2018 , there were 204 Total direct staff Citigroup 2011 2018 in the United States .


Example 681:
data: Year|2019|x|line_chart Gross_profit_in_million_U.S._dollars|1980.78|y|line_chart Year|2018|x|line_chart Gross_profit_in_million_U.S._dollars|1798.68|y|line_chart Year|2017|x|line_chart Gross_profit_in_million_U.S._dollars|1824.57|y|line_chart Year|2016|x|line_chart Gross_profit_in_million_U.S._dollars|2546.69|y|line_chart Year|2015|x|line_chart Gross_profit_in_million_U.S._dollars|2806.36|y|line_chart Year|2014|x|line_chart Gross_profit_in_million_U.S._dollars|3001.02|y|line_chart Year|2013|x|line_chart Gross_profit_in_million_U.S._dollars|3478.88|y|line_chart Year|2012|x|line_chart Gross_profit_in_million_U.S._dollars|3409.2|y|line_chart Year|2011|x|line_chart Gross_profit_in_million_U.S._dollars|3145.83|y|line_chart Year|2010|x|line_chart Gross_profit_in_million_U.S._dollars|2954.97|y|line_chart Year|2009|x|line_chart Gross_profit_in_million_U.S._dollars|2714.7|y|line_chart Year|2008|x|line_chart Gross_profit_in_million_U.S._dollars|2684.41|y|line_chart Year|2007|x|line_chart Gross_profit_in_million_U.S._dollars|2777.3|y|line_chart Year|2006|x|line_chart Gross_profit_in_million_U.S._dollars|2611.79|y|line_chart 
title: Gross profit of toy manufacturer Mattel 2006 - 2019

gold: This statistic shows the gross profit of the U.S. toy manufacturer Mattel worldwide from 2006 to 2019 . In 2019 , their gross profit came to around 1.98 billion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitle[4] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , their templateYLabel[0] templateYLabel[1] came to around templateYValue[0] billion templateYLabel[3] templateYLabel[4] .

generated_template: U.S. fashion fashion retailer templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] 100,000 of templateYValue[0] in templateXValue[max] , down from templateYValue[1] percent in templateXValue[1] when compared with the previous templateXLabel[0] . The United States had the highest level of templateYLabel[1] ranged at templateYValue[max] percent of templateYLabel[1] at the end of templateXValue[max] . U.S. templateYLabel[1] represents a templateXLabel[0] represents in templateYValue[0] percent .
generated: U.S. fashion retailer manufacturer Mattel 2006 in million 100,000 of 1980.78 in 2019 , down from 1798.68 percent in 2018 when compared with the previous Year . The United States had the highest level of profit ranged at 3478.88 percent of profit at the end of 2019 . U.S. profit represents a Year represents in 1980.78 percent .


Example 682:
data: Country|Total_(worldwide)|x|bar_chart Number_of_cases|88948|y|bar_chart Country|China|x|bar_chart Number_of_cases|80174|y|bar_chart Country|Republic_of_Korea|x|bar_chart Number_of_cases|4212|y|bar_chart Country|Italy|x|bar_chart Number_of_cases|1689|y|bar_chart Country|Iran_(Islamic_Republic_of)|x|bar_chart Number_of_cases|978|y|bar_chart Country|Cases_on_an_international_conveyance_(Japan)|x|bar_chart Number_of_cases|706|y|bar_chart Country|Japan|x|bar_chart Number_of_cases|254|y|bar_chart Country|Germany|x|bar_chart Number_of_cases|129|y|bar_chart Country|Singapore|x|bar_chart Number_of_cases|106|y|bar_chart Country|France|x|bar_chart Number_of_cases|100|y|bar_chart Country|United_States_of_America|x|bar_chart Number_of_cases|62|y|bar_chart Country|Kuwait|x|bar_chart Number_of_cases|56|y|bar_chart Country|Bahrain|x|bar_chart Number_of_cases|47|y|bar_chart Country|Spain|x|bar_chart Number_of_cases|45|y|bar_chart Country|Thailand|x|bar_chart Number_of_cases|42|y|bar_chart Country|United_Kingdom|x|bar_chart Number_of_cases|36|y|bar_chart Country|Australia|x|bar_chart Number_of_cases|27|y|bar_chart Country|Malaysia|x|bar_chart Number_of_cases|24|y|bar_chart Country|Switzerland|x|bar_chart Number_of_cases|24|y|bar_chart Country|United_Arab_Emirates|x|bar_chart Number_of_cases|21|y|bar_chart Country|Norway|x|bar_chart Number_of_cases|19|y|bar_chart Country|Iraq|x|bar_chart Number_of_cases|19|y|bar_chart Country|Canada|x|bar_chart Number_of_cases|19|y|bar_chart Country|Viet_Nam|x|bar_chart Number_of_cases|16|y|bar_chart Country|Sweden|x|bar_chart Number_of_cases|14|y|bar_chart Country|Netherlands|x|bar_chart Number_of_cases|13|y|bar_chart Country|Lebanon|x|bar_chart Number_of_cases|10|y|bar_chart Country|Austria|x|bar_chart Number_of_cases|10|y|bar_chart Country|Israel|x|bar_chart Number_of_cases|7|y|bar_chart Country|Croatia|x|bar_chart Number_of_cases|7|y|bar_chart Country|Greece|x|bar_chart Number_of_cases|7|y|bar_chart Country|Oman|x|bar_chart Number_of_cases|6|y|bar_chart Country|Finland|x|bar_chart Number_of_cases|6|y|bar_chart Country|Mexico|x|bar_chart Number_of_cases|5|y|bar_chart Country|Pakistan|x|bar_chart Number_of_cases|4|y|bar_chart Country|Denmark|x|bar_chart Number_of_cases|4|y|bar_chart Country|India|x|bar_chart Number_of_cases|3|y|bar_chart Country|Czechia|x|bar_chart Number_of_cases|3|y|bar_chart Country|Romania|x|bar_chart Number_of_cases|3|y|bar_chart Country|Georgia|x|bar_chart Number_of_cases|3|y|bar_chart Country|Philippines|x|bar_chart Number_of_cases|3|y|bar_chart Country|Azerbaijan|x|bar_chart Number_of_cases|3|y|bar_chart Country|Qatar|x|bar_chart Number_of_cases|3|y|bar_chart Country|Indonesia|x|bar_chart Number_of_cases|2|y|bar_chart Country|Iceland|x|bar_chart Number_of_cases|2|y|bar_chart Country|Egypt|x|bar_chart Number_of_cases|2|y|bar_chart Country|Brazil|x|bar_chart Number_of_cases|2|y|bar_chart Country|Russian_Federation|x|bar_chart Number_of_cases|2|y|bar_chart Country|Armenia|x|bar_chart Number_of_cases|1|y|bar_chart Country|Ecuador|x|bar_chart Number_of_cases|1|y|bar_chart Country|Dominican_Republic|x|bar_chart Number_of_cases|1|y|bar_chart Country|Estonia|x|bar_chart Number_of_cases|1|y|bar_chart Country|Ireland|x|bar_chart Number_of_cases|1|y|bar_chart Country|Lithuania|x|bar_chart Number_of_cases|1|y|bar_chart Country|Luxembourg|x|bar_chart Number_of_cases|1|y|bar_chart Country|Monaco|x|bar_chart Number_of_cases|1|y|bar_chart Country|Algeria|x|bar_chart Number_of_cases|1|y|bar_chart Country|New_Zealand|x|bar_chart Number_of_cases|1|y|bar_chart Country|Cambodia|x|bar_chart Number_of_cases|1|y|bar_chart Country|North_Macedonia|x|bar_chart Number_of_cases|1|y|bar_chart Country|San_Marino|x|bar_chart Number_of_cases|1|y|bar_chart Country|Nepal|x|bar_chart Number_of_cases|1|y|bar_chart Country|Sri_Lanka|x|bar_chart Number_of_cases|1|y|bar_chart Country|Afghanistan|x|bar_chart Number_of_cases|1|y|bar_chart Country|Nigeria|x|bar_chart Number_of_cases|1|y|bar_chart Country|Belarus|x|bar_chart Number_of_cases|1|y|bar_chart Country|Belgium|x|bar_chart Number_of_cases|1|y|bar_chart 
title: COVID-19 cases worldwide as of March 2 , 2020 , by country

gold: As of March 2 , 2020 , the outbreak of the coronavirus disease ( COVID-19 ) had been confirmed in 65 countries , with the overwhelming majority of cases reported in China . The virus had infected 88,948 people worldwide , and the number of deaths had totaled 3,043 . The most severely affected countries outside of China were the Republic of Korea and Italy .
gold_template: As of templateTitle[3] templateYValue[43] , templateTitle[5] , the outbreak of the coronavirus disease ( templateTitle[0] ) had been confirmed in 65 countries , with the overwhelming majority of templateXValue[5] reported in templateXValue[1] . The virus had infected templateYValue[max] people templateTitle[2] , and the templateYLabel[0] of deaths had totaled 3,043 . The most severely affected countries outside of templateXValue[1] were the templateXValue[2] of templateXValue[2] and templateXValue[3] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the participating in the United States in templateTitle[7] , templateTitle[4] templateTitle[5] of templateTitle[6] . In templateTitle[7] , there were a total of templateYValue[min] templateYLabel[1] during this period .
generated: This statistic shows the Number of cases of the participating in the United States in country , 2 2020 of by . In country , there were a total of 1 cases during this period .


Example 683:
data: Year|2018|x|line_chart Number_of_Navy_personnel|325395|y|line_chart Year|2017|x|line_chart Number_of_Navy_personnel|319492|y|line_chart Year|2016|x|line_chart Number_of_Navy_personnel|320101|y|line_chart Year|2015|x|line_chart Number_of_Navy_personnel|323334|y|line_chart Year|2014|x|line_chart Number_of_Navy_personnel|321599|y|line_chart Year|2013|x|line_chart Number_of_Navy_personnel|319838|y|line_chart Year|2012|x|line_chart Number_of_Navy_personnel|314339|y|line_chart Year|2011|x|line_chart Number_of_Navy_personnel|320141|y|line_chart Year|2010|x|line_chart Number_of_Navy_personnel|323139|y|line_chart Year|2009|x|line_chart Number_of_Navy_personnel|324239|y|line_chart Year|2008|x|line_chart Number_of_Navy_personnel|326684|y|line_chart Year|2007|x|line_chart Number_of_Navy_personnel|332269|y|line_chart Year|2006|x|line_chart Number_of_Navy_personnel|345098|y|line_chart Year|2005|x|line_chart Number_of_Navy_personnel|357853|y|line_chart Year|2000|x|line_chart Number_of_Navy_personnel|367371|y|line_chart Year|1995|x|line_chart Number_of_Navy_personnel|429630|y|line_chart 
title: Active Duty U.S. Navy personnel numbers from 1995 to 2018

gold: This graph shows the number of active duty U.S. Navy personnel from 1995 to 2018 . In 2018 , there were 325,395 active duty Navy members in the United States Department of Defense . In 2000 , there were 367,371 active duty members .
gold_template: This graph shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateTitle[0] templateTitle[1] templateYLabel[1] members in the templateTitle[2] Department of Defense . In templateXValue[14] , there were templateYValue[14] templateTitle[0] templateTitle[1] members .

generated_template: There were templateYValue[0] templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] in templateXValue[max] United States between the years from templateXValue[min] to templateXValue[max] . Over this time , there were templateYValue[0] templateYLabel[0] templateYLabel[1] in templateTitle[3] templateTitle[4] templateTitle[5] the United States .
generated: There were 325395 Navy in U.S. Navy personnel numbers in personnel in 2018 United States between the years from 1995 to 2018 . Over this time , there were 325395 Number Navy in Navy personnel numbers the United States .


Example 684:
data: Year|2015|x|line_chart Price_per_tonne_in_GBP|127.15|y|line_chart Year|2014|x|line_chart Price_per_tonne_in_GBP|143.06|y|line_chart Year|2013|x|line_chart Price_per_tonne_in_GBP|175.95|y|line_chart Year|2012|x|line_chart Price_per_tonne_in_GBP|179.26|y|line_chart Year|2011|x|line_chart Price_per_tonne_in_GBP|169.17|y|line_chart Year|2010|x|line_chart Price_per_tonne_in_GBP|123.76|y|line_chart Year|2009|x|line_chart Price_per_tonne_in_GBP|107.05|y|line_chart Year|2008|x|line_chart Price_per_tonne_in_GBP|137.87|y|line_chart Year|2007|x|line_chart Price_per_tonne_in_GBP|120.97|y|line_chart Year|2006|x|line_chart Price_per_tonne_in_GBP|78.88|y|line_chart Year|2005|x|line_chart Price_per_tonne_in_GBP|67.43|y|line_chart Year|2004|x|line_chart Price_per_tonne_in_GBP|80.3|y|line_chart Year|2003|x|line_chart Price_per_tonne_in_GBP|79.32|y|line_chart Year|2002|x|line_chart Price_per_tonne_in_GBP|65.02|y|line_chart 
title: Commodity prices of wheat in the United Kingdom ( UK ) 2002 - 2015

gold: This statistic shows the average price per tonne of wheat in the United Kingdom ( UK ) between 2002 and 2015 by year , according to published agricultural and commodity price figures . In 2012 , the price of wheat was 179.26 British Pound Sterling ( GBP ) per tonne .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[2] in the templateTitle[3] templateTitle[4] ( templateTitle[5] ) between templateXValue[min] and templateXValue[max] by templateXLabel[0] , according to published agricultural and templateTitle[0] templateYLabel[0] figures . In templateXValue[3] , the templateYLabel[0] of templateTitle[2] was templateYValue[max] British Pound Sterling ( templateYLabel[3] ) templateYLabel[1] templateYLabel[2] .

generated_template: templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] templateYLabel[3] of templateTitle[5] templateYLabel[1] 100,000 people .
generated: 2015 had the highest Price per tonne of United Kingdom UK in the United States from 2002 to 2015 . In 2015 , about 127.15 GBP of UK per 100,000 people .


Example 685:
data: Year|16_to_24|x|line_chart Share_of_respondents|59|y|line_chart Year|25_to_34|x|line_chart Share_of_respondents|69|y|line_chart Year|35_to_49|x|line_chart Share_of_respondents|76|y|line_chart Year|50_to_64|x|line_chart Share_of_respondents|79|y|line_chart Year|65_to_74|x|line_chart Share_of_respondents|82|y|line_chart Year|75_and_over|x|line_chart Share_of_respondents|83|y|line_chart 
title: Share of the population who gave to charity in England 2019 , by age

gold: This statistic shows the share of the population who said they gave to charity in the last four weeks in 2018/19 , by age group . Proportionally , those aged 75 and more gave most to charity . At 59 percent , 16 to 24 year olds had the smallest proportion of charitable givers .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] said they templateTitle[3] to templateTitle[4] in the last four weeks in 2018/19 , templateTitle[7] templateTitle[8] group . Proportionally , those aged templateXValue[last] and more templateTitle[3] most to templateTitle[4] . At templateYValue[min] percent , templateXValue[0] to templateXValue[0] templateXLabel[0] olds had the smallest proportion of charitable givers .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitle[3] templateTitle[4] who were using templateTitle[0] as of February templateTitle[5] , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] percent of templateYLabel[1] aged between templateXValue[1] and templateXValue[1] to templateXValue[0] .
generated: This statistic shows the Share of adults in the gave charity who were using Share as of February England , sorted 2019 by age . During that period of time , 83 percent of respondents aged between 25_to_34 and 25_to_34 to 16_to_24 .


Example 686:
data: Year|2018|x|line_chart Number_of_fatalities|1867|y|line_chart Year|2017|x|line_chart Number_of_fatalities|1951|y|line_chart Year|2016|x|line_chart Number_of_fatalities|1913|y|line_chart Year|2015|x|line_chart Number_of_fatalities|1893|y|line_chart Year|2014|x|line_chart Number_of_fatalities|1818|y|line_chart Year|2013|x|line_chart Number_of_fatalities|1861|y|line_chart Year|2012|x|line_chart Number_of_fatalities|2042|y|line_chart Year|2011|x|line_chart Number_of_fatalities|2018|y|line_chart Year|2010|x|line_chart Number_of_fatalities|2377|y|line_chart Year|2009|x|line_chart Number_of_fatalities|2797|y|line_chart Year|2008|x|line_chart Number_of_fatalities|3065|y|line_chart Year|2007|x|line_chart Number_of_fatalities|2800|y|line_chart Year|2006|x|line_chart Number_of_fatalities|2587|y|line_chart 
title: Number of road deaths in Romania 2006 - 2018

gold: This statistic illustrates the annual number of road traffic fatalities in Romania between 2006 and 2018 . In the period of consideration , road fatalities presented a trend of decline in Romania despite some oscillation . The peak was recorded in 2008 , with 3,065 fatalities on Romanian roads .
gold_template: This statistic illustrates the annual templateYLabel[0] of templateTitle[1] traffic templateYLabel[1] in templateTitle[3] between templateXValue[min] and templateXValue[max] . In the period of consideration , templateTitle[1] templateYLabel[1] presented a trend of decline in templateTitle[3] despite some oscillation . The peak was recorded in templateXValue[10] , with templateYValue[max] templateYLabel[1] on Romanian roads .

generated_template: There were templateYValue[0] reported templateTitle[1] templateTitle[2] in templateTitle[3] in templateXValue[max] . This was a slight increase compared to the previous templateXLabel[0] , but an increase of 14 incidents compared to the low reported in templateXValue[1] . The Northern European island state is known for enforcing a strict templateTitle[1] safety policy in order to ensure the security of its residents of tourists in the country .
generated: There were 1867 reported road deaths in Romania in 2018 . This was a slight increase compared to the previous Year , but an increase of 14 incidents compared to the low reported in 2017 . The Northern European island state is known for enforcing a strict road safety policy in order to ensure the security of its residents of tourists in the country .


Example 687:
data: Year|2018|x|line_chart Sales_volume_in_millions|12.88|y|line_chart Year|2017|x|line_chart Sales_volume_in_millions|13.51|y|line_chart Year|2016|x|line_chart Sales_volume_in_millions|16.17|y|line_chart Year|2015|x|line_chart Sales_volume_in_millions|15.94|y|line_chart Year|2014|x|line_chart Sales_volume_in_millions|15.46|y|line_chart Year|2013|x|line_chart Sales_volume_in_millions|13.5|y|line_chart Year|2012|x|line_chart Sales_volume_in_millions|15.85|y|line_chart Year|2011|x|line_chart Sales_volume_in_millions|13.82|y|line_chart Year|2010|x|line_chart Sales_volume_in_millions|11.78|y|line_chart Year|2009|x|line_chart Sales_volume_in_millions|12.99|y|line_chart Year|2008|x|line_chart Sales_volume_in_millions|13.0|y|line_chart Year|2007|x|line_chart Sales_volume_in_millions|8.18|y|line_chart Year|2006|x|line_chart Sales_volume_in_millions|5.0|y|line_chart Year|2005|x|line_chart Sales_volume_in_millions|3.2|y|line_chart Year|2004|x|line_chart Sales_volume_in_millions|2.03|y|line_chart 
title: Sales volume of USB flash drives in Germany 2004 - 2018

gold: USB flash drives experienced fluctuating sales numbers in recent years , with almost 12.9 million units sold in 2018 . Meanwhile , revenue generated amounted to 155 million euros in the same year , a decrease on the one before . Storage media USB flash drives revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard drives and optical storage units like CD-R and CD-RW discs .
gold_template: templateTitle[2] templateTitle[3] templateTitle[4] experienced fluctuating templateYLabel[0] numbers in recent years , with almost templateYValue[0] million units sold in templateXValue[max] . Meanwhile , revenue generated amounted to 155 million euros in the same templateXLabel[0] , a decrease on the one before . Storage media templateTitle[2] templateTitle[3] templateTitle[4] revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard templateTitle[4] and optical storage units like CD-R and CD-RW discs .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[0] million templateYLabel[1] in the United States .
generated: This statistic shows the Sales volume of flash drives in the United States from 2004 to 2018 . In 2018 , there were approximately 12.88 million volume in the United States .


Example 688:
data: Year|2018|x|line_chart Working_age_population_in_millions|72.59|y|line_chart Year|2017|x|line_chart Working_age_population_in_millions|71.89|y|line_chart Year|2016|x|line_chart Working_age_population_in_millions|70.94|y|line_chart Year|2015|x|line_chart Working_age_population_in_millions|69.74|y|line_chart Year|2014|x|line_chart Working_age_population_in_millions|69.34|y|line_chart Year|2013|x|line_chart Working_age_population_in_millions|68.69|y|line_chart Year|2012|x|line_chart Working_age_population_in_millions|68.19|y|line_chart Year|2011|x|line_chart Working_age_population_in_millions|67.38|y|line_chart Year|2010|x|line_chart Working_age_population_in_millions|65.71|y|line_chart Year|2009|x|line_chart Working_age_population_in_millions|64.44|y|line_chart 
title: Working age population in Vietnam 2009 - 2018

gold: In 2018 , the working age population in Vietnam amounted to approximately 72.59 million people . In that year , the labor participation rate was at 76.7 percent while the employment rate was at 75.2 percent .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] amounted to approximately templateYValue[max] million people . In that templateXLabel[0] , the labor participation rate was at 76.7 percent while the employment rate was at 75.2 percent .

generated_template: In templateXValue[max] , there were templateYValue[0] templateYLabel[1] in the United States . The templateYLabel[0] templateYLabel[1] observed in the United States had increased since templateXValue[min] , reaching templateYValue[min] percent in the templateXLabel[0] .
generated: In 2018 , there were 72.59 age in the United States . The Working age observed in the United States had increased since 2009 , reaching 64.44 percent in the Year .


Example 689:
data: Response|NFL|x|bar_chart Share_of_respondents|33|y|bar_chart Response|MLB|x|bar_chart Share_of_respondents|16|y|bar_chart Response|NBA|x|bar_chart Share_of_respondents|10|y|bar_chart Response|NHL|x|bar_chart Share_of_respondents|5|y|bar_chart Response|MLS|x|bar_chart Share_of_respondents|3|y|bar_chart Response|I_don't_follow_any_of_these_leagues|x|bar_chart Share_of_respondents|32|y|bar_chart 
title: Most followed sports leagues in the U.S. 2019

gold: There are widely considered to be four major professional men 's sports leagues in the United States and Canada - NFL , NBA , MLB , and NHL . The professional soccer league ( MLS ) has also achieved some popularity in the United States in recent years . During a 2019 survey , 33 percent of respondents stated that the National Football League , NFL , was their favorite men 's U.S. professional sports league to follow .
gold_template: There are widely considered to be four major professional men 's templateTitle[2] templateXValue[last] in the templateTitle[4] and Canada - templateXValue[0] , templateXValue[2] , templateXValue[1] , and templateXValue[3] . The professional soccer league ( templateXValue[4] ) has also achieved some popularity in the templateTitle[4] in recent years . During a templateTitle[5] survey , templateYValue[max] percent of templateYLabel[1] stated that the National Football League , templateXValue[0] , was their favorite men 's templateTitle[4] professional templateTitle[2] league to templateXValue[last] .

generated_template: This statistic shows the results of a survey among Indian templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States in templateTitle[6] . During the survey , templateYValue[max] percent of the templateYLabel[1] stated that they templateTitle[3] templateTitle[4] templateXValue[0] templateXValue[0] a templateXValue[0] .
generated: This statistic shows the results of a survey among Indian followed sports leagues U.S. 2019 in the United States in titleErr . During the survey , 33 percent of the respondents stated that they leagues U.S. NFL a NFL .


Example 690:
data: Annual_household_income|Under_$30000|x|bar_chart Reach|18|y|bar_chart Annual_household_income|$30000-$74999|x|bar_chart Reach|27|y|bar_chart Annual_household_income|$75000+|x|bar_chart Reach|41|y|bar_chart 
title: Pinterest usage reach in the United States 2019 , by household income

gold: This statistic shows the share of adults in the United States who were using Pinterest as of February 2019 , sorted by income . During that period of time , 18 percent of respondents earning 30,000 U.S. dollars or less used the social networking site .
gold_template: This statistic shows the share of adults in the templateTitle[3] templateTitle[4] who were using templateTitle[0] as of February templateTitle[5] , sorted templateTitle[6] templateXLabel[2] . During that period of time , templateYValue[min] percent of respondents earning 30,000 templateTitle[4] dollars or less used the social networking site .

generated_template: This statistic shows the share of internet users in the templateTitle[3] templateTitle[4] who were using templateTitle[0] as of February templateTitle[5] , templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] percent of respondents stated that they used the social networking site .
generated: This statistic shows the share of internet users in the United States who were using Pinterest as of February 2019 , by household income . During that period of time , 41 percent of respondents stated that they used the social networking site .


Example 691:
data: Year|2024|x|line_chart Inhabitants_in_millions|52.91|y|line_chart Year|2023|x|line_chart Inhabitants_in_millions|52.69|y|line_chart Year|2022|x|line_chart Inhabitants_in_millions|52.48|y|line_chart Year|2021|x|line_chart Inhabitants_in_millions|52.27|y|line_chart Year|2020|x|line_chart Inhabitants_in_millions|52.06|y|line_chart Year|2019|x|line_chart Inhabitants_in_millions|51.85|y|line_chart Year|2018|x|line_chart Inhabitants_in_millions|51.64|y|line_chart Year|2017|x|line_chart Inhabitants_in_millions|51.43|y|line_chart Year|2016|x|line_chart Inhabitants_in_millions|51.22|y|line_chart Year|2015|x|line_chart Inhabitants_in_millions|51.02|y|line_chart Year|2014|x|line_chart Inhabitants_in_millions|50.75|y|line_chart 
title: Total population of South Korea 2024

gold: The statistic shows the total population of South Korea from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population of South Korea was about 51.64 million people . Population of South Korea South Korea , also called Republic of Korea , has one of the highest population densities worldwide , i.e .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] was about templateYValue[6] million people . templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[2] templateTitle[3] , also called Republic of templateTitle[3] , has one of the highest templateTitle[1] densities worldwide , i.e .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] amounted to around templateYValue[6] million templateYLabel[0] . templateTitle[1] of templateTitle[2] As of templateXValue[6] , the templateTitle[1] of templateTitle[2] has increased by around templateYValue[6] percent of the people .
generated: This statistic shows the Total population of South from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of South amounted to around 51.64 million Inhabitants . population of South As of 2018 , the population of South has increased by around 51.64 percent of the people .


Example 692:
data: Quarter|Q3_'19|x|bar_chart Market_capitalization_in_trillion_Euros|5.3|y|bar_chart Quarter|Q2_'19|x|bar_chart Market_capitalization_in_trillion_Euros|5.2|y|bar_chart Quarter|Q1_'19|x|bar_chart Market_capitalization_in_trillion_Euros|5.2|y|bar_chart Quarter|Q4_'18|x|bar_chart Market_capitalization_in_trillion_Euros|4.8|y|bar_chart Quarter|Q3_'18|x|bar_chart Market_capitalization_in_trillion_Euros|5.3|y|bar_chart Quarter|Q2_'18|x|bar_chart Market_capitalization_in_trillion_Euros|5.2|y|bar_chart Quarter|Q1_'18|x|bar_chart Market_capitalization_in_trillion_Euros|5.4|y|bar_chart Quarter|Q4_'17|x|bar_chart Market_capitalization_in_trillion_Euros|5.6|y|bar_chart Quarter|Q3_'17|x|bar_chart Market_capitalization_in_trillion_Euros|5.4|y|bar_chart Quarter|Q2_'17|x|bar_chart Market_capitalization_in_trillion_Euros|5.3|y|bar_chart Quarter|Q1_'17|x|bar_chart Market_capitalization_in_trillion_Euros|5.4|y|bar_chart Quarter|Q4_'16|x|bar_chart Market_capitalization_in_trillion_Euros|5.2|y|bar_chart Quarter|Q3_'16|x|bar_chart Market_capitalization_in_trillion_Euros|4.4|y|bar_chart Quarter|Q2_'16|x|bar_chart Market_capitalization_in_trillion_Euros|4.1|y|bar_chart Quarter|Q1_'16|x|bar_chart Market_capitalization_in_trillion_Euros|4.2|y|bar_chart 
title: Market capitalization of leading 100 banks worldwide 2016 - 2019

gold: This statistic presents the market capitalization of leading 100 banks worldwide from the first quarter 2016 to the third quarter 2019 . The market cap of top 100 global banks amounted to 5.3 trillion Euros in the third quarter of 2019 .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from the first templateXLabel[0] templateTitle[6] to the third templateXLabel[0] templateTitle[7] . The templateYLabel[0] cap of top templateTitle[3] global templateTitle[4] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] in the third templateXLabel[0] of templateTitle[7] .

generated_template: This statistic gives information on the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[4] templateTitle[5] as of the fourth templateXLabel[0] of templateTitle[6] . As of the last reported templateXLabel[0] , the templateYLabel[1] of templateTitle[4] templateTitle[5] amounted to templateYValue[0] percent .
generated: This statistic gives information on the Market capitalization of Market capitalization banks worldwide as of the fourth Quarter of 2016 . As of the last reported Quarter , the capitalization of banks worldwide amounted to 5.3 percent .


Example 693:
data: Response|0_to_3_nights|x|bar_chart Share_of_respondents|21|y|bar_chart Response|4_to_5_nights|x|bar_chart Share_of_respondents|28|y|bar_chart Response|6_to_7_nights|x|bar_chart Share_of_respondents|53|y|bar_chart 
title: Frequency of American families having dinner together at home 2013

gold: This statistic shows the results of a survey , conducted in 2013 , among adult Americans on the frequency of having dinner at home as a family . In December 2013 , 53 percent of the respondents answered that their family eat dinner together at home on 6 to 7 nights a week .
gold_template: This statistic shows the results of a survey , conducted in templateTitle[7] , among adult Americans on the templateTitle[0] of templateTitle[3] templateTitle[4] at templateTitle[6] as a family . In December templateTitle[7] , templateYValue[max] percent of the templateYLabel[1] answered that their family eat templateTitle[4] templateTitle[5] at templateTitle[6] on templateXValue[last] to templateXValue[last] templateXValue[0] a week .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] people in the United States as of templateTitle[8] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated they had purchased goods and templateXValue[0] templateXValue[0] templateXValue[0] a templateXValue[0] .
generated: This statistic shows the Frequency American families having dinner together home people in the United States as of titleErr . During the survey period , 53 percent of respondents stated they had purchased goods and 0_to_3_nights 0_to_3_nights a 0_to_3_nights .


Example 694:
data: Year|2024|x|line_chart Share_in_GDP|30.26|y|line_chart Year|2023|x|line_chart Share_in_GDP|29.18|y|line_chart Year|2022|x|line_chart Share_in_GDP|28.24|y|line_chart Year|2021|x|line_chart Share_in_GDP|28.06|y|line_chart Year|2020|x|line_chart Share_in_GDP|28.79|y|line_chart Year|2019|x|line_chart Share_in_GDP|30.67|y|line_chart Year|2018|x|line_chart Share_in_GDP|32.18|y|line_chart Year|2017|x|line_chart Share_in_GDP|39.53|y|line_chart Year|2016|x|line_chart Share_in_GDP|47.47|y|line_chart Year|2015|x|line_chart Share_in_GDP|38.42|y|line_chart Year|2014|x|line_chart Share_in_GDP|11.82|y|line_chart 
title: Iran 's national debt in relation to gross domestic product 2024

gold: This statistic shows the national debt of Iran in relation to gross domestic product ( GDP ) from 2014 to 2018 , with projections up until 2024 . In 2018 , Iran 's national debt amounted to 32.18 percent of gross domestic product .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitle[0] in templateTitle[4] to templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[1] ) from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] amounted to templateYValue[6] percent of templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[6] , there were some templateYValue[0] people throughout the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Share GDP of relation gross in the United States from 2014 to 2024 . In 2018 , there were some 30.26 people throughout the national debt relation gross in the United States .


Example 695:
data: Year|2026|x|line_chart GDP_contribution_in_billion_U.S._dollars|20.9|y|line_chart Year|2016|x|line_chart GDP_contribution_in_billion_U.S._dollars|11.4|y|line_chart Year|2006|x|line_chart GDP_contribution_in_billion_U.S._dollars|4.0|y|line_chart 
title: Direct tourism contribution of Dubai to GDP of the UAE 2006 - 2026

gold: This statistic described the direct tourism contribution of Dubai to the gross domestic product of the United Arab Emirates from 2006 to 2016 and a forecast for 2026 . The forecast of the direct tourism contribution of Dubai to the GDP of the United Arab Emirates for 2026 was approximately 20.9 billion U.S. dollars .
gold_template: This statistic described the templateTitle[0] templateTitle[1] templateYLabel[1] of templateTitle[3] to the gross domestic product of the United Arab Emirates from templateXValue[min] to templateXValue[1] and a forecast for templateXValue[max] . The forecast of the templateTitle[0] templateTitle[1] templateYLabel[1] of templateTitle[3] to the templateYLabel[0] of the United Arab Emirates for templateXValue[max] was approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: templateXValue[0] templateXValue[0] had the templateYLabel[0] templateYLabel[1] of templateTitle[6] , according to templateYValue[max] million people in the United States . The least once every templateXLabel[0] , about templateYValue[1] percent of the templateXLabel[0] . templateYLabel[1] Just like templateTitle[3] templateTitle[4] – additional information The number of eggs worldwide , this figure has seen as a steady growth in the United States .
generated: 2026 had the GDP contribution of 2006 , according to 20.9 million people in the United States . The least once every Year , about 11.4 percent of the Year . contribution Just like Dubai GDP – additional information The number of eggs worldwide , this figure has seen as a steady growth in the United States .


Example 696:
data: Year|2018|x|line_chart Number_of_participants_in_millions|15.69|y|line_chart Year|2017|x|line_chart Number_of_participants_in_millions|15.63|y|line_chart Year|2016|x|line_chart Number_of_participants_in_millions|15.47|y|line_chart Year|2015|x|line_chart Number_of_participants_in_millions|15.53|y|line_chart Year|2014|x|line_chart Number_of_participants_in_millions|14.85|y|line_chart Year|2013|x|line_chart Number_of_participants_in_millions|13.53|y|line_chart Year|2012|x|line_chart Number_of_participants_in_millions|14.71|y|line_chart Year|2011|x|line_chart Number_of_participants_in_millions|14.89|y|line_chart Year|2010|x|line_chart Number_of_participants_in_millions|14.01|y|line_chart Year|2009|x|line_chart Number_of_participants_in_millions|15.27|y|line_chart Year|2008|x|line_chart Number_of_participants_in_millions|13.98|y|line_chart Year|2007|x|line_chart Number_of_participants_in_millions|14.14|y|line_chart Year|2006|x|line_chart Number_of_participants_in_millions|15.1|y|line_chart 
title: Participants in hunting in the U.S. from 2006 to 2018

gold: This statistic shows the number of participants in hunting in the United States from 2006 to 2018 . In 2018 , the number of participants ( aged six years and older ) in hunting amounted to approximately 15.69 million .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[max] million .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the United States templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[0] million .
generated: This statistic shows the Number of participants in hunting in the United States from 2006 to 2018 . In 2018 , the Number of participants ( aged six years and older ) in hunting amounted to approximately 15.69 million .


Example 697:
data: Year|2017|x|line_chart Kilograms_per_person_per_year|403|y|line_chart Year|2016|x|line_chart Kilograms_per_person_per_year|412|y|line_chart Year|2015|x|line_chart Kilograms_per_person_per_year|406|y|line_chart Year|2014|x|line_chart Kilograms_per_person_per_year|413|y|line_chart Year|2013|x|line_chart Kilograms_per_person_per_year|402|y|line_chart Year|2012|x|line_chart Kilograms_per_person_per_year|412|y|line_chart Year|2011|x|line_chart Kilograms_per_person_per_year|421|y|line_chart Year|2010|x|line_chart Kilograms_per_person_per_year|425|y|line_chart 
title: Total household waste in England 2010 - 2017

gold: Household waste volumes per person in England remained at a similar level between 2010 and 2017 . Although there was an overall decrease during this period , the household volumes were still over 400 kilograms per person in 2017 . The region which generated the largest volume of residual waste per household was the North East of England , where an average of 601 kilograms of waste was generated per person in 2017/2018 .
gold_template: templateTitle[1] templateTitle[2] volumes templateYLabel[1] templateYLabel[2] in templateTitle[3] remained at a similar level between templateXValue[min] and templateXValue[max] . Although there was an overall decrease during this period , the templateTitle[1] volumes were still over 400 templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The region which generated the largest volume of residual templateTitle[2] templateYLabel[1] templateTitle[1] was the North East of templateTitle[3] , where an average of 601 templateYLabel[0] of templateTitle[2] was generated templateYLabel[1] templateYLabel[2] in 2017/2018 .

generated_template: As of templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[max] . The templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] is an increase from templateYValue[1] million in the previous templateXLabel[0] . templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] The templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] can be accessed here .
generated: As of 2017 , the Kilograms per of England 2010 2017 in the United States amounted to approximately 425 person per in 2017 . The waste England 2010 2017 is an increase from 412 million in the previous Year . waste England 2010 2017 The waste England 2010 2017 can be accessed here .


Example 698:
data: Preferred_mode_of_travel|Car|x|bar_chart Share_of_respondents|63|y|bar_chart Preferred_mode_of_travel|Plane|x|bar_chart Share_of_respondents|31|y|bar_chart Preferred_mode_of_travel|RV|x|bar_chart Share_of_respondents|3|y|bar_chart Preferred_mode_of_travel|Train|x|bar_chart Share_of_respondents|2|y|bar_chart Preferred_mode_of_travel|Other|x|bar_chart Share_of_respondents|1|y|bar_chart 
title: Preferred modes of transportation when taking a family vacation in the U.S. 2015

gold: The statistic shows the preferred ways to travel when taking a family vacation in the United States in 2015 . The survey revealed that 63 percent of respondents prefer to travel by car .
gold_template: The statistic shows the templateXLabel[0] ways to templateXLabel[2] templateTitle[3] templateTitle[4] a templateTitle[5] templateTitle[6] in the templateTitle[7] in templateTitle[8] . The survey revealed that templateYValue[max] percent of templateYLabel[1] prefer to templateXLabel[2] by templateXValue[0] .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[4] , sorted templateTitle[5] templateXLabel[0] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated they used templateXValue[0] templateXValue[0] or a templateXValue[0] templateXValue[0] for the templateTitle[0] templateTitle[1] in the United States .
generated: This statistic shows the results of a survey conducted in the United States in taking , sorted family Preferred . During the survey period , 63 percent of respondents stated they used Car or a Car for the Preferred modes in the United States .


Example 699:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|4|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|4|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|3.9|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|3.8|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|3.75|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|3.6|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|3.54|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|3.52|y|line_chart Year|2016|x|line_chart Inflation_rate_compared_to_previous_year|2.67|y|line_chart Year|2015|x|line_chart Inflation_rate_compared_to_previous_year|0.63|y|line_chart Year|2014|x|line_chart Inflation_rate_compared_to_previous_year|4.09|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|6.6|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|9.1|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|18.67|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|9.21|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|6.72|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|23.12|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|8.35|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|7.5|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|8.39|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|7.89|y|line_chart Year|2003|x|line_chart Inflation_rate_compared_to_previous_year|3.3|y|line_chart Year|2002|x|line_chart Inflation_rate_compared_to_previous_year|4.08|y|line_chart Year|2001|x|line_chart Inflation_rate_compared_to_previous_year|-0.31|y|line_chart Year|2000|x|line_chart Inflation_rate_compared_to_previous_year|-1.77|y|line_chart Year|1999|x|line_chart Inflation_rate_compared_to_previous_year|4.11|y|line_chart Year|1998|x|line_chart Inflation_rate_compared_to_previous_year|8.11|y|line_chart Year|1997|x|line_chart Inflation_rate_compared_to_previous_year|3.1|y|line_chart Year|1996|x|line_chart Inflation_rate_compared_to_previous_year|5.59|y|line_chart Year|1995|x|line_chart Inflation_rate_compared_to_previous_year|16.93|y|line_chart Year|1994|x|line_chart Inflation_rate_compared_to_previous_year|9.49|y|line_chart Year|1993|x|line_chart Inflation_rate_compared_to_previous_year|8.38|y|line_chart Year|1992|x|line_chart Inflation_rate_compared_to_previous_year|37.71|y|line_chart Year|1991|x|line_chart Inflation_rate_compared_to_previous_year|81.82|y|line_chart Year|1990|x|line_chart Inflation_rate_compared_to_previous_year|36.03|y|line_chart Year|1989|x|line_chart Inflation_rate_compared_to_previous_year|95.77|y|line_chart Year|1988|x|line_chart Inflation_rate_compared_to_previous_year|374.35|y|line_chart Year|1987|x|line_chart Inflation_rate_compared_to_previous_year|360.36|y|line_chart Year|1986|x|line_chart Inflation_rate_compared_to_previous_year|453.54|y|line_chart Year|1985|x|line_chart Inflation_rate_compared_to_previous_year|91.6|y|line_chart Year|1984|x|line_chart Inflation_rate_compared_to_previous_year|64.9|y|line_chart 
title: Inflation rate in Vietnam 2024*

gold: In 2018 , the average inflation rate in Vietnam amounted to 3.54 percent compared to the previous year . After a severe drop below one percent in 2015 , Vietnam 's inflation seems to have stabilized again and is expected to level off at around four percent in the next few years . Vietnam 's economic struggles Around 2012 , Vietnam suffered the consequences of the global economic crisis and domestic economic mismanagement , which saw enterprises going bankrupt , inflation peaking at over nine percent , and gross domestic product slumping to a dramatic low .
gold_template: In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . After a severe drop below templateYValue[9] percent in templateXValue[9] , templateTitle[2] 's templateYLabel[0] seems to have stabilized again and is expected to level off at around templateYValue[0] percent in the next few years . templateTitle[2] 's economic struggles Around templateXValue[12] , templateTitle[2] suffered the consequences of the global economic crisis and domestic economic mismanagement , which saw enterprises going bankrupt , templateYLabel[0] peaking at over templateYValue[12] percent , and gross domestic product slumping to a dramatic low .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Vietnam from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Vietnam amounted to about 3.54 percent compared to the previous Year .


Example 700:
data: Year|2022|x|line_chart Revenue_in_million_U.S._dollars|19718.88|y|line_chart Year|2021|x|line_chart Revenue_in_million_U.S._dollars|15686.56|y|line_chart Year|2020|x|line_chart Revenue_in_million_U.S._dollars|10936.67|y|line_chart Year|2019|x|line_chart Revenue_in_million_U.S._dollars|7275.43|y|line_chart Year|2018|x|line_chart Revenue_in_million_U.S._dollars|4421.74|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|409.67|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|138.61|y|line_chart 
title: Global smart augmented reality glasses revenue 2016 - 2022

gold: The statistic shows smart AR glasses revenue worldwide from 2016 to 2022 . Smart augmented reality glasses revenue reached 138.6 million U.S. dollars in 2016 and is forecast to amount to around 19.7 billion U.S. dollars by 2022 .
gold_template: The statistic shows templateTitle[1] AR templateTitle[4] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] reached templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[min] and is forecast to amount to around templateYValue[max] billion templateYLabel[2] templateYLabel[3] by templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] ( UK ) templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the source templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] of the templateTitle[2] templateTitle[3] was templateYValue[6] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the smart augmented ( UK ) Global smart augmented reality from 2016 to 2022 . In 2016 , the source Global smart augmented Revenue of the augmented reality was 138.61 million U.S. dollars .


Example 701:
data: Year|2018|x|line_chart Household_income_in_current_U.S._dollars|50573|y|line_chart Year|2017|x|line_chart Household_income_in_current_U.S._dollars|45392|y|line_chart Year|2016|x|line_chart Household_income_in_current_U.S._dollars|44354|y|line_chart Year|2015|x|line_chart Household_income_in_current_U.S._dollars|42824|y|line_chart Year|2014|x|line_chart Household_income_in_current_U.S._dollars|39552|y|line_chart Year|2013|x|line_chart Household_income_in_current_U.S._dollars|40241|y|line_chart Year|2012|x|line_chart Household_income_in_current_U.S._dollars|43553|y|line_chart Year|2011|x|line_chart Household_income_in_current_U.S._dollars|41821|y|line_chart Year|2010|x|line_chart Household_income_in_current_U.S._dollars|42777|y|line_chart Year|2009|x|line_chart Household_income_in_current_U.S._dollars|40490|y|line_chart Year|2008|x|line_chart Household_income_in_current_U.S._dollars|37994|y|line_chart Year|2007|x|line_chart Household_income_in_current_U.S._dollars|42091|y|line_chart Year|2006|x|line_chart Household_income_in_current_U.S._dollars|38419|y|line_chart Year|2005|x|line_chart Household_income_in_current_U.S._dollars|36445|y|line_chart Year|2004|x|line_chart Household_income_in_current_U.S._dollars|33373|y|line_chart Year|2003|x|line_chart Household_income_in_current_U.S._dollars|32763|y|line_chart Year|2002|x|line_chart Household_income_in_current_U.S._dollars|29359|y|line_chart Year|2001|x|line_chart Household_income_in_current_U.S._dollars|29673|y|line_chart Year|2000|x|line_chart Household_income_in_current_U.S._dollars|29411|y|line_chart Year|1999|x|line_chart Household_income_in_current_U.S._dollars|29297|y|line_chart Year|1998|x|line_chart Household_income_in_current_U.S._dollars|26704|y|line_chart Year|1997|x|line_chart Household_income_in_current_U.S._dollars|27488|y|line_chart Year|1996|x|line_chart Household_income_in_current_U.S._dollars|25247|y|line_chart Year|1995|x|line_chart Household_income_in_current_U.S._dollars|24880|y|line_chart Year|1994|x|line_chart Household_income_in_current_U.S._dollars|23564|y|line_chart Year|1993|x|line_chart Household_income_in_current_U.S._dollars|22421|y|line_chart Year|1992|x|line_chart Household_income_in_current_U.S._dollars|20271|y|line_chart Year|1991|x|line_chart Household_income_in_current_U.S._dollars|23147|y|line_chart Year|1990|x|line_chart Household_income_in_current_U.S._dollars|22137|y|line_chart 
title: West Virginia - Median household income 1990 - 2018

gold: This statistic shows the median household income in West Virginia from 1990 to 2018 . In 2018 , the median household income in West Virginia amounted to 50,573 U.S. dollars .
gold_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] templateTitle[1] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[3] .
generated: The statistic shows the Household income in West Virginia from 1990 to 2018 . In 2018 , the West Virginia Household income in West amounted to 50573 U.S. .


Example 702:
data: Quarter|Q2_'20|x|bar_chart Revenue_in_million_U.S._dollars|1348|y|bar_chart Quarter|Q1_'20|x|bar_chart Revenue_in_million_U.S._dollars|1209|y|bar_chart Quarter|Q4_'19|x|bar_chart Revenue_in_million_U.S._dollars|1238|y|bar_chart Quarter|Q3_'19|x|bar_chart Revenue_in_million_U.S._dollars|1289|y|bar_chart Quarter|Q2_'19|x|bar_chart Revenue_in_million_U.S._dollars|1286|y|bar_chart Quarter|Q1_'19|x|bar_chart Revenue_in_million_U.S._dollars|1137|y|bar_chart Quarter|Q4_'18|x|bar_chart Revenue_in_million_U.S._dollars|1582|y|bar_chart Quarter|Q3_'18|x|bar_chart Revenue_in_million_U.S._dollars|1160|y|bar_chart Quarter|Q2_'18|x|bar_chart Revenue_in_million_U.S._dollars|959|y|bar_chart Quarter|Q1_'18|x|bar_chart Revenue_in_million_U.S._dollars|1449|y|bar_chart Quarter|Q4_'17|x|bar_chart Revenue_in_million_U.S._dollars|1527|y|bar_chart Quarter|Q3_'17|x|bar_chart Revenue_in_million_U.S._dollars|1149|y|bar_chart Quarter|Q2_'17|x|bar_chart Revenue_in_million_U.S._dollars|898|y|bar_chart Quarter|Q1_'17|x|bar_chart Revenue_in_million_U.S._dollars|1271|y|bar_chart Quarter|Q4_'16|x|bar_chart Revenue_in_million_U.S._dollars|1308|y|bar_chart Quarter|Q3_'16|x|bar_chart Revenue_in_million_U.S._dollars|1070|y|bar_chart Quarter|Q2_'16|x|bar_chart Revenue_in_million_U.S._dollars|815|y|bar_chart Quarter|Q1_'16|x|bar_chart Revenue_in_million_U.S._dollars|1203|y|bar_chart Quarter|Q4_'15|x|bar_chart Revenue_in_million_U.S._dollars|1185|y|bar_chart Quarter|Q3_'15|x|bar_chart Revenue_in_million_U.S._dollars|1126|y|bar_chart Quarter|Q2_'15|x|bar_chart Revenue_in_million_U.S._dollars|990|y|bar_chart Quarter|Q1_'15|x|bar_chart Revenue_in_million_U.S._dollars|1214|y|bar_chart Quarter|Q4_'14|x|bar_chart Revenue_in_million_U.S._dollars|1123|y|bar_chart Quarter|Q3_'14|x|bar_chart Revenue_in_million_U.S._dollars|808|y|bar_chart Quarter|Q2_'14|x|bar_chart Revenue_in_million_U.S._dollars|695|y|bar_chart Quarter|Q1_'14|x|bar_chart Revenue_in_million_U.S._dollars|949|y|bar_chart Quarter|Q4_'13|x|bar_chart Revenue_in_million_U.S._dollars|1209|y|bar_chart Quarter|Q3_'13|x|bar_chart Revenue_in_million_U.S._dollars|922|y|bar_chart Quarter|Q2_'13|x|bar_chart Revenue_in_million_U.S._dollars|711|y|bar_chart Quarter|Q1_'13|x|bar_chart Revenue_in_million_U.S._dollars|955|y|bar_chart Quarter|Q4_'12|x|bar_chart Revenue_in_million_U.S._dollars|1368|y|bar_chart Quarter|Q3_'12|x|bar_chart Revenue_in_million_U.S._dollars|1061|y|bar_chart Quarter|Q2_'12|x|bar_chart Revenue_in_million_U.S._dollars|715|y|bar_chart Quarter|Q1_'12|x|bar_chart Revenue_in_million_U.S._dollars|999|y|bar_chart Quarter|Q4_'11|x|bar_chart Revenue_in_million_U.S._dollars|1090|y|bar_chart Quarter|Q3_'11|x|bar_chart Revenue_in_million_U.S._dollars|1053|y|bar_chart Quarter|Q2_'11|x|bar_chart Revenue_in_million_U.S._dollars|631|y|bar_chart Quarter|Q1_'11|x|bar_chart Revenue_in_million_U.S._dollars|815|y|bar_chart Quarter|Q4_'10|x|bar_chart Revenue_in_million_U.S._dollars|979|y|bar_chart Quarter|Q3_'10|x|bar_chart Revenue_in_million_U.S._dollars|1243|y|bar_chart 
title: Quarterly revenue of Electronic Arts from Q3 2010 to Q2 2020

gold: This time series depicts the quarterly revenue of Electronic Arts from the third quarter of the fiscal year 2010 to the second quarter of the fiscal year 2020 . In the second fiscal quarter of 2020 , which ended on September 30 , 2019 , Electronic Arts generated a net revenue of 1.35 billion U.S. dollars . Here you can find information about EA 's quarterly net income .
gold_template: This time series depicts the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] the third templateXLabel[0] of the fiscal year templateTitle[6] to the second templateXLabel[0] of the fiscal year templateTitle[8] . In the second fiscal templateXLabel[0] of templateTitle[8] , which ended on September 30 , 2019 , templateTitle[2] templateTitle[3] generated a net templateYLabel[0] of templateYValue[0] billion templateYLabel[2] templateYLabel[3] . Here you can find information about EA 's templateTitle[0] net income .

generated_template: The statistic shows the total templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from the fourth templateXLabel[0] of 2012 to the third templateXLabel[0] of templateXValue[max] . In the last reported templateXLabel[0] , the company 's templateTitle[2] amounted to around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the same templateXLabel[0] .
generated: The statistic shows the total Revenue of Quarterly revenue Electronic from the fourth Quarter of 2012 to the third Quarter of Q2_'20 . In the last reported Quarter , the company 's Electronic amounted to around 1582 million U.S. dollars in the same Quarter .


Example 703:
data: Month|Booking.com|x|bar_chart Number_of_site_visits_in_millions|166.0|y|bar_chart Month|TripAdvisor_Family|x|bar_chart Number_of_site_visits_in_millions|159.9|y|bar_chart Month|Expedia_Family|x|bar_chart Number_of_site_visits_in_millions|59.3|y|bar_chart Month|Hotels.com|x|bar_chart Number_of_site_visits_in_millions|34.5|y|bar_chart Month|Priceline.com|x|bar_chart Number_of_site_visits_in_millions|31.3|y|bar_chart Month|Agoda.com|x|bar_chart Number_of_site_visits_in_millions|30.7|y|bar_chart Month|Hotelurbano|x|bar_chart Number_of_site_visits_in_millions|25.5|y|bar_chart Month|Kayak.com|x|bar_chart Number_of_site_visits_in_millions|24.4|y|bar_chart Month|Travel.yahoo.com|x|bar_chart Number_of_site_visits_in_millions|24.1|y|bar_chart Month|Cheapoair.com|x|bar_chart Number_of_site_visits_in_millions|20.2|y|bar_chart Month|Makemytrip.com|x|bar_chart Number_of_site_visits_in_millions|17.5|y|bar_chart Month|Orbitz.com|x|bar_chart Number_of_site_visits_in_millions|17.2|y|bar_chart Month|Travelocity|x|bar_chart Number_of_site_visits_in_millions|15.0|y|bar_chart Month|Hotwire.com|x|bar_chart Number_of_site_visits_in_millions|13.2|y|bar_chart Month|Airbnb.com|x|bar_chart Number_of_site_visits_in_millions|12.4|y|bar_chart Month|Travelzoo.com|x|bar_chart Number_of_site_visits_in_millions|12.2|y|bar_chart Month|Decolar.com|x|bar_chart Number_of_site_visits_in_millions|11.3|y|bar_chart Month|Slyscanner.com|x|bar_chart Number_of_site_visits_in_millions|9.6|y|bar_chart Month|Ctrip.com|x|bar_chart Number_of_site_visits_in_millions|8.6|y|bar_chart Month|HomeAway.com|x|bar_chart Number_of_site_visits_in_millions|7.4|y|bar_chart 
title: Leading global travel booking sites by number of page visits 2014

gold: This statistic shows the number of visits to travel booking sites worldwide in January 2014 . Booking.com had the most visits in January 2014 , with an estimated number of visits of 166 million .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[2] to templateTitle[2] templateTitle[3] templateTitle[4] worldwide in January templateTitle[9] . templateXValue[0] had the most templateYLabel[2] in January templateTitle[9] , with an estimated templateYLabel[0] of templateYLabel[2] of templateYValue[max] million .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateYLabel[2] in the United States in templateTitle[4] , templateTitle[5] templateXLabel[0] . In templateTitle[4] , templateYValue[max] templateYLabel[3] of templateTitle[1] templateYLabel[1] from the United States in templateTitle[6] .
generated: This statistic shows the Number site of global visits in the United States in sites , by Month . In sites , 166.0 millions of global site from the United States in number .


Example 704:
data: Year|2019|x|line_chart Share_of_individuals|4.3|y|line_chart Year|2018|x|line_chart Share_of_individuals|4.8|y|line_chart Year|2017|x|line_chart Share_of_individuals|5.9|y|line_chart Year|2016|x|line_chart Share_of_individuals|7.3|y|line_chart Year|2015|x|line_chart Share_of_individuals|8.6|y|line_chart Year|2014|x|line_chart Share_of_individuals|9|y|line_chart Year|2013|x|line_chart Share_of_individuals|8.9|y|line_chart Year|2012|x|line_chart Share_of_individuals|7.1|y|line_chart Year|2011|x|line_chart Share_of_individuals|6.1|y|line_chart Year|2010|x|line_chart Share_of_individuals|6.1|y|line_chart Year|2009|x|line_chart Share_of_individuals|5.5|y|line_chart 
title: Unemployment rate in the Netherlands 2009 - 2019

gold: In 2019 , the unemployment rate in the Netherlands was just over four percent . Unemployment peaked in 2013 and 2014 . At the height of the financial crisis , the annual unemployment rate in the country reached 8.9 and 9 percent respectively .
gold_template: In templateXValue[max] , the templateTitle[0] templateTitle[1] in the templateTitle[2] was just over templateYValue[min] percent . templateTitle[0] peaked in templateXValue[6] and templateXValue[5] . At the height of the financial crisis , the annual templateTitle[0] templateTitle[1] in the country reached templateYValue[6] and templateYValue[4] percent respectively .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[6] templateTitle[7] and templateTitle[8] templateTitle[9] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] percent of the templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Share individuals of titleErr titleErr and titleErr titleErr in the United States from 2009 to 2019 . In 2019 , there were 4.3 percent of the 2019 titleErr in the United States .


Example 705:
data: Response|Sexual_harassment|x|bar_chart Share_of_respondents|40|y|bar_chart Response|Sexual_violence|x|bar_chart Share_of_respondents|37|y|bar_chart Response|Physical_violence|x|bar_chart Share_of_respondents|32|y|bar_chart Response|Domestic_abuse|x|bar_chart Share_of_respondents|19|y|bar_chart Response|Equal_pay|x|bar_chart Share_of_respondents|19|y|bar_chart Response|Workplace_discrimination|x|bar_chart Share_of_respondents|18|y|bar_chart Response|Gender_stereotyping|x|bar_chart Share_of_respondents|16|y|bar_chart Response|Sexualization_of_women_and_girls_in_the_media|x|bar_chart Share_of_respondents|15|y|bar_chart Response|Access_to_employment|x|bar_chart Share_of_respondents|9|y|bar_chart Response|Balancing_work_and_caring_responsibilities|x|bar_chart Share_of_respondents|8|y|bar_chart Response|Lack_of_women_in_leadership_roles_in_business_and_public_life|x|bar_chart Share_of_respondents|7|y|bar_chart Response|Abuse_on_social_media|x|bar_chart Share_of_respondents|7|y|bar_chart Response|Support_for_pregnant_women_and_new_mothers|x|bar_chart Share_of_respondents|6|y|bar_chart Response|The_amount_of_unpaid_work_that_women_do_(e.g._cooking_cleaning_childcare)|x|bar_chart Share_of_respondents|6|y|bar_chart Response|Lack_of_financial/economic_independence|x|bar_chart Share_of_respondents|5|y|bar_chart 
title: Mexico : most important issues facing women and girls in 2019

gold: The statistic presents the results of a survey conducted in December 2018 and January 2019 to find out about the situation of women and gender ( in ) equality across 27 countries . When asked which were the main issues that women and girls were facing in Mexico , 40 percent of respondents answered sexual harassment .
gold_template: The statistic presents the results of a survey conducted in December 2018 and January templateTitle[7] to find out about the situation of templateXValue[7] and templateXValue[6] ( in ) equality across 27 countries . When asked which were the main templateTitle[3] templateXValue[13] templateXValue[7] and templateXValue[7] were templateTitle[4] in templateTitle[0] , templateYValue[max] percent of templateYLabel[1] answered templateXValue[0] templateXValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States as of March templateTitle[7] . During the survey , templateYValue[max] percent of the templateYLabel[1] cited templateXValue[1] templateXValue[1] at and templateYValue[1] percent reported in templateTitle[5] .
generated: This statistic shows the Mexico most important issues facing women in the United States as of March 2019 . During the survey , 40 percent of the respondents cited Sexual_violence at and 37 percent reported in women .


Example 706:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|5.04|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|4.94|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|4.79|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|4.67|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|4.19|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|5.63|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|4.97|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|3.85|y|line_chart Year|2016|x|line_chart Inflation_rate_compared_to_previous_year|3.52|y|line_chart Year|2015|x|line_chart Inflation_rate_compared_to_previous_year|4|y|line_chart Year|2014|x|line_chart Inflation_rate_compared_to_previous_year|6.04|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|7.14|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|7.19|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|8.08|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|5.46|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|3.69|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|19.83|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|11.13|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|9.14|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|9.6|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|8.47|y|line_chart Year|2003|x|line_chart Inflation_rate_compared_to_previous_year|5.3|y|line_chart Year|2002|x|line_chart Inflation_rate_compared_to_previous_year|3.75|y|line_chart Year|2001|x|line_chart Inflation_rate_compared_to_previous_year|7.36|y|line_chart Year|2000|x|line_chart Inflation_rate_compared_to_previous_year|11.55|y|line_chart Year|1999|x|line_chart Inflation_rate_compared_to_previous_year|11.21|y|line_chart Year|1998|x|line_chart Inflation_rate_compared_to_previous_year|13.05|y|line_chart Year|1997|x|line_chart Inflation_rate_compared_to_previous_year|9.19|y|line_chart Year|1996|x|line_chart Inflation_rate_compared_to_previous_year|11.65|y|line_chart Year|1995|x|line_chart Inflation_rate_compared_to_previous_year|11.12|y|line_chart Year|1994|x|line_chart Inflation_rate_compared_to_previous_year|3.7|y|line_chart Year|1993|x|line_chart Inflation_rate_compared_to_previous_year|13.5|y|line_chart Year|1992|x|line_chart Inflation_rate_compared_to_previous_year|21.9|y|line_chart Year|1991|x|line_chart Inflation_rate_compared_to_previous_year|116.6|y|line_chart Year|1990|x|line_chart Inflation_rate_compared_to_previous_year|3004.1|y|line_chart Year|1989|x|line_chart Inflation_rate_compared_to_previous_year|7428.7|y|line_chart Year|1988|x|line_chart Inflation_rate_compared_to_previous_year|4775.2|y|line_chart Year|1987|x|line_chart Inflation_rate_compared_to_previous_year|13109.5|y|line_chart Year|1986|x|line_chart Inflation_rate_compared_to_previous_year|885.2|y|line_chart Year|1985|x|line_chart Inflation_rate_compared_to_previous_year|571.4|y|line_chart Year|1984|x|line_chart Inflation_rate_compared_to_previous_year|141.3|y|line_chart 
title: Inflation rate in Nicaragua 2024

gold: This statistic shows the average inflation rate in Nicaragua from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Nicaragua amounted to about 4.97 percent compared to the previous year . Nicaragua 's economy Nicaragua 's inflation rate has been on the decline since 2011 , but it is expected to rise again in 2016 .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . templateTitle[2] 's economy templateTitle[2] 's templateYLabel[0] templateYLabel[1] has been on the decline since templateXValue[13] , but it is expected to rise again in templateXValue[8] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Nicaragua from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Nicaragua amounted to about 4.97 percent compared to the previous Year .


Example 707:
data: Year|2018|x|line_chart Index_score|0.72|y|line_chart Year|2017|x|line_chart Index_score|0.7|y|line_chart Year|2016|x|line_chart Index_score|0.7|y|line_chart Year|2015|x|line_chart Index_score|0.7|y|line_chart Year|2014|x|line_chart Index_score|0.7|y|line_chart 
title: Chile : gender gap index 2014 - 2018

gold: The graph presents the gender gap index in Chile from 2014 to 2018 . In 2018 , Chile scored 0.72 points , which shows a gender gap of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . In 2018 , the gender gap in the area of political empowerment in Chile amounted to 69 percent .
gold_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] scored templateYValue[max] points , which shows a templateTitle[1] templateTitle[2] of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . In templateXValue[max] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitle[0] amounted to 69 percent .

generated_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] scored templateYValue[max] , which shows a templateTitle[1] templateTitle[2] of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . That same templateXLabel[0] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitle[0] amounted to 72 percent .
generated: The graph presents the gender gap Index in Chile from 2014 to 2018 . In 2018 , Chile scored 0.72 , which shows a gender gap of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . That same Year , the gender gap in the area of political empowerment in Chile amounted to 72 percent .


Example 708:
data: Age_group|18-29|x|bar_chart Share_of_respondents|67|y|bar_chart Age_group|30-49|x|bar_chart Share_of_respondents|47|y|bar_chart Age_group|50-64|x|bar_chart Share_of_respondents|23|y|bar_chart Age_group|65+|x|bar_chart Share_of_respondents|8|y|bar_chart 
title: Instagram usage reach in the United States 2019 , by age group

gold: As of February 2019 , 67 percent of U.S. adults aged between 18 and 29 years used the photo sharing app Instagram . Furthermore , it was found that 43 percent of female adults in the United States used Instagram compared to only 31 percent of adult men . Instagram usage in the United StatesInstagram is one of the most popular social networks in the United States with a 37 percent usage reach among the adult population .
gold_template: As of February templateTitle[5] , templateYValue[max] percent of templateTitle[4] adults aged between 18 and 29 years used the photo sharing app templateTitle[0] . Furthermore , it was found that 43 percent of female adults in the templateTitle[3] templateTitle[4] used templateTitle[0] compared to only 31 percent of adult men . templateTitle[0] templateTitle[1] in the templateTitle[3] StatesInstagram is one of the most popular social networks in the templateTitle[3] templateTitle[4] with a 37 percent templateTitle[1] templateTitle[2] among the adult population .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitle[3] templateTitle[4] who were using templateTitle[0] as of February templateTitle[5] , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] percent of templateYLabel[1] aged between 18 and 29 years stated that they used the visual blogging site .
generated: This statistic shows the Share of adults in the United States who were using Instagram as of February 2019 , sorted by age group . During that period of time , 67 percent of respondents aged between 18 and 29 years stated that they used the visual blogging site .


Example 709:
data: Year|2018|x|line_chart Household_income_in_current_U.S._dollars|85750|y|line_chart Year|2017|x|line_chart Household_income_in_current_U.S._dollars|83382|y|line_chart Year|2016|x|line_chart Household_income_in_current_U.S._dollars|70982|y|line_chart Year|2015|x|line_chart Household_income_in_current_U.S._dollars|70071|y|line_chart Year|2014|x|line_chart Household_income_in_current_U.S._dollars|68277|y|line_chart Year|2013|x|line_chart Household_income_in_current_U.S._dollars|60675|y|line_chart Year|2012|x|line_chart Household_income_in_current_U.S._dollars|65246|y|line_chart Year|2011|x|line_chart Household_income_in_current_U.S._dollars|55251|y|line_chart Year|2010|x|line_chart Household_income_in_current_U.S._dollars|56928|y|line_chart Year|2009|x|line_chart Household_income_in_current_U.S._dollars|53141|y|line_chart Year|2008|x|line_chart Household_income_in_current_U.S._dollars|55590|y|line_chart Year|2007|x|line_chart Household_income_in_current_U.S._dollars|50783|y|line_chart Year|2006|x|line_chart Household_income_in_current_U.S._dollars|48477|y|line_chart Year|2005|x|line_chart Household_income_in_current_U.S._dollars|44993|y|line_chart Year|2004|x|line_chart Household_income_in_current_U.S._dollars|43451|y|line_chart Year|2003|x|line_chart Household_income_in_current_U.S._dollars|45044|y|line_chart Year|2002|x|line_chart Household_income_in_current_U.S._dollars|39070|y|line_chart Year|2001|x|line_chart Household_income_in_current_U.S._dollars|41169|y|line_chart Year|2000|x|line_chart Household_income_in_current_U.S._dollars|41222|y|line_chart Year|1999|x|line_chart Household_income_in_current_U.S._dollars|38670|y|line_chart Year|1998|x|line_chart Household_income_in_current_U.S._dollars|33433|y|line_chart Year|1997|x|line_chart Household_income_in_current_U.S._dollars|31860|y|line_chart Year|1996|x|line_chart Household_income_in_current_U.S._dollars|31966|y|line_chart Year|1995|x|line_chart Household_income_in_current_U.S._dollars|30748|y|line_chart Year|1994|x|line_chart Household_income_in_current_U.S._dollars|30116|y|line_chart Year|1993|x|line_chart Household_income_in_current_U.S._dollars|27304|y|line_chart Year|1992|x|line_chart Household_income_in_current_U.S._dollars|30247|y|line_chart Year|1991|x|line_chart Household_income_in_current_U.S._dollars|29885|y|line_chart Year|1990|x|line_chart Household_income_in_current_U.S._dollars|27392|y|line_chart 
title: District of Colombia - median household income 1990 - 2018

gold: This statistic shows the median household income in the District of Colombia from 1990 to 2018 . In 2018 , the median household income in the District of Colombia amounted to 85,750 U.S. dollars .
gold_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in the templateTitle[0] of templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in the templateTitle[0] of templateTitle[1] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[3] was templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the District Colombia Household income household from 1990 to 2018 . In 2018 , the District Colombia Household income in household was 85750 U.S. dollars .


Example 710:
data: Year|2024|x|line_chart Inhabitants_in_millions|44.47|y|line_chart Year|2023|x|line_chart Inhabitants_in_millions|43.35|y|line_chart Year|2022|x|line_chart Inhabitants_in_millions|42.25|y|line_chart Year|2021|x|line_chart Inhabitants_in_millions|41.18|y|line_chart Year|2020|x|line_chart Inhabitants_in_millions|40.13|y|line_chart Year|2019|x|line_chart Inhabitants_in_millions|39.12|y|line_chart Year|2018|x|line_chart Inhabitants_in_millions|38.12|y|line_chart Year|2017|x|line_chart Inhabitants_in_millions|37.14|y|line_chart Year|2016|x|line_chart Inhabitants_in_millions|36.17|y|line_chart Year|2015|x|line_chart Inhabitants_in_millions|35.21|y|line_chart Year|2014|x|line_chart Inhabitants_in_millions|35.0|y|line_chart 
title: Total population of Iraq 2024

gold: This statistic shows the total population of Iraq from 2014 to 2024 . In 2018 , the estimated total population of Iraq amounted to approximately 38.12 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitle[2] amounted to approximately templateYValue[6] million templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] amounted to approximately templateYValue[6] million templateYLabel[0] . templateTitle[1] of templateTitle[2] is the ten largest countries in the world , in terms of area size , although its templateTitle[0] templateTitle[1] is low in numbers compared to other countries .
generated: The statistic shows the Total population of Iraq from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Iraq amounted to approximately 38.12 million Inhabitants . population of Iraq is the ten largest countries in the world , in terms of area size , although its Total population is low in numbers compared to other countries .


Example 711:
data: Club_Name|Boussia_Dortmund|x|bar_chart Average_attendance|80295|y|bar_chart Club_Name|Manchester_United|x|bar_chart Average_attendance|75205|y|bar_chart Club_Name|Barcelona|x|bar_chart Average_attendance|72115|y|bar_chart Club_Name|Real_Madrid|x|bar_chart Average_attendance|71565|y|bar_chart Club_Name|Bayern_Munich|x|bar_chart Average_attendance|71000|y|bar_chart Club_Name|Schalke_04|x|bar_chart Average_attendance|61750|y|bar_chart Club_Name|Arsenal|x|bar_chart Average_attendance|60015|y|bar_chart Club_Name|Borussia_Mönchengladbach|x|bar_chart Average_attendance|52240|y|bar_chart Club_Name|Hertha_BSC|x|bar_chart Average_attendance|51890|y|bar_chart Club_Name|Hamburger_SV|x|bar_chart Average_attendance|51825|y|bar_chart Club_Name|Ajax_Amsterdam|x|bar_chart Average_attendance|50905|y|bar_chart Club_Name|VfB_Stuttgart|x|bar_chart Average_attendance|50500|y|bar_chart Club_Name|Newcastle_United|x|bar_chart Average_attendance|50395|y|bar_chart Club_Name|Manchester_City|x|bar_chart Average_attendance|47075|y|bar_chart Club_Name|Eintracht_Frankfurt|x|bar_chart Average_attendance|47055|y|bar_chart Club_Name|Celtic_FC|x|bar_chart Average_attendance|46810|y|bar_chart Club_Name|FC_Internazionale|x|bar_chart Average_attendance|46245|y|bar_chart Club_Name|Atletico_Madrid|x|bar_chart Average_attendance|46245|y|bar_chart Club_Name|FC_Köln|x|bar_chart Average_attendance|46235|y|bar_chart Club_Name|Feyenoord|x|bar_chart Average_attendance|45755|y|bar_chart Club_Name|Hannover_96|x|bar_chart Average_attendance|45635|y|bar_chart Club_Name|Paris_Saint_Germain|x|bar_chart Average_attendance|45420|y|bar_chart Club_Name|Liverpool|x|bar_chart Average_attendance|44670|y|bar_chart Club_Name|SL_Benfica|x|bar_chart Average_attendance|43615|y|bar_chart Club_Name|Rangers_FC|x|bar_chart Average_attendance|42935|y|bar_chart 
title: European football clubs average attendance 2013/14

gold: The statistic shows the European football clubs with the highest average per game attendance in the 2013/14 season . Germany 's Borussia Dortmund had the highest average attendance throughout Europe , with an average of over 80,000 fans attending each of their home games .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] with the highest templateYLabel[0] per game templateYLabel[1] in the templateTitle[5] season . Germany 's templateXValue[7] templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] throughout Europe , with an templateYLabel[0] of over 80,000 fans attending each of their home games .

generated_template: This graph depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the templateTitle[5] templateTitle[6] templateTitle[7] of America in templateTitle[8] . In templateTitle[8] , the templateYLabel[0] templateYLabel[1] at templateTitle[3] games of the templateTitle[5] templateTitle[6] templateTitle[7] was templateYValue[0] .
generated: This graph depicts the Average football clubs average attendance of the 2013/14 titleErr titleErr of America in titleErr . In titleErr , the Average attendance at average games of the 2013/14 titleErr titleErr was 80295 .


Example 712:
data: Year|2019|x|line_chart Unemployment_rate|2.7|y|line_chart Year|2018|x|line_chart Unemployment_rate|3.6|y|line_chart Year|2017|x|line_chart Unemployment_rate|4.6|y|line_chart Year|2016|x|line_chart Unemployment_rate|5.7|y|line_chart Year|2015|x|line_chart Unemployment_rate|6.1|y|line_chart Year|2014|x|line_chart Unemployment_rate|6.4|y|line_chart Year|2013|x|line_chart Unemployment_rate|7.5|y|line_chart Year|2012|x|line_chart Unemployment_rate|7.4|y|line_chart Year|2011|x|line_chart Unemployment_rate|7.2|y|line_chart Year|2010|x|line_chart Unemployment_rate|7.1|y|line_chart Year|2009|x|line_chart Unemployment_rate|6.4|y|line_chart Year|2008|x|line_chart Unemployment_rate|4.4|y|line_chart Year|2007|x|line_chart Unemployment_rate|3.9|y|line_chart Year|2006|x|line_chart Unemployment_rate|4.4|y|line_chart Year|2005|x|line_chart Unemployment_rate|4.6|y|line_chart Year|2004|x|line_chart Unemployment_rate|5|y|line_chart Year|2003|x|line_chart Unemployment_rate|5.6|y|line_chart Year|2002|x|line_chart Unemployment_rate|5.9|y|line_chart Year|2001|x|line_chart Unemployment_rate|6|y|line_chart Year|2000|x|line_chart Unemployment_rate|6.2|y|line_chart 
title: Unemployment rate in Northern Ireland ( UK ) 2000 - 2019

gold: This statistic shows the unemployment rate in Northern Ireland from 2000 to 2019 . Unemployment in Northern Ireland peaked in 2013 when there were 7.5 percent of the population unemployed , compared with just 2.7 percent in the most recent reporting year of 2019 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . templateYLabel[0] in templateTitle[2] templateTitle[3] peaked in templateXValue[6] when there were templateYValue[max] percent of the population unemployed , compared with just templateYValue[min] percent in the most recent reporting templateXLabel[0] of templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] between templateXValue[min] and templateXValue[max] . The templateYLabel[0] templateYLabel[1] was at its lowest in the most recent period , in templateXValue[max] , having fallen to templateYValue[min] percentage points since the peak of templateYValue[max] percent in templateXValue[10] .
generated: This statistic shows the Unemployment rate in Northern between 2000 and 2019 . The Unemployment rate was at its lowest in the most recent period , in 2019 , having fallen to 2.7 percentage points since the peak of 7.5 percent in 2009 .


Example 713:
data: Year|2022|x|line_chart Market_in_million_U.S._dollars|2997.8|y|line_chart Year|2021|x|line_chart Market_in_million_U.S._dollars|2692.7|y|line_chart Year|2020|x|line_chart Market_in_million_U.S._dollars|2347.1|y|line_chart Year|2019|x|line_chart Market_in_million_U.S._dollars|1998.4|y|line_chart Year|2018|x|line_chart Market_in_million_U.S._dollars|1665.5|y|line_chart Year|2017|x|line_chart Market_in_million_U.S._dollars|1405.1|y|line_chart Year|2016|x|line_chart Market_in_million_U.S._dollars|1137.7|y|line_chart Year|2015|x|line_chart Market_in_million_U.S._dollars|858.0|y|line_chart 
title: Video analytics market revenues worldwide 2015 - 2022

gold: The statistic shows the size of the video analytics market worldwide , from 2015 to 2022 . In 2015 , revenues from video analytics reached 858 million U.S. dollars .
gold_template: The statistic shows the size of the templateTitle[0] templateTitle[1] templateYLabel[0] templateTitle[4] , from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateTitle[3] from templateTitle[0] templateTitle[1] reached templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: U.S. than templateYValue[0] percent of the templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] between templateXValue[min] and templateXValue[max] . This figure is expected to increase by around templateYValue[0] percent in the U.S. templateYLabel[1] templateYLabel[2] of the previous templateXLabel[0] . The number of U.S. dollars in templateYLabel[1] U.S. dollars in templateTitle[3] templateTitle[4] templateTitle[5] is expected to increase by around 1.45 percent since many years .
generated: U.S. than 2997.8 percent of the revenues worldwide 2015 in U.S. between 2015 and 2022 . This figure is expected to increase by around 2997.8 percent in the U.S. million U.S. of the previous Year . The number of U.S. dollars in million U.S. dollars in revenues worldwide 2015 is expected to increase by around 1.45 percent since many years .


Example 714:
data: Year|2019|x|line_chart ACSI_score|81|y|line_chart Year|2018|x|line_chart ACSI_score|81|y|line_chart Year|2017|x|line_chart ACSI_score|78|y|line_chart Year|2016|x|line_chart ACSI_score|81|y|line_chart Year|2015|x|line_chart ACSI_score|82|y|line_chart Year|2014|x|line_chart ACSI_score|82|y|line_chart Year|2013|x|line_chart ACSI_score|81|y|line_chart Year|2012|x|line_chart ACSI_score|80|y|line_chart Year|2011|x|line_chart ACSI_score|82|y|line_chart Year|2010|x|line_chart ACSI_score|81|y|line_chart Year|2009|x|line_chart ACSI_score|84|y|line_chart Year|2008|x|line_chart ACSI_score|80|y|line_chart Year|2007|x|line_chart ACSI_score|81|y|line_chart 
title: American Customer Satisfaction Index : full-service restaurants in the U.S. 2007 - 2019

gold: This statistic shows the American Customer Satisfaction Index scores for full-service restaurants in the United States from 2007 to 2019 . In 2019 , the ACSI score for full-service restaurants in the U.S. was 81 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] scores for templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] for templateTitle[4] templateTitle[5] in the templateTitle[6] was templateYValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] scores for templateTitle[4] templateTitle[5] templateTitle[6] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] for templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[7] was templateYValue[min] , down from templateYValue[1] the previous templateXLabel[0] .
generated: This statistic shows the American Customer Satisfaction Index scores for full-service restaurants U.S. in the United States from 2007 to 2019 . In 2019 , the ACSI for full-service restaurants U.S. in the 2007 was 78 , down from 81 the previous Year .


Example 715:
data: Year|2018|x|line_chart Revenue_in_million_U.S._dollars|10151|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|9549|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|9008|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|9648|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|9527|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|9350|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|8102|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|7178|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|5842|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|4396|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|5936|y|line_chart 
title: Eastman Chemical 's revenue 2008 - 2018

gold: This statistic shows the revenues of Eastman Chemical from 2007 to 2018 . United States-based Eastman Chemical Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In 2018 , the company generated approximately 10.15 billion U.S. dollars of sales revenues .
gold_template: This statistic shows the revenues of templateTitle[0] templateTitle[1] from 2007 to templateXValue[max] . United States-based templateTitle[0] templateTitle[1] Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In templateXValue[max] , the company generated approximately templateYValue[max] billion templateYLabel[2] templateYLabel[3] of sales revenues .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the women templateTitle[1] templateTitle[2] was templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Eastman Chemical 's Revenue in the United States from 2008 to 2018 . In 2017 , the women Chemical 's was 10151 million U.S. dollars .


Example 716:
data: Year|2017|x|line_chart Number_of_stores|1002|y|line_chart Year|2016|x|line_chart Number_of_stores|1430|y|line_chart Year|2015|x|line_chart Number_of_stores|1672|y|line_chart Year|2014|x|line_chart Number_of_stores|1725|y|line_chart Year|2013|x|line_chart Number_of_stores|2429|y|line_chart Year|2012|x|line_chart Number_of_stores|2548|y|line_chart Year|2011|x|line_chart Number_of_stores|4010|y|line_chart Year|2010|x|line_chart Number_of_stores|3949|y|line_chart Year|2009|x|line_chart Number_of_stores|3862|y|line_chart 
title: Number of stores of Sears Holdings worldwide 2009 - 2017

gold: This statistic depicts the total number of stores of Sears Holdings from 2009 to 2017 . In 2017 , Sears Holdings had a total of 1,002 stores worldwide . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the United States .
gold_template: This statistic depicts the total templateYLabel[0] of templateYLabel[1] of templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateTitle[3] had a total of templateYValue[min] templateYLabel[1] templateTitle[4] . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the United States .

generated_template: As of templateXValue[max] , the store store chain Rossman opened its store templateYLabel[0] of each templateXLabel[0] . The templateYLabel[0] of templateYLabel[1] has risen over templateYValue[max] locations in U.S. dollars . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] ? templateTitle[1] is a chain of discount store brand .
generated: As of 2017 , the store chain Rossman opened its store Number of each Year . The Number of stores has risen over 4010 locations in U.S. dollars . stores Sears Holdings worldwide ? stores is a chain of discount store brand .


Example 717:
data: Year|2017|x|line_chart Percentage_of_population|10.8|y|line_chart Year|2016|x|line_chart Percentage_of_population|11.8|y|line_chart Year|2015|x|line_chart Percentage_of_population|11.9|y|line_chart Year|2014|x|line_chart Percentage_of_population|13.1|y|line_chart Year|2013|x|line_chart Percentage_of_population|14.2|y|line_chart Year|2012|x|line_chart Percentage_of_population|15.4|y|line_chart Year|2011|x|line_chart Percentage_of_population|16.3|y|line_chart Year|2010|x|line_chart Percentage_of_population|18.3|y|line_chart Year|2009|x|line_chart Percentage_of_population|20.5|y|line_chart Year|2008|x|line_chart Percentage_of_population|22.4|y|line_chart Year|2005|x|line_chart Percentage_of_population|23.4|y|line_chart 
title: Colombia : poverty headcount ratio at 3.20 U.S. dollars a day 2005 - 2017

gold: In Colombia , the poverty rate has been decreasing throughout recent years . In 2017 , approximately 10.8 percent of Colombians were living on less than 3.20 U.S. dollars per day , down from 23.4 percent of the country 's population in 2005.Moreover , it was recently found that the incidence rate of poverty in Colombia is higher in families whose heads of household were women .
gold_template: In templateTitle[0] , the templateTitle[1] rate has been decreasing throughout recent years . In templateXValue[max] , approximately templateYValue[min] percent of Colombians were living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] percent of the country 's templateYLabel[1] in 2005.Moreover , it was recently found that the incidence rate of templateTitle[1] in templateTitle[0] is higher in families whose heads of household were women .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitle[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the poverty headcount in Colombia from 2005 to 2017 . In 2017 , 10.8 percent of Colombia 's population lived below the poverty line .


Example 718:
data: Country|China|x|bar_chart Oil_imports_in_thousand_barrels_per_day|543|y|bar_chart Country|European_Union_(total)|x|bar_chart Oil_imports_in_thousand_barrels_per_day|450|y|bar_chart Country|Japan|x|bar_chart Oil_imports_in_thousand_barrels_per_day|341|y|bar_chart Country|India|x|bar_chart Oil_imports_in_thousand_barrels_per_day|328|y|bar_chart Country|South_Korea|x|bar_chart Oil_imports_in_thousand_barrels_per_day|244|y|bar_chart Country|Italy|x|bar_chart Oil_imports_in_thousand_barrels_per_day|183|y|bar_chart Country|Turkey|x|bar_chart Oil_imports_in_thousand_barrels_per_day|182|y|bar_chart Country|Spain|x|bar_chart Oil_imports_in_thousand_barrels_per_day|137|y|bar_chart Country|France|x|bar_chart Oil_imports_in_thousand_barrels_per_day|49|y|bar_chart Country|Netherlands|x|bar_chart Oil_imports_in_thousand_barrels_per_day|33|y|bar_chart Country|Germany|x|bar_chart Oil_imports_in_thousand_barrels_per_day|17|y|bar_chart Country|United_Kingdom|x|bar_chart Oil_imports_in_thousand_barrels_per_day|11|y|bar_chart 
title: Iran 's oil exports 2011

gold: This statistic depicts the volume of crude oil imported from Iran by its leading destination countries between January and June 2011 . The European Union imported a total of around 450,000 barrels of oil per day from Iran during that period . Iran has stopped oil exports to France , where crude oil is the second most important energy source and Britain , where crude oil production has been declining since 2002 .
gold_template: This statistic depicts the volume of crude templateYLabel[0] imported from templateTitle[0] by its leading destination countries between January and June templateTitle[4] . The templateXValue[1] templateXValue[1] imported a total of around templateYValue[1] templateYLabel[3] of templateYLabel[0] templateYLabel[4] templateYLabel[5] from templateTitle[0] during that period . templateTitle[0] has stopped templateYLabel[0] templateTitle[3] to templateXValue[8] , where crude templateYLabel[0] is the second most important energy source and Britain , where crude templateYLabel[0] production has been declining since 2002 .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the world templateTitle[5] templateXLabel[0] . In templateTitle[6] , there were about templateYValue[max] million templateYLabel[1] in the templateXValue[0] templateXValue[0] .
generated: This statistic shows the Oil imports of Iran 's oil exports 2011 in the world titleErr Country . In titleErr , there were about 543 million imports in the China .


Example 719:
data: Quarter|Q4_'19|x|bar_chart Operating_profit_in_trillion_South_Korean_won|7.16|y|bar_chart Quarter|Q3_'19|x|bar_chart Operating_profit_in_trillion_South_Korean_won|7.78|y|bar_chart Quarter|Q2_'19|x|bar_chart Operating_profit_in_trillion_South_Korean_won|6.6|y|bar_chart Quarter|Q1_'19|x|bar_chart Operating_profit_in_trillion_South_Korean_won|6.23|y|bar_chart Quarter|Q4_'18|x|bar_chart Operating_profit_in_trillion_South_Korean_won|10.8|y|bar_chart Quarter|Q3_'18|x|bar_chart Operating_profit_in_trillion_South_Korean_won|17.57|y|bar_chart Quarter|Q2_'18|x|bar_chart Operating_profit_in_trillion_South_Korean_won|14.87|y|bar_chart Quarter|Q1_'18|x|bar_chart Operating_profit_in_trillion_South_Korean_won|15.64|y|bar_chart Quarter|Q4_'17|x|bar_chart Operating_profit_in_trillion_South_Korean_won|15.15|y|bar_chart Quarter|Q3_'17|x|bar_chart Operating_profit_in_trillion_South_Korean_won|14.53|y|bar_chart Quarter|Q2_'17|x|bar_chart Operating_profit_in_trillion_South_Korean_won|14.07|y|bar_chart Quarter|Q1_'17|x|bar_chart Operating_profit_in_trillion_South_Korean_won|9.9|y|bar_chart Quarter|Q4_'16|x|bar_chart Operating_profit_in_trillion_South_Korean_won|9.22|y|bar_chart Quarter|Q3_'16|x|bar_chart Operating_profit_in_trillion_South_Korean_won|5.2|y|bar_chart Quarter|Q2_'16|x|bar_chart Operating_profit_in_trillion_South_Korean_won|8.14|y|bar_chart Quarter|Q1_'16|x|bar_chart Operating_profit_in_trillion_South_Korean_won|6.68|y|bar_chart Quarter|Q4_'15|x|bar_chart Operating_profit_in_trillion_South_Korean_won|6.14|y|bar_chart Quarter|Q3_'15|x|bar_chart Operating_profit_in_trillion_South_Korean_won|7.39|y|bar_chart Quarter|Q2_'15|x|bar_chart Operating_profit_in_trillion_South_Korean_won|6.9|y|bar_chart Quarter|Q1_'15|x|bar_chart Operating_profit_in_trillion_South_Korean_won|5.98|y|bar_chart Quarter|Q4_'14|x|bar_chart Operating_profit_in_trillion_South_Korean_won|5.29|y|bar_chart Quarter|Q3_'14|x|bar_chart Operating_profit_in_trillion_South_Korean_won|4.06|y|bar_chart Quarter|Q2_'14|x|bar_chart Operating_profit_in_trillion_South_Korean_won|7.2|y|bar_chart Quarter|Q1_'14|x|bar_chart Operating_profit_in_trillion_South_Korean_won|8.5|y|bar_chart Quarter|Q4_'13|x|bar_chart Operating_profit_in_trillion_South_Korean_won|8.3|y|bar_chart Quarter|Q3_'13|x|bar_chart Operating_profit_in_trillion_South_Korean_won|10.2|y|bar_chart Quarter|Q2_'13|x|bar_chart Operating_profit_in_trillion_South_Korean_won|9.5|y|bar_chart Quarter|Q1_'13|x|bar_chart Operating_profit_in_trillion_South_Korean_won|8.8|y|bar_chart Quarter|Q4_'12|x|bar_chart Operating_profit_in_trillion_South_Korean_won|8.8|y|bar_chart Quarter|Q3_'12|x|bar_chart Operating_profit_in_trillion_South_Korean_won|8.1|y|bar_chart Quarter|Q2_'12|x|bar_chart Operating_profit_in_trillion_South_Korean_won|6.5|y|bar_chart Quarter|Q1_'12|x|bar_chart Operating_profit_in_trillion_South_Korean_won|5.7|y|bar_chart Quarter|Q4_'11|x|bar_chart Operating_profit_in_trillion_South_Korean_won|4.7|y|bar_chart Quarter|Q3_'11|x|bar_chart Operating_profit_in_trillion_South_Korean_won|4.3|y|bar_chart Quarter|Q2_'11|x|bar_chart Operating_profit_in_trillion_South_Korean_won|3.8|y|bar_chart Quarter|Q1_'11|x|bar_chart Operating_profit_in_trillion_South_Korean_won|2.8|y|bar_chart Quarter|Q4_'10|x|bar_chart Operating_profit_in_trillion_South_Korean_won|3.0|y|bar_chart Quarter|Q3_'10|x|bar_chart Operating_profit_in_trillion_South_Korean_won|4.9|y|bar_chart Quarter|Q2_'10|x|bar_chart Operating_profit_in_trillion_South_Korean_won|5.0|y|bar_chart Quarter|Q1_'10|x|bar_chart Operating_profit_in_trillion_South_Korean_won|4.4|y|bar_chart Quarter|Q4_'09|x|bar_chart Operating_profit_in_trillion_South_Korean_won|3.4|y|bar_chart Quarter|Q3_'09|x|bar_chart Operating_profit_in_trillion_South_Korean_won|4.2|y|bar_chart Quarter|Q2_'09|x|bar_chart Operating_profit_in_trillion_South_Korean_won|2.7|y|bar_chart Quarter|Q1_'09|x|bar_chart Operating_profit_in_trillion_South_Korean_won|0.6|y|bar_chart 
title: Samsung Electronics ' operating profit 2009 - 2019 , by quarter

gold: In the fourth quarter of 2019 , Korean consumer electronics company Samsung Electronics reported an operating profit of nearly 7.16 trillion Korean Won or around 6.5 billion U.S. dollars . Many of the company 's segments experienced increases in their overall sales numbers in the third quarter of 2019 , but growing competition throughout the consumer electronics industry meant that profitability fell . Samsung Samsung ranks among the largest companies in the world in terms of market value and is perhaps best known for its various lines of successful consumer electronics products .
gold_template: In the fourth templateXLabel[0] of templateTitle[6] , templateYLabel[4] consumer templateTitle[1] company templateTitle[0] templateTitle[1] reported an templateYLabel[0] templateYLabel[1] of nearly templateYValue[0] templateYLabel[2] templateYLabel[4] templateYLabel[5] or around templateYValue[30] billion U.S. dollars . Many of the company 's segments experienced increases in their overall sales numbers in the third templateXLabel[0] of templateTitle[6] , but growing competition throughout the consumer templateTitle[1] industry meant that profitability fell . templateTitle[0] ranks among the largest companies in the world in terms of market value and is perhaps best known for its various lines of successful consumer templateTitle[1] products .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] ( templateTitle[4] ) from the fourth templateXLabel[0] of templateTitle[6] to the fourth templateXLabel[0] of templateTitle[7] . In the fourth templateXLabel[0] of templateTitle[6] , templateYValue[0] people were living in the United States .
generated: This statistic shows the Samsung Operating profit of operating profit ( profit ) from the fourth Quarter of 2019 to the fourth Quarter of by . In the fourth Quarter of 2019 , 7.16 people were living in the United States .


Example 720:
data: Year|2018|x|line_chart Retail_sales_in_million_U.S._dollars|35725.0|y|line_chart Year|2017|x|line_chart Retail_sales_in_million_U.S._dollars|34782.0|y|line_chart Year|2016|x|line_chart Retail_sales_in_million_U.S._dollars|40357.8|y|line_chart Year|2015|x|line_chart Retail_sales_in_million_U.S._dollars|39767.0|y|line_chart Year|2014|x|line_chart Retail_sales_in_million_U.S._dollars|37595.7|y|line_chart Year|2013|x|line_chart Retail_sales_in_million_U.S._dollars|36236.2|y|line_chart 
title: Retail sales of the vision care market in the U.S. 2013 - 2018

gold: This statistic depicts retail sales of the vision care market in the United States from 2013 to 2018 . In 2016 , the U.S. vision care market generated approximately 40.36 billion U.S. dollars , up from 39.77 billion U.S. dollars the previous year .
gold_template: This statistic depicts templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitle[4] generated approximately templateYValue[max] billion templateYLabel[3] templateYLabel[4] , up from templateYValue[3] billion templateYLabel[3] templateYLabel[4] the previous templateXLabel[0] .

generated_template: More than templateYValue[max] percent of the templateTitle[3] templateTitle[4] templateTitle[5] in templateXValue[max] , up from templateYValue[1] percent in the previous templateXLabel[0] . The United States had in templateTitle[3] templateTitle[4] templateTitle[5] roads by around templateYValue[min] percent of the previous templateXLabel[0] .
generated: More than 40357.8 percent of the care market U.S. in 2018 , up from 34782.0 percent in the previous Year . The United States had in care market U.S. roads by around 34782.0 percent of the previous Year .


Example 721:
data: Year|2018|x|line_chart Number_of_UFC_events|39|y|line_chart Year|2017|x|line_chart Number_of_UFC_events|39|y|line_chart Year|2016|x|line_chart Number_of_UFC_events|41|y|line_chart Year|2015|x|line_chart Number_of_UFC_events|41|y|line_chart Year|2014|x|line_chart Number_of_UFC_events|46|y|line_chart Year|2013|x|line_chart Number_of_UFC_events|33|y|line_chart Year|2012|x|line_chart Number_of_UFC_events|31|y|line_chart 
title: UFC : number of events 2012 - 2018

gold: In 2018 , a total of 39 Ultimate Fighting Championship ( UFC ) events were hosted around the world featuring 474 fights . The highest live attendance in 2018 was at UFC Fight Night 136 – Hunt vs Oleinik with 22,603 ringside spectators and the highest attendance to date was at UFC 193 – Rousey vs Holm in 2015 with 56,214 attendees . Pay-Per-View In 2017 , the UFC was among the 10 most valuable sports business brands worldwide which is in part thanks to its ' Pay-Per-View ( PPV ) business model .
gold_template: In templateXValue[max] , a total of templateYValue[0] Ultimate Fighting Championship ( templateYLabel[1] ) templateYLabel[2] were hosted around the world featuring 474 fights . The highest live attendance in templateXValue[max] was at templateYLabel[1] Fight Night 136 – Hunt vs Oleinik with 22,603 ringside spectators and the highest attendance to date was at templateYLabel[1] 193 – Rousey vs Holm in templateXValue[3] with 56,214 attendees . Pay-Per-View In templateXValue[1] , the templateYLabel[1] was among the 10 most valuable sports business brands worldwide which is in part thanks to its ' Pay-Per-View ( PPV ) business model .

generated_template: In templateXValue[max] , Chinese templateYLabel[0] templateYLabel[1] in templateTitle[3] amounted to templateYValue[max] percent in the United States , up from templateYValue[1] percent in the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] increased during this period was founded in San Francisco , California in templateXValue[4] , where the most populous city . After a quarter of templateTitle[5] , there were just templateYValue[min] percent of templateYLabel[1] in templateTitle[3] .
generated: In 2018 , Chinese Number UFC in 2012 amounted to 46 percent in the United States , up from 39 percent in the previous Year . The Number UFC increased during this period was founded in San Francisco , California in 2014 , where the most populous city . After a quarter of titleErr , there were just 31 percent of UFC in 2012 .


Example 722:
data: Year|2024|x|line_chart Ratio_of_government_expenditure_to_GDP|36.77|y|line_chart Year|2023|x|line_chart Ratio_of_government_expenditure_to_GDP|36.75|y|line_chart Year|2022|x|line_chart Ratio_of_government_expenditure_to_GDP|36.73|y|line_chart Year|2021|x|line_chart Ratio_of_government_expenditure_to_GDP|36.51|y|line_chart Year|2020|x|line_chart Ratio_of_government_expenditure_to_GDP|36.41|y|line_chart Year|2019|x|line_chart Ratio_of_government_expenditure_to_GDP|36.19|y|line_chart Year|2018|x|line_chart Ratio_of_government_expenditure_to_GDP|35.14|y|line_chart Year|2017|x|line_chart Ratio_of_government_expenditure_to_GDP|35.25|y|line_chart Year|2016|x|line_chart Ratio_of_government_expenditure_to_GDP|35.46|y|line_chart Year|2015|x|line_chart Ratio_of_government_expenditure_to_GDP|35.15|y|line_chart Year|2014|x|line_chart Ratio_of_government_expenditure_to_GDP|35.47|y|line_chart 
title: Ratio of government expenditure to gross domestic product ( GDP ) in the United States

gold: The statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in the United States from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure amounted to 35.14 percent of the gross domestic product . See the US GDP for further information .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in the templateTitle[7] templateTitle[8] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] amounted to templateYValue[min] percent of the templateTitle[3] templateTitle[4] templateTitle[5] . See the US templateYLabel[3] for further information .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to the templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitle[7] amounted to about templateYValue[6] percent of the templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to the gross domestic product ( GDP ) in United from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in United amounted to about 35.14 percent of the gross domestic product .


Example 723:
data: Year|2018|x|line_chart Number_of_fatalities|317|y|line_chart Year|2017|x|line_chart Number_of_fatalities|331|y|line_chart Year|2016|x|line_chart Number_of_fatalities|307|y|line_chart Year|2015|x|line_chart Number_of_fatalities|348|y|line_chart Year|2014|x|line_chart Number_of_fatalities|308|y|line_chart Year|2013|x|line_chart Number_of_fatalities|368|y|line_chart Year|2012|x|line_chart Number_of_fatalities|393|y|line_chart Year|2011|x|line_chart Number_of_fatalities|418|y|line_chart Year|2010|x|line_chart Number_of_fatalities|426|y|line_chart Year|2009|x|line_chart Number_of_fatalities|548|y|line_chart Year|2008|x|line_chart Number_of_fatalities|664|y|line_chart Year|2007|x|line_chart Number_of_fatalities|619|y|line_chart Year|2006|x|line_chart Number_of_fatalities|614|y|line_chart 
title: Croatia : Number of road deaths 2006 - 2018

gold: This statistic illustrates the number of road traffic fatalities per year in Croatia between 2006 and 2018 . In the period of consideration , road fatalities presented an overall trend of decline . The year with the lowest amount of fatalities was 2016 , with a total of 207 road traffic fatalities in Croatia .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[2] traffic templateYLabel[1] per templateXLabel[0] in templateTitle[0] between templateXValue[min] and templateXValue[max] . In the period of consideration , templateTitle[2] templateYLabel[1] presented an overall trend of decline . The templateXLabel[0] with the lowest amount of templateYLabel[1] was templateXValue[2] , with a total of 207 templateTitle[2] traffic templateYLabel[1] in templateTitle[0] .

generated_template: This statistic shows the templateYLabel[0] of lives lost due to templateTitle[0] and flash templateTitle[0] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[0] templateYLabel[1] reported due to flash templateTitle[0] and river templateTitle[0] in the United States .
generated: This statistic shows the Number of lives lost due to Croatia and flash Croatia in the United States from 2006 to 2018 . In 2018 , there were a total of 317 fatalities reported due to flash Croatia and river Croatia in the United States .


Example 724:
data: Year|2017|x|line_chart Fatalities_per_100,000_licensed_drivers|16.48|y|line_chart Year|2016|x|line_chart Fatalities_per_100,000_licensed_drivers|17.05|y|line_chart Year|2015|x|line_chart Fatalities_per_100,000_licensed_drivers|16.27|y|line_chart Year|2014|x|line_chart Fatalities_per_100,000_licensed_drivers|15.29|y|line_chart Year|2013|x|line_chart Fatalities_per_100,000_licensed_drivers|15.5|y|line_chart Year|2012|x|line_chart Fatalities_per_100,000_licensed_drivers|15.95|y|line_chart Year|2011|x|line_chart Fatalities_per_100,000_licensed_drivers|15.33|y|line_chart Year|2010|x|line_chart Fatalities_per_100,000_licensed_drivers|15.71|y|line_chart Year|2009|x|line_chart Fatalities_per_100,000_licensed_drivers|16.16|y|line_chart Year|2008|x|line_chart Fatalities_per_100,000_licensed_drivers|17.96|y|line_chart Year|2007|x|line_chart Fatalities_per_100,000_licensed_drivers|20.05|y|line_chart Year|2006|x|line_chart Fatalities_per_100,000_licensed_drivers|21.06|y|line_chart Year|2005|x|line_chart Fatalities_per_100,000_licensed_drivers|21.7|y|line_chart Year|2000|x|line_chart Fatalities_per_100,000_licensed_drivers|22.0|y|line_chart Year|1995|x|line_chart Fatalities_per_100,000_licensed_drivers|23.68|y|line_chart Year|1990|x|line_chart Fatalities_per_100,000_licensed_drivers|26.7|y|line_chart 
title: Fatality rate per 100,000 drivers licensed in the U.S. 1990 - 2017

gold: The timeline shows the fatality rate per 100,000 drivers licensed to operate a motor vehicle in the United States from 1990 to 2017 . The fatality rate stood at 16.5 deaths per 100,000 licensed drivers in 2017 .
gold_template: The timeline shows the templateTitle[0] templateTitle[1] templateYLabel[1] 100,000 templateYLabel[4] templateYLabel[3] to operate a motor vehicle in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] stood at templateYValue[0] deaths templateYLabel[1] 100,000 templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in the United States templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the United States amounted to templateYValue[0] people .
generated: This statistic shows the Fatalities per in the United States 100,000 1990 to 2017 . In 2017 , the Fatalities per of the United States amounted to 16.48 people .


Example 725:
data: Year|2019|x|line_chart Franchise_value_in_million_U.S._dollars|1600|y|line_chart Year|2018|x|line_chart Franchise_value_in_million_U.S._dollars|1500|y|line_chart Year|2017|x|line_chart Franchise_value_in_million_U.S._dollars|1350|y|line_chart Year|2016|x|line_chart Franchise_value_in_million_U.S._dollars|1050|y|line_chart Year|2015|x|line_chart Franchise_value_in_million_U.S._dollars|975|y|line_chart Year|2014|x|line_chart Franchise_value_in_million_U.S._dollars|695|y|line_chart Year|2013|x|line_chart Franchise_value_in_million_U.S._dollars|692|y|line_chart Year|2012|x|line_chart Franchise_value_in_million_U.S._dollars|600|y|line_chart Year|2011|x|line_chart Franchise_value_in_million_U.S._dollars|526|y|line_chart Year|2010|x|line_chart Franchise_value_in_million_U.S._dollars|466|y|line_chart Year|2009|x|line_chart Franchise_value_in_million_U.S._dollars|450|y|line_chart Year|2008|x|line_chart Franchise_value_in_million_U.S._dollars|443|y|line_chart Year|2007|x|line_chart Franchise_value_in_million_U.S._dollars|381|y|line_chart Year|2006|x|line_chart Franchise_value_in_million_U.S._dollars|315|y|line_chart Year|2005|x|line_chart Franchise_value_in_million_U.S._dollars|262|y|line_chart Year|2004|x|line_chart Franchise_value_in_million_U.S._dollars|248|y|line_chart Year|2003|x|line_chart Franchise_value_in_million_U.S._dollars|233|y|line_chart Year|2002|x|line_chart Franchise_value_in_million_U.S._dollars|223|y|line_chart 
title: Franchise value of the Chicago White Sox 2002 - 2019

gold: This graph depicts the value of the Chicago White Sox franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 1.6 billion U.S. dollars . The Chicago White Sox are owned by Jerry Reinsdorf , who bought the franchise for 20 million U.S. dollars in 1981 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[4] are owned by Jerry Reinsdorf , who bought the templateYLabel[0] for 20 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1981 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[4] are owned by the Steinbrenner Family , who bought the templateYLabel[0] for 380 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[min] .
generated: This graph depicts the value of the Chicago White Sox Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 1600 billion U.S. dollars . The Chicago White Sox are owned by the Steinbrenner Family , who bought the Franchise for 380 million U.S. dollars in 2002 .


Example 726:
data: Quarter|Q4_'19|x|bar_chart Number_of_Bitcoins_in_millions|18.13|y|bar_chart Quarter|Q3_'19|x|bar_chart Number_of_Bitcoins_in_millions|17.97|y|bar_chart Quarter|Q2_'19|x|bar_chart Number_of_Bitcoins_in_millions|17.79|y|bar_chart Quarter|Q1_'19|x|bar_chart Number_of_Bitcoins_in_millions|17.62|y|bar_chart Quarter|Q4_'18|x|bar_chart Number_of_Bitcoins_in_millions|17.45|y|bar_chart Quarter|Q3_'18|x|bar_chart Number_of_Bitcoins_in_millions|17.3|y|bar_chart Quarter|Q2_'18|x|bar_chart Number_of_Bitcoins_in_millions|17.12|y|bar_chart Quarter|Q1_'18|x|bar_chart Number_of_Bitcoins_in_millions|16.95|y|bar_chart Quarter|Q4_'17|x|bar_chart Number_of_Bitcoins_in_millions|16.78|y|bar_chart Quarter|Q3_'17|x|bar_chart Number_of_Bitcoins_in_millions|16.6|y|bar_chart Quarter|Q2_'17|x|bar_chart Number_of_Bitcoins_in_millions|16.42|y|bar_chart Quarter|Q1_'17|x|bar_chart Number_of_Bitcoins_in_millions|16.25|y|bar_chart Quarter|Q4_'16|x|bar_chart Number_of_Bitcoins_in_millions|16.08|y|bar_chart Quarter|Q3_'16|x|bar_chart Number_of_Bitcoins_in_millions|15.9|y|bar_chart Quarter|Q2_'16|x|bar_chart Number_of_Bitcoins_in_millions|15.72|y|bar_chart Quarter|Q1_'16|x|bar_chart Number_of_Bitcoins_in_millions|15.38|y|bar_chart Quarter|Q4_'15|x|bar_chart Number_of_Bitcoins_in_millions|15.03|y|bar_chart Quarter|Q3_'15|x|bar_chart Number_of_Bitcoins_in_millions|14.67|y|bar_chart Quarter|Q2_'15|x|bar_chart Number_of_Bitcoins_in_millions|14.33|y|bar_chart Quarter|Q1_'15|x|bar_chart Number_of_Bitcoins_in_millions|14.0|y|bar_chart Quarter|Q4_'14|x|bar_chart Number_of_Bitcoins_in_millions|13.67|y|bar_chart Quarter|Q3_'14|x|bar_chart Number_of_Bitcoins_in_millions|13.33|y|bar_chart Quarter|Q2_'14|x|bar_chart Number_of_Bitcoins_in_millions|12.97|y|bar_chart Quarter|Q1_'14|x|bar_chart Number_of_Bitcoins_in_millions|12.59|y|bar_chart Quarter|Q4_'13|x|bar_chart Number_of_Bitcoins_in_millions|12.2|y|bar_chart Quarter|Q3_'13|x|bar_chart Number_of_Bitcoins_in_millions|11.77|y|bar_chart Quarter|Q2_'13|x|bar_chart Number_of_Bitcoins_in_millions|11.35|y|bar_chart Quarter|Q1_'13|x|bar_chart Number_of_Bitcoins_in_millions|10.97|y|bar_chart Quarter|Q4_'12|x|bar_chart Number_of_Bitcoins_in_millions|10.61|y|bar_chart 
title: Number of Bitcoins in circulation 2012 - 2019

gold: In the fourth quarter of 2019 , there were 18.13 million Bitcoins in the world . Bitcoin is a virtual currency . It can be bought and sold through online exchanges or ATMs .
gold_template: In the fourth templateXLabel[0] of templateTitle[4] , there were templateYValue[max] million templateYLabel[1] in the world . Bitcoin is a virtual currency . It can be bought and sold through online exchanges or ATMs .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] in the United States from the fourth templateXLabel[0] of templateTitle[6] to the third templateXLabel[0] of templateXValue[0] . In the fourth templateXLabel[0] of templateTitle[6] , templateYValue[0] people were living in the United States .
generated: This statistic shows the Number Bitcoins of 2012 2019 in the United States from the fourth Quarter of titleErr to the third Quarter of Q4_'19 . In the fourth Quarter of titleErr , 18.13 people were living in the United States .


Example 727:
data: Year|2018|x|line_chart Revenue_in_billion_U.S._dollars|10.58|y|line_chart 
title: WarnerMedia television network revenue 2018

gold: This statistic contains data on the revenue that WarnerMedia generated with its TV network business in 2018 . In 2018 , the media giant generated 10.58 billion U.S. dollars with , among others , HBO , CNN and Cartoon Network . Due to the acquisition of Time Warner by AT & T and its subsequent renaming ( now WarnerMedia ) , results for previous years are not considered meaningful and as such were not reported by AT & T in 2018 .
gold_template: This statistic contains data on the templateYLabel[0] that templateTitle[0] generated with its TV templateTitle[2] business in templateXValue[max] . In templateXValue[max] , the media giant generated templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] with , among others , HBO , CNN and Cartoon templateTitle[2] . Due to the acquisition of Time Warner by AT & T and its subsequent renaming ( now templateTitle[0] ) , results for previous years are not considered meaningful and as such were not reported by AT & T in templateXValue[max] .

generated_template: This statistic presents the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] 's templateYLabel[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic presents the Revenue of television billion U.S. dollars in WarnerMedia from 2018 to 2018 . In 2018 , WarnerMedia 's Revenue amounted to 10.58 billion U.S. dollars .


Example 728:
data: Year|2009|x|line_chart Average_hotel_rate_in_U.S._dollars|155|y|line_chart Year|2010|x|line_chart Average_hotel_rate_in_U.S._dollars|162|y|line_chart Year|2011|x|line_chart Average_hotel_rate_in_U.S._dollars|174|y|line_chart Year|2012|x|line_chart Average_hotel_rate_in_U.S._dollars|171|y|line_chart Year|2013|x|line_chart Average_hotel_rate_in_U.S._dollars|171|y|line_chart Year|2014|x|line_chart Average_hotel_rate_in_U.S._dollars|174|y|line_chart Year|2015|x|line_chart Average_hotel_rate_in_U.S._dollars|179|y|line_chart 
title: Average global hotel rates from 2009 to 2015

gold: This statistic shows average global hotel rates from 2009 to 2015 . In 2013 , the average global hotel rate was 171 U.S. dollars . This figure was forecasted to increase to 174 U.S. dollars in 2014 and again to 179 dollars in 2015 .
gold_template: This statistic shows templateYLabel[0] templateTitle[1] templateYLabel[1] templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateYLabel[0] templateTitle[1] templateYLabel[1] templateYLabel[2] was templateYValue[3] templateYLabel[3] templateYLabel[4] . This figure was forecasted to increase to templateYValue[2] templateYLabel[3] templateYLabel[4] in templateXValue[5] and again to templateYValue[max] templateYLabel[4] in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] people died as a result of templateTitle[3] templateTitle[4] . The number was one of the lowest result of the templateTitle[2] templateTitle[3] templateTitle[4] .
generated: The statistic shows the Average hotel of hotel rates from worldwide from 2009 to 2015 . In 2015 , about 155 people died as a result of rates from . The number was one of the lowest result of the hotel rates from .


Example 729:
data: Year|2019|x|line_chart Unemployment_rate|12.35|y|line_chart Year|2018|x|line_chart Unemployment_rate|12.15|y|line_chart Year|2017|x|line_chart Unemployment_rate|12|y|line_chart Year|2016|x|line_chart Unemployment_rate|10.2|y|line_chart Year|2015|x|line_chart Unemployment_rate|11.21|y|line_chart Year|2014|x|line_chart Unemployment_rate|10.21|y|line_chart Year|2013|x|line_chart Unemployment_rate|9.82|y|line_chart Year|2012|x|line_chart Unemployment_rate|10.97|y|line_chart Year|2011|x|line_chart Unemployment_rate|9.96|y|line_chart Year|2010|x|line_chart Unemployment_rate|9.96|y|line_chart Year|2009|x|line_chart Unemployment_rate|10.16|y|line_chart Year|2008|x|line_chart Unemployment_rate|11.33|y|line_chart Year|2007|x|line_chart Unemployment_rate|13.79|y|line_chart Year|2006|x|line_chart Unemployment_rate|12.27|y|line_chart Year|2005|x|line_chart Unemployment_rate|15.27|y|line_chart Year|2004|x|line_chart Unemployment_rate|17.65|y|line_chart Year|2003|x|line_chart Unemployment_rate|23.72|y|line_chart Year|2002|x|line_chart Unemployment_rate|25.9|y|line_chart Year|2001|x|line_chart Unemployment_rate|27.3|y|line_chart Year|2000|x|line_chart Unemployment_rate|29.77|y|line_chart Year|1999|x|line_chart Unemployment_rate|28.45|y|line_chart 
title: Unemployment rate in Algeria 2019

gold: This statistic shows the unemployment rate in Algeria from 1998 to 2019 . In 2019 , the unemployment rate in Algeria was 12.35 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from 1998 to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Algeria from 1999 to 2019 . In 2019 , the Unemployment rate in Algeria was at approximately 12.35 percent .


Example 730:
data: Year|2024|x|line_chart Inhabitants_in_millions|56.43|y|line_chart Year|2023|x|line_chart Inhabitants_in_millions|54.96|y|line_chart Year|2022|x|line_chart Inhabitants_in_millions|53.52|y|line_chart Year|2021|x|line_chart Inhabitants_in_millions|52.11|y|line_chart Year|2020|x|line_chart Inhabitants_in_millions|50.72|y|line_chart Year|2019|x|line_chart Inhabitants_in_millions|49.36|y|line_chart Year|2018|x|line_chart Inhabitants_in_millions|48.03|y|line_chart Year|2017|x|line_chart Inhabitants_in_millions|46.73|y|line_chart Year|2016|x|line_chart Inhabitants_in_millions|45.45|y|line_chart Year|2015|x|line_chart Inhabitants_in_millions|44.2|y|line_chart Year|2014|x|line_chart Inhabitants_in_millions|43.0|y|line_chart 
title: Total population of Kenya 2024

gold: This statistic shows the total population of Kenya from 2014 to 2024 . In 2018 , the total population of Kenya was estimated at approximately 48.03 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] was estimated at approximately templateYValue[6] million templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] amounted to approximately templateYValue[6] million templateYLabel[0] . templateTitle[1] of templateTitle[2] The templateTitle[0] templateTitle[1] of templateTitle[2] was expected to reach 116.02 million people by the end of 2013 .
generated: This statistic shows the Total population of Kenya from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Kenya amounted to approximately 48.03 million Inhabitants . population of Kenya The Total population of Kenya was expected to reach 116.02 million people by the end of 2013 .


Example 731:
data: Year|2018|x|line_chart Share_of_urban_population_in_total_population|99.14|y|line_chart Year|2017|x|line_chart Share_of_urban_population_in_total_population|99.08|y|line_chart Year|2016|x|line_chart Share_of_urban_population_in_total_population|99.02|y|line_chart Year|2015|x|line_chart Share_of_urban_population_in_total_population|98.95|y|line_chart Year|2014|x|line_chart Share_of_urban_population_in_total_population|98.87|y|line_chart Year|2013|x|line_chart Share_of_urban_population_in_total_population|98.79|y|line_chart Year|2012|x|line_chart Share_of_urban_population_in_total_population|98.7|y|line_chart Year|2011|x|line_chart Share_of_urban_population_in_total_population|98.6|y|line_chart Year|2010|x|line_chart Share_of_urban_population_in_total_population|98.5|y|line_chart Year|2009|x|line_chart Share_of_urban_population_in_total_population|98.34|y|line_chart Year|2008|x|line_chart Share_of_urban_population_in_total_population|98.14|y|line_chart 
title: Urbanization in Qatar 2018

gold: This statistic shows the degree of urbanization in Qatar from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 99.14 percent of Qatar 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of the templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Qatar 2018 from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 99.14 percent of the total population lived in urban areas and cities .


Example 732:
data: Year|2018|x|line_chart Real_GDP_in_billion_U.S._dollars|468.39|y|line_chart Year|2017|x|line_chart Real_GDP_in_billion_U.S._dollars|456.0|y|line_chart Year|2016|x|line_chart Real_GDP_in_billion_U.S._dollars|448.71|y|line_chart Year|2015|x|line_chart Real_GDP_in_billion_U.S._dollars|440.31|y|line_chart Year|2014|x|line_chart Real_GDP_in_billion_U.S._dollars|430.5|y|line_chart Year|2013|x|line_chart Real_GDP_in_billion_U.S._dollars|424.32|y|line_chart Year|2012|x|line_chart Real_GDP_in_billion_U.S._dollars|418.86|y|line_chart Year|2011|x|line_chart Real_GDP_in_billion_U.S._dollars|411.47|y|line_chart Year|2010|x|line_chart Real_GDP_in_billion_U.S._dollars|400.94|y|line_chart Year|2009|x|line_chart Real_GDP_in_billion_U.S._dollars|380.09|y|line_chart Year|2008|x|line_chart Real_GDP_in_billion_U.S._dollars|416.7|y|line_chart Year|2007|x|line_chart Real_GDP_in_billion_U.S._dollars|441.15|y|line_chart Year|2006|x|line_chart Real_GDP_in_billion_U.S._dollars|443.31|y|line_chart Year|2005|x|line_chart Real_GDP_in_billion_U.S._dollars|450.75|y|line_chart Year|2004|x|line_chart Real_GDP_in_billion_U.S._dollars|444.2|y|line_chart Year|2003|x|line_chart Real_GDP_in_billion_U.S._dollars|443.79|y|line_chart Year|2002|x|line_chart Real_GDP_in_billion_U.S._dollars|435.25|y|line_chart Year|2001|x|line_chart Real_GDP_in_billion_U.S._dollars|423.62|y|line_chart Year|2000|x|line_chart Real_GDP_in_billion_U.S._dollars|438.28|y|line_chart 
title: Michigan - real GDP 2000 - 2018

gold: This statistic shows the development of Michigan 's real GDP from 2000 to 2018 . In 2018 , the real GDP of Michigan was 468.39 billion U.S. dollars .
gold_template: This statistic shows the development of templateTitle[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[0] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitle[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[0] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The annual templateYLabel[0] templateYLabel[1] growth of the templateYLabel[3] can be accessed here .
generated: This statistic shows the development of Michigan 's Real GDP from 2000 to 2018 . In 2018 , the Real GDP of Michigan was about 468.39 billion U.S. dollars . The annual Real GDP growth of the U.S. can be accessed here .


Example 733:
data: Year|2018|x|line_chart Share_of_urban_population_in_total_population|100|y|line_chart Year|2017|x|line_chart Share_of_urban_population_in_total_population|100|y|line_chart Year|2016|x|line_chart Share_of_urban_population_in_total_population|100|y|line_chart Year|2015|x|line_chart Share_of_urban_population_in_total_population|100|y|line_chart Year|2014|x|line_chart Share_of_urban_population_in_total_population|100|y|line_chart Year|2013|x|line_chart Share_of_urban_population_in_total_population|100|y|line_chart Year|2012|x|line_chart Share_of_urban_population_in_total_population|100|y|line_chart Year|2011|x|line_chart Share_of_urban_population_in_total_population|100|y|line_chart Year|2010|x|line_chart Share_of_urban_population_in_total_population|100|y|line_chart Year|2009|x|line_chart Share_of_urban_population_in_total_population|100|y|line_chart Year|2008|x|line_chart Share_of_urban_population_in_total_population|100|y|line_chart 
title: Urbanization in Kuwait 2018

gold: This statistic shows the degree of urbanization in Kuwait from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 100 percent of Kuwait 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] in templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in cities .
generated: This statistic shows the Share of Kuwait in 2018 titleErr from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 100 percent of Kuwait 's total population lived in cities .


Example 734:
data: State|Syracuse|x|bar_chart Average_attendance|21992|y|bar_chart State|Kentucky|x|bar_chart Average_attendance|21695|y|bar_chart State|North_Carolina|x|bar_chart Average_attendance|19715|y|bar_chart State|Tennessee|x|bar_chart Average_attendance|19034|y|bar_chart State|Wisconsin|x|bar_chart Average_attendance|17170|y|bar_chart State|Louisville|x|bar_chart Average_attendance|16601|y|bar_chart State|Kansas|x|bar_chart Average_attendance|16236|y|bar_chart State|Creighton|x|bar_chart Average_attendance|15980|y|bar_chart State|Marquette|x|bar_chart Average_attendance|15611|y|bar_chart State|Nebraska|x|bar_chart Average_attendance|15341|y|bar_chart State|Arkansas|x|bar_chart Average_attendance|15278|y|bar_chart State|Indiana|x|bar_chart Average_attendance|15206|y|bar_chart State|Michigan_St.|x|bar_chart Average_attendance|14797|y|bar_chart State|Perdue|x|bar_chart Average_attendance|14467|y|bar_chart State|Iowa_St.|x|bar_chart Average_attendance|14099|y|bar_chart State|Virginia|x|bar_chart Average_attendance|14087|y|bar_chart State|Memphis|x|bar_chart Average_attendance|14065|y|bar_chart State|Maryland|x|bar_chart Average_attendance|14009|y|bar_chart State|Ohio_St.|x|bar_chart Average_attendance|13922|y|bar_chart State|NC_State|x|bar_chart Average_attendance|13897|y|bar_chart State|Arizona|x|bar_chart Average_attendance|13744|y|bar_chart State|Dayton|x|bar_chart Average_attendance|12957|y|bar_chart State|Iowa|x|bar_chart Average_attendance|12869|y|bar_chart State|Michigan|x|bar_chart Average_attendance|12505|y|bar_chart State|Illinois|x|bar_chart Average_attendance|12456|y|bar_chart State|Texas_Tech|x|bar_chart Average_attendance|12098|y|bar_chart State|BYU|x|bar_chart Average_attendance|11958|y|bar_chart State|South_Carolina|x|bar_chart Average_attendance|11472|y|bar_chart State|Cincinnati|x|bar_chart Average_attendance|11256|y|bar_chart State|New_Mexico|x|bar_chart Average_attendance|11107|y|bar_chart 
title: NCAA division I men 's basketball attendance leaders 2019

gold: While the players on the court might still be college students , the National Collegiate Athletic Association men 's basketball top division still draws in big crowds . The Syracuse Orange men 's basketball , which represents Syracuse University in New York , attracted the highest average attendance during the 2019 season . The team , traditionally known as the Syracuse Orangemen , had an average home audience of almost 22 thousand in 2019 .
gold_template: While the players on the court might still be college students , the National Collegiate Athletic Association templateTitle[3] templateTitle[4] templateTitle[5] top templateTitle[1] still draws in big crowds . The templateXValue[0] Orange templateTitle[3] templateTitle[4] templateTitle[5] , which represents templateXValue[0] University in templateXValue[last] York , attracted the highest templateYLabel[0] templateYLabel[1] during the templateTitle[8] season . The team , traditionally known as the templateXValue[0] Orangemen , had an templateYLabel[0] home audience of almost templateYValue[max] thousand in templateTitle[8] .

generated_template: This graph shows the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] of the United States in templateTitle[7] , templateTitle[4] templateTitle[5] templateXLabel[0] . In templateTitle[7] , the female had the highest templateYLabel[0] templateYLabel[1] at templateTitle[3] templateYValue[1] .
generated: This graph shows the Average division I men attendance of the United States in leaders , 's basketball State . In leaders , the female had the highest Average attendance at men 21695 .


Example 735:
data: Year|2018|x|line_chart Percentage_of_population|9.5|y|line_chart Year|2017|x|line_chart Percentage_of_population|10|y|line_chart Year|2016|x|line_chart Percentage_of_population|10.4|y|line_chart Year|2015|x|line_chart Percentage_of_population|10.8|y|line_chart Year|2014|x|line_chart Percentage_of_population|11.1|y|line_chart Year|2013|x|line_chart Percentage_of_population|11.4|y|line_chart Year|2012|x|line_chart Percentage_of_population|10.8|y|line_chart Year|2011|x|line_chart Percentage_of_population|10.4|y|line_chart Year|2010|x|line_chart Percentage_of_population|10.3|y|line_chart Year|2009|x|line_chart Percentage_of_population|9.4|y|line_chart Year|2008|x|line_chart Percentage_of_population|8.7|y|line_chart Year|2007|x|line_chart Percentage_of_population|8.6|y|line_chart Year|2006|x|line_chart Percentage_of_population|8.7|y|line_chart Year|2005|x|line_chart Percentage_of_population|8.7|y|line_chart Year|2004|x|line_chart Percentage_of_population|8.5|y|line_chart Year|2003|x|line_chart Percentage_of_population|8.4|y|line_chart Year|2002|x|line_chart Percentage_of_population|7.5|y|line_chart Year|2001|x|line_chart Percentage_of_population|7.9|y|line_chart Year|2000|x|line_chart Percentage_of_population|7.9|y|line_chart 
title: New Jersey - poverty rate 2000 - 2018

gold: This statistic shows the poverty rate in New Jersey from 2000 to 2018 . For instance , 9.5 percent of New Jersey 's population lived below the poverty line in 2018
gold_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . For instance , templateYValue[0] percent of templateTitle[0] templateTitle[1] 's templateYLabel[1] lived below the templateTitle[2] line in templateXValue[max]

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitle[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the Jersey poverty in New from 2000 to 2018 . In 2018 , 9.5 percent of New 's population lived below the Jersey line .


Example 736:
data: Month|Ivan_Rakitic|x|bar_chart Market_value_in_million_euros|50.0|y|bar_chart Month|Ivan_Perisic|x|bar_chart Market_value_in_million_euros|40.0|y|bar_chart Month|Mateo_Kovacic|x|bar_chart Market_value_in_million_euros|30.0|y|bar_chart Month|Andrej_Kramaric|x|bar_chart Market_value_in_million_euros|27.0|y|bar_chart Month|Marcelo_Brozovic|x|bar_chart Market_value_in_million_euros|27.0|y|bar_chart Month|Luka_Modric|x|bar_chart Market_value_in_million_euros|25.0|y|bar_chart Month|Sime_Vrsaljko|x|bar_chart Market_value_in_million_euros|25.0|y|bar_chart Month|Dejan_Lovren|x|bar_chart Market_value_in_million_euros|20.0|y|bar_chart Month|Mario_Mandzukic|x|bar_chart Market_value_in_million_euros|18.0|y|bar_chart Month|Milan_Badelj|x|bar_chart Market_value_in_million_euros|15.0|y|bar_chart Month|Marko_Pjaca|x|bar_chart Market_value_in_million_euros|15.0|y|bar_chart Month|Nikola_Kalinic|x|bar_chart Market_value_in_million_euros|14.0|y|bar_chart Month|Ante_Rebic|x|bar_chart Market_value_in_million_euros|10.0|y|bar_chart Month|Duje_Caleta–Car|x|bar_chart Market_value_in_million_euros|10.0|y|bar_chart Month|Domagoj_Vida|x|bar_chart Market_value_in_million_euros|7.0|y|bar_chart Month|Lovre_Kalinic|x|bar_chart Market_value_in_million_euros|6.5|y|bar_chart Month|Tin_Jedvaj|x|bar_chart Market_value_in_million_euros|5.0|y|bar_chart Month|Danijel_Subasic|x|bar_chart Market_value_in_million_euros|4.5|y|bar_chart Month|Vedran_Corluka|x|bar_chart Market_value_in_million_euros|4.0|y|bar_chart Month|Ivan_Strinic|x|bar_chart Market_value_in_million_euros|4.0|y|bar_chart Month|Filip_Bradaric|x|bar_chart Market_value_in_million_euros|3.5|y|bar_chart Month|Josip_Pivaric|x|bar_chart Market_value_in_million_euros|2.0|y|bar_chart Month|Dominik_Livakovic|x|bar_chart Market_value_in_million_euros|1.5|y|bar_chart 
title: Leading Croatian national team players at FIFA World Cup 2018 , by market value

gold: The statistic displays the leading players of the national football team of Croatia at FIFA World Cup as of June 2018 , by market value . The most valuable player was Ivan Rakitic , with a market value of 50 million euros .
gold_template: The statistic displays the templateTitle[0] templateTitle[4] of the templateTitle[2] football templateTitle[3] of Croatia at templateTitle[5] templateTitle[6] templateTitle[7] as of June templateTitle[8] , templateTitle[9] templateYLabel[0] templateYLabel[1] . The most valuable player was templateXValue[0] templateXValue[0] , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[6] templateTitle[7] templateTitle[8] templateTitle[9] templateYLabel[1] valued at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . templateXValue[1] ranked second with a templateYLabel[0] templateYLabel[1] of templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in valued at templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Leading Croatian of national team players in the World Cup 2018 by value valued at 50.0 million euros yLabelErr yLabelErr . Ivan_Perisic ranked second with a Market value of 40.0 million euros yLabelErr yLabelErr in valued at 40.0 million euros yLabelErr yLabelErr .


Example 737:
data: Response|In-store|x|bar_chart Share_of_respondents|41|y|bar_chart Response|Other_online|x|bar_chart Share_of_respondents|14|y|bar_chart Response|Buy_buttons|x|bar_chart Share_of_respondents|9|y|bar_chart Response|Other_mobile_transfers|x|bar_chart Share_of_respondents|8|y|bar_chart Response|P2P_transfer|x|bar_chart Share_of_respondents|7|y|bar_chart Response|Mobile_messenger_apps|x|bar_chart Share_of_respondents|7|y|bar_chart Response|QR_codes|x|bar_chart Share_of_respondents|5|y|bar_chart Response|Other_in-app_payments|x|bar_chart Share_of_respondents|4|y|bar_chart Response|Smart_home_device|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Wearables_/_contactless|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Other|x|bar_chart Share_of_respondents|2|y|bar_chart 
title: Distribution of consumer transactions worldwide 2018 , by payment channel

gold: This statistic presents the most popular payment methods for everyday transactions according to internet users worldwide as of June 2018 . When asked to think about they payment methods for their ten most recent transactions , it was found that seven percent were made via P2P transfer . In-store still accounted for the single largest share of everyday transactions with 41 percent .
gold_template: This statistic presents the most popular templateTitle[6] methods for everyday templateTitle[2] according to internet users templateTitle[3] as of June templateTitle[4] . When asked to think about they templateTitle[6] methods for their ten most recent templateTitle[2] , it was found that templateYValue[4] percent were made via templateXValue[4] templateXValue[4] . templateXValue[0] still accounted for the single largest templateYLabel[0] of everyday templateTitle[2] with templateYValue[max] percent .

generated_template: This statistic shows the results of a survey conducted in the templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] templateTitle[4] on templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated they would templateXValue[0] faith templateXValue[1] templateXValue[1] to templateXValue[1] templateXValue[0] and templateYValue[1] percent of the U.S. dollars .
generated: This statistic shows the results of a survey conducted in the Distribution consumer in transactions worldwide 2018 on by payment channel titleErr . During the survey period , 41 percent of respondents stated they would In-store faith Other_online to Other_online In-store and 14 percent of the U.S. dollars .


Example 738:
data: Year|2018|x|line_chart Percentage_of_all_births|10.02|y|line_chart Year|2017|x|line_chart Percentage_of_all_births|9.93|y|line_chart Year|2016|x|line_chart Percentage_of_all_births|9.85|y|line_chart Year|2015|x|line_chart Percentage_of_all_births|9.63|y|line_chart Year|2014|x|line_chart Percentage_of_all_births|9.57|y|line_chart Year|2013|x|line_chart Percentage_of_all_births|9.62|y|line_chart Year|2012|x|line_chart Percentage_of_all_births|9.76|y|line_chart Year|2011|x|line_chart Percentage_of_all_births|9.81|y|line_chart Year|2010|x|line_chart Percentage_of_all_births|9.98|y|line_chart Year|2009|x|line_chart Percentage_of_all_births|10.07|y|line_chart Year|2008|x|line_chart Percentage_of_all_births|10.36|y|line_chart Year|2007|x|line_chart Percentage_of_all_births|10.44|y|line_chart Year|2006|x|line_chart Percentage_of_all_births|12.8|y|line_chart Year|2005|x|line_chart Percentage_of_all_births|12.73|y|line_chart Year|2000|x|line_chart Percentage_of_all_births|11.64|y|line_chart Year|1990|x|line_chart Percentage_of_all_births|10.62|y|line_chart 
title: U.S. preterm birth rate 1990 - 2018

gold: This statistic depicts the percentage of births that were preterm births in the United States from 1990 to 2018 . In 1990 , some 10.6 percent of all births in the United States were preterm births . A preterm birth means that a child was delivered after less than 37 weeks of gestation .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[2] that were templateTitle[1] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , some templateYValue[15] percent of templateYLabel[1] templateYLabel[2] in the templateTitle[0] were templateTitle[1] templateYLabel[2] . A templateTitle[1] templateTitle[2] means that a child was delivered after less than 37 weeks of gestation .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] ( templateTitle[4] ) amounted to templateYValue[0] million people . This figure is an increase from the previous templateXLabel[0] . In this period , the templateYLabel[0] templateYLabel[1] distribution of the population .
generated: In 2018 , the Percentage all of birth rate ( 1990 ) amounted to 10.02 million people . This figure is an increase from the previous Year . In this period , the Percentage all distribution of the population .


Example 739:
data: Country|China|x|bar_chart Percentage_of_newly_installed_capacity|45|y|bar_chart Country|India|x|bar_chart Percentage_of_newly_installed_capacity|11|y|bar_chart Country|US|x|bar_chart Percentage_of_newly_installed_capacity|11|y|bar_chart Country|Japan|x|bar_chart Percentage_of_newly_installed_capacity|7|y|bar_chart Country|Australia|x|bar_chart Percentage_of_newly_installed_capacity|4|y|bar_chart Country|Germany|x|bar_chart Percentage_of_newly_installed_capacity|3|y|bar_chart Country|Mexico|x|bar_chart Percentage_of_newly_installed_capacity|3|y|bar_chart Country|Republic_of_Korea|x|bar_chart Percentage_of_newly_installed_capacity|2|y|bar_chart Country|Turkey|x|bar_chart Percentage_of_newly_installed_capacity|2|y|bar_chart Country|Netherlands|x|bar_chart Percentage_of_newly_installed_capacity|1|y|bar_chart 
title: Solar PV capacity - new installations worldwide by country 2018

gold: This statistic shows the share of new installed solar PV capacity worldwide in 2018 , by country . In 2018 , new solar PV capacity installations in China accounted for around 45 percent of the world 's total new installed grid-connected PV capacity .
gold_template: This statistic shows the share of templateTitle[3] templateYLabel[2] templateTitle[0] templateTitle[1] templateYLabel[3] templateTitle[5] in templateTitle[8] , templateTitle[6] templateXLabel[0] . In templateTitle[8] , templateTitle[3] templateTitle[0] templateTitle[1] templateYLabel[3] templateTitle[4] in templateXValue[0] accounted for around templateYValue[max] percent of the world 's total templateTitle[3] templateYLabel[2] grid-connected templateTitle[1] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitle[6] . During the survey , it was found that templateYValue[max] percent of the templateYLabel[1] templateYLabel[2] lived in templateXValue[0] .
generated: This statistic shows the Percentage newly installed capacity new installations worldwide in by . During the survey , it was found that 45 percent of the newly installed lived in China .


Example 740:
data: Year|2019|x|line_chart Net_income_in_million_U.S._dollars|-11.7|y|line_chart Year|2018|x|line_chart Net_income_in_million_U.S._dollars|1.99|y|line_chart Year|2017|x|line_chart Net_income_in_million_U.S._dollars|26.63|y|line_chart Year|2016|x|line_chart Net_income_in_million_U.S._dollars|-183.32|y|line_chart Year|2015|x|line_chart Net_income_in_million_U.S._dollars|33.68|y|line_chart Year|2014|x|line_chart Net_income_in_million_U.S._dollars|-63.92|y|line_chart Year|2013|x|line_chart Net_income_in_million_U.S._dollars|-88.95|y|line_chart Year|2012|x|line_chart Net_income_in_million_U.S._dollars|-51.03|y|line_chart Year|2011|x|line_chart Net_income_in_million_U.S._dollars|-297.76|y|line_chart Year|2010|x|line_chart Net_income_in_million_U.S._dollars|-413.39|y|line_chart Year|2009|x|line_chart Net_income_in_million_U.S._dollars|-1.34|y|line_chart 
title: Groupon : annual net income 2009 - 2019

gold: The statistic above shows the annual net income of Groupon from 2008 to 2019 . In 2019 , the coupon site accumulated a net loss of more than 11.6 million dollars , an decline from the previous year 's net income of two million US dollars .
gold_template: The statistic above shows the templateTitle[1] templateYLabel[0] templateYLabel[1] of templateTitle[0] from 2008 to templateXValue[max] . In templateXValue[max] , the coupon site accumulated a templateYLabel[0] loss of more than 11.6 templateYLabel[2] templateYLabel[4] , an decline from the previous templateXLabel[0] 's templateYLabel[0] templateYLabel[1] of templateYValue[1] templateYLabel[2] US templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] had a templateYLabel[0] templateYLabel[1] of templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net income of net income 2009 2019 titleErr from 2009 to 2019 . In 2019 , Groupon annual net income 2009 2019 had a Net income of -11.7 million U.S. dollars .


Example 741:
data: Quarter|Q1_2018|x|bar_chart Shipments_in_million_units|109.6|y|bar_chart Quarter|2017|x|bar_chart Shipments_in_million_units|454.4|y|bar_chart Quarter|2016|x|bar_chart Shipments_in_million_units|448.5|y|bar_chart Quarter|2015|x|bar_chart Shipments_in_million_units|385.3|y|bar_chart Quarter|2014|x|bar_chart Shipments_in_million_units|392.8|y|bar_chart Quarter|2013|x|bar_chart Shipments_in_million_units|359.0|y|bar_chart 
title: China smartphone unit shipments 2013 - 2018

gold: The statistic shows the smartphone unit shipments in China from 2013 to Q1 2018 . In Q1 2018 , 109.6 million smartphones were shipped in China .
gold_template: The statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitle[0] from templateXValue[last] to templateXValue[0] templateXValue[0] . In templateXValue[0] templateXValue[0] , templateYValue[min] templateYLabel[1] smartphones were shipped in templateTitle[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in templateTitle[3] from the first templateXLabel[0] of templateXValue[2] to the second templateXLabel[0] of templateXValue[0] . In the second templateXLabel[0] of templateXValue[0] , templateYValue[2] templateYLabel[1] were sold in templateTitle[3] .
generated: The statistic shows the China smartphone Shipments in shipments from the first Quarter of 2016 to the second Quarter of Q1_2018 . In the second Quarter of Q1_2018 , 448.5 million were sold in shipments .


Example 742:
data: Year|2018|x|line_chart Share_of_urban_population_in_total_population|57.43|y|line_chart Year|2017|x|line_chart Share_of_urban_population_in_total_population|57.34|y|line_chart Year|2016|x|line_chart Share_of_urban_population_in_total_population|57.26|y|line_chart Year|2015|x|line_chart Share_of_urban_population_in_total_population|57.19|y|line_chart Year|2014|x|line_chart Share_of_urban_population_in_total_population|57.12|y|line_chart Year|2013|x|line_chart Share_of_urban_population_in_total_population|57.05|y|line_chart Year|2012|x|line_chart Share_of_urban_population_in_total_population|56.97|y|line_chart Year|2011|x|line_chart Share_of_urban_population_in_total_population|56.9|y|line_chart Year|2010|x|line_chart Share_of_urban_population_in_total_population|56.83|y|line_chart Year|2009|x|line_chart Share_of_urban_population_in_total_population|56.76|y|line_chart Year|2008|x|line_chart Share_of_urban_population_in_total_population|56.68|y|line_chart 
title: Urbanization in Kazakhstan 2018

gold: This statistic shows the degree of urbanization in Kazakhstan from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 57.43 percent of Kazakhstan 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[1] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Kazakhstan from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 57.43 percent of Kazakhstan 's total population lived in urban areas and cities .


Example 743:
data: Quarter|Q4_'19|x|bar_chart Revenue_in_million_U.S._dollars|269|y|bar_chart Quarter|Q3_'19|x|bar_chart Revenue_in_million_U.S._dollars|265|y|bar_chart Quarter|Q2_'19|x|bar_chart Revenue_in_million_U.S._dollars|271|y|bar_chart Quarter|Q1_'19|x|bar_chart Revenue_in_million_U.S._dollars|256|y|bar_chart Quarter|Q4_'18|x|bar_chart Revenue_in_million_U.S._dollars|263|y|bar_chart Quarter|Q3_'18|x|bar_chart Revenue_in_million_U.S._dollars|254|y|bar_chart Quarter|Q2_'18|x|bar_chart Revenue_in_million_U.S._dollars|259|y|bar_chart Quarter|Q1_'18|x|bar_chart Revenue_in_million_U.S._dollars|246|y|bar_chart Quarter|Q4_'17|x|bar_chart Revenue_in_million_U.S._dollars|244|y|bar_chart Quarter|Q3_'17|x|bar_chart Revenue_in_million_U.S._dollars|235|y|bar_chart Quarter|Q2_'17|x|bar_chart Revenue_in_million_U.S._dollars|219|y|bar_chart Quarter|Q1_'17|x|bar_chart Revenue_in_million_U.S._dollars|199|y|bar_chart Quarter|Q4_'16|x|bar_chart Revenue_in_million_U.S._dollars|201|y|bar_chart Quarter|Q3_'16|x|bar_chart Revenue_in_million_U.S._dollars|197|y|bar_chart Quarter|Q2_'16|x|bar_chart Revenue_in_million_U.S._dollars|207|y|bar_chart Quarter|Q1_'16|x|bar_chart Revenue_in_million_U.S._dollars|186|y|bar_chart Quarter|Q4_'15|x|bar_chart Revenue_in_million_U.S._dollars|183|y|bar_chart Quarter|Q3_'15|x|bar_chart Revenue_in_million_U.S._dollars|178|y|bar_chart Quarter|Q2_'15|x|bar_chart Revenue_in_million_U.S._dollars|180|y|bar_chart Quarter|Q1_'15|x|bar_chart Revenue_in_million_U.S._dollars|162|y|bar_chart Quarter|Q4_'14|x|bar_chart Revenue_in_million_U.S._dollars|180|y|bar_chart 
title: eBay : quarterly classifieds revenue 2014 - 2019

gold: eBay 's classifieds revenue in the fourth quarter of 2019 amounted to 269 million U.S. dollars . This represents a three percent year-on-year change . The classifieds revenue is counted towards the company 's marketing services and other revenues segment .
gold_template: templateTitle[0] 's templateTitle[2] templateYLabel[0] in the fourth templateXLabel[0] of templateTitle[5] amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . This represents a three percent year-on-year change . The templateTitle[2] templateYLabel[0] is counted towards the company 's marketing services and other revenues segment .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] from the first templateXLabel[0] of templateTitle[3] to the fourth templateXLabel[0] of templateTitle[4] . In the last reported templateXLabel[0] , the templateYLabel[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the eBay quarterly from the first Quarter of revenue to the fourth Quarter of 2014 . In the last reported Quarter , the Revenue amounted to 271 million U.S. dollars .


Example 744:
data: Country|Cook_Islands|x|bar_chart Population_decline_compared_to_the_previous_year|2.79|y|bar_chart Country|Puerto_Rico|x|bar_chart Population_decline_compared_to_the_previous_year|1.74|y|bar_chart Country|American_Samoa|x|bar_chart Population_decline_compared_to_the_previous_year|1.3|y|bar_chart Country|Lebanon|x|bar_chart Population_decline_compared_to_the_previous_year|1.1|y|bar_chart Country|Saint_Pierre_and_Miquelon|x|bar_chart Population_decline_compared_to_the_previous_year|1.08|y|bar_chart Country|Latvia|x|bar_chart Population_decline_compared_to_the_previous_year|1.08|y|bar_chart Country|Lithuania|x|bar_chart Population_decline_compared_to_the_previous_year|1.08|y|bar_chart Country|Moldova|x|bar_chart Population_decline_compared_to_the_previous_year|1.05|y|bar_chart Country|Bulgaria|x|bar_chart Population_decline_compared_to_the_previous_year|0.61|y|bar_chart Country|Estonia|x|bar_chart Population_decline_compared_to_the_previous_year|0.57|y|bar_chart Country|Federated_States_of_Micronesia|x|bar_chart Population_decline_compared_to_the_previous_year|0.52|y|bar_chart Country|Northern_Mariana_Islands|x|bar_chart Population_decline_compared_to_the_previous_year|0.51|y|bar_chart Country|Croatia|x|bar_chart Population_decline_compared_to_the_previous_year|0.5|y|bar_chart Country|Serbia|x|bar_chart Population_decline_compared_to_the_previous_year|0.46|y|bar_chart Country|Ukraine|x|bar_chart Population_decline_compared_to_the_previous_year|0.41|y|bar_chart Country|Romania|x|bar_chart Population_decline_compared_to_the_previous_year|0.33|y|bar_chart Country|Slovenia|x|bar_chart Population_decline_compared_to_the_previous_year|0.31|y|bar_chart Country|Cuba|x|bar_chart Population_decline_compared_to_the_previous_year|0.29|y|bar_chart Country|Montenegro|x|bar_chart Population_decline_compared_to_the_previous_year|0.28|y|bar_chart Country|Virgin_Islands|x|bar_chart Population_decline_compared_to_the_previous_year|0.25|y|bar_chart 
title: Countries with the highest population decline rate 2017

gold: This statistic shows the 20 countries with the highest population decline rate in 2017 . In the Cook Islands , the population decreased by about 2.8 percent compared to the previous year , making it the country with the highest population decline rate in 2017 . The population decline of Syria Due to the conflicts and instability in Syria in 2014 , thousands of people have fled have fled the country to cross into surrounding countries such as Turkey .
gold_template: This statistic shows the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitle[6] . In the templateXValue[0] templateXValue[0] , the templateYLabel[0] decreased by about templateYValue[max] percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] , making it the templateXLabel[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitle[6] . The templateYLabel[0] templateYLabel[1] of Syria Due to the conflicts and instability in Syria in 2014 , thousands of people have fled have fled the templateXLabel[0] to cross into surrounding templateTitle[0] such as Turkey .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in templateTitle[4] templateTitle[5] in templateTitle[6] . In that year , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] , with approximately templateYValue[max] million people living in the United States .
generated: This statistic shows the Population decline of population decline in rate 2017 in titleErr . In that year , Cook_Islands had the highest Population decline of rate 2017 , with approximately 2.79 million people living in the United States .


Example 745:
data: Number_of_employees|3_to_49|x|bar_chart Share_of_companies|8|y|bar_chart Number_of_employees|50_to_199|x|bar_chart Share_of_companies|20|y|bar_chart Number_of_employees|200_to_999|x|bar_chart Share_of_companies|48|y|bar_chart Number_of_employees|1000_and_more|x|bar_chart Share_of_companies|80|y|bar_chart 
title: Percentage of U.S. companies using self-insured health plans for employees 2010

gold: This statistic shows the percentage of U.S. companies using self-insured health plans for employees in 2010 , by the number of employees . 80 percent of companies with 1,000 and more employees used self-insured health plans in 2010 .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitle[9] , by the templateXLabel[0] of templateXLabel[1] . templateYValue[max] percent of templateYLabel[1] with templateXValue[last] and templateXValue[last] templateXLabel[1] used templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[9] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] in templateTitle[6] , templateTitle[7] templateXLabel[0] . In that year , templateYValue[max] percent of all templateTitle[3] templateTitle[4] were recorded in the United States .
generated: This statistic shows the Share companies of companies using self-insured in health in plans , for Number . In that year , 80 percent of all using self-insured were recorded in the United States .


Example 746:
data: Year|2018|x|line_chart Revenue_in_million_U.S._dollars|56399|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|45440|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|33188|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|43235|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|65424|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|70374|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|72225|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|75754|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|66266|y|line_chart 
title: BP 's revenue - Upstream segment 2010 - 2018

gold: This statistic shows the revenue of the BP Upstream segment from 2010 to 2018 . In 2018 , BP Upstream reported some 56.4 billion U.S. dollars of revenue . BP is a globally operating oil and gas company , and the eighth largest company generally worldwide as of 2017 , measured by revenue .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[3] reported some templateYValue[0] billion templateYLabel[2] templateYLabel[3] of templateYLabel[0] . templateTitle[0] is a globally operating oil and gas company , and the eighth largest company generally worldwide as of templateXValue[1] , measured by templateYLabel[0] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in annual templateTitle[2] .
generated: The statistic shows the Revenue of the BP 's revenue from 2010 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 56399 million U.S. dollars in annual revenue .


Example 747:
data: Response|Boys_13-14|x|bar_chart Share_of_respondents|33|y|bar_chart Response|Boys_15-17|x|bar_chart Share_of_respondents|51|y|bar_chart Response|Girls_13-14|x|bar_chart Share_of_respondents|56|y|bar_chart Response|Girls_15-17|x|bar_chart Share_of_respondents|64|y|bar_chart 
title: Share of U.S. teenagers who use Instagram 2015 , by gender and age

gold: This statistic shows the share of teenagers in the United States who were Instagram users as of March 2015 , sorted by gender and age group . During that period of time , 64 percent of female U.S. teens aged 15 to 17 years used the social networking app .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] in the templateTitle[1] templateTitle[3] were templateTitle[5] users as of March templateTitle[6] , sorted templateTitle[7] templateTitle[8] and templateTitle[9] group . During that period of time , templateYValue[max] percent of female templateTitle[1] teens aged 15 to 17 years used the social networking app .

generated_template: This statistic shows the results of a survey among female templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . According to the survey , templateYValue[max] percent of female students in the United States were templateXValue[0] templateXValue[0] for the United States in templateTitle[7] .
generated: This statistic shows the results of a survey among female U.S. teenagers who use Instagram 2015 in by . According to the survey , 64 percent of female students in the United States were Boys_13-14 for the United States in by .


Example 748:
data: Response|Yes|x|bar_chart Share_of_respondents|13|y|bar_chart Response|Never|x|bar_chart Share_of_respondents|87|y|bar_chart Response|Spontaneous|x|bar_chart Share_of_respondents|0|y|bar_chart 
title: Proportion of individuals who have tried waterpipe , shisha or hooka in EU-28 2017

gold: This statistic displays the proportion of individuals who have tried water pipe , shisha or hookah in EU-28 countries in 2017 . A majority of 87 percent of respondents said they have never tried water pipe , shisha or hookah products . Additionally , the proportion of individuals who have tried oral , nasal or chewing tobacco can be found at the following .
gold_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] water pipe , templateTitle[6] or hookah in templateTitle[8] countries in templateTitle[9] . A majority of templateYValue[max] percent of templateYLabel[1] said they templateTitle[3] templateXValue[1] templateTitle[4] water pipe , templateTitle[6] or hookah products . Additionally , the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] oral , nasal or chewing tobacco can be found at the following .

generated_template: This statistic shows the results of a survey , conducted in the United States in templateTitle[4] templateTitle[5] . During the survey , templateYValue[max] percent of templateYLabel[1] stated that they would be observed as any of their templateTitle[2] templateTitle[3] .
generated: This statistic shows the results of a survey , conducted in the United States in tried waterpipe . During the survey , 87 percent of respondents stated that they would be observed as any of their who have .


Example 749:
data: City,_country_(date),_organisation|Ghazni_Afghanistan_(Taliban)_(8/10/2018)|x|bar_chart Number_of_fatalities|466|y|bar_chart City,_country_(date),_organisation|Farah_Afghanistan_(Taliban)_(5/15/2018)|x|bar_chart Number_of_fatalities|330|y|bar_chart City,_country_(date),_organisation|Darengarh_Pakistan_(Khorasan_Chapter_of_the_Islamic_State)_(7/13/2018)|x|bar_chart Number_of_fatalities|150|y|bar_chart City,_country_(date),_organisation|Kabul_Afghanistan_(Taliban)_(1/27/2018)|x|bar_chart Number_of_fatalities|104|y|bar_chart City,_country_(date),_organisation|Dila_District_Afghanistan_(Taliban)_(10/12/2018)|x|bar_chart Number_of_fatalities|77|y|bar_chart City,_country_(date),_organisation|Kabul_Afghanistan_(Khorasan_Chapter_of_the_Islamic_State)_(4/22/2018)|x|bar_chart Number_of_fatalities|70|y|bar_chart City,_country_(date),_organisation|Muhmand_Dara_District_Afghanistan_(Unknown)_(9/11/2018)|x|bar_chart Number_of_fatalities|69|y|bar_chart City,_country_(date),_organisation|Day_Mirdad_District_Afghanistan_(Taliban)_(9/9/2018)|x|bar_chart Number_of_fatalities|62|y|bar_chart City,_country_(date),_organisation|Maywand_District_Afghanistan_(Taliban)_(9/11/2018)|x|bar_chart Number_of_fatalities|61|y|bar_chart City,_country_(date),_organisation|Farah_Afghanistan_(Taliban)_(5/12/2018)|x|bar_chart Number_of_fatalities|61|y|bar_chart City,_country_(date),_organisation|Gwaska_Nigeria_(Attributed_to_"Fulani_Extremists")_(5/5/2018)|x|bar_chart Number_of_fatalities|58|y|bar_chart City,_country_(date),_organisation|Kabul_Afghanistan_(Khorasan_Chapter_of_the_Islamic_State)_(11/20/2018)|x|bar_chart Number_of_fatalities|56|y|bar_chart City,_country_(date),_organisation|Sari_Pul_Afghanistan_(Taliban)_(9/10/2018)|x|bar_chart Number_of_fatalities|56|y|bar_chart City,_country_(date),_organisation|Chora_District_Afghanistan_(Taliban)_(8/3/2018)|x|bar_chart Number_of_fatalities|51|y|bar_chart City,_country_(date),_organisation|Pur_Chaman_District_Afghanistan_(Taliban)_(6/12/2018)|x|bar_chart Number_of_fatalities|51|y|bar_chart City,_country_(date),_organisation|Albu_Kamal_Syria_(ISIL)_(6/8/2018)|x|bar_chart Number_of_fatalities|51|y|bar_chart City,_country_(date),_organisation|Azra_District_Afghanistan_(Taliban)_(8/6/2018)|x|bar_chart Number_of_fatalities|50|y|bar_chart City,_country_(date),_organisation|Kabul_Afghanistan_(Taliban)_(12/24/2018)|x|bar_chart Number_of_fatalities|47|y|bar_chart City,_country_(date),_organisation|Oshan_Afghanistan_(Taliban)_(5/11/2018)|x|bar_chart Number_of_fatalities|46|y|bar_chart City,_country_(date),_organisation|Tagbara_Central_African_Republic_(Anti-Balaka_Militia)_(4/3/2018)|x|bar_chart Number_of_fatalities|44|y|bar_chart 
title: The 20 worst terrorist attacks by number of fatalities 2018

gold: The statistic shows the 20 worst terrorist attacks of 2018 , by number of fatalities . The worst terrorist attack in 2018 occurred on August 10 , 2018 , was carried out by the Taliban in Ghazni , Afghanistan , and caused 466 fatalities .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[7] , templateTitle[4] templateYLabel[0] of templateYLabel[1] . The templateTitle[1] templateTitle[2] attack in templateTitle[7] occurred on August 10 , templateTitle[7] , was carried out templateTitle[4] the Taliban in templateXValue[0] , templateXValue[0] , and caused templateYValue[max] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of fatal templateYLabel[1] in the United States in templateTitle[3] templateTitle[4] , sorted templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[0] . During the survey period , templateYValue[max] percent of templateXValue[0] were templateYLabel[1] in the United States .
generated: This statistic shows the Number of fatal fatalities in the United States in attacks by , sorted number fatalities 2018 City, . During the survey period , 466 percent of Ghazni_Afghanistan_(Taliban)_(8/10/2018) were fatalities in the United States .


Example 750:
data: Year|2017|x|line_chart Number_of_hospitals|247|y|line_chart Year|2016|x|line_chart Number_of_hospitals|262|y|line_chart Year|2015|x|line_chart Number_of_hospitals|268|y|line_chart Year|2014|x|line_chart Number_of_hospitals|258|y|line_chart Year|2013|x|line_chart Number_of_hospitals|259|y|line_chart Year|2012|x|line_chart Number_of_hospitals|263|y|line_chart Year|2011|x|line_chart Number_of_hospitals|275|y|line_chart Year|2010|x|line_chart Number_of_hospitals|280|y|line_chart Year|2009|x|line_chart Number_of_hospitals|298|y|line_chart Year|2008|x|line_chart Number_of_hospitals|320|y|line_chart Year|2007|x|line_chart Number_of_hospitals|325|y|line_chart 
title: Number of hospitals in Finland 2007 - 2017

gold: The number of hospitals in Finland was down at the lowest point of the observed period in 2017 , when there were 247 hospitals . At the beginning of the observed period , in 2007 , the number of hospitals amounted to 325 .
gold_template: The templateYLabel[0] of templateYLabel[1] in templateTitle[2] was down at the lowest point of the observed period in templateXValue[max] , when there were templateYValue[min] templateYLabel[1] . At the beginning of the observed period , in templateXValue[min] , the templateYLabel[0] of templateYLabel[1] amounted to templateYValue[max] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in templateTitle[0] was templateYValue[max] templateYLabel[1] .
generated: The statistic shows the Number of hospitals in hospitals from 2007 to 2017 . In 2017 , the Number of hospitals in Number was 325 hospitals .


Example 751:
data: Brand|Just_For_Men|x|bar_chart Sales_in_million_U.S._dollars|138.0|y|bar_chart Brand|Just_For_Men_Autostop|x|bar_chart Sales_in_million_U.S._dollars|27.2|y|bar_chart Brand|Just_For_Men_Control_GX|x|bar_chart Sales_in_million_U.S._dollars|18.2|y|bar_chart Brand|Just_For_Men_Touch_of_Gray|x|bar_chart Sales_in_million_U.S._dollars|9.7|y|bar_chart Brand|Softsheen-Carson_Dark_&_Natural|x|bar_chart Sales_in_million_U.S._dollars|5.7|y|bar_chart Brand|Private_label|x|bar_chart Sales_in_million_U.S._dollars|2.8|y|bar_chart Brand|Grecian_Formula_16|x|bar_chart Sales_in_million_U.S._dollars|2.3|y|bar_chart Brand|Just_For_Men_Original_Formula|x|bar_chart Sales_in_million_U.S._dollars|0.5|y|bar_chart Brand|Creme_of_Nature|x|bar_chart Sales_in_million_U.S._dollars|0.3|y|bar_chart Brand|Grecian_5|x|bar_chart Sales_in_million_U.S._dollars|0.1|y|bar_chart 
title: Leading men 's hair coloring brands in the U.S. 2019

gold: In 2019 , Just For Men was the leading men 's hair coloring brand in the United States with sales of approximately 138 million U.S. dollars . Ranked second , the Just For Men Autostop brand generated sales of around 27.2 million U.S. dollars that year .
gold_template: In templateTitle[7] , templateXValue[0] templateXValue[0] templateXValue[0] was the templateTitle[0] templateXValue[0] templateTitle[2] templateTitle[3] templateTitle[4] templateXLabel[0] in the templateTitle[6] with templateYLabel[0] of approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Ranked second , the templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[1] templateXLabel[0] generated templateYLabel[0] of around templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] that year .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] men 's templateTitle[1] templateTitle[2] templateTitle[3] in the United States in templateTitle[5] . In that year , templateXValue[0] was the templateTitle[0] men 's templateTitle[1] templateTitle[2] templateXLabel[0] with templateYLabel[0] that amounted to about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Sales of the Leading men 's men 's hair in the United States in brands . In that year , Just_For_Men was the Leading men 's men 's Brand with Sales that amounted to about 138.0 million U.S. dollars .


Example 752:
data: Month|Trentino-South_Tyrol|x|bar_chart Birth_rate_per_thousand_inhabitants|9.0|y|bar_chart Month|Campania|x|bar_chart Birth_rate_per_thousand_inhabitants|8.3|y|bar_chart Month|Sicily|x|bar_chart Birth_rate_per_thousand_inhabitants|8.1|y|bar_chart Month|Calabria|x|bar_chart Birth_rate_per_thousand_inhabitants|7.8|y|bar_chart Month|Lombardy|x|bar_chart Birth_rate_per_thousand_inhabitants|7.5|y|bar_chart Month|Emilia-Romagna|x|bar_chart Birth_rate_per_thousand_inhabitants|7.3|y|bar_chart Month|Lazio|x|bar_chart Birth_rate_per_thousand_inhabitants|7.2|y|bar_chart Month|Apulia|x|bar_chart Birth_rate_per_thousand_inhabitants|7.2|y|bar_chart Month|Aosta_Valley|x|bar_chart Birth_rate_per_thousand_inhabitants|7.2|y|bar_chart Month|Veneto|x|bar_chart Birth_rate_per_thousand_inhabitants|7.2|y|bar_chart Month|Abruzzo|x|bar_chart Birth_rate_per_thousand_inhabitants|6.8|y|bar_chart Month|Piedmont|x|bar_chart Birth_rate_per_thousand_inhabitants|6.7|y|bar_chart Month|Tuscany|x|bar_chart Birth_rate_per_thousand_inhabitants|6.7|y|bar_chart Month|Marche|x|bar_chart Birth_rate_per_thousand_inhabitants|6.7|y|bar_chart Month|Umbria|x|bar_chart Birth_rate_per_thousand_inhabitants|6.6|y|bar_chart Month|Basilicata|x|bar_chart Birth_rate_per_thousand_inhabitants|6.6|y|bar_chart Month|Friuli-Venezia_Giulia|x|bar_chart Birth_rate_per_thousand_inhabitants|6.4|y|bar_chart Month|Molise|x|bar_chart Birth_rate_per_thousand_inhabitants|6.2|y|bar_chart Month|Liguria|x|bar_chart Birth_rate_per_thousand_inhabitants|5.8|y|bar_chart Month|Sardinia|x|bar_chart Birth_rate_per_thousand_inhabitants|5.7|y|bar_chart 
title: Birth rate in Italy 2018 , by region

gold: In 2018 , Trentino-South Tyrol was the region in Italy with the highest birth rate nationwide , with nine births per every 1,000 inhabitants . The following three positions of the ranking were occupied by Southern regions : Campania , Sicily , and Calabria . Indeed , South-Italy was the macro-region with the largest birth-rate in Italy .
gold_template: In templateTitle[3] , templateXValue[0] templateXValue[0] was the templateTitle[5] in templateTitle[2] with the highest templateYLabel[0] templateYLabel[1] nationwide , with templateYValue[max] births templateYLabel[2] every 1,000 templateYLabel[4] . The following three positions of the ranking were occupied templateTitle[4] Southern regions : templateXValue[1] , templateXValue[2] , and templateXValue[3] . Indeed , South-Italy was the macro-region with the largest birth-rate in templateTitle[2] .

generated_template: In templateTitle[3] , the Italian regions with the highest templateYLabel[0] templateYLabel[1] was registered at templateYValue[max] million , followed by templateXValue[1] templateXValue[1] , and templateYValue[1] templateYLabel[0] templateYLabel[1] . The highest templateYLabel[0] templateYLabel[1] was also the highest level of Italian Italian regions with the highest templateYLabel[0] templateYLabel[1] , as well as of templateXValue[12] . Still , the registered in the United States also the highest templateYLabel[0] templateYLabel[1] in the citizens of templateTitle[2] 's highest templateYLabel[0] templateYLabel[1] was about two infants .
generated: In 2018 , the Italian regions with the highest Birth rate was registered at 9.0 million , followed by Campania , and 8.3 Birth rate . The highest Birth rate was also the highest level of Italian regions with the highest Birth rate , as well as of Tuscany . Still , the registered in the United States also the highest Birth rate in the citizens of Italy 's highest Birth rate was about two infants .


Example 753:
data: Response|Use_ad_blocker|x|bar_chart Share_of_respondents|41|y|bar_chart Response|Don't_use_ad_blocker|x|bar_chart Share_of_respondents|53|y|bar_chart Response|Don't_know|x|bar_chart Share_of_respondents|6|y|bar_chart 
title: Ad blocker usage in the United Kingdom ( UK ) 2018

gold: This statistic shows the survey on ad blocker usage in the United Kingdom in 2018 . According to the survey , 41 percent of the respondents used an ad blocker , while 53 percent did not . Six percent of respondents said they did n't know if they used ad blocking software .
gold_template: This statistic shows the survey on templateXValue[0] templateXValue[0] templateTitle[2] in the templateTitle[3] templateTitle[4] in templateTitle[6] . According to the survey , templateYValue[0] percent of the templateYLabel[1] used an templateXValue[0] templateXValue[0] , while templateYValue[max] percent did not . templateYValue[min] percent of templateYLabel[1] said they did n't templateXValue[last] if they used templateXValue[0] blocking software .

generated_template: This statistic shows the results of a survey among female templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] . According to the source , templateYValue[max] percent of templateYLabel[1] cited templateXValue[1] templateXValue[1] or second templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States .
generated: This statistic shows the results of a survey among female blocker usage United Kingdom UK 2018 titleErr titleErr . According to the source , 53 percent of respondents cited Don't_use_ad_blocker or second Ad blocker usage United Kingdom in the United States .


Example 754:
data: Year|2018|x|line_chart Number_of_pubs_in_thousands|47.6|y|line_chart Year|2017|x|line_chart Number_of_pubs_in_thousands|48.35|y|line_chart Year|2016|x|line_chart Number_of_pubs_in_thousands|50.3|y|line_chart Year|2015|x|line_chart Number_of_pubs_in_thousands|50.8|y|line_chart Year|2014|x|line_chart Number_of_pubs_in_thousands|51.9|y|line_chart Year|2013|x|line_chart Number_of_pubs_in_thousands|52.5|y|line_chart Year|48|x|line_chart Number_of_pubs_in_thousands|53.8|y|line_chart Year|2011|x|line_chart Number_of_pubs_in_thousands|54.7|y|line_chart Year|2010|x|line_chart Number_of_pubs_in_thousands|55.4|y|line_chart Year|2009|x|line_chart Number_of_pubs_in_thousands|52.5|y|line_chart Year|2008|x|line_chart Number_of_pubs_in_thousands|54.8|y|line_chart Year|2007|x|line_chart Number_of_pubs_in_thousands|56.8|y|line_chart Year|2006|x|line_chart Number_of_pubs_in_thousands|58.2|y|line_chart Year|2005|x|line_chart Number_of_pubs_in_thousands|58.6|y|line_chart Year|2004|x|line_chart Number_of_pubs_in_thousands|59.0|y|line_chart Year|2003|x|line_chart Number_of_pubs_in_thousands|59.4|y|line_chart Year|2002|x|line_chart Number_of_pubs_in_thousands|60.1|y|line_chart Year|2001|x|line_chart Number_of_pubs_in_thousands|60.7|y|line_chart Year|2000|x|line_chart Number_of_pubs_in_thousands|60.8|y|line_chart 
title: Number of pubs in the United Kingdom ( UK ) 2000 - 2018

gold: How many pubs are there in the UK ? There were approximately 47,600 pubs operating in the United Kingdom in 2018 . This represented a decrease of approximately 7,200 pubs in the last ten years , and over 13,200 pubs since 2000 . Pubs in decline Several factors have been suggested for the decline in pubs in the UK .
gold_template: How many templateYLabel[1] are there in the templateTitle[4] ? There were approximately templateYValue[min] templateYLabel[1] operating in the templateTitle[2] templateTitle[3] in templateXValue[max] . This represented a decrease of approximately 7,200 templateYLabel[1] in the last ten years , and over 13,200 templateYLabel[1] since templateXValue[18] . templateYLabel[1] in decline Several factors have been suggested for the decline in templateYLabel[1] in the templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were about templateYValue[max] million templateYLabel[1] in templateTitle[2] templateTitle[3] in the United States .
generated: This statistic shows the Number pubs of United Kingdom in the United States from 48 to 2018 . In 2018 , there were about 60.8 million pubs in United Kingdom in the United States .


Example 755:
data: Year|2018|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|70642|y|line_chart Year|2017|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|69851|y|line_chart Year|2016|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|66440|y|line_chart Year|2015|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|65133|y|line_chart Year|2014|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|62453|y|line_chart Year|2013|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|63588|y|line_chart Year|2012|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|60979|y|line_chart Year|2011|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|60526|y|line_chart Year|2010|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|61361|y|line_chart Year|2009|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|62374|y|line_chart Year|2008|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|63378|y|line_chart Year|2007|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|65089|y|line_chart Year|2006|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|63892|y|line_chart Year|2005|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|63900|y|line_chart Year|2004|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|63627|y|line_chart Year|2003|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|63832|y|line_chart Year|2002|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|64084|y|line_chart Year|2001|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|62773|y|line_chart Year|2000|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|63609|y|line_chart Year|1999|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|63654|y|line_chart Year|1998|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|62480|y|line_chart Year|1997|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|60548|y|line_chart Year|1996|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|59128|y|line_chart Year|1995|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|58184|y|line_chart Year|1994|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|56297|y|line_chart Year|1993|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|55914|y|line_chart Year|1992|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|55842|y|line_chart Year|1991|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|55568|y|line_chart Year|1990|x|line_chart Median_income_in_2018_CPI-U-RS_adjusted_U.S._dollars|56917|y|line_chart 
title: Household income of white families in the U.S. 1990 - 2018

gold: This statistic shows the household income of white families in the U.S. from 1990 to 2018 . The median income in 2018 was at 70,642 U.S. dollars for white , non-Hispanic families . The median household income of the United States can be accessed here .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateYLabel[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[max] templateYLabel[5] templateYLabel[6] for templateTitle[2] , non-Hispanic templateTitle[3] . The templateYLabel[0] templateTitle[0] templateYLabel[1] of the templateTitle[4] can be accessed here .

generated_template: More than templateYValue[0] percent of the templateTitle[3] templateTitle[4] templateTitle[5] in the United States between templateXValue[min] and templateXValue[max] , there were an increase of templateYValue[0] percent compared with the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] represents in the United States was a certain time , many of different at least once since templateXValue[10] , edition of the templateXLabel[0] .
generated: More than 70642 percent of the families U.S. 1990 in the United States between 1990 and 2018 , there were an increase of 70642 percent compared with the previous Year . The Median income represents in the United States was a certain time , many of different at least once since 2008 , edition of the Year .


Example 756:
data: Year|2018|x|line_chart Expenditure_in_U.S._dollars|3459|y|line_chart Year|2017|x|line_chart Expenditure_in_U.S._dollars|3365|y|line_chart Year|2016|x|line_chart Expenditure_in_U.S._dollars|3154|y|line_chart Year|2015|x|line_chart Expenditure_in_U.S._dollars|3008|y|line_chart Year|2014|x|line_chart Expenditure_in_U.S._dollars|2787|y|line_chart Year|2013|x|line_chart Expenditure_in_U.S._dollars|2625|y|line_chart Year|2012|x|line_chart Expenditure_in_U.S._dollars|2678|y|line_chart Year|2011|x|line_chart Expenditure_in_U.S._dollars|2620|y|line_chart Year|2010|x|line_chart Expenditure_in_U.S._dollars|2505|y|line_chart 
title: Average annual food away-from-home expenditures of U.S. households 2010 - 2018

gold: This timeline depicts the average annual food away-from-home expenditure of United States households from 2010 to 2018 . In 2018 , average food away-from-home expenditure of U.S. households amounted to about 3,459 U.S. dollars .
gold_template: This timeline depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] of templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[2] templateTitle[3] templateYLabel[0] of templateYLabel[1] templateTitle[6] amounted to about templateYValue[max] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] of templateTitle[3] from templateXValue[min] to templateXValue[max] . There were templateYValue[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] in templateXValue[max] . The United States has been increasing dramatically in recent years .
generated: This statistic shows the Average Expenditure of U.S. dollars of away-from-home from 2010 to 2018 . There were 3459 U.S. dollars of away-from-home expenditures in 2018 . The United States has been increasing dramatically in recent years .


Example 757:
data: Year|2024|x|line_chart Budgetary_balance_in_relation_to_the_gross_domestic_product|35.66|y|line_chart Year|2023|x|line_chart Budgetary_balance_in_relation_to_the_gross_domestic_product|35.68|y|line_chart Year|2022|x|line_chart Budgetary_balance_in_relation_to_the_gross_domestic_product|35.64|y|line_chart Year|2021|x|line_chart Budgetary_balance_in_relation_to_the_gross_domestic_product|35.61|y|line_chart Year|2020|x|line_chart Budgetary_balance_in_relation_to_the_gross_domestic_product|35.17|y|line_chart Year|2019|x|line_chart Budgetary_balance_in_relation_to_the_gross_domestic_product|34.81|y|line_chart Year|2018|x|line_chart Budgetary_balance_in_relation_to_the_gross_domestic_product|34.61|y|line_chart Year|2017|x|line_chart Budgetary_balance_in_relation_to_the_gross_domestic_product|33.62|y|line_chart Year|2016|x|line_chart Budgetary_balance_in_relation_to_the_gross_domestic_product|35.08|y|line_chart Year|2015|x|line_chart Budgetary_balance_in_relation_to_the_gross_domestic_product|33.37|y|line_chart Year|2014|x|line_chart Budgetary_balance_in_relation_to_the_gross_domestic_product|33.23|y|line_chart 
title: Ratio of government expenditure in relation to gross domestic product ( GDP ) in Turkey

gold: This statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in Turkey from 2014 to 2018 , with projections up until 2024 . In 2018 , the ratio in relation to the GDP in Turkey was at approximately 34.61 percent .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] to templateYLabel[3] templateYLabel[4] templateYLabel[5] ( templateTitle[7] ) in templateTitle[8] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] in templateYLabel[2] to the templateTitle[7] in templateTitle[8] was at approximately templateYValue[6] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at templateYValue[min] percent .
generated: This statistic shows the Budgetary balance in expenditure from 2014 to 2024 . In 2018 , the Budgetary balance in expenditure was at 33.23 percent .


Example 758:
data: Response|Women|x|bar_chart Share_of_respondents|74|y|bar_chart Response|Men|x|bar_chart Share_of_respondents|67|y|bar_chart 
title: Travelers in the U.S. who find family vacation planning stressful in 2014 , by gender

gold: This statistic shows the share of travelers who find family vacation planning stressful in the United States as of May 2014 , by gender . During the survey , 74 percent of women said that they found family vacation planning stressful .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[1] as of May templateTitle[8] , templateTitle[9] templateTitle[10] . During the survey , templateYValue[max] percent of templateXValue[0] said that they found templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: The statistic shows the templateYLabel[0] of adults in the templateTitle[3] templateTitle[4] who were using templateTitle[0] as of February templateTitle[5] , sorted templateTitle[6] templateTitle[7] . During that period of time , templateYValue[max] percent of female templateYLabel[1] stated that they used the social networking site .
generated: The statistic shows the Share of adults in the find family who were using Travelers as of February vacation , sorted planning stressful . During that period of time , 74 percent of female respondents stated that they used the social networking site .


Example 759:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|1|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|1|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|1.1|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|1.1|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|1.06|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|0.89|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|1.09|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|1.01|y|line_chart Year|2016|x|line_chart Inflation_rate_compared_to_previous_year|0.6|y|line_chart Year|2015|x|line_chart Inflation_rate_compared_to_previous_year|-0.73|y|line_chart Year|2014|x|line_chart Inflation_rate_compared_to_previous_year|1.14|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|0.76|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|1.73|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|5.13|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|1.18|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|0.54|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|7.26|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|4.58|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|4.04|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|4.69|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|4.45|y|line_chart Year|2003|x|line_chart Inflation_rate_compared_to_previous_year|2.12|y|line_chart Year|2002|x|line_chart Inflation_rate_compared_to_previous_year|1.87|y|line_chart Year|2001|x|line_chart Inflation_rate_compared_to_previous_year|3.75|y|line_chart Year|2000|x|line_chart Inflation_rate_compared_to_previous_year|2.27|y|line_chart Year|1999|x|line_chart Inflation_rate_compared_to_previous_year|0.51|y|line_chart Year|1998|x|line_chart Inflation_rate_compared_to_previous_year|2.55|y|line_chart Year|1997|x|line_chart Inflation_rate_compared_to_previous_year|4.49|y|line_chart Year|1996|x|line_chart Inflation_rate_compared_to_previous_year|9.79|y|line_chart Year|1995|x|line_chart Inflation_rate_compared_to_previous_year|10.03|y|line_chart Year|1994|x|line_chart Inflation_rate_compared_to_previous_year|10.58|y|line_chart Year|1993|x|line_chart Inflation_rate_compared_to_previous_year|18.51|y|line_chart Year|1992|x|line_chart Inflation_rate_compared_to_previous_year|11.22|y|line_chart Year|1991|x|line_chart Inflation_rate_compared_to_previous_year|14.41|y|line_chart Year|1990|x|line_chart Inflation_rate_compared_to_previous_year|28.29|y|line_chart Year|1989|x|line_chart Inflation_rate_compared_to_previous_year|17.65|y|line_chart Year|1988|x|line_chart Inflation_rate_compared_to_previous_year|19.77|y|line_chart Year|1987|x|line_chart Inflation_rate_compared_to_previous_year|24.85|y|line_chart Year|1986|x|line_chart Inflation_rate_compared_to_previous_year|31.95|y|line_chart Year|1985|x|line_chart Inflation_rate_compared_to_previous_year|22.32|y|line_chart Year|1984|x|line_chart Inflation_rate_compared_to_previous_year|11.71|y|line_chart 
title: Inflation rate in El Salvador 2024

gold: This statistic shows the average inflation rate in El Salvador from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in El Salvador amounted to about 1.09 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in El from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in El amounted to about 1.09 percent compared to the previous Year .


Example 760:
data: Country|Brazil|x|bar_chart Export_volume_in_thousand_metric_tons|3775|y|bar_chart Country|United_States|x|bar_chart Export_volume_in_thousand_metric_tons|3248|y|bar_chart Country|EU|x|bar_chart Export_volume_in_thousand_metric_tons|1500|y|bar_chart Country|Thailand|x|bar_chart Export_volume_in_thousand_metric_tons|900|y|bar_chart Country|China|x|bar_chart Export_volume_in_thousand_metric_tons|475|y|bar_chart Country|Turkey|x|bar_chart Export_volume_in_thousand_metric_tons|400|y|bar_chart Country|Ukraine|x|bar_chart Export_volume_in_thousand_metric_tons|350|y|bar_chart Country|Belarus|x|bar_chart Export_volume_in_thousand_metric_tons|185|y|bar_chart Country|Russia|x|bar_chart Export_volume_in_thousand_metric_tons|180|y|bar_chart Country|Argentina|x|bar_chart Export_volume_in_thousand_metric_tons|145|y|bar_chart Country|Canada|x|bar_chart Export_volume_in_thousand_metric_tons|130|y|bar_chart Country|Others|x|bar_chart Export_volume_in_thousand_metric_tons|331|y|bar_chart 
title: Global exports of broiler meat 2019 , by country

gold: This statistic depicts the export volume of broiler meat worldwide in 2019 , by leading country , in thousand metric tons . The broiler meat exports of the United States amounted to approximately 3.25 million metric tons in that year .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] worldwide in templateTitle[4] , templateTitle[5] leading templateXLabel[0] , in thousand templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[1] of the templateXValue[1] templateXValue[1] amounted to approximately templateYValue[1] million templateYLabel[3] templateYLabel[4] in that year .

generated_template: This statistic depicts the forecast templateYLabel[0] templateYLabel[1] of the worldwide in templateTitle[3] , templateTitle[4] leading templateXLabel[0] . The estimated templateTitle[1] templateTitle[2] of the templateXValue[1] templateXValue[1] were amounted to approximately templateYValue[1] million templateYLabel[3] templateYLabel[4] in templateTitle[3] .
generated: This statistic depicts the forecast Export volume of the worldwide in meat , 2019 leading Country . The estimated exports broiler of the United_States were amounted to approximately 3248 million metric tons in meat .


Example 761:
data: Year|Under_11_years_old|x|line_chart Share_of_respondents|16|y|line_chart Year|11_to_12_years_old|x|line_chart Share_of_respondents|12|y|line_chart Year|13_to_14_years_old|x|line_chart Share_of_respondents|31|y|line_chart Year|15_years_old|x|line_chart Share_of_respondents|16|y|line_chart Year|16_years_old|x|line_chart Share_of_respondents|10|y|line_chart Year|17_years_old_and_over|x|line_chart Share_of_respondents|13|y|line_chart Year|Is_not_pronounced|x|line_chart Share_of_respondents|2|y|line_chart 
title: Distribution of young people according to the age of their first kiss in France 2013

gold: In 2013 , it appears that the majority of French teenagers were in middle school when they had their first kiss . Love appears to be an important area of life at a young age , with more than 50 percent of young French people stating that love relationships were important for them . First love experiences Even though new technologies and smartphones may have changed the way teenagers live their love life , it seems that the age for first love and sex experiences has not really changed over the years .
gold_template: In templateTitle[9] , it appears that the majority of French teenagers were in middle school when they had templateTitle[5] templateTitle[6] templateTitle[7] . Love appears to be an important area of life at a templateTitle[1] templateTitle[4] , with more than 50 percent of templateTitle[1] French templateTitle[2] stating that love relationships were important for them . templateTitle[6] love experiences Even though new technologies and smartphones may have changed the way teenagers live templateTitle[5] love life , it seems that the templateTitle[4] for templateTitle[6] love and sex experiences has templateXValue[last] really changed templateXValue[5] the templateXValue[0] .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[5] on how long the templateYLabel[1] thought the feeling of templateTitle[3] could last in a templateTitle[4] . According to templateYValue[max] percent of templateYLabel[1] , the current templateTitle[2] templateTitle[3] templateTitle[4] in the United States .
generated: This statistic shows the results of a survey conducted in the United States in their on how long the respondents thought the feeling of according could last in a age . According to 31 percent of respondents , the current people according age in the United States .


Example 762:
data: Country|Mexico|x|bar_chart Exports_in_metric_tons|894043|y|bar_chart Country|Haiti|x|bar_chart Exports_in_metric_tons|508527|y|bar_chart Country|Japan|x|bar_chart Exports_in_metric_tons|302752|y|bar_chart Country|Venezuela|x|bar_chart Exports_in_metric_tons|240063|y|bar_chart Country|Canada|x|bar_chart Exports_in_metric_tons|221833|y|bar_chart Country|Costa_Rica|x|bar_chart Exports_in_metric_tons|164114|y|bar_chart Country|Korea_South|x|bar_chart Exports_in_metric_tons|152098|y|bar_chart Country|Jordan|x|bar_chart Exports_in_metric_tons|146558|y|bar_chart Country|Honduras|x|bar_chart Exports_in_metric_tons|137420|y|bar_chart Country|Saudi_Arabia|x|bar_chart Exports_in_metric_tons|124913|y|bar_chart 
title: U.S. rice exports - top destination country 2017

gold: This statistic shows the major nations to which the U.S. exported rice ( milled basis ) in 2017 . Some 894,043 metric tons were exported to Mexico that year . Thus , Mexico was ranked first among the most important destinations for U.S. rice exports in 2017 .
gold_template: This statistic shows the major nations to which the templateTitle[0] exported templateTitle[1] ( milled basis ) in templateTitle[6] . Some templateYValue[max] templateYLabel[1] templateYLabel[2] were exported to templateXValue[0] that year . Thus , templateXValue[0] was ranked first among the most important destinations for templateTitle[0] templateTitle[1] templateYLabel[0] in templateTitle[6] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] worldwide templateTitle[7] world templateTitle[5] to templateTitle[8] . According to the report , the templateTitle[0] templateYLabel[0] in the United States exported approximately templateYValue[1] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateXValue[1] .
generated: This statistic shows the U.S. rice exports of top destination country worldwide titleErr world country to titleErr . According to the report , the U.S. Exports in the United States exported approximately 508527 metric tons of destination Haiti .


Example 763:
data: Year|2018|x|line_chart Operating_income_in_million_U.S._dollars|420.0|y|line_chart Year|2017|x|line_chart Operating_income_in_million_U.S._dollars|365.0|y|line_chart Year|2016|x|line_chart Operating_income_in_million_U.S._dollars|350.0|y|line_chart Year|2015|x|line_chart Operating_income_in_million_U.S._dollars|300.0|y|line_chart Year|2014|x|line_chart Operating_income_in_million_U.S._dollars|270.0|y|line_chart Year|2013|x|line_chart Operating_income_in_million_U.S._dollars|245.7|y|line_chart Year|2012|x|line_chart Operating_income_in_million_U.S._dollars|250.5|y|line_chart Year|2011|x|line_chart Operating_income_in_million_U.S._dollars|226.7|y|line_chart Year|2010|x|line_chart Operating_income_in_million_U.S._dollars|119.0|y|line_chart Year|2009|x|line_chart Operating_income_in_million_U.S._dollars|143.3|y|line_chart Year|2008|x|line_chart Operating_income_in_million_U.S._dollars|9.2|y|line_chart Year|2007|x|line_chart Operating_income_in_million_U.S._dollars|30.6|y|line_chart Year|2006|x|line_chart Operating_income_in_million_U.S._dollars|4.3|y|line_chart Year|2005|x|line_chart Operating_income_in_million_U.S._dollars|37.1|y|line_chart Year|2004|x|line_chart Operating_income_in_million_U.S._dollars|54.3|y|line_chart Year|2003|x|line_chart Operating_income_in_million_U.S._dollars|37.5|y|line_chart Year|2002|x|line_chart Operating_income_in_million_U.S._dollars|52.3|y|line_chart Year|2001|x|line_chart Operating_income_in_million_U.S._dollars|75.0|y|line_chart 
title: National Football League : operating income of the Dallas Cowboys 2001 - 2018

gold: The statistic depicts the operating income of the Dallas Cowboys , a franchise of the National Football League , from 2001 to 2018 . In the 2018 season , the operating income of the Dallas Cowboys was at 420 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[5] templateTitle[6] , a franchise of the templateTitle[0] templateTitle[1] templateTitle[2] , from templateXValue[min] to templateXValue[max] . In the templateXValue[max] season , the templateYLabel[0] templateYLabel[1] of the templateTitle[5] templateTitle[6] was at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] franchise amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic depicts the Operating income of the National Football League from 2001 to 2018 . In 2018 , the Operating income of the National Football League franchise amounted to 420.0 million U.S. dollars .


Example 764:
data: Quarter|Q4_2019|x|bar_chart Number_of_deliveries_in_units|112000|y|bar_chart Quarter|Q3_2019|x|bar_chart Number_of_deliveries_in_units|97000|y|bar_chart Quarter|Q2_2019|x|bar_chart Number_of_deliveries_in_units|95200|y|bar_chart Quarter|Q1_2019|x|bar_chart Number_of_deliveries_in_units|63000|y|bar_chart Quarter|Q4_2018|x|bar_chart Number_of_deliveries_in_units|90700|y|bar_chart Quarter|Q3_2018|x|bar_chart Number_of_deliveries_in_units|83500|y|bar_chart Quarter|Q2_2018|x|bar_chart Number_of_deliveries_in_units|40740|y|bar_chart Quarter|Q1_2018|x|bar_chart Number_of_deliveries_in_units|29980|y|bar_chart Quarter|Q4_2017|x|bar_chart Number_of_deliveries_in_units|29870|y|bar_chart Quarter|Q3_2017|x|bar_chart Number_of_deliveries_in_units|26150|y|bar_chart Quarter|Q2_2017|x|bar_chart Number_of_deliveries_in_units|22000|y|bar_chart Quarter|Q1_2017|x|bar_chart Number_of_deliveries_in_units|25000|y|bar_chart Quarter|Q4_2016|x|bar_chart Number_of_deliveries_in_units|22200|y|bar_chart Quarter|Q3_2016|x|bar_chart Number_of_deliveries_in_units|24500|y|bar_chart Quarter|Q2_2016|x|bar_chart Number_of_deliveries_in_units|14370|y|bar_chart Quarter|Q1_2016|x|bar_chart Number_of_deliveries_in_units|14820|y|bar_chart Quarter|Q4_2015|x|bar_chart Number_of_deliveries_in_units|17400|y|bar_chart 
title: Tesla 's vehicle deliveries by quarter 2019

gold: How many Tesla vehicles were delivered in 2019 ? Annual deliveries rose by almost 50 percent between 2018 and 2019 . Year-to-date deliveries increased to between 367,000 and 368,000 units in 2019 , and Tesla delivered around 112,000 vehicles during the fourth quarter of 2019 alone . The quarterly figure represents a new record following the electric carmaker 's previous quarter which set the record at 97,000 deliveries worldwide .
gold_template: How many templateTitle[0] vehicles were delivered in templateXValue[0] ? Annual templateYLabel[1] rose templateTitle[4] almost 50 percent between templateXValue[4] and templateXValue[0] . Year-to-date templateYLabel[1] increased to between 367,000 and 368,000 templateYLabel[2] in templateXValue[0] , and templateTitle[0] delivered around templateYValue[max] vehicles during the fourth templateXLabel[0] of templateXValue[0] alone . The quarterly figure represents a new record following the electric carmaker templateTitle[1] previous templateXLabel[0] which set the record at templateYValue[1] templateYLabel[1] worldwide .

generated_template: More than templateYValue[max] billion monthly templateTitle[1] templateYLabel[1] templateYLabel[2] in the United States during the fourth templateXLabel[0] of templateXValue[0] , an increase of templateYValue[0] percent since the first templateXLabel[0] of templateXValue[0] templateXValue[0] . However , the templateYLabel[1] templateYLabel[2] person in recent years observed within the first templateXLabel[0] of templateXValue[0] , which has increased dramatically in the first templateXLabel[0] of 2006 .
generated: More than 112000 billion monthly 's deliveries units in the United States during the fourth Quarter of Q4_2019 , an increase of 112000 percent since the first Quarter of Q4_2019 . However , the deliveries units person in recent years observed within the first Quarter of Q4_2019 , which has increased dramatically in the first Quarter of 2006 .


Example 765:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|11|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|11.14|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|11.4|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|11.31|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|11.73|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|11.32|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|12.09|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|16.5|y|line_chart Year|2016|x|line_chart Inflation_rate_compared_to_previous_year|15.7|y|line_chart Year|2015|x|line_chart Inflation_rate_compared_to_previous_year|9.01|y|line_chart Year|2014|x|line_chart Inflation_rate_compared_to_previous_year|8.05|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|8.5|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|12.23|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|10.83|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|13.74|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|12.54|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|11.58|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|5.4|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|8.22|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|17.86|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|15|y|line_chart 
title: Inflation rate in Nigeria 2024

gold: Nigeria 's inflation has been higher than the average for African and Sub-Saharan countries for years now , and even exceeded 16 percent in 2017 – and a real , significant decrease is nowhere in sight . The bigger problem is its unsteadiness , however : An inflation rate that is bouncing all over the place , like this one , is usually a sign of a struggling economy , causing prices to fluctuate , and unemployment and poverty to increase . Nigeria 's economy - a so-called `` mixed economy '' , which means the market economy is at least in part regulated by the state – is not entirely in bad shape , though .
gold_template: templateTitle[2] 's templateYLabel[0] has been higher than the average for African and Sub-Saharan countries for years now , and even exceeded templateYValue[8] percent in templateXValue[7] – and a real , significant decrease is nowhere in sight . The bigger problem is its unsteadiness , however : An templateYLabel[0] templateYLabel[1] that is bouncing all over the place , like this one , is usually a sign of a struggling economy , causing prices to fluctuate , and unemployment and poverty to increase . templateTitle[2] 's economy - a so-called `` mixed economy '' , which means the market economy is at least in part regulated by the state – is not entirely in bad shape , though .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price increase of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: The statistic shows the Inflation rate in Nigeria from 2004 to 2018 , with projections up until 2024 . The Inflation rate is calculated using the price increase of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the Year .


Example 766:
data: Year|Men|x|line_chart Share_of_respondents|15|y|line_chart Year|Women|x|line_chart Share_of_respondents|18|y|line_chart Year|16-24|x|line_chart Share_of_respondents|14|y|line_chart Year|25-34|x|line_chart Share_of_respondents|16|y|line_chart Year|35-44|x|line_chart Share_of_respondents|26|y|line_chart Year|45-54|x|line_chart Share_of_respondents|16|y|line_chart Year|55-64|x|line_chart Share_of_respondents|15|y|line_chart Year|65+|x|line_chart Share_of_respondents|12|y|line_chart 
title: Medicine : Online purchasing in Great Britain 2019 , by demographic

gold: This statistic displays the share of individuals in Great Britain who purchased medicine online in 2019 , by age and gender . Purchasing online was most common among individuals within the 35 to 44 age demographic , at 26 percent of respondents .
gold_template: This statistic displays the templateYLabel[0] of individuals in templateTitle[3] templateTitle[4] who purchased templateTitle[0] templateTitle[1] in templateTitle[5] , templateTitle[6] age and gender . templateTitle[2] templateTitle[1] was most common among individuals within the 35 to 44 age templateTitle[7] , at templateYValue[max] percent of templateYLabel[1] .

generated_template: This statistic presents the templateTitle[7] distribution of templateTitle[1] at templateTitle[0] in the templateTitle[2] templateTitle[3] as of templateTitle[5] . According to their annual report , templateYValue[max] percent of templateTitle[0] templateTitle[1] are in the templateTitle[7] templateTitle[8] 45 - 54 . templateTitle[0] is a British retailer selling clothing , furniture and other household items in a department store format .
generated: This statistic presents the demographic distribution of Online at Medicine in the purchasing Great as of 2019 . According to their annual report , 26 percent of Medicine Online are in the demographic titleErr 45 - 54 . Medicine is a British retailer selling clothing , furniture and other household items in a department store format .


Example 767:
data: Year|2018|x|line_chart Number_of_fatalities|678|y|line_chart Year|2017|x|line_chart Number_of_fatalities|613|y|line_chart Year|2016|x|line_chart Number_of_fatalities|629|y|line_chart Year|2015|x|line_chart Number_of_fatalities|621|y|line_chart Year|2014|x|line_chart Number_of_fatalities|570|y|line_chart Year|2013|x|line_chart Number_of_fatalities|570|y|line_chart Year|2012|x|line_chart Number_of_fatalities|650|y|line_chart Year|2011|x|line_chart Number_of_fatalities|661|y|line_chart Year|2010|x|line_chart Number_of_fatalities|640|y|line_chart Year|2009|x|line_chart Number_of_fatalities|720|y|line_chart Year|2008|x|line_chart Number_of_fatalities|750|y|line_chart Year|2007|x|line_chart Number_of_fatalities|791|y|line_chart Year|2006|x|line_chart Number_of_fatalities|811|y|line_chart 
title: Number of road deaths in the Netherlands 2006 - 2018

gold: In 2018 , 678 people were killed on roads in the Netherlands . Between 2006 and 2018 , road traffic fatalities had seen a net decline of 16 percent , with the peak recorded at the beginning of the reporting period in 2006 . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the number of road deaths down to below 500 by 2020 .
gold_template: In templateXValue[max] , templateYValue[0] people were killed on roads in the templateTitle[3] . Between templateXValue[min] and templateXValue[max] , templateTitle[1] traffic templateYLabel[1] had seen a net decline of 16 percent , with the peak recorded at the beginning of the reporting period in templateXValue[min] . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the templateYLabel[0] of templateTitle[1] templateTitle[2] down to below 500 by 2020 .

generated_template: There were templateYValue[0] reported templateTitle[1] templateTitle[2] in templateTitle[3] in templateXValue[max] . This was a slight increase compared to the previous templateXLabel[0] , but an increase of 14 incidents compared to the low reported in templateXValue[4] . The Northern European island state is known for enforcing a strict templateTitle[1] safety policy in order to ensure the security of its residents and tourists in the country .
generated: There were 678 reported road deaths in Netherlands in 2018 . This was a slight increase compared to the previous Year , but an increase of 14 incidents compared to the low reported in 2014 . The Northern European island state is known for enforcing a strict road safety policy in order to ensure the security of its residents and tourists in the country .


Example 768:
data: Year|2018|x|line_chart Average_number_of_employees|26000|y|line_chart Year|2017|x|line_chart Average_number_of_employees|28000|y|line_chart Year|2016|x|line_chart Average_number_of_employees|22450|y|line_chart Year|2015|x|line_chart Average_number_of_employees|23316|y|line_chart Year|2014|x|line_chart Average_number_of_employees|39751|y|line_chart Year|2013|x|line_chart Average_number_of_employees|41221|y|line_chart Year|2012|x|line_chart Average_number_of_employees|50304|y|line_chart Year|2011|x|line_chart Average_number_of_employees|50301|y|line_chart 
title: Balfour Beatty Group 's average number of employees 2011 - 2018

gold: Balfour Beatty was employer to some 26,000 people in 2018 . The United Kingdom based heavy construction company let go 2,000 employees between 2017 and 2018 , whilst the overall decline in the period of consideration was even more prominent . Overall , employment figures have fallen by nearly 50 percent since a high of 50,304 was reported in 2012 .
gold_template: templateTitle[0] templateTitle[1] was employer to some templateYValue[0] people in templateXValue[max] . The United Kingdom based heavy construction company let go 2,000 templateYLabel[2] between templateXValue[1] and templateXValue[max] , whilst the overall decline in the period of consideration was even more prominent . Overall , employment figures have fallen by nearly 50 percent since a high of templateYValue[max] was reported in templateXValue[6] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of U.S. dollars at templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people living in templateTitle[2] at least templateYValue[0] percent .
generated: This statistic shows the Average number employees of U.S. dollars at Group of 's average number in the United States from 2011 to 2018 . In 2018 , there were 26000 people living in Group at least 26000 percent .


Example 769:
data: Year|2019|x|line_chart Revenue_in_billion_U.S._dollars|6.89|y|line_chart Year|2018|x|line_chart Revenue_in_billion_U.S._dollars|6.48|y|line_chart Year|2017|x|line_chart Revenue_in_billion_U.S._dollars|5.32|y|line_chart Year|2016|x|line_chart Revenue_in_billion_U.S._dollars|4.8|y|line_chart Year|2015|x|line_chart Revenue_in_billion_U.S._dollars|4.37|y|line_chart Year|2014|x|line_chart Revenue_in_billion_U.S._dollars|4.09|y|line_chart Year|2013|x|line_chart Revenue_in_billion_U.S._dollars|3.88|y|line_chart Year|2012|x|line_chart Revenue_in_billion_U.S._dollars|3.76|y|line_chart 
title: Cintas - annual revenue 2012 - 2019

gold: This statistic depicts the annual revenue of Cintas Corporation between the fiscal year of 2012 and the fiscal year of 2019 . For the fiscal year of 2019 , the Cincinnati-based specialized facility services company reported an annual revenue of just under 6.9 billion U.S. dollars .
gold_template: This statistic depicts the templateTitle[1] templateYLabel[0] of templateTitle[0] Corporation between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . For the fiscal templateXLabel[0] of templateXValue[max] , the Cincinnati-based specialized facility services company reported an templateTitle[1] templateYLabel[0] of just under templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of the over-the-counter and templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateYLabel[0] amounted to templateYValue[3] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Cintas Revenue of the over-the-counter and 2012 2019 from 2012 to 2019 . In 2016 , the Cintas Revenue amounted to 4.8 billion U.S. dollars .


Example 770:
data: Year|17|x|line_chart Sales_in_billion_U.S._dollars|166.31|y|line_chart Year|16|x|line_chart Sales_in_billion_U.S._dollars|161.16|y|line_chart Year|15|x|line_chart Sales_in_billion_U.S._dollars|152.57|y|line_chart Year|14|x|line_chart Sales_in_billion_U.S._dollars|147.34|y|line_chart Year|13|x|line_chart Sales_in_billion_U.S._dollars|145.86|y|line_chart Year|12|x|line_chart Sales_in_billion_U.S._dollars|136.77|y|line_chart Year|11|x|line_chart Sales_in_billion_U.S._dollars|129.43|y|line_chart Year|10|x|line_chart Sales_in_billion_U.S._dollars|122.9|y|line_chart Year|09|x|line_chart Sales_in_billion_U.S._dollars|120.76|y|line_chart Year|08|x|line_chart Sales_in_billion_U.S._dollars|121.58|y|line_chart Year|07|x|line_chart Sales_in_billion_U.S._dollars|115.57|y|line_chart Year|06|x|line_chart Sales_in_billion_U.S._dollars|108.56|y|line_chart Year|05|x|line_chart Sales_in_billion_U.S._dollars|103.91|y|line_chart Year|04|x|line_chart Sales_in_billion_U.S._dollars|96.25|y|line_chart Year|03|x|line_chart Sales_in_billion_U.S._dollars|91.23|y|line_chart Year|02|x|line_chart Sales_in_billion_U.S._dollars|87.56|y|line_chart 
title: U.S. wholesale sales of beer and wine 2002 - 2017

gold: The timeline shows the beer , wine , and distilled alcoholic beverages sales of merchant wholesalers in the United States from 2002 to 2017 . In 2017 , the beer , wine , and distilled alcoholic beverages sales of U.S. merchant wholesalers amounted to about 166.31 billion U.S. dollars . Alcohol in the United States During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .
gold_template: The timeline shows the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of merchant wholesalers in the templateTitle[0] from templateTitle[5] to templateTitle[6] . In templateTitle[6] , the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of templateYLabel[2] merchant wholesalers amounted to about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Alcohol in the templateTitle[0] During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] and templateTitle[4] reached approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the United States .
generated: This statistic shows the U.S. Sales of beer wine 2002 in the United States from 02 to 17 . In 17 , U.S. wholesale Sales of beer and wine reached approximately 166.31 billion U.S. dollars in the United States .


Example 771:
data: Year|2006|x|line_chart Average_estimated_amount_in_U.S._dollars|907|y|line_chart Year|2007|x|line_chart Average_estimated_amount_in_U.S._dollars|909|y|line_chart Year|2008|x|line_chart Average_estimated_amount_in_U.S._dollars|801|y|line_chart Year|2009|x|line_chart Average_estimated_amount_in_U.S._dollars|740|y|line_chart Year|2010|x|line_chart Average_estimated_amount_in_U.S._dollars|715|y|line_chart Year|2011|x|line_chart Average_estimated_amount_in_U.S._dollars|712|y|line_chart 
title: Average spending on Christmas gifts in the U.S .

gold: The statistic depicts the results of a survey about the average Christmas spending of U.S. consumers from 2006 to 2011 . October of 2007 was the most generous regarding spending on gifts among Americans , estimating a likelihood of spending around 909 U.S. dollars ( on average ) . Since then , the amount reserved for Christmas presents has steadily declined .
gold_template: The statistic depicts the results of a survey about the templateYLabel[0] templateTitle[2] templateTitle[1] of templateYLabel[3] consumers from templateXValue[min] to templateXValue[max] . October of templateXValue[1] was the most generous regarding templateTitle[1] on templateTitle[3] among Americans , estimating a likelihood of templateTitle[1] around templateYValue[max] templateYLabel[3] templateYLabel[4] ( on templateYLabel[0] ) . Since then , the templateYLabel[2] reserved for templateTitle[2] presents has steadily declined .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people living in the United States .
generated: This statistic shows the Average estimated amount of gifts U.S titleErr in the United States from 2006 to 2011 . In 2011 , there were 907 people living in the United States .


Example 772:
data: Year|2006|x|line_chart Revenue_in_billion_U.S._dollars|7.14|y|line_chart Year|2007|x|line_chart Revenue_in_billion_U.S._dollars|7.97|y|line_chart Year|2008|x|line_chart Revenue_in_billion_U.S._dollars|7.36|y|line_chart Year|2009|x|line_chart Revenue_in_billion_U.S._dollars|7.16|y|line_chart Year|2011|x|line_chart Revenue_in_billion_U.S._dollars|6.55|y|line_chart Year|2012|x|line_chart Revenue_in_billion_U.S._dollars|6.78|y|line_chart Year|2013|x|line_chart Revenue_in_billion_U.S._dollars|6.56|y|line_chart Year|2014|x|line_chart Revenue_in_billion_U.S._dollars|8.0|y|line_chart 
title: Advance Publications ' revenue 2006 - 2014

gold: The timeline shows estimated data on the revenue of the American media corporation Advance Publications , Inc. from 2006 to 2014 . Advance Publications is a privately-owned , unlisted company and owns such brands as Reddit - a social news and entertainment website and Conde Nast - a magazine publisher . Its 2006 revenue is estimated to have amounted to 7.14 billion US dollars .
gold_template: The timeline shows estimated data on the templateYLabel[0] of the American media corporation templateTitle[0] templateTitle[1] , Inc. from templateXValue[min] to templateXValue[max] . templateTitle[0] templateTitle[1] is a privately-owned , unlisted company and owns such brands as Reddit - a social news and entertainment website and Conde Nast - a magazine publisher . Its templateXValue[min] templateYLabel[0] is estimated to have amounted to templateYValue[0] templateYLabel[1] US templateYLabel[3] .

generated_template: This statistic depicts the templateTitle[0] templateYLabel[0] of the United States templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] generated approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitle[0] templateYLabel[0] - additional information The templateTitle[0] templateTitle[1] is one of the largest staffing and temporary employment company in the world , behind the Swiss firm Adecco and Randstad .
generated: This statistic depicts the Advance Revenue of the United States 2006 2006 to 2014 . In 2014 , the Advance Publications generated approximately 7.14 billion U.S. dollars . Advance Revenue - additional information The Advance Publications is one of the largest staffing and temporary employment company in the world , behind the Swiss firm Adecco and Randstad .


Example 773:
data: Year|2020|x|line_chart Brand_value_in_million_U.S._dollars|34792|y|line_chart Year|2019|x|line_chart Brand_value_in_million_U.S._dollars|32421|y|line_chart Year|2018|x|line_chart Brand_value_in_million_U.S._dollars|28030|y|line_chart Year|2017|x|line_chart Brand_value_in_million_U.S._dollars|31762|y|line_chart Year|2016|x|line_chart Brand_value_in_million_U.S._dollars|28041|y|line_chart 
title: Global brand value of Nike from 2016 to 2020

gold: In 2020 , the Nike brand was valued at approximately 34.8 billion U.S. dollars , which was an increase of over two billion U.S. dollars from 2019 . Nike 's popularity Nike 's footwear segment was the source of the most revenue for the company in 2019 , netting over 24 billion U.S. dollars that year . Among U.S. consumers , Nike was the most popular sports shoe , ahead of its main competitors Adidas .
gold_template: In templateXValue[max] , the templateTitle[3] templateYLabel[0] was valued at approximately templateYValue[max] billion templateYLabel[3] templateYLabel[4] , which was an increase of over two billion templateYLabel[3] templateYLabel[4] templateTitle[4] templateXValue[1] . templateTitle[3] 's popularity templateTitle[3] 's footwear segment was the source of the most revenue for the company in templateXValue[1] , netting over 24 billion templateYLabel[3] templateYLabel[4] that templateXLabel[0] . Among templateYLabel[3] consumers , templateTitle[3] was the most popular sports shoe , ahead of its main competitors Adidas .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] ( GPV ) in the United Kingdom ( UK ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] was around templateYValue[6] percent templateYLabel[2] templateYLabel[3] templateXLabel[0] . The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] - additional information The templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] has increased annually as in recent years , reaching a decline in templateXValue[1] , reaching a value of templateYValue[1] percent in templateXValue[1] , the years .
generated: The statistic shows the Global brand value Nike ( GPV ) in the United Kingdom ( UK ) from 2016 to 2020 . In xValErr , the Global brand value Nike was around yValErr percent million U.S. Year . The Global brand value Nike - additional information The Global brand value Nike has increased annually as in recent years , reaching a decline in 2019 , reaching a value of 32421 percent in 2019 , the years .


Example 774:
data: Year|2017|x|line_chart Turnover_in_million_GBP|4784|y|line_chart Year|2016|x|line_chart Turnover_in_million_GBP|4892|y|line_chart Year|2015|x|line_chart Turnover_in_million_GBP|4372|y|line_chart Year|2014|x|line_chart Turnover_in_million_GBP|4304|y|line_chart Year|2013|x|line_chart Turnover_in_million_GBP|4620|y|line_chart Year|2012|x|line_chart Turnover_in_million_GBP|4397|y|line_chart Year|2011|x|line_chart Turnover_in_million_GBP|4432|y|line_chart Year|2010|x|line_chart Turnover_in_million_GBP|4025|y|line_chart Year|2009|x|line_chart Turnover_in_million_GBP|4423|y|line_chart Year|2008|x|line_chart Turnover_in_million_GBP|4446|y|line_chart 
title: Stationery retail sales turnover in the United Kingdom ( UK ) 2008 - 207

gold: This statistic shows the total annual turnover of the retail sales of stationery and drawing materials and miscellaneous printed matter in the United Kingdom , from 2008 to 2017 . In 2017 , turnover from stationery and drawing material retail sales reached 4.78 billion British pounds which was the highest point of turnover over the nine year period .
gold_template: This statistic shows the total annual templateYLabel[0] of the templateTitle[1] templateTitle[2] of templateTitle[0] and drawing materials and miscellaneous printed matter in the templateTitle[4] templateTitle[5] , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] from templateTitle[0] and drawing material templateTitle[1] templateTitle[2] reached templateYValue[0] billion British pounds which was the highest point of templateYLabel[0] over the nine templateXLabel[0] period .

generated_template: Over the past decade the templateYLabel[0] of the British templateTitle[0] templateTitle[1] templateTitle[2] in the United Kingdom ( UK ) has decreased from templateYValue[max] in templateXValue[min] to templateYValue[min] in templateXValue[max] . This is an increase of over the previous templateXLabel[0] . The United Kingdom of templateTitle[4] can be seen in templateXValue[min] , the templateYLabel[0] of the British pounds in templateXValue[max] .
generated: Over the past decade the Turnover of the British Stationery retail sales in the United Kingdom ( UK ) has decreased from 4892 in 2008 to 4025 in 2017 . This is an increase of over the previous Year . The United Kingdom of United can be seen in 2008 , the Turnover of the British pounds in 2017 .


Example 775:
data: Year|2024|x|line_chart Smartphone_users_in_millions|5.19|y|line_chart Year|2023|x|line_chart Smartphone_users_in_millions|5.15|y|line_chart Year|2022|x|line_chart Smartphone_users_in_millions|5.11|y|line_chart Year|2021|x|line_chart Smartphone_users_in_millions|5.0|y|line_chart Year|2020|x|line_chart Smartphone_users_in_millions|4.89|y|line_chart Year|2019|x|line_chart Smartphone_users_in_millions|4.77|y|line_chart Year|2018|x|line_chart Smartphone_users_in_millions|4.64|y|line_chart 
title: Forecast of smartphone user numbers in Norway 2018 - 2024

gold: This statistic displays the development in smartphone user numbers in Norway in 2018 with a forecast from 2019 to 2024 . In 2018 , the number of smartphone users amounted to 4.64 million . In the same year , smartphone penetration rate was at 86.95 percent .
gold_template: This statistic displays the development in templateYLabel[0] templateTitle[2] templateTitle[3] in templateTitle[4] in templateXValue[min] with a templateTitle[0] from templateXValue[5] to templateXValue[max] . In templateXValue[min] , the number of templateYLabel[0] templateYLabel[1] amounted to templateYValue[min] million . In the same templateXLabel[0] , templateYLabel[0] penetration rate was at 86.95 percent .

generated_template: The statistic shows the number of templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the number of templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] million . templateYLabel[0] templateYLabel[1] in templateTitle[2] – additional information Advances in people are expected to reach roughly templateYValue[6] million inhabitants .
generated: The statistic shows the number of Smartphone users in user from 2018 to 2018 , with projections up until 2024 . In 2018 , the number of Smartphone users in user amounted to about 4.64 million . Smartphone users in user – additional information Advances in people are expected to reach roughly 4.64 million inhabitants .


Example 776:
data: Year|2018|x|line_chart Deaths_per_1,000_live_births|49.5|y|line_chart Year|2017|x|line_chart Deaths_per_1,000_live_births|50.8|y|line_chart Year|2016|x|line_chart Deaths_per_1,000_live_births|52.1|y|line_chart Year|2015|x|line_chart Deaths_per_1,000_live_births|53.3|y|line_chart Year|2014|x|line_chart Deaths_per_1,000_live_births|54.5|y|line_chart Year|2013|x|line_chart Deaths_per_1,000_live_births|55.7|y|line_chart Year|2012|x|line_chart Deaths_per_1,000_live_births|56.8|y|line_chart Year|2011|x|line_chart Deaths_per_1,000_live_births|57.9|y|line_chart Year|2010|x|line_chart Deaths_per_1,000_live_births|85.6|y|line_chart Year|2009|x|line_chart Deaths_per_1,000_live_births|60.2|y|line_chart Year|2008|x|line_chart Deaths_per_1,000_live_births|61.4|y|line_chart 
title: Infant mortality rate in Haiti 2018

gold: The statistic shows the infant mortality rate in Haiti from 2008 to 2018 . In 2018 , the infant mortality rate in Haiti was at about 49.5 deaths per 1,000 live births .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[3] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[3] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality rate in Haiti from 2008 to 2018 . In 2018 , the Infant mortality rate in Haiti was at about 49.5 Deaths per 1,000 live births .


Example 777:
data: Response|The_variety_of_music_available|x|bar_chart Share_of_respondents|81|y|bar_chart Response|Low_price_point|x|bar_chart Share_of_respondents|80|y|bar_chart Response|The_ability_to_listen_on_multiple_divices|x|bar_chart Share_of_respondents|68|y|bar_chart Response|Clean_user_interface|x|bar_chart Share_of_respondents|66|y|bar_chart Response|Good_algorithms_to_find_new_music|x|bar_chart Share_of_respondents|58|y|bar_chart Response|The_ability_to_combine_your_music_library_with_your_streaming_service_library|x|bar_chart Share_of_respondents|64|y|bar_chart Response|The_ability_to_stream_on_smart_home_devices|x|bar_chart Share_of_respondents|57|y|bar_chart Response|Curated_playlists|x|bar_chart Share_of_respondents|52|y|bar_chart Response|Artist_exclusives|x|bar_chart Share_of_respondents|46|y|bar_chart 
title: Important features of music streaming services in the U.S. 2018

gold: This statistic presents data on the most important features of music streaming services among adults in the United States as of March 2018 . During a survey , 81 percent of respondents stated that the variety of music available was the most important feature of music streaming services .
gold_template: This statistic presents data on the most templateTitle[0] templateTitle[1] of templateXValue[0] templateXValue[5] templateTitle[4] among adults in the templateTitle[5] as of March templateTitle[6] . During a survey , templateYValue[max] percent of templateYLabel[1] stated that the templateXValue[0] of templateXValue[0] templateXValue[0] was the most templateTitle[0] feature of templateXValue[0] templateXValue[5] templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitle[3] templateTitle[4] who were using templateTitle[0] as of February templateTitle[5] , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] percent of templateYLabel[1] stated that they used templateXValue[0] templateXValue[0] would be the award .
generated: This statistic shows the Share of adults in the streaming services who were using Important as of February U.S. , sorted 2018 titleErr titleErr . During that period of time , 81 percent of respondents stated that they used The_variety_of_music_available would be the award .


Example 778:
data: Year|2018|x|line_chart Revenue_in_billion_U.S._dollars|2.92|y|line_chart Year|2017|x|line_chart Revenue_in_billion_U.S._dollars|3.15|y|line_chart Year|2016|x|line_chart Revenue_in_billion_U.S._dollars|3.05|y|line_chart Year|2015|x|line_chart Revenue_in_billion_U.S._dollars|2.89|y|line_chart Year|2014|x|line_chart Revenue_in_billion_U.S._dollars|3.17|y|line_chart Year|2013|x|line_chart Revenue_in_billion_U.S._dollars|3.32|y|line_chart 
title: Gannett 's revenue 2013 - 2018

gold: This statistic presents Gannett Company 's annual revenue from 2013 to 2018 . In 2018 , the publisher of USA Today generated a total revenue of 2.92 billion U.S. dollars .
gold_template: This statistic presents templateTitle[0] Company templateTitle[1] annual templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the publisher of USA Today generated a total templateYLabel[0] of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the company generated around templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYLabel[0] . templateTitle[0] Founded by Warren A. templateTitle[0] in 1898 , the templateTitle[0] Corporation is the largest construction and engineering company in the United States and the world .
generated: This statistic shows Gannett 's Revenue from the fiscal Year of 2013 to the fiscal Year of 2018 . In the fiscal Year of 2018 , the company generated around 2.92 billion U.S. dollars of Revenue . Gannett Founded by Warren A. Gannett in 1898 , the Gannett Corporation is the largest construction and engineering company in the United States and the world .


Example 779:
data: Year|2024|x|line_chart Change_from_year_to_year|2|y|line_chart Year|2023|x|line_chart Change_from_year_to_year|0.5|y|line_chart Year|2022|x|line_chart Change_from_year_to_year|0.5|y|line_chart Year|2021|x|line_chart Change_from_year_to_year|0.8|y|line_chart Year|2020|x|line_chart Change_from_year_to_year|2.2|y|line_chart 
title: Forecast on U.S. petroleum refinery end-use market output 2020 - 2024

gold: This statistic displays a forecast of the petroleum and refinery end-use market output in the United States from 2020 to 2024 . Through 2020 , the petroleum and refinery end-use market output is expected to increase by 2.2 percent . U.S. petroleum refinery market It is projected that the growth of output from the U.S. petroleum refinery end-use market will slow , from a rate of 2.2 percent in 2020 to 0.5 percent in 2023 , and grow again to 2.2 percent in 2024 .
gold_template: This statistic displays a templateTitle[0] of the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[1] templateYLabel[1] templateXValue[min] to templateXValue[max] . Through templateXValue[min] , the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] is expected to increase by templateYValue[max] percent . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[5] It is projected that the growth of templateTitle[6] templateYLabel[1] the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] will slow , templateYLabel[1] a rate of templateYValue[max] percent in templateXValue[min] to templateYValue[min] percent in templateXValue[1] , and grow again to templateYValue[max] percent in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[5] , about templateYValue[5] percent of the total U.S. templateYLabel[1] accessed the templateTitle[2] templateTitle[3] templateTitle[4] compared to previous templateXLabel[0] .
generated: This statistic shows the Change from year in refinery end-use in the United States from 2020 to 2024 . In xValErr , about yValErr percent of the total U.S. from accessed the petroleum refinery end-use compared to previous Year .


Example 780:
data: Year|2018|x|line_chart Total_consumption_in_million_kilograms|164.9|y|line_chart Year|2017|x|line_chart Total_consumption_in_million_kilograms|162.4|y|line_chart Year|2016|x|line_chart Total_consumption_in_million_kilograms|164.1|y|line_chart Year|2015|x|line_chart Total_consumption_in_million_kilograms|165.5|y|line_chart Year|2014|x|line_chart Total_consumption_in_million_kilograms|163.6|y|line_chart Year|2013|x|line_chart Total_consumption_in_million_kilograms|165.3|y|line_chart Year|2012|x|line_chart Total_consumption_in_million_kilograms|158.7|y|line_chart Year|2011|x|line_chart Total_consumption_in_million_kilograms|151.5|y|line_chart Year|2010|x|line_chart Total_consumption_in_million_kilograms|150.54|y|line_chart Year|2009|x|line_chart Total_consumption_in_million_kilograms|147.44|y|line_chart Year|2008|x|line_chart Total_consumption_in_million_kilograms|143.6|y|line_chart 
title: Chocolate and cocoa products consumption in Spanish households 2008 - 2018

gold: Chocolate has inspired uncountable desserts in many countries across the globe and throughout history . This delicacy originating in Mesoamerica is the favorite sweet of many places including Spain , which imported this delight to Europe during the country 's explorations in the Americas . Spain 's sweet tooth has been on the rise in recent years , with a peak in chocolate and cocoa product consumption amounting to 165.5 million kilograms in 2013 .
gold_template: templateTitle[0] has inspired uncountable desserts in many countries across the globe and throughout history . This delicacy originating in Mesoamerica is the favorite sweet of many places including Spain , which imported this delight to Europe during the country 's explorations in the Americas . Spain 's sweet tooth has been on the rise in recent years , with a peak in templateTitle[0] and templateTitle[1] product templateYLabel[1] amounting to templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[5] .

generated_template: During templateXValue[max] , some templateYValue[0] people died in templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States . The templateYLabel[0] templateYLabel[1] thought the highest templateTitle[3] templateTitle[4] templateTitle[5] in the United States dropped from templateYValue[1] templateYLabel[1] templateYLabel[2] in templateXValue[1] . Over the years observed within the time period shown 's templateYLabel[0] templateYLabel[1] has gradually risen dramatically in the past few years .
generated: During 2018 , some 164.9 people died in products consumption Spanish households in the United States . The Total consumption thought the highest consumption Spanish households in the United States dropped from 162.4 consumption million in 2017 . Over the years observed within the time period shown 's Total consumption has gradually risen dramatically in the past few years .


Example 781:
data: Year|2024|x|line_chart Inflation_rate_compared_to_previous_year|8.97|y|line_chart Year|2023|x|line_chart Inflation_rate_compared_to_previous_year|8.97|y|line_chart Year|2022|x|line_chart Inflation_rate_compared_to_previous_year|8.97|y|line_chart Year|2021|x|line_chart Inflation_rate_compared_to_previous_year|8.97|y|line_chart Year|2020|x|line_chart Inflation_rate_compared_to_previous_year|8.97|y|line_chart Year|2019|x|line_chart Inflation_rate_compared_to_previous_year|7.32|y|line_chart Year|2018|x|line_chart Inflation_rate_compared_to_previous_year|1.24|y|line_chart Year|2017|x|line_chart Inflation_rate_compared_to_previous_year|16.64|y|line_chart Year|2016|x|line_chart Inflation_rate_compared_to_previous_year|5.53|y|line_chart Year|2015|x|line_chart Inflation_rate_compared_to_previous_year|5.55|y|line_chart Year|2014|x|line_chart Inflation_rate_compared_to_previous_year|4.42|y|line_chart Year|2013|x|line_chart Inflation_rate_compared_to_previous_year|7.94|y|line_chart Year|2012|x|line_chart Inflation_rate_compared_to_previous_year|18.18|y|line_chart Year|2011|x|line_chart Inflation_rate_compared_to_previous_year|9.58|y|line_chart Year|2010|x|line_chart Inflation_rate_compared_to_previous_year|6.5|y|line_chart Year|2009|x|line_chart Inflation_rate_compared_to_previous_year|10.56|y|line_chart Year|2008|x|line_chart Inflation_rate_compared_to_previous_year|24.41|y|line_chart Year|2007|x|line_chart Inflation_rate_compared_to_previous_year|8.41|y|line_chart Year|2006|x|line_chart Inflation_rate_compared_to_previous_year|2.74|y|line_chart Year|2005|x|line_chart Inflation_rate_compared_to_previous_year|13.25|y|line_chart Year|2004|x|line_chart Inflation_rate_compared_to_previous_year|8.18|y|line_chart Year|2003|x|line_chart Inflation_rate_compared_to_previous_year|10.57|y|line_chart Year|2002|x|line_chart Inflation_rate_compared_to_previous_year|-1.26|y|line_chart Year|2001|x|line_chart Inflation_rate_compared_to_previous_year|7.87|y|line_chart Year|2000|x|line_chart Inflation_rate_compared_to_previous_year|25.52|y|line_chart Year|1999|x|line_chart Inflation_rate_compared_to_previous_year|3.52|y|line_chart Year|1998|x|line_chart Inflation_rate_compared_to_previous_year|12.47|y|line_chart Year|1997|x|line_chart Inflation_rate_compared_to_previous_year|31.06|y|line_chart Year|1996|x|line_chart Inflation_rate_compared_to_previous_year|26.42|y|line_chart Year|1995|x|line_chart Inflation_rate_compared_to_previous_year|19.36|y|line_chart Year|1994|x|line_chart Inflation_rate_compared_to_previous_year|14.71|y|line_chart Year|1993|x|line_chart Inflation_rate_compared_to_previous_year|9.71|y|line_chart Year|1992|x|line_chart Inflation_rate_compared_to_previous_year|5.33|y|line_chart Year|1991|x|line_chart Inflation_rate_compared_to_previous_year|9.01|y|line_chart Year|1990|x|line_chart Inflation_rate_compared_to_previous_year|6.99|y|line_chart Year|1989|x|line_chart Inflation_rate_compared_to_previous_year|11.67|y|line_chart Year|1988|x|line_chart Inflation_rate_compared_to_previous_year|4.49|y|line_chart Year|1987|x|line_chart Inflation_rate_compared_to_previous_year|7.11|y|line_chart Year|1986|x|line_chart Inflation_rate_compared_to_previous_year|1.67|y|line_chart Year|1985|x|line_chart Inflation_rate_compared_to_previous_year|3.82|y|line_chart Year|1984|x|line_chart Inflation_rate_compared_to_previous_year|14.3|y|line_chart 
title: Inflation rate in Burundi 2024

gold: This statistic shows the average inflation rate in Burundi from 1984 to 2017 , with projections up until 2024 . In 2017 , the average inflation rate in Burundi amounted to about 16.64 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[7] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[2] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Burundi from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Burundi amounted to about 1.24 percent compared to the previous Year .


Example 782:
data: Response|By_phone|x|bar_chart Share_of_respondents|50.5|y|bar_chart Response|In_person|x|bar_chart Share_of_respondents|43.5|y|bar_chart Response|Online|x|bar_chart Share_of_respondents|32.1|y|bar_chart Response|Via_mobile_app|x|bar_chart Share_of_respondents|3.9|y|bar_chart Response|Other|x|bar_chart Share_of_respondents|0.2|y|bar_chart 
title: Methods of ordering food for takeout or delivery in the U.S. as of April 2014

gold: This statistic shows the methods which consumers used to order food for takeout or delivery in the United States as of April 2014 . During the survey , 32.1 percent of respondents said they ordered food for takeout or delivery online .
gold_template: This statistic shows the templateTitle[0] which consumers used to order templateTitle[2] templateTitle[3] templateTitle[4] or templateTitle[5] in the templateTitle[6] as of templateTitle[7] templateTitle[8] . During the survey , templateYValue[2] percent of templateYLabel[1] said they ordered templateTitle[2] templateTitle[3] templateTitle[4] or templateTitle[5] templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] in the United States as of templateTitle[6] . During the survey , it was found that templateYValue[max] percent of templateYLabel[1] stated they used the templateXValue[0] templateXValue[0] to templateXValue[0] .
generated: This statistic shows the Methods ordering food of for in the United States as of U.S. . During the survey , it was found that 50.5 percent of respondents stated they used the By_phone to By_phone .


Example 783:
data: Year|18/19|x|line_chart Expenditure_as_share_of_GDP|34.1|y|line_chart Year|17/18|x|line_chart Expenditure_as_share_of_GDP|34.4|y|line_chart Year|16/17|x|line_chart Expenditure_as_share_of_GDP|34.8|y|line_chart Year|15/16|x|line_chart Expenditure_as_share_of_GDP|35.7|y|line_chart Year|14/15|x|line_chart Expenditure_as_share_of_GDP|36.4|y|line_chart Year|13/14|x|line_chart Expenditure_as_share_of_GDP|37.3|y|line_chart Year|12/13|x|line_chart Expenditure_as_share_of_GDP|38.3|y|line_chart Year|11/12|x|line_chart Expenditure_as_share_of_GDP|39.1|y|line_chart Year|10/11|x|line_chart Expenditure_as_share_of_GDP|39.7|y|line_chart Year|09/10|x|line_chart Expenditure_as_share_of_GDP|39.4|y|line_chart Year|08/09|x|line_chart Expenditure_as_share_of_GDP|36.9|y|line_chart Year|07/08|x|line_chart Expenditure_as_share_of_GDP|34.9|y|line_chart Year|06/07|x|line_chart Expenditure_as_share_of_GDP|34.5|y|line_chart Year|05/06|x|line_chart Expenditure_as_share_of_GDP|34.7|y|line_chart Year|04/05|x|line_chart Expenditure_as_share_of_GDP|34.9|y|line_chart Year|03/04|x|line_chart Expenditure_as_share_of_GDP|34|y|line_chart Year|02/03|x|line_chart Expenditure_as_share_of_GDP|32.8|y|line_chart Year|01/02|x|line_chart Expenditure_as_share_of_GDP|32.1|y|line_chart Year|00/01|x|line_chart Expenditure_as_share_of_GDP|31.8|y|line_chart 
title: Public sector expenditure as a share of GDP in the United Kingdom ( UK ) 2000 - 2019

gold: This statistic shows total public sector current expenditure as a share of GDP in the United Kingdom ( UK ) from 2000/01 to 2018/19 . During this period public sector spending fluctuated , peaking in 2010/11 at 39.7 percent of GDP .
gold_template: This statistic shows total templateTitle[0] templateTitle[1] current templateYLabel[0] as a templateYLabel[1] of templateYLabel[2] in the templateTitle[5] templateTitle[6] ( templateTitle[7] ) from 2000/01 to 2018/19 . During this period templateTitle[0] templateTitle[1] spending fluctuated , peaking in 2010/11 at templateYValue[max] percent of templateYLabel[2] .

generated_template: In the most recent fiscal templateXLabel[0] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States amounted to templateYValue[max] templateYLabel[3] , up from templateYValue[1] people in the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] indicator indicator in the United States has been steadily since templateYValue[max] people died as a result of the population remaining two percent of the United States .
generated: In the most recent fiscal Year , the Expenditure share GDP in the United States amounted to 39.7 yLabelErr , up from 34.4 people in the previous Year . The Expenditure share indicator in the United States has been steadily since 39.7 people died as a result of the population remaining two percent of the United States .


Example 784:
data: Year|2018|x|line_chart Number_of_arrivals_in_thousands|1139.0|y|line_chart Year|2017|x|line_chart Number_of_arrivals_in_thousands|1156.0|y|line_chart Year|2016|x|line_chart Number_of_arrivals_in_thousands|1161.8|y|line_chart Year|2015|x|line_chart Number_of_arrivals_in_thousands|1196.1|y|line_chart Year|2014|x|line_chart Number_of_arrivals_in_thousands|1142.9|y|line_chart Year|2013|x|line_chart Number_of_arrivals_in_thousands|1044.3|y|line_chart Year|2012_|x|line_chart Number_of_arrivals_in_thousands|1021.7|y|line_chart Year|2011|x|line_chart Number_of_arrivals_in_thousands|935.0|y|line_chart Year|2010|x|line_chart Number_of_arrivals_in_thousands|854.72|y|line_chart Year|2009|x|line_chart Number_of_arrivals_in_thousands|907.53|y|line_chart Year|2008|x|line_chart Number_of_arrivals_in_thousands|936.65|y|line_chart Year|2007|x|line_chart Number_of_arrivals_in_thousands|979.21|y|line_chart Year|2006|x|line_chart Number_of_arrivals_in_thousands|967.88|y|line_chart 
title: Number of arrivals in tourist accommodation Luxembourg 2006 - 2018

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Luxembourg from 2006 to 2018 . There were around 1.1 million arrivals at accommodation establishments in Luxembourg in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitle[4] from templateXValue[last] to templateXValue[0] . There were around templateYValue[0] million templateYLabel[1] at templateTitle[3] establishments in templateTitle[4] in templateXValue[0] .

generated_template: There were templateYValue[0] templateTitle[1] templateTitle[2] at templateTitle[3] templateTitle[4] templateTitle[5] in the United States in templateXValue[max] , an increase of templateYValue[0] percent compared with the previous templateXLabel[0] . The company 's templateYLabel[0] templateYLabel[1] represents growth in templateTitle[3] templateTitle[4] Japan , reached its highest level , many of templateYLabel[1] at 100 percent in templateXValue[1] .
generated: There were 1139.0 arrivals tourist at accommodation Luxembourg 2006 in the United States in 2018 , an increase of 1139.0 percent compared with the previous Year . The company 's Number arrivals represents growth in accommodation Luxembourg Japan , reached its highest level , many of arrivals at 100 percent in 2017 .


Example 785:
data: Year|2018|x|line_chart Revenue_in_million_euros|35075.9|y|line_chart Year|2017|x|line_chart Revenue_in_million_euros|31263.26|y|line_chart Year|2016|x|line_chart Revenue_in_million_euros|28759.15|y|line_chart Year|2015|x|line_chart Revenue_in_million_euros|31418.69|y|line_chart Year|2014|x|line_chart Revenue_in_million_euros|30032.27|y|line_chart Year|2013|x|line_chart Revenue_in_million_euros|31077.11|y|line_chart Year|2012|x|line_chart Revenue_in_million_euros|34201.0|y|line_chart Year|2011|x|line_chart Revenue_in_million_euros|31648.0|y|line_chart Year|2010|x|line_chart Revenue_in_million_euros|30431.0|y|line_chart Year|2009|x|line_chart Revenue_in_million_euros|25891.9|y|line_chart 
title: Iberdrola - revenue 2009 - 2018

gold: This statistic represents Iberdrola 's global revenue between the fiscal year of 2009 and the fiscal year of 2018 . The Spain-based multinational electric utility company with headquarters in Bilbao generated around 35 billion euros in revenue in the fiscal year of 2018 .
gold_template: This statistic represents templateTitle[0] 's global templateYLabel[0] between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . The Spain-based multinational electric utility company with headquarters in Bilbao generated around templateYValue[max] billion templateYLabel[2] in templateYLabel[0] in the fiscal templateXLabel[0] of templateXValue[max] .

generated_template: templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] rose by templateYValue[6] percent in templateXValue[1] to templateYValue[1] percent in templateXValue[1] . The annual templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the last five years observed since templateXValue[6] , the templateYLabel[1] templateYLabel[2] in templateXValue[max] at templateYValue[0] percent . templateYLabel[0] growth of U.S. dollars in templateXValue[1] , which was expected to increase in templateXValue[6] .
generated: Iberdrola revenue 2009 2018 rose by 34201.0 percent in 2017 to 31263.26 percent in 2017 . The annual Revenue of million euros in the last five years observed since 2012 , the million euros in 2018 at 35075.9 percent . Revenue growth of U.S. dollars in 2017 , which was expected to increase in 2012 .


Example 786:
data: Month|Religion/Bible_says_it_is_wrong|x|bar_chart Share_of_respondents|47|y|bar_chart Month|Marriage_should_be_between_a_man_and_a_woman|x|bar_chart Share_of_respondents|20|y|bar_chart Month|Morally_wrong/Have_traditional_beliefs|x|bar_chart Share_of_respondents|16|y|bar_chart Month|Civil_unions_are_sufficient|x|bar_chart Share_of_respondents|6|y|bar_chart Month|Unnatural/Against_laws_of_nature|x|bar_chart Share_of_respondents|5|y|bar_chart Month|Undermines_traditional_family_structure/Mother_and_father|x|bar_chart Share_of_respondents|5|y|bar_chart Month|Other|x|bar_chart Share_of_respondents|7|y|bar_chart Month|No_opinion|x|bar_chart Share_of_respondents|4|y|bar_chart 
title: Reasons for opposing same-sex marriage in the United States in 2012

gold: This statistic shows the results of a 2012 survey among American adults opposing legal same-sex marriage . They were asked to give reasons for this decision . 47 percent of respondents stated that they oppose same-sex marriage because their religion and/or the Bible says it 's wrong .
gold_template: This statistic shows the results of a templateTitle[7] survey among American adults templateTitle[2] legal templateTitle[3] templateXValue[1] . They were asked to give templateTitle[0] templateTitle[1] this decision . templateYValue[max] percent of templateYLabel[1] stated that they oppose templateTitle[3] templateXValue[1] because their religion and/or the Bible templateXValue[0] it 's templateXValue[0] .

generated_template: The statistic shows the results of a survey conducted in the United States in February templateTitle[8] on whether templateTitle[5] templateTitle[6] templateTitle[7] templateXValue[1] templateXValue[0] templateTitle[2] the American President Donald templateTitle[3] . During the survey , it was found that templateYValue[max] percent of templateYLabel[1] aged between 16 and templateYValue[2] percent of American consumers templateXValue[6] templateXValue[0] templateXValue[0] templateXValue[0] .
generated: The statistic shows the results of a survey conducted in the United States in February titleErr on whether United States 2012 Marriage_should_be_between_a_man_and_a_woman Religion/Bible_says_it_is_wrong opposing the American President Donald same-sex . During the survey , it was found that 47 percent of respondents aged between 16 and 16 percent of American consumers Other Religion/Bible_says_it_is_wrong Religion/Bible_says_it_is_wrong .


Example 787:
data: Year|2018|x|line_chart Number_of_units_sold|487017|y|line_chart Year|2017|x|line_chart Number_of_units_sold|560415|y|line_chart Year|2016|x|line_chart Number_of_units_sold|547343|y|line_chart Year|2015|x|line_chart Number_of_units_sold|554046|y|line_chart Year|2014|x|line_chart Number_of_units_sold|477703|y|line_chart Year|2013|x|line_chart Number_of_units_sold|421134|y|line_chart Year|2012|x|line_chart Number_of_units_sold|436169|y|line_chart Year|2011|x|line_chart Number_of_units_sold|470004|y|line_chart Year|2010|x|line_chart Number_of_units_sold|411084|y|line_chart Year|2009|x|line_chart Number_of_units_sold|372096|y|line_chart Year|2008|x|line_chart Number_of_units_sold|338169|y|line_chart Year|2007|x|line_chart Number_of_units_sold|313437|y|line_chart Year|2006|x|line_chart Number_of_units_sold|332150|y|line_chart Year|2005|x|line_chart Number_of_units_sold|387325|y|line_chart Year|2004|x|line_chart Number_of_units_sold|409717|y|line_chart Year|2003|x|line_chart Number_of_units_sold|409511|y|line_chart 
title: Nissan car sales in Europe 2003 - 2018

gold: This statistic shows the number of cars sold by Nissan in Europe between 2003 and 2018 . European sales of the Nissan cars rose from 400 thousand units sold in 2003 to over 560 thousand units sold by 2017 . In 2018 , there were 487 thousand units of Nissan cars sold in Europe .
gold_template: This statistic shows the templateYLabel[0] of cars templateYLabel[2] by templateTitle[0] in templateTitle[3] between templateXValue[min] and templateXValue[max] . European templateTitle[2] of the templateTitle[0] cars rose from 400 thousand templateYLabel[1] templateYLabel[2] in templateXValue[min] to over templateYValue[max] thousand templateYLabel[1] templateYLabel[2] by templateXValue[1] . In templateXValue[max] , there were templateYValue[0] thousand templateYLabel[1] of templateTitle[0] cars templateYLabel[2] in templateTitle[3] .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] reached templateYValue[max] in templateTitle[5] and templateYValue[0] percent . The templateYLabel[0] templateYLabel[1] templateYLabel[2] in the source since templateXValue[6] , which was around templateYValue[6] percent compared to the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[1] , the annual templateYLabel[0] templateYLabel[1] templateYLabel[2] peaked in templateXValue[6] .
generated: In 2018 , the Number units sold of Europe 2003 reached 560415 in 2018 and 487017 percent . The Number units sold in the source since 2012 , which was around 436169 percent compared to the previous Year . The Number units sold in 2017 , the annual Number units sold peaked in 2012 .


Example 788:
data: Year|2018|x|line_chart Year-over-year_growth_in_average_hotel_rates|3.7|y|line_chart Year|2017|x|line_chart Year-over-year_growth_in_average_hotel_rates|2.5|y|line_chart Year|2016|x|line_chart Year-over-year_growth_in_average_hotel_rates|2.5|y|line_chart Year|2015|x|line_chart Year-over-year_growth_in_average_hotel_rates|2.6|y|line_chart Year|2014|x|line_chart Year-over-year_growth_in_average_hotel_rates|1.8|y|line_chart Year|2013|x|line_chart Year-over-year_growth_in_average_hotel_rates|0|y|line_chart Year|2012|x|line_chart Year-over-year_growth_in_average_hotel_rates|-1.5|y|line_chart Year|2011|x|line_chart Year-over-year_growth_in_average_hotel_rates|7.4|y|line_chart Year|2010|x|line_chart Year-over-year_growth_in_average_hotel_rates|4.7|y|line_chart 
title: Annual growth in average global hotel rates 2010 - 2018

gold: This statistic shows annual growth in average global hotel rates from 2010 to 2018 . Global hotel rates were forecasted to increase by 3.7 percent in 2018 . The average daily rate of the hotel industry in the Americas reached around 123.37 U.S. dollars in 2016 .
gold_template: This statistic shows templateTitle[0] templateYLabel[1] in templateYLabel[2] templateTitle[3] templateYLabel[3] templateYLabel[4] from templateXValue[min] to templateXValue[max] . templateTitle[3] templateYLabel[3] templateYLabel[4] were forecasted to increase by templateYValue[0] percent in templateXValue[max] . The templateYLabel[2] daily rate of the templateYLabel[3] industry in the Americas reached around 123.37 U.S. dollars in templateXValue[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] percent of the total templateYLabel[1] in the United States .
generated: This statistic shows the Year-over-year growth of global hotel rates 2010 in the United States from 2010 to 2018 . In 2018 , there were 3.7 percent of the total growth in the United States .


Example 789:
data: Country|Spain|x|bar_chart Number_of_Muslims|847801|y|bar_chart Country|Morocco|x|bar_chart Number_of_Muslims|769050|y|bar_chart Country|Pakistan|x|bar_chart Number_of_Muslims|82738|y|bar_chart Country|Senegal|x|bar_chart Number_of_Muslims|66046|y|bar_chart Country|Algeria|x|bar_chart Number_of_Muslims|60820|y|bar_chart Country|Nigeria|x|bar_chart Number_of_Muslims|39374|y|bar_chart Country|Mali|x|bar_chart Number_of_Muslims|23685|y|bar_chart Country|Gambia|x|bar_chart Number_of_Muslims|19381|y|bar_chart Country|Bangladesh|x|bar_chart Number_of_Muslims|15979|y|bar_chart Country|Guinea|x|bar_chart Number_of_Muslims|10186|y|bar_chart Country|Others|x|bar_chart Number_of_Muslims|58615|y|bar_chart 
title: Muslims in Spain 2018 , by nationality

gold: This statistic presents the number of Muslims in Spain in 2018 , broken down by nationality . That year , there were a total of approximately two million Muslims in Spain . Almost 848 thousand had Spanish nationality , followed by Muslims with a Moroccan nationality with figures that almost reached 770 thousand individuals .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] in templateXValue[0] in templateTitle[2] , broken down templateTitle[3] templateTitle[4] . That year , there were a total of approximately two million templateYLabel[1] in templateXValue[0] . Almost templateYValue[max] thousand had Spanish templateTitle[4] , followed templateTitle[3] templateYLabel[1] with a Moroccan templateTitle[4] with figures that almost reached 770 thousand individuals .

generated_template: The graph shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateYLabel[1] templateYLabel[2] in templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] as of September templateTitle[8] . templateYValue[max] percent of templateYLabel[1] templateYLabel[2] in templateXValue[0] had the United States .
generated: The graph shows the Muslims Spain 2018 of Muslims yLabelErr in nationality titleErr titleErr titleErr as of September titleErr . 847801 percent of Muslims yLabelErr in Spain had the United States .


Example 790:
data: Year|2018|x|line_chart Revenue_in_million_U.S._dollars|400|y|line_chart Year|2017|x|line_chart Revenue_in_million_U.S._dollars|383|y|line_chart Year|2016|x|line_chart Revenue_in_million_U.S._dollars|367|y|line_chart Year|2015|x|line_chart Revenue_in_million_U.S._dollars|341|y|line_chart Year|2014|x|line_chart Revenue_in_million_U.S._dollars|313|y|line_chart Year|2013|x|line_chart Revenue_in_million_U.S._dollars|275|y|line_chart Year|2012|x|line_chart Revenue_in_million_U.S._dollars|267|y|line_chart Year|2011|x|line_chart Revenue_in_million_U.S._dollars|258|y|line_chart Year|2010|x|line_chart Revenue_in_million_U.S._dollars|245|y|line_chart Year|2009|x|line_chart Revenue_in_million_U.S._dollars|246|y|line_chart Year|2008|x|line_chart Revenue_in_million_U.S._dollars|241|y|line_chart Year|2007|x|line_chart Revenue_in_million_U.S._dollars|224|y|line_chart Year|2006|x|line_chart Revenue_in_million_U.S._dollars|205|y|line_chart Year|2005|x|line_chart Revenue_in_million_U.S._dollars|203|y|line_chart Year|2004|x|line_chart Revenue_in_million_U.S._dollars|195|y|line_chart Year|2003|x|line_chart Revenue_in_million_U.S._dollars|175|y|line_chart Year|2002|x|line_chart Revenue_in_million_U.S._dollars|168|y|line_chart Year|2001|x|line_chart Revenue_in_million_U.S._dollars|151|y|line_chart 
title: Revenue of the Tampa Bay Buccaneers ( NFL ) 2001 - 2018

gold: The statistic depicts the revenue of the Tampa Bay Buccaneers , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Tampa Bay Buccaneers was 400 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] dollars.The templateTitle[0] templateTitle[1] templateTitle[2] are owned by William DeWittJr. , who bought the franchise for 150 templateYLabel[1] templateYLabel[2] templateYLabel[3] in 1996 .
generated: The statistic depicts the Revenue of the Revenue Tampa Bay from 2001 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 400 million U.S. dollars.The Revenue Tampa Bay are owned by William DeWittJr. , who bought the franchise for 150 million U.S. dollars in 1996 .


Example 791:
data: Year|2018|x|line_chart Population_growth_compared_to_previous_year|2.38|y|line_chart Year|2017|x|line_chart Population_growth_compared_to_previous_year|2.55|y|line_chart Year|2016|x|line_chart Population_growth_compared_to_previous_year|2.78|y|line_chart Year|2015|x|line_chart Population_growth_compared_to_previous_year|3.08|y|line_chart Year|2014|x|line_chart Population_growth_compared_to_previous_year|3.36|y|line_chart Year|2013|x|line_chart Population_growth_compared_to_previous_year|3.49|y|line_chart Year|2012|x|line_chart Population_growth_compared_to_previous_year|3.41|y|line_chart Year|2011|x|line_chart Population_growth_compared_to_previous_year|3.14|y|line_chart Year|2010|x|line_chart Population_growth_compared_to_previous_year|2.75|y|line_chart Year|2009|x|line_chart Population_growth_compared_to_previous_year|2.4|y|line_chart Year|2008|x|line_chart Population_growth_compared_to_previous_year|2.27|y|line_chart 
title: Population growth in Afghanistan 2018

gold: This timeline shows the population growth in Afghanistan from 2008 to 2018 . In 2018 , Afghanistan 's population grew by an estimated 2.38 percent compared to the previous year . See Afghanistan 's population figures for comparison .
gold_template: This timeline shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] 's templateYLabel[0] grew by an estimated templateYValue[0] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . See templateTitle[2] 's templateYLabel[0] figures for comparison .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] million people were living in templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States .
generated: This statistic presents the Population growth of Afghanistan 2018 titleErr titleErr in the United States from 2008 to 2018 . In 2018 , about 2.38 million people were living in Afghanistan 2018 titleErr titleErr in the United States .


Example 792:
data: Year|2023|x|line_chart Share_of_population|55|y|line_chart Year|2022|x|line_chart Share_of_population|54|y|line_chart Year|2021|x|line_chart Share_of_population|54|y|line_chart Year|2020|x|line_chart Share_of_population|53|y|line_chart Year|2019|x|line_chart Share_of_population|52|y|line_chart Year|2018|x|line_chart Share_of_population|51|y|line_chart Year|2017|x|line_chart Share_of_population|49|y|line_chart 
title: Philippines social media user penetration 2017 - 2023

gold: The social media penetration in the Philippines was at 49 percent in 2017 , amounting to about 54 million people using a social network in the Philippines as of 2018 . Considering that the number of internet users in the Philippines was at just under 70 million in that year , the social media penetration was projected to increase to 55 percent of the population by 2023 . Social media in the Philippines The Philippines are an archipelagic country , which poses logistical problems for social interaction and communication between residents from the various islands .
gold_template: The templateTitle[1] templateTitle[2] templateTitle[4] in the templateTitle[0] was at templateYValue[min] percent in templateXValue[min] , amounting to about templateYValue[1] million people using a templateTitle[1] network in the templateTitle[0] as of templateXValue[5] . Considering that the number of internet users in the templateTitle[0] was at just under 70 million in that templateXLabel[0] , the templateTitle[1] templateTitle[2] templateTitle[4] was projected to increase to templateYValue[max] percent of the templateYLabel[1] by templateXValue[max] . templateTitle[1] templateTitle[2] in the templateTitle[0] The templateTitle[0] are an archipelagic country , which poses logistical problems for templateTitle[1] interaction and communication between residents from the various islands .

generated_template: This statistic presents the templateTitle[0] networking reach in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[1] in the country accessed templateTitle[0] templateTitle[1] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] percent .
generated: This statistic presents the Philippines networking reach in penetration from 2017 to 2023 . In 2017 , 49 percent of the population in the country accessed Philippines social . In 2019 , this Share is projected to reach 52 percent .


Example 793:
data: Year|'18|x|line_chart Rate_per_100,000_population|376.0|y|line_chart Year|'17|x|line_chart Rate_per_100,000_population|429.7|y|line_chart Year|'16|x|line_chart Rate_per_100,000_population|468.9|y|line_chart Year|'15|x|line_chart Rate_per_100,000_population|494.7|y|line_chart Year|'14|x|line_chart Rate_per_100,000_population|537.2|y|line_chart Year|'13|x|line_chart Rate_per_100,000_population|610.5|y|line_chart Year|'12|x|line_chart Rate_per_100,000_population|672.2|y|line_chart Year|'11|x|line_chart Rate_per_100,000_population|701.3|y|line_chart Year|'10|x|line_chart Rate_per_100,000_population|701.0|y|line_chart Year|'09|x|line_chart Rate_per_100,000_population|717.7|y|line_chart Year|'08|x|line_chart Rate_per_100,000_population|733.0|y|line_chart Year|'07|x|line_chart Rate_per_100,000_population|726.1|y|line_chart Year|'06|x|line_chart Rate_per_100,000_population|733.1|y|line_chart Year|'05|x|line_chart Rate_per_100,000_population|726.9|y|line_chart Year|'04|x|line_chart Rate_per_100,000_population|730.3|y|line_chart Year|'03|x|line_chart Rate_per_100,000_population|741.0|y|line_chart Year|'02|x|line_chart Rate_per_100,000_population|747.0|y|line_chart Year|'01|x|line_chart Rate_per_100,000_population|740.8|y|line_chart Year|'00|x|line_chart Rate_per_100,000_population|728.8|y|line_chart Year|'99|x|line_chart Rate_per_100,000_population|770.4|y|line_chart Year|'98|x|line_chart Rate_per_100,000_population|863.0|y|line_chart Year|'97|x|line_chart Rate_per_100,000_population|919.6|y|line_chart Year|'96|x|line_chart Rate_per_100,000_population|944.8|y|line_chart Year|'95|x|line_chart Rate_per_100,000_population|987.1|y|line_chart Year|'94|x|line_chart Rate_per_100,000_population|1042.0|y|line_chart Year|'93|x|line_chart Rate_per_100,000_population|1099.2|y|line_chart Year|'92|x|line_chart Rate_per_100,000_population|1168.2|y|line_chart Year|'91|x|line_chart Rate_per_100,000_population|1252.0|y|line_chart Year|'90|x|line_chart Rate_per_100,000_population|1235.9|y|line_chart 
title: USA - reported burglary rate 1990 - 2018

gold: This graph shows the reported burglary rate in the United States from 1990 to 2018 . In 2018 , the nationwide rate was 376 cases per 100,000 of the population .
gold_template: This graph shows the templateTitle[1] templateTitle[2] templateYLabel[0] in the United States from templateTitle[4] to templateTitle[5] . In templateTitle[5] , the nationwide templateYLabel[0] was templateYValue[min] cases templateYLabel[1] 100,000 of the templateYLabel[3] .

generated_template: This graph shows the templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] in the United States from templateTitle[4] to templateTitle[5] . In templateTitle[5] , the nationwide templateYLabel[0] was templateYValue[0] cases templateYLabel[1] 100,000 of the templateYLabel[3] .
generated: This graph shows the reported burglary rate Rate in the United States from 1990 to 2018 . In 2018 , the nationwide Rate was 376.0 cases per 100,000 of the population .


Example 794:
data: Year|2018|x|line_chart Price_per_pound_in_U.S._cents|86.85|y|line_chart Year|2017|x|line_chart Price_per_pound_in_U.S._cents|84.48|y|line_chart Year|2016|x|line_chart Price_per_pound_in_U.S._cents|64.7|y|line_chart Year|2015|x|line_chart Price_per_pound_in_U.S._cents|61.49|y|line_chart Year|2014|x|line_chart Price_per_pound_in_U.S._cents|74.9|y|line_chart Year|2013|x|line_chart Price_per_pound_in_U.S._cents|77.23|y|line_chart Year|2012|x|line_chart Price_per_pound_in_U.S._cents|79.5|y|line_chart Year|2011|x|line_chart Price_per_pound_in_U.S._cents|88.02|y|line_chart Year|2010|x|line_chart Price_per_pound_in_U.S._cents|70.95|y|line_chart Year|2009|x|line_chart Price_per_pound_in_U.S._cents|49.15|y|line_chart Year|2008|x|line_chart Price_per_pound_in_U.S._cents|60.79|y|line_chart Year|2007|x|line_chart Price_per_pound_in_U.S._cents|49.96|y|line_chart Year|2006|x|line_chart Price_per_pound_in_U.S._cents|47.53|y|line_chart Year|2005|x|line_chart Price_per_pound_in_U.S._cents|42.69|y|line_chart Year|2004|x|line_chart Price_per_pound_in_U.S._cents|54.3|y|line_chart Year|2003|x|line_chart Price_per_pound_in_U.S._cents|51.65|y|line_chart Year|2002|x|line_chart Price_per_pound_in_U.S._cents|33.63|y|line_chart Year|2001|x|line_chart Price_per_pound_in_U.S._cents|38.86|y|line_chart Year|2000|x|line_chart Price_per_pound_in_U.S._cents|49.81|y|line_chart Year|1995|x|line_chart Price_per_pound_in_U.S._cents|77.21|y|line_chart Year|1990|x|line_chart Price_per_pound_in_U.S._cents|64.83|y|line_chart 
title: Cotton price received by U.S. farmers 1990 - 2018

gold: This statistic shows the average cotton price per pound as received by U.S. farmers from 1990 to 2018 . In the 1990 calendar year , a U.S. cotton farmer received an average price of 64.83 cents per one pound of upland cotton .
gold_template: This statistic shows the average templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] as templateTitle[2] templateTitle[3] templateYLabel[3] templateTitle[5] from templateXValue[min] to templateXValue[max] . In the templateXValue[min] calendar templateXLabel[0] , a templateYLabel[3] templateTitle[0] farmer templateTitle[2] an average templateYLabel[0] of templateYValue[20] templateYLabel[4] templateYLabel[1] one templateYLabel[2] of upland templateTitle[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In calendar templateXLabel[0] templateXValue[1] , the average templateTitle[1] templateYLabel[0] templateYLabel[1] one templateYLabel[2] of templateTitle[0] templateTitle[2] was about templateYValue[1] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Cotton price received Price per pound in the United States from 1990 to 2018 . In calendar Year 2017 , the average price Price per one pound of Cotton received was about 84.48 U.S. cents .


Example 795:
data: Year|2018|x|line_chart Number_of_deaths|474523|y|line_chart Year|2017|x|line_chart Number_of_deaths|424523|y|line_chart Year|2016|x|line_chart Number_of_deaths|410611|y|line_chart Year|2015|x|line_chart Number_of_deaths|422568|y|line_chart Year|2014|x|line_chart Number_of_deaths|395830|y|line_chart Year|2013|x|line_chart Number_of_deaths|390419|y|line_chart Year|2012|x|line_chart Number_of_deaths|402950|y|line_chart Year|2011|x|line_chart Number_of_deaths|387911|y|line_chart Year|2010|x|line_chart Number_of_deaths|382047|y|line_chart Year|2009|x|line_chart Number_of_deaths|384933|y|line_chart Year|2008|x|line_chart Number_of_deaths|386324|y|line_chart Year|2007|x|line_chart Number_of_deaths|385361|y|line_chart Year|2006|x|line_chart Number_of_deaths|371478|y|line_chart 
title: Number of deaths in Spain 2006 - 2018

gold: According to data provided by the Spanish Statistics Institute , more people died in Spain than were being born in 2018 , with figures reaching over 427 thousand deaths versus 370 thousand newborns . The number of deaths experienced an upward trend over the 11-year period , presumably due to Spain 's aging population . Circulatory system diseases and cancer ranked as the most common causes of death in Spain The cause of death can vary significantly across the globe and depends highly on economic development , presence of a competent healthcare system and one 's choices in lifestyle .
gold_template: According to data provided by the Spanish Statistics Institute , more people died in templateTitle[2] than were being born in templateXValue[max] , with figures reaching over 427 thousand templateYLabel[1] versus 370 thousand newborns . The templateYLabel[0] of templateYLabel[1] experienced an upward trend over the 11-year period , presumably due to templateTitle[2] 's aging population . Circulatory system diseases and cancer ranked as the most common causes of death in templateTitle[2] The cause of death can vary significantly across the globe and depends highly on economic development , presence of a competent healthcare system and one 's choices in lifestyle .

generated_template: templateYLabel[1] related to templateTitle[2] templateTitle[3] has been declining in recent years , reaching templateYValue[max] in the templateXLabel[0] . In templateXValue[1] , the highest amount of recorded templateYLabel[1] increased in the last ten years . The templateYLabel[0] of templateYLabel[1] – additional information In templateXValue[1] , the annual templateYLabel[0] of templateYLabel[1] gradually increased annually , in templateTitle[3] .
generated: deaths related to Spain 2006 has been declining in recent years , reaching 474523 in the Year . In 2017 , the highest amount of recorded deaths increased in the last ten years . The Number of deaths – additional information In 2017 , the annual Number of deaths gradually increased annually , in 2006 .


Example 796:
data: Year|2014_Sochi|x|line_chart Number_of_participants|2800|y|line_chart Year|2010_Vancouver|x|line_chart Number_of_participants|2536|y|line_chart Year|2006_Torino|x|line_chart Number_of_participants|2494|y|line_chart Year|2002_Salt_Lake_City|x|line_chart Number_of_participants|2402|y|line_chart Year|1998_Nagano|x|line_chart Number_of_participants|2180|y|line_chart Year|1994_Lillehammer|x|line_chart Number_of_participants|1738|y|line_chart Year|1992_Albertville|x|line_chart Number_of_participants|1801|y|line_chart Year|1988_Calgary|x|line_chart Number_of_participants|1424|y|line_chart Year|1984_Sarajevo|x|line_chart Number_of_participants|1273|y|line_chart Year|1980_Lake_Placid|x|line_chart Number_of_participants|1072|y|line_chart Year|1976_Innsbruck|x|line_chart Number_of_participants|1129|y|line_chart Year|1972_Sapporo|x|line_chart Number_of_participants|1008|y|line_chart Year|1968_Grenoble|x|line_chart Number_of_participants|1160|y|line_chart Year|1964_Innsbruck|x|line_chart Number_of_participants|1094|y|line_chart Year|1960_Squaw_Valley|x|line_chart Number_of_participants|665|y|line_chart Year|1956_Cortina_d'Ampezzo|x|line_chart Number_of_participants|821|y|line_chart Year|1952_Oslo|x|line_chart Number_of_participants|694|y|line_chart Year|1948_St._Moritz|x|line_chart Number_of_participants|668|y|line_chart Year|1936_Garmisch-Partenkirchen|x|line_chart Number_of_participants|668|y|line_chart Year|1932_Lake_Placid|x|line_chart Number_of_participants|252|y|line_chart Year|1928_St._Moritz|x|line_chart Number_of_participants|461|y|line_chart Year|1924_Chamonix|x|line_chart Number_of_participants|292|y|line_chart 
title: Number of participants Winter Olympic Games 2014

gold: The statistic shows the number of participants in the Winter Olympic Games from 1924 to 2014 . At the first Olympic Winter Games in Chamonix in 1924 , 292 athletes participated . This figure grew to 2,536 participating athletes from 82 nations during the 2010 Vancouver Winter Olympics .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[last] to templateXValue[0] . At the first templateTitle[3] templateTitle[2] templateTitle[4] in templateXValue[last] in templateXValue[last] , templateYValue[21] athletes participated . This figure grew to templateYValue[1] participating athletes from 82 nations during the templateXValue[1] templateXValue[1] templateTitle[2] Olympics .

generated_template: In an annual survey by Sport templateTitle[2] , the sports governing body for templateTitle[2] , about the templateYLabel[0] of people who play templateTitle[0] , it was found that as of templateXValue[max] , roughly templateYValue[0] people play templateTitle[0] at least twice a month , considering any intensity and duration . The survey was conducted online and the latest update on it was conducted for the time period between May templateXValue[1] and May May templateXValue[0] , having children play templateTitle[0] fans who play templateTitle[0] AssociationTennis in templateTitle[2] , figures for the second , with an annual survey conducted among the Isle of Man , is governed by the Lawn templateTitle[0] Association ( LTA ) .
generated: In an annual survey by Sport Winter , the sports governing body for Winter , about the Number of people who play Number , it was found that as of 2014_Sochi , roughly 2800 people play Number at least twice a month , considering any intensity and duration . The survey was conducted online and the latest update on it was conducted for the time period between May 2010_Vancouver and May 2014_Sochi , having children play Number fans who play Number AssociationTennis in Winter , figures for the second , with an annual survey conducted among the Isle of Man , is governed by the Lawn Number Association ( LTA ) .


Example 797:
data: Year|2018|x|line_chart Real_GDP_in_billion_U.S._dollars|2677.94|y|line_chart Year|2017|x|line_chart Real_GDP_in_billion_U.S._dollars|2587.57|y|line_chart Year|2016|x|line_chart Real_GDP_in_billion_U.S._dollars|2498.84|y|line_chart Year|2015|x|line_chart Real_GDP_in_billion_U.S._dollars|2426.14|y|line_chart Year|2014|x|line_chart Real_GDP_in_billion_U.S._dollars|2309.93|y|line_chart Year|2013|x|line_chart Real_GDP_in_billion_U.S._dollars|2220.87|y|line_chart Year|2012|x|line_chart Real_GDP_in_billion_U.S._dollars|2144.5|y|line_chart Year|2011|x|line_chart Real_GDP_in_billion_U.S._dollars|2091.59|y|line_chart Year|2010|x|line_chart Real_GDP_in_billion_U.S._dollars|2058.14|y|line_chart Year|2009|x|line_chart Real_GDP_in_billion_U.S._dollars|2026.49|y|line_chart Year|2008|x|line_chart Real_GDP_in_billion_U.S._dollars|2111.14|y|line_chart Year|2007|x|line_chart Real_GDP_in_billion_U.S._dollars|2103.62|y|line_chart Year|2006|x|line_chart Real_GDP_in_billion_U.S._dollars|2072.18|y|line_chart Year|2005|x|line_chart Real_GDP_in_billion_U.S._dollars|1990.14|y|line_chart Year|2004|x|line_chart Real_GDP_in_billion_U.S._dollars|1902.32|y|line_chart Year|2003|x|line_chart Real_GDP_in_billion_U.S._dollars|1825.42|y|line_chart Year|2002|x|line_chart Real_GDP_in_billion_U.S._dollars|1743.65|y|line_chart Year|2001|x|line_chart Real_GDP_in_billion_U.S._dollars|1702.78|y|line_chart Year|2000|x|line_chart Real_GDP_in_billion_U.S._dollars|1709.94|y|line_chart 
title: California - real GDP 2000 - 2018

gold: This statistic shows the development of California 's real GDP from 2000 to 2018 . In 2018 , the real GDP of California was 2.67 trillion U.S. dollars .
gold_template: This statistic shows the development of templateTitle[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[0] was 2.67 trillion templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitle[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The annual templateYLabel[0] templateYLabel[1] growth of the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the development of California 's Real GDP from 2000 to 2018 . In 2018 , the Real GDP of 2000 was about 2677.94 billion U.S. dollars . The annual Real GDP growth of the U.S. Year .


Example 798:
data: Year|2024|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1988.11|y|line_chart Year|2023|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1884.06|y|line_chart Year|2022|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1789.96|y|line_chart Year|2021|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1702.14|y|line_chart Year|2020|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1626.55|y|line_chart Year|2019|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1629.53|y|line_chart Year|2018|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1720.49|y|line_chart Year|2017|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1623.9|y|line_chart Year|2016|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1500.48|y|line_chart Year|2015|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1465.77|y|line_chart Year|2014|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1484.32|y|line_chart Year|2013|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1305.61|y|line_chart Year|2012|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1222.81|y|line_chart Year|2011|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1202.46|y|line_chart Year|2010|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1094.5|y|line_chart Year|2009|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|901.94|y|line_chart Year|2008|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1002.22|y|line_chart Year|2007|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1122.68|y|line_chart Year|2006|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|1011.8|y|line_chart Year|2005|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|898.14|y|line_chart Year|2004|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|764.88|y|line_chart Year|2003|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|680.52|y|line_chart Year|2002|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|609.02|y|line_chart Year|2001|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|533.05|y|line_chart Year|2000|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|561.63|y|line_chart Year|1999|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|485.25|y|line_chart Year|1998|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|374.24|y|line_chart Year|1997|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|557.5|y|line_chart Year|1996|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|598.1|y|line_chart Year|1995|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|556.13|y|line_chart Year|1994|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|455.61|y|line_chart Year|1993|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|386.3|y|line_chart Year|1992|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|350.05|y|line_chart Year|1991|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|325.73|y|line_chart Year|1990|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|279.35|y|line_chart Year|1989|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|243.53|y|line_chart Year|1988|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|196.97|y|line_chart Year|1987|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|146.13|y|line_chart Year|1986|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|115.54|y|line_chart Year|1985|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|100.27|y|line_chart Year|1984|x|line_chart Gross_domestic_product_in_billion_U.S._dollars|96.6|y|line_chart 
title: Gross domestic product ( GDP ) in South Korea 2024

gold: The statistic shows gross domestic product ( GDP ) of South Korea from 1984 to 2018 , with projections up until 2024 . GDP or gross domestic product is the sum of all goods and services produced in a country in a year ; it is a strong indicator of economic strength . In 2018 , South Korea 's GDP was around 1.72 trillion U.S. dollars .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) of templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateTitle[3] or templateYLabel[0] templateYLabel[1] templateYLabel[2] is the sum of all goods and services produced in a country in a templateXLabel[0] ; it is a strong indicator of economic strength . In templateXValue[6] , templateTitle[4] templateTitle[5] 's templateTitle[3] was around templateYValue[6] trillion templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in South Korea from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year . GDP is an important indicator of a country 's economic power .


Example 799:
data: Year|2018|x|line_chart Imports_in_thousand_barrels_per_day|521|y|line_chart Year|2017|x|line_chart Imports_in_thousand_barrels_per_day|604|y|line_chart Year|2016|x|line_chart Imports_in_thousand_barrels_per_day|424|y|line_chart Year|2015|x|line_chart Imports_in_thousand_barrels_per_day|229|y|line_chart Year|2014|x|line_chart Imports_in_thousand_barrels_per_day|369|y|line_chart Year|2013|x|line_chart Imports_in_thousand_barrels_per_day|341|y|line_chart Year|2012|x|line_chart Imports_in_thousand_barrels_per_day|476|y|line_chart Year|2011|x|line_chart Imports_in_thousand_barrels_per_day|459|y|line_chart Year|2010|x|line_chart Imports_in_thousand_barrels_per_day|415|y|line_chart Year|2009|x|line_chart Imports_in_thousand_barrels_per_day|450|y|line_chart Year|2008|x|line_chart Imports_in_thousand_barrels_per_day|627|y|line_chart Year|2007|x|line_chart Imports_in_thousand_barrels_per_day|484|y|line_chart Year|2006|x|line_chart Imports_in_thousand_barrels_per_day|553|y|line_chart Year|2005|x|line_chart Imports_in_thousand_barrels_per_day|531|y|line_chart Year|2004|x|line_chart Imports_in_thousand_barrels_per_day|656|y|line_chart Year|2003|x|line_chart Imports_in_thousand_barrels_per_day|481|y|line_chart Year|2002|x|line_chart Imports_in_thousand_barrels_per_day|459|y|line_chart Year|2001|x|line_chart Imports_in_thousand_barrels_per_day|795|y|line_chart Year|2000|x|line_chart Imports_in_thousand_barrels_per_day|620|y|line_chart 
title: U.S. petroleum imports from Iraq 2000 - 2018

gold: This statistic represents U.S. petroleum imports from Iraq between 2000 and 2018 . In 2018 , the United States imported an average of approximately 521,000 barrels of petroleum per day from the Middle Eastern country .
gold_template: This statistic represents templateTitle[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateTitle[4] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateTitle[0] imported an average of approximately templateYValue[0] templateYLabel[2] of templateTitle[1] templateYLabel[3] templateYLabel[4] templateTitle[3] the Middle Eastern country .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] people living in the templateTitle[4] templateTitle[5] in the United States .
generated: This statistic shows the Imports thousand of imports from Iraq 2000 in the United States from 2000 to 2018 . In 2018 , there were 521 people living in the Iraq 2000 in the United States .


Example 800:
data: Year|2019|x|line_chart Net_revenue_in_million_U.S._dollars|1080.0|y|line_chart Year|2018|x|line_chart Net_revenue_in_million_U.S._dollars|924.9|y|line_chart Year|2017|x|line_chart Net_revenue_in_million_U.S._dollars|771.5|y|line_chart Year|2016|x|line_chart Net_revenue_in_million_U.S._dollars|727.5|y|line_chart Year|2015|x|line_chart Net_revenue_in_million_U.S._dollars|726.0|y|line_chart 
title: Cree 's revenue 2015 - 2019

gold: This statistic represents Cree 's revenue from the fiscal year of 2015 to the fiscal year of 2019 . In the fiscal year of 2019 , the LED technology company reported revenue of about 1.08 billion U.S. dollars .
gold_template: This statistic represents templateTitle[0] templateTitle[1] templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the LED technology company reported templateYLabel[1] of about templateYValue[max] billion templateYLabel[3] templateYLabel[4] .

generated_template: This graph shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] billion templateYLabel[3] templateYLabel[4] . The templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[2] templateYLabel[1] are expected to reach 50 billion U.S. dollars in annual report .
generated: This graph shows the Cree 's revenue Net revenue of 2019 in the United States from 2015 to 2019 . In 2019 , Cree 's revenue Net revenue amounted to 1080.0 billion U.S. dollars . The Cree 's revenue Net revenue revenue are expected to reach 50 billion U.S. dollars in annual report .


Example 801:
data: Year|2018|x|line_chart Population_growth_compared_to_previous_year|1.35|y|line_chart Year|2017|x|line_chart Population_growth_compared_to_previous_year|1.36|y|line_chart Year|2016|x|line_chart Population_growth_compared_to_previous_year|1.36|y|line_chart Year|2015|x|line_chart Population_growth_compared_to_previous_year|1.34|y|line_chart Year|2014|x|line_chart Population_growth_compared_to_previous_year|1.34|y|line_chart Year|2013|x|line_chart Population_growth_compared_to_previous_year|1.37|y|line_chart Year|2012|x|line_chart Population_growth_compared_to_previous_year|1.45|y|line_chart Year|2011|x|line_chart Population_growth_compared_to_previous_year|1.56|y|line_chart Year|2010|x|line_chart Population_growth_compared_to_previous_year|1.69|y|line_chart Year|2009|x|line_chart Population_growth_compared_to_previous_year|1.82|y|line_chart Year|2008|x|line_chart Population_growth_compared_to_previous_year|1.91|y|line_chart 
title: Population growth in Malaysia 2018

gold: This statistic shows the population growth in Malaysia from 2008 to 2018 . In 2018 , Malaysia 's population increased by approximately 1.35 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] 's templateYLabel[0] increased by approximately templateYValue[0] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[max] percent of the templateYLabel[1] templateYLabel[2] lived in cities .
generated: The statistic shows the Population growth compared in 2018 titleErr titleErr from 2008 to 2018 . In 2018 , about 1.91 percent of the growth compared lived in cities .


Example 802:
data: Year|2024|x|line_chart Inhabitants_in_millions|30.36|y|line_chart Year|2023|x|line_chart Inhabitants_in_millions|29.97|y|line_chart Year|2022|x|line_chart Inhabitants_in_millions|29.59|y|line_chart Year|2021|x|line_chart Inhabitants_in_millions|29.2|y|line_chart Year|2020|x|line_chart Inhabitants_in_millions|28.83|y|line_chart Year|2019|x|line_chart Inhabitants_in_millions|28.46|y|line_chart Year|2018|x|line_chart Inhabitants_in_millions|28.09|y|line_chart Year|2017|x|line_chart Inhabitants_in_millions|27.63|y|line_chart Year|2016|x|line_chart Inhabitants_in_millions|27.26|y|line_chart Year|2015|x|line_chart Inhabitants_in_millions|27.02|y|line_chart Year|2014|x|line_chart Inhabitants_in_millions|26.91|y|line_chart 
title: Total population of Nepal 2024

gold: This statistic represents the total population of Nepal from 2014 to 2015 , with projections up until 2024 . In 2018 , the estimated total population of Nepal amounted to around 28.09 million people .
gold_template: This statistic represents the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[9] , with projections up until templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitle[2] amounted to around templateYValue[6] million people .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] amounted to approximately templateYValue[6] million templateYLabel[0] . templateTitle[1] of templateTitle[2] Although templateTitle[0] templateTitle[1] increased on a yearly basis from 2004 to templateXValue[min] , templateTitle[1] growth has slowly decreased annually as of 2011 , despite remaining positive .
generated: The statistic shows the Total population of Nepal from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Nepal amounted to approximately 28.09 million Inhabitants . population of Nepal Although Total population increased on a yearly basis from 2004 to 2014 , population growth has slowly decreased annually as of 2011 , despite remaining positive .


Example 803:
data: Response|Google_(Gmail)|x|bar_chart Share_of_respondents|53|y|bar_chart Response|Yahoo|x|bar_chart Share_of_respondents|18|y|bar_chart Response|Outlook_(Hotmail)|x|bar_chart Share_of_respondents|14|y|bar_chart Response|AOL|x|bar_chart Share_of_respondents|8|y|bar_chart Response|Other|x|bar_chart Share_of_respondents|4|y|bar_chart Response|iCloud|x|bar_chart Share_of_respondents|2|y|bar_chart Response|Comcast|x|bar_chart Share_of_respondents|1|y|bar_chart 
title: Leading U.S. consumer e-mail providers 2016

gold: This statistic shows the most popular e-mail providers according to consumers in the United States as of 2016 . During the consumer survey , 53 percent of respondents stated that they used Gmail as their primary e-mail provider . Yahoo was ranked second with 18 percent .
gold_template: This statistic shows the most popular templateTitle[3] templateTitle[4] according to consumers in the templateTitle[1] as of templateTitle[5] . During the templateTitle[2] survey , templateYValue[max] percent of templateYLabel[1] stated that they used Gmail as their primary templateTitle[3] provider . templateXValue[1] was ranked second with templateYValue[1] percent .

generated_template: templateXValue[0] templateXValue[0] was the templateTitle[0] popular templateTitle[2] of templateTitle[3] templateTitle[4] in the United States in templateTitle[5] . During a survey , templateYValue[max] percent of templateYLabel[1] stated that they would be the list of the templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] to second and templateYValue[1] percent of templateYLabel[1] claimed to women in a templateXValue[1] templateXValue[1] .
generated: Google_(Gmail) was the Leading popular consumer of e-mail providers in the United States in 2016 . During a survey , 53 percent of respondents stated that they would be the list of the Yahoo Yahoo Yahoo to second and 18 percent of respondents claimed to women in a Yahoo .


Example 804:
data: Country|China|x|bar_chart Price_in_U.S._dollars_per_ton|17400|y|bar_chart Country|United_States|x|bar_chart Price_in_U.S._dollars_per_ton|8800|y|bar_chart Country|Russia|x|bar_chart Price_in_U.S._dollars_per_ton|7100|y|bar_chart Country|Saudi_Arabia|x|bar_chart Price_in_U.S._dollars_per_ton|6600|y|bar_chart Country|Canada|x|bar_chart Price_in_U.S._dollars_per_ton|5300|y|bar_chart Country|Other|x|bar_chart Price_in_U.S._dollars_per_ton|3900|y|bar_chart Country|Kazakhstan|x|bar_chart Price_in_U.S._dollars_per_ton|3600|y|bar_chart Country|United_Arab_Emirates|x|bar_chart Price_in_U.S._dollars_per_ton|3400|y|bar_chart Country|India|x|bar_chart Price_in_U.S._dollars_per_ton|3400|y|bar_chart Country|Japan|x|bar_chart Price_in_U.S._dollars_per_ton|3400|y|bar_chart Country|South_Korea|x|bar_chart Price_in_U.S._dollars_per_ton|3100|y|bar_chart Country|Iran|x|bar_chart Price_in_U.S._dollars_per_ton|2200|y|bar_chart Country|Qatar|x|bar_chart Price_in_U.S._dollars_per_ton|2100|y|bar_chart Country|Chile|x|bar_chart Price_in_U.S._dollars_per_ton|1500|y|bar_chart Country|Poland|x|bar_chart Price_in_U.S._dollars_per_ton|1230|y|bar_chart Country|Finland|x|bar_chart Price_in_U.S._dollars_per_ton|940|y|bar_chart Country|Kuwait|x|bar_chart Price_in_U.S._dollars_per_ton|900|y|bar_chart Country|Australia|x|bar_chart Price_in_U.S._dollars_per_ton|900|y|bar_chart Country|Germany|x|bar_chart Price_in_U.S._dollars_per_ton|870|y|bar_chart Country|Venezuela|x|bar_chart Price_in_U.S._dollars_per_ton|700|y|bar_chart Country|Italy|x|bar_chart Price_in_U.S._dollars_per_ton|550|y|bar_chart Country|Netherlands|x|bar_chart Price_in_U.S._dollars_per_ton|520|y|bar_chart Country|Brazil|x|bar_chart Price_in_U.S._dollars_per_ton|500|y|bar_chart 
title: Global sulfur production by country 2019

gold: In 2019 , China produced around 17.4 megatons of sulfur , which makes China the world 's leading sulfur producer . China 's sulfur production includes byproduct elemental sulfur recovered from natural gas and petroleum , the estimated sulfur content of byproduct sulfuric acid from metallurgy , and the sulfur content of sulfuric acid from pyrite .
gold_template: In templateTitle[5] , templateXValue[0] produced around 17.4 megatons of templateTitle[1] , which makes templateXValue[0] the world 's leading templateTitle[1] producer . templateXValue[0] 's templateTitle[1] templateTitle[2] includes byproduct elemental templateTitle[1] recovered from natural gas and petroleum , the estimated templateTitle[1] content of byproduct sulfuric acid from metallurgy , and the templateTitle[1] content of sulfuric acid from pyrite .

generated_template: In templateTitle[4] , templateXValue[0] was the largest templateTitle[1] producer of templateTitle[3] with an templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[max] templateYLabel[4] , followed by templateXValue[1] at templateYValue[1] templateYLabel[3] in templateXValue[2] templateXValue[2] . The global templateYLabel[1] templateYLabel[2] rates are about 100 million people living in comparison .
generated: In country , China was the largest sulfur producer of by with an Price U.S. dollars of 17400 ton , followed by United_States at 8800 per in Russia . The global U.S. dollars rates are about 100 million people living in comparison .


Example 805:
data: Year|2018/19|x|line_chart Revenue_in_million_U.S._dollars|149|y|line_chart Year|2017/18|x|line_chart Revenue_in_million_U.S._dollars|142|y|line_chart Year|2016/17|x|line_chart Revenue_in_million_U.S._dollars|139|y|line_chart Year|2015/16|x|line_chart Revenue_in_million_U.S._dollars|136|y|line_chart Year|2014/15|x|line_chart Revenue_in_million_U.S._dollars|125|y|line_chart Year|2013/14|x|line_chart Revenue_in_million_U.S._dollars|111|y|line_chart Year|2012/13|x|line_chart Revenue_in_million_U.S._dollars|81|y|line_chart Year|2011/12|x|line_chart Revenue_in_million_U.S._dollars|99|y|line_chart Year|2010/11|x|line_chart Revenue_in_million_U.S._dollars|97|y|line_chart Year|2009/10|x|line_chart Revenue_in_million_U.S._dollars|92|y|line_chart Year|2008/09|x|line_chart Revenue_in_million_U.S._dollars|95|y|line_chart Year|2007/08|x|line_chart Revenue_in_million_U.S._dollars|94|y|line_chart Year|2006/07|x|line_chart Revenue_in_million_U.S._dollars|78|y|line_chart Year|2005/06|x|line_chart Revenue_in_million_U.S._dollars|71|y|line_chart 
title: Minnesota Wilds ' revenue 2005 - 2019

gold: This graph depicts the annual National Hockey League revenue of the Minnesota Wild from the 2005/06 season to the 2018/19 season . The revenue of the Minnesota Wild amounted to 149 million U.S. dollars in the 2018/19 season .
gold_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitle[0] Wild from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitle[0] Wild amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] franchise from the templateXValue[last] season to the templateXValue[0] season . In templateXValue[0] , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Minnesota Wilds franchise from the 2005/06 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 149 million U.S. dollars .


Example 806:
data: Year|2018|x|line_chart Volume_in_billion_U.S._dollars|517|y|line_chart Year|2017|x|line_chart Volume_in_billion_U.S._dollars|550|y|line_chart Year|2016|x|line_chart Volume_in_billion_U.S._dollars|325|y|line_chart Year|2015|x|line_chart Volume_in_billion_U.S._dollars|333|y|line_chart Year|2014|x|line_chart Volume_in_billion_U.S._dollars|393|y|line_chart Year|2010|x|line_chart Volume_in_billion_U.S._dollars|126|y|line_chart Year|2005|x|line_chart Volume_in_billion_U.S._dollars|474|y|line_chart Year|2000|x|line_chart Volume_in_billion_U.S._dollars|240|y|line_chart 
title: New issue volume of U.S. asset-backed securities 2000 - 2018

gold: This statistic presents the new issue volume of the asset-backed securities of the United States from 2000 to 2018 . In 2018 , the new issue volume of the asset-backed securities of the United States was 517 billion U.S. dollars .
gold_template: This statistic presents the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[4] templateTitle[5] of the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[4] templateTitle[5] of the templateTitle[3] was templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitle[5] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States was at templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the New Volume of volume U.S. asset-backed securities in the United States from 2000 to 2018 . In 2018 , the Volume of securities volume U.S. asset-backed securities in the United States was at 126 billion U.S. dollars .


Example 807:
data: Geographical_area|North_America|x|bar_chart Share_of_net_sales|58|y|bar_chart Geographical_area|Europe|x|bar_chart Share_of_net_sales|21|y|bar_chart Geographical_area|Asia-Pacific|x|bar_chart Share_of_net_sales|13|y|bar_chart Geographical_area|Latin_America|x|bar_chart Share_of_net_sales|6|y|bar_chart Geographical_area|Rest_of_the_world|x|bar_chart Share_of_net_sales|2|y|bar_chart 
title: Share of global net sales of Luxottica by geographical area 2018

gold: This statistic depicts the share of net sales of Luxottica worldwide in 2018 , by geographical area . In that year , 58 percent of Luxottica 's global net sales came from North America . Founded in 1961 in Agordo , Italy , the Luxottica Group S.p.A. is the world 's largest eyewear company .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[1] templateYLabel[2] of templateTitle[4] worldwide in templateTitle[8] , templateTitle[5] templateXLabel[0] templateXLabel[1] . In that year , templateYValue[max] percent of templateTitle[4] 's templateTitle[1] templateYLabel[1] templateYLabel[2] came from templateXValue[0] templateXValue[0] . Founded in 1961 in Agordo , Italy , the templateTitle[4] Group S.p.A. is the templateXValue[last] 's largest eyewear company .

generated_template: This statistic shows the templateYLabel[0] of people employed in the United States as of March templateTitle[5] , templateYLabel[2] templateTitle[6] templateXLabel[0] templateXLabel[1] . During the survey , it was found that templateYValue[2] percent of templateYLabel[1] had a templateXValue[0] templateXValue[0] throughout templateYValue[max] percent .
generated: This statistic shows the Share of people employed in the United States as of March by , sales geographical Geographical area . During the survey , it was found that 13 percent of net had a North_America throughout 58 percent .


Example 808:
data: Year|2017|x|line_chart Retail_sales_in_million_U.S._dollars|9732.2|y|line_chart Year|2016|x|line_chart Retail_sales_in_million_U.S._dollars|9710.0|y|line_chart Year|2015|x|line_chart Retail_sales_in_million_U.S._dollars|9627.0|y|line_chart Year|2014|x|line_chart Retail_sales_in_million_U.S._dollars|9153.0|y|line_chart Year|2013|x|line_chart Retail_sales_in_million_U.S._dollars|8871.0|y|line_chart Year|2012|x|line_chart Retail_sales_in_million_U.S._dollars|8475.0|y|line_chart Year|2011|x|line_chart Retail_sales_in_million_U.S._dollars|8213.0|y|line_chart 
title: Retail sales of the frame market for eyewear in the U.S. 2011 - 2017

gold: This statistic depicts the retail sales of the frame market for eyewear in the United States from 2011 to 2017 . In 2017 , the U.S. frame market for eyewear generated about 9.73 billion U.S. dollars in retail sales .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] generated about templateYValue[max] billion templateYLabel[3] templateYLabel[4] in templateYLabel[0] templateYLabel[1] .

generated_template: U.S. fashion fashion retailer templateTitle[3] templateTitle[4] templateTitle[5] in templateYLabel[2] United States increased from templateYValue[max] percent in templateXValue[max] , an increase of templateYValue[0] percent compared with the previous templateXLabel[0] . The templateYLabel[0] templateYLabel[1] - additional information The highest number of templateYLabel[1] templateYLabel[2] services , which rose from just templateYValue[min] percent since templateXValue[9] .
generated: U.S. fashion retailer market for eyewear in million United States increased from 9732.2 percent in 2017 , an increase of 9732.2 percent compared with the previous Year . The Retail sales - additional information The highest number of sales million services , which rose from just 8213.0 percent since xValErr .


Example 809:
data: Response|Absolutely_certain_that_there_is_a_God|x|bar_chart Share_of_respondents|54|y|bar_chart Response|Somewhat_certain_that_there_is_a_God|x|bar_chart Share_of_respondents|15|y|bar_chart Response|Somewhat_certain_that_there_is_no_God|x|bar_chart Share_of_respondents|7|y|bar_chart Response|Absolutely_certain_that_there_is_no_God|x|bar_chart Share_of_respondents|9|y|bar_chart Response|Not_sure_whether_or_not_there_is_a_God|x|bar_chart Share_of_respondents|16|y|bar_chart 
title: American teenagers ' belief in existence of a God

gold: This survey , conducted by Harris Poll across the United States in February 2014 , shows the share of American teenagers who are certain or uncertain about the existence of a God . 54 percent of American teenagers are absolutely certain that there is a God .
gold_template: This survey , conducted by Harris Poll across the United States in February 2014 , shows the templateYLabel[0] of templateTitle[0] templateTitle[1] who are templateXValue[0] or uncertain about the templateTitle[4] of a templateXValue[0] . templateYValue[max] percent of templateTitle[0] templateTitle[1] are templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] is a templateXValue[0] .

generated_template: This statistic shows the results of a survey conducted in the United States in templateTitle[5] . According to the survey , templateYValue[max] percent of templateYLabel[1] stated that they purchase templateXValue[last] a templateXValue[0] templateXValue[0] templateXValue[0] or their templateTitle[2] templateTitle[3] templateTitle[4] templateXValue[0] .
generated: This statistic shows the results of a survey conducted in the United States in God . According to the survey , 54 percent of respondents stated that they purchase Not_sure_whether_or_not_there_is_a_God a Absolutely_certain_that_there_is_a_God Absolutely_certain_that_there_is_a_God or their ' belief existence Absolutely_certain_that_there_is_a_God .


Example 810:
data: Year|2018|x|line_chart Inhabitants_per_square_kilometer|195.94|y|line_chart Year|2017|x|line_chart Inhabitants_per_square_kilometer|192.73|y|line_chart Year|2016|x|line_chart Inhabitants_per_square_kilometer|190.17|y|line_chart Year|2015|x|line_chart Inhabitants_per_square_kilometer|188.46|y|line_chart Year|2014|x|line_chart Inhabitants_per_square_kilometer|187.7|y|line_chart Year|2013|x|line_chart Inhabitants_per_square_kilometer|187.78|y|line_chart Year|2012|x|line_chart Inhabitants_per_square_kilometer|188.28|y|line_chart Year|2011|x|line_chart Inhabitants_per_square_kilometer|188.64|y|line_chart Year|2010|x|line_chart Inhabitants_per_square_kilometer|188.44|y|line_chart Year|2009|x|line_chart Inhabitants_per_square_kilometer|187.54|y|line_chart Year|2008|x|line_chart Inhabitants_per_square_kilometer|186.02|y|line_chart 
title: Population density in Nepal 2008 - 2018

gold: The statistic shows the population density in Nepal from 2008 to 2018 . In 2018 , the population density in Nepal amounted to about 195.94 inhabitants per square kilometer .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[2] amounted to about templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The templateTitle[0] templateTitle[1] in templateTitle[2] was templateYValue[max] people templateYLabel[1] templateYLabel[2] templateYLabel[3] ( 47.24 templateYLabel[1] templateYLabel[2] mile ) in templateXValue[max] . This value has increased since the last ten years and its overall templateTitle[0] was valued at over time period , but not necessarily economic growth . templateTitle[2] had the highest templateTitle[0] templateTitle[1] In templateXValue[max] , the average templateTitle[0] templateTitle[1] templateTitle[2] had increased again in relation to its overall templateTitle[0] templateTitle[1] peaked in the highest of average growth of about templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The Population density in Nepal was 195.94 people per square kilometer ( 47.24 per square mile ) in 2018 . This value has increased since the last ten years and its overall Population was valued at over time period , but not necessarily economic growth . Nepal had the highest Population density In 2018 , the average Population density Nepal had increased again in relation to its overall Population density peaked in the highest of average growth of about 195.94 Inhabitants per square kilometer .


Example 811:
data: Year|1999|x|line_chart Vended_volume_(in_billion_U.S._dollars)|36.6|y|line_chart Year|2000|x|line_chart Vended_volume_(in_billion_U.S._dollars)|38.7|y|line_chart Year|2001|x|line_chart Vended_volume_(in_billion_U.S._dollars)|41.0|y|line_chart Year|2002|x|line_chart Vended_volume_(in_billion_U.S._dollars)|41.1|y|line_chart Year|2003|x|line_chart Vended_volume_(in_billion_U.S._dollars)|42.2|y|line_chart Year|2004|x|line_chart Vended_volume_(in_billion_U.S._dollars)|44.2|y|line_chart Year|2005|x|line_chart Vended_volume_(in_billion_U.S._dollars)|46.0|y|line_chart Year|2006|x|line_chart Vended_volume_(in_billion_U.S._dollars)|46.8|y|line_chart Year|2007|x|line_chart Vended_volume_(in_billion_U.S._dollars)|47.5|y|line_chart Year|2008|x|line_chart Vended_volume_(in_billion_U.S._dollars)|45.6|y|line_chart Year|2009|x|line_chart Vended_volume_(in_billion_U.S._dollars)|42.9|y|line_chart Year|2010|x|line_chart Vended_volume_(in_billion_U.S._dollars)|42.2|y|line_chart 
title: Vending machines : sales volume of vended products 2010

gold: This graph depicts the total sales volume of products sold through vending machines in the U.S. from 1999 to 2010 . In 1999 , the sales volume was 36.6 billion U.S. dollars .
gold_template: This graph depicts the total templateTitle[2] templateYLabel[1] of templateTitle[5] sold through templateTitle[0] templateTitle[1] in the templateYLabel[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[2] templateYLabel[1] was templateYValue[min] templateYLabel[3] templateYLabel[4] dollars .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] people lived in the United States .
generated: This statistic shows the Vended volume in sales volume from 1999 to 2010 . In 2010 , 36.6 people lived in the United States .


Example 812:
data: Year|2018|x|line_chart Household_income_in_current_U.S._dollars|61633|y|line_chart Year|2017|x|line_chart Household_income_in_current_U.S._dollars|59768|y|line_chart Year|2016|x|line_chart Household_income_in_current_U.S._dollars|53985|y|line_chart Year|2015|x|line_chart Household_income_in_current_U.S._dollars|53301|y|line_chart Year|2014|x|line_chart Household_income_in_current_U.S._dollars|49644|y|line_chart Year|2013|x|line_chart Household_income_in_current_U.S._dollars|46398|y|line_chart Year|2012|x|line_chart Household_income_in_current_U.S._dollars|44375|y|line_chart Year|2011|x|line_chart Household_income_in_current_U.S._dollars|44648|y|line_chart Year|2010|x|line_chart Household_income_in_current_U.S._dollars|45886|y|line_chart Year|2009|x|line_chart Household_income_in_current_U.S._dollars|45879|y|line_chart Year|2008|x|line_chart Household_income_in_current_U.S._dollars|46934|y|line_chart Year|2007|x|line_chart Household_income_in_current_U.S._dollars|49099|y|line_chart Year|2006|x|line_chart Household_income_in_current_U.S._dollars|45900|y|line_chart Year|2005|x|line_chart Household_income_in_current_U.S._dollars|44203|y|line_chart Year|2004|x|line_chart Household_income_in_current_U.S._dollars|43055|y|line_chart Year|2003|x|line_chart Household_income_in_current_U.S._dollars|43520|y|line_chart Year|2002|x|line_chart Household_income_in_current_U.S._dollars|42684|y|line_chart Year|2001|x|line_chart Household_income_in_current_U.S._dollars|41785|y|line_chart Year|2000|x|line_chart Household_income_in_current_U.S._dollars|42962|y|line_chart Year|1999|x|line_chart Household_income_in_current_U.S._dollars|39489|y|line_chart Year|1998|x|line_chart Household_income_in_current_U.S._dollars|38925|y|line_chart Year|1997|x|line_chart Household_income_in_current_U.S._dollars|36134|y|line_chart Year|1996|x|line_chart Household_income_in_current_U.S._dollars|34070|y|line_chart Year|1995|x|line_chart Household_income_in_current_U.S._dollars|34941|y|line_chart Year|1994|x|line_chart Household_income_in_current_U.S._dollars|31855|y|line_chart Year|1993|x|line_chart Household_income_in_current_U.S._dollars|31285|y|line_chart Year|1992|x|line_chart Household_income_in_current_U.S._dollars|31404|y|line_chart Year|1991|x|line_chart Household_income_in_current_U.S._dollars|29790|y|line_chart Year|1990|x|line_chart Household_income_in_current_U.S._dollars|30013|y|line_chart 
title: Ohio - Median household income 1990 - 2018

gold: This statistic shows the median household income in Ohio from 1990 to 2018 . In 2018 , the median household income in Ohio amounted to 61,633 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitle[3] was templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Ohio Median Household income income from 1990 to 2018 . In 2018 , the Ohio Median Household income in income was 61633 U.S. dollars .


Example 813:
data: Age_in_years|Under_1_year|x|bar_chart Number_of_persons|50|y|bar_chart Age_in_years|1_to_4_years|x|bar_chart Number_of_persons|2442|y|bar_chart Age_in_years|5_to_9_years|x|bar_chart Number_of_persons|2914|y|bar_chart Age_in_years|10_to_14_years|x|bar_chart Number_of_persons|2706|y|bar_chart Age_in_years|15_to_19_years|x|bar_chart Number_of_persons|2692|y|bar_chart Age_in_years|20_to_24_years|x|bar_chart Number_of_persons|2383|y|bar_chart Age_in_years|25_to_29_years|x|bar_chart Number_of_persons|1952|y|bar_chart Age_in_years|30_to_34_years|x|bar_chart Number_of_persons|1910|y|bar_chart Age_in_years|35_to_39_years|x|bar_chart Number_of_persons|1418|y|bar_chart Age_in_years|40_to_44_years|x|bar_chart Number_of_persons|1073|y|bar_chart Age_in_years|45_to_49_years|x|bar_chart Number_of_persons|872|y|bar_chart Age_in_years|50_to_54_years|x|bar_chart Number_of_persons|621|y|bar_chart Age_in_years|55_to_59_years|x|bar_chart Number_of_persons|447|y|bar_chart Age_in_years|60_to_64_years|x|bar_chart Number_of_persons|334|y|bar_chart Age_in_years|65_to_69_years|x|bar_chart Number_of_persons|269|y|bar_chart Age_in_years|70_to_74_years|x|bar_chart Number_of_persons|159|y|bar_chart Age_in_years|75_years_and_over|x|bar_chart Number_of_persons|163|y|bar_chart 
title: Refugees arriving by age U.S. 2018

gold: This statistic shows the number of refugees arriving in the United States in 2018 , by age . In 2018 , about 163 refugees arrived in the United States aged 75 years or over . The total number of refugee arrivals amounted to 22,405 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitle[4] in templateTitle[5] , templateTitle[2] templateXLabel[0] . In templateTitle[5] , about templateYValue[16] templateTitle[0] arrived in the templateTitle[4] aged templateXValue[last] templateXValue[1] or templateXValue[last] . The total templateYLabel[0] of refugee arrivals amounted to 22,405 .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of different regions in the United States in templateTitle[4] , ranked templateTitle[5] . In templateTitle[4] , templateYValue[max] percent of templateYLabel[1] to the templateTitle[2] templateYLabel[1] to the United States .
generated: This statistic shows the Number of persons of different regions in the United States in U.S. , ranked 2018 . In U.S. , 2914 percent of persons to the by persons to the United States .


Example 814:
data: Response|I_have_been_cyber_bullied|x|bar_chart Share_of_respondents|17.4|y|bar_chart Response|Mean_or_hurtful_comments_online|x|bar_chart Share_of_respondents|24.9|y|bar_chart Response|Rumors_online|x|bar_chart Share_of_respondents|22.2|y|bar_chart Response|Threatened_to_hurt_me_through_a_cell_phone_text|x|bar_chart Share_of_respondents|12.2|y|bar_chart Response|Posted_mean_names_or_comments_online_about_me_with_a_sexual_meaning|x|bar_chart Share_of_respondents|12|y|bar_chart Response|Threatened_to_hurt_me_online|x|bar_chart Share_of_respondents|11.7|y|bar_chart Response|Posted_a_mean_or_hurtful_picture_online_of_me|x|bar_chart Share_of_respondents|10.8|y|bar_chart Response|Pretended_to_be_me_online|x|bar_chart Share_of_respondents|10.1|y|bar_chart Response|Posted_mean_names_or_comments_about_my_race_or_color|x|bar_chart Share_of_respondents|9.5|y|bar_chart Response|Posted_a_mean_or_hurtful_video_online_of_me|x|bar_chart Share_of_respondents|7.1|y|bar_chart Response|Posten_mean_names_or_comments_online_about_my_religion|x|bar_chart Share_of_respondents|6.7|y|bar_chart Response|Created_a_mean_or_hurtful_web_page_about_me|x|bar_chart Share_of_respondents|6.4|y|bar_chart Response|One_or_more_of_above_two_or_more_times|x|bar_chart Share_of_respondents|30.1|y|bar_chart 
title: Cyber bullying : common types of bullying 2019

gold: This statistic presents the percentage of middle and high school students in the United States who were cyber bullied , divided by the type of cyber bullying endured . During the April 2019 survey , 10.1 percent of cyber bullying victims had been impersonated online during the last 30 days . Cyber bullying includes unwanted contact by threatening or insulting the respondent via digital communications as well as sharing private information online .
gold_template: This statistic presents the percentage of middle and high school students in the United States who were templateXValue[0] templateXValue[0] , divided by the type of templateXValue[0] templateTitle[1] endured . During the April templateTitle[5] survey , templateYValue[7] percent of templateXValue[0] templateTitle[1] victims had templateXValue[0] impersonated templateXValue[1] during the last templateYValue[max] days . templateXValue[0] templateTitle[1] includes unwanted contact by threatening or insulting the respondent via digital communications as well as sharing private information templateXValue[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in U.S. templateTitle[8] . According to the survey , templateYValue[max] percent of templateYLabel[1] cited templateXValue[1] and templateYValue[1] percent of templateYLabel[1] said that they would be U.S. dollars in the United States .
generated: This statistic shows the Cyber bullying of common types bullying 2019 titleErr titleErr in U.S. titleErr . According to the survey , 30.1 percent of respondents cited Mean_or_hurtful_comments_online and 24.9 percent of respondents said that they would be U.S. dollars in the United States .


Example 815:
data: Year|2018|x|line_chart Operating_profit_in_million_Euros|1440.6|y|line_chart Year|2017|x|line_chart Operating_profit_in_million_Euros|1391.21|y|line_chart Year|2016|x|line_chart Operating_profit_in_million_Euros|1674.3|y|line_chart Year|2015|x|line_chart Operating_profit_in_million_Euros|1645.35|y|line_chart Year|2014|x|line_chart Operating_profit_in_million_Euros|1302.45|y|line_chart Year|2013|x|line_chart Operating_profit_in_million_Euros|1117.43|y|line_chart Year|2012|x|line_chart Operating_profit_in_million_Euros|1019.49|y|line_chart Year|2011|x|line_chart Operating_profit_in_million_Euros|762.19|y|line_chart Year|2010|x|line_chart Operating_profit_in_million_Euros|667.1|y|line_chart Year|2009|x|line_chart Operating_profit_in_million_Euros|389.5|y|line_chart 
title: LEGO Group operating profit 2009 - 2018

gold: This statistic shows the operating profit of the LEGO Group from 2009 to 2018 . In 2015 , the LEGO Group 's operating profit amounted to approximately 1.65 billion euros .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[3] billion templateYLabel[3] .

generated_template: More than templateYValue[0] templateYLabel[1] were recorded in templateTitle[3] templateTitle[4] templateTitle[5] in templateXValue[max] . templateTitle[1] citizens between the years and templateYValue[0] percent of the templateXLabel[0] 's history with an increase of templateYValue[max] percent in templateYLabel[1] compared with the previous templateXLabel[0] . The source templateYLabel[1] Japan , many birth rate , was one of the highest in templateXValue[1] .
generated: More than 1440.6 profit were recorded in profit 2009 2018 in 2018 . Group citizens between the years and 1440.6 percent of the Year 's history with an increase of 1674.3 percent in profit compared with the previous Year . The source profit Japan , many birth rate , was one of the highest in 2017 .


Example 816:
data: Quarter|Q4_'19|x|bar_chart Share_of_organic_search_visits|58|y|bar_chart Quarter|Q3_'19|x|bar_chart Share_of_organic_search_visits|60|y|bar_chart Quarter|Q2_'19|x|bar_chart Share_of_organic_search_visits|59|y|bar_chart Quarter|Q1_'19|x|bar_chart Share_of_organic_search_visits|59|y|bar_chart Quarter|Q4_'18|x|bar_chart Share_of_organic_search_visits|57|y|bar_chart Quarter|Q3_'18|x|bar_chart Share_of_organic_search_visits|56|y|bar_chart Quarter|Q2_'18|x|bar_chart Share_of_organic_search_visits|55|y|bar_chart Quarter|Q1_'18|x|bar_chart Share_of_organic_search_visits|53|y|bar_chart Quarter|Q4_'17|x|bar_chart Share_of_organic_search_visits|53|y|bar_chart Quarter|Q3_'17|x|bar_chart Share_of_organic_search_visits|53|y|bar_chart Quarter|Q2_'17|x|bar_chart Share_of_organic_search_visits|51|y|bar_chart Quarter|Q1_'17|x|bar_chart Share_of_organic_search_visits|53|y|bar_chart Quarter|Q4_'16|x|bar_chart Share_of_organic_search_visits|51|y|bar_chart Quarter|Q3_'16|x|bar_chart Share_of_organic_search_visits|48|y|bar_chart Quarter|Q2_'16|x|bar_chart Share_of_organic_search_visits|46|y|bar_chart Quarter|Q1_'16|x|bar_chart Share_of_organic_search_visits|45|y|bar_chart Quarter|Q4_'15|x|bar_chart Share_of_organic_search_visits|43|y|bar_chart Quarter|Q3_'15|x|bar_chart Share_of_organic_search_visits|45|y|bar_chart Quarter|Q2_'15|x|bar_chart Share_of_organic_search_visits|45|y|bar_chart Quarter|Q1_'15|x|bar_chart Share_of_organic_search_visits|45|y|bar_chart Quarter|Q4_'14|x|bar_chart Share_of_organic_search_visits|39|y|bar_chart Quarter|Q3_'14|x|bar_chart Share_of_organic_search_visits|38|y|bar_chart Quarter|Q2_'14|x|bar_chart Share_of_organic_search_visits|34|y|bar_chart Quarter|Q1_'14|x|bar_chart Share_of_organic_search_visits|34|y|bar_chart Quarter|Q4_'13|x|bar_chart Share_of_organic_search_visits|33|y|bar_chart Quarter|Q3_'13|x|bar_chart Share_of_organic_search_visits|27|y|bar_chart 
title: Mobile share of U.S. organic search engine visits 2013 - 2019

gold: This statistic highlights the mobile share of organic search engine visits in the United States . As of the fourth quarter of 2019 , it was found that mobile devices accounted for 58 percent of organic search engine visits .
gold_template: This statistic highlights the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[5] templateYLabel[3] in the templateTitle[2] . As of the fourth templateXLabel[0] of templateTitle[8] , it was found that templateTitle[0] devices accounted for templateYValue[0] percent of templateYLabel[1] templateYLabel[2] templateTitle[5] templateYLabel[3] .

generated_template: In the fourth templateXLabel[0] of templateTitle[6] , there were templateYValue[max] templateYLabel[1] in the United States , up from templateYValue[1] million in the corresponding templateXLabel[0] of templateTitle[4] in the preceding templateXLabel[0] . Over the period under consideration , the templateYLabel[1] of templateTitle[0] templateTitle[1] templateYLabel[1] has increased significantly in the last decades .
generated: In the fourth Quarter of visits , there were 60 organic in the United States , up from 60 million in the corresponding Quarter of search in the preceding Quarter . Over the period under consideration , the organic of Mobile share organic has increased significantly in the last decades .


Example 817:
data: Year|2018|x|line_chart Direct_investments_in_billion_U.S._dollars|116.52|y|line_chart Year|2017|x|line_chart Direct_investments_in_billion_U.S._dollars|107.56|y|line_chart Year|2016|x|line_chart Direct_investments_in_billion_U.S._dollars|97.29|y|line_chart Year|2015|x|line_chart Direct_investments_in_billion_U.S._dollars|92.15|y|line_chart Year|2014|x|line_chart Direct_investments_in_billion_U.S._dollars|82.24|y|line_chart Year|2013|x|line_chart Direct_investments_in_billion_U.S._dollars|60.45|y|line_chart Year|2012|x|line_chart Direct_investments_in_billion_U.S._dollars|54.51|y|line_chart Year|2011|x|line_chart Direct_investments_in_billion_U.S._dollars|53.66|y|line_chart Year|2010|x|line_chart Direct_investments_in_billion_U.S._dollars|59.0|y|line_chart Year|2009|x|line_chart Direct_investments_in_billion_U.S._dollars|54.07|y|line_chart Year|2008|x|line_chart Direct_investments_in_billion_U.S._dollars|53.93|y|line_chart Year|2007|x|line_chart Direct_investments_in_billion_U.S._dollars|29.71|y|line_chart Year|2006|x|line_chart Direct_investments_in_billion_U.S._dollars|26.46|y|line_chart Year|2005|x|line_chart Direct_investments_in_billion_U.S._dollars|19.02|y|line_chart Year|2004|x|line_chart Direct_investments_in_billion_U.S._dollars|17.62|y|line_chart Year|2003|x|line_chart Direct_investments_in_billion_U.S._dollars|11.26|y|line_chart Year|2002|x|line_chart Direct_investments_in_billion_U.S._dollars|10.57|y|line_chart Year|2001|x|line_chart Direct_investments_in_billion_U.S._dollars|12.08|y|line_chart Year|2000|x|line_chart Direct_investments_in_billion_U.S._dollars|11.14|y|line_chart 
title: Direct investment position of the U.S. in China 2000 - 2018

gold: This statistic shows the direct investment position of the United States in China from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in China were valued at approximately 117 billion U.S. dollars . Direct investment position of the United States - additional information Foreign direct investment ( FDI ) , simply put , is an investment of one company into another company located in a different country .
gold_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitle[4] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitle[4] were valued at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] - additional information Foreign templateYLabel[0] templateTitle[1] ( FDI ) , simply put , is an templateTitle[1] of one company into another company located in a different country .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the United States in templateTitle[4] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitle[4] were valued at approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] templateTitle[1] abroad is defined as ownership by a templateYLabel[3] investor of at least templateYValue[3] percent of a foreign business .
generated: This statistic shows the Direct investment position of the United States in China from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in China were valued at approximately 116.52 billion U.S. dollars . U.S. Direct investment abroad is defined as ownership by a U.S. investor of at least 92.15 percent of a foreign business .


Example 818:
data: Month|June_2015_to_June_2016|x|bar_chart Earnings_in_million_U.S._dollars|9.5|y|bar_chart Month|June_2014_to_June_2015|x|bar_chart Earnings_in_million_U.S._dollars|12.0|y|bar_chart Month|June_2013_to_June_2014|x|bar_chart Earnings_in_million_U.S._dollars|12.0|y|bar_chart Month|June_2012_to_June_2013|x|bar_chart Earnings_in_million_U.S._dollars|12.0|y|bar_chart Month|May_2011_to_May_2012|x|bar_chart Earnings_in_million_U.S._dollars|15.0|y|bar_chart 
title: George R.R . Martin - earnings 2011 - 2016

gold: The statistic presents data on the annual earnings of George R.R . Martin from May 2011 to June 2016 . The author earned 12 million U.S. dollars in the period June 2014 to June 2015 .
gold_template: The statistic presents data on the annual templateYLabel[0] of templateTitle[0] templateTitle[1] . templateTitle[2] from templateXValue[last] templateXValue[last] to templateXValue[0] templateXValue[0] . The author earned templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the period templateXValue[0] templateXValue[1] to templateXValue[0] templateXValue[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] templateTitle[6] in templateTitle[7] , broken down templateTitle[7] templateXLabel[0] . According to the report , the highest templateYLabel[0] of templateXValue[1] templateXValue[1] reached templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[7] .
generated: The statistic shows the Earnings of R.R million U.S. titleErr in titleErr , broken down titleErr Month . According to the report , the highest Earnings of June_2014_to_June_2015 reached 12.0 million U.S. dollars in titleErr .


Example 819:
data: Month|Dec_19|x|bar_chart Units_sold|745|y|bar_chart Month|Nov_19|x|bar_chart Units_sold|1044|y|bar_chart Month|Oct_19|x|bar_chart Units_sold|1098|y|bar_chart Month|Sep_19|x|bar_chart Units_sold|3852|y|bar_chart Month|Aug_19|x|bar_chart Units_sold|424|y|bar_chart Month|Jul_19|x|bar_chart Units_sold|1118|y|bar_chart Month|Jun_19|x|bar_chart Units_sold|1199|y|bar_chart Month|May_19|x|bar_chart Units_sold|943|y|bar_chart Month|Apr_19|x|bar_chart Units_sold|884|y|bar_chart Month|Mar_19|x|bar_chart Units_sold|3137|y|bar_chart Month|Feb_19|x|bar_chart Units_sold|262|y|bar_chart Month|Jan_19|x|bar_chart Units_sold|1007|y|bar_chart Month|Dec_18|x|bar_chart Units_sold|420|y|bar_chart Month|Nov_18|x|bar_chart Units_sold|663|y|bar_chart Month|Oct_18|x|bar_chart Units_sold|674|y|bar_chart Month|Sep_18|x|bar_chart Units_sold|2686|y|bar_chart Month|Aug_18|x|bar_chart Units_sold|270|y|bar_chart Month|Jul_18|x|bar_chart Units_sold|784|y|bar_chart Month|Jun_18|x|bar_chart Units_sold|1306|y|bar_chart Month|May_18|x|bar_chart Units_sold|851|y|bar_chart Month|Apr_18|x|bar_chart Units_sold|678|y|bar_chart Month|Mar_18|x|bar_chart Units_sold|3006|y|bar_chart Month|Feb_18|x|bar_chart Units_sold|180|y|bar_chart Month|Jan_18|x|bar_chart Units_sold|887|y|bar_chart Month|Dec_17|x|bar_chart Units_sold|772|y|bar_chart Month|Nov_17|x|bar_chart Units_sold|775|y|bar_chart Month|Oct_17|x|bar_chart Units_sold|725|y|bar_chart Month|Sep_17|x|bar_chart Units_sold|2908|y|bar_chart Month|Aug_17|x|bar_chart Units_sold|205|y|bar_chart Month|Jul_17|x|bar_chart Units_sold|843|y|bar_chart Month|Jun_17|x|bar_chart Units_sold|1161|y|bar_chart Month|May_17|x|bar_chart Units_sold|715|y|bar_chart Month|Apr_17|x|bar_chart Units_sold|672|y|bar_chart Month|Mar_17|x|bar_chart Units_sold|2888|y|bar_chart Month|Feb_17|x|bar_chart Units_sold|206|y|bar_chart Month|Jan_17|x|bar_chart Units_sold|800|y|bar_chart Month|Dec_16|x|bar_chart Units_sold|750|y|bar_chart Month|Nov_16|x|bar_chart Units_sold|931|y|bar_chart Month|Oct_16|x|bar_chart Units_sold|812|y|bar_chart Month|Sep_16|x|bar_chart Units_sold|2998|y|bar_chart Month|Aug_16|x|bar_chart Units_sold|234|y|bar_chart Month|Jul_16|x|bar_chart Units_sold|774|y|bar_chart 
title: Lexus car sales in the United Kingdom ( UK ) 2016 - 2019

gold: This statistic shows the monthly amount of cars sold by Lexus in the United Kingdom ( UK ) between July 2016 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In September 2019 , 3,852 new Lexus cars were sold in the UK
gold_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitle[0] in the templateTitle[3] templateTitle[4] ( templateTitle[5] ) between July templateTitle[6] and December templateTitle[7] . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In September templateTitle[7] , templateYValue[max] new templateTitle[0] cars were templateYLabel[1] in the templateTitle[5]

generated_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitle[0] in the templateTitle[3] templateTitle[4] ( templateTitle[5] ) between July templateTitle[6] and December templateTitle[7] . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December templateTitle[7] , templateTitle[0] templateYLabel[1] templateYValue[0] cars had been templateYLabel[1] .
generated: This statistic shows the monthly amount of cars sold by Lexus in the United Kingdom ( UK ) between July 2016 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December 2019 , Lexus sold 745 cars had been sold .


Example 820:
data: Year|2019|x|line_chart Number_of_customers_in_millions|145.0|y|line_chart Year|2018|x|line_chart Number_of_customers_in_millions|144.0|y|line_chart Year|2017|x|line_chart Number_of_customers_in_millions|133.0|y|line_chart Year|2016|x|line_chart Number_of_customers_in_millions|125.0|y|line_chart Year|2015|x|line_chart Number_of_customers_in_millions|121.0|y|line_chart Year|2014|x|line_chart Number_of_customers_in_millions|117.0|y|line_chart Year|2013|x|line_chart Number_of_customers_in_millions|106.6|y|line_chart Year|2012|x|line_chart Number_of_customers_in_millions|102.1|y|line_chart 
title: Banco Santander : customer numbers globally 2012 - 2019

gold: Between 2018 and 2019 , the Banco Santander Group increased by one million customers worldwide . In 2019 , the Spanish giant had approximately196 thousand employees , across nearly12 thousand branches looking after the needs of its customers globally . As of 2019 , Banco Santander 's largest contributor to the company 's profit was Europe .
gold_template: Between templateXValue[1] and templateXValue[max] , the templateTitle[0] templateTitle[1] Group increased by one million templateYLabel[1] worldwide . In templateXValue[max] , the Spanish giant had approximately196 thousand employees , across nearly12 thousand branches looking after the needs of its templateYLabel[1] templateTitle[4] . As of templateXValue[max] , templateTitle[0] templateTitle[1] 's largest contributor to the company 's profit was Europe .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateYLabel[1] in the templateTitle[2] templateTitle[3] templateTitle[4] and templateTitle[5] in the United States .
generated: This statistic shows the Number customers of customer numbers globally 2012 in the United States from 2012 to 2019 . In 2019 , there were 145.0 customers in the customer numbers globally and 2012 in the United States .


Example 821:
data: Year|2019|x|line_chart Unemployment_rate|2.75|y|line_chart Year|2018|x|line_chart Unemployment_rate|2.73|y|line_chart Year|2017|x|line_chart Unemployment_rate|2.68|y|line_chart Year|2016|x|line_chart Unemployment_rate|2.83|y|line_chart Year|2015|x|line_chart Unemployment_rate|2.51|y|line_chart Year|2014|x|line_chart Unemployment_rate|2.72|y|line_chart Year|2013|x|line_chart Unemployment_rate|3.02|y|line_chart Year|2012|x|line_chart Unemployment_rate|2.77|y|line_chart Year|2011|x|line_chart Unemployment_rate|4.13|y|line_chart Year|2010|x|line_chart Unemployment_rate|3.5|y|line_chart Year|2009|x|line_chart Unemployment_rate|3.31|y|line_chart Year|2008|x|line_chart Unemployment_rate|2.84|y|line_chart Year|2007|x|line_chart Unemployment_rate|2.8|y|line_chart Year|2006|x|line_chart Unemployment_rate|2.89|y|line_chart Year|2005|x|line_chart Unemployment_rate|2.99|y|line_chart Year|2004|x|line_chart Unemployment_rate|2.97|y|line_chart Year|2003|x|line_chart Unemployment_rate|2.81|y|line_chart Year|2002|x|line_chart Unemployment_rate|2.85|y|line_chart Year|2001|x|line_chart Unemployment_rate|2.78|y|line_chart Year|2000|x|line_chart Unemployment_rate|2.9|y|line_chart Year|1999|x|line_chart Unemployment_rate|2.92|y|line_chart 
title: Unemployment rate in Guatemala 2019

gold: This statistic shows the unemployment rate in Guatemala from 1999 to 2019 . In 2019 , the unemployment rate in Guatemala was 2.75 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[min] percent .
generated: The statistic shows the Unemployment rate in Guatemala from 1999 to 2019 . In 2019 , the Unemployment rate in Guatemala was at approximately 2.51 percent .


Example 822:
data: Year|2018|x|line_chart Unemployment_rate|5.5|y|line_chart Year|2017|x|line_chart Unemployment_rate|6.2|y|line_chart Year|2016|x|line_chart Unemployment_rate|7.3|y|line_chart Year|2015|x|line_chart Unemployment_rate|5.6|y|line_chart Year|2014|x|line_chart Unemployment_rate|7.1|y|line_chart Year|2013|x|line_chart Unemployment_rate|9.3|y|line_chart Year|2012|x|line_chart Unemployment_rate|12.9|y|line_chart Year|2011|x|line_chart Unemployment_rate|10.7|y|line_chart Year|2010|x|line_chart Unemployment_rate|13|y|line_chart Year|2009|x|line_chart Unemployment_rate|13.8|y|line_chart Year|2008|x|line_chart Unemployment_rate|9|y|line_chart Year|2007|x|line_chart Unemployment_rate|6.9|y|line_chart Year|2006|x|line_chart Unemployment_rate|5.9|y|line_chart Year|2005|x|line_chart Unemployment_rate|8.5|y|line_chart Year|2004|x|line_chart Unemployment_rate|8.7|y|line_chart Year|2003|x|line_chart Unemployment_rate|11.2|y|line_chart Year|2002|x|line_chart Unemployment_rate|10.3|y|line_chart Year|2001|x|line_chart Unemployment_rate|9.2|y|line_chart 
title: Unemployment in U.S. motion picture and recording industries 2001 - 2018

gold: The statistic above presents the yearly unemployment rate for the U.S. motion picture and sound recording industry from 2001 to 2018 . In this industry , 5.5 percent of all private wage and salary workers were unemployed in 2018 .
gold_template: The statistic above presents the yearly templateYLabel[0] templateYLabel[1] for the templateTitle[1] templateTitle[2] templateTitle[3] and sound templateTitle[4] industry from templateXValue[min] to templateXValue[max] . In this industry , templateYValue[min] percent of all private wage and salary workers were unemployed in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at templateYValue[min] percent .
generated: The statistic shows the Unemployment rate in motion from 2001 to 2018 . In 2018 , the Unemployment rate in motion was at 5.5 percent .


Example 823:
data: Yearly_expenses|under_500$|x|bar_chart Percentage_of_boat_owners|30.5|y|bar_chart Yearly_expenses|$500_to_$999|x|bar_chart Percentage_of_boat_owners|15.5|y|bar_chart Yearly_expenses|$1000_to_$1999|x|bar_chart Percentage_of_boat_owners|18.3|y|bar_chart Yearly_expenses|$2000_to_$4999|x|bar_chart Percentage_of_boat_owners|17.4|y|bar_chart Yearly_expenses|over_$5000|x|bar_chart Percentage_of_boat_owners|18.3|y|bar_chart 
title: Survey on amount of money spent on boating in the U.S. 2012

gold: The statistic depicts the amount of money boat owners in the U.S. spent on boating in 2012 . 18.3 percent of the respondents stated that they spent between $ 1,000 and $ 1,999 on boating in 2012 .
gold_template: The statistic depicts the templateTitle[1] of templateTitle[2] templateYLabel[1] templateYLabel[2] in the templateTitle[5] templateTitle[3] on templateTitle[4] in templateTitle[6] . templateYValue[2] percent of the respondents stated that they templateTitle[3] between $ 1,000 and $ 1,999 on templateTitle[4] in templateTitle[6] .

generated_template: This statistic shows the results of a survey about the number of people in the United States in templateTitle[4] templateTitle[5] . In templateTitle[4] , it was found that templateYValue[0] percent of U.S. dollars members in the United States .
generated: This statistic shows the results of a survey about the number of people in the United States in boating U.S. . In boating , it was found that 30.5 percent of U.S. dollars members in the United States .


Example 824:
data: Response|Watch_TV|x|bar_chart Share_of_respondents|43|y|bar_chart Response|Reading|x|bar_chart Share_of_respondents|24|y|bar_chart Response|Computer/internet|x|bar_chart Share_of_respondents|20|y|bar_chart Response|Playing_video_games_and_computer/internet_games|x|bar_chart Share_of_respondents|13|y|bar_chart Response|Spending_time_with_families_and_friends|x|bar_chart Share_of_respondents|13|y|bar_chart Response|Watching/going_to_the_movies|x|bar_chart Share_of_respondents|11|y|bar_chart Response|Exercise/working_out|x|bar_chart Share_of_respondents|10|y|bar_chart Response|Concerts/listening_to/playing_music|x|bar_chart Share_of_respondents|10|y|bar_chart Response|Walking/running/jogging|x|bar_chart Share_of_respondents|7|y|bar_chart Response|Golf|x|bar_chart Share_of_respondents|7|y|bar_chart 
title: Most popular leisure activities among men in the U.S. 2013

gold: This statistic shows the most popular leisure activities among men in the United States as of September 2013 . During the survey , 43 percent of the male respondents named watching TV as their most preferred activity during leisure time .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of September templateTitle[7] . During the survey , templateYValue[max] percent of the male templateYLabel[1] named watching templateXValue[0] as their templateTitle[0] preferred activity during templateTitle[2] templateXValue[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the United States as of September templateTitle[8] . During the survey , templateYValue[2] percent of templateYLabel[1] from templateTitle[5] templateTitle[6] stated templateXValue[3] templateXValue[3] templateXValue[3] templateXValue[3] and templateXValue[3] as a favorite templateTitle[2] activity .
generated: This statistic shows the Most popular leisure activities among men U.S. in the United States as of September titleErr . During the survey , 20 percent of respondents from men U.S. stated Playing_video_games_and_computer/internet_games Playing_video_games_and_computer/internet_games and Playing_video_games_and_computer/internet_games as a favorite leisure activity .


Example 825:
data: Month|Canadian_Tire|x|bar_chart Number_of_stores|503|y|bar_chart Month|SportChek|x|bar_chart Number_of_stores|409|y|bar_chart Month|Mark's|x|bar_chart Number_of_stores|386|y|bar_chart Month|Canadian_Tire_gas_bar_locations|x|bar_chart Number_of_stores|297|y|bar_chart Month|Other|x|bar_chart Number_of_stores|105|y|bar_chart 
title: Number of stores operated by Canadian Tire Corporation in Canada by brand 2018

gold: This statistic shows the number of stores of the retail company Canadian Tire Corporation in Canada in 2018 , by brand . There were SportChek stores operated by Canadian Tire Corporation in Canada in that year .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the retail company templateXValue[0] templateXValue[0] templateTitle[6] in templateTitle[7] in templateTitle[10] , templateTitle[3] templateTitle[9] . There were templateXValue[1] templateYLabel[1] templateTitle[2] templateTitle[3] templateXValue[0] templateXValue[0] templateTitle[6] in templateTitle[7] in that year .

generated_template: templateTitle[1] Inc. of the Canadian owned retail chain headquartered in Montreal . There were 1,225 templateTitle[1] templateYLabel[1] in templateTitle[3] as of December templateTitle[6] . templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] per game had a total of templateYValue[max] .
generated: stores Inc. of the Canadian owned retail chain headquartered in Montreal . There were 1,225 stores stores in by as of December Corporation . Canadian_Tire had the highest Number of stores per game had a total of 503 .


Example 826:
data: Fossil_Fuel|Gas_mains|x|bar_chart Share_of_deaths|35|y|bar_chart Fossil_Fuel|Solid|x|bar_chart Share_of_deaths|31|y|bar_chart Fossil_Fuel|Gas_portable|x|bar_chart Share_of_deaths|16|y|bar_chart Fossil_Fuel|Petrol/diesel|x|bar_chart Share_of_deaths|15|y|bar_chart Fossil_Fuel|Unknown|x|bar_chart Share_of_deaths|2|y|bar_chart Fossil_Fuel|Oil|x|bar_chart Share_of_deaths|1|y|bar_chart Fossil_Fuel|Parafin|x|bar_chart Share_of_deaths|0.4|y|bar_chart 
title: Deaths from unintentional carbon monoxide poisoning in the United Kingdom 1995 - 2018

gold: This statistic shows the distribution of deaths from unintentional carbon monoxide poisoning in the United Kingdom ( UK ) from 1995 to 2018 , by fuel type . In this period , 35 percent of unintentional carbon monoxide poisoning were caused by gas mains during this period .
gold_template: This statistic shows the distribution of templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] templateTitle[7] ( UK ) templateTitle[1] templateTitle[8] to templateTitle[9] , by templateXLabel[1] type . In this period , templateYValue[max] percent of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] were caused by templateXValue[0] templateXValue[0] during this period .

generated_template: This statistic presents the templateYLabel[0] of people in the United States in templateTitle[4] templateTitle[5] as of April templateTitle[6] . During the survey , it was found that templateYValue[max] percent of templateYLabel[1] templateYLabel[2] 100,000 people in the United States .
generated: This statistic presents the Share of people in the United States in monoxide poisoning as of April United . During the survey , it was found that 35 percent of deaths yLabelErr 100,000 people in the United States .


Example 827:
data: Year|2019|x|line_chart Unemployment_rate|4.35|y|line_chart Year|2018|x|line_chart Unemployment_rate|4.39|y|line_chart Year|2017|x|line_chart Unemployment_rate|4.39|y|line_chart Year|2016|x|line_chart Unemployment_rate|4.42|y|line_chart Year|2015|x|line_chart Unemployment_rate|4|y|line_chart Year|2014|x|line_chart Unemployment_rate|4.16|y|line_chart Year|2013|x|line_chart Unemployment_rate|3.69|y|line_chart Year|2012|x|line_chart Unemployment_rate|3.85|y|line_chart Year|2011|x|line_chart Unemployment_rate|4.3|y|line_chart Year|2010|x|line_chart Unemployment_rate|4.89|y|line_chart Year|2009|x|line_chart Unemployment_rate|7.33|y|line_chart Year|2008|x|line_chart Unemployment_rate|5.88|y|line_chart Year|2007|x|line_chart Unemployment_rate|6.41|y|line_chart Year|2006|x|line_chart Unemployment_rate|6.57|y|line_chart Year|2005|x|line_chart Unemployment_rate|7.22|y|line_chart Year|2004|x|line_chart Unemployment_rate|6.05|y|line_chart Year|2003|x|line_chart Unemployment_rate|6.26|y|line_chart Year|2002|x|line_chart Unemployment_rate|5.73|y|line_chart Year|2001|x|line_chart Unemployment_rate|6.96|y|line_chart Year|2000|x|line_chart Unemployment_rate|6.96|y|line_chart Year|1999|x|line_chart Unemployment_rate|6.68|y|line_chart 
title: Unemployment rate in El Salvador 2019

gold: This statistic shows the unemployment rate in El Salvador from 1999 to 2019 . In 2019 , the unemployment rate in El Salvador amounted to approximately 4.35 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] templateTitle[3] amounted to approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[2] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in El from 1999 to 2019 . In 2019 , the Unemployment rate in El was at approximately 4.35 percent .


Example 828:
data: Year|2024|x|line_chart Inhabitants_in_millions|39.22|y|line_chart Year|2023|x|line_chart Inhabitants_in_millions|38.87|y|line_chart Year|2022|x|line_chart Inhabitants_in_millions|38.52|y|line_chart Year|2021|x|line_chart Inhabitants_in_millions|38.17|y|line_chart Year|2020|x|line_chart Inhabitants_in_millions|37.81|y|line_chart Year|2019|x|line_chart Inhabitants_in_millions|37.46|y|line_chart Year|2018|x|line_chart Inhabitants_in_millions|36.99|y|line_chart Year|2017|x|line_chart Inhabitants_in_millions|36.49|y|line_chart Year|2016|x|line_chart Inhabitants_in_millions|36.05|y|line_chart Year|2015|x|line_chart Inhabitants_in_millions|35.68|y|line_chart Year|2014|x|line_chart Inhabitants_in_millions|35.39|y|line_chart 
title: Total population in Canada 2024

gold: The statistic shows the total population in Canada from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population in Canada amounted to about 36.99 million inhabitants . Population of Canada Canada ranks second among the largest countries in the world in terms of area size , right behind Russia , despite having a relatively low total population .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] in templateTitle[2] amounted to about templateYValue[6] million templateYLabel[0] . templateTitle[1] of templateTitle[2] ranks second among the largest countries in the world in terms of area size , right behind Russia , despite having a relatively low templateTitle[0] templateTitle[1] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[2] was estimated at approximately templateYValue[6] million templateYLabel[0] . templateTitle[1] of templateTitle[2] templateTitle[2] Despite its small size , the templateTitle[2] is the twenty-third smallest nation in the European Union , and it is one of the most important nations in Europe and the world .
generated: The statistic shows the Total population of Canada from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Canada was estimated at approximately 36.99 million Inhabitants . population of Canada Despite its small size , the Canada is the twenty-third smallest nation in the European Union , and it is one of the most important nations in Europe and the world .


Example 829:
data: Month|Sep_19|x|bar_chart Index_number_of_sales_per_week|140.0|y|bar_chart Month|Aug_19|x|bar_chart Index_number_of_sales_per_week|136.8|y|bar_chart Month|Jul_19|x|bar_chart Index_number_of_sales_per_week|138.8|y|bar_chart Month|Jun_19|x|bar_chart Index_number_of_sales_per_week|139.3|y|bar_chart Month|May_19|x|bar_chart Index_number_of_sales_per_week|140.3|y|bar_chart Month|Apr_19|x|bar_chart Index_number_of_sales_per_week|132.8|y|bar_chart Month|Mar_19|x|bar_chart Index_number_of_sales_per_week|134.7|y|bar_chart Month|Feb_19|x|bar_chart Index_number_of_sales_per_week|126.7|y|bar_chart Month|Jan_19|x|bar_chart Index_number_of_sales_per_week|124.8|y|bar_chart Month|Dec_18|x|bar_chart Index_number_of_sales_per_week|129.6|y|bar_chart Month|Nov_18|x|bar_chart Index_number_of_sales_per_week|132.5|y|bar_chart Month|Oct_18|x|bar_chart Index_number_of_sales_per_week|131.2|y|bar_chart Month|Sep_18|x|bar_chart Index_number_of_sales_per_week|133.0|y|bar_chart Month|Aug_18|x|bar_chart Index_number_of_sales_per_week|135.8|y|bar_chart Month|Jul_18|x|bar_chart Index_number_of_sales_per_week|137.5|y|bar_chart Month|Jun_18|x|bar_chart Index_number_of_sales_per_week|112.8|y|bar_chart Month|May_18|x|bar_chart Index_number_of_sales_per_week|124.1|y|bar_chart Month|Apr_18|x|bar_chart Index_number_of_sales_per_week|118.7|y|bar_chart Month|Mar_18|x|bar_chart Index_number_of_sales_per_week|111.5|y|bar_chart Month|Feb_18|x|bar_chart Index_number_of_sales_per_week|116.8|y|bar_chart Month|Jan_18|x|bar_chart Index_number_of_sales_per_week|120.2|y|bar_chart Month|Dec_17|x|bar_chart Index_number_of_sales_per_week|114.3|y|bar_chart Month|Nov_17|x|bar_chart Index_number_of_sales_per_week|128.1|y|bar_chart Month|Oct_17|x|bar_chart Index_number_of_sales_per_week|115.7|y|bar_chart Month|Sep_17|x|bar_chart Index_number_of_sales_per_week|118.8|y|bar_chart Month|Aug_17|x|bar_chart Index_number_of_sales_per_week|118.6|y|bar_chart Month|Jul_17|x|bar_chart Index_number_of_sales_per_week|117.0|y|bar_chart Month|Jun_17|x|bar_chart Index_number_of_sales_per_week|114.4|y|bar_chart Month|May_17|x|bar_chart Index_number_of_sales_per_week|113.3|y|bar_chart Month|Apr_17|x|bar_chart Index_number_of_sales_per_week|116.5|y|bar_chart Month|Mar_17|x|bar_chart Index_number_of_sales_per_week|115.2|y|bar_chart Month|Feb_17|x|bar_chart Index_number_of_sales_per_week|118.3|y|bar_chart Month|Jan_17|x|bar_chart Index_number_of_sales_per_week|114.8|y|bar_chart Month|Dec_16|x|bar_chart Index_number_of_sales_per_week|107.9|y|bar_chart Month|Nov_16|x|bar_chart Index_number_of_sales_per_week|104.0|y|bar_chart Month|Oct_16|x|bar_chart Index_number_of_sales_per_week|107.3|y|bar_chart Month|Sep_16|x|bar_chart Index_number_of_sales_per_week|101.2|y|bar_chart Month|Aug_16|x|bar_chart Index_number_of_sales_per_week|100.3|y|bar_chart Month|Jul_16|x|bar_chart Index_number_of_sales_per_week|99.6|y|bar_chart Month|Jun_16|x|bar_chart Index_number_of_sales_per_week|104.1|y|bar_chart Month|May_16|x|bar_chart Index_number_of_sales_per_week|97.2|y|bar_chart Month|Apr_16|x|bar_chart Index_number_of_sales_per_week|97.7|y|bar_chart Month|Mar_16|x|bar_chart Index_number_of_sales_per_week|92.5|y|bar_chart Month|Feb_16|x|bar_chart Index_number_of_sales_per_week|95.3|y|bar_chart Month|Jan_16|x|bar_chart Index_number_of_sales_per_week|91.6|y|bar_chart 
title: Monthly watch and jewelry retail sales value index in Great Britain 2016 - 2019

gold: This statistic shows the monthly trend in the amount spent on watches and jewelry ( sales value ) in Great Britain from January 2016 to September 2019 , as an index of sales per week . During this period of time , retail sales increased significantly , measuring at 140 index points in September 2019 . The figures are seasonally adjusted estimates , measured using the Retail Sales Index ( RSI ) and published in index form with a reference year of 2016 equal to 100 .
gold_template: This statistic shows the templateTitle[0] trend in the amount spent on watches and templateTitle[2] ( templateYLabel[2] templateTitle[5] ) in templateTitle[7] templateTitle[8] from January templateTitle[9] to September templateTitle[10] , as an templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] . During this period of time , templateTitle[3] templateYLabel[2] increased significantly , measuring at templateYValue[0] templateYLabel[0] points in September templateTitle[10] . The figures are seasonally adjusted estimates , measured using the templateTitle[3] templateYLabel[2] templateYLabel[0] ( RSI ) and published in templateYLabel[0] form with a reference year of templateTitle[9] equal to templateYValue[37] .

generated_template: This statistic shows the monthly templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] in the templateTitle[4] templateTitle[5] ( templateTitle[6] ) between January templateTitle[7] and December templateTitle[8] . The most templateYLabel[0] templateYLabel[1] and templateTitle[1] templateTitle[2] in the templateTitle[4] templateTitle[5] amounted to templateYValue[0] percent , respectively .
generated: This statistic shows the monthly Index number of Monthly watch in the sales value ( index ) between January Great and December Britain . The most Index number and watch jewelry in the sales value amounted to 140.0 percent , respectively .


Example 830:
data: Year|2019|x|line_chart Inhabitants_in_millions|5.33|y|line_chart Year|2018|x|line_chart Inhabitants_in_millions|5.3|y|line_chart Year|2017|x|line_chart Inhabitants_in_millions|5.26|y|line_chart Year|2016|x|line_chart Inhabitants_in_millions|5.21|y|line_chart Year|2015|x|line_chart Inhabitants_in_millions|5.17|y|line_chart Year|2014|x|line_chart Inhabitants_in_millions|5.12|y|line_chart Year|2013|x|line_chart Inhabitants_in_millions|5.05|y|line_chart Year|2012|x|line_chart Inhabitants_in_millions|4.99|y|line_chart Year|2011|x|line_chart Inhabitants_in_millions|4.92|y|line_chart Year|2010|x|line_chart Inhabitants_in_millions|4.86|y|line_chart Year|2009|x|line_chart Inhabitants_in_millions|4.8|y|line_chart 
title: Population in Norway 2009 - 2019

gold: This statistic shows the population in Norway over the years from 2009 to 2019 . In 2009 , the population of Norway was around 4.8 million people . In 2019 , the number of inhabitants increased to around 5.33 million .
gold_template: This statistic shows the templateTitle[0] in templateTitle[1] over the years from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] of templateTitle[1] was around templateYValue[min] million people . In templateXValue[max] , the number of templateYLabel[0] increased to around templateYValue[max] million .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] of templateTitle[2] was at approximately templateYValue[min] million templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] , down from templateYValue[1] million in templateXValue[min] to the previous templateXLabel[0] .
generated: This statistic shows the Population Norway of 2009 from 2009 to 2019 . The Population Norway of 2009 was at approximately 4.8 million Inhabitants millions yLabelErr in 2019 , down from 5.3 million in 2009 to the previous Year .


Example 831:
data: Response|Yes|x|bar_chart Share_of_respondents|94|y|bar_chart Response|No|x|bar_chart Share_of_respondents|6|y|bar_chart 
title: Share of Americans who believe in true love in 2019

gold: This statistic shows the results of a survey conducted in the United States in 2017 on whether the respondents believe in love , or not . During the survey , some 94 percent of respondents stated they believe in true love .
gold_template: This statistic shows the results of a survey conducted in the United States in 2017 on whether the templateYLabel[1] templateTitle[3] in templateTitle[5] , or not . During the survey , some templateYValue[max] percent of templateYLabel[1] stated they templateTitle[3] in templateTitle[4] templateTitle[5] .

generated_template: The statistic shows the results of a templateTitle[0] among adult Americans who have one or more templateTitle[2] . The templateTitle[0] was conducted in templateTitle[4] , asking the templateYLabel[1] whether they ever regret getting any of their templateTitle[2] , or not . templateYValue[max] percent of templateYLabel[1] stated they do not regret getting any of their templateTitle[2] .
generated: The statistic shows the results of a Share among adult Americans who have one or more who . The Share was conducted in true , asking the respondents whether they ever regret getting any of their who , or not . 94 percent of respondents stated they do not regret getting any of their who .


