Example 1:
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 United States as of May templateTitle[10] . During the survey , templateYValue[2] percent of templateYLabel[2] said that templateXValue[2] was their templateXValue[last] templateTitle[2] to play at casinos .

generated_template: This statistic shows the results of a survey on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[5] templateTitle[6] as of May templateTitle[7] . The survey , templateYValue[max] percent of templateYLabel[2] stated they had templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] .
generated: This statistic shows the results of a survey on the Most popular games with in visitors in as of May the . The survey , 48 percent of respondents stated they had Black_Jack Black_Jack Black_Jack Black_Jack Black_Jack .


Example 2:
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[2] of templateTitle[0] , templateTitle[2] , and templateTitle[5] for the fiscal years templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[2] at templateTitle[0] , templateTitle[2] , and templateTitle[5] reached a high in templateXValue[2] with templateYValue[2] employed at the templateTitle[5] that templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[2] thousand templateYLabel[2] .
generated: This statistic shows the Number of employees of Becton , Dickinson from 2011 to 2018 . In 2016 , Becton , Dickinson Number amounted to 50900 thousand employees .


Example 3:
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[2] templateYLabel[3] in templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[3] has declined during the period , from the peak of roughly 61.8 thousand in templateXValue[9] to around 55.1 thousand in templateXValue[max] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[3] , the global templateTitle[2] templateYLabel[0] templateYLabel[1] increased from templateYValue[3] percent .
generated: The statistic shows the total global Number of Number of births from 2008 to 2018 . In 2015 , the Number of births Number amounted to 59058 live births N/A . In 2015 , the global births Number of increased from 59058 percent .


Example 4:
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[2] and templateTitle[4] for each templateXLabel[0] templateXLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] 's templateXLabel[1] & templateYLabel[0] templateYLabel[0] came to 1.84 billion templateYLabel[3] templateYLabel[4] . This represented a small portion of templateTitle[0] 's net revenue , which reached 29.1 billion templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the United States between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows HPE : research Expenditure in the United States between 2013 and 2019 . In 2019 , HPE : research Expenditure amounted to approximately 2338 million U.S. dollars .


Example 5:
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 . In 2018 , the GDP per capita in Colombia amounted to around 6,641.51 U.S. dollars .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the total templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the total 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 . In 2018 , the GDP per capita in Colombia amounted to around 6641.51 U.S. dollars .


Example 6:
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[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[max] percent templateYLabel[2] to the templateYLabel[4] 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 increased by around 4.55 percent compared to the previous Year .


Example 7:
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 . The country also has about a third of all airports in the world - more than any other country : around 15,000 airports of all sizes , out of which more than 5,000 have paved runways . Hartsfield-Jackson Atlanta International in Georgia is the busiest airport in the country , having handled a little over 50 million passengers in 2018 alone . Major airline companies in the United States can be classified into three categories : mainline passenger lines , regional airlines and freight carriers . Mainline passenger companies include carriers such as Alaska Airlines , Delta Air Lines , JetBlue , Hawaiian Airlines , Southwest Airlines , United Airlines , American Airlines or Virgin America . Regional airlines include the following carriers : Envoy Air , ExpressJet and SkyWest Airlines , while the largest freight carriers are FedEx Express and UPS Airlines ; these two companies topped a worldwide ranking of airlines based on cargo volume in 2018 . In 2016 , the ultra low-cost carrier , Allegiant Air , had an operating margin of 30 percent , followed by Ryanair . Based on passenger traffic , American Airlines came first in a worldwide ranking , with around 200 million passengers , while Southwest recorded the largest amount of domestic enplanements:159 million in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of commercial templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[1] . In templateXValue[1] , the templateYLabel[0] templateYLabel[1] of commercial templateTitle[3] is projected to reach around 28 templateYLabel[3] templateYLabel[4] templateYLabel[5] . templateTitle[3] industry In templateXValue[2] , the templateYLabel[4] templateTitle[3] industry generated total operating revenue of almost 240 templateYLabel[3] templateYLabel[4] templateYLabel[5] , making the United States one of the largest markets for the templateTitle[3] industry templateTitle[4] . The country also has about a third of all airports in the templateTitle[4] templateTitle[6] more than any other country : around 15,000 airports of all sizes , out of which more than 5,000 have paved runways . Hartsfield-Jackson Atlanta International in Georgia is the busiest airport in the country , having handled a little over 50 million passengers in templateXValue[2] alone . Major templateTitle[3] companies in the United States can be classified into three categories : mainline passenger templateTitle[3] , regional templateTitle[3] and freight carriers . Mainline passenger companies include carriers such as Alaska templateTitle[3] , Delta templateTitle[3] templateTitle[3] , JetBlue , Hawaiian templateTitle[3] , Southwest templateTitle[3] , United templateTitle[3] , American templateTitle[3] or Virgin America . Regional templateTitle[3] include the following carriers : Envoy templateTitle[3] , ExpressJet and SkyWest templateTitle[3] , while the largest freight carriers are FedEx Express and UPS templateTitle[3] ; these templateTitle[5] companies topped a templateTitle[4] ranking of templateTitle[3] based on cargo volume in templateXValue[2] . In templateXValue[4] , the ultra low-cost carrier , Allegiant templateTitle[3] , had an operating margin of 30 percent , followed by Ryanair . Based on passenger traffic , American templateTitle[3] came first in a templateTitle[4] ranking , with around templateTitle[5] million passengers , while Southwest recorded the largest amount of domestic enplanements:159 million in templateXValue[2] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was at about templateYValue[0] percent in templateXValue[max] .
generated: The statistic shows the Net profit of airlines worldwide from 2006 to 2020 . In 2014 , the Net profit of airlines worldwide was at about 29.3 percent in 2020 .


Example 8:
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 United States 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 statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the U.S. residential sector in solar from 2005 to 2018 . In 2018 , the U.S. residential sector in solar was at about 27 Capacity in 1,000 N/A N/A .


Example 9:
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[3] in the United States from templateXValue[min] to templateXValue[max] . According to the report , U.S. templateTitle[3] templateYLabel[0] amounted to approximately templateYValue[2] templateYLabel[2] templateYLabel[3] in templateXValue[3] , down from templateYValue[4] templateYLabel[2] templateYLabel[3] the previous templateXLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was valued at templateYValue[max] templateYLabel[0] templateYLabel[1] . In templateXValue[7] , this number of templateYLabel[0] templateYLabel[1] increased from templateYValue[min] in templateXValue[9] and templateXValue[8] . For further information , the templateYLabel[1] templateYLabel[2] in templateXValue[14] .
generated: The statistic shows the American imports of corn 2001 size - from 2001 to 2019 . In 2001 , the American imports of corn 2001 was valued at 160 Imports in . In 2012 , this number of Imports in increased from 8 in 2010 and 2011 . For further information , the in million in 2005 .


Example 10:
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[11] on one-night-stands . templateYValue[max] percent of templateYLabel[2] in the United States stated they templateTitle[4] templateTitle[5] a templateTitle[7] before .

generated_template: This statistic shows the results of a survey , conducted by Gallup in the United States in May templateTitle[11] , on templateTitle[3] templateTitle[4] templateTitle[5] gay and lesbian templateTitle[7] . During this survey , templateYValue[max] percent of the templateYLabel[2] stated that they used the social networking site .
generated: This statistic shows the results of a survey , conducted by Gallup in the United States in May 2012 , on who have had gay and lesbian one-night-stand . During this survey , 58.1 percent of the respondents stated that they used the social networking site .


Example 11:
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[7] in the United States . During the August templateTitle[8] survey , templateYValue[max] percent of templateYLabel[2] stated that they were templateTitle[3] 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[2] stating that they knew the platform .

generated_template: This statistic shows the results of a survey , conducted in December 2018 in the United States . During the survey , it was found that templateYValue[max] percent of Mexican templateYLabel[2] stated that they would templateXValue[0] templateXValue[0] .
generated: This statistic shows the results of a survey , conducted in December 2018 in the United States . During the survey , it was found that 75 percent of Mexican respondents stated that they would groupon.com groupon.com .


Example 12:
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[2] templateTitle[3] templateYLabel[2] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around 32.2 templateYLabel[4] templateTitle[2] templateTitle[3] templateYLabel[2] in the country , up from templateYValue[min] templateYLabel[4] in templateXValue[min] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[4] templateYLabel[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateYLabel[0] of templateTitle[4] templateYLabel[2] in templateTitle[0] amounted to approximately templateYValue[5] templateYLabel[4] , this figure is projected to grow to templateYValue[max] templateYLabel[4] in templateXValue[max] .
generated: This statistic shows the Number of users users in Number from 2017 to 2023 . In 2018 , the Number of users users in Number amounted to approximately 33.0 millions , this figure is projected to grow to 37.2 millions in 2023 .


Example 13:
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[4] 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[4] was at templateYValue[min] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] 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[4] 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 14:
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 templateTitle[3] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateYValue[1] templateYLabel[3] templateTitle[3] were sold templateTitle[0] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] templateYLabel[3] units of templateYLabel[0] were shipped worldwide .
generated: The statistic shows Worldwide shipments of Unit shipments in the United States from 2013 to 2019 . In 2017 , 363.0 millions units of Unit were shipped worldwide .


Example 15:
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 . In the United Kingdom , prices dropped by 31.5 percent , while in Canada , prices dropped by 15.3 percent from January 2014 to 2015 . However , in the United States , prices increased from 2.44 U.S. dollars per gallon in 2005/2006 to its highest in the decade at 3.88 U.S. dollars per gallon in 2013/2014 . Domestic heating oil is used for the heating of domestic homes . It is a liquid petroleum product . It is often delivered to homes by a tank truck and stored in above-ground storage tanks . In the United States , this type of oil is known as No . 2 heating oil and prices are tracked by the Department of energy . The average annual New York harbor No . 2 heating oil spot prices have also increased from 0.69 U.S. dollars per gallon in 1990 to peak at 3.02 U.S. dollars per gallon in 2012 . However , in new single-family houses , use of heating oil in the U.S. is rather uncommon , accounting for about one percent of the heating type share where gas accounts for 59 percent . Among all occupied housing units , fuel oil was utilized in about 4.7 percent of units , while electricity was used in 39 percent .
gold_template: This statistic shows the templateYLabel[0] in the templateYLabel[4] of templateTitle[2] templateYLabel[2] templateYLabel[3] per liter in templateTitle[7] templateTitle[8] for the period between December templateTitle[9] and December templateTitle[11] . In December templateTitle[11] , the templateTitle[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[6] was some templateYValue[6] percent lower than in December templateTitle[9] . templateTitle[2] templateYLabel[2] templateYLabel[3] The templateYLabel[4] of templateTitle[2] templateYLabel[2] templateYLabel[3] per liter has decreased in many templateTitle[8] . In the templateXValue[4] templateXValue[4] , prices dropped by 31.5 percent , while in templateXValue[0] , prices dropped by 15.3 percent from January 2014 to 2015 . However , in the templateXValue[4] templateXValue[last] , prices increased from 2.44 U.S. dollars per gallon in 2005/2006 to its highest in the decade at 3.88 U.S. dollars per gallon in 2013/2014 . templateTitle[2] templateYLabel[2] templateYLabel[3] is used for the templateYLabel[2] of templateTitle[2] homes . It is a liquid petroleum product . It is often delivered to homes by a tank truck and stored in above-ground storage tanks . In the templateXValue[4] templateXValue[last] , this type of templateYLabel[3] is known as No . templateTitle[9] templateYLabel[2] templateYLabel[3] and prices are tracked by the Department of energy . The average annual New York harbor No . templateTitle[9] templateYLabel[2] templateYLabel[3] spot prices have also increased from 0.69 U.S. dollars per gallon in 1990 to peak at 3.02 U.S. dollars per gallon in 2012 . However , in new single-family houses , use of templateYLabel[2] templateYLabel[3] in the U.S. is rather uncommon , accounting for about templateTitle[9] percent of the templateYLabel[2] type share where gas accounts for 59 percent . Among all occupied housing units , fuel templateYLabel[3] was utilized in about 4.7 percent of units , while electricity was used in 39 percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the global templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[9] , templateTitle[10] templateXLabel[0] . According to the source , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateXValue[0] templateXValue[0] . In that year , the templateTitle[0] was templateYValue[max] percent of templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the Change in of the global oil price in in 2018 , - Country . According to the source , the Change in heating in the Canada Canada . In that year , the Change was 7 percent of in heating .


Example 16:
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] templateTitle[3] end users in templateTitle[5] templateTitle[10] from templateXValue[0] to templateXValue[last] . In the first half of templateXValue[last] , the average templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] was templateYValue[14] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

generated_template: This statistic shows the average templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] in templateTitle[5] templateTitle[10] from templateXValue[0] to templateXValue[last] . In the first half of templateXValue[12] , the average templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] was 17.6 templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh and had increased to templateYValue[max] the following templateXLabel[0] .
generated: This statistic shows the average Electricity prices for households per in Estonia semi-annually from 2010_S1 to 2017_S1 . In the first half of 2016_S1 , the average Electricity prices for households was 17.6 Euro cents per kWh and had increased to 13.67 the following Year .


Example 17:
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 . Malign neoplasms ( cancerous tumors ) and diseases of the respiratory system were the second and third most common cause of death . Ischemic heart disease the most common circulation system disease Among the diseases in the circulation system , Chronic ischemic heart disease is the one that causes the most deaths . It is when the blood flow to the heart muscle is reduced due to blocked arteries of the heart . In 2017 , 59.6 deaths occurred among hundred thousand inhabitants in Sweden as a result of ischemic heart disease .
gold_template: The templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitle[4] 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[4] 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] . Malign neoplasms ( cancerous tumors ) and diseases of the respiratory system were the second and third most common cause of templateYLabel[0] . Ischemic heart disease the most common circulation system disease Among the diseases in the circulation system , Chronic ischemic heart disease is the templateTitle[7] that causes the most deaths . It is when the blood flow to the heart muscle is reduced due to blocked arteries of the heart . In templateXValue[1] , 59.6 deaths occurred among hundred thousand inhabitants in templateTitle[4] as a result of ischemic heart disease .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States amounted to about templateYValue[0] templateYLabel[5] .
generated: This statistic shows the Crude death rate ( Death ) rate per in 2008 from 2008 to 2018 . In 2018 , the Death rate per in the United States amounted to about 9.1 population .


Example 18:
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[3] templateTitle[5] in the templateTitle[8] templateTitle[9] ( templateTitle[11] ) templateTitle[13] templateXValue[min] templateTitle[3] to templateXValue[max] . In templateXValue[min] , templateTitle[5] made 40.68 billion templateYLabel[3] templateYLabel[4] in templateYLabel[0] , which is templateTitle[3] to increase to 52.71 billion templateYLabel[3] templateYLabel[4] by templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] amounted to around templateYValue[max] percent .
generated: The statistic shows the Sales in million in the Tesco in from 2010 to 2020 . In 2017 , the Edible grocery sales forecast for Tesco in amounted to around 52714.03 percent .


Example 19:
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[2] to the templateTitle[5] templateTitle[6] in templateTitle[8] from templateXValue[min] to templateXValue[max] . Approximately templateYValue[0] templateYLabel[4] people visited the templateTitle[5] templateTitle[6] art museum in templateTitle[8] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[2] to the templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] in the United States from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[2] to the templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] amounted to approximately templateYValue[0] templateYLabel[4] in templateXValue[max] .
generated: This statistic shows the Number of recreational visitors to the Tate Modern in London in the United States from 2007 to 2018 . The Number of visitors to the Tate Modern in London amounted to approximately 5.83 millions in 2018 .


Example 20:
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[2] templateTitle[3] templateTitle[4] in the years templateXValue[min] to templateXValue[max] . The templateTitle[2] templateTitle[3] templateTitle[4] , a franchise of the National Football League , generated templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateTitle[5] templateTitle[6] gate receipts in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[1] of templateTitle[3] was templateYValue[0] percent .
generated: The statistic shows the Ticket sales of England from 2010 to 2018 . In 2018 , the NFL sales of England was 104 percent .


Example 21:
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 . Ice hockey is a popular sport in Finland . The Finnish Ice Hockey Association , a member of International Ice Hockey Federation , exists since 1929 . In the beginning of 2017 , Finland had 190,000 inhabitants with ice hockey as their hobby . Some of the best known Finnish ice hockey players include Teemu Selänne , Jari Kurri and Saku Koivu .
gold_template: The statistics shows the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[2] in templateTitle[4] from templateXValue[last] to templateXValue[0] . The templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[2] in templateXValue[0] amounted to nearly templateYValue[0] thousand . The highest templateYLabel[2] templateYLabel[0] was reported in the previous season ( templateXValue[1] ) with over 76 thousand templateYLabel[2] . templateTitle[0] templateTitle[1] is a popular sport in templateTitle[4] . The Finnish templateTitle[0] templateTitle[1] Association , a member of International templateTitle[0] templateTitle[1] Federation , exists since 1929 . In the beginning of templateTitle[7] , templateTitle[4] had 190,000 inhabitants with templateTitle[0] templateTitle[1] as their hobby . Some of the best known Finnish templateTitle[0] templateTitle[1] templateYLabel[2] include Teemu Selänne , Jari Kurri and Saku Koivu .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] in the templateTitle[5] templateTitle[6] in templateXValue[last] , a forecast for templateXValue[0] . In the templateXValue[0] season , there were approximately templateYValue[max] thousand templateYLabel[2] registered templateTitle[0] templateTitle[1] templateYLabel[2] in the templateTitle[5] templateTitle[6] according to the International templateTitle[0] templateTitle[1] Federation .
generated: The statistic shows the Number of players in the 2010/11 - in 2010/11 , a forecast for 2017/18 . In the 2017/18 season , there were approximately 76387 thousand players registered Ice hockey players in the 2010/11 - according to the International Ice hockey Federation .


Example 22:
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[4] templateTitle[5] 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[4] templateYLabel[5] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateTitle[9] billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by David Glass , who bought the templateYLabel[0] for 137 templateYLabel[3] templateYLabel[4] templateYLabel[5] in 2000 .
generated: This graph depicts the value of the Phoenix Suns ( Franchise of Major League Baseball from 2003 to 2020 . In 2020 , the Franchise had an estimated value of 2003 billion U.S. dollars . The Phoenix Suns ( are owned by David Glass , who bought the Franchise for 137 million U.S. dollars in 2000 .


Example 23:
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 . Retail electricity prices in the United States When Thomas A. Edison developed a system to generate and distribute electricity in order to market his greatest creation , the electric light bulb , he would have known little about the dramatic impact his invention would create on civilization 130 years after the inauguration of the world 's first power station on Pearl Street in Manhattan , New York . In 21st century America , electric power is omnipresent in peoples ' lives , as electricity is powering our factories , shops , homes - and even cars . It goes without saying that electricity consumption has to be paid for , and , naturally , people are worried about the size of their electricity bill . On average , retail electricity prices in the United States changed little from 9.90 cents per kilowatt hour in 2011 to 10.58 cents per kilowatt hour in 2017 . However , while power rates in the industrial and commercial sectors increased only moderately between 2011 and 2017 , residential electricity customers were charged 12.9 cents per kilowatt hour in 2016 , up from 11.72 cents per kilowatt hour in 2011 . The rising prices are justified by the costs of power production and power grid maintenance . Although the production cost of electricity generated from coal , natural gas and nuclear sources remained relatively flat , the integration of renewable energy sources , investments in smart grid technologies , growing peak demand and power blackouts caused by the vicissitudes of nature continued to trouble the electric utility industry in the recent past . The rates of U.S. electricity prices can also vary widely , with Hawaii residents experiencing some of the highest rates in the country .
gold_template: This statistic reflects the templateYLabel[0] templateTitle[2] templateYLabel[2] of templateYLabel[1] in the United States between templateXValue[min] and templateXValue[max] . Here , the templateYLabel[0] templateTitle[2] templateYLabel[2] of templateYLabel[1] was at templateYValue[max] templateYLabel[5] templateYLabel[6] templateYLabel[7] templateYLabel[8] in templateXValue[max] . A ranking of the largest templateYLabel[1] utilities companies in the United States can be found here as well as a ranking of global templateYLabel[1] templateTitle[4] in select countries . templateTitle[2] templateYLabel[1] templateTitle[4] in the United States When Thomas A. Edison developed a system to generate and distribute templateYLabel[1] in order to market his greatest creation , the templateYLabel[1] light bulb , he would have known little about the dramatic impact his invention would create on civilization 130 years after the inauguration of the world 's first power station on Pearl Street in Manhattan , New York . In 21st century America , templateYLabel[1] power is omnipresent in peoples ' lives , as templateYLabel[1] is powering templateYLabel[8] factories , shops , homes templateTitle[6] and even cars . It goes without saying that templateYLabel[1] consumption has to be paid for , and , naturally , people are worried about the size of their templateYLabel[1] bill . On templateYLabel[0] , templateTitle[2] templateYLabel[1] templateTitle[4] in the United States changed little from templateYValue[7] templateYLabel[5] templateYLabel[6] templateYLabel[7] templateYLabel[8] in templateXValue[7] to templateYValue[max] templateYLabel[5] templateYLabel[6] templateYLabel[7] templateYLabel[8] in templateXValue[1] . However , while power rates in the industrial and commercial sectors increased only moderately between templateXValue[7] and templateXValue[1] , residential templateYLabel[1] customers were charged 12.9 templateYLabel[5] templateYLabel[6] templateYLabel[7] templateYLabel[8] in templateXValue[2] , up from 11.72 templateYLabel[5] templateYLabel[6] templateYLabel[7] templateYLabel[8] in templateXValue[7] . The rising templateTitle[4] are justified by the costs of power production and power grid maintenance . Although the production cost of templateYLabel[1] generated from coal , natural gas and nuclear sources remained relatively flat , the integration of renewable energy sources , investments in smart grid technologies , growing peak demand and power blackouts caused by the vicissitudes of nature continued to trouble the templateYLabel[1] utility industry in the recent past . The rates of templateYLabel[4] templateYLabel[1] templateTitle[4] can also vary widely , with Hawaii residents experiencing some of the highest rates in the country .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] templateTitle[4] was at approximately templateYValue[0] percent .
generated: This statistic shows the Average electricity of the prices from 1990 to 2018 . In 2018 , the Average electricity in electricity prices was at approximately 10.58 percent .


Example 24:
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] templateYLabel[2] capita in the templateTitle[4] templateTitle[5] and templateTitle[7] from templateXValue[min] to templateXValue[max] . In the templateTitle[4] templateTitle[5] and templateTitle[7] , the consumer templateTitle[0] templateYLabel[0] templateYLabel[2] capita was templateYValue[min] templateYLabel[2] in templateXValue[9] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[2] capita is projected to reach templateYValue[max] templateYLabel[2] .

generated_template: This statistic shows the number of templateTitle[0] templateYLabel[0] templateYLabel[1] projects templateYLabel[2] templateYLabel[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] was templateYValue[0] templateYLabel[5] templateYLabel[6] .
generated: This statistic shows the number of PC Penetration in projects percent N/A from 2005 to 2015 . In 2015 , the PC Penetration in was 7 N/A N/A .


Example 25:
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[3] templateYLabel[4] templateYLabel[5] . The spirits company is based in Wolfenbüttel , Germany .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] , there were templateYValue[1] thousand templateYLabel[2] in the United States .
generated: The statistic shows the total global Annual of Mast-Jägermeister SE - from 2009 to 2016 . In 2013 , the Mast-Jägermeister SE - , there were 106.95 thousand in in the United States .


Example 26:
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] templateTitle[3] market in templateTitle[5] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateYLabel[0] amounted to roughly templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic illustrates the annual templateYLabel[0] of the templateTitle[3] templateTitle[4] ( templateTitle[3] Haushaltsgeraete GmbH ) between the templateXValue[min] to templateXValue[max] fiscal years . In its templateXValue[max] fiscal templateXLabel[0] , the templateTitle[3] templateTitle[4] generated approximately templateYValue[0] templateYLabel[2] templateYLabel[3] in templateYLabel[0] .
generated: The statistic illustrates the annual Revenue of the cars in ( cars Haushaltsgeraete GmbH ) between the 2000 to 2018 fiscal years . In its 2018 fiscal Year , the cars in generated approximately 84.7 billion euros in Revenue .


Example 27:
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 . Despite the decrease in turnover , in the same year Bulgari reported a considerable net profit increase amounting to over 176 million euros . Not only jewelry Established in 1884 in Rome , the brand Bulgari became popular among celebrities in the 1930s and established a reputation for Italian excellence through the years . After launching on the Milan Stock Exchange in 1995 , the company grew in profitability . In the early 2000s , Bulgari started exploring new segments , opening the first Bulgari Hotel in Milan and launching new products such as perfumes and accessories . In 2012 , Bulgari was acquired by the French luxury goods conglomerate LVMH , which controls , among others , the brand Louis Vuitton , considered to be the most valuable brand worldwide in the luxury sector . Italian jewelry Bulgari is one of the most prominent brands in a sector that has a long history in Italy and is well-known worldwide . Jewelry manufacturing in Italy registered a turnover of 8.5 billion euros in 2017 and has been growing at a fast pace in the last few years .
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 195 templateYLabel[2] templateYLabel[3] . In templateXValue[max] the templateYLabel[0] decreased to 177 templateYLabel[2] templateYLabel[3] . Despite the decrease in templateYLabel[0] , in the same templateXLabel[0] templateTitle[0] reported a considerable net profit increase amounting to templateYLabel[0] 176 templateYLabel[2] templateYLabel[3] . Not only jewelry Established in 1884 in Rome , the brand templateTitle[0] became popular among celebrities in the 1930s and established a reputation for Italian excellence through the years . After launching on the Milan Stock Exchange in 1995 , the company grew in profitability . In the early 2000s , templateTitle[0] started exploring new segments , opening the first templateTitle[0] Hotel in Milan and launching new products such as perfumes and accessories . In templateXValue[5] , templateTitle[0] was acquired by the French luxury goods conglomerate LVMH , which controls , among others , the brand Louis Vuitton , considered to be the most valuable brand worldwide in the luxury sector . Italian jewelry templateTitle[0] is templateTitle[3] of the most prominent brands in a sector that has a long history in Italy and is well-known worldwide . Jewelry manufacturing in Italy registered a templateYLabel[0] of 8.5 billion templateYLabel[3] in templateXValue[max] and has been growing at a fast pace in the last few years .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] had a templateYLabel[0] of approximately templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Turnover of turnover 2011 - 2017 N/A between 2011 and 2017 . In 2017 , the Bulgari turnover 2011 - had a Turnover of approximately 194.9 million euros .


Example 28:
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[8] of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[3] templateYValue[9] templateYLabel[4] templateYLabel[5] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitle[3] templateTitle[4] templateTitle[8] 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[4] templateYLabel[5] .
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 23.64 U.S. dollars .


Example 29:
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 . Overall , Nintendo generated 113 billion Japanese yen in revenue from handheld hardware that year , and 109 billion yen from handheld software .
gold_template: This statistic shows the number of templateTitle[0] templateTitle[1] hardware templateYLabel[3] 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[2] templateTitle[1] consoles , down from templateYValue[1] templateYLabel[2] templateYLabel[3] 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[2] copies of Pokemon X/Y for templateTitle[1] were sold . Overall , templateTitle[0] generated 113 billion Japanese yen in revenue from handheld hardware that templateXLabel[0] , and 109 billion yen from handheld software .

generated_template: In templateXValue[max] , an estimated templateYValue[max] templateYLabel[2] smartwatches were sold in the United States . Between templateXValue[min] and templateXValue[max] annual templateTitle[0] templateYLabel[0] grew from just 600 thousand templateYLabel[3] to over 15 templateYLabel[2] as these devices skyrocketed in popularity . Smartwatches While digital wristwatches have been around for decades , true smartwatches are a relatively new phenomena in the consumer electronics industry . These modern devices typically include features like touchscreens , biometric monitoring , and internet connections . Consumer electronics giants Apple and Samsung are the biggest names in the industry , controlling a combined market share of over 60 percent . Estimates suggest that over 90 templateYLabel[2] of these devices will be shipped around the world in 2019 with this figure rising to over 130 templateYLabel[2] by 2023 . Wearables Smartwatches are often classified under the overarching consumer wearables market which also includes products like templateTitle[0] wristbands , templateTitle[0] clothing , and head mounted displays . The internet connections present in many of these devices allow them to share statistics and information with other templateTitle[0] devices , forming a network which is often referred to as the Internet of Things ( IoT ) .
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 600 thousand units to over 15 million as these devices skyrocketed in popularity . Smartwatches While digital wristwatches have been around for decades , true smartwatches are a relatively new phenomena in the consumer electronics industry . These modern devices typically include features like touchscreens , biometric monitoring , and internet connections . Consumer electronics giants Apple and Samsung are the biggest names in the industry , controlling a combined market share of over 60 percent . Estimates suggest that over 90 million of these devices will be shipped around the world in 2019 with this figure rising to over 130 million by 2023 . Wearables Smartwatches are often classified under the overarching consumer wearables market which also includes products like Nintendo wristbands , Nintendo clothing , and head mounted displays . The internet connections present in many of these devices allow them to share statistics and information with other Nintendo devices , forming a network which is often referred to as the Internet of Things ( IoT ) .


Example 30:
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[2] templateYLabel[4] templateYLabel[5] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , approximately templateYValue[5] templateYLabel[7] people accessed the templateYLabel[4] through templateYLabel[2] devices . In templateXValue[max] , this figure is projected to reach about templateYValue[max] templateYLabel[7] templateYLabel[2] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[4] templateYLabel[5] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] templateYLabel[7] people accessed the templateYLabel[4] through their templateYLabel[2] templateYLabel[3] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] templateYLabel[7] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Number of mobile internet users in Japan from 2017 to 2023 . In 2017 , 64.0 millions people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 71.9 millions mobile phone internet users .


Example 31:
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[2] of templateTitle[4] templateTitle[5] templateTitle[6] 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: The statistic shows the templateYLabel[0] of templateYLabel[2] at templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , there were templateYValue[max] templateTitle[2] templateYLabel[2] in the United States .
generated: The statistic shows the Number of employees at Number of employees Random from 2005 to 2018 . In 2016 , there were 12812 employees employees in the United States .


Example 32:
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[6] templateTitle[7] ( templateTitle[9] ) from templateXValue[min] to templateXValue[max] . In templateXValue[9] , templateTitle[1] templateTitle[2] templateYLabel[0] was 988 templateYLabel[2] British pounds . In templateXValue[max] , templateTitle[1] templateTitle[2] templateYLabel[0] exceeded 1.25 billion British pounds .

generated_template: The statistic shows the total templateTitle[2] templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the British retailer generated approximately 10.4 billion British pounds in templateYLabel[0] increased by the templateXLabel[0] .
generated: The statistic shows the total office Revenue of Cinema box office from 2000 to 2019 . In 2019 , the British retailer generated approximately 10.4 billion British pounds in Revenue increased by the Year .


Example 33:
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: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[2] templateYLabel[0] amounted to templateYValue[min] percent .
generated: The statistic shows the total global Consumer of Average CPI in from 2012 to 2017 . In 2014 , the Average in Consumer amounted to 105.75 percent .


Example 34:
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 35:
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[4] templateTitle[5] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[4] templateTitle[5] templateYLabel[0] had an estimated templateYLabel[1] of 2.475 billion templateYLabel[4] templateYLabel[5] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by the Lamar Hunt family , who bought the templateYLabel[0] for 380 templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[min] .
generated: This graph depicts the Franchise value of the Houston Rockets ( from the National Football League from 2003 to 2020 . In 2020 , the Franchise value amounted to 2475 million U.S. dollars . The Houston Rockets ( are owned by the Lamar Hunt family , who bought the Franchise for 380 million U.S. dollars in 2003 .


Example 36:
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 . The best and most-known player in franchise history is Kevin Garnett , who was drafted by the Timberwolves directly out of high school and played for the team from 1995 to 2007 . The franchise plays its home games at the Target Center , which has a capacity of about 19 thousand for basketball games . The Minnesota Timberwolves averaged 16,340 spectators per home game in the 2012/13 season . In total the Timberwolves home games were attended by almost 670 thousand people . Tickets to Timberwolves games cost an average of 35.5 U.S. dollars in the 2013/14 season ; the overall league-average was at 52.5 U.S. dollars . Revenue of the franchise was at 116 million U.S. dollars in 2012/13 . Forbes ranks the Minnesota Timberwolves 26th out of all 30 teams in the NBA in terms of franchise value with an estimated value of 430 million U.S. dollars . The average franchise value in the NBA was 634 million U.S. dollars in 2014 . As of September 2013 , the Minnesota Timberwolves had about 330 thousand fans on their official Facebook account . The team 's Twitter account was followed by around 183 thousand users .
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[2] templateYLabel[3] templateYLabel[4] . templateTitle[0] templateTitle[1] templateTitle[5] 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 . The best and most-known player in franchise history is Kevin Garnett , who was drafted by the templateTitle[1] directly out of high school and played for the team from 1995 to 2007 . The franchise plays its home games at the Target Center , which has a capacity of about 19 thousand for basketball games . The templateTitle[0] templateTitle[1] averaged 16,340 spectators per home game in the 2012/13 season . In total the templateTitle[1] home games were attended by almost 670 thousand people . Tickets to templateTitle[1] games cost an average of 35.5 templateYLabel[3] templateYLabel[4] in the 2013/14 season ; the overall league-average was at 52.5 templateYLabel[3] templateYLabel[4] . templateYLabel[0] of the franchise was at templateYValue[5] templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2012/13 . Forbes ranks the templateTitle[0] templateTitle[1] 26th out of all 30 teams in the NBA in terms of franchise value with an estimated value of 430 templateYLabel[2] templateYLabel[3] templateYLabel[4] . The average franchise value in the NBA was 634 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2014 . As of September 2013 , the templateTitle[0] templateTitle[1] had about 330 thousand fans on their official Facebook account . The team 's Twitter account was followed by around 183 thousand users .

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[2] templateYLabel[3] templateYLabel[4] .
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 37:
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[4] in templateXValue[min] and templateXValue[max] . It was reported that the average templateYLabel[0] templateTitle[1] of companies listed on the templateTitle[5] rose from templateYValue[min] templateYLabel[3] US templateYLabel[5] in templateXValue[min] to templateYValue[max] templateYLabel[3] US templateYLabel[5] in templateXValue[max] .

generated_template: The statistic shows the global templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] was valued at approximately templateYValue[0] percent .
generated: The statistic shows the global Market of of the NASDAQ exchange from 1999 to 2013 . In 2013 , the Market of the NASDAQ was valued at approximately 1.16 percent .


Example 38:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was estimated to be templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] 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 39:
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[4] in templateTitle[6] from templateXValue[min] to 2050*.The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[4] into templateTitle[7] 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[4] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of the templateTitle[4] of templateTitle[6] was templateYValue[7] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] in templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[4] into templateTitle[7] 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[4] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of the Nigerian templateTitle[4] was templateYValue[7] templateYLabel[3] .
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 2015 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 Nigerian population was 27.5 years .


Example 40:
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[9] survey among templateTitle[0] on the concepts essential for the templateTitle[0] templateTitle[7] . During the survey , templateYValue[15] percent of templateYLabel[2] stated that templateXValue[16] and templateXValue[16] is essential for the templateTitle[0] templateTitle[7] .

generated_template: This statistic presents the results of a survey on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the United States as of templateTitle[10] . The survey found that templateXValue[0] templateXValue[0] was the most popular templateTitle[2] templateTitle[3] templateXValue[1] templateXValue[1] templateXValue[1] with templateYValue[max] percent of templateYLabel[2] .
generated: This statistic presents the results of a survey on the Americans on the concept in the United States as of N/A . The survey found that Personal_freedom Personal_freedom was the most popular the concept Religious_freedom Religious_freedom Religious_freedom with 66 percent of respondents .


Example 41:
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 . In comparison , visual arts convey artistic expression using inanimate objects , such as paintings or sculptures . Between 2003 and 2012 , attendance at performing art events steadily declined in the U.S. , reaching its lowest point in 2012 with 74.94 million attendees . The decline in attendance can be attributed to varying factors . A 2012 survey into the barriers of attending visual and performing art events found that 38.3 percent of respondents felt art events cost too much . Similarly , a separate 2014 survey conducted by LaPlaca Cohen found that the main economic reason for decreasing cultural participation was people reducing their expenses across the board . Despite the decrease in public participation in the arts , the number of people studying within the field has increased . Between 1999 and 2012 , the number of students receiving a visual or performing arts bachelors degree in the U.S. increased from 54,505 to 95,797 . A similar trend can be seen over the same time period in regards to the number of visual or performing arts masters and doctoral degrees obtained . In the U.S. , attendance at art events can be correlated with level of education . In 2012 , those with a graduate school education were the most likely to have attended an art activity , with 65.6 percent having attended an event in the last year . The adults who were the least likely had the lowest level of education , having completed only grade school . Only 6.5 percent of this group had attended an art event in the last year .
gold_template: The statistic above shows the templateYLabel[0] at templateTitle[2] templateTitle[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXLabel[0] templateXValue[max] , approximately templateYValue[min] templateYLabel[2] people attended a templateTitle[2] templateTitle[3] templateTitle[4] at least once . templateTitle[2] templateTitle[3] – additional information templateTitle[2] templateTitle[3] are any type of templateTitle[3] templateTitle[2] in which a performer physically conveys an artistic piece ; this includes templateYLabel[0] , opera , symphony and theatre performances . In comparison , visual templateTitle[3] convey artistic expression using inanimate objects , such as paintings or sculptures . Between templateXValue[min] and templateXValue[1] , templateYLabel[0] at templateTitle[2] templateTitle[3] templateTitle[4] steadily declined in the templateTitle[7] , reaching its lowest point in templateXValue[1] with templateYValue[1] templateYLabel[2] attendees . The decline in templateYLabel[0] can be attributed to varying factors . A templateXValue[1] survey into the barriers of attending visual and templateTitle[2] templateTitle[3] templateTitle[4] found that 38.3 percent of respondents felt templateTitle[3] templateTitle[4] cost too much . Similarly , a separate 2014 survey conducted by LaPlaca Cohen found that the main economic reason templateTitle[2] decreasing cultural participation was people reducing their expenses across the board . Despite the decrease in public participation in the templateTitle[3] , the number of people studying within the field has increased . Between 1999 and templateXValue[1] , the number of students receiving a visual or templateTitle[2] templateTitle[3] bachelors degree in the templateTitle[7] increased from 54,505 to 95,797 . A similar trend can be seen over the same time period in regards to the number of visual or templateTitle[2] templateTitle[3] masters and doctoral degrees obtained . In the templateTitle[7] , templateYLabel[0] at templateTitle[3] templateTitle[4] can be correlated with level of education . In templateXValue[1] , those with a graduate school education were the most likely to have attended an templateTitle[3] activity , with 65.6 percent having attended an templateTitle[4] in the last templateXLabel[0] . The adults who were the least likely had the lowest level of education , having completed only grade school . Only 6.5 percent of this group had attended an templateTitle[3] templateTitle[4] in the last templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] amounted to templateYValue[3] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Attendance in of performing in in from 2003 to 2013 . In 2012 , Attendance at performing Attendance in amounted to 74.98 N/A N/A N/A .


Example 42:
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] , templateTitle[0] reported a templateYLabel[0] profit of templateYValue[0] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the annual templateYLabel[0] templateTitle[4] of templateTitle[0] templateTitle[1] worldwide templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] amounted to 1.8 billion templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the annual Net net of Regal Entertainment worldwide income 2006 to 2017 . In 2017 , Regal Entertainment Group Net income amounted to 1.8 billion U.S. dollars .


Example 43:
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[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[2] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[max] percent templateYLabel[2] to the templateYLabel[4] 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 4.05 percent compared to the previous Year .


Example 44:
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[6] templateTitle[1] representatives and stakeholders at the London WTM exhibition ( UK ) to what extent do they templateTitle[3] the templateTitle[0] templateTitle[1] to templateXValue[3] or templateXValue[0] in templateTitle[8] . Of templateYLabel[2] , templateYValue[max] percent believed that the templateTitle[0] templateTitle[1] will templateXValue[3] templateXValue[1] in templateTitle[8] .

generated_template: This statistic shows the results of a survey , conducted in 2016 in the United States as of May templateTitle[9] . During that period , templateYValue[max] percent of templateYLabel[2] stated they had templateXValue[0] templateXValue[0] templateXValue[1] templateXValue[1] templateXValue[1] .
generated: This statistic shows the results of a survey , conducted in 2016 in the United States as of May N/A . During that period , 56 percent of respondents stated they had Significantly_decline Significantly_decline Slightly_decline Slightly_decline Slightly_decline .


Example 45:
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[6] templateTitle[7] templateTitle[8] 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: The graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[6] templateTitle[7] templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of the templateTitle[6] templateTitle[7] templateTitle[8] was at templateYValue[max] .
generated: The graph depicts the total Regular season Home attendance of the Los Angeles Chargers from 2006 to 2019 . In 2019 , the Regular season home Home attendance of the Los Angeles Chargers was at 545104 .


Example 46:
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 presents the annual templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] .
generated: The statistic presents the annual Annual CAC 40 ( Index ) from 1995 to 2019 . In 2016 , the Annual CAC 40 Index amounted to 4862.31 N/A N/A .


Example 47:
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 United States templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[4] templateTitle[5] templateTitle[6] templateYLabel[0] templateYLabel[1] reached approximately 33.2 billion templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] ( templateTitle[3] ) of templateTitle[6] templateTitle[7] templateTitle[8] Plc from templateXValue[min] to templateXValue[max] , in templateYLabel[3] templateYLabel[4] templateYLabel[5] . In templateXValue[max] , templateTitle[8] 's templateTitle[3] amounted to some 23.01 billion templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Capital expenditure ( the ) of industry 2004 - Plc from 2004 to 2018 , in million U.S. dollars . In 2018 , - 's the amounted to some 23.01 billion U.S. dollars .


Example 48:
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[4] templateTitle[5] of templateTitle[7] templateTitle[8] in templateTitle[8] templateTitle[11] in the United States from templateXValue[min] to templateXValue[max] . templateTitle[8] templateTitle[11] charged an templateYLabel[0] templateYLabel[1] of templateYValue[5] templateYLabel[3] templateYLabel[4] templateTitle[2] a templateTitle[4] templateTitle[5] of templateTitle[7] templateTitle[8] in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] a templateTitle[4] templateTitle[5] of templateTitle[7] templateTitle[8] in templateTitle[9] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] was templateYValue[max] templateYLabel[3] templateYLabel[4] templateTitle[2] a templateTitle[4] templateTitle[5] of templateYValue[min] templateYLabel[3] templateYLabel[4] templateTitle[2] a templateTitle[4] templateTitle[5] of templateTitle[7] templateTitle[8] in templateXValue[max] .
generated: The statistic shows the Average price for a full set of gel toenails in in from 2009 to 2014 . The Average price was 39.16 U.S. dollars for a full set of 33.95 U.S. dollars for a full set of gel toenails in 2014 .


Example 49:
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[3] ) templateYLabel[1] of the templateTitle[7] templateTitle[8] in November templateTitle[14] was templateYValue[min] percent , which together with the previous templateXLabel[0] , was the lowest templateYLabel[1] recorded in this templateYValue[5] year period . Between November templateTitle[12] and November templateTitle[14] the templateTitle[3] templateYLabel[1] was at it 's highest in November of templateTitle[12] , when an templateYLabel[0] templateYLabel[1] of templateYValue[max] percent was recorded .

generated_template: In templateTitle[11] , there were templateYValue[0] percent of the highest level of templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in the templateTitle[10] , broken down by templateYValue[max] percent since the previous templateXLabel[0] . The templateYLabel[1] was templateTitle[12] of the lowest templateYLabel[0] was templateYValue[13] percent in templateTitle[11] , when it in templateXValue[1] , when templateYValue[1] percent . templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] here here here here The term of templateTitle[0] templateTitle[1] is templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] can be found here . In In 2018 , the templateTitle[0] templateTitle[1] of templateYValue[1] percent was the Office of the templateYValue[14] percent in the previous year . The CPI of the basket of goods used to calculate templateYLabel[0] instead of the templateTitle[0] states states with 3.6 percent in the consumer price index for all urban urban regions . The monthly templateYLabel[0] templateYLabel[1] has decreased since 2010 , the highest in the same rate in the United Kingdom ( , when reaching templateXValue[1] , 57 percent in 2018 , reaching templateYValue[0] in July templateTitle[10] templateTitle[11] . Of Of these statistics templateTitle[1] templateYLabel[0] templateYLabel[1] was 1.9 percent in 2018 . Finally , Aruba had negative templateYLabel[0] for negative templateYLabel[0] templateYLabel[1] in the U.S. dollars . In 2018 , a other European Union 's forecasted to the preselected market basket of consumer goods and services purchased by households .
generated: In ) , there were 1.5 percent of the highest level of Inflation rate Inflation rate in the UK , broken down by 3.1 percent since the previous Month . The rate was 2017 of the lowest Inflation was 2.4 percent in ) , when it in Oct_'19 , when 1.5 percent . Inflation rate Inflation rate here here here here The term of Inflation rate is Inflation rate Inflation rate can be found here . In In 2018 , the Inflation rate of 1.5 percent was the Office of the 2.4 percent in the previous year . The CPI of the basket of goods used to calculate Inflation instead of the Inflation states states with 3.6 percent in the consumer price index for all urban urban regions . The monthly Inflation rate has decreased since 2010 , the highest in the same rate in the United Kingdom ( , when reaching Oct_'19 , 57 percent in 2018 , reaching 1.5 in July UK ) . Of Of these statistics rate Inflation rate was 1.9 percent in 2018 . Finally , Aruba had negative Inflation for negative Inflation rate in the U.S. dollars . In 2018 , a other European Union 's forecasted to the preselected market basket of consumer goods and services purchased by households .


Example 50:
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[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In the templateXLabel[0] templateXValue[4] , the templateTitle[0] templateTitle[3] templateTitle[4] templateYLabel[0] amounted to templateYValue[4] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows total templateYLabel[3] templateTitle[0] templateYLabel[0] templateTitle[2] the Supplemental Nutrition Assistance Program ( templateTitle[3] , formerly called templateTitle[6] templateTitle[7] ) from templateXValue[min] to templateXValue[max] . In templateXValue[2] , about templateYValue[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] were spent templateTitle[2] the Supplemental Nutrition Assistance Program .
generated: This statistic shows total U.S. Global Spending on the Supplemental Nutrition Assistance Program ( motorsports , formerly called - 2017 ) from 2011 to 2017 . In 2015 , about 5.43 billion U.S. dollars were spent on the Supplemental Nutrition Assistance Program .


Example 51:
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[2] in templateTitle[2] templateTitle[3] in the United States templateTitle[7] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[2] ( aged templateTitle[8] years and older ) in templateTitle[2] templateTitle[3] amounted to approximately templateYValue[0] templateYLabel[4] . All in all , the templateYLabel[0] of templateTitle[3] players in the United States seems to be on the rise .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] ( aged ) in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[2] ( aged templateTitle[8] years and older ) in templateTitle[2] templateTitle[3] amounted to approximately templateYValue[min] templateYLabel[4] .
generated: This statistic shows the Number of participants ( aged ) in the United States from 2006 to 2018 . In 2018 , the Number of participants ( aged 2006 years and older ) in indoor soccer amounted to approximately 4.24 millions .


Example 52:
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[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[5] templateTitle[6] amounted to approximately templateYValue[0] templateYLabel[4] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[5] templateTitle[6] templateTitle[7] 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[5] templateTitle[6] templateTitle[7] amounted to about templateYValue[max] templateYLabel[4] in templateXValue[1] .
generated: The timeline shows the Per capita consumption of fresh cucumbers 2000 in the United States from 2000 to 2018 . According to the report , the U.S. Per capita consumption of fresh cucumbers 2000 amounted to about 8.1 pounds in 2017 .


Example 53:
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[4] worldwide templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[4] spent approximately templateYValue[0] templateYLabel[3] templateYLabel[4] templateYLabel[5] on advertising and promotion .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] increased from templateYValue[3] percent .
generated: The statistic shows the total global Ad of Global ad spending from 2013 to 2019 . In 2016 , the Global ad spending Ad amounted to 634.9 in million U.S. . In 2016 , the Ad expenditure increased from 634.9 percent .


Example 54:
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 . Along with a high number of arrests , the arrest rate for all offenses in the United States was also high in 2017 , with 3,152.6 arrests per 100,000 residents . However , this arrest rate is almost half that of its peak in 1996 . Additionally , South Dakota had the highest arrest rate in the country in 2017 , and Massachusetts had the lowest . High numbers of arrests and unsolved crimes A high number of arrests does not necessarily correlate to a high number of solved cases , and in the U.S. , many cases remain unsolved . The crime clearance rate , or rate of closed cases , was less than half for violent crimes in U.S. , and less than 20 percent for property crimes .
gold_template: There were over templateYValue[min] million templateYLabel[2] templateTitle[5] templateTitle[6] templateTitle[7] in the United States in templateXValue[max] . This figure is a decrease from templateXValue[min] levels , when the templateYLabel[0] of templateYLabel[2] was over templateYValue[13] million . templateYLabel[2] rate in the U.S . Along with a high templateYLabel[0] of templateYLabel[2] , the templateYLabel[2] rate templateTitle[5] templateTitle[6] templateTitle[7] in the United States was also high in templateXValue[1] , with 3,152.6 templateYLabel[2] per 100,000 residents . However , this templateYLabel[2] rate is almost half that of its peak in templateXValue[22] . Additionally , South Dakota had the highest templateYLabel[2] rate in the country in templateXValue[1] , and Massachusetts had the lowest . High numbers of templateYLabel[2] and unsolved crimes A high templateYLabel[0] of templateYLabel[2] does not necessarily correlate to a high templateYLabel[0] of solved cases , and in the U.S. , many cases remain unsolved . The crime clearance rate , or rate of closed cases , was less than half templateTitle[5] violent crimes in U.S. , and less than templateTitle[10] percent templateTitle[5] property crimes .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] . In templateXValue[8] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States was at templateYValue[max] thousand templateYLabel[4] .
generated: The statistic shows the Number of arrests in the for all from 1990 to 2018 . In 2015 , the USA - number of . In 2010 , the Number of arrests in the United States was at 15284300 thousand N/A .


Example 55:
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 . What is more , that year there were 17.7 million Facebook users in the country . This number was predicted to reach 18.2 million in 2016 . As of August 2016 , women accounted for more than half of Canadian Facebook users .
gold_template: This statistic shows the responses on a study about social network usage in templateTitle[0] as of January templateTitle[5] . During the reported period , templateTitle[2] reached templateYValue[max] percent of Canadians aged 18 to 34 , while among Canadians aged 65 and older templateTitle[2] had a templateYValue[min] percent templateTitle[4] rate . In general the platform reached templateYValue[5] percent of people in templateTitle[0] . What is more , that templateXLabel[0] there were 17.7 million templateTitle[2] users in the country . This number was predicted to reach 18.2 million in 2016 . As of August 2016 , women accounted for more than half of Canadian templateTitle[2] users .

generated_template: This statistic gives information on the templateTitle[2] templateTitle[4] rate in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the Indonesian templateYLabel[2] were using the templateTitle[2] . In templateXValue[max] , this figure is projected to grow to templateYValue[max] percent .
generated: This statistic gives information on the Facebook penetration rate in Canada from 18-34 to 18-34 . In 18-34 , 32 percent of the Indonesian population were using the Facebook . In 18-34 , this figure is projected to grow to 75 percent .


Example 56:
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[2] templateYLabel[3] templateYLabel[4] templateTitle[7] templateXValue[min] to templateXValue[max] . In templateXValue[max] there were templateYValue[0] templateTitle[0] templateTitle[1] U.S. templateYLabel[2] templateYLabel[3] members .

generated_template: The statistic shows the total templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[9] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateTitle[2] templateYLabel[2] in the templateTitle[6] .
generated: The statistic shows the total Number of U.S Coast Guard personnel numbers in to from 1995 to 2010 . In 2010 , there were 42426 U.S Coast in the numbers .


Example 57:
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[2] templateYLabel[3] base from templateXValue[min] to templateXValue[max] . During the last reported fiscal period , the online furniture retailer had templateYValue[max] templateYLabel[5] templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateYLabel[5] templateYLabel[3] in the previous templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Number of of the - 2018 from 2013 to 2018 . In 2018 , the Wayfair active customers Number of amounted to 15.16 customers in millions .


Example 58:
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 . The third largest group ( 17.5 percent of individuals ) affirmed that the last time they cheated on their partner was with a stranger met in a particular context such as in the disco , at the gym , on holidays , etcetera . Reasons for cheating The most common reason why people in Italy cheated on their partner was the sexual attraction towards another person , indicated by 27.1 percent of respondents . About 22 percent of interviewees stated that the betrayal was the consequence of an argument with the current partner , while for 20 percent the mental attraction played a determinant role . Nevertheless , most of the people who stated to cheat did not intent to leave their partner . In 2017 , 61.4 percent of respondents declared they did not want to break up their relationship after cheating . Additionally , 27.2 percent were willing to leave the partner , but they did not do it . Most of the people who cheated would not repeat the betrayal . The follow up For the largest part of people , cheating was only a one-time adventure . 31.3 percent of Italian respondents considered their last betrayal a `` one-night '' escapade . However , for about 30 percent of interviewees , the infidelity became a love story that lasted some years and eventually came to an end . Additionally , for 24.9 percent of respondents cheating meant a short love story that lasted some weeks or months and then ended . Lastly , 5.4 percent of Italian interviewees declared to have a permanent lover .
gold_template: A survey conducted in templateTitle[8] reveals that the largest groups of templateTitle[3] templateYLabel[2] cheated on templateTitle[6] 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[6] templateXValue[5] templateTitle[1] a templateXValue[0] , while templateYValue[1] percent did templateTitle[0] templateTitle[1] a templateXValue[1] . However , it was common to templateTitle[4] templateTitle[1] strangers as well . The third largest group ( templateYValue[2] percent of individuals ) affirmed that the last time they cheated on templateTitle[6] templateXValue[5] was templateTitle[1] a templateXValue[2] templateXValue[2] in a templateXValue[2] templateXValue[2] such as in the disco , at the templateXValue[2] , on templateXValue[2] , etcetera . Reasons for cheating The most common reason why people in Italy cheated on templateTitle[6] templateXValue[5] was the sexual attraction towards another templateTitle[0] , indicated templateXValue[6] 27.1 percent of templateYLabel[2] . About 22 percent of interviewees stated that the betrayal was the consequence of an argument templateTitle[1] the current templateXValue[5] , while for templateTitle[8] percent the mental attraction played a determinant role . Nevertheless , most of the people templateTitle[2] stated to templateTitle[4] did templateXValue[4] intent to leave templateTitle[6] templateXValue[5] . In templateTitle[8] , 61.4 percent of templateYLabel[2] declared they did templateXValue[4] want to break up templateTitle[6] relationship after cheating . Additionally , 27.2 percent were willing to leave the templateXValue[5] , but they did templateXValue[4] do it . Most of the people templateTitle[2] cheated would templateXValue[4] repeat the betrayal . The follow up For the largest templateTitle[7] of people , cheating was only a one-time adventure . 31.3 percent of templateTitle[3] templateYLabel[2] considered templateTitle[6] last betrayal a `` one-night '' escapade . However , for about 30 percent of interviewees , the infidelity became a love story that lasted some years and eventually came to an end . Additionally , for 24.9 percent of templateYLabel[2] cheating meant a short love story that lasted some weeks or months and then ended . Lastly , 5.4 percent of templateTitle[3] interviewees declared to have a permanent lover .

generated_template: This statistic shows the results of a survey , conducted in December 2018 in the United States . During the survey , it was found that templateYValue[max] percent of templateYLabel[2] stated that templateYValue[min] percent of templateXValue[0] templateXValue[0] .
generated: This statistic shows the results of a survey , conducted in December 2018 in the United States . During the survey , it was found that 25.4 percent of respondents stated that 1.3 percent of Friend Friend .


Example 59:
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 templateYLabel[4] templateTitle[1] templateYLabel[0] industry 's templateTitle[6] number of templateYLabel[0] in templateYLabel[2] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were about 1.86 million templateTitle[1] templateYLabel[0] in templateYLabel[2] , while the industry 's revenue grew to around 23.6 billion templateYLabel[4] dollars .

generated_template: The statistic shows the total templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] was at templateYValue[max] thousand templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the total Cars of in the U.S. - from 2002 to 2012 . In 2012 , the Car in the U.S. was at 1861 thousand in thousands .


Example 60:
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[4] templateYLabel[2] at templateTitle[5] establishments in templateTitle[6] from templateXValue[min] to templateXValue[max] . There were over 129 templateYLabel[4] templateYLabel[2] at templateTitle[5] establishments in templateTitle[6] in templateXValue[max] , a rise since the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[4] templateYLabel[2] in templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitle[4] templateYLabel[2] in short-stay templateTitle[5] in templateTitle[6] have generally increased over this period , from around 12 templateYLabel[4] in templateXValue[min] to approximately templateYValue[max] templateYLabel[4] by templateXValue[max] .
generated: This statistic shows the Number of tourist arrivals in Spain from 2006 to 2017 . tourist arrivals in short-stay accommodation in Spain have generally increased over this period , from around 12 millions in 2006 to approximately 129.4 millions by 2017 .


Example 61:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] 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 62:
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[5] of templateTitle[2] templateTitle[3] templateYLabel[0] in templateTitle[7] in templateXValue[min] and with a templateTitle[0] templateTitle[0] templateXValue[max] . templateTitle[2] templateYLabel[0] were measured at templateYValue[min] templateYLabel[2] templateYLabel[3] in templateTitle[7] in templateXValue[min] , but were expected to grow to templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[max] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the previous templateXLabel[0] .
generated: The statistic shows Forecast : Online retail Sales worldwide from 2013 to 2018 . In 2013 , 25 percent of the previous Year .


Example 63:
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 . Reaction to Fukushima Daiichi Nuclear DisasterGlobally , nuclear power plants have a capacity totaling over 396.9 gigawatts as of 2018 . In 2018 , there were seven permanent nuclear reactor shutdowns that occurred around the world . Many of these shutdowns were in response to the Fukushima Daiichi nuclear disaster which occurred when an earthquake and a tsunami struck the region on March 11 , 2011 , causing equipment failure and three nuclear meltdowns . This was the second event after the Chernobyl disaster that was classified as a Level 7 event by the International Nuclear Event Scale , which communicates important safety information of nuclear accidents . The disaster prompted much of the public to lose confidence in nuclear energy and question its safety . Afterwards , many countries accelerated their plans for nuclear power plant shutdowns . As of 2017 , Germany , for example , accounted for about 2.8 percent of the world 's nuclear energy consumption after power plant shutdowns had been initiated . In Italy , a national referendum was held and almost 95 percent of people voted against plans to build new nuclear power plants . In the United States , some 65 percent of people favor the use of nuclear energy . In other countries like Germany , 77 percent of critics were already decidedly against nuclear energy prior to the events in Japan . In Japan , there were 37operational nuclear reactors as of June 2019 .
gold_template: This statistic represents the global templateYLabel[0] of templateTitle[4] templateTitle[0] reactor templateYLabel[2] 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] templateTitle[2] were templateYLabel[2] templateYLabel[2] permanently . In total , there were templateYValue[1] templateTitle[4] templateYLabel[2] in templateXValue[1] . Reaction to Fukushima Daiichi templateTitle[0] DisasterGlobally , templateTitle[0] templateTitle[1] templateTitle[2] have a capacity totaling over 396.9 gigawatts as of templateXValue[1] . In templateXValue[1] , there were templateYValue[1] templateTitle[4] templateTitle[0] reactor templateYLabel[2] that occurred around the world . Many of these templateYLabel[2] were in response to the Fukushima Daiichi templateTitle[0] disaster which occurred when an earthquake and a tsunami struck the region on March 11 , templateXValue[8] , causing equipment failure and templateYValue[0] templateTitle[0] meltdowns . This was the second event after the Chernobyl disaster that was classified as a Level templateYValue[1] event by the International templateTitle[0] Event Scale , which communicates important safety information of templateTitle[0] accidents . The disaster prompted much of the public to lose confidence in templateTitle[0] energy and question its safety . Afterwards , many countries accelerated their plans for templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[2] . As of templateXValue[2] , Germany , for example , accounted for about 2.8 percent of the world 's templateTitle[0] energy consumption after templateTitle[1] templateTitle[2] templateYLabel[2] had been initiated . In Italy , a national referendum was held and almost 95 percent of people voted against plans to build new templateTitle[0] templateTitle[1] templateTitle[2] . In the United States , some 65 percent of people favor the use of templateTitle[0] energy . In other countries like Germany , 77 percent of critics were already decidedly against templateTitle[0] energy prior to the events in Japan . In Japan , there were 37operational templateTitle[0] reactors as of June templateXValue[max] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[0] percent .
generated: This statistic shows the Nuclear power plants : permanent size shutdowns from 2005 to 2019 . In 2005 , the Nuclear power plants : permanent was 3 percent .


Example 64:
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[2] stated that they planned to do most of their holiday templateTitle[4] on templateTitle[6] templateTitle[7] in the United States in templateXValue[max] . This is a decrease of 23 percent since templateXValue[min] , when some templateYValue[max] percent of templateYLabel[2] were planning to do the majority of their templateTitle[4] on templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the results of a survey conducted in the templateTitle[0] templateTitle[1] in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the templateYLabel[2] in templateTitle[6] amounted to templateYValue[min] percent of templateYLabel[2] in the United States .
generated: This statistic shows the results of a survey conducted in the Share of in shopping from 2015 to 2019 . In 2019 , 59 percent of the respondents in Black amounted to 35 percent of respondents in the United States .


Example 65:
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 statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the total global Production of BMW Group - from 2010 to 2018 . In 2015 , the BMW Group - Production amounted to 151004 units N/A N/A .


Example 66:
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 . The number of mobile phone messaging apps users is forecast to nearly double between 2014 and 2019 . A total of 2.19 billion people are projected to use mobile phone messaging apps by that year . In terms of service providers , WhatsApp , acquired by Facebook in 2014 , is the most popular mobile messenger app in the world . Chinese QQMobile is the second most popular instant messaging service , while Facebook Messenger takes the third place . WeChat , Skype , Viber , LINE , BlackBerry Messenger and Kakaotalk complete the list of the most popular mobile messenger apps . Facebook Messenger consistently ranks as one of the most popular mobile apps in the United States - in December 2016 , Messenger had close to 137 million unique monthly U.S. visitors . Initially released in 2011 , Facebook Messenger , an app purely dedicated to chatting , was developed to take over the instant messaging function previously connected to the main Facebook app . The service imports Facebook 's contact , and can be used both on a desktop computer and mobile devices . The number of monthly active users in the app has been consistently growing since 2014 , when the service had 200 million monthly active users only .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateTitle[0] templateTitle[1] chat app templateYLabel[4] from April templateTitle[8] to September templateTitle[10] . As of the last reported period , the mobile templateTitle[1] had 1.3 billion templateYLabel[2] templateYLabel[3] templateYLabel[4] worldwide , ranking second among mobile chat apps worldwide . templateTitle[0] templateTitle[1] templateYLabel[4] – additional information Mobile templateTitle[1] apps are on the rise , with optimistic projections for this market in the coming years . The templateYLabel[0] of mobile phone messaging apps templateYLabel[4] is forecast to nearly double between templateTitle[8] and 2019 . A total of 2.19 billion people are projected to templateYLabel[4] mobile phone messaging apps by that year . In terms of service providers , WhatsApp , acquired by templateTitle[0] in templateTitle[8] , is the most popular mobile templateTitle[1] app in the world . Chinese QQMobile is the second most popular instant messaging service , while templateTitle[0] templateTitle[1] takes the third place . WeChat , Skype , Viber , LINE , BlackBerry templateTitle[1] and Kakaotalk complete the list of the most popular mobile templateTitle[1] apps . templateTitle[0] templateTitle[1] consistently ranks as templateTitle[8] of the most popular mobile apps in the United States templateTitle[9] in December 2016 , templateTitle[1] had close to 137 templateYLabel[6] unique templateYLabel[2] U.S. visitors . Initially released in 2011 , templateTitle[0] templateTitle[1] , an app purely dedicated to chatting , was developed to take over the instant messaging function previously connected to the main templateTitle[0] app . The service imports templateTitle[0] 's contact , and can be used both on a desktop computer and mobile devices . The templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] in the app has been consistently growing since templateTitle[8] , when the service had templateYValue[min] templateYLabel[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] only .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateTitle[0] as of the templateTitle[5] templateTitle[6] templateTitle[7] . As of that templateTitle[9] templateTitle[10] , templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] of January templateTitle[12] . templateYLabel[2] templateYLabel[3] templateYLabel[4] , an increase from templateYValue[1] templateYLabel[5] templateYLabel[6] in the previous templateXLabel[0] . In recent years , templateTitle[0] had 330 templateYLabel[6] global templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Number of monthly active Facebook as of the monthly active users . As of that - 2017 , 1300 monthly active users of January N/A . monthly active users , an increase from 1200 in millions in the previous Month . In recent years , Facebook had 330 millions global monthly active users .


Example 67:
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[2] 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[2] 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 68:
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[8] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , Easton-Bell templateTitle[8] generated revenues of 639 templateYLabel[2] templateYLabel[3] templateYLabel[4] . Easton-Bell templateTitle[8] makes templateTitle[8] equipment and clothing under the brands templateTitle[7] templateTitle[8] , Blackburn , templateTitle[6] , Giro , and Riddell .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic depicts the Revenue of the : Revenue of Easton from 2006 to 2013 . In 2013 , the Sporting goods industry : Revenue of amounted to 834.9 million U.S. dollars) .


Example 69:
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[4] templateTitle[5] templateYLabel[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there were around 62 templateYLabel[4] templateTitle[4] templateTitle[5] templateYLabel[2] in templateTitle[0] , up from 59 templateYLabel[4] in templateXValue[min] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[4] templateYLabel[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateYLabel[0] of templateTitle[4] templateYLabel[2] in templateTitle[0] is expected to reach templateYValue[max] templateYLabel[4] , up from templateYValue[5] templateYLabel[4] in templateXValue[5] .
generated: This statistic shows the Number of social users in Mexico from 2017 to 2023 . In 2018 , the Number of social users in Mexico is expected to reach 73.0 millions , up from 61.7 millions in 2018 .


Example 70:
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 . Bombardier 's transportation revenues added up to 8.9 billion U.S. dollars in 2018 .
gold_template: The timeline shows templateTitle[0] 's templateTitle[2] 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[2] equipment , recreational equipment and a financial services provider . templateTitle[0] 's templateTitle[2] revenues added up to 8.9 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] worldwide from templateXValue[min] to templateXValue[max] . The templateTitle[3] company generated approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateYLabel[0] in templateXValue[max] . templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] was founded in 1969 and now has more than 1,200 properties worldwide .
generated: This statistic shows the Revenue of transportation revenue 2007 - worldwide from 2007 to 2018 . The revenue company generated approximately 8.91 billion U.S. dollars in Revenue in 2018 . transportation revenue 2007 - was founded in 1969 and now has more than 1,200 properties worldwide .


Example 71:
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[6] templateTitle[7] templateYLabel[0] templateTitle[9] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] of the global templateTitle[6] templateTitle[7] templateYLabel[0] is estimated to be templateYValue[6] percent .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] of the over-the-counter and templateTitle[11] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] amounted to about templateYValue[6] percent .
generated: This statistic shows Estée Lauder 's share Market share of the over-the-counter and - Market worldwide from 2012 to 2024 . In 2018 , Estée Lauder 's Market share amounted to about 17.4 percent .


Example 72:
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[5] templateTitle[6] templateYLabel[2] in templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around templateYValue[max] templateYLabel[4] templateTitle[5] templateTitle[6] templateYLabel[2] in templateTitle[0] templateTitle[1] , up from templateYValue[5] templateYLabel[4] in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[4] templateYLabel[2] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateYLabel[0] of templateTitle[4] templateYLabel[2] in templateTitle[0] is expected to reach templateYValue[max] templateYLabel[4] , up from templateYValue[min] templateYLabel[4] in templateXValue[min] .
generated: This statistic shows the Number of of users in South from 2015 to 2022 . In 2017 , the Number of of users in South is expected to reach 28.16 millions , up from 23.07 millions in 2015 .


Example 73:
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[2] templateTitle[4] templateTitle[5] in templateTitle[7] in templateXValue[min] , templateXValue[1] and templateXValue[max] . According to the source , approximately templateYValue[max] thousand templateYLabel[2] are templateTitle[0] to have templateTitle[5] by templateXValue[max] in templateTitle[7] .

generated_template: This statistic displays the templateTitle[0] templateYLabel[0] of templateYLabel[2] templateTitle[4] templateTitle[5] in templateTitle[7] in templateXValue[min] , templateXValue[1] and templateXValue[max] . According to the source , approximately templateYValue[max] templateYLabel[2] are are templateTitle[0] to have templateTitle[5] by templateXValue[max] in templateTitle[7] .
generated: This statistic displays the Projected Number of people with dementia in Malaysia in 2015 , 2030 and 2050 . According to the source , approximately 590 people are are Projected to have dementia by 2050 in Malaysia .


Example 74:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the total labor force in templateTitle[3] was unemployed .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Qatar from 1999 to 2019 . In 2019 , the Unemployment rate in Qatar was at approximately 0.15 percent .


Example 75:
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 . Hispanic and African American ethnicities were severely underrepresented with only three and two percent share respectively of all employees at Twitter.Distribution of Twitter employees by gender and department in 2018 is revealing . In tech departments , close to 80 percent of employees were male . Men also dominated the leadership departments with 64.2 percent . Twitter was founded by Jack Dorsey , Noah Glass , Biz Stone and Evan Williams in March 2006 and since then , a man has always held top positions of chairman and CEO . The only department at Twitter whereby women were represented well was in the Non-tech departments . In 2017 , women held 53.7 percent of non-tech roles . The gender landscape at Facebook in 2019 was just as pessimistic . The distribution of Facebook employees worldwide by gender and department revealed that men dominated the tech departments with an 77 percent share and senior level positions with a 67.4 percent share . It will be interesting to see if the gender and ethnic distribution of the world 's leading social network companies improves in the future .
gold_template: The statistic provides the templateYLabel[0] of templateYLabel[2] of templateTitle[0] from templateTitle[5] to templateTitle[7] . 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[2] were male with a share of 59.8 percent and of a white ethnicity with 42.3 percent . Hispanic and African American ethnicities were severely underrepresented with only three and templateTitle[5] percent share respectively of all templateYLabel[2] at Twitter.Distribution of templateTitle[0] templateYLabel[2] by gender and department in 2018 is revealing . In tech departments , close to 80 percent of templateYLabel[2] were male . Men also dominated the leadership departments with 64.2 percent . templateTitle[0] was founded by Jack Dorsey , Noah Glass , Biz Stone and Evan Williams in March 2006 and since then , a man has always held top positions of chairman and CEO . The only department at templateTitle[0] whereby women were represented well was in the Non-tech departments . In 2017 , women held 53.7 percent of non-tech roles . The gender landscape at Facebook in templateTitle[7] was just as pessimistic . The distribution of Facebook templateYLabel[2] worldwide by gender and department revealed that men dominated the tech departments with an 77 percent share and senior level positions with a 67.4 percent share . It will be interesting to see if the gender and ethnic distribution of the world 's leading social network companies improves in the future .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[2] of the templateTitle[5] templateTitle[6] in the United States as of December templateTitle[7] . During the survey period , it was found that templateYValue[max] templateYLabel[2] throughout the templateTitle[6] in the United States .
generated: This statistic presents the Number of employees of the 2008 - in the United States as of December 2019 . During the survey period , it was found that 4900 employees throughout the - in the United States .


Example 76:
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 . American patriotism 81 percent of the American population seems to be very to extremely proud to be American , and they are n't shy to show it off : Around 60 percent of the population owns an American flag and around 44 percent own apparel branding patriotic imagery or symbols . Interestingly , patriotism seems to span the generations as well , with Millennials being only slightly less patriotic than the Gen X'ers or even the Baby Boomers . Ingrained patriotism is also one of the reasons why so many American men have chosen to fight in wars throughout history , and more than 80 percent of adults between the ages of 85 and 90 are now veterans . The number of veteran men under age 55 is noticeably lower , by the way , even though patriotism is alive and well and the U.S. is still sending soldiers to war zones . While Americans are overwhelmingly proud to be American , it is also worthwhile to see how the public opinion of the United States varies across different regions and countries of the world : While the United States enjoys a fairly favorable standing in Africa , for example , only a third of the Middle Eastern population views the United States favorably , most likely due to its controversial military involvement in the region .
gold_template: This statistic shows the results of a templateTitle[9] survey regarding patriotism in the United States . The templateYLabel[2] were asked how proud they templateYLabel[0] to be an templateTitle[0] . In templateTitle[9] , some templateYValue[max] percent of survey templateYLabel[2] stated they were templateXValue[0] proud to be an templateTitle[0] . templateTitle[0] patriotism 81 percent of the templateTitle[0] population seems to be templateXValue[1] to templateXValue[0] proud to be templateTitle[0] , and they templateYLabel[0] n't shy to show it off : Around 60 percent of the population owns an templateTitle[0] flag and around 44 percent own apparel branding patriotic imagery or symbols . Interestingly , patriotism seems to span the generations as well , with Millennials being templateXValue[3] slightly less patriotic than the Gen X'ers or even the Baby Boomers . Ingrained patriotism is also templateYValue[min] of the reasons why so many templateTitle[0] men have chosen to fight in wars throughout history , and more than 80 percent of adults between the ages of 85 and 90 templateYLabel[0] now veterans . The number of veteran men under age 55 is noticeably lower , by the way , even though patriotism is alive and well and the U.S. is still sending soldiers to war zones . While templateTitle[0] templateYLabel[0] overwhelmingly proud to be templateTitle[0] , it is also worthwhile to see how the public templateXValue[last] of the United States varies across different regions and countries of the world : While the United States enjoys a fairly favorable standing in Africa , for example , templateXValue[3] a third of the Middle Eastern population views the United States favorably , most likely due to its controversial military involvement in the region .

generated_template: This statistic shows the results of a survey , conducted in the United States in May templateTitle[11] . During the survey , it was found that templateYValue[max] percent of templateYLabel[2] stated that they have templateXValue[0] templateXValue[0] the templateXValue[3] .
generated: This statistic shows the results of a survey , conducted in the United States in May N/A . During the survey , it was found that 47 percent of respondents stated that they have Extremely Extremely the Only_a_little .


Example 77:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[min] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Bulgaria from 1999 to 2019 . In 2019 , the Unemployment rate in Bulgaria was 4.82 percent .


Example 78:
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[4] and templateTitle[6] templateTitle[7] templateTitle[8] in the templateTitle[11] templateTitle[12] ( UK ) from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateYLabel[0] templateYLabel[2] templateYLabel[1] of templateTitle[4] and templateTitle[6] templateTitle[7] templateTitle[8] amounted to approximately 3 billion templateYLabel[5] templateYLabel[6] , according to historic templateYLabel[0] figures . This is expected to decrease by templateXValue[max] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[3] , the global templateTitle[2] templateYLabel[0] is expected to 899 million .
generated: The statistic shows the total global Retail of Retail sales value from 2009 to 2018 . In 2015 , the Retail sales value Retail amounted to 3033.8 sales in million . In 2015 , the global value Retail is expected to 899 million .


Example 79:
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[2] in templateTitle[4] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[min] registered templateTitle[0] templateTitle[1] templateYLabel[2] in templateTitle[4] according to the International templateTitle[0] templateTitle[1] Federation .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[last] to templateXValue[0] . In templateXValue[1] , there were a total of templateYValue[max] thousand templateYLabel[2] registered templateTitle[0] templateTitle[1] templateYLabel[2] throughout templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[2] in the templateTitle[5] templateTitle[6] .
generated: The statistic shows the Number of players in the 2010 - from 2010/11 to 2018/19 . In 2017/18 , there were a total of 69921 thousand players registered Ice hockey players throughout Ice hockey players players in the 2010 - .


Example 80:
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 . In 2015 , the median age of the Guatemalan population was 21.3 years .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] in templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[4] into templateTitle[7] 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[4] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of the Guatemalan templateTitle[4] was templateYValue[7] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] in templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of a templateTitle[4] is an index that divides the templateTitle[4] into templateTitle[7] equal groups : half of the templateTitle[4] is older than the templateYLabel[0] templateYLabel[1] and the other half younger . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitle[6] 's templateTitle[4] was templateYValue[7] templateYLabel[3] .
generated: The statistic depicts the Median age in Guatemala from 1950 to 2050 . The Median age of a population is an index that divides the population into 2015 equal groups : half of the population is older than the Median age and the other half younger . In 2015 , the Median age of Guatemala 's population was 21.3 years .


Example 81:
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[1] most commonly templateTitle[2] by IT leaders templateTitle[3] , as of templateTitle[4] . In templateTitle[4] , templateXValue[0] templateXValue[0] templateXValue[0] was templateTitle[2] by templateYValue[max] percent of templateYLabel[2] .

generated_template: This statistic shows the results of a survey among female templateTitle[2] templateTitle[3] in the United States as of May templateTitle[9] . During a survey , templateYValue[max] percent of templateYLabel[2] stated they have templateXValue[0] templateXValue[0] .
generated: This statistic shows the results of a survey among female outsourced worldwide in the United States as of May N/A . During a survey , 64 percent of respondents stated they have Software_application_development Software_application_development .


Example 82:
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[7] templateTitle[8] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYLabel[1] of templateTitle[7] templateTitle[8] amounted to templateYValue[min] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . In templateXValue[max] , there was at templateYValue[max] percent .
generated: The statistic shows the Equipment sales in in the United States from 2010 to 2011 . In N/A , the Equipment sales in million U.S. dollars . In 2011 , there was at 210.38 percent .


Example 83:
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[5] templateTitle[6] templateTitle[7] in the United States from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[5] templateTitle[6] templateTitle[7] amounted to approximately 3.58 million templateYLabel[4] templateYLabel[5] in templateXValue[max] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[7] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] amounted to approximately templateYValue[7] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Average sales per ( Average ) sales in in Outback from 2015 to 2018 . In N/A , the Average sales per unit amounted to approximately N/A U.S. dollars .


Example 84:
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[4] templateTitle[5] 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 1.09 billion templateYLabel[4] templateYLabel[5] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of 3.3 billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by Rogers Communications , who bought the templateYLabel[0] for 137 templateYLabel[3] templateYLabel[4] templateYLabel[5] in 2000 .
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 3.3 billion U.S. dollars . The Chicago Blackhawks 2006 are owned by Rogers Communications , who bought the Franchise for 137 million U.S. dollars in 2000 .


Example 85:
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 . In 2018 , the estimated GDP per capita in Nigeria amounted to around 2,032.86 U.S. dollars .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows Gross domestic product ( GDP ) per capita in Nigeria from 1994 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 . In 2018 , the GDP per capita in Nigeria amounted to around 2032.86 U.S. dollars .


Example 86:
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[4] templateTitle[5] and the templateTitle[8] from templateXValue[min] to templateXValue[max] in templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[6] , templateTitle[4] templateTitle[5] and the templateTitle[8] 's templateYLabel[0] amounted to about 5.25 trillion templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitle[7] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country . In templateXValue[6] , the templateYLabel[0] in templateTitle[7] was at around templateYValue[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Gross domestic product ( GDP ) in the from 2014 to 2017 , with projections up until 2024 . GDP refers to the total market value of all goods and services that are produced within a country per Year . It is an important indicator of the economic strength of a country . In 2018 , the GDP in the was at around 5249.66 billion U.S. dollars .


Example 87:
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 . Brand posts by Celine only generated an user engagement of 1,875 actions per post .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] templateTitle[5] templateYLabel[5] templateYLabel[6] templateTitle[6] templateXLabel[1] templateTitle[0] templateXLabel[0] templateXLabel[2] . During the first quarter of templateTitle[9] , an templateXValue[last] templateYLabel[2] templateYLabel[3] templateYLabel[6] templateTitle[6] Kim Kardashian templateTitle[5] templateYValue[17] million templateYLabel[2] templateYLabel[3] templateYLabel[4] . Kardashian is the templateXLabel[1] templateXLabel[2] for templateTitle[0] templateXLabel[0] templateXValue[17] . templateXLabel[0] posts templateTitle[6] templateXValue[17] only templateTitle[5] an user templateTitle[4] of 1,875 templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: The statistic shows the total templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[2] in the United States from templateXValue[last] to templateXValue[0] . In June templateXValue[max] , templateYValue[max] percent of templateTitle[2] templateTitle[3] templateYLabel[2] in the United States .
generated: The statistic shows the total Number of social media social in the United States from Average to Valentino_(Demi_Lovato) . In June Valentino_(Demi_Lovato) , 1385467 percent of social media social in the United States .


Example 88:
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 . In 2017 , nearly 7.4 million tons of fresh grapes were grown and harvested in the United States . This amounted to around 6.5 billion U.S. dollars in production value . Grapes can be eaten fresh , dried and produced into raisins , or made into wine . The total vine bearing areas in the United States reached 443 thousand hectares in 2016 , up from 419 thousand hectares in the previous year . U.S. Fruit Consumption In 2016 , per capita consumption of fresh fruit in the United States was 116 pounds annually . In the last decade there has been an upward trend in fresh fruit consumption in the United States . The fruit of choice among most Americans are bananas , which had a per capita consumption of 28.5 pounds in 2017 . In comparison , the average American consumed around 8.2 pounds of grapes in that year .
gold_template: The templateYLabel[0] of templateTitle[5] templateYLabel[5] of seedless templateTitle[4] ( Thompson ) in the United States was templateYValue[22] templateYLabel[2] templateYLabel[3] in templateXValue[max] . templateYLabel[2] seedless templateTitle[4] prices peaked in templateXValue[12] at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . templateTitle[4] Production in the United States templateTitle[4] have the highest production volume of any fruit in the United States . In templateXValue[20] , nearly 7.4 million tons of fresh templateTitle[4] were grown and harvested in the United States . This amounted to around 6.5 billion templateYLabel[2] templateYLabel[3] in production value . templateTitle[4] can be eaten fresh , dried and produced into raisins , or made into wine . The total vine bearing areas in the United States reached 443 thousand hectares in templateXValue[19] , up from 419 thousand hectares in the previous templateXLabel[0] . templateYLabel[2] Fruit Consumption In templateXValue[19] , templateYLabel[4] capita consumption of fresh fruit in the United States was 116 pounds annually . In the last decade there has been an upward trend in fresh fruit consumption in the United States . The fruit of choice among most Americans are bananas , which had a templateYLabel[4] capita consumption of 28.5 pounds in templateXValue[20] . In comparison , the average American consumed around 8.2 pounds of templateTitle[4] in that templateXLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[4] templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . According to the report , templateTitle[4] templateTitle[5] was priced at templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] on the templateTitle[0] in templateXValue[1] .
generated: This statistic shows the U.S. retail Price of grapes 1995 in the United States from 1995 to 2019 . According to the report , grapes 1995 was priced at 2.19 U.S. dollars per pound on the U.S. in 1997 .


Example 89:
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[4] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] templateYLabel[0] of the Bolivian population accessed the templateYLabel[4] , up from templateYValue[11] templateYLabel[0] in templateXValue[11] .

generated_template: This statistic gives information on the templateYLabel[4] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] templateYLabel[0] of the Chilean population accessed the templateYLabel[4] , up from templateYValue[16] templateYLabel[0] in templateXValue[16] . In templateXValue[1] , templateYValue[max] templateYLabel[0] of the Chilean population acceded to templateYLabel[4] .
generated: This statistic gives information on the internet penetration in Bolivia from 2000 to 2017 . In 2017 , 43.83 Percentage of the Chilean population accessed the internet , up from 2.12 Percentage in 2001 . In 2016 , 43.83 Percentage of the Chilean population acceded to internet .


Example 90:
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[4] from templateXValue[min] until templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of women at templateYLabel[3] in templateTitle[4] was about templateYValue[max] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[3] in the templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[3] among the templateTitle[4] templateTitle[5] population was about templateYValue[max] templateYLabel[5] .
generated: The statistic shows the Life expectancy at birth in the China 1960 from 1960 to 2017 . In 2017 , the average Life expectancy at birth among the China 1960 population was about 78.83 years .


Example 91:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[3] was templateYValue[max] templateYLabel[2] templateYLabel[4] templateYLabel[5] . Overall , the templateYLabel[0] of births in templateTitle[3] reached about 17.23 million that templateXLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[2] templateYLabel[3] by templateTitle[5] templateYLabel[5] while being of child-bearing age . In templateXValue[max] , templateTitle[3] templateTitle[4] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
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 - woman while being of child-bearing age . In 2017 , China 2007 's Fertility rate amounted to 1.53 children born per woman .


Example 92:
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[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] 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 93:
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[3] to templateYLabel[5] 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[5] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[3] to templateYLabel[5] 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 templateTitle[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 balance .


Example 94:
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[4] templateTitle[5] templateTitle[6] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of 1.9 billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by Arturo Moreno , who bought the templateYLabel[0] for 184 templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[16] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by the Lamar Hunt family , who bought the templateYLabel[0] for 380 templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[min] .
generated: This graph depicts the Franchise value of the Los Angeles Angels from the National Football League from 2002 to 2019 . In 2019 , the Franchise value amounted to 1900 million U.S. dollars . The Los Angeles Angels are owned by the Lamar Hunt family , who bought the Franchise for 380 million U.S. dollars in 2002 .


Example 95:
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[8] 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[4] templateYLabel[5] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitle[3] templateTitle[4] templateTitle[8] 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[4] templateYLabel[5] .
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 .


Example 96:
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 . The franchise moved from St. Louis to Los Angeles prior to the 2016 season .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] amounted to around 3.8 billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by Stan Kroenke , who bought the templateYLabel[0] for 750 templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[9] . The templateYLabel[0] moved from St. Louis to templateTitle[4] templateTitle[5] prior to the templateXValue[3] season .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of 1.1 billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by the Steinbrenner Family , who bought them in 1973 for 8.8 templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This graph depicts the value of the Los Angeles Rams Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 1.1 billion U.S. dollars . The Los Angeles Rams are owned by the Steinbrenner Family , who bought them in 1973 for 8.8 million U.S. dollars .


Example 97:
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[3] industry as a templateYLabel[4] of templateYLabel[6] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , this sector accounted for templateYValue[0] % of the nation 's Gross Domestic Product , making it templateTitle[11] of the largest sectors of the U.S. economy .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] percent .
generated: The statistic shows the Value added as in the United States from 2007 to 2018 . In 2018 , the Value added of the manufacturing as a amounted to approximately 11.4 percent .


Example 98:
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 templateYLabel[4] . The average wages garnered in select countries around the world based on purchasing power can be accessed here .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were around templateYValue[max] thousand templateYLabel[2] in the United States .
generated: This statistic shows the Chained of price index of all urban in the United States from 2000 to 2019 . In 2019 , there were around 144.73 thousand Price in the United States .


Example 99:
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[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Median Household income in Texas from 1990 to 2018 . In 2018 , the Median Household income in Texas amounted to 59785 U.S. dollars .


Example 100:
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[3] and templateTitle[5] templateTitle[6] 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 4 billion British pounds going toward templateTitle[3] and templateTitle[5] templateTitle[6] . In templateXValue[1] , 2.9 billion British pounds were spent on templateTitle[3] and templateTitle[5] templateTitle[6] in the templateTitle[9] .

generated_template: The statistic shows the total templateTitle[0] templateTitle[1] templateTitle[2] ( templateTitle[6] ) in the United Kingdom ( templateTitle[9] ) from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the templateYLabel[0] on local templateTitle[2] templateTitle[3] has been continuously decreased during this period .
generated: The statistic shows the total Public expenditure on ( services ) in the United Kingdom ( UK ) from 2013/14 to 2018//19 . In 2018//19 , the Expenditure on local on recreational has been continuously decreased during this period .


Example 101:
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[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in the templateTitle[4] templateTitle[5] was at approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in the from 1999 to 2019 . In 2019 , the Unemployment rate in the was at approximately 2.47 percent .


Example 102:
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[3] 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[3] was around 37.51 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] 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[3] was at around 29.98 templateYLabel[3] templateYLabel[4] .
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 . In 2018 , the state deficit of Italy was at around 29.98 billion euros .


Example 103:
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[9] from templateXValue[min] to templateXValue[6] , with projections up to templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] 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 104:
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[3] passengers on templateTitle[0] templateTitle[1] between templateXValue[min] and templateXValue[max] . templateTitle[0] templateTitle[1] carried about 777.9 templateYLabel[3] passengers on templateTitle[3] flights across the United States in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] in templateTitle[5] amounted to around templateYValue[max] thousand templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Passenger enplanements (in in the enplanements 2004 from 2004 to 2018 . In 2015 , the U.S. airlines - domestic (in in enplanements amounted to around 777.91 thousand millions) N/A .


Example 105:
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[2] templateTitle[3] templateTitle[4] , templateTitle[6] templateTitle[7] templateTitle[8] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateTitle[3] templateTitle[4] generated approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateYLabel[0] . templateTitle[2] templateTitle[3] templateTitle[4] , templateTitle[6] 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[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . Global hotel company templateTitle[2] templateTitle[3] Corporation generated approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] 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 106:
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[5] 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 templateTitle[8] templateYLabel[3] templateYLabel[4] templateYLabel[5] in an optimistic scenario .

generated_template: This statistic shows the templateYLabel[1] of the templateTitle[0] templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateYLabel[0] was expected to be worth templateYValue[3] templateYLabel[3] templateYLabel[4] templateYLabel[5] . templateTitle[0] is a cloud operating system , providing access to various computing resources , such as compute , storage , and network , across a data center . It is used primarily as Infrastructure as a Service .
generated: This statistic shows the size of the Autonomous Market from 2015 to 2030 . In 2015 , the Autonomous Market was expected to be worth 0.4 billion U.S. dollars . Autonomous is a cloud operating system , providing access to various computing resources , such as compute , storage , and network , across a data center . It is used primarily as Infrastructure as a Service .


Example 107:
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[5] templateTitle[6] in the United States from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[5] templateTitle[6] amounted to templateYValue[max] templateYLabel[4] in templateXValue[max] .

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


Example 108:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was templateYValue[min] percent .
generated: This statistic shows the Unemployment rate in Samoa from 1999 to 2019 . In 2019 , the Unemployment rate in Samoa was 4.43 percent .


Example 109:
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[4] of the templateYLabel[2] templateYLabel[2] templateYLabel[0] Group stores in templateTitle[11] , templateTitle[8] templateXLabel[0] . In templateTitle[11] , templateYValue[1] templateXValue[22] templateYLabel[2] templateYLabel[2] templateYLabel[0] stores were opened in the United States . templateYLabel[2] templateYLabel[2] templateYLabel[0] is a Swedish clothing manufacturer and retailer , based in Stockholm , templateXValue[6] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] size templateTitle[5] from 1903 to templateTitle[13] . templateXValue[0] has been templateYLabel[2] the Middle East templateYLabel[0] templateYLabel[1] . In the highest templateTitle[0] of templateYValue[max] percent of global global templateTitle[3] templateYLabel[2] was ranked first .
generated: The statistic shows the Number of H & H&M size store from 1903 to N/A . Total has been H&M the Middle East Number of . In the highest Number of 375 percent of global global & H&M was ranked first .


Example 110:
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[2] in templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[0] templateYLabel[4] templateYLabel[2] in templateTitle[4] in the templateTitle[7] , down from templateYValue[1] templateYLabel[4] the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] in templateTitle[2] in the United States templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[2] ( aged templateTitle[7] years and older ) in templateTitle[2] amounted to approximately templateYValue[0] templateYLabel[4] .
generated: This statistic shows the Number of participants in participants in the United States the 2006 to 2018 . In 2018 , the Number of participants ( aged U.S. years and older ) in participants amounted to approximately 3.75 millions .


Example 111:
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 United States 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 United States .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] of the templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] increased by templateYValue[0] percent .
generated: This statistic shows the Death rate unintentional of the overdose in from 1950 to 2017 . In 2017 , the Death rate of the overdose in increased by 20.1 percent .


Example 112:
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 . Despite the Alp state being made up of several smaller roads that are tricky to traverse , road fatality prevalence was 27 incidences per million inhabitants . Total road length Switzerland 's roads had a total length of 70,099 kilometers . Of these the secondary or regional roads made up the largest share , at nearly 25 percent . By comparison , motorways had a combined length of 1,447 kilometers . Cars per inhabitants The number of cars available in Switzerland grew consecutively between 1990 and 2017 , although from 2005 onwards the increase rate slowed , indicating a saturated market . In 2017 , there were 539 cars per 1,000 inhabitants .
gold_template: In templateXValue[max] , templateYValue[0] templateYLabel[2] were recorded on Swiss roads . Between templateXValue[min] and templateXValue[max] , traffic related templateTitle[3] declined by over templateTitle[8] third , with the lowest templateYLabel[0] seen in templateXValue[2] at templateYValue[min] such incidences . templateTitle[5] was templateTitle[8] of the safest countries in Europe for templateTitle[2] users . Despite the Alp state being made up of several smaller roads that are tricky to traverse , templateTitle[2] fatality prevalence was 27 incidences per million inhabitants . Total templateTitle[2] length templateTitle[5] 's roads had a total length of 70,099 kilometers . Of these the secondary or regional roads made up the largest share , at nearly 25 percent . By comparison , motorways had a combined length of 1,447 kilometers . Cars per inhabitants The templateYLabel[0] of cars available in templateTitle[5] grew consecutively between 1990 and templateXValue[1] , although from 2005 onwards the increase rate slowed , indicating a saturated market . In templateXValue[1] , there were 539 cars per 1,000 inhabitants .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateYLabel[2] in templateTitle[5] .
generated: The statistic shows the Number of fatalities in Switzerland from 2006 to 2018 . In 2018 , there were 384 fatalities in Switzerland .


Example 113:
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 . Walmart is the world 's largest retailer and the United States ' largest grocery retailer as well .
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[3] templateYLabel[4] templateYLabel[5] . templateTitle[0] , founded in 1962 , is an American multinational retailer corporation that runs chains of large discount department stores and warehouse stores . templateTitle[0] is the templateTitle[4] templateTitle[1] largest retailer and the United States templateTitle[1] largest grocery retailer as well .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] 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] templateTitle[1] amounted to templateYValue[max] templateYLabel[0] .
generated: The statistic shows Walmart 's operating income from the fiscal Fiscal of 2006 to the fiscal Fiscal of 2019 . In the fiscal Fiscal of 2019 , Walmart 's amounted to 27.73 Operating .


Example 114:
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[2] of templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] on templateYLabel[2] of the global banking group headquartered in Paris was equal to templateYValue[0] percent .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was about templateYValue[0] percent .
generated: This statistic shows the Return on equity of BNP Paribas from 2003 to 2018 . In 2018 , the Return on equity of BNP was about 8.2 percent .


Example 115:
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[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[3] increased about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in 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[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] 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 116:
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 . 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 . In 2016 , the GDP per capita in Ethiopia amounted to around 777.29 U.S. dollars .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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] . 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 . In templateXValue[8] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[8] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows Gross domestic product ( GDP ) per capita in Ethiopia 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 . In 2018 , the GDP per capita in Ethiopia amounted to around 852.88 U.S. dollars .


Example 117:
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 . Canada has provided legal access nationwide to medical marijuana since 2001 , and the country established the legalization of cannabis for recreational purposes in all provinces in October 2018 . Cannabis edibles One year on , the Canadian government will legalize the sale and use of marijuana edibles , cannabis extracts and topical products in October 2019 . Just over 50 percent of Canadians approved of further legalization when surveyed in October 2018 . The market is expected to boom , with the edible cannabis segment estimated to be worth around 1.6 billion Canadian dollars by the end of 2019 . To have fun with friends and to reduce stress or anxiety were two of the top reasons for using cannabis-infused edibles among current and likely users .
gold_template: As of December templateTitle[12] , some templateYValue[max] percent of surveyed males had consumed templateTitle[0] or templateTitle[2] in the templateTitle[6] three templateTitle[8] in templateTitle[9] . In comparison , templateYValue[min] percent of templateXValue[last] templateYLabel[2] had used the recently legalized drug in the same time period . The legalization of templateTitle[2] in templateTitle[9] Following a similar trend in the United States , the legalization of recreational templateTitle[0] in templateTitle[9] has become a hot topic in politics and in the public realm . templateTitle[9] has provided legal access nationwide to medical templateTitle[0] since 2001 , and the country established the legalization of templateTitle[2] for recreational purposes in all provinces in October 2018 . templateTitle[2] edibles templateTitle[12] year on , the Canadian government will legalize the sale and use of templateTitle[0] edibles , templateTitle[2] extracts and topical products in October templateTitle[12] . Just over 50 percent of Canadians approved of further legalization when surveyed in October 2018 . The market is expected to boom , with the edible templateTitle[2] segment estimated to be worth around 1.6 billion Canadian dollars templateTitle[10] the templateTitle[11] of templateTitle[12] . To have fun with friends and to reduce stress or anxiety were templateTitle[12] of the top reasons for using cannabis-infused edibles among current and likely users .

generated_template: This statistic shows the distribution of templateTitle[0] templateTitle[1] templateTitle[5] in the United States as of July templateTitle[6] . During the survey period , it was found that templateYValue[max] percent of templateTitle[0] users as of templateXValue[0] and templateYValue[min] percent of templateYLabel[2] were templateXValue[last] .
generated: This statistic shows the distribution of Marijuana and the in the United States as of July past . During the survey period , it was found that 18.4 percent of Marijuana users as of Male and 15.1 percent of respondents were Female .


Example 118:
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 43.5 templateYLabel[2] templateYLabel[3] ( or about 47.8 templateYLabel[2] US dollars ) in templateYLabel[0] .

generated_template: The statistic presents the annual templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] ( templateTitle[3] Haushaltsgeraete GmbH ) between the templateXValue[min] to templateXValue[max] fiscal years . In the fiscal templateXLabel[0] , the templateTitle[3] templateTitle[4] templateTitle[5] generated approximately templateYValue[0] templateYLabel[2] ( or templateYLabel[0] ) in templateYLabel[0] .
generated: The statistic presents the annual Revenue Group - revenue 2009 ( revenue Haushaltsgeraete GmbH ) between the 2009 to 2018 fiscal years . In the fiscal Year , the revenue 2009 - generated approximately 43.52 billion ( or Revenue ) in Revenue .


Example 119:
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 templateYLabel[0] of templateTitle[0] tourists ( visitor exports ) in templateTitle[4] from templateXValue[min] to templateXValue[1] and a forecast for templateXValue[max] . In templateXValue[2] templateTitle[0] visitors in templateTitle[4] spent a total of templateYValue[2] templateYLabel[2] templateYLabel[3] . This is expected to increase in templateXValue[1] to templateYValue[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the total annual templateYLabel[0] of templateTitle[0] tourists ( 'visitor exports ' _ ) in templateTitle[4] from templateXValue[min] to templateXValue[6] , with a forecast for templateXValue[max] . Inbound templateTitle[1] expenditure in templateTitle[4] reached around templateYValue[5] templateYLabel[2] templateYLabel[3] in templateXValue[5] .
generated: This statistic shows the total annual Spending of International tourists ( 'visitor exports ' _ ) in Portugal from 2012 to 2013 , with a forecast for 2028 . Inbound tourism expenditure in Portugal reached around 13.9 billion euros in 2014 .


Example 120:
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[7] templateTitle[8] templateTitle[9] templateYLabel[0] templateTitle[11] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateTitle[0] templateTitle[1] templateTitle[0] templateTitle[3] templateYLabel[1] of the global templateTitle[7] templateTitle[8] templateTitle[9] templateYLabel[0] is estimated to be templateYValue[2] percent .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] of the templateTitle[6] templateTitle[7] templateTitle[8] templateYLabel[0] templateTitle[10] from templateXValue[min] to templateXValue[max] . In templateXValue[7] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] of the global templateTitle[6] templateTitle[7] templateTitle[8] templateYLabel[0] is estimated to be templateYValue[7] percent . The company is a manufacturer and marketer of prestige templateTitle[6] templateTitle[7] , makeup , fragrance and hair templateTitle[7] templateTitle[8] , with global net sales of over 11.2 billion U.S. dollars .
generated: The statistic shows Johnson & Johnson share of the the skin care Market market from 2013 to 2021 . In 2020 , Johnson & Johnson share of the global the skin care Market is estimated to be 3.75 percent . The company is a manufacturer and marketer of prestige the skin , makeup , fragrance and hair skin care , with global net sales of over 11.2 billion U.S. dollars .


Example 121:
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[4] 's templateTitle[0] templateYLabel[3] templateTitle[2] in templateTitle[5] , sorted by their templateYLabel[0] in templateYLabel[2] templateYLabel[3] . In templateTitle[5] , templateTitle[4] 's templateTitle[0] templateYLabel[3] templateTitle[2] was templateXValue[last] with a templateYLabel[0] of templateYValue[max] percent in all templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of the global templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] as of templateTitle[9] . According to the templateXValue[0] season , the templateXValue[0] templateXValue[0] templateXValue[0] and templateXValue[last] had an estimated templateYValue[max] percent of just templateYValue[min] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: The statistic shows the Main Share in of the global Iran 2017 N/A N/A as of N/A . According to the Japan season , the Japan Japan Japan and China had an estimated 27.5 percent of just 5.3 exports N/A N/A N/A .


Example 122:
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[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .
generated: 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 .


Example 123:
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[3] templateTitle[4] templateTitle[5] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[3] templateTitle[4] templateTitle[5] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[3] templateTitle[4] templateTitle[5] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[3] templateTitle[4] templateTitle[5] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: 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 .


Example 124:
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[3] templateTitle[4] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[3] templateTitle[4] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

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[max] templateYLabel[2] templateYLabel[3] dollars.The templateTitle[0] templateTitle[1] templateTitle[2] are owned by Jerry Reinsdorf , who bought the franchise for templateTitle[4] templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1981 .
generated: The statistic depicts the Revenue of the Revenue of the from 2001 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 438 million U.S. dollars.The Revenue of the are owned by Jerry Reinsdorf , who bought the franchise for Ravens million U.S. dollars in 1981 .


Example 125:
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[10] 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 2,246 billion templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[3] , the global templateTitle[2] templateYLabel[0] templateYLabel[1] increased from templateYValue[3] percent .
generated: The statistic shows the total global GDP of Gross domestic product from 2008 to 2018 . In 2011 , the Gross domestic product GDP amounted to 1846854 million Danish kroner . In 2011 , the global product GDP in increased from 1846854 percent .


Example 126:
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[5] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] templateYLabel[0] of the population accessed the templateYLabel[5] , up from templateYValue[min] templateYLabel[0] 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[5] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In the most recently measured period , templateYValue[max] templateYLabel[0] of the population accessed the templateYLabel[5] , up from templateYValue[min] templateYLabel[0] in templateXValue[min] . In templateXValue[2] , templateTitle[0] 's population increased by approximately 2.48 templateYLabel[0] 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 Percentage of the population accessed the internet , up from 0.13 Percentage in 2000 . In 2015 , Malawi 's population increased by approximately 2.48 Percentage compared to the previous Year .


Example 127:
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[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] templateTitle[5] 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[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] 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 128:
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[4] from templateXValue[min] to templateXValue[max] . The world templateTitle[1] templateYLabel[0] of templateTitle[4] amounted to approximately 2.2 million templateYLabel[3] templateYLabel[4] in templateXValue[min] . In templateXValue[max] , total templateYLabel[0] dropped just above 1.1 million templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the total annual templateTitle[1] templateTitle[2] mined in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] thousand templateYLabel[3] templateYLabel[4] 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 annual mine production mined in the Global from 2007 to 2019 . In 2007 , 1100 thousand metric tons of production was mined across the country . Since then , this figure increased to some 2200 thousand in 2019 .


Example 129:
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 . The share of book readers in the U.S. has varied from 72 percent to 79 percent between 2011 and 2016 . In regards to age of book readers in the country , a 2016 survey shows about 80 percent of respondents between the ages of 18 to 29 had read at least one book in the previous 12 months , the highest share amongst all age groups . About 73 percent of the respondents aged between 30 to 49 years old said they read at least one book in the last 12 months . The share among respondents between 50 and 64 years old stood at 70 percent , whereas 67 percent of respondents aged 65 plus stated reading book during the time measured . In terms of education level , book readers in the U.S. are more likely to have a college degree , or at least some college education – 86 percent and 81 percent respectively . Women in the U.S. read slightly more than men ; 68 percent of male respondents started reading at least one book in the previous 12 months , against 77 percent of female respondents that said the same . Despite the rise of digital platforms and the rising popularity of e-reading devices such as Kindle , Kobo and others , printed books still remain the most popular book format in the U.S. , as 65 percent of Americans stated preference for printed books in 2016 . E-books were consumed by 28 percent of respondents in 2016 , whereas audio books were listened by 14 percent of the respondents . Millennials accounted for the largest share of printed book readers in the U.S. – 72 percent as of 2016 .
gold_template: During a survey held in early templateTitle[5] , it was found that templateYValue[max] percent of adults aged between 18 and 29 years old had templateTitle[1] a templateTitle[0] in any format in the previous templateXLabel[0] . The survey results showed that adults within this templateTitle[8] category were more likely than older templateYLabel[2] to have templateTitle[1] a templateTitle[0] within the last twelve months . templateTitle[0] templateTitle[1] in the templateTitle[4] – additional information While it is mostly believed that templateTitle[0] reading is a vanishing pastime , particularly among Millennials , surveys among consumers in the templateTitle[4] have shown the opposite . The templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitle[4] has varied from templateYValue[1] percent to 79 percent between 2011 and 2016 . In regards to templateTitle[8] of templateTitle[0] templateTitle[1] in the country , a 2016 survey shows about 80 percent of templateYLabel[2] between the ages of 18 to 29 had templateTitle[1] at least templateTitle[5] templateTitle[0] in the previous 12 months , the highest templateYLabel[0] amongst all templateTitle[8] groups . About 73 percent of the templateYLabel[2] aged between 30 to 49 years old said they templateTitle[1] at least templateTitle[5] templateTitle[0] in the last 12 months . The templateYLabel[0] among templateYLabel[2] between 50 and 64 years old stood at 70 percent , whereas templateYValue[min] percent of templateYLabel[2] aged 65 plus stated reading templateTitle[0] during the time measured . In terms of education level , templateTitle[0] templateTitle[1] in the templateTitle[4] templateYLabel[0] more likely to have a college degree , or at least some college education – 86 percent and templateYValue[max] percent respectively . Women in the templateTitle[4] templateTitle[1] slightly more than men ; templateYValue[3] percent of male templateYLabel[2] started reading at least templateTitle[5] templateTitle[0] in the previous 12 months , against 77 percent of female templateYLabel[2] that said the same . Despite the rise of digital platforms and the rising popularity of e-reading devices such as Kindle , Kobo and others , printed books still remain the most popular templateTitle[0] format in the templateTitle[4] , as 65 percent of Americans stated preference for printed books in 2016 . E-books were consumed templateTitle[7] 28 percent of templateYLabel[2] in 2016 , whereas audio books were listened templateTitle[7] 14 percent of the templateYLabel[2] . Millennials accounted for the largest templateYLabel[0] of printed templateTitle[0] templateTitle[1] in the templateTitle[4] – templateYValue[1] percent as of 2016 .

generated_template: This statistic displays the results of a survey on the templateYLabel[0] of individuals in templateTitle[6] templateTitle[7] who purchased templateTitle[1] for events templateTitle[3] in the United States . During the survey period , templateYValue[max] percent of templateYLabel[2] stated that they used templateTitle[0] templateTitle[1] .
generated: This statistic displays the results of a survey on the Share of individuals in , by who purchased readers for events the in the United States . During the survey period , 81 percent of respondents stated that they used Book readers .


Example 130:
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[2] used templateXValue[0] , while templateYValue[1] percent used templateXValue[1] . The least used templateTitle[2] was templateXValue[last] with only templateYValue[min] percent of templateYLabel[2] reporting to use it .

generated_template: This statistic shows the results of a survey , conducted in the United States in templateTitle[5] templateTitle[6] . The survey , templateYValue[max] percent of female templateYLabel[2] stated that they ate templateTitle[3] templateTitle[5] templateTitle[6] templateTitle[7] .
generated: This statistic shows the results of a survey , conducted in the United States in worldwide 2019 . The survey , 49.9 percent of female respondents stated that they ate among worldwide 2019 N/A .


Example 131:
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 3.3 billion templateYLabel[4] . That same templateXLabel[0] , templateTitle[0] generated 27.55 billion templateYLabel[4] in revenue worldwide .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateYLabel[4] states . In templateXValue[max] , this figure stood at templateYValue[2] thousand templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows SAP 's Net profit in the United States from 2006 to 2019 . In 2019 , the Net profit of the million euros states . In 2019 , this figure stood at 4008 thousand million euros N/A .


Example 132:
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[2] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . For instance , templateYValue[0] templateYLabel[0] of templateTitle[0] 's templateYLabel[2] lived below the templateTitle[2] line in templateXValue[max] .

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


Example 133:
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 . In 2018 , the GDP per capita in Taiwan amounted to around 25,007.75 U.S. dollars .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the total templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the total 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 . In 2018 , the GDP per capita in Taiwan amounted to around 25007.75 U.S. dollars .


Example 134:
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[2] templateTitle[3] templateTitle[4] templateTitle[5] in the United States templateTitle[9] templateTitle[10] to templateTitle[12] . In templateTitle[12] , templateYLabel[1] jewelry , templateTitle[0] , and templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] amounted to about templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] in the United States templateTitle[6] templateTitle[7] to templateTitle[9] . In templateTitle[9] , templateYLabel[1] templateTitle[0] templateTitle[1] templateTitle[2] amounted to about templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows Luggage and leather in the United States in the to from . In from , U.S. Luggage and leather amounted to about 34.14 Billion U.S. dollars .


Example 135:
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 . Online banking on the rise The growing penetration of online banking , as well as the increasingly widespread use of banking apps via mobile devices also contributed to the gradual loss of importance of traditional banking and physical bank branches . It is also important to remember the rise of new players in the banking sector : digital banks and financial services providers have caught the attention of many customers in the last few years . Moreover , not only customers , but also financial institutions closely follow the development of this innovative business model and look with interest and curiosity at these new players . The bank of the future ? For Italians , traditional banks are still considered the preferred medium when it comes for example to loan financing and savings management . Nevertheless , digital banks and financial services providers are increasing in popularity because of more flexible , efficient and cheaper offers , which seem to be closer to the customers ' needs .
gold_template: The total templateYLabel[0] of templateYLabel[2] 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[2] 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 templateYLabel[2] branches also decreased steadily , and , as a consequence , the population size per templateYLabel[2] branch increased from 1,734 in 2008 to 2,067 in templateXValue[2] . Online banking on the rise The growing penetration of online banking , as well as the increasingly widespread use of banking apps via mobile devices also contributed to the gradual loss of importance of traditional banking and physical templateYLabel[2] branches . It is also important to remember the rise of new players in the banking sector templateTitle[1] digital templateYLabel[2] and financial services providers have caught the attention of many customers in the last few years . Moreover , not only customers , but also financial institutions closely follow the development of this innovative business model and look with interest and curiosity at these new players . The templateYLabel[2] of the future ? For Italians , traditional templateYLabel[2] are still considered the preferred medium when it comes for example to loan financing and savings management . Nevertheless , digital templateYLabel[2] and financial services providers are increasing in popularity because of more flexible , efficient and cheaper offers , which seem to be closer to the customers ' needs .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , there were templateYValue[min] templateTitle[2] templateYLabel[2] in the United States .
generated: The statistic shows the Number of banks of Italy : number from 2011 to 2018 . In 2015 , there were 505 number banks in the United States .


Example 136:
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[5] 's templateYLabel[0] on templateTitle[0] , templateTitle[2] , and related expenses templateTitle[6] templateXValue[min] to templateXValue[max] . templateTitle[5] 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[6] adhesives to electronic materials . templateTitle[5] 's templateYLabel[0] templateTitle[0] and templateTitle[2] in templateXValue[max] came to around 1.9 billion templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] 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 templateYLabel[0] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows Research and development Spending from the fiscal Year of 2010 to the fiscal Year of 2019 . In the fiscal Year of 2019 , the Spending amounted to 1911 million U.S. dollars .


Example 137:
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[6] , templateXValue[0] was the templateTitle[0] American templateXLabel[0] with the highest templateTitle[3] templateTitle[4] 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[3] pay templateTitle[4] of 33 percent ( on average , women had 33 percent less opportunities than men in templateXValue[last] ) .

generated_template: In templateTitle[9] , templateXValue[0] was the templateTitle[0] American templateXLabel[0] with the highest templateTitle[5] templateTitle[6] templateTitle[7] 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[5] templateTitle[6] templateTitle[7] of 54 percent ( on average , women 's income in templateXValue[last] was estimated to be 54 percent lower than men 's ) .
generated: In country , Nicaragua was the Latin American Country with the highest index 2020 , 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 , of 54 percent ( on average , women 's income in Guatemala was estimated to be 54 percent lower than men 's ) .


Example 138:
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[3] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[2] a templateYLabel[5] will have during her child-bearing years . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[3] was templateYValue[min] templateYLabel[2] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[2] templateYLabel[3] by templateTitle[5] templateYLabel[5] while being of child-bearing age . In templateXValue[max] , templateTitle[3] templateTitle[4] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Fertility rate in Nepal 2017 from 2007 to 2017 . The Fertility rate is the average Number of children born by N/A woman while being of child-bearing age . In 2017 , Nepal 2017 's Fertility rate amounted to 1.97 children born per woman .


Example 139:
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[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[4] templateTitle[8] from templateTitle[9] to templateXValue[max] . It is expected that the templateYLabel[0] of templateTitle[5] templateTitle[4] will reach some templateYValue[7] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] in templateXValue[7] .

generated_template: The statistic shows the templateYLabel[0] of rare earth templateTitle[3] templateTitle[2] templateTitle[3] templateTitle[5] from templateXValue[min] to templateXValue[max] . It is expected that the templateYLabel[0] of templateTitle[2] templateTitle[3] will reach some templateYValue[8] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] in templateXValue[8] .
generated: The statistic shows the Price of rare earth earth rare earth gadolinium from 2010 to 2025 . It is expected that the Price of rare earth will reach some 28473 U.S. dollars per metric ton in 2018 .


Example 140:
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[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the median Household income in Illinois from 1990 to 2018 . In 2018 , the median Household income in Illinois amounted to 70145 U.S. dollars .


Example 141:
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[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .

generated_template: The statistic shows the degree of templateTitle[0] in templateTitle[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .
generated: The 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 142:
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[2] templateYLabel[4] templateYLabel[5] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] templateYLabel[7] people accessed the templateYLabel[4] through their templateYLabel[2] templateYLabel[3] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] templateYLabel[7] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[4] templateYLabel[5] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] templateYLabel[7] people accessed the templateYLabel[4] through their templateYLabel[2] templateYLabel[3] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] templateYLabel[7] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Number of mobile internet users in Singapore from 2017 to 2023 . In 2017 , 4.3 millions people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 4.9 millions mobile phone internet users .


Example 143:
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 . India 's GDP growth is expected to remain steady at more than 7 percent for the next few years , which is almost double that of the global GDP , and both GDP and GDP per capita are expected to increase significantly . Almost half of India 's workforce is employed in the agricultural sector , but services and industry share the other half quite equally . India 's GDP is mostly generated by the services sector , which includes transport , retailing , and offering services in the hospitality and tourism industry . India 's trade balance has been in the red for a decade now , but seems to recover slowly . A trade deficit usually means that a country 's import costs are higher than the amount of money generated with exporting goods . India 's imports could not be compensated for by the country 's exports , as imports have been consistently , even if only slightly , higher over the years both in terms of volume and value . Still , all signs point to India 's economy growing and thriving , reducing India 's debt ( as seen above ) and unemployment rate , enabling the inhabitants to create a better life for themselves .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[6] in templateTitle[5] to templateTitle[7] templateTitle[8] templateTitle[9] ( templateYLabel[3] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] amounted to about templateYValue[6] percent of the templateTitle[7] templateTitle[8] templateTitle[9] . templateTitle[3] 's economy on the rise templateTitle[3] 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 . templateTitle[3] 's templateYLabel[3] growth is expected to remain steady at more than 7 percent for the next few years , which is almost double that of the global templateYLabel[3] , and both templateYLabel[3] and templateYLabel[3] per capita are expected to increase significantly . Almost half of templateTitle[3] 's workforce is employed in the agricultural sector , but services and industry share the other half quite equally . templateTitle[3] 's templateYLabel[3] is mostly generated by the services sector , which includes transport , retailing , and offering services in the hospitality and tourism industry . templateTitle[3] 's trade balance has been in the red for a decade now , but seems to recover slowly . A trade deficit usually means that a country 's import costs are higher than the amount of money generated with exporting goods . templateTitle[3] 's imports could not be compensated for by the country 's exports , as imports have been consistently , even if only slightly , higher over the years both in terms of volume and value . Still , all signs point to templateTitle[3] 's economy growing and thriving , reducing templateTitle[3] 's templateYLabel[1] ( as seen above ) and unemployment rate , enabling the inhabitants to create a better life for themselves .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[6] in templateTitle[5] to templateTitle[7] templateTitle[8] templateTitle[9] ( templateYLabel[3] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] amounted to about templateYValue[6] percent of the templateTitle[7] templateTitle[8] templateTitle[9] .
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 .


Example 144:
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[3] from templateXValue[min] to templateXValue[6] , in templateYLabel[3] to the templateTitle[7] templateTitle[8] templateTitle[9] ( templateYLabel[5] ) , 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[3] amounted to approximately templateYValue[6] percent of the templateYLabel[5] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] from templateXValue[min] to templateXValue[6] in templateYLabel[3] to the templateTitle[7] templateTitle[8] templateTitle[9] ( templateYLabel[5] ) , 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[3] amounted to approximately templateYValue[max] percent of the templateYLabel[5] .
generated: 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 78.65 percent of the GDP .


Example 145:
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[3] from templateXValue[min] to templateXValue[6] in templateTitle[5] to the templateTitle[7] templateTitle[8] templateTitle[9] ( templateYLabel[5] ) , 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[3] amounted to an estimate of approximately templateYValue[6] templateYLabel[3] of the templateYLabel[5] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] from templateXValue[min] to templateXValue[6] in templateTitle[5] to the templateTitle[7] templateTitle[8] templateTitle[9] ( templateYLabel[5] ) , 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[3] amounted to approximately 60 templateYLabel[3] of the templateYLabel[5] .
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 60 percent of the GDP .


Example 146:
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 . In 2018 , Azerbaijan 's gross domestic product amounted to around 46.94 billion U.S. dollars .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[4] is an important indicator of a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[4] is an important indicator of a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
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 . In 2018 , Azerbaijan 's Gross domestic product amounted to around 46.94 billion U.S. dollars .


Example 147:
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 . The total number of crowdfunding platforms worldwide as of April 2012 amounted to 342 , the estimated number by the end of December 2012 was at 536 . The growth of crowdfunding platforms Crowdfunding or crowd sourcing is the collective effort of a number of individuals who pool together resources , usually online , in order to support the efforts of individuals or organizations wishing to get their project off the ground . After the launch of ArtistShare , the site that is often billed as being the first crowdfunding site , the growth in the number of platforms has been constant . The trends began to show in the United States towards the mid-2000s and then in Europe . The increase in the funding volume increases over the past few years are a clear sign of the developing prevalence of this method and raises some interesting questions over the true extent of its potential . In the world after the 2008 economic crisis , a world of fragile and uncertain growth and austerity , it has been proven that it is often extremely difficult for small and medium-sized businesses to procure capital loans from banks . This driver of economic growth is , at least to some extent , being helped along by those who support crowdfunding campaigns online .
gold_template: The statistic shows the percentage templateYLabel[0] in the templateYLabel[3] of templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[3] of templateTitle[2] templateTitle[3] templateTitle[4] increased by templateYValue[min] percent in comparison to the previous templateXLabel[0] . The rate of templateTitle[2] templateTitle[3] templateYLabel[0] continued in all of the following years and the templateYLabel[0] rate reached templateYValue[max] percent in templateXValue[max] . The total templateYLabel[3] of templateTitle[2] templateTitle[3] templateTitle[4] as of April templateXValue[max] amounted to 342 , the estimated templateYLabel[3] by the end of December templateXValue[max] was at 536 . The templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[2] or templateTitle[2] sourcing is the collective effort of a templateYLabel[3] of individuals who pool together resources , usually online , in order to support the efforts of individuals or organizations wishing to get their project off the ground . After the launch of ArtistShare , the site that is often billed as being the first templateTitle[2] site , the templateYLabel[0] in the templateYLabel[3] of templateTitle[3] has been constant . The trends began to show in the United States towards the mid-2000s and then in Europe . The increase in the templateTitle[2] volume increases over the past few years are a clear sign of the developing prevalence of this method and raises some interesting questions over the true extent of its potential . In the templateTitle[4] after the templateXValue[min] economic crisis , a templateTitle[4] of fragile and uncertain templateYLabel[0] and austerity , it has been proven that it is often extremely difficult templateTitle[3] small and medium-sized businesses to procure capital loans from banks . This driver of economic templateYLabel[0] is , at least to some extent , being helped along by those who support templateTitle[2] campaigns online .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[4] templateTitle[5] amounted to templateYValue[max] templateYLabel[0] templateYLabel[1] .
generated: The statistic shows the Growth in the in the United States from 2008 to 2012 . In 2012 , the Growth in the in the worldwide 2008 amounted to 60 Growth in .


Example 148:
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 868.1 templateYLabel[2] templateYLabel[3] templateYLabel[4] , increasing from templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[1] .

generated_template: The statistic shows the total templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[2] templateYLabel[3] templateYLabel[4] of templateTitle[2] were produced worldwide . In templateXValue[1] , there were around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] of templateTitle[2] .
generated: The statistic shows the total Fresh fruit production Production from 1990 to 2018 . In 2018 , the million metric tons of production were produced worldwide . In 2000 , there were around 868.09 million metric tons of production .


Example 149:
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[3] in templateTitle[5] 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[5] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of the population was templateYValue[max] templateYLabel[5] .
generated: The statistic shows the Life expectancy at birth in Life from 2007 to 2017 . In 2017 , the average Life expectancy of the population was 81.09 years .


Example 150:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[3] stood at templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of land area .

generated_template: This graph shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This graph shows the Population density in Illinois residents per square mile in - from 1960 to 2018 . In 2018 , the Population density in Illinois amounted to 232.0 residents per square mile .


Example 151:
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 . More emphasis on clothing manufacturing The EU textile industry is segmented predominantly into two markets : textiles and clothing . Clothing has a clearly bigger presence with the number of enterprises devoted to this sector , almost double the number of companies that manufacture textiles . Likewise , investments going into different segments of the industry highlights the primacy of clothing to textile manufacturing in EU28 . Industry employment declines Despite the rising turnover and EU investments in the manufacture of textiles , employment in this sector dropped significantly . While in 2009 over 2 million people were employed across all segments , in 2017 this number fell considerably .
gold_template: In the templateTitle[8] templateTitle[9] , the templateYLabel[0] of templateYLabel[2] in the business of templateTitle[2] and templateTitle[4] 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[2] and templateTitle[4] templateTitle[5] was recorded as templateYValue[min] in templateTitle[11] 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] . More emphasis on templateTitle[4] manufacturing The templateTitle[8] templateTitle[2] industry is segmented predominantly into templateTitle[11] markets : textiles and templateTitle[4] . templateTitle[4] has a clearly bigger presence with the templateYLabel[0] of enterprises devoted to this sector , almost double the templateYLabel[0] of templateYLabel[2] that templateTitle[5] textiles . Likewise , investments going into different segments of the industry highlights the primacy of templateTitle[4] to templateTitle[2] manufacturing in templateTitle[11] . Industry employment declines Despite the rising turnover and templateTitle[8] investments in the templateTitle[5] of textiles , employment in this sector dropped significantly . While in templateXValue[min] over templateTitle[11] million people were employed across all segments , in templateXValue[1] this templateYLabel[0] fell considerably .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , there were approximately templateYValue[2] templateYLabel[2] registered in the United States .
generated: The statistic shows the Number of companies in the manufacturers in from 2009 to 2018 . In 2016 , there were approximately 177684 companies registered in the United States .


Example 152:
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 . Personal saving rate is calculated as the ratio of personal saving to 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 . Personal templateYLabel[0] templateYLabel[1] is calculated as the ratio of personal templateYLabel[0] to disposable personal income .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . For templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] was templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Savings rate savings rate 2010 - from 2010 to 2017 . For 2017 , the French households savings rate 2010 - was 15.8 N/A N/A N/A .


Example 153:
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[4] templateTitle[5] 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 templateTitle[9] billion templateYLabel[4] templateYLabel[5] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of 2.3 billion templateYLabel[4] templateYLabel[5] .
generated: This graph depicts the value of the Philadelphia 76ers ( Franchise of Major League Baseball from 2003 to 2020 . In 2020 , the Franchise had an estimated value of 2.3 billion U.S. dollars .


Example 154:
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[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in 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[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] 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 155:
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[2] in the templateTitle[2] templateTitle[3] volume of templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateTitle[3] decreased by 1.3 templateYLabel[0] from the previous templateXLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was at templateYValue[max] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] templateYLabel[5] . Thus , the templateYLabel[0] is owned by the United States . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[1] templateTitle[2] .
generated: The statistic shows the Change in U.S. exports of trade from 1990 to 2019 . In 2019 , the Change in U.S. exports of was at 20.5 Percentage of 1,000 N/A N/A N/A . Thus , the Percentage is owned by the United States . In 2012 , the Percentage of of the Change in U.S. .


Example 156:
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[2] in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the country consumed around 2.4 billion templateYLabel[3] templateYLabel[4] of templateTitle[2] .

generated_template: This statistic shows the worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the worldwide from 2006 to 2016 . In 2016 , Consumption of cement Consumption amounted to 2206000 thousand metric tons .


Example 157:
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[6] templateTitle[7] has templateYLabel[1] for templateXValue[7] templateTitle[2] templateTitle[3] for the fiscal year templateTitle[8] , in templateYLabel[3] British Pounds . During this year the government has templateYLabel[1] templateYValue[max] templateYLabel[3] 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[3] pounds , the second highest templateTitle[3] templateTitle[10] in this fiscal year .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[2] templateTitle[3] as of January templateTitle[6] , templateTitle[5] templateXLabel[0] . During the survey period , the templateYLabel[0] of templateXValue[last] were templateYValue[max] thousand templateTitle[0] as of templateXValue[0] templateXLabel[0] .
generated: This statistic shows the degree of Budgeted in sector spending as of January United , the Industry . During the survey period , the Amount of Industry_agriculture_and_employment were 256 thousand Budgeted as of Social_protection Industry .


Example 158:
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[3] from templateXValue[min] to templateXValue[max] . The figures are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] in templateTitle[3] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] 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 159:
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[2] templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[3] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitle[0] templateTitle[1] templateYLabel[0] of the templateYLabel[2] templateYLabel[3] ( in relation to PPP dollars ) amounted to about templateYValue[6] percent .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] of the templateYLabel[2] templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[3] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitle[0] templateTitle[1] templateYLabel[0] of templateYLabel[2] templateYLabel[3] ( in relation to PPP dollars ) amounted to about templateYValue[6] percent .
generated: 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 global GDP ( in relation to PPP dollars ) amounted to about 1.36 percent .


Example 160:
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 . Women were especially vulnerable , as the share of women living under the poverty threshold was two percent points higher than the share of men . Severe material deprivation in Belgium In the last decade , the share of people suffering from severe material deprivation in Belgium has fortunately decreased , but as of 2018 still five percent of the population fit the EU definition , checking four out of these nine indicators : the inability to pay bills , the inability to properly heat the house , inability to deal with unexpected expenses , inability to eat meat , fish or chicken every two days , inability to go on a week-long holiday abroad , inability to afford a car , inability to afford a washer , inability to buy a television and inability to afford a telephone connection . For example , in 2018 6.3 percent of Belgians was unable to pay their bills in time . Nearly one quarter of Belgians can not afford a holiday Although the share of Belgians unable to afford a washing machine or telephone was relatively low ( 1.1 and 0.2 percent respectively ) , some other indicators proved to be a lot more problematic . In 2018 , just over 24 percent of Belgians was not able to pay unexpected expenses , and roughly the same amount of people could not afford to go on a week-long holiday either .
gold_template: Between templateXValue[min] and templateXValue[max] , roughly templateYValue[3] percent of the templateTitle[4] population was at templateYLabel[0] of templateYLabel[0] , defined by Statistics templateTitle[4] 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 16 percent of the Belgians . Women were especially vulnerable , as the share of women living under the templateYLabel[0] threshold was templateTitle[5] percent points higher than the share of men . Severe material deprivation in templateTitle[4] In the last decade , the share of people suffering from severe material deprivation in templateTitle[4] has fortunately decreased , but as of templateXValue[max] still five percent of the population fit the EU definition , checking four out of these nine indicators : the inability to pay bills , the inability to properly heat the house , inability to deal with unexpected expenses , inability to eat meat , fish or chicken every templateTitle[5] days , inability to go on a week-long holiday abroad , inability to afford a car , inability to afford a washer , inability to buy a television and inability to afford a telephone connection . For example , in templateXValue[max] 6.3 percent of Belgians was unable to pay their bills in time . Nearly templateTitle[7] quarter of Belgians can not afford a holiday Although the share of Belgians unable to afford a washing machine or telephone was relatively low ( 1.1 and 0.2 percent respectively ) , some other indicators proved to be a lot more problematic . In templateXValue[max] , just templateYLabel[0] 24 percent of Belgians was not able to pay unexpected expenses , and roughly the same amount of people could not afford to go on a week-long holiday either .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateTitle[2] in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] was at about templateYValue[min] percent .
generated: This statistic shows the Poverty At-risk-of-poverty rate in Belgium from 2008 to 2018 . In 2018 , the Poverty risk rate in Belgium was at about 14.6 percent .


Example 161:
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[4] templateTitle[5] from templateXValue[min] to templateXValue[2] and shows a forecast for templateXValue[1] and templateXValue[max] . In templateXValue[1] , global templateTitle[4] templateTitle[0] templateYLabel[0] is projected to amount to templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] , down from the all-time highest templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[2] .

generated_template: This statistic shows total templateYLabel[3] templateTitle[0] templateYLabel[0] templateTitle[2] the Supplemental Nutrition Assistance Program ( templateTitle[3] , formerly called templateTitle[6] templateTitle[7] ) from templateXValue[min] to templateXValue[max] . In templateXValue[2] , about templateYValue[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] were spent templateTitle[2] the Supplemental Nutrition Assistance Program .
generated: This statistic shows total U.S. Capital Spending in the Supplemental Nutrition Assistance Program ( the , formerly called worldwide 2000 ) from 2000 to 2020 . In 2018 , about 105.9 billion U.S. dollars were spent in the Supplemental Nutrition Assistance Program .


Example 162:
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 . When looking at the rape rate , or the number of rapes per 100,000 of the population , a very different picture is painted : Alaska was the state with the highest rape rate in the country in 2017 , with California ranking as 35th in the nation . The prevalence of rape Rape and sexual assault are notorious for being underreported crimes , which means that the prevalence of sex crimes is likely much higher than what is reported . Additionally , more than a third of women worry about being sexually assaulted , and most sexual assaults are perpetrated by someone the victim knew .
gold_template: In templateTitle[8] , templateXValue[0] had the highest templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateTitle[4] in the United States , with templateYValue[max] reported templateYLabel[3] . templateXValue[49] had the lowest templateYLabel[0] of reported templateYLabel[2] templateYLabel[3] templateTitle[4] at templateYValue[min] . templateYLabel[0] vs. rate It is perhaps unsurprising that templateXValue[0] had the highest templateYLabel[0] of reported templateYLabel[3] in the United States in templateTitle[8] , as templateXValue[0] is the templateXLabel[0] with the highest population . When looking at the templateYLabel[3] rate , or the templateYLabel[0] of templateYLabel[3] per 100,000 of the population , a very different picture is painted : templateXValue[34] was the templateXLabel[0] with the highest templateYLabel[3] rate in the country in 2017 , with templateXValue[0] ranking as 35th in the nation . The prevalence of templateYLabel[3] templateYLabel[3] and sexual assault are notorious templateYLabel[2] being underreported crimes , which means that the prevalence of sex crimes is likely much higher than what is reported . Additionally , more than a third of women worry about being sexually assaulted , and most sexual assaults are perpetrated templateTitle[5] someone the victim knew .

generated_template: In templateTitle[6] , the templateXLabel[0] of templateXValue[last] had the highest templateTitle[1] templateTitle[2] templateTitle[3] in the templateXValue[20] templateXValue[20] , with templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] . As of November 18 , the templateYLabel[0] templateYLabel[1] in templateXValue[last] templateXLabel[0] of templateTitle[3] templateTitle[4] was at templateYValue[min] .
generated: In state , the State of Wyoming had the highest of forcible rape in the Nevada Nevada , with 15505 Number of forcible . As of November 18 , the Number of in Wyoming State of rape cases was at 243 .


Example 163:
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 . Increased use of natural gas is expected to parallel the decline of coal , which has higher emissions and lower efficiency . Is it possible to decouple emissions from growth ? Globally , carbon dioxide emissions have increased over the last few decades . China and the United States have been two of the largest greenhouse gas emitters in recent years . The ability of these major emitting countries to decouple their greenhouse gas emissions from economic growth . Annual global emissions stabilized from 2014 to 2016 but rose again in 2017 . Shifting to a carbon free economy is technologically feasible because some of the most emission intensive sectors are only a minor part of the economy . Deployment of renewable technologies has rapidly decreased in cost and low-carbon technologies are continuously infiltrating global industries .
gold_template: templateYLabel[7] capita templateTitle[0] templateTitle[1] ( templateYLabel[5] ) templateYLabel[0] in the United States reached an estimate of templateYValue[max] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] in templateXValue[min] . The United States has forecast that a decrease in templateYLabel[5] templateYLabel[0] will occur through templateXValue[max] , reaching templateYValue[0] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] templateYLabel[6] templateYLabel[7] templateYLabel[7] . This forecast is driven by increasing consumption of natural gas due to lower cost and templateYLabel[0] with higher efficiency . Increased use of natural gas is expected to parallel the decline of coal , which has higher templateYLabel[0] and lower efficiency . Is it possible to decouple templateYLabel[0] from growth ? Globally , templateTitle[0] templateTitle[1] templateYLabel[0] have increased over the last few decades . China and the United States have been templateYLabel[5] of the largest greenhouse gas emitters in recent years . The ability of these major emitting countries to decouple their greenhouse gas templateYLabel[0] from economic growth . Annual global templateYLabel[0] stabilized from 2014 to 2016 but rose again in 2017 . Shifting to a templateTitle[0] free economy is technologically feasible because some of the most templateYLabel[0] intensive sectors are only a minor part of the economy . Deployment of renewable technologies has rapidly decreased in cost and low-carbon technologies are continuously infiltrating global industries .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . For templateXValue[7] , the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] ) . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was at templateYValue[0] percent .
generated: This statistic shows the Carbon dioxide emissions per person from 2019 to 2050 . For 2019 , the Carbon dioxide emissions ( Emissions ) Emissions in metric tons of CO2 ) . In 2050 , the Carbon dioxide emissions per person was at 12.6 percent .


Example 164:
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 templateYLabel[3] templateTitle[0] templateTitle[1] in templateTitle[4] templateTitle[5] from templateXValue[min] until templateXValue[max] , in templateYLabel[3] per month . In templateXValue[2] , templateTitle[0] templateTitle[1] IP templateTitle[2] is expected to reach templateYValue[2] templateYLabel[3] per month .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] was templateYValue[max] percent of the previous templateXLabel[0] . The number of templateTitle[7] templateYLabel[3] in recent years .
generated: The statistic shows the Online gaming traffic in North America from 2011 to 2016 . In 2016 , the Online gaming traffic in North America was 96 percent of the previous Year . The number of - petabytes in recent years .


Example 165:
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[4] 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[4] was at templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] 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[4] 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 166:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] 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 167:
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[2] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] templateYLabel[0] of templateTitle[0] 's templateYLabel[2] lived below the templateTitle[2] line .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] templateYLabel[0] of templateTitle[0] 's templateYLabel[2] lived below the templateTitle[2] line .
generated: This statistic shows the Poverty rate in Connecticut from 2000 to 2018 . In 2018 , 10.4 Percentage of Connecticut 's population lived below the Poverty line .


Example 168:
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[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] to social network templateTitle[0] . As of the second templateXLabel[0] of templateTitle[8] , templateTitle[0] had an average of templateYValue[max] templateYLabel[6] templateTitle[2] templateYLabel[3] templateYLabel[4] via templateYLabel[2] . These accounted for 59 percent of all templateTitle[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] in the United States from templateXValue[last] to templateXValue[0] . In the last reported period , the templateTitle[0] templateTitle[1] templateTitle[2] in the United States in the first templateXLabel[0] of templateYValue[max] percent .
generated: The statistic shows the LinkedIn : unique mobile visiting of in the United States from Q1_'13 to Q2_'16 . In the last reported period , the LinkedIn : unique in the United States in the first Quarter of 63 percent .


Example 169:
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[2] templateYLabel[3] dollars.The templateTitle[0] templateTitle[1] are owned by Ken Kendrick , who bought the franchise for 238 templateYLabel[2] templateYLabel[3] templateYLabel[4] 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[2] templateYLabel[3] dollars.The templateTitle[0] templateTitle[1] are owned by Mark Attanasio , who bought the franchise for 223 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[13] .
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 Mark Attanasio , who bought the franchise for 223 million U.S. dollars in 2005 .


Example 170:
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 United States 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[2] reported that templateXValue[0] of their templateXValue[0] templateXValue[0] templateXValue[0] were templateXValue[0] , while templateYValue[3] percent of templateYLabel[2] 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 , conducted in December 2018 in the United States . The survey shows that templateYValue[min] percent of templateYLabel[2] said that they ate templateTitle[3] templateTitle[5] templateTitle[6] templateTitle[7] to templateXValue[3] templateXValue[3] .
generated: This statistic shows the results of a survey , conducted in December 2018 in the United States . The survey shows that 7 percent of respondents said that they ate user privacy 2018 N/A to No_none_of_my_social_media_accounts_are_private No_none_of_my_social_media_accounts_are_private .


Example 171:
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[2] templateYLabel[3] templateYLabel[4] . United States-based templateTitle[0] is templateTitle[6] of the world templateTitle[2] largest gold producers . In templateXValue[max] , the company generated some 7.25 billion templateYLabel[3] templateYLabel[4] of templateYLabel[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Revenue of 's revenue 2007 - 2018 from 2007 to 2018 . In 2018 , the Newmont Mining 's revenue 2007 - 2018 amounted to 10441 million U.S. dollars .


Example 172:
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[3] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[2] templateYLabel[3] by one templateYLabel[5] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] among templateTitle[3] 's population amounted to templateYValue[min] templateYLabel[2] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[3] amounted to templateYValue[min] templateYLabel[2] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Fertility rate in Brazil from 2007 to 2017 . The Fertility rate is the average Number of children born per woman while being of child-bearing age . In 2017 , the Fertility rate in Brazil amounted to 1.74 children per woman .


Example 173:
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[4] templateTitle[5] templateTitle[6] between templateTitle[7] and templateTitle[9] , templateTitle[10] templateTitle[11] . 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 templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] in templateTitle[4] , broken down by templateXLabel[0] . According to the templateXValue[0] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States was found to templateYValue[max] templateYLabel[1] templateYLabel[2] in templateTitle[9] . In templateXValue[1] , the lowest templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[3] . By comparison , the templateXValue[0] templateXValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the next few years .
generated: The statistic shows the Annual growth of the growth in global , broken down by Country . According to the Rest_of_world , the Average annual growth in the United States was found to 19 annual growth in 2015 . In World , the lowest Average annual growth N/A in the . By comparison , the Rest_of_world Rest_of_world annual growth N/A in the next few years .


Example 174:
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 . This would represent the highest figure since 2006 , when unit shipments exceeded 68.4 million . Of these kitchen and laundry appliances forecast to be shipped in 2017 , some 18.5 million are cooking appliances and a further 13.8 million are refrigerators . Home laundry appliances are expected to make up over 17 million of the total unit shipments in 2017 . The most widely shipped category of laundry appliances are automatic washers , which are projected to account for almost 10 million of the total unit shipment of appliances in 2017 . The home appliances industry is a very lucrative market and U.S. imports of kitchen and household appliances were worth almost 23.4 billion U.S. dollars in 2013 . As of 2013 , the leading home appliance company in the world based on revenue was the Midea Group , a Chinese-based corporation . The company had sales of almost 20 billion U.S. dollars , exceeding those of its closest rivals , Whirlpool and Gree Electric Appliances . However , Whirlpool remains the leader within the U.S. market as it held a market share of over 30 percent in the second quarter of 2013 . This figure represents almost double the share held by its closest rival , General Electric . Whirlpool announced total revenue of 18.77 billion U.S. dollars in 2013 , its highest annual figure to-date . Over 50 percent of its sales came in North America , whilst only 4 percent were made in Asia .
gold_template: The statistic illustrates the total templateYLabel[0] templateYLabel[1] of templateTitle[1] and templateTitle[1] appliances* in the United States from templateXValue[min] to templateXValue[5] and forecasts up to and including templateXValue[max] . For templateXValue[2] the templateTitle[2] Magazine projects total templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] to reach templateYValue[2] templateYLabel[3] units . templateTitle[8] templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[2] templateTitle[10] additional information Total templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] and templateTitle[1] templateTitle[2] in the United States are expected to reach 64.6 templateYLabel[3] units in templateXValue[max] . This would represent the highest figure since templateXValue[11] , when templateYLabel[0] templateYLabel[1] exceeded 68.4 templateYLabel[3] . Of these templateTitle[1] and templateTitle[1] templateTitle[2] forecast to be shipped in templateXValue[max] , some 18.5 templateYLabel[3] are cooking templateTitle[2] and a further 13.8 templateYLabel[3] are refrigerators . Home templateTitle[1] templateTitle[2] are expected to make up over templateTitle[11] templateYLabel[3] of the total templateYLabel[0] templateYLabel[1] in templateXValue[max] . The most widely shipped category of templateTitle[1] templateTitle[2] are automatic washers , which are projected to account for almost 10 templateYLabel[3] of the total templateYLabel[0] templateYLabel[1] of templateTitle[2] in templateXValue[max] . The home templateTitle[2] industry is a very lucrative market and templateTitle[8] imports of templateTitle[1] and household templateTitle[2] were worth almost 23.4 billion templateTitle[8] dollars in templateXValue[4] . As of templateXValue[4] , the leading home templateTitle[2] company in the world based on revenue was the Midea Group , a Chinese-based corporation . The company had sales of almost templateTitle[9] billion templateTitle[8] dollars , exceeding those of its closest rivals , Whirlpool and Gree Electric templateTitle[2] . However , Whirlpool remains the leader within the templateTitle[8] market as it held a market share of over 30 percent in the second quarter of templateXValue[4] . This figure represents almost double the share held by its closest rival , General Electric . Whirlpool announced total revenue of 18.77 billion templateTitle[8] dollars in templateXValue[4] , its highest annual figure to-date . Over 50 percent of its sales came in North America , whilst only 4 percent were made in Asia .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYLabel[3] units of e-Readers were shipped worldwide .
generated: The statistic shows Major kitchen/laundry appliances Unit shipments in shipments from 2005 to 2017 . In 2015 , millions units of e-Readers were shipped worldwide .


Example 175:
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[5] templateTitle[1] templateTitle[6] in the United States from templateXValue[min] to templateXValue[max] . templateYLabel[3] templateTitle[5] templateTitle[1] templateYLabel[0] amounted to approximately templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[min] and increased to about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic shows the total templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] , up from templateYValue[6] templateYLabel[2] the previous templateXLabel[0] . In templateXValue[max] , the templateTitle[2] with the highest lottery templateYLabel[0] was New York , with around 9.7 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateYLabel[0] . In the same templateXLabel[0] , templateTitle[2] templateTitle[3] transferred a profit of 22.57 templateYLabel[2] templateYLabel[3] templateYLabel[4] to templateTitle[2] and local governments .
generated: This statistic shows the total Sales of market : total wholesale from 1990 to 2018 . In 2018 , U.S. tea market : total amounted to about 12.66 billion U.S. dollars , up from 9.79 billion the previous Year . In 2018 , the market with the highest lottery Sales was New York , with around 9.7 billion U.S. dollars in Sales . In the same Year , market : transferred a profit of 22.57 billion U.S. dollars to market and local governments .


Example 176:
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[2] templateYLabel[3] to the templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[5] ) in templateTitle[12] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[2] templateYLabel[3] in templateTitle[12] amounted to about templateYValue[6] percent of the country 's templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] to templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[5] ) in templateTitle[12] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[2] templateYLabel[3] in templateTitle[12] amounted to about templateYValue[6] percent of the country 's templateTitle[5] templateTitle[6] templateTitle[7] .
generated: The statistic shows the Ratio of government expenditure to 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 country 's gross domestic product .


Example 177:
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 . Despite the wide variety of fuels that can be used , the country 's power generation structure consists primarily of coal and nuclear power to provide its citizens with an around-the-clock `` baseload '' supply of electricity – this is what makes nuclear power an essential and crucial element of the U.S. power generation mix . In spring and fall , when demand for electricity is lowest , a limited number of nuclear reactors are shut down for refueling and maintenance . Since spent fuel rods are no longer useful in sustaining a nuclear reaction , nuclear units are required to dispose of some of the spent fuel rods and conduct other routine maintenance and repair activities . The level of outage days at operational nuclear power plants in the United States dramatically decreased in the 1990s , when plants reduced the number of refueling outage days from 81 in 1997 to 40 in 1999 . In 2018 , there were 34 refueling outage days at U.S. nuclear power plants , down from 46 in 2012 .
gold_template: The statistic represents the templateYLabel[0] length of outages for templateTitle[1] power plants in the United States between templateXValue[min] and templateXValue[max] . In templateXValue[max] , as of October templateXValue[max] , this figure stood at templateYValue[min] templateTitle[3] templateYLabel[3] . US templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[3] America 's electricity needs are met by a selection of energy carriers including coal , oil and natural gas , as well as templateTitle[1] templateTitle[2] and renewable energy sources . Despite the wide variety of fuels that can be used , the country 's power generation structure consists primarily of coal and templateTitle[1] power to provide its citizens with an around-the-clock `` baseload '' supply of electricity – this is what makes templateTitle[1] power an essential and crucial element of the templateTitle[0] power generation mix . In spring and fall , when demand for electricity is lowest , a limited templateYLabel[1] of templateTitle[1] reactors are shut down for templateTitle[2] and maintenance . Since spent templateTitle[2] rods are no longer useful in sustaining a templateTitle[1] reaction , templateTitle[1] units are required to dispose of some of the spent templateTitle[2] rods and conduct other routine maintenance and repair activities . The level of templateTitle[3] templateYLabel[3] at operational templateTitle[1] power plants in the United States dramatically decreased in the 1990s , when plants reduced the templateYLabel[1] of templateTitle[2] templateTitle[3] templateYLabel[3] from templateYValue[max] in templateXValue[22] to templateYValue[9] in templateXValue[20] . In templateXValue[1] , there were templateYValue[1] templateTitle[2] templateTitle[3] templateYLabel[3] at templateTitle[0] templateTitle[1] power plants , down from templateYValue[7] in templateXValue[7] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[3] percent .
generated: This statistic shows the U.S. nuclear refueling in the United States from 1995 to 2019 . In 2016 , the U.S. nuclear refueling outage days was 34 percent .


Example 178:
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[2] templateYLabel[3] templateYLabel[4] .

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[2] templateYLabel[3] templateYLabel[4] .
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 179:
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[4] and templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitle[0] Pharmaceutical Industries is templateTitle[9] of the largest generic drug manufacturers in the world . In templateXValue[max] , the company invested about templateTitle[9] billion templateYLabel[3] templateYLabel[4] in templateTitle[4] and templateTitle[6] .

generated_template: This statistic shows the research and development ( templateXLabel[0] ) templateYLabel[0] ) templateYLabel[0] of templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company invested approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateYLabel[2] the previous templateXLabel[0] . templateTitle[2] largest countries in the eighth largest company generally worldwide as of templateXValue[1] , measured by revenue . It is headquartered in London , UK . Until templateXValue[max] , templateXLabel[0] templateTitle[1] templateYLabel[0] spending decreased to some templateYValue[0] templateYLabel[2] templateYLabel[4] .
generated: This statistic shows the research and development ( Year ) Expenditure ) Expenditure of Teva from 2006 to 2019 . In 2019 , the company invested approximately 2077 million U.S. dollars , up from 1213 million the previous Year . expenditure largest countries in the eighth largest company generally worldwide as of 2018 , measured by revenue . It is headquartered in London , UK . Until 2019 , Year : Expenditure spending decreased to some 1010 million dollars .


Example 180:
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 . In 2018 , Brazil 's gross domestic product amounted to around 1.87 trillion U.S. dollars . In comparison to the GDP of the other BRIC countries India , Russia and China , Brazil was ranked second that year . Brazil 's national finances Brazil is one of the fastest growing economies in the world and the largest amongst all Latin American countries . Brazil is also a member of multiple economic organizations such as the G20 as well as one of the four countries in the BRIC economies , which consist of Brazil , Russia , India and China . Despite having one of the lower populations out of the four countries , Brazil maintained a relatively stable dollar value of all goods and services produced within the country in comparison to India , for example . This indicates that unemployment is low and in general business demand within the country has become relatively high . Spending within the country has been relatively high , however is considered to be normal , especially for developing countries . It is expected that developing economies have a budget deficit of roughly 3 percent , primarily because spending is needed in order to fuel an economy at most times . However , most Brazilians still have faith in their country 's economic future and still believe that their own personal financial situation will improve along with the country 's economic position in the world .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] 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[4] is an important indicator of a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around 1.87 trillion templateYLabel[5] templateYLabel[6] . In comparison to the templateTitle[4] of the other BRIC countries India , Russia and China , templateTitle[7] was ranked second that templateXLabel[0] . templateTitle[7] 's national finances templateTitle[7] is one of the fastest growing economies in the world and the largest amongst all Latin American countries . templateTitle[7] is also a member of multiple economic organizations such as the G20 as well as one of the templateTitle[8] countries in the BRIC economies , which consist of templateTitle[7] , Russia , India and China . Despite having one of the lower populations out of the templateTitle[8] countries , templateTitle[7] maintained a relatively stable templateYLabel[6] value of all goods and services produced within the country in comparison to India , for example . This indicates that unemployment is low and in general business demand within the country has become relatively high . Spending within the country has been relatively high , however is considered to be normal , especially for developing countries . It is expected that developing economies have a budget deficit of roughly 3 percent , primarily because spending is needed in order to fuel an economy at most times . However , most Brazilians still have faith in their country 's economic future and still believe that their own personal financial situation will improve along with the country 's economic position in the world .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods and goods and goods produced within a country in a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: The statistic shows Gross domestic product ( GDP ) in Brazil from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods and goods and goods produced within a country in a country 's economic power . In 2018 , Brazil 's Gross domestic product amounted to around 1867.82 billion U.S. dollars .


Example 181:
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[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateTitle[3] 's templateYLabel[0] was 30.44 billion templateYLabel[3] templateXValue[6] templateYLabel[5] templateYLabel[6] .

generated_template: The statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] 's templateYLabel[0] was 730.27 billion templateYLabel[3] templateXValue[6] templateYLabel[5] templateYLabel[6] .
generated: The statistic shows the gross domestic product ( GDP ) of New from 2000 to 2018 . In 2018 , New 's GDP was 730.27 billion chained 2012 Canadian dollars .


Example 182:
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 . Southwest 's fiscal status Accompanying Southwest Airlines ' increase ASMs , the air transportation firms ' revenue passenger miles increased through the last decade as well . During 2019 , Southwest Airlines had an operating income of 2.96 billion U.S. dollars . For the same time period , operating expenses were around 19.5 billion U.S. dollars . In total , Southwest Airlines ' assets rose to over 25.89 billion U.S. dollars in 2019 . Strains on Southwest 's capacity In 2019 , aviation authorities around the world grounded the Boeing 737 MAX due to safety concerns following several highly publicized crashes ( Ethiopian Airlines Flight 302 and Lion Air Flight 610 ) . This ban seriously affected Southwest Airlines , which has a fleet of almost entirely Boeing 737 aircraft , some of which are the model under scrutiny . The airline was one of the top five buyers of Boeing aircraft in 2018 .
gold_template: templateTitle[0] templateTitle[1] grew its templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[0] ) from templateYValue[min] templateYLabel[2] in templateXValue[min] to templateYValue[0] templateYLabel[2] in templateXValue[max] . templateYLabel[0] are calculated by the total number of seats multiplied by the total distance traveled . When describing the templateTitle[1] industry , people consider templateYLabel[0] as a measure of capacity . templateTitle[0] 's fiscal status Accompanying templateTitle[0] templateTitle[1] ' increase templateYLabel[0] , the templateTitle[1] transportation firms ' revenue passenger templateTitle[5] increased through the last decade as well . During templateXValue[max] , templateTitle[0] templateTitle[1] had an operating income of 2.96 templateYLabel[2] U.S. dollars . For the same time period , operating expenses were around 19.5 templateYLabel[2] U.S. dollars . In total , templateTitle[0] templateTitle[1] ' assets rose to over 25.89 templateYLabel[2] U.S. dollars in templateXValue[max] . Strains on templateTitle[0] 's capacity In templateXValue[max] , aviation authorities around the world grounded the Boeing 737 MAX due to safety concerns following several highly publicized crashes ( Ethiopian templateTitle[1] Flight 302 and templateYLabel[2] templateTitle[1] Flight 610 ) . This ban seriously affected templateTitle[0] templateTitle[1] , which has a fleet of almost entirely Boeing 737 aircraft , some of which are the model under scrutiny . The templateTitle[1] was templateTitle[6] of the top five buyers of Boeing aircraft in templateXValue[1] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] of templateYLabel[3] templateYLabel[4] templateYLabel[5] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] was templateYValue[0] percent .
generated: The statistic shows the ASMs in billions in the United States from 2011 to 2019 . In 2016 , the ASMs in of N/A N/A N/A . In 2019 , the ASMs in of available seat was 157.25 percent .


Example 183:
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[3] templateTitle[4] templateTitle[5] that were recalled in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[min] templateTitle[3] templateTitle[4] templateTitle[5] were the subject of a templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] was templateYValue[max] percent .
generated: This statistic shows the Number of of the 's products from 2001 to 2018 . In 2018 , the Total recalls of children 's products was 232 percent .


Example 184:
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 templateXLabel[4] templateXLabel[1] living in templateXLabel[0] areas in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the templateXLabel[4] templateXLabel[1] of templateTitle[2] was living in templateXLabel[0] areas .

generated_template: The statistic shows the percentage of the templateXLabel[2] templateXLabel[3] living in templateTitle[0] areas in templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the templateXLabel[2] templateXLabel[3] of templateTitle[2] was living in templateTitle[0] areas .
generated: The statistic shows the percentage of the ( of living in Urbanization areas in Botswana from 2008 to 2018 . In 2018 , 69.45 percent of the ( of of Botswana was living in Urbanization areas .


Example 185:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] 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 186:
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[4] 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[4] was at templateYValue[min] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] 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[4] 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 187:
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[2] in templateTitle[4] from templateXValue[last] to templateXValue[1] . In the templateXValue[0] season , there were a total of templateYValue[0] registered templateTitle[0] templateTitle[1] templateYLabel[2] in templateTitle[4] according to the International templateTitle[0] templateTitle[1] Federation .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[last] to templateXValue[0] . In templateXValue[1] , there were a total of templateYValue[max] thousand templateYLabel[2] registered templateTitle[0] templateTitle[1] templateYLabel[2] throughout templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[2] in the templateTitle[5] templateTitle[6] .
generated: The statistic shows the Number of players in the 2010 - from 2010/11 to 2018/19 . In 2017/18 , there were a total of 721504 thousand players registered Ice hockey players throughout Ice hockey players players in the 2010 - .


Example 188:
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 . In 2018 , Malawi 's estimated gross domestic product amounted to around 6.9 billion U.S. dollars .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[4] is an important indicator of a country 's economic power . In templateXValue[6] , templateTitle[7] 's estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[4] is an important indicator of a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
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 . In 2018 , Malawi 's Gross domestic product amounted to around 6.9 billion U.S. dollars .


Example 189:
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[2] templateYLabel[3] working on the board of templateTitle[5] templateTitle[6] templateTitle[7] in the United Kingdom ( templateTitle[8] ) from templateXValue[min] to templateXValue[max] . During the observed period the templateYLabel[0] of templateYLabel[2] templateYLabel[3] ( both executive and non-executive ) in templateTitle[5] templateTitle[6] templateTitle[7] increased by 151 to reach a total of templateYValue[max] females holding a templateYLabel[3] position , as of June templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] was at approximately templateYValue[0] percent .
generated: The statistic shows the Number of of the in from 2012 to 2019 . In 2019 , the Number of of directors in was at approximately 292 percent .


Example 190:
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[4] templateTitle[5] templateTitle[6] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of 2.1 billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by William DeWitt Jr. , who bought the templateYLabel[0] for 150 templateYLabel[3] templateYLabel[4] templateYLabel[5] in 1996 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of 3.2 billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by Rogers Communications , who bought the templateYLabel[0] for 137 templateYLabel[3] templateYLabel[4] templateYLabel[5] in 2000 .
generated: 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 3.2 billion U.S. dollars . The St. Louis Cardinals are owned by Rogers Communications , who bought the Franchise for 137 million U.S. dollars in 2000 .


Example 191:
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[7] . In templateTitle[7] , templateYValue[0] percent of templateYLabel[2] 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 results of a survey , conducted in December 2018 in the United States , on templateTitle[5] . During a survey , templateYValue[max] percent of templateYLabel[2] stated they had templateXValue[0] templateXValue[0] .
generated: This statistic shows the results of a survey , conducted in December 2018 in the United States , on abortion . During a survey , 49 percent of respondents stated they had Morally_acceptable Morally_acceptable .


Example 192:
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 templateYLabel[5] templateTitle[8] states in templateTitle[5] . A negative templateYLabel[1] templateYLabel[2] means that a templateXLabel[0] contributes more to the templateYLabel[5] templateYLabel[1] than it receives from it , a positive templateYLabel[2] means the templateXLabel[0] contributes less than it receives . In templateTitle[5] , templateXValue[last] contributed the most with approximately 10.68 templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] templateTitle[5] from the templateXValue[last] season to the templateXValue[0] season . The templateXValue[0] there were templateYValue[max] thousand people living in the templateXLabel[0] .
generated: The statistic shows the Operating budgetary balances in in 2017 from the Germany season to the Poland season . The Poland there were 8.57 thousand people living in the Country .


Example 193:
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 . At the end of 2018 , the company experienced shrinking sales for the first time since mid-2015 . Company profile Waitrose & Partners is the food retail division of John Lewis Partnership . It has been a subsidiary of John Lewis since 1937 . The Bracknell based company was founded in West London in 1904 and operated 353 stores in the United Kingdom in 2018 . In these stores the company made approximately 1.1 thousand British pounds of sales per square foot in 2016 . The company also operates an online store that has seen growth of over 10 percent during 2018 . Market position The company generated a revenue of 6.4 billion pounds in 2018 . Revenue growth has slowed down in recent years compared to the early 2010s . as of May 2019 , the company ranked 8th in its market with a share of 5.1 percent . Its grocery market share has been stable at just over five percent for a number of years now . In 2018 , the company was overtaken by the German discounter Lidl .
gold_template: templateTitle[0] templateTitle[1] have decreased by 1.9 templateYLabel[0] in templateTitle[5] templateTitle[6] over a 12-week templateXLabel[2] templateXLabel[3] July templateXLabel[0] , templateTitle[9] compared to the same time templateXLabel[2] in 2018 . templateTitle[0] has seen its templateTitle[1] templateYLabel[1] 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 templateTitle[7] templateYLabel[0] . At the templateXLabel[3] of 2018 , the company experienced shrinking templateTitle[1] for the first time since mid-2015 . Company profile templateTitle[0] & Partners is the food retail division of John Lewis Partnership . It has been a subsidiary of John Lewis since 1937 . The Bracknell based company was founded in West London in 1904 and operated 353 stores in the United Kingdom in 2018 . In these stores the company made approximately templateYValue[31] thousand British pounds of templateTitle[1] templateXLabel[2] square foot in 2016 . The company also operates an online store that has seen templateYLabel[1] of over templateXValue[10] templateYLabel[0] during 2018 . Market position The company generated a revenue of 6.4 billion pounds in 2018 . Revenue templateYLabel[1] has slowed down in recent years compared to the early 2010s . as of templateXValue[25] templateTitle[9] , the company ranked 8th in its market with a share of 5.1 templateYLabel[0] . Its grocery market share has been stable at just over templateTitle[7] templateYLabel[0] for a number of years now . In 2018 , the company was overtaken by the German discounter Lidl .

generated_template: In the three months leading up to November 5 , templateXValue[last] , the templateTitle[0] templateTitle[1] in templateTitle[5] templateTitle[6] grew by templateYValue[7] templateYLabel[2] compared to the same period of the same templateXLabel[0] 's templateYLabel[0] compared to the last last three years this constitutes a sizeable increase . The entire templateTitle[0] templateTitle[1] amounted to 185.2 billion British pounds in templateXValue[last] . Compared to the prior templateXLabel[0] it had increased by just five and a half billion pounds . templateTitle[1] shares of templateTitle[0] stores As of May 2019 , the four most important players on the templateTitle[0] templateTitle[1] were Tesco , Sainsbury 's templateTitle[4] . In the technology , the four most templateXValue[last] , Sainsbury , just over templateXValue[3] templateYLabel[0] . By the templateTitle[1] systems , Sainsbury 's were able to claim to pay channels . The templateTitle[1] leader Tesco had a share of over a quarter of the templateTitle[1] , despite the companies ' channels channels . In 2016 . The next few years , Aldi was still first templateXLabel[0] , 66 percent in the Europe .
generated: In the three months leading up to November 5 , 19_Jul_15 , the Waitrose sales in Great Britain grew by -0.7 (year-on-year) compared to the same period of the same 12 's Percentage compared to the last last three years this constitutes a sizeable increase . The entire Waitrose sales amounted to 185.2 billion British pounds in 19_Jul_15 . Compared to the prior 12 it had increased by just five and a half billion pounds . sales shares of Waitrose stores As of May 2019 , the four most important players on the Waitrose sales were Tesco , Sainsbury 's in . In the technology , the four most 19_Jul_15 , Sainsbury , just over 24_Mar_19 Percentage . By the sales systems , Sainsbury 's were able to claim to pay channels . The sales leader Tesco had a share of over a quarter of the sales , despite the companies ' channels channels . In 2016 . The next few years , Aldi was still first 12 , 66 percent in the Europe .


Example 194:
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[2] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] templateYLabel[0] of templateTitle[0] 's templateYLabel[2] lived below the templateTitle[2] line .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] templateYLabel[0] of templateTitle[0] 's templateYLabel[2] lived below the templateTitle[2] line .
generated: This statistic shows the poverty rate in Louisiana from 2000 to 2018 . In 2018 , 18.6 Percentage of Louisiana 's population lived below the poverty line .


Example 195:
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 . However , the value of the U.S. footwear retail market increased from 41.8 billion U.S. dollars in 2004 to 46.8 billion U.S. dollars in 2008 . *
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[5] templateTitle[6] and templateTitle[8] templateTitle[9] templateTitle[10] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[5] templateTitle[6] and templateTitle[8] templateTitle[9] templateTitle[10] templateYLabel[1] was templateYValue[3] percent . Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[5] templateTitle[9] templateTitle[10] templateYLabel[1] decreased by 3.0 percent . However , the value of the templateTitle[5] templateTitle[9] templateTitle[10] templateYLabel[1] increased from 41.8 billion templateTitle[5] dollars in templateXValue[min] to 46.8 billion templateTitle[5] dollars in templateXValue[max] . *

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[5] percent .
generated: The statistic shows the Global market share of the size U.S. from 2004 to 2008 . In N/A , the Global market share of the was N/A percent .


Example 196:
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[2] templateTitle[3] templateTitle[4] templateTitle[5] templateYLabel[2] at templateTitle[8] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] on templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateYLabel[2] at templateTitle[8] amounted to templateYValue[0] percent in templateXValue[max] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in the United States was at templateYValue[max] percent .
generated: This statistic shows the Return on average ordinary shareholders ' from 2009 to 2019 . In 2019 , the Return on average in the United States was at 10.9 percent .


Example 197:
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[2] templateTitle[3] in the templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateTitle[3] was templateTitle[0] by an average templateYValue[min] thousand templateTitle[0] per issue .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[8] , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] had a decrease of templateYValue[max] percent .
generated: This statistic shows the Readership of FourFourTwo magazine in size the from 2006 to 2016 . In 2008 , Readership of FourFourTwo magazine in had a decrease of 664 percent .


Example 198:
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 . In 2018 , the GDP per capita in Denmark amounted to around 60,897.23 U.S. dollars .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the total templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the total product ( GDP ) per capita in Denmark 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 . In 2018 , the GDP per capita in Denmark amounted to around 60897.23 U.S. dollars .


Example 199:
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 . In 2018 , the GDP per capita in Lithuania amounted to around 18,994.38 U.S. dollars .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .
generated: 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 . In 2018 , the GDP per capita in Lithuania amounted to around 18994.38 U.S. dollars .


Example 200:
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 United States as of May templateTitle[6] , templateTitle[8] templateTitle[9] of templateYLabel[2] . templateXValue[0] had templateYValue[max] templateYLabel[2] , which made it the largest templateTitle[2] in terms of templateYLabel[2] as of May templateTitle[6] .

generated_template: The statistic shows the degree of templateTitle[0] in templateTitle[2] templateTitle[3] templateYLabel[2] templateYLabel[3] in the United States as of May templateTitle[7] , templateTitle[6] templateXLabel[0] . During that period of time , templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] .
generated: The statistic shows the degree of Leading in platforms in offerings N/A in the United States as of May , , 2017 Country . During that period of time , 95 Amount of offerings N/A in the Wefunder .


Example 201:
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[3] and templateTitle[5] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[min] , this figure stood at templateYValue[16] templateYLabel[4] templateYLabel[5] and rose to templateYValue[0] templateYLabel[4] templateYLabel[5] by templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateYLabel[5] . In templateXValue[max] , the estimated to increase of templateYValue[max] the previous templateXLabel[0] .
generated: The statistic shows the Value per of sheep and from 2001 to 2017 . In 2017 , the Value per head in the dollars . In 2017 , the estimated to increase of 221 the previous Year .


Example 202:
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 . In 2018 , the GDP per capita in Portugal amounted to around 23,437.39 U.S. dollars .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the total templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the total 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 . In 2018 , the GDP per capita in Portugal amounted to around 23437.39 U.S. dollars .


Example 203:
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[3] templateYLabel[0] templateYLabel[1] in templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[3] templateYLabel[0] templateYLabel[1] in templateTitle[0] templateTitle[1] amounted to templateYValue[0] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the - Household income in North from 1990 to 2018 . In 2018 , the - Household income in North amounted to 53764 U.S. dollars .


Example 204:
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[4] templateYLabel[2] at templateTitle[5] establishments in templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] there were around templateYValue[max] templateYLabel[4] templateYLabel[2] at templateTitle[5] establishments in templateTitle[6] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[4] templateYLabel[2] in templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitle[4] templateYLabel[2] in short-stay templateTitle[5] in templateTitle[6] have generally increased over this period , from around 12 templateYLabel[4] in templateXValue[min] to approximately templateYValue[max] templateYLabel[4] by templateXValue[max] .
generated: This statistic shows the Number of tourist arrivals in Slovakia from 2006 to 2018 . tourist arrivals in short-stay accommodation in Slovakia have generally increased over this period , from around 12 millions in 2006 to approximately 5.49 millions by 2018 .


Example 205:
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 . Bricks are one of the main elements needed to fuel the construction industry in the country . Most common uses Bricks delivered in Great Britain were most commonly used for facings . 85 percent of bricks delivered in 2018 were meant for surface work on walls , which made this the peak year for facing type bricks . Production value of construction industry The production value of the UK construction industry reached 288.9 billion euros in 2016 , which was a decrease compared to 2015 , but still a significant increase from figures recorded at the beginning of the reporting period in 2010 .
gold_template: British producers had manufactured nearly 2.03 billion templateYLabel[4] in templateXValue[max] . This was the peak since the beginning of the reporting period in templateXValue[min] and the first time figures exceeded templateTitle[11] billion units . Following increased demand , the templateYLabel[4] 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 . templateYLabel[4] are templateTitle[11] of the main elements needed to fuel the construction industry in the country . Most common uses templateYLabel[4] delivered in templateTitle[6] templateTitle[7] were most commonly used for facings . 85 percent of templateYLabel[4] delivered in templateXValue[max] were meant for surface work on walls , which made this the peak templateXLabel[0] for facing type templateYLabel[4] . templateYLabel[0] value of construction industry The templateYLabel[0] value of the UK construction industry reached 288.9 billion euros in templateXValue[2] , which was a decrease compared to templateXValue[3] , but still a significant increase from figures recorded at the beginning of the reporting period in 2010 .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] was templateYValue[max] percent .
generated: The statistic shows the Production level of brick production in from 2013 to 2018 . In 2018 , the Annual levels of brick production in was 2025 percent .


Example 206:
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[2] rate in templateTitle[0] has been decreasing lately . In templateXValue[max] , approximately templateYValue[0] templateYLabel[0] of the Ecuadorian templateYLabel[2] was living on less than templateTitle[6] templateTitle[7] templateTitle[8] templateYLabel[0] templateTitle[10] , down from templateYValue[max] templateYLabel[0] in 2005.Still , social inequality remains a challenge in templateTitle[0] and Latin America as a whole .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] templateYLabel[0] of templateTitle[0] 's templateYLabel[2] lived below the templateTitle[2] line .
generated: This statistic shows the poverty headcount in Ecuador from 2005 to 2017 . In 2017 , 8.7 Percentage of Ecuador 's population lived below the poverty line .


Example 207:
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 . If a population is expected to live longer than the generations before , the economy will have to change as well in order to fulfill the needs of the citizens . In addition , the birth rate in the U.S. has been falling over the last 20 years , meaning that there are not as many young people to replace the individuals leaving the workforce . The future population It 's not only the American population that is aging -- the global population is , too . By 2020 , the median age of the global workforce is expected to be 39 years , up from 33.8 years in 1990 . Additionally , it is projected that there will be over three million people worldwide aged 100 years and over by 2050 .
gold_template: In 2017 , about templateYValue[4] templateYLabel[0] of the American templateYLabel[3] was 65 years old or over ; a figure which is expected to reach templateYValue[max] templateYLabel[0] by templateXValue[max] . This is a significant increase from templateXValue[min] , when only templateYValue[min] templateYLabel[0] of the templateYLabel[3] was 65 or over . A rapidly aging templateYLabel[3] In recent years , the aging templateYLabel[3] 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 . If a templateYLabel[3] is expected to live longer than the generations before , the economy will have to change as well in order to fulfill the needs of the citizens . In addition , the birth rate in the templateTitle[0] has been falling over the last templateTitle[11] years , meaning that there are not as many young people to replace the individuals leaving the workforce . The future templateYLabel[3] It 's not only the American templateYLabel[3] that is aging -- the global templateYLabel[3] is , too . By templateXValue[3] , the median templateYLabel[0] of the global workforce is expected to be 39 years , up from 33.8 years in templateXValue[7] . Additionally , it is projected that there will be over three million people worldwide aged 100 years and over by templateXValue[max] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . For templateXValue[max] , the global templateYLabel[0] templateYLabel[1] was templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the U.S. - seniors as a in the United States from 1950 to 2050 . For 2050 , the global Percentage of was 22 population N/A N/A .


Example 208:
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[4] templateTitle[5] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to 1.9 billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] are owned by Terry and Kim Pegula .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of 3.3 billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by Rogers Communications , who bought the templateYLabel[0] for 137 templateYLabel[3] templateYLabel[4] templateYLabel[5] in 2000 .
generated: This graph depicts the value of the Buffalo Bills ( Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 3.3 billion U.S. dollars . The Buffalo Bills ( are owned by Rogers Communications , who bought the Franchise for 137 million U.S. dollars in 2000 .


Example 209:
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[2] rate in templateTitle[0] has been decreasing recently . In templateXValue[max] , approximately templateYValue[min] templateYLabel[0] of Uruguayans was living on less than templateTitle[6] templateTitle[7] templateTitle[8] templateYLabel[0] templateTitle[10] , down from templateYValue[max] templateYLabel[0] of the country 's templateYLabel[2] in 2006.Still , social inequality remains a challenge in Latin America as a whole .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] templateYLabel[0] of templateTitle[0] 's templateYLabel[2] lived below the templateTitle[2] line .
generated: This statistic shows the poverty headcount in Uruguay from 2006 to 2017 . In 2017 , about 0.4 Percentage of Uruguay 's population lived below the poverty line .


Example 210:
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 United States . During the February templateTitle[7] survey period , templateYValue[0] percent of C-level templateYLabel[2] 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 , conducted in December 2018 in the United States . During the survey , templateYValue[max] percent of templateYLabel[2] stated that they used templateTitle[2] templateTitle[3] on templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] .
generated: This statistic shows the results of a survey , conducted in December 2018 in the United States . During the survey , 21 percent of respondents stated that they used business cyber on 3_to_4 3_to_4 3_to_4 3_to_4 .


Example 211:
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[6] templateTitle[7] 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: The graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[6] templateTitle[7] templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of the templateTitle[6] templateTitle[7] templateTitle[8] was at templateYValue[max] .
generated: The graph depicts the total Regular season Home attendance of the Denver Broncos 2006 from 2006 to 2019 . In 2019 , the Regular season home Home attendance of the Denver Broncos 2006 was at 615517 .


Example 212:
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[2] templateYLabel[3] to the templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[5] ) in templateTitle[12] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[2] templateYLabel[3] in templateTitle[12] amounted to about templateYValue[6] percent of the templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] to templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[5] ) in templateTitle[12] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[2] templateYLabel[3] in templateTitle[12] amounted to about templateYValue[6] percent of the country 's templateTitle[5] templateTitle[6] templateTitle[7] .
generated: The statistic shows the Ratio of government expenditure to 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 country 's gross domestic product .


Example 213:
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[3] from templateXValue[min] to templateXValue[6] , with projections up to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] was at approximately templateYValue[6] templateYLabel[2] templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] amounted to approximately templateYValue[6] templateYLabel[2] templateYLabel[0] . templateTitle[1] of templateTitle[3] templateTitle[3] is the second largest countries in the world , in terms of area size , although its templateTitle[0] templateTitle[1] is low in numbers compared to other countries . Not all of templateTitle[3] 's land mass has been reclaimed and urbanized , as most of templateTitle[3] 's templateYLabel[0] live in metropolises and cities on the country 's coasts . The majority of Australians is of European descent ( predominantly British ) , and only a small minority belongs to the indigenous templateTitle[1] , the Aborigines . Although the number of Australians is rising slowly and continuously , year-on-year templateTitle[1] growth in templateTitle[3] has only recently recovered from a slight slump in 2010 and 2011 , while the fertility rate has been stable over the last decade . Standard of living is fairly high life expectancy , which can be seen when looking at the Human Development Index , which ranks countries by their level of human development and living standards , such as their unemployment rate .
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 millions Inhabitants . population of Ireland Ireland is the second largest countries in the world , in terms of area size , although its Total population is low in numbers compared to other countries . Not all of Ireland 's land mass has been reclaimed and urbanized , as most of Ireland 's Inhabitants live in metropolises and cities on the country 's coasts . The majority of Australians is of European descent ( predominantly British ) , and only a small minority belongs to the indigenous population , the Aborigines . Although the number of Australians is rising slowly and continuously , year-on-year population growth in Ireland has only recently recovered from a slight slump in 2010 and 2011 , while the fertility rate has been stable over the last decade . Standard of living is fairly high life expectancy , which can be seen when looking at the Human Development Index , which ranks countries by their level of human development and living standards , such as their unemployment rate .


Example 214:
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 . With almost 12.1 million inhabitants , Moscow is largest of them . Russia 's economy Russia holds the sixth largest economy in the world and is one of wealthiest nations . Following the 1998 Russian financial crisis , Russia introduced several structural reforms that allowed for a fast economical recovery . Following these reforms , Russia experienced significant economic growth from the early 2000s and improved living standards in general for the country . A reason for the momentous economical boost was the rise in commodity prices as well as a boom in the total amount of consumer credit . Additionally , Russia is highly dependent on the mining and production of natural resources , primarily in the energy department , in order to promote economic growth in the country . Due to large energy reserves throughout the country , Russia has developed a stable economy capable of sustaining itself for many years into the future . The majority of Russian oil and energy reserves are located in the Western Siberian areas . These natural gas liquids , along with oil reserves that consist of crude oil , shale oil and oil sands are constantly used for the production of consumable oil , which is an annually growing industry in Russia . Oil products are one of Russia 's primary exports and the country is able to profit entirely off of sales due to high prices as well as high demand for such goods .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] was around templateYValue[6] templateYLabel[2] people . Only a fraction of them live in the major Russian cities . With almost 12.1 templateYLabel[2] templateYLabel[0] , Moscow is largest of them . templateTitle[3] 's economy templateTitle[3] holds the sixth largest economy in the world and is one of wealthiest nations . Following the 1998 Russian financial crisis , templateTitle[3] introduced several structural reforms that allowed for a fast economical recovery . Following these reforms , templateTitle[3] experienced significant economic growth from the early 2000s and improved living standards in general for the country . A reason for the momentous economical boost was the rise in commodity prices as well as a boom in the templateTitle[0] amount of consumer credit . Additionally , templateTitle[3] is highly dependent on the mining and production of natural resources , primarily in the energy department , in order to promote economic growth in the country . Due to large energy reserves throughout the country , templateTitle[3] has developed a stable economy capable of sustaining itself for many years into the future . The majority of Russian oil and energy reserves are located in the Western Siberian areas . These natural gas liquids , along with oil reserves that consist of crude oil , shale oil and oil sands are constantly used for the production of consumable oil , which is an annually growing industry in templateTitle[3] . Oil products are one of templateTitle[3] 's primary exports and the country is able to profit entirely off of sales due to high prices as well as high demand for such goods .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitle[4] was around templateYValue[6] templateYLabel[2] people . templateTitle[1] of templateTitle[3] is the second largest countries in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC . With a rapidly growing templateTitle[0] templateTitle[1] of just under 78 templateYLabel[2] templateYLabel[0] , it is the world 's seventeenth most populous nation . templateTitle[3] 's templateTitle[0] templateTitle[1] has grown by 10 templateYLabel[2] templateYLabel[0] over the past decade . In 2010 , templateTitle[3] 's gross domestic product ( GDP ) . In 2010 , the templateTitle[0] templateTitle[1] increased by around 15 years , the life expectancy is growing templateTitle[1] growth rates and has the island nation is predominantly whiteChristians , but a steady net influx of immigrants , despite a slight increase in the wide-reaching former British Empire , has grown by around templateTitle[5] percent . Looking at the age structure of the templateTitle[3] templateTitle[4] is due to assume that this is true .
generated: The statistic shows the Total population of Russia 2024 from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Russia 2024 was around 146.8 millions people . population of Russia is the second largest countries in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC . With a rapidly growing Total population of just under 78 millions Inhabitants , it is the world 's seventeenth most populous nation . Russia 's Total population has grown by 10 millions Inhabitants over the past decade . In 2010 , Russia 's gross domestic product ( GDP ) . In 2010 , the Total population increased by around 15 years , the life expectancy is growing population growth rates and has the island nation is predominantly whiteChristians , but a steady net influx of immigrants , despite a slight increase in the wide-reaching former British Empire , has grown by around N/A percent . Looking at the age structure of the Russia 2024 is due to assume that this is true .


Example 215:
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 . Despite a steady decline observed after 2014 , youth unemployment still stood at almost 29 percent in 2019 . Dream job versus harsh reality Newly graduated and often looking for a first job , young people are particularly vulnerable to stagnation in the labor market . Considering the difficulties in finding a job during and after the years of the financial crisis , about 48 percent of young Italians declared in 2018 that they would accept a job that does not meet their career aspiration . One fourth of the respondents stated that they would a accept a monthly salary of 500 euros . Greece and Italy with have the highest youth unemployment rates in the EU With about 33 percent of potential young workers without a job , Italy was the country with the second highest youth unemployment rate among the EU member states at the beginning of 2019 . The country with the highest youth unemployment was Greece , where about four in ten young individuals were unemployed .
gold_template: The problem of templateYLabel[0] in templateTitle[4] 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 24 years increased by more than 15 percent . Despite a steady decline observed after templateXValue[10] , templateTitle[0] templateYLabel[0] still stood at almost 29 percent in templateXValue[max] . Dream job versus harsh reality Newly graduated and often looking for a first job , young people are particularly vulnerable to stagnation in the labor market . Considering the difficulties in finding a job during and after the years of the financial crisis , about 48 percent of young Italians declared in templateXValue[14] that they would accept a job that does not meet their career aspiration . templateTitle[7] fourth of the respondents stated that they would a accept a monthly salary of 500 euros . Greece and templateTitle[4] with have the highest templateTitle[0] templateYLabel[0] rates in the EU With about 33 percent of potential young workers without a job , templateTitle[4] was the country with the second highest templateTitle[0] templateYLabel[0] templateYLabel[1] among the EU member states at the beginning of templateXValue[max] . The country with the highest templateTitle[0] templateYLabel[0] was Greece , where about templateTitle[5] in ten young individuals were unemployed .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in in from 2004 to 2019 . In 2019 , the Unemployment rate in in was at approximately 23.5 percent .


Example 216:
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[2] templateTitle[4] templateTitle[6] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the Brazilian templateYLabel[2] accessed templateTitle[4] from their templateTitle[2] device . This figure is expected to grow to templateYValue[max] percent in templateXValue[max] .

generated_template: This statistic presents the templateTitle[0] penetration in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the local templateYLabel[2] accessed the social network . This templateYLabel[0] is projected to grow to templateYValue[2] percent in templateXValue[2] .
generated: This statistic presents the Brazil penetration in the penetration from 2017 to 2023 . In 2017 , 41 percent of the local population accessed the social network . This Share is projected to grow to 49 percent in 2021 .


Example 217:
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[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[5] templateTitle[6] amounted to approximately templateYValue[min] templateYLabel[4] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of 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[5] templateTitle[6] amounted to approximately templateYValue[0] templateYLabel[4] 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 218:
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[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[5] templateTitle[6] amounted to approximately templateYValue[0] templateYLabel[4] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of 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[5] templateTitle[6] amounted to approximately templateYValue[0] templateYLabel[4] in templateXValue[max] .
generated: 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 .


Example 219:
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[2] templateTitle[3] states in May templateTitle[5] . templateXValue[last] reported a templateYLabel[0] of templateYValue[min] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the United States in templateTitle[9] . According to the source , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[last] and templateTitle[5] templateTitle[6] in templateTitle[9] amounted to templateYValue[min] percent in templateTitle[11] .
generated: The statistic shows the Debt in of debtor nations 2011 N/A in the United States in N/A . According to the source , the Debt in billion in Belarus and 2011 N/A in N/A amounted to 2.5 percent in N/A .


Example 220:
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 . In 2010 , Syria 's gross domestic product amounted to around 60.04 billion U.S. dollars .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[4] is an important indicator of a country 's economic power . In templateXValue[max] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[max] templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[4] is an important indicator of a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: 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 . In 2004 , Syria 's Gross domestic product amounted to around 25.2 billion U.S. dollars .


Example 221:
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 . According to the European Mortgage Federation ( EMF ) , real estate prices in Spain initiated a solid recovery in 2015 , reaching 78.7 house price index points in 2018 from a lowest point of 70.8 index points recorded in 2014 . Spain 's real estate market behind others The property market has made great progress , but it is still far off the rest of its European counterparts , and it is positioned , in fact , at the bottom of the European list of the EMF 's house price index , which is led by Sweden at 160.6 index points . In 2016 , 200 thousand euros could still buy a 119 square-meter home on average in Spain , whereas it would only allow for a 50 square-meter apartment in France . Renting becoming more popular in Spain As happens with many other countries , home rent and purchase prices will differ considerably dependent on the area . There are some indicators though that pointed to renting being slightly more favorable than buying a home in Spain as of 2015 , which might be the reason why more people are renting in recent years than before the financial crisis .
gold_template: In December templateXValue[max] , a house in templateTitle[4] would cost around 1.699 thousand templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] . 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 . According to the European Mortgage Federation ( EMF ) , real estate templateTitle[3] in templateTitle[4] initiated a solid recovery in templateXValue[8] , reaching 78.7 house templateYLabel[0] index points in templateXValue[max] from a lowest point of 70.8 index points recorded in templateXValue[7] . templateTitle[4] 's real estate market behind others The property market has made great progress , but it is still far off the rest of its European counterparts , and it is positioned , in fact , at the bottom of the European list of the EMF 's house templateYLabel[0] index , which is led by Sweden at 160.6 index points . In templateXValue[9] , templateTitle[5] thousand templateYLabel[2] could still buy a 119 square-meter home on templateTitle[1] in templateTitle[4] , whereas it would only allow for a 50 square-meter apartment in France . Renting becoming more popular in templateTitle[4] As happens with many other countries , home rent and purchase templateTitle[3] will differ considerably dependent on the area . There templateYLabel[4] some indicators though that pointed to renting being slightly more favorable than buying a home in templateTitle[4] as of templateXValue[8] , which might be the reason why more people templateYLabel[4] renting in recent years than before the financial crisis .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] amounted to templateYValue[3] percent .
generated: The statistic shows the Price in euros in the United States from 2007 to 2018 . In 2010 , the Annual average housing prices amounted to 2060 percent .


Example 222:
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[4] templateTitle[5] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[4] 's net sales was at about 4.11 billion templateYLabel[3] dollars.The templateTitle[4] templateTitle[5] 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 templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Revenue of of the Brunswick Corporation 2007 from 2007 to 2019 . In 2019 , the Global revenue of the Brunswick Corporation 2007 amounted to 5671.2 million U.S. dollars .


Example 223:
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[2] in templateTitle[4] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[2] in templateTitle[4] according to the International templateTitle[0] templateTitle[1] Federation .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[last] to templateXValue[0] . In templateXValue[1] , there were a total of templateYValue[max] thousand templateYLabel[2] registered templateTitle[0] templateTitle[1] templateYLabel[2] throughout templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[2] in the templateTitle[5] templateTitle[6] .
generated: The statistic shows the Number of players in the 2010 - from 2010/11 to 2018/19 . In 2017/18 , there were a total of 112236 thousand players registered Ice hockey players throughout Ice hockey players players in the 2010 - .


Example 224:
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[3] templateTitle[4] 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 2.3 billion templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of The templateTitle[4] templateTitle[5] templateTitle[6] worldwide from templateXValue[min] to templateXValue[max] . Fast food chain templateTitle[4] templateTitle[5] had a templateYLabel[0] templateYLabel[1] of approximately templateYValue[0] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[max] . This shows a 70 percent decrease over previous templateXLabel[0] templateTitle[5] total amounting to templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Net income of The Airlines 2010 - worldwide from 2010 to 2019 . Fast food chain Airlines 2010 had a Net income of approximately 2300 million U.S. dollars in 2019 . This shows a 70 percent decrease over previous Year 2010 total amounting to 3357 million U.S. dollars .


Example 225:
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 . FIFA World Ranking The FIFA World ranking is made by the International Federation of Association Football ( FIFA ) . It is based on match results , according to which the most successful team is ranked highest . Since August 2018 , a new calculation model modified from the Elo rating system is in use . The football teams get points for single games , and the number of points is determined by the relative strength of both opponents . To get the final ranking position , these points must be added/subtracted . In April 2019 , the position of the Faroe Island 's national football team in the world ranking was 102 . At that time , the team had already participated in two qualifiers for the upcoming UEFA European Championship in 2020 and lost both . By contrast , the three leading national teams were Belgium on the first position , followed by France and Brazil .
gold_template: In templateXValue[23] , the templateTitle[3] templateTitle[4] 's templateTitle[6] templateTitle[7] templateTitle[8] , controlled by the templateTitle[7] Association of the templateTitle[3] templateTitle[4] , reached its highest templateYLabel[3] in the templateYLabel[0] templateYLabel[1] templateYLabel[2] . The templateTitle[8] took part in the qualifying for the UEFA European Championship templateXValue[23] . Out of the ten qualifying matches , the templateTitle[3] templateTitle[4] 's templateTitle[6] templateTitle[7] templateTitle[8] won both matches against Greece . templateYLabel[0] templateYLabel[1] templateYLabel[2] The templateYLabel[0] templateYLabel[1] templateYLabel[2] is made by the International Federation of Association templateTitle[7] ( templateYLabel[0] ) . It is based on match results , according to which the most successful templateTitle[8] is ranked highest . Since August templateXValue[25] , a new calculation model modified from the Elo rating system is in use . The templateTitle[7] teams get points for single games , and the number of points is determined by the relative strength of both opponents . To get the final templateYLabel[2] templateYLabel[3] , these points must be added/subtracted . In April templateXValue[max] , the templateYLabel[3] of the templateTitle[3] templateTitle[4] 's templateTitle[6] templateTitle[7] templateTitle[8] in the templateYLabel[1] templateYLabel[2] was templateYValue[26] . At that time , the templateTitle[8] had already participated in templateTitle[11] qualifiers for the upcoming UEFA European Championship in 2020 and lost both . By contrast , the templateTitle[9] leading templateTitle[6] teams were Belgium on the first templateYLabel[3] , followed by France and Brazil .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the Norwegian templateTitle[5] templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[max] . The highest templateYLabel[3] ever reached was templateYValue[min] in templateXValue[min] . templateYLabel[2] templateYValue[max] was the lowest result of the templateTitle[7] , which was reached in templateXValue[23] . As of templateXValue[max] , templateTitle[3] templateTitle[4] templateYLabel[3] in the templateYLabel[1] templateYLabel[2] was templateYValue[5] .
generated: This statistic shows the FIFA World Ranking of the Norwegian ' national football from 1993 to 2019 . The highest position ever reached was 83 in 1993 . Ranking 194 was the lowest result of the football , which was reached in 2016 . As of 2019 , Faroe Islands position in the World Ranking was 125 .


Example 226:
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[3] or templateTitle[5] templateTitle[6] in the United States as of January templateTitle[10] , sorted templateTitle[12] templateTitle[13] . According to the source , templateYValue[max] percent of templateXValue[last] who subscribe to service templateTitle[6] had a templateTitle[3] or templateTitle[5] templateTitle[6] as of January templateTitle[10] .

generated_template: This statistic shows the results of a survey , conducted in December 2018 in the United States , on templateTitle[5] . During the survey , templateYValue[max] percent of templateYLabel[2] stated they had templateXValue[1] templateXValue[1] templateXValue[1] via templateXValue[0] .
generated: This statistic shows the results of a survey , conducted in December 2018 in the United States , on magazine . During the survey , 54 percent of respondents stated they had Millennials Millennials Millennials via Total .


Example 227:
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[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateTitle[4] in templateXValue[min] , with a projection from templateXValue[3] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] amounted to about templateYValue[min] percent . In templateXValue[max] , the percentage of the templateYLabel[2] above the age of templateYLabel[5] was forecasted to reach templateYValue[max] percent .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . For templateXValue[4] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] is estimated to reach templateYValue[4] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: This statistic shows the Share of aging population Thailand size 2015 from 2015 to 2035 . For 2015 , the Share of aging population Thailand is estimated to reach 10.6 population older than 65 years .


Example 228:
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[3] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the state deficit of templateTitle[3] was at about 17.27 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the total annual templateYLabel[1] in the templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] billion templateYLabel[3] templateYLabel[4] templateYLabel[5] . In templateXValue[7] , this number was at around templateYValue[min] percent .
generated: The statistic shows the total annual balance in the N/A N/A from 2014 to 2018 , with projections up until 2024 . In 2018 , the Budget balance in in N/A billion trillion yen N/A . In 2017 , this number was at around -28.96 percent .


Example 229:
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[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitle[0] spendings on templateTitle[3] templateTitle[4] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the total templateTitle[0] templateYLabel[0] templateTitle[2] the global templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] were sold worldwide .
generated: This statistic shows the total Global Spending on the global sponsorships 2010 from 2010 to 2016 . In 2014 , 1.65 billion U.S. dollars were sold worldwide .


Example 230:
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[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] fell by around 1.2 percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] 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 increased by around -1.2 percent compared to the previous Year .


Example 231:
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[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] in daily attendance in templateTitle[1] elementary and secondary templateTitle[2] in the United States from the academic templateXLabel[0] of templateXValue[min] to templateXValue[max] . An templateTitle[4] of templateYValue[max] templateYLabel[4] templateYLabel[5] was spent on each templateYLabel[2] in templateTitle[1] elementary and secondary templateTitle[2] in the academic templateXLabel[0] of templateXValue[max] .

generated_template: The statistic shows the total templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] was at templateYValue[max] thousand .
generated: The statistic shows the total Expenditures of schools - average expenditure from 1980 to 2016 . In 2016 , the Expenditures of schools - average expenditure was at 12617 thousand .


Example 232:
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[3] templateYLabel[0] volume of templateTitle[3] templateTitle[4] to the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[3] templateYLabel[0] amounted to about 1.65 trillion templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] in templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] thousand templateYLabel[2] in the United States .
generated: The statistic shows the Exports of billion in goods to the from 1987 to 2019 . In 2019 , there were 1665.99 thousand billion in the United States .


Example 233:
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 templateTitle[2] comics outside of school in the templateTitle[8] templateTitle[9] 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: This statistic shows the results of a survey conducted in the templateTitle[0] templateTitle[1] in templateTitle[5] in templateTitle[6] between templateXValue[min] and templateXValue[max] . During the survey period , it was found that templateYValue[min] percent of templateYLabel[2] stated that they used their templateTitle[3] .
generated: This statistic shows the results of a survey conducted in the Comic book in people in in between 2005 and 2015 . During the survey period , it was found that 25.1 percent of respondents stated that they used their by .


Example 234:
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[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateYLabel[0] on templateTitle[3] templateTitle[4] reached approximately templateYValue[14] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic illustrates the templateYLabel[0] of templateYLabel[2] at templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] were templateYValue[min] templateYLabel[2] templateYLabel[3] in the United States . templateTitle[5] was the highest amount to templateYValue[max] templateYLabel[2] by templateXValue[1] . It is a slight increase .
generated: The statistic illustrates the Expenditure of billion at Consumer expenditure from 1999 to 2013 . In 2013 , the Expenditure of billion U.S. dollars were 3.93 billion U.S. in the United States . in was the highest amount to 5.52 billion by 2000 . It is a slight increase .


Example 235:
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[3] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateXValue[min] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitle[9] amounted to around templateYValue[min] percent .
generated: This statistic shows the Retail revenue of smart wearable devices worldwide in 2014 , with projections up until 2019 . In N/A , the Retail revenue in 2019 amounted to around 4.5 percent .


Example 236:
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[4] templateTitle[5] from templateXValue[min] through templateXValue[max] . In templateXValue[max] , the templateYLabel[3] templateTitle[7] for templateTitle[0] templateTitle[1] was sized at around 401.6 templateYLabel[2] templateYLabel[3] templateYLabel[4] ( or about 326 templateYLabel[2] euros ) .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateTitle[2] templateYLabel[2] a total of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Revenue of billion U.S. dollars in United from 2006 to 2017 . In 2017 , there were 423.76 billion U.S. dollars in billion a total of 423.76 billion U.S. dollars .


Example 237:
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 . Steeped in history and culture , Italy 's capital city Rome is a leading European city destination for international tourism , ranking above other popular Italian cities including Florence , Milan and Venice . Major international tourist markets visiting Rome include the United States , the United Kingdom and Germany . Although occupancy rates were lower than in other major European cities in 2017 , hotel occupancy in Rome has continued to remain stable and hotel performance overall shows a positive trend . Looking at other key hotel industry performance measures , the revenue per available room ( RevPar ) in Rome has been slowly increasing , as well as the average daily room rate ( ADR ) , both of which are expected to show growth in 2018 and 2019 . Occupancy rates are a key measure of performance in the hotel industry . They are calculated by dividing the number of occupied rooms by the number of available rooms and multiplying by 100 to form a percentage .
gold_template: This statistic illustrates the templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitle[4] 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 templateTitle[5] percentage point in templateXValue[max] . Steeped in history and culture , Italy 's capital city templateTitle[4] is a leading European city destination for international tourism , ranking above other popular Italian cities including Florence , Milan and Venice . Major international tourist markets visiting templateTitle[4] include the United States , the United Kingdom and Germany . Although templateYLabel[0] rates were lower than in other major European cities in templateXValue[6] , templateTitle[0] templateYLabel[0] in templateTitle[4] has continued to remain stable and templateTitle[0] performance overall shows a positive trend . Looking at other key templateTitle[0] industry performance measures , the revenue per available room ( RevPar ) in templateTitle[4] has been slowly increasing , as well as the average daily room templateYLabel[1] ( ADR ) , both of which are expected to show growth in templateXValue[7] and templateXValue[max] . templateYLabel[0] rates are a key measure of performance in the templateTitle[0] industry . They are calculated by dividing the number of occupied rooms by the number of available rooms and multiplying by 100 to form a percentage .

generated_template: This statistic illustrates the templateTitle[0] templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateYLabel[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[3] in the United States . templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] - additional information The templateTitle[0] is templateTitle[0] to reach templateYValue[max] templateYLabel[3] by templateXValue[max] . In templateXValue[1] , the templateYLabel[0] is likely to reach templateYValue[1] templateYLabel[3] by the coming years . This number of templateTitle[0] – additional information templateTitle[0] is expected to grow to templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[max] . The templateTitle[0] is forecast to grow to nearly 12 billion U.S. dollars in templateXValue[max] . In the biggest online in templateTitle[5] templateTitle[6] billion U.S. dollars in revenue of laptops for example , a normative status symbol for being held the biggest market capitalization . Of the templateTitle[4] templateTitle[5] templateTitle[6] , the `` like '' button is templateTitle[7] third leading countries . '' , there were imported products . The leading players in the world , Estonians purchase the sports business units . Social media network , amidst the United States , marking a dramatic increase from this figure from the United States . The revenue in the first templateXLabel[0] . By templateXValue[max] , chemicals , or Volkswagen Group . By templateXValue[max] . The revenue in templateTitle[5] templateTitle[6] , England were sold to over 16 million U.S. dollars in advertising , Beyonce ranked first day , attracting nearly 15.5 billion U.S. dollars in templateTitle[5] billion U.S. dollars , such as templateTitle[0] . As a much smaller number of 1.26 million U.S. dollars . As a result , also topped the United States .
generated: This statistic illustrates the Hotel Occupancy rate of the Rome 2011 - from 2011 to 2019 . In 2019 , there were 71 N/A Rome 2011 - in in the United States . Hotel occupancy Occupancy rate - additional information The Hotel is Hotel to reach 71 N/A by 2019 . In 2012 , the Occupancy is likely to reach 66 N/A by the coming years . This number of Hotel – additional information Hotel is expected to grow to 71 N/A N/A N/A in 2019 . The Hotel is forecast to grow to nearly 12 billion U.S. dollars in 2019 . In the biggest online in 2011 - billion U.S. dollars in revenue of laptops for example , a normative status symbol for being held the biggest market capitalization . Of the Rome 2011 - , the `` like '' button is 2019 third leading countries . '' , there were imported products . The leading players in the world , Estonians purchase the sports business units . Social media network , amidst the United States , marking a dramatic increase from this figure from the United States . The revenue in the first Year . By 2019 , chemicals , or Volkswagen Group . By 2019 . The revenue in 2011 - , England were sold to over 16 million U.S. dollars in advertising , Beyonce ranked first day , attracting nearly 15.5 billion U.S. dollars in 2011 billion U.S. dollars , such as Hotel . As a much smaller number of 1.26 million U.S. dollars . As a result , also topped the United States .


Example 238:
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[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[max] percent templateYLabel[2] to the templateYLabel[4] 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 4.14 percent compared to the previous Year .


Example 239:
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[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[0] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Median Household income in Wisconsin from 1990 to 2018 . In 2018 , the Median Household income in Wisconsin amounted to 63451 U.S. dollars .


Example 240:
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 . The company is headquartered in the Hague , Netherlands .
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[2] templateYLabel[3] templateYLabel[4] for such purposes . templateTitle[0] templateTitle[1] templateTitle[2] is templateTitle[6] of the top oil and gas companies worldwide , operating in every segment of the oil and gas industry . The company is headquartered in the Hague , Netherlands .

generated_template: The statistic presents the templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the Costs of million U.S. dollars in the United States from 2010 to 2018 . In 2018 , the Costs amounted to approximately 5278 million U.S. dollars .


Example 241:
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[3] from templateXValue[min] to templateXValue[6] with forecasts up to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] amounted to approximately templateYValue[6] templateYLabel[2] templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] amounted to approximately templateYValue[6] templateYLabel[2] templateYLabel[0] .
generated: The 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 millions Inhabitants .


Example 242:
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 26 templateYLabel[3] templateYLabel[4] templateYLabel[5] , and is forecasted to reach to about 37.2 templateYLabel[3] templateYLabel[4] templateYLabel[5] by 2025 .

generated_template: This statistic shows the templateYLabel[1] of the templateTitle[0] templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateYLabel[0] is expected to be worth templateYValue[3] templateYLabel[3] templateYLabel[4] templateYLabel[5] . templateTitle[0] is a cloud operating system , providing access to various computing resources , such as compute , storage , and network , across a data center . It is used primarily as Infrastructure as a Service .
generated: This statistic shows the size of the Global Market from 2017 to 2023 . In 2020 , the Global Market is expected to be worth 31.2 billion U.S. dollars . Global is a cloud operating system , providing access to various computing resources , such as compute , storage , and network , across a data center . It is used primarily as Infrastructure as a Service .


Example 243:
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 . In 2017 , Belgium numbered around 353,000 unemployed . Unemployment per region Unemployment in Belgium differed from region to region . The unemployment rate was lowest in the Flemish Region , the Dutch-speaking part of the country . In 2018 , unemployment reached 3.5 percent here . By comparison , in the Brussels-Capital Region the share of people without paid employment was nearly four times as high , at over 13 percent . Forecast for 2019 For 2019 , a slight decrease in the unemployment rate was foreseen . According to a recent forecast , the unemployment rate would drop below six percent this year , to 5.9 . This would be a drop of two percent in just four years .
gold_template: In 2019 , the templateYLabel[0] templateYLabel[1] in templateTitle[3] 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 . In templateXValue[1] , templateTitle[3] numbered around 353,000 unemployed . templateYLabel[0] per region templateYLabel[0] in templateTitle[3] differed from region to region . The templateYLabel[0] templateYLabel[1] was lowest in the Flemish Region , the Dutch-speaking part of the country . In templateXValue[max] , templateYLabel[0] reached 3.5 percent here . By comparison , in the Brussels-Capital Region the share of people without paid templateYLabel[0] was nearly four times as high , at over 13 percent . Forecast for 2019 For 2019 , a slight decrease in the templateYLabel[0] templateYLabel[1] was foreseen . According to a recent forecast , the templateYLabel[0] templateYLabel[1] would drop below templateYValue[min] percent this templateXLabel[0] , to 5.9 . This would be a drop of templateTitle[4] percent in just four years .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Belgium from 2008 to 2018 . In 2018 , the Unemployment rate in Belgium was 6 percent .


Example 244:
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 United States templateTitle[1] templateTitle[5] templateTitle[6] templateTitle[7] or templateTitle[9] . During the April templateTitle[10] survey , templateYValue[max] percent of responding current or former templateTitle[6] website or templateTitle[9] users said they used templateTitle[6] websites and templateTitle[9] to templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] their templateXValue[0] or templateXValue[0] .

generated_template: This statistic shows the results of a survey , conducted in the United States in templateTitle[10] . The survey was found that templateYValue[min] percent of templateYLabel[2] stated they used templateTitle[5] .
generated: This statistic shows the results of a survey , conducted in the United States in 2017 . The survey was found that 7 percent of respondents stated they used online .


Example 245:
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 templateYLabel[3] 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 325 thousand templateYLabel[3] in templateTitle[0] and the templateYLabel[1] grew up to more than 420 thousand as of templateXValue[2] . By templateXValue[max] , the templateYLabel[1] of templateYLabel[3] in templateTitle[0] had decreased to approximately templateYValue[0] thousand .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the total global Total of European ATM numbers 2018 from 2005 to 2018 . In 2015 , European ATM numbers Total amounted to 411243 of ATMs N/A .


Example 246:
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 templateTitle[3] & templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] & templateTitle[3] was at templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] 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 247:
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[3] templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[3] templateTitle[4] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in 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[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] 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 248:
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 . In 2018 , Bahrain 's gross domestic product amounted to around 37.75 billion U.S. dollars .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[4] is an important indicator of a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[4] is an important indicator of a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
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 . In 2018 , Bahrain 's Gross domestic product amounted to around 37.75 billion U.S. dollars .


Example 249:
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[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic depicts the total 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[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] templateYLabel[7] .
generated: This statistic depicts the total 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 250:
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[3] templateTitle[4] professional templateTitle[5] templateTitle[6] ( templateTitle[8] Five ) from templateXValue[last] to templateXValue[0] . In the templateXValue[2] season , the total templateYLabel[0] of the `` templateTitle[8] Five '' templateTitle[6] was estimated at templateYValue[2] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic illustrates the annual templateYLabel[0] of the European templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , the total templateYLabel[0] of the European templateTitle[3] templateTitle[4] templateTitle[5] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic illustrates the annual Revenue of the European top European soccer from 2006/07 to 2019/20 . In the 2019/20 season , the total Revenue of the European top European soccer amounted to 17.95 billion euros .


Example 251:
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 . In 2015 , the median age of Jordan 's population was 22.1 years .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] in templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[4] into templateTitle[7] 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[4] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitle[6] 's templateTitle[4] was templateYValue[7] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] in templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[4] into templateTitle[7] 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[4] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of the Iraqi templateTitle[4] was templateYValue[7] templateYLabel[3] .
generated: 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 2015 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 Iraqi population was 22.1 years .


Example 252:
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[5] and templateTitle[3] to templateYLabel[3] templateTitle[10] 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[5] and templateTitle[3] to the templateTitle[7] of templateYLabel[3] templateTitle[10] in templateXValue[max] will be around 573 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic shows the total global templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateTitle[0] templateTitle[2] templateYLabel[0] amounted to templateYValue[2] percent .
generated: This statistic shows the total global Value of of travel tourism from 2012 to 2028 . In 2017 , the Total of Value amounted to 240.9 percent .


Example 253:
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[3] to templateYLabel[5] 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[5] .

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[3] to templateYLabel[5] 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[5] .
generated: 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 Budget surplus amounted to around 8.77 percent of GDP .


Example 254:
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 templateYLabel[2] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateYLabel[2] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[min] thousand people committed committed templateYLabel[2] in templateTitle[5] . The numbers peaked in templateXValue[min] with around 32.8 thousand templateYLabel[2] victims in templateTitle[0] .
generated: The statistic shows the U.S active of suicides in 1990 from 1990 to 2010 . In 2010 , approximately 150 thousand people committed committed suicides in 1990 . The numbers peaked in 1990 with around 32.8 thousand suicides victims in U.S .


Example 255:
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[5] from templateXValue[min] to templateXValue[max] . It was estimated that the templateXValue[max] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] for templateTitle[5] would be templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[4] 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[2] templateYLabel[3] dollars.The templateTitle[0] templateTitle[1] templateTitle[2] are owned by John Henry and Thomas Werner , who bought the franchise for 380 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[min] .
generated: The statistic depicts the Revenue of the in PC online from 2012 to 2014 . In 2014 , the Revenue of the Major League Baseball franchise amounted to 66.5 million U.S. dollars.The PC online games are owned by John Henry and Thomas Werner , who bought the franchise for 380 million U.S. dollars in 2012 .


Example 256:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately 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 approximately 2.03 percent .


Example 257:
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[4] from templateXValue[min] and templateXValue[max] . The data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] was at templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] 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[4] 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 258:
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 templateYLabel[2] in templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the last reported period . The templateYLabel[2] in templateTitle[4] templateTitle[5] templateTitle[6] amounted to templateYValue[max] thousand .
generated: The statistic shows the Circulation of N/A in ) : circulation from July_2017-June_2018 to July_2017-June_2018 . In July_2017-June_2018 , the Circulation of the last reported period . The N/A in ) : circulation amounted to 469183 thousand .


Example 259:
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 . In the most recent surveyed time period , the number of illegal border-crossings had significantly dropped from the following years , amounting to roughly 150 thousand individuals .
gold_template: This statistic shows the total templateYLabel[0] of individuals detected entering the European Union ( templateTitle[8] ) illegally templateTitle[4] border-crossing points ( templateTitle[5] ) from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there was a total of approximately 107 thousand templateYLabel[2] templateYLabel[3] templateTitle[4] templateTitle[5] , making it a 48 percent increase on the previous templateXLabel[0] . By templateXValue[3] the templateYLabel[0] of individuals had increased to almost templateTitle[9] million templateYLabel[2] templateYLabel[3] . In the most recent surveyed time period , the templateYLabel[0] of templateYLabel[2] border-crossings had significantly dropped from the following years , amounting to roughly 150 thousand individuals .

generated_template: The statistic shows the number of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] was at approximately templateYValue[min] percent .
generated: The statistic shows the number of illegal entries between BCPs from 2009 to 2018 . In 2015 , the Number of illegal entries between BCPs was at approximately 72.44 percent .


Example 260:
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 United States from templateXValue[min] to templateXValue[max] . The templateTitle[0] online templateTitle[1] market , in the templateYLabel[3] , reached $ templateYValue[max] templateYLabel[2] in revenues in templateXValue[min] . The figure is estimated to decrease to $ templateYValue[min] templateYLabel[2] by templateXValue[max] .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] templateTitle[4] the Chinese search engine templateTitle[0] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateYLabel[0] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the industry Revenue in the Chinese search engine Self-paced industry from 2016 to 2021 . In 2021 , industry Revenue amounted to 20.85 billion U.S. dollars .


Example 261:
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 . Over two-thirds of the adults population in the United States takes dietary supplements . The nutritional supplement industry has seen a large growth in the market , especially in protein supplements and vitamins .
gold_template: This statistic indicates the templateYLabel[0] of templateYLabel[2] templateYLabel[3] that take templateTitle[0] supplements , distributed templateTitle[6] templateTitle[7] . The statistic is based on a survey conducted in August templateTitle[8] . Among templateYLabel[2] templateYLabel[3] males , some templateYValue[min] templateYLabel[0] reported taking templateTitle[0] supplements . Over two-thirds of the templateYLabel[3] population in the United States takes templateTitle[0] supplements . The nutritional templateTitle[1] industry has seen a large growth in the market , especially in protein supplements and vitamins .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the United States as of January templateTitle[6] , sorted templateTitle[8] templateTitle[9] . During the survey period , templateYValue[max] percent of templateTitle[0] templateTitle[5] were templateXValue[0] and templateYValue[min] percent were templateXValue[last] .
generated: This statistic shows the Dietary supplement usage in the United States as of January by , sorted 2018 N/A . During the survey period , 77 percent of Dietary adults were Female and 73 percent were Male .


Example 262:
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[2] templateYLabel[3] templateYLabel[4] in the templateXValue[0] season .

generated_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[2] templateYLabel[3] templateYLabel[4] in the templateXValue[0] season .
generated: 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 .


Example 263:
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[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] 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 264:
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[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . The net templateTitle[0] of templateTitle[2] templateTitle[3] amounted to about 113 million templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[2] , and forecasted to decrease to 96.4 million metric tones by templateXValue[max] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[7] , the templateYLabel[0] of templateTitle[3] templateTitle[4] templateTitle[5] amounted to approximately templateYValue[min] percent .
generated: The statistic shows the total global Thousand of Production of pork worldwide 2013 - from 2013 to 2020 . In 2013 , the Thousand of worldwide 2013 - amounted to approximately 96.38 percent .


Example 265:
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[9] 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[9] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[5] from templateXValue[last] to templateXValue[0] . In templateXValue[3] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[min] thousand templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the total global Bonus of UEFA Champions League and from 2005/06 to 2017/18 . In 2014/15 , UEFA Champions League Bonus amounted to 437.1 thousand million euros .


Example 266:
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 . In 2013 , the company invested 1.38 billion U.S. dollars in R & D .
gold_template: The statistic shows templateTitle[0] AG templateTitle[1] templateYLabel[0] on research and development ( templateXLabel[0] templateTitle[3] templateYLabel[0] ) templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitle[0] is templateTitle[9] of the largest global agribusiness companies . It is based in Basel , Switzerland , and notably markets seeds and pesticides . In templateXValue[5] , the company invested 1.38 billion templateYLabel[3] templateYLabel[4] in templateXLabel[0] templateTitle[3] templateYLabel[0] .

generated_template: This statistic shows the total annual templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] ( templateTitle[5] ) in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company generated templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the total annual Expenditure of Syngenta 's R & ( expenditure ) in the United States from 2009 to 2018 . In 2018 , the company generated 1430 million U.S. dollars .


Example 267:
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[1] templateTitle[2] templateYLabel[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , U.S.-based templateYLabel[2] chain The templateTitle[1] templateTitle[2] had templateYValue[max] templateYLabel[2] , up from templateYValue[1] the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[2] in templateTitle[6] templateTitle[7] from templateXValue[min] to templateXValue[max] . At the end of the templateXValue[max] fiscal templateXLabel[0] , there were templateYValue[max] templateTitle[2] templateTitle[3] templateYLabel[2] in templateTitle[6] templateTitle[7] .
generated: This statistic shows the Number of Factory 's restaurants in establishments 2009 from 2009 to 2018 . At the end of the 2018 fiscal Year , there were 201 Factory 's restaurants in establishments 2009 .


Example 268:
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[3] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[3] in templateTitle[5] was templateYValue[max] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[3] in the templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[3] among the templateTitle[4] templateTitle[5] population was about templateYValue[max] templateYLabel[5] .
generated: The statistic shows the Life expectancy at birth in the in Vietnam from 2007 to 2017 . In 2017 , the average Life expectancy at birth among the in Vietnam population was about 75.24 years .


Example 269:
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 . Decreasing population growth in Eritrea Eritrea is a small country located in East Africa , and like in many other African nations , its population is growing fast . In 2010 , Eritrea had a population of 5.71 million inhabitants and the population is expected to increase to almost 8 million inhabitants by the end of this decade . The high fertility rate is the largest and biggest contributor to population growth in Eritrea , especially considering that the average life expectancy is quite low at around 64 years of age . The fertility rate has decreased somewhat since 2005 , but it was still as high as 4.21 children per woman as of 2015 . This reduction is likely not a result of socio-cultural-economic changes , but rather as a result of conflict ; between 2004 and 2007 , significant reductions to the annual population growth rate can be seen , which is likely because of emigration . It is estimated that hundreds of thousands of Eritreans have fled the country due of poverty , the perceived threat of war and human rights abuse . As of 2015 , more than 379,000 Eritrean citizens had fled their homes , and tensions with neighbor Ethiopia have been and continue to be high .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] from templateXValue[min] to templateXValue[max] . All figures are estimates . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] was estimated to amount to approximately templateYValue[6] templateYLabel[2] templateYLabel[0] . Decreasing templateTitle[1] growth in templateTitle[3] templateTitle[3] is a small country located in East Africa , and like in many other African nations , its templateTitle[1] is growing fast . In 2010 , templateTitle[3] had a templateTitle[1] of 5.71 templateYLabel[2] templateYLabel[0] and the templateTitle[1] is expected to increase to almost 8 templateYLabel[2] templateYLabel[0] by the end of this decade . The high fertility rate is the largest and biggest contributor to templateTitle[1] growth in templateTitle[3] , especially considering that the average life expectancy is quite low at around 64 years of age . The fertility rate has decreased somewhat since 2005 , but it was still as high as 4.21 children per woman as of templateXValue[9] . This reduction is likely not a result of socio-cultural-economic changes , but rather as a result of conflict ; between 2004 and 2007 , significant reductions to the annual templateTitle[1] growth rate can be seen , which is likely because of emigration . It is estimated that hundreds of thousands of Eritreans have fled the country due of poverty , the perceived threat of war and human rights abuse . As of templateXValue[9] , more than 379,000 Eritrean citizens had fled their homes , and tensions with neighbor Ethiopia have been and continue to be high .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] amounted to approximately templateYValue[6] templateYLabel[2] templateYLabel[0] .
generated: This 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 millions Inhabitants .


Example 270:
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[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[4] templateTitle[8] from 2009 to templateXValue[max] . It expected that the templateYLabel[0] of templateTitle[5] templateTitle[4] will reach some templateYValue[7] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[7] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] templateTitle[4] amounted to approximately templateYValue[max] percent .
generated: This statistic shows the Forecast of rare earth oxide in the United States from 2010 to 2025 . In 2017 , the Price in in earth oxide amounted to approximately 303 percent .


Example 271:
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[2] of templateTitle[4] templateTitle[5] on templateTitle[7] templateTitle[8] as of January templateTitle[9] . During the measured period , the largest templateTitle[7] templateTitle[8] presence of the brand was on templateXValue[0] with templateYValue[max] templateYLabel[4] templateYLabel[2] , as opposed to its templateYValue[min] templateYLabel[4] templateYLabel[2] base on templateXValue[last] .

generated_template: The statistic shows the global templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . During the survey period of time , it was found that templateYValue[max] percent of templateTitle[5] were templateXValue[0] templateXValue[0] .
generated: The statistic shows the global Number of followers of Michael Kors from Facebook to Facebook . During the survey period of time , it was found that 17.91 percent of Kors were Facebook Facebook .


Example 272:
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[2] templateYLabel[3] on templateTitle[5] between December templateTitle[6] and October templateTitle[8] . As of that templateXLabel[0] , the social check-in app community had accumulated over templateYValue[max] templateYLabel[5] templateYLabel[3] worldwide .

generated_template: This statistic presents the templateYLabel[0] of templateTitle[0] in the United States from July templateTitle[6] to January templateTitle[9] . During the last reported period , templateYValue[max] percent of the templateYLabel[3] were born in the United States .
generated: This statistic presents the Number of Number in the United States from July 2010 to January N/A . During the last reported period , 55 percent of the members were born in the United States .


Example 273:
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[6] templateTitle[7] 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: The graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[6] templateTitle[7] templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of the templateTitle[6] templateTitle[7] templateTitle[8] was at templateYValue[max] .
generated: The graph depicts the total Regular season Home attendance of the Atlanta Falcons 2006 from 2006 to 2019 . In 2019 , the Regular season home Home attendance of the Atlanta Falcons 2006 was at 583184 .


Example 274:
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[2] templateTitle[3] templateTitle[4] in templateTitle[6] in templateTitle[7] . templateXValue[0] had the highest templateTitle[0] of templateTitle[2] templateTitle[3] templateTitle[4] with templateYValue[max] certified templateTitle[2] templateTitle[3] templateTitle[4] sources .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . The templateXValue[0] templateXValue[0] has been templateYLabel[2] templateYValue[max] times . In that year , the highest templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] eight eight percent of global templateTitle[4] templateYLabel[2] . templateXValue[0] is the second and the most important indicator of the world 's largest banks in North America . In 2017 , the fifth largest templateYLabel[0] templateYLabel[1] held in the leading countries in this statistic statistic refers to the European Union . The value added through their life expectancy in the total templateYLabel[1] templateYLabel[2] prices for example , the previous years . For example , the global gross domestic product product sector sector had an overall templateYLabel[0] templateYLabel[1] templateYLabel[2] of approximately 4.2 billion British pounds which was at around 120,000 templateYLabel[4] templateYLabel[5] templateYLabel[6] templateYLabel[7] templateYLabel[8] . For example , smaller than that sector had an increase from the templateYLabel[1] capita consumption of templateTitle[2] . For this sector has been able to the fifth largest countries in Spain when it is cementing itself with a track record high number of global box for commercial sector , respectively . The Middle East market , more than others . templateYLabel[0] in the global global financial crisis and the global global average number of these actions from the world , snowboarding is also lead to shield the world .
generated: The statistic shows the Litres consumed per in waters in Europe in 2016 . The Germany Germany has been per 821 times . In that year , the highest Number of natural mineral waters in eight eight percent of global waters per . Germany is the second and the most important indicator of the world 's largest banks in North America . In 2017 , the fifth largest Litres consumed held in the leading countries in this statistic statistic refers to the European Union . The value added through their life expectancy in the total consumed per prices for example , the previous years . For example , the global gross domestic product product sector sector had an overall Litres consumed per of approximately 4.2 billion British pounds which was at around 120,000 N/A N/A N/A N/A N/A . For example , smaller than that sector had an increase from the consumed capita consumption of natural . For this sector has been able to the fifth largest countries in Spain when it is cementing itself with a track record high number of global box for commercial sector , respectively . The Middle East market , more than others . Litres in the global global financial crisis and the global global average number of these actions from the world , snowboarding is also lead to shield the world .


Example 275:
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[0] the templateYLabel[0] of templateTitle[5] templateYLabel[2] templateYLabel[3] templateTitle[8] in templateTitle[10] templateTitle[11] templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateYValue[min] templateTitle[5] templateYLabel[2] templateYLabel[3] opened in the European templateYLabel[2] market . It was forecasted that templateYValue[0] templateTitle[5] templateYLabel[2] templateYLabel[3] would templateTitle[8] in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[2] templateYLabel[3] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateTitle[2] templateTitle[3] templateYLabel[2] templateYLabel[3] in templateTitle[5] .
generated: The statistic shows the Number of the number hotel rooms worldwide from 2012 to 2016 . In 2016 , there were 39178 the number hotel rooms in new .


Example 276:
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 . In 2018 , Finland 's gross domestic product amounted to around 274.21 billion U.S. dollars .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[4] is an important indicator of a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods and goods and goods produced within a country in a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: 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 and goods and goods produced within a country in a country 's economic power . In 2018 , Finland 's Gross domestic product amounted to around 274.21 billion U.S. dollars .


Example 277:
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[3] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] amounted to around templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] 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 templateTitle[3] amounted to around templateYValue[6] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the National debt of Switzerland from 2014 to 2018 , with projections up until 2024 . In 2018 , the National debt of Switzerland amounted to around 280.14 billion U.S. dollars .


Example 278:
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[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[2] users accessed the templateTitle[4] through their templateTitle[2] 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[min] , templateYValue[min] percent of the templateYLabel[2] 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 Mexico networking reach in internet from 2017 to 2023 . In 2017 , 43 percent of the population in the country accessed Mexico : . In 2019 , this Share is projected to reach 50 percent .


Example 279:
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[5] and templateTitle[7] templateYLabel[0] of templateTitle[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] templateTitle[1] templateTitle[3] , templateTitle[5] and templateTitle[7] templateYLabel[0] have gradually increased since templateXValue[min] , reaching templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] . The templateTitle[0] Corporation is a templateYLabel[3] medical technology company headquartered in Kalamazoo , Michigan .

generated_template: This statistic shows the templateTitle[4] templateTitle[5] of the NRA in the United States from templateXValue[min] to templateXValue[max] . As of July templateXValue[max] , the NRA spent about 860,000 templateYLabel[3] templateYLabel[4] on templateTitle[4] . This is a significant decrease from templateXValue[1] , when the NRA spent about templateYValue[1] templateYLabel[2] on templateTitle[4] templateYLabel[0] .
generated: This statistic shows the , development of the NRA in the United States from 2011 to 2019 . As of July 2019 , the NRA spent about 860,000 U.S. dollars on , . This is a significant decrease from 2018 , when the NRA spent about 862 million on , Expenses .


Example 280:
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 . Production outside the EU The United States produced about 11.5 to 13 million tons in the early 2000s . However , by 2017 the US production had fallen to just 300 thousand tons above Spanish production . 2017 was the year that for the first-time Mexican production surpassed that of the US . Turkish production almost reached 2 million tons in 2017 and the country surpassed Italian production by roughly 800 thousand tons , making it the second largest in the whole of Europe . Consumption of fruits and juice Orange juice consumption in countries such as Germany , the US , China and Canada is down and only the Mexican market shows a stable trend in consumption . Fruit consumption is down in many all parts of Latin America ( Mexico , Argentina , Brazil ) or stagnating ( Guatemala , Costa Rica ) . With only a very few markets showing upwards trends , such as orange consumption in Austria , a further decrease in production or stagnation in production can be expected .
gold_template: In 2018/2019 , templateXValue[0] was the leading producer of templateTitle[0] oranges in the templateTitle[6] templateTitle[7] ( EU28 ) , with over 3.7 million templateYLabel[3] of templateTitle[0] oranges produced . The Spanish templateTitle[2] was more than templateTitle[8] times the templateTitle[2] of templateXValue[1] , the second largest producer of oranges . The other three producers in the templateTitle[6] produced less than templateYLabel[2] million templateYLabel[3] during this year . templateTitle[2] outside the templateTitle[6] The United States produced about 11.5 to 13 million templateYLabel[3] in the early 2000s . However , templateTitle[10] 2017 the US templateTitle[2] had fallen to just 300 thousand templateYLabel[3] above Spanish templateTitle[2] . 2017 was the year that for the first-time Mexican templateTitle[2] surpassed that of the US . Turkish templateTitle[2] almost reached templateTitle[8] million templateYLabel[3] in 2017 and the templateXLabel[0] surpassed Italian templateTitle[2] templateTitle[10] roughly 800 thousand templateYLabel[3] , making it the second largest in the whole of templateTitle[6] . Consumption of fruits and juice templateTitle[1] juice consumption in countries such as Germany , the US , China and Canada is down and only the Mexican market shows a stable trend in consumption . Fruit consumption is down in many all parts of Latin America ( Mexico , Argentina , Brazil ) or stagnating ( Guatemala , Costa Rica ) . With only a very few markets showing upwards trends , such as templateTitle[1] consumption in Austria , a further decrease in templateTitle[2] or stagnation in templateTitle[2] can be expected .

generated_template: The statistic shows the global templateYLabel[0] templateYLabel[1] of the European Union templateTitle[5] from templateXValue[last] to templateXValue[0] . The templateYLabel[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the United States in templateXValue[last] . In 2017 , templateXValue[0] and the world was templateYValue[max] percent .
generated: The statistic shows the global Volume in of the European Union the from Cyprus to Spain . The Volume in volume in the European in the United States in Cyprus . In 2017 , Spain and the world was 3731 percent .


Example 281:
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 . Since 2011 , the figure was more or less stable and ranged between 2.87 and 3.17 people per household . The average Chinese household still counts as rather large in comparison to other industrial countries . In 2019 , an average American household consisted of only 2.52 people . Comparable figures have already been reached in the bigger cities and coastal areas of China , but in the rural provinces the household size is still much larger . According to the National Bureau of Statistics of China , the household size in China was diametrically correlated to its income . The receding size of Chinese households may be linked to the controversial one-child policy introduced in 1979 . The main aim of the policy was to control population growth . In 2017 , the fertility rate in China resided at 1.6 children per woman , much less than in the United States or in the United Kingdom . A partial ease in the one-child policy was introduced in 2013 , due to which couples where at least one parent was an only child were allowed to have a second child . In October 2015 , the law was changed into a two-child policy becoming effective in January 2016 .
gold_template: This graph shows the templateTitle[0] templateTitle[1] of templateTitle[3] in templateTitle[5] 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 templateYLabel[2] templateTitle[3] in templateTitle[5] – additional information A templateTitle[3] is commonly defined as templateTitle[6] templateYLabel[2] living alone or a group of people living together and sharing certain living accommodations . The templateTitle[0] templateYLabel[0] of people living in templateTitle[6] templateTitle[3] in templateTitle[5] dropped from templateYValue[max] in templateXValue[min] to templateYValue[min] in templateXValue[7] . Since templateXValue[7] , the figure was more or less stable and ranged between templateYValue[min] and templateYValue[1] people templateYLabel[2] templateTitle[3] . The templateTitle[0] Chinese templateTitle[3] still counts as rather large in comparison to other industrial countries . In 2019 , an templateTitle[0] American templateTitle[3] consisted of only 2.52 people . Comparable figures have already been reached in the bigger cities and coastal areas of templateTitle[5] , but in the rural provinces the templateTitle[3] templateTitle[1] is still much larger . According to the National Bureau of Statistics of templateTitle[5] , the templateTitle[3] templateTitle[1] in templateTitle[5] was diametrically correlated to its income . The receding templateTitle[1] of Chinese templateTitle[3] may be linked to the controversial one-child policy introduced in 1979 . The main aim of the policy was to control population growth . In templateXValue[1] , the fertility rate in templateTitle[5] resided at 1.6 children templateYLabel[2] woman , much less than in the United States or in the United Kingdom . A partial ease in the one-child policy was introduced in templateXValue[5] , due to which couples where at least templateTitle[6] parent was an only child were allowed to have a second child . In October templateXValue[3] , the law was changed into a two-child policy becoming effective in January templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[3] percent . templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] has been increasing since the financial crisis . In templateXValue[9] , the largest countries in the world in the United States . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] increased to templateYValue[2] percent .
generated: This statistic shows the Average size of in the United States from 1990 to 2018 . In 2015 , the Average size of households in was 3.1 percent . Number of persons in the Average size of households in has been increasing since the financial crisis . In 2009 , the largest countries in the world in the United States . In 2011 , the Number of increased to 3.11 percent .


Example 282:
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[2] templateYLabel[3] templateYLabel[4] .

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[2] templateYLabel[3] templateYLabel[4] .
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 283:
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[2] of templateTitle[0] templateYLabel[2] worldwide as of January templateTitle[6] , sorted templateTitle[8] templateTitle[9] . During the survey period , templateYValue[min] templateYLabel[0] of templateTitle[0] templateTitle[5] were templateXValue[0] and templateYValue[max] templateYLabel[0] were templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[2] of templateTitle[0] templateYLabel[2] in the United States as of Fall templateTitle[6] , sorted templateTitle[8] templateTitle[9] . During the survey period , templateYValue[max] templateYLabel[0] of templateTitle[0] templateYLabel[2] were templateXValue[0] and templateYValue[min] templateYLabel[0] of templateYLabel[2] were templateXValue[last] .
generated: This statistic gives information on the distribution of LinkedIn users in the United States as of Fall 2020 , sorted by gender . During the survey period , 57 Percentage of LinkedIn users were Female and 43 Percentage of users were Male .


Example 284:
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[6] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , all templateTitle[0] templateTitle[1] templateTitle[6] combined received a templateTitle[2] of 2.46 billion British Pounds in templateYLabel[0] templateYLabel[1] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[5] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , the templateYLabel[0] of templateTitle[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in the United States .
generated: The statistic shows the total global Broadcasting of Premier League total to from 2010/11 to 2018/19 . In the 2018/19 season , the Broadcasting of total in million GBP N/A in the United States .


Example 285:
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[2] templateYLabel[2] templateYLabel[0] in templateTitle[0] from templateXValue[min] to templateXValue[max] . There were about templateYValue[min] arsons templateYLabel[3] 100,000 templateYLabel[5] in templateTitle[0] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] was at templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Rate of reported in the United States from 2000 to 2018 . In 2018 , the Rate of of the rate 2000 was at 46.27 per 100,000 residents .


Example 286:
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[2] 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[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] of templateTitle[0] templateTitle[1] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] employed templateYValue[max] people employed by the templateTitle[4] templateTitle[5] templateTitle[6] .
generated: This statistic shows the Number of employees of Marathon Oil employees 2010 from 2010 to 2018 . In 2018 , Marathon employed 29677 people employed by the of employees 2010 .


Example 287:
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 United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around 344 thousand templateYLabel[3] of templateTitle[1] cherries were produced in the templateTitle[0] .

generated_template: This statistic shows the total templateTitle[5] templateTitle[0] templateTitle[1] templateYLabel[0] from templateXValue[min] to templateXValue[max] . According to the report , approximately 28.6 million templateYLabel[3] of templateTitle[0] templateTitle[1] were produced in the United States in templateXValue[max] .
generated: This statistic shows the total - U.S. sweet Production from 2000 to 2018 . According to the report , approximately 28.6 million tons of U.S. sweet were produced in the United States in 2018 .


Example 288:
data: Quarter|Q4_'19|x|bar_chart Percent_change_from_preceding_quarter|1.4|y|bar_chart Quarter|Q3_'19|x|bar_chart Percent_change_from_preceding_quarter|-0.2|y|bar_chart Quarter|Q2_'19|x|bar_chart Percent_change_from_preceding_quarter|2.5|y|bar_chart Quarter|Q1_'19|x|bar_chart Percent_change_from_preceding_quarter|3.5|y|bar_chart Quarter|Q4_'18|x|bar_chart Percent_change_from_preceding_quarter|0.1|y|bar_chart Quarter|Q3_'18|x|bar_chart Percent_change_from_preceding_quarter|1.2|y|bar_chart Quarter|Q2_'18|x|bar_chart Percent_change_from_preceding_quarter|1.8|y|bar_chart Quarter|Q1_'18|x|bar_chart Percent_change_from_preceding_quarter|0.9|y|bar_chart Quarter|Q4_'17|x|bar_chart Percent_change_from_preceding_quarter|0.9|y|bar_chart Quarter|Q3_'17|x|bar_chart Percent_change_from_preceding_quarter|3|y|bar_chart Quarter|Q2_'17|x|bar_chart Percent_change_from_preceding_quarter|0.5|y|bar_chart Quarter|Q1_'17|x|bar_chart Percent_change_from_preceding_quarter|1|y|bar_chart 
title: U.S. labor productivity - quarterly change in the nonfarm business sector 2017 - 2019

gold: This statistic shows the quarterly percent change in nonfarm business sector labor productivity in the United States from 2017 to 2019 . The data are seasonally adjusted at annual rates . Productivity is the output per hour of all persons . Nonfarm business sector labor productivity increased by 1.4 percent in the fourth quarter of 2019 compared to the previous quarter .
gold_template: This statistic shows the templateTitle[4] templateYLabel[0] templateYLabel[1] in templateTitle[8] templateTitle[9] templateTitle[10] templateTitle[1] templateTitle[2] in the United States templateYLabel[2] templateTitle[11] to templateTitle[13] . The data are seasonally adjusted at annual rates . templateTitle[2] is the output templateYLabel[0] hour of all persons . templateTitle[8] templateTitle[9] templateTitle[10] templateTitle[1] templateTitle[2] increased by templateYValue[0] templateYLabel[0] in the fourth templateXLabel[0] of templateTitle[13] compared to the previous templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the global templateTitle[4] templateTitle[5] templateTitle[6] from templateTitle[9] to templateTitle[11] . In the last reported period , the templateYLabel[0] templateYLabel[1] was at templateYValue[max] percent .
generated: The statistic shows the Percent change of the global quarterly change in from business to 2017 . In the last reported period , the Percent change was at 3.5 percent .


Example 289:
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[6] templateTitle[7] templateTitle[8] 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[6] templateTitle[7] before the templateXValue[3] templateTitle[1] .

generated_template: The graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[6] templateTitle[7] templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of the templateTitle[6] templateTitle[7] templateTitle[8] was at templateYValue[max] .
generated: The graph depicts the total Regular season Home attendance of the Los Angeles Rams from 2006 to 2019 . In 2019 , the Regular season home Home attendance of the Los Angeles Rams was at 665318 .


Example 290:
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[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] and templateTitle[2] templateTitle[5] templateYLabel[2] amounted to templateYValue[0] templateYLabel[4] templateYLabel[5] templateYLabel[0] templateYLabel[1] .

generated_template: The statistic shows the templateTitle[0] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateYLabel[2] amounted to about templateYValue[max] templateYLabel[4] templateYLabel[5] templateYLabel[0] templateYLabel[1] .
generated: The statistic shows the Estimated clothing Per capita sales in the United States per 2000 to 2017 . In 2017 , clothing sales amounted to about 804 U.S. dollars Per capita .


Example 291:
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 . Physicians , patients , and digital health Usage of smartphones among physicians in the United States has risen . From 2012 to 2015 , 68 percent to 84 percent of physicians , respectively , reported using their smartphones for professional purposes . Digital health has become more widespread globally among the medical industry . In the United States , 85 percent of physicians reported that they had already implemented electronic medical records by 2014 . However , it has been proposed that the adoption of smartphone usage among doctors has started to plateau . The use of tablets is also becoming more common among doctors in daily professional procedures . Digital technologies provide a new approach for consumers in regards to the accessibility to information and to physicians . Over half of U.S. adults used the internet to search for a specific disease or medical problem in 2012 . Digital technologies have altered the way that patients undergo treatment . For example , over half of U.S. consumers would send a digital photo of a rash or skin problem to dermatologists for an opinion and 38.6 percent of patients would be willing to have a live visit with a physician via a smartphone app . The global mobile health industry has risen rapidly and is expected to reach 55.9 billion U.S. dollars by 2020 .
gold_template: This survey indicates the templateYLabel[0] of templateTitle[7] in the United States 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] templateYLabel[0] 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 . templateTitle[7] , patients , and digital health Usage of smartphones templateTitle[5] templateTitle[7] in the United States has risen . From templateXValue[last] to templateXValue[0] , templateYValue[min] templateYLabel[0] to templateYValue[max] templateYLabel[0] of templateTitle[7] , respectively , reported using their smartphones templateTitle[2] templateTitle[3] purposes . Digital health has become more widespread globally templateTitle[5] the medical industry . In the United States , 85 templateYLabel[0] of templateTitle[7] reported that they had already implemented electronic medical records by templateXValue[1] . However , it has been proposed that the adoption of templateTitle[0] usage templateTitle[5] doctors has started to plateau . The templateTitle[1] of tablets is also becoming more common templateTitle[5] doctors in daily templateTitle[3] procedures . Digital technologies provide a new approach templateTitle[2] consumers in regards to the accessibility to information and to templateTitle[7] . Over half of templateTitle[6] adults used the internet to search templateTitle[2] a specific disease or medical problem in templateXValue[last] . Digital technologies have altered the way that patients undergo treatment . templateTitle[2] example , over half of templateTitle[6] consumers would send a digital photo of a rash or skin problem to dermatologists templateTitle[2] an opinion and 38.6 templateYLabel[0] of patients would be willing to have a live visit with a templateTitle[7] via a templateTitle[0] app . The global mobile health industry has risen rapidly and is expected to reach 55.9 billion templateTitle[6] dollars by 2020 .

generated_template: As of templateXValue[0] templateXValue[0] , templateYValue[max] percent of the most popular templateTitle[5] templateTitle[6] templateTitle[3] in the United Kingdom ( templateTitle[9] ) from templateXValue[last] to templateXValue[0] . templateXValue[1] templateXValue[1] and templateXValue[2] templateXValue[2] accounted for templateYValue[2] percent of templateYLabel[2] respectively . The templateTitle[2] templateTitle[3] in the UK ) has increased between 18 and 2018 .
generated: As of March_2015 March_2015 , 84 percent of the most popular among U.S. professional in the United Kingdom ( - ) from March_2012 to March_2015 . March_2014 March_2014 and March_2013 March_2013 accounted for 76 percent of respondents respectively . The for professional in the UK ) has increased between 18 and 2018 .


Example 292:
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 . Favorable weather also increases demand for fertilizers in major agricultural regions .
gold_template: This statistic displays a templateTitle[0] of templateYLabel[0] global templateYLabel[1] templateTitle[0] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . By templateXValue[max] , the annual templateYLabel[1] templateTitle[0] templateTitle[2] templateTitle[3] is expected to reach some 69 templateYLabel[3] templateYLabel[4] templateYLabel[5] . Increasing crop prices lead to increased templateTitle[3] demands and has been especially noted in recent years in South Asia . Favorable weather also increases templateYLabel[1] templateTitle[0] fertilizers in major agricultural regions .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Forecast of sulfur Total worldwide from 2014 to 2020 . In 2017 , the Forecast of sulfur fertilizer amounted to 68.08 in million metric tons .


Example 293:
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[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] amounted to around templateYValue[6] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] 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 templateTitle[3] amounted to around templateYValue[6] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: 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 .


Example 294:
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[6] templateTitle[7] in the United States as of the third quarter of templateTitle[8] , templateTitle[10] templateTitle[11] . 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[6] templateTitle[7] , while the templateXValue[2] templateXValue[2] industry reported in templateYValue[2] percent .

generated_template: As of December templateTitle[9] , templateXValue[0] , the United States , also known as of January templateTitle[6] . During the survey , the last reported period , the templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] was templateYValue[max] percent of templateYLabel[2] . The number of templateTitle[5] , with record templateYValue[max] percent of templateXValue[2] and templateYValue[2] templateYLabel[2] nationwide nationwide in the United States .
generated: As of December , , Utilities , the United States , also known as of January social . During the survey , the last reported period , the Retail Retail Retail Retail was 18 percent of rate . The number of on , with record 18 percent of Consumer_Goods and 14 rate nationwide nationwide in the United States .


Example 295:
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 . The largest city in Italy , with the largest amount of inhabitants , is Rome , with almost 3 million inhabitants .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[3] amounted to about templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . See the templateTitle[0] of templateTitle[3] for comparison . The largest city in templateTitle[3] , with the largest amount of templateYLabel[0] , is Rome , with almost 3 million templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[3] amounted to approximately templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Population density in Italy from 2008 to 2018 . In 2018 , the Population density in Italy amounted to approximately 206.67 Inhabitants per square kilometer .


Example 296:
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 . February 2017 marks the time when the population in Sweden surpassed 10 million and there were 10.01 million people living in Sweden . The population growth in Sweden is forecasted to continue . In 2060 , it is estimated that the population will increase to 11.7 million .
gold_template: This statistic shows the total templateTitle[0] in templateTitle[2] from templateXValue[min] to templateXValue[max] . The number of templateYLabel[0] in templateTitle[2] has increased by nearly templateTitle[5] templateYLabel[2] in this time period . In templateXValue[min] , there were approximately templateYValue[min] templateYLabel[2] templateYLabel[0] in templateTitle[2] and by the end of templateXValue[max] the Swedish templateTitle[0] reached templateYValue[max] templateYLabel[2] people . February templateXValue[2] marks the time when the templateTitle[0] in templateTitle[2] surpassed 10 templateYLabel[2] and there were 10.01 templateYLabel[2] people living in templateTitle[2] . The templateTitle[0] growth in templateTitle[2] is forecasted to continue . In 2060 , it is estimated that the templateTitle[0] will increase to 11.7 templateYLabel[2] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] amounted to approximately templateYValue[6] templateYLabel[2] templateYLabel[0] .
generated: The statistic shows the Population in of 2009 from 2009 to 2019 . In 2013 , the Population in of 2009 amounted to approximately 9.64 millions Inhabitants .


Example 297:
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 . A second forecast predicts that a large number of Dutch households will experience an increase in purchasing power . Just over 22,000 households were forecast to see a growth of five percent or more . Singles with average income see purchasing power grow the most The singles with average incomes ( 35,000 euros ) were expected to see the most significant increase in their purchasing power in 2019 , at over two percent . With the exception of couples with one child on a double income ( earning between 10,000 and 25,000 euros ) though , all other household types could foresee a growth in purchasing power . Purchasing power under Rutte III-government Pensioners and people on social security benefits are especially vulnerable to the dynamics of a changing economic situation . Another forecast released just before the Rutte III government was inaugurated in October 2017 predicts a growth of the purchasing power for all groups under the new government : the working , the pensioners and the recipients of benefits . It was however expected that the purchasing power of the employed group would increase far more than the purchasing power of pensioners and people on social security : 1.4 percent vs. 0.6 and 0.7 percent .
gold_template: In templateXValue[2] , the templateTitle[0] templateTitle[1] in the templateTitle[5] 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 templateTitle[6] years , the templateTitle[0] templateTitle[1] was forecast to increase further . A second forecast predicts that a large number of Dutch households will experience an increase in templateTitle[0] templateTitle[1] . Just over 22,000 households were forecast to see a growth of templateTitle[6] templateYLabel[3] or more . Singles with average income see templateTitle[0] templateTitle[1] grow the most The singles with average incomes ( 35,000 euros ) were expected to see the most significant increase in their templateTitle[0] templateTitle[1] in templateXValue[1] , at over templateTitle[6] templateYLabel[3] . With the exception of couples with templateYValue[5] child on a double income ( earning between 10,000 and 25,000 euros ) though , all other household types could foresee a growth in templateTitle[0] templateTitle[1] . templateTitle[0] templateTitle[1] under Rutte III-government Pensioners and people on social security benefits are especially vulnerable to the dynamics of a changing economic situation . Another forecast released just before the Rutte III government was inaugurated in October templateXValue[3] predicts a growth of the templateTitle[0] templateTitle[1] for all groups under the new government : the working , the pensioners and the recipients of benefits . It was however expected that the templateTitle[0] templateTitle[1] of the employed group would increase far more than the templateTitle[0] templateTitle[1] of pensioners and people on social security : 1.4 templateYLabel[3] vs. 0.6 and 0.7 templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[3] templateYLabel[4] on the previous templateXLabel[0] of templateTitle[0] prices in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateTitle[0] market value of templateTitle[0] prices in templateTitle[5] are expected to increase with 2.3 templateYLabel[3] compared to the previous templateXLabel[0] . In recent years , the templateTitle[0] market in templateTitle[5] remained stable . Between 2005 and 2015 templateTitle[3] example , the number of real estate transactions in templateTitle[0] in the Flemish region increased from approximately 39,500 transactions in 2005 to a value of approximately 43,000 transactions in 2014 . This was due to templateTitle[5] 's fiscal system . When purchasing a house in templateTitle[5] , consumers pay the asking templateTitle[1] plus ten ( in Flanders ) or 12 templateYLabel[3] ( in Wallonia and the Brussels capital region ) in registration rights .
generated: This statistic shows the percentage change on the previous Year of Purchasing prices in Netherlands from 2015 to 2020 . In 2019 , the Purchasing market value of Purchasing prices in Netherlands are expected to increase with 2.3 percentage compared to the previous Year . In recent years , the Purchasing market in Netherlands remained stable . Between 2005 and 2015 in example , the number of real estate transactions in Purchasing in the Flemish region increased from approximately 39,500 transactions in 2005 to a value of approximately 43,000 transactions in 2014 . This was due to Netherlands 's fiscal system . When purchasing a house in Netherlands , consumers pay the asking power plus ten ( in Flanders ) or 12 percentage ( in Wallonia and the Brussels capital region ) in registration rights .


Example 298:
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] templateYLabel[1] production of templateTitle[0] templateTitle[1] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , GM templateYLabel[2] 6.7 templateYLabel[4] templateYLabel[0] templateYLabel[1] templateTitle[6] . The U.S. automaker is templateTitle[6] 's fourth largest manufacturer of templateYLabel[0] templateYLabel[1] in terms of production .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] production of templateTitle[0] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateYLabel[2] around templateYValue[max] templateYLabel[4] templateYLabel[0] templateYLabel[1] templateTitle[5] . templateTitle[0] is ranked among the 15 largest automakers templateTitle[5] .
generated: The timeline shows the Passenger cars production of General produced from 1999 to 2014 . In 2014 , General produced around 6.87 millions) Passenger cars produced . General is ranked among the 15 largest automakers produced .


Example 299:
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[3] templateTitle[4] Index ( templateYLabel[0] ) of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] websites in templateTitle[8] . Overall , templateXValue[0] scored the highest level of templateTitle[3] templateTitle[4] templateTitle[5] templateYValue[max] index points . templateXValue[last] was ranked last templateTitle[5] an index templateYLabel[1] rating of templateYValue[min] / templateYLabel[2] index points .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States in templateTitle[5] , internet templateXLabel[0] . During the last reported period , it was found that around templateYValue[max] percent of all templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: This statistic shows the ACSI - U.S. customer satisfaction in the United States in with , internet Platform . During the last reported period , it was found that around 80 percent of all scale) N/A N/A N/A .


Example 300:
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[6] templateTitle[7] templateTitle[8] among consumers in the United States in templateTitle[12] . In the study it was found that templateYValue[2] percent of consumers preferred to use templateXValue[last] templateTitle[3] making purchases at templateTitle[6] templateTitle[7] templateTitle[8] .

generated_template: This statistic shows the results of a survey among female templateTitle[2] high school students templateTitle[4] templateTitle[5] templateTitle[6] as of May templateTitle[7] . The survey , templateYValue[max] percent of templateYLabel[2] stated they had templateXValue[1] templateXValue[1] templateXValue[1] .
generated: This statistic shows the results of a survey among female preference high school students shopping at fast as of May food . The survey , 44 percent of respondents stated they had Debit_card Debit_card Debit_card .


Example 301:
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[2] templateTitle[3] templateTitle[4] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[2] in templateTitle[0] were accessing the templateTitle[2] . This figure is projected to grow to templateYValue[max] percent by templateXValue[max] .

generated_template: This statistic gives information on the templateTitle[2] templateTitle[4] rate in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the Singaporean templateYLabel[2] were using the templateTitle[2] . 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 302:
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 . Regional differences in Belgium Within Belgium , there were marked regional differences as well . The Flemish Region , the Dutch-speaking part of the country , had a migration balance of nearly 25,000 in 2018 . By comparison , the migration balance of the French-speaking Walloon Region was roughly one third of this , at just over 8,000 . Opinions on immigration in Belgium Between 2011 and 2018 , Ipsos surveyed opinions on immigration in Belgium multiple times . The share of respondents who believe immigration has a positive impact on the country was continuously low though , never reaching above 16 percent . On a more positive note though , this peak was reached in the most recent survey , held at the end of 2018 . Surveys from 2011 and 2013 saw considerable fewer positive responses of nine and eight percent respectively . After 2013 , the share of respondents positive about immigration remained above ten percent in all years , until it reached its peak in 2018 .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was roughly 50,000 , meaning that the number of immigrants moving to templateTitle[3] 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 . Regional differences in templateTitle[3] Within templateTitle[3] , there were marked regional differences as well . The Flemish Region , the Dutch-speaking part of the country , had a templateYLabel[0] templateYLabel[1] of nearly 25,000 in templateXValue[max] . By comparison , the templateYLabel[0] templateYLabel[1] of the French-speaking Walloon Region was roughly templateTitle[4] third of this , at just over 8,000 . Opinions on immigration in templateTitle[3] Between templateXValue[7] and templateXValue[max] , Ipsos surveyed opinions on immigration in templateTitle[3] multiple times . The share of respondents who believe immigration has a positive impact on the country was continuously low though , never reaching above 16 percent . On a more positive note though , this peak was reached in the most recent survey , held at the end of templateXValue[max] . Surveys from templateXValue[7] and templateXValue[5] saw considerable fewer positive responses of nine and templateTitle[6] percent respectively . After templateXValue[5] , the share of respondents positive about immigration remained above templateTitle[4] percent in all years , until it reached its peak in templateXValue[max] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the total global Migration of Migration balance in from 2010 to 2018 . In 2015 , the Migration balance in Migration amounted to 47682 N/A N/A N/A .


Example 303:
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[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In January templateXValue[max] , over 733 thousand people were estimated to be working in print or software templateTitle[3] companies , down from the templateYValue[1] thousand people recorded in January of the previous templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] data for the templateTitle[2] templateTitle[3] templateTitle[4] and sound templateTitle[6] industry from templateXValue[min] to templateXValue[max] . In January templateXValue[max] , there were approximately templateYValue[max] thousand templateYLabel[2] compared to the previous templateXLabel[0] .
generated: The statistic shows the Employment data for the U.S. publishing industries and sound - industry from 2001 to 2019 . In January 2019 , there were approximately 1045.7 thousand 1,000s compared to the previous Year .


Example 304:
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[5] 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[5] amounted to templateYValue[max] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) of templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] amounted to around templateYValue[0] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Number of employed persons ( Employed ) of persons from 2010 to 2020 . In 2020 , the Employed persons of persons in amounted to around 5.02 millions N/A N/A .


Example 305:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . Today , templateTitle[3] 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[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[2] templateYLabel[3] by templateTitle[5] templateYLabel[5] while being of child-bearing age . In templateXValue[max] , templateTitle[3] templateTitle[4] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Fertility rate in Afghanistan 2017 from 2007 to 2017 . The Fertility rate is the average Number of children born by N/A woman while being of child-bearing age . In 2017 , Afghanistan 2017 's Fertility rate amounted to 4.63 children born per woman .


Example 306:
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[3] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[2] a templateYLabel[5] will have during her child-bearing years . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[3] 's population was templateYValue[0] templateYLabel[2] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[2] templateYLabel[3] by templateTitle[5] templateYLabel[5] while being of child-bearing age . In templateXValue[max] , templateTitle[3] templateTitle[4] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Fertility rate in Russia 2017 from 2007 to 2017 . The Fertility rate is the average Number of children born by N/A woman while being of child-bearing age . In 2017 , Russia 2017 's Fertility rate amounted to 1.42 children born per woman .


Example 307:
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 . The majority of company revenue is generated via in-game sales of virtual items .
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 750 templateYLabel[3] templateYLabel[4] templateYLabel[5] . Popular templateTitle[0] titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga . The majority of company revenue is generated via in-game sales of virtual items .

generated_template: The statistic shows 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[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows 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 308:
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 templateYLabel[0] templateTitle[1] of templateTitle[3] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[min] children were born templateYLabel[1] 1,000 of templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in the templateTitle[4] templateTitle[5] was at approximately templateYValue[0] percent .
generated: The statistic shows the Births per of the in the from 1990 to 2018 . In 2018 , the Births per in the in the was at approximately 14.8 percent .


Example 309:
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[2] in templateTitle[4] that had templateTitle[2] to the templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[1] penetration grew in templateTitle[4] during this period . In templateXValue[max] , templateYValue[max] percent of Greek templateYLabel[2] had templateTitle[1] templateTitle[2] .

generated_template: Following templateTitle[12] consecutive years at templateYValue[1] percent , the templateYLabel[0] of templateYLabel[2] with templateTitle[1] access in the templateTitle[5] templateTitle[6] ( templateTitle[8] ) 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] . As of templateXValue[2] , China was the country with this highest number of templateTitle[1] users . The templateTitle[8] was home to 63.06 million templateTitle[1] users in that templateXLabel[0] . templateTitle[1] templateTitle[2] rates around the world Northern America and Northern Europe featured the highest templateTitle[1] templateTitle[2] of any region globally , at 95 percent . Middle Africa showed the lowest levels , at just 12 percent . With regard to mobile templateTitle[1] , the templateTitle[5] Arab Emirates showed the highest templateTitle[2] rate , at 96 percent .
generated: Following N/A consecutive years at 71 percent , the Share of households with internet access in the 2007 - ( N/A ) 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 N/A , to 3.9 billion users in 2017 . As of 2016 , China was the country with this highest number of internet users . The N/A was home to 63.06 million internet users in that Year . internet access rates around the world Northern America and Northern Europe featured the highest internet access of any region globally , at 95 percent . Middle Africa showed the lowest levels , at just 12 percent . With regard to mobile internet , the 2007 Arab Emirates showed the highest access rate , at 96 percent .


Example 310:
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[6] templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[min] templateYLabel[4] templateYLabel[2] templateYLabel[0] in universities in templateTitle[6] templateTitle[7] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was at approximately templateYValue[min] percent .
generated: The statistic shows the Number of enrolled university students size in from 2011 to 2018 . In 2011 , the Number of enrolled university students was at approximately 2.03 percent .


Example 311:
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[2] templateYLabel[3] templateYLabel[4] as of October templateTitle[5] , by templateXLabel[0] . During the measured period , templateXValue[0] accounted for over half of templateYLabel[2] templateYLabel[3] templateYLabel[4] volume . Within that templateXLabel[0] , Netflix was by far the market leader in terms of global templateXValue[0] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the global templateTitle[4] templateTitle[5] templateTitle[6] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] was templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Share of of the global worldwide 2018 N/A in the United States from Video to Video . In Video , the Share of of the worldwide 2018 N/A was 57.7 internet traffic N/A .


Example 312:
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[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . The total templateYLabel[0] of templateYLabel[2] will keep on increasing . According to the templateTitle[0] there will be roughly over 6 templateYLabel[4] of people living in templateTitle[5] by templateXValue[max] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was estimated to increase by about templateYValue[min] percent .
generated: The statistic shows the Forecast of population growth in size Denmark from 2019 to 2029 . In 2024 , the Forecast of population growth in was estimated to increase by about 5.83 percent .


Example 313:
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 United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] templateYLabel[2] templateYLabel[3] 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: The statistic shows the templateYLabel[0] of templateYLabel[2] in templateTitle[4] from templateXValue[min] to templateXValue[max] . Approximately templateYValue[max] templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] was produced in templateTitle[4] in templateXValue[max] , templateTitle[1] from templateYValue[1] templateYLabel[2] templateYLabel[3] the previous templateXLabel[0] . templateTitle[0] templateTitle[1] in templateTitle[4] templateTitle[6] additional information templateTitle[0] templateTitle[1] is most commonly associated with templateTitle[4] , the templateYLabel[0] 's largest producer . The national flag of templateTitle[4] also features a large red templateTitle[0] leaf . It is believed that the templateYLabel[0] of templateTitle[0] templateTitle[1] dates back to the indigenous people of North America who discovered a way of processing templateTitle[0] tree sap into templateTitle[1] and sugar . templateTitle[0] sap is usually gathered in springtime when the weather conditions make it easy to collect the sap by boring holes into the trunk of the templateTitle[0] tree . The sap is then boiled down into templateTitle[0] templateTitle[1] . templateTitle[0] templateTitle[1] is commonly used as a sweetener , a topping for pancakes and waffles or as an ingredient in baking . The volume of templateTitle[0] templateTitle[1] produced in templateTitle[4] fluctuates each templateXLabel[0] , but , on the whole , has almost doubled from just over five templateYLabel[2] templateYLabel[3] in templateXValue[min] to nearly ten templateYLabel[2] templateYLabel[3] in templateXValue[5] .
generated: The statistic shows the Production of million in the from 2006 to 2018 . Approximately 78.6 million gallons of Frozen yogurt was produced in the in 2018 , yogurt from 62.5 million gallons the previous Year . Frozen yogurt in the 2006 additional information Frozen yogurt is most commonly associated with the , the Production 's largest producer . The national flag of the also features a large red Frozen leaf . It is believed that the Production of Frozen yogurt dates back to the indigenous people of North America who discovered a way of processing Frozen tree sap into yogurt and sugar . Frozen sap is usually gathered in springtime when the weather conditions make it easy to collect the sap by boring holes into the trunk of the Frozen tree . The sap is then boiled down into Frozen yogurt . Frozen yogurt is commonly used as a sweetener , a topping for pancakes and waffles or as an ingredient in baking . The volume of Frozen yogurt produced in the fluctuates each Year , but , on the whole , has almost doubled from just over five million gallons in 2006 to nearly ten million gallons in 2013 .


Example 314:
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[5] 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: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States amounted to templateYValue[1] templateYLabel[5] .
generated: This statistic shows the Welsh Assembly elections ( Turnout ) rate N/A in rates from 1999 to 2011 . In N/A , the Turnout rate N/A in the United States amounted to 38.2 N/A .


Example 315:
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 . Trade is a great door-opener for countries , as it shows that they are willing to work together , and as the most powerful economy in the world , the U.S. is an ideal trading partner . The benefits of free trade Free trade is especially beneficial for countries who work closely together , as it can help to grow their overall trade volume . The United States , for example , is part of the North American Free Trade Agreement ( NAFTA ) , which is the largest free trade zone in the world . Since the implementation of NAFTA in 1994 , Canada , Mexico , and the United States have all benefitted from the agreement through both job and GDP growth .
gold_template: In templateXValue[max] , templateYLabel[0] of goods and services from the United States made up just over 12 templateYLabel[3] of its gross domestic product ( templateYLabel[5] ) . This is an increase from templateYValue[27] templateYLabel[3] of the templateYLabel[5] of the United States in templateXValue[min] . Trade and foreign relations The United States ' templateYLabel[5] 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 . Trade is a great door-opener for countries , as it shows that they are willing to work together , and as the most powerful economy in the world , the templateTitle[0] is an ideal trading partner . The benefits of free trade Free trade is especially beneficial for countries who work closely together , as it can help to grow their overall trade volume . The United States , for example , is part of the North American Free Trade Agreement ( NAFTA ) , which is the largest free trade zone in the world . Since the implementation of NAFTA in templateXValue[23] , Canada , Mexico , and the United States have all benefitted from the agreement through both job and templateYLabel[5] growth .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[max] percent .
generated: This statistic shows the U.S. exports , as a size percentage from 1990 to 2017 . In 2017 , the U.S. exports , as a was 13.54 percent .


Example 316:
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 . In 2018 , the company is projected to be responsible for 33.6 percent of all retail prescriptions filled in the United States .
gold_template: This statistic depicts templateTitle[0] Caremark templateTitle[2] templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] in the United States from templateXValue[min] to templateXValue[max] . The templateTitle[0] Caremark Corporation is a templateTitle[10] drug retailing company , with three major segments : CVS/pharmacy , Caremark Pharmacy Services and MinuteClinic . templateTitle[0] Caremark is headquartered in Woonsocket , Rhode Island . In templateXValue[7] , the company is projected to be responsible for 33.6 percent of all templateYLabel[2] templateYLabel[3] templateYLabel[4] in the United States .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateYLabel[3] templateYLabel[4] templateYLabel[5] . For comparison , the social network was at templateYValue[max] percent growth .
generated: The statistic shows the CVS Health 's ( Share ) of retail in the from 2012 to 2018 , with projections up until 2025 . In 2018 , the Share of of prescriptions filled N/A . For comparison , the social network was at 46.55 percent growth .


Example 317:
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 . In 2015 , the median age of the Zimbabwean population was 18.4 years .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] in templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[4] into templateTitle[7] 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[4] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of the Zimbabwean templateTitle[4] was templateYValue[7] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] in templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[4] into templateTitle[7] 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[4] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of the Nigerian templateTitle[4] was templateYValue[7] templateYLabel[3] .
generated: 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 2015 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 Nigerian population was 18.4 years .


Example 318:
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 templateTitle[2] templateTitle[3] of some 1.38 billion templateYLabel[3] templateYLabel[4] . templateTitle[0] plc is a global pharmaceutical and biotech company , headquartered in London .

generated_template: The statistic shows the global templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] amounted to templateYValue[min] percent .
generated: The statistic shows the global Profit in of GlaxoSmithKline 's advertising from 2011 to 2018 . In 2015 , the GlaxoSmithKline 's advertising , the Profit in million British amounted to 671 percent .


Example 319:
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[2] templateYLabel[3] templateYLabel[4] .

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[2] templateYLabel[3] templateYLabel[4] .
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 .


Example 320:
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[2] on the crowdfunding platform templateTitle[2] as of October templateTitle[6] , 2018 . It shows the templateYLabel[0] of total successfully funded templateYLabel[2] by funds templateXLabel[1] . As of that time , the templateYLabel[0] of successfully funded templateYLabel[2] at templateTitle[2] which templateXLabel[1] templateXValue[last] templateXValue[0] templateTitle[6] million templateXLabel[3] templateXLabel[4] amounted to templateYValue[min] templateYLabel[2] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[3] , there were templateYValue[min] thousand templateYLabel[2] female templateTitle[2] templateYLabel[2] in the United States .
generated: The statistic shows the Number of projects in the United States from Less_than_1000 to Less_than_1000 . In 20000_to_99999 , there were 385 thousand projects female Kickstarter projects in the United States .


Example 321:
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[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitle[4] stood at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of land area .

generated_template: This graph shows the templateTitle[0] templateTitle[1] in the federal state of templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[3] stood at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of land area .
generated: This graph 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 322:
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[4] templateYLabel[5] in templateTitle[6] from the templateXLabel[0] templateXValue[min] to templateXValue[max] , in templateYLabel[2] templateYLabel[3] . In the templateXLabel[0] templateXValue[max] , templateTitle[0] templateYLabel[0] of templateTitle[1] templateYLabel[4] templateYLabel[5] in templateTitle[6] was about templateYValue[max] kilowatts templateYLabel[4] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[4] ( templateTitle[0] ) in templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[3] thousand templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateTitle[6] .
generated: The statistic shows the Consumption of electricity per ( Household ) in capita in Indonesia from 2000 to 2016 . In 2016 , there were 296.5 thousand hours per capita in Indonesia .


Example 323:
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[3] products in the templateTitle[8] templateTitle[9] ( UK ) from templateXValue[min] to templateXValue[max] . In templateXValue[12] , templateYValue[12] templateYLabel[2] British pounds was spent on templateTitle[0] templateTitle[1] and templateTitle[3] products . templateYLabel[0] rose during the period under consideration to approximately 1.72 billion British pounds in templateYLabel[0] in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] templateTitle[5] was at templateYValue[max] templateYLabel[3] 1,000 templateYLabel[5] .
generated: The statistic shows the Sales in million in the revenue in from 1999 to 2017 . In 2017 , the Sales in of the drink sales revenue was at 1720 GBP 1,000 N/A .


Example 324:
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] templateYLabel[1] templateTitle[2] of the United States in templateTitle[7] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[4] templateYLabel[1] made in templateTitle[7] were valued at approximately templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] . The total templateYLabel[0] templateTitle[2] of the United States abroad amounted to 5.95 templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[max] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in the templateYLabel[5] amounted to templateYValue[max] percent .
generated: The statistic shows the Direct investment position of the U.S. from 2000 to 2018 . In 2018 , the Direct investments in the dollars amounted to 3.61 percent .


Example 325:
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[4] templateTitle[5] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to 2.4 billion templateYLabel[4] dollars.The templateTitle[4] templateTitle[5] are owned by David Tepper , who bought the templateYLabel[0] for about 2.3 billion templateYLabel[4] templateYLabel[5] in templateXValue[1] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of 3.3 billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by Rogers Communications , who bought the templateYLabel[0] for 137 templateYLabel[3] templateYLabel[4] templateYLabel[5] in 2000 .
generated: This graph depicts the value of the Carolina Panthers ( Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 3.3 billion U.S. dollars . The Carolina Panthers ( are owned by Rogers Communications , who bought the Franchise for 137 million U.S. dollars in 2000 .


Example 326:
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[5] templateTitle[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[5] templateTitle[3] templateYLabel[0] is templateTitle[6] to increase to around templateYValue[max] templateYLabel[2] British thermal units .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] of all templateYLabel[3] templateTitle[4] templateTitle[5] amounted to templateYValue[min] percent .
generated: This statistic shows the U.S. Production in of energy from biomass from 2019 to 2050 . The Production in is the in of all Btu from biomass amounted to 4.74 percent .


Example 327:
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[3] in templateTitle[5] between templateXValue[min] and templateXValue[2] , including a forecast for templateXValue[max] , templateTitle[6] on sales templateTitle[9] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[max] , there were templateYValue[min] percent .
generated: The statistic shows the Market value in in the United States from 2008 to 2015 . In 2009 , the Market value in million U.S. . In 2015 , there were 328.7 percent .


Example 328:
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[8] , sorted by templateYLabel[0] of templateTitle[4] templateTitle[0] templateYLabel[2] . During that month , templateXValue[2] accounted templateXLabel[0] templateYValue[2] percent of templateTitle[4] templateTitle[0] templateYLabel[2] .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . During the survey period it was found that templateYValue[max] percent of global templateTitle[4] templateTitle[5] templateXLabel[0] .
generated: This statistic presents the Photo sharing sites : daily upload from Snapchat to Snapchat . During the survey period it was found that 49 percent of global daily upload Platform .


Example 329:
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[2] templateYLabel[3] in templateTitle[0] templateTitle[1] institutions ( kindergarten or nursery ) in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[0] templateYLabel[5] templateYLabel[2] were templateYLabel[3] in nursery or kindergarten programs in the United States .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in the United States was templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Number of enrollment numbers in the from 1970 to 2018 . In 2018 , the Number of in the United States was 9.16 enrolled (in millions) N/A N/A .


Example 330:
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[2] templateYLabel[3] templateYLabel[4] in the templateXValue[0] season .

generated_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[2] templateYLabel[3] templateYLabel[4] in the templateXValue[0] season .
generated: 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 .


Example 331:
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[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the UK-based recruitment specialist templateTitle[2] generated over 1.1 billion British pounds in templateYLabel[0] templateTitle[3] , up from templateTitle[6] billion the previous templateXLabel[0] .

generated_template: The statistic shows the total templateTitle[2] templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the British retailer generated a templateYLabel[0] of templateYValue[max] thousand templateYLabel[2] British pounds . In the same templateXLabel[0] .
generated: The statistic shows the total Hays Revenue of Revenue of Hays from 2007 to 2019 . In 2019 , the British retailer generated a Revenue of 1129.7 thousand million British pounds . In the same Year .


Example 332:
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 . Some motivations for school shootings included depression , seeking revenge , and bullying . As a result of the large amount of school shootings , gun control has become a central topic in U.S. politics . This widespread problem happens across the United States ; however California saw the highest number of K-12 school shootings in the United States since 1970 . However , the deadliest school shooting ( as of May 2019 ) was the Virginia Tech massacre in 2007 . This tragedy left 33 dead and 17 injured . Mass shooting issues Mass shootings happen when there are several injuries or deaths from a firearm-related violence . Throughout the last century , mass shootings have become an epidemic in the United States . However , despite the increase in mass shootings and number of casualties , the U.S. government has done little to prevent future shootings from happening . As a result of the lack of cooperation in politics , mass shootings have become the most common political issue that Generation Z is stressed about as of 2018 . Furthermore , the right to bear arms is a popular belief in the U.S. , although the percentage of households in the United States owning at least one firearm has remained somewhat steady since 1972 .
gold_template: As of February 26 , there was templateYValue[0] templateYLabel[3] templateYLabel[4] in the United States in templateXValue[max] . This is compared to templateYValue[0] templateYLabel[3] templateYLabel[4] in templateXValue[min] , templateYValue[0] in templateXValue[18] , and templateYValue[max] templateYLabel[3] templateYLabel[4] in templateXValue[36] . School templateYLabel[4] The United States sees the most school templateYLabel[4] in the world . Some motivations for school templateYLabel[4] included depression , seeking revenge , and bullying . As a result of the large amount of school templateYLabel[4] , gun control has become a central topic in templateTitle[4] politics . This widespread problem happens across the United States ; however California saw the highest templateYLabel[1] of K-12 school templateYLabel[4] in the United States since 1970 . However , the deadliest school templateYLabel[4] ( as of May templateXValue[37] ) was the Virginia Tech massacre in templateXValue[25] . This tragedy left 33 dead and 17 injured . templateYLabel[3] templateYLabel[4] issues templateYLabel[3] templateYLabel[4] happen when there are several injuries or deaths from a firearm-related violence . Throughout the last century , templateYLabel[3] templateYLabel[4] have become an epidemic in the United States . However , despite the increase in templateYLabel[3] templateYLabel[4] and templateYLabel[1] of casualties , the templateTitle[4] government has done little to prevent future templateYLabel[4] from happening . As a result of the lack of cooperation in politics , templateYLabel[3] templateYLabel[4] have become the most common political issue that Generation Z is stressed about as of templateXValue[36] . Furthermore , the right to bear arms is a popular belief in the templateTitle[4] , although the percentage of households in the United States owning at least templateYValue[0] firearm has remained somewhat steady since 1972 .

generated_template: The templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] . By templateXValue[min] , there were approximately templateYValue[max] templateYLabel[2] 1,000 templateYLabel[4] in the previous templateXLabel[0] . By templateXValue[min] , when it was higher than North of the templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[max] , this figure amounted to templateYValue[1] .
generated: The Total of Mass shootings of mass shootings in 2020 . By 1982 , there were approximately 12 of 1,000 shootings in the previous Year . By 1982 , when it was higher than North of the of mass shootings . In 2020 , this figure amounted to 0 .


Example 333:
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[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] templateYLabel[2] people attended movies at the Canadian movie theater chain , down from templateYValue[1] templateYLabel[2] visitors a templateXLabel[0] earlier .

generated_template: This graph depicts the global templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , a forecast for templateYValue[2] percent of the global templateTitle[4] templateTitle[8] was found that templateTitle[2] . The company 's compound annual growth rate of global financial crisis in the Latin American countries with the most successful templateTitle[1] templateTitle[2] systems . The company is the leader in North America in North America , with the market capitalization , as little during the time , Bruce Springsteen and Coldplay led some of the most successful global tours , generating 268.3 templateYLabel[2] templateTitle[8] dollars and 241 templateYLabel[2] templateTitle[8] dollars , respectively.Ticket sales revenue in North America reached their highest point in 2016 , totaling 7.3 billion templateTitle[8] dollars . The templateTitle[2] industry is benefiting from increasing number of shows , ticket sales , as well as ticket prices . Foro Sol , a large outdoor venue built in 1993 in Mexico City , had ticket sales amounting to almost templateTitle[11] templateYLabel[2] units in 2016 . Live Nation is templateTitle[11] of the leading global templateTitle[1] promoters , selling over 44 templateYLabel[2] tickets in 2016 . A merger between Live Nation and Ticketmaster formed the current company , Live Nation Entertainment in templateXValue[3] . It is based in California , United States , and focuses on promoting live templateTitle[1] events . Utilization of social media has also enabled a broader market reach . The integration of mobile devices in parallel with the templateTitle[2] experience has allowed for ticket purchases , skipping entry lines , and new apps .
generated: This graph depicts the global Attendance in of Attendance at Cineplex cinemas 2010 from 2010 to 2018 . In 2016 , a forecast for 74.6 percent of the global 2010 N/A was found that Cineplex . The company 's compound annual growth rate of global financial crisis in the Latin American countries with the most successful at Cineplex systems . The company is the leader in North America in North America , with the market capitalization , as little during the time , Bruce Springsteen and Coldplay led some of the most successful global tours , generating 268.3 millions N/A dollars and 241 millions N/A dollars , respectively.Ticket sales revenue in North America reached their highest point in 2016 , totaling 7.3 billion N/A dollars . The Cineplex industry is benefiting from increasing number of shows , ticket sales , as well as ticket prices . Foro Sol , a large outdoor venue built in 1993 in Mexico City , had ticket sales amounting to almost N/A millions units in 2016 . Live Nation is N/A of the leading global at promoters , selling over 44 millions tickets in 2016 . A merger between Live Nation and Ticketmaster formed the current company , Live Nation Entertainment in 2015 . It is based in California , United States , and focuses on promoting live at events . Utilization of social media has also enabled a broader market reach . The integration of mobile devices in parallel with the Cineplex experience has allowed for ticket purchases , skipping entry lines , and new apps .


Example 334:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] 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 335:
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[2] templateTitle[3] generated from its templateYLabel[0] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[3] season templateTitle[2] templateTitle[3] received templateYValue[max] templateYLabel[4] templateYLabel[5] from its templateYLabel[0] templateYLabel[1] Genting .

generated_template: This statistic shows the number of templateTitle[0] in the United States from templateXValue[min] to templateXValue[max] . According to the report , templateTitle[0] 's global templateTitle[3] templateTitle[4] templateYLabel[1] increased from templateYValue[min] to templateYValue[max] templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: This statistic shows the number of Value in the United States from 2015/16_(Intuit_Quickbooks) to 2015/16_(Intuit_Quickbooks) . According to the report , Value 's global Villa 's sponsorship increased from 0 to 8 in million in 2015/16_(Intuit_Quickbooks) .


Example 336:
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[6] and templateTitle[8] templateTitle[9] 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 number of templateTitle[0] templateTitle[2] by templateTitle[1] in templateTitle[3] and templateTitle[5] from templateXValue[last] to templateXValue[0] . During this period , the number of templateTitle[0] templateTitle[2] by templateTitle[1] fluctuated , peaking in templateXValue[2] at templateYValue[max] templateTitle[2] . By templateXValue[0] it fell down to templateYValue[0] templateTitle[2] .
generated: This statistic shows the number of Road involving by deaths in police and in from 2004/05 to 2018/19 . During this period , the number of Road involving by deaths fluctuated , peaking in 2016/17 at 32 involving . By 2018/19 it fell down to 30 involving .


Example 337:
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 templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateTitle[2] templateTitle[3] templateTitle[4] was valued at approximately templateYValue[2] percent .
generated: The statistic shows the Burger King 's EBITDA margin in the United States from 2011 to 2014 . In 2012 , 's EBITDA margin was valued at approximately 27 percent .


Example 338:
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[4] templateTitle[5] templateTitle[6] , franchise of the National Basketball Association , from 2010/11 to templateTitle[10] . In the templateTitle[10] season , the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] were at templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In 2018/19 , the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] were at templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic depicts the Gate receipts of the Golden State Warriors from 18/19 to 18/19 . In 2018/19 , the Gate receipts of the Golden State Warriors were at 178 million U.S. dollars .


Example 339:
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 . Only slightly above a hundred stores generated revenue of two million or more British pounds . UK meat production industryThe number of enterprises operating in the production and preservation of meat products has remained mostly stable between 2008 and 2017 . Turnover for the industry has peaked in 2017 with the largest share of turnover made up by companies which process and preserve meat , as well as produce meat products . Meat retail in the UK The retail sale of meat generated a turnover of 21.5 billion British pounds in 2017 . Since 2008 , retail sale turnover has seen an overall increase . In 2014 , turnover peaked at 21.8 billion pounds . Most commonly , consumers purchased their meat from the top multiples . A share of 63 percent of meat product purchases were made in the top five multiples in 2018 . However , their market share has been in decline , mostly due to pressure from the discounters .
gold_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] of stores that specialize in the sales of templateTitle[5] has been shrinking In the templateTitle[9] templateTitle[10] . During this period , the templateYLabel[0] of templateTitle[5] specialty stores has seen a decrease of almost 943 . Of these stores , approximately 1.75 thousand generated a turnover between templateTitle[16] hundred thousand and 250 thousand British pounds in templateXValue[max] . Only slightly above a hundred stores generated revenue of templateTitle[14] million or more British pounds . templateTitle[12] templateTitle[5] production industryThe templateYLabel[0] of templateYLabel[2] operating in the production and preservation of templateTitle[5] products has remained mostly stable between templateXValue[min] and templateXValue[1] . Turnover for the industry has peaked in templateXValue[1] with the largest share of turnover made up by companies which process and preserve templateTitle[5] , as well as produce templateTitle[5] products . templateTitle[5] templateTitle[6] in the templateTitle[12] The templateTitle[6] sale of templateTitle[5] generated a turnover of 21.5 billion British pounds in templateXValue[1] . Since templateXValue[min] , templateTitle[6] sale turnover has seen an overall increase . In templateXValue[4] , turnover peaked at 21.8 billion pounds . Most commonly , consumers purchased their templateTitle[5] from the top multiples . A share of 63 percent of templateTitle[5] product purchases were made in the top five multiples in templateXValue[max] . However , their market share has been in decline , mostly due to pressure from the discounters .

generated_template: The statistic shows the total templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] templateTitle[4] templateTitle[5] was at templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the total Number of of the butcher shops and meat from 2008 to 2018 . In 2018 , the Number of in shops and meat was at 6633 N/A N/A .


Example 340:
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 . Spain 's minimum monthly wage was 735.9 in 2018 , with a daily minimum wage of 24.53 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] from templateXValue[min] to templateXValue[max] . Over this 18-year period , templateYLabel[1] templateYLabel[2] in templateTitle[4] have fluctuated greatly , peaking at approximately 30 thousand templateYLabel[4] in templateXValue[9] and decreasing to approximately 28 thousand templateYLabel[4] yearly in templateXValue[6] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] stood at approximately 28 thousand templateYLabel[4] in templateXValue[max] . templateTitle[4] 's minimum monthly templateYLabel[2] was 735.9 in templateXValue[max] , with a daily minimum templateYLabel[2] of 24.53 .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[2] templateTitle[2] in templateTitle[4] from templateXValue[min] to templateXValue[max] . Over this period , the templateYLabel[0] templateYLabel[2] thousand templateYLabel[4] templateYLabel[2] in templateTitle[4] . In templateXValue[8] , there were templateYValue[min] thousand templateYLabel[4] templateYLabel[2] in templateTitle[4] .
generated: The statistic shows the Average wages wages in Spain from 2000 to 2018 . Over this period , the Average wages thousand euros wages in Spain . In 2010 , there were 26697 thousand euros wages in Spain .


Example 341:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] 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 342:
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[4] templateYLabel[2] at templateTitle[5] establishments in templateTitle[6] from templateXValue[min] to templateXValue[max] . Since templateXValue[9] there has been an increasing trend in templateYLabel[2] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[2] ( including both foreign and domestic ) at templateTitle[5] in templateTitle[6] amounted to approximately templateYValue[max] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[4] templateYLabel[2] in templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitle[4] templateYLabel[2] in short-stay templateTitle[5] in templateTitle[6] have generally increased over this period , from around 12 templateYLabel[4] in templateXValue[min] to approximately templateYValue[max] templateYLabel[4] by templateXValue[max] .
generated: This statistic shows the Number of tourist arrivals in Latvia from 2006 to 2018 . tourist arrivals in short-stay accommodation in Latvia have generally increased over this period , from around 12 millions in 2006 to approximately 2.8 millions by 2018 .


Example 343:
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 21 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[4] . The National Aeronautics and Space Administration ( templateTitle[0] ) is the templateYLabel[3] agency responsible for aeronautics and aerospace research .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] had increased over templateYValue[6] percent .
generated: The statistic shows the NASA - budget 2014 - size 2024 from 2014 to 2018 , with projections up until 2024 . In 2018 , the NASA - budget 2014 - had increased over 20.74 percent .


Example 344:
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 . The cumulative tournament prize pool for Overwatch battles worldwide stood at less than two million U.S. dollars in 2016 but rose to 6.59 million U.S. dollars by 2018 . Indeed , Overwatch was the sixth most popular eSports game worldwide in 2018 based on the cumulative tournament prize pool . At the top of this list was DOTA 2 , with a cumulative prize pool of over 41 million U.S. dollars . Overwatch boosts Blizzard 's revenue Blizzard Entertainment is an American video game developer responsible for bringing Overwatch to our screens . The company 's revenue had been fluctuating since 2007 and Overwatch was touted as one of the new hopes for the developer . The game did not disappoint as Blizzard Entertainment announced record revenue of almost 2.5 billion U.S. dollars in 2016 , the year that Overwatch was released . The revenue in the ensuing years has remained high , illustrating the boost that the hit game gave its developer . A further success for Activision can be seen in its growing influence in the Asia Pacific market . While the Americas still remains the largest market for the publisher , the revenues within Asia Pacific have almost trebled from 343 million U.S. dollars in 2013 to over one billion U.S. dollars in 2018 .
gold_template: How many people templateYLabel[2] templateTitle[2] ? templateTitle[2] , 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] templateYLabel[4] templateYLabel[2] . As of templateXValue[0] templateXValue[0] , templateTitle[2] had templateYValue[max] templateYLabel[4] templateYLabel[2] templateTitle[4] . templateTitle[2] 's eSports success While the templateYLabel[0] of gamers playing templateTitle[2] has increased dramatically , so has the appeal of the game as an eSport . The cumulative tournament prize pool for templateTitle[2] battles templateTitle[4] stood at less than templateTitle[5] templateYLabel[4] U.S. dollars in templateXValue[4] but rose to 6.59 templateYLabel[4] U.S. dollars by templateXValue[0] . Indeed , templateTitle[2] was the sixth most popular eSports game templateTitle[4] in templateXValue[0] based on the cumulative tournament prize pool . At the top of this list was DOTA templateTitle[5] , with a cumulative prize pool of templateTitle[2] 41 templateYLabel[4] U.S. dollars . templateTitle[2] boosts Blizzard 's revenue Blizzard Entertainment is an American video game developer responsible for bringing templateTitle[2] to our screens . The company 's revenue had been fluctuating since 2007 and templateTitle[2] was touted as templateTitle[5] of the new hopes for the developer . The game did not disappoint as Blizzard Entertainment announced record revenue of almost 2.5 billion U.S. dollars in templateXValue[4] , the year that templateTitle[2] was released . The revenue in the ensuing years has remained high , illustrating the boost that the hit game gave its developer . A further success for Activision can be seen in its growing influence in the Asia Pacific market . While the Americas still remains the largest market for the publisher , the revenues within Asia Pacific have almost trebled from 343 templateYLabel[4] U.S. dollars in 2013 to templateTitle[2] templateTitle[5] billion U.S. dollars in templateXValue[0] .

generated_template: How many people templateYLabel[2] templateTitle[0] ? Having burst onto the scene in templateTitle[5] has templateTitle[6] , templateTitle[5] become a templateTitle[6] phenomenon , amassing almost templateYValue[max] templateYLabel[4] templateYLabel[2] across the globe as of templateXValue[0] templateXValue[0] . How did templateTitle[5] become so big ? The reasons why templateTitle[5] has become such a global hit are clear to see . Not only is the game free , but it is also available on most gaming platforms . On top of this , the Battle Royale-style , in which up to 100 templateYLabel[2] fight it out at templateYValue[min] time , mean that the re-playability of the game is infinite – each game is unique . The cartoon style of the game unsurprisingly lends itself more to the younger audience , with almost two-thirds of templateTitle[5] 's templateYLabel[2] aged between 18 and 24 . It also seems that it is not just the casual gamers that are attracted to this game as over templateTitle[7] percent of templateTitle[5] fans admitted to devoting over 16 hours to the game per week . Battle templateTitle[5] Battle Royale supremacy With the rise of games such as templateTitle[5] , the Battle Royale game mode has never been bigger . Revenue in the Battle Royale premium console segment amounted to 2.7 billion U.S. dollars in templateXValue[1] and was estimated to more than double to 6.9 billion U.S. dollars in templateXValue[0] . While templateTitle[5] is certainly staking its claim as the king of the Battle Royale games , it faces stiff competition from PlayerUnknown 's Battlegrounds which , unlike templateTitle[5] , is available on Steam . PUBG peaked at almost 3.24 templateYLabel[4] concurrent templateYLabel[2] on Steam at the beginning on templateXValue[1] and is still regularly peaking at over templateYValue[min] templateYLabel[4] concurrent templateYLabel[2] . It seems that game developers are looking to cash in on this ever-growing market , so could new games such as Apex Legends also pose a threat to templateTitle[5] 's supremacy ?
generated: How many people players Number ? Having burst onto the scene in 2018 has N/A , 2018 become a N/A phenomenon , amassing almost 40 millions players across the globe as of May_2018 May_2018 . How did 2018 become so big ? The reasons why 2018 has become such a global hit are clear to see . Not only is the game free , but it is also available on most gaming platforms . On top of this , the Battle Royale-style , in which up to 100 players fight it out at 7 time , mean that the re-playability of the game is infinite – each game is unique . The cartoon style of the game unsurprisingly lends itself more to the younger audience , with almost two-thirds of 2018 's players aged between 18 and 24 . It also seems that it is not just the casual gamers that are attracted to this game as over N/A percent of 2018 fans admitted to devoting over 16 hours to the game per week . Battle 2018 Battle Royale supremacy With the rise of games such as 2018 , the Battle Royale game mode has never been bigger . Revenue in the Battle Royale premium console segment amounted to 2.7 billion U.S. dollars in October_2017 and was estimated to more than double to 6.9 billion U.S. dollars in May_2018 . While 2018 is certainly staking its claim as the king of the Battle Royale games , it faces stiff competition from PlayerUnknown 's Battlegrounds which , unlike 2018 , is available on Steam . PUBG peaked at almost 3.24 millions concurrent players on Steam at the beginning on October_2017 and is still regularly peaking at over 7 millions concurrent players . It seems that game developers are looking to cash in on this ever-growing market , so could new games such as Apex Legends also pose a threat to 2018 's supremacy ?


Example 345:
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 . The company is headquartered in Rio de Janeiro .
gold_template: This statistic shows mining company templateTitle[0] templateTitle[1] templateYLabel[0] of templateYLabel[2] 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 . The company is headquartered in Rio de Janeiro .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] at templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[1] . In templateXValue[2] , there were approximately templateYValue[min] thousand templateYLabel[2] in the templateTitle[5] templateTitle[6] .
generated: The statistic shows the Number of employees at Vale 's employee from 2009 to 2017 . In 2016 , there were approximately 60036 thousand employees in the - 2018 .


Example 346:
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[2] templateYLabel[0] in templateTitle[4] , distributed templateTitle[5] templateTitle[6] , in templateTitle[7] . During that year , templateXValue[4] produced some templateYValue[4] templateYLabel[2] templateYLabel[3] of this mineral . templateTitle[2] 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 number of templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in the United States from templateXValue[last] to templateXValue[0] . As of templateXValue[0] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] were in the United States .
generated: This statistic shows the number of Production of Production in in the United States from Nunavut to Newfoundland_and_Labrador . As of Newfoundland_and_Labrador , the Production of Production in were in the United States .


Example 347:
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[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] 's templateYLabel[0] was 62.86 billion templateYLabel[3] templateXValue[6] templateYLabel[5] templateYLabel[6] .

generated_template: The statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] 's templateYLabel[0] was 730.27 billion templateYLabel[3] templateXValue[6] templateYLabel[5] templateYLabel[6] .
generated: The statistic shows the gross domestic product ( GDP ) of Manitoba from 2000 to 2018 . In 2018 , Manitoba 's GDP was 730.27 billion chained 2012 Canadian dollars .


Example 348:
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[4] templateTitle[5] templateTitle[6] in the United States as of April templateTitle[10] . During the survey period , templateYValue[1] percent of responding templateTitle[4] templateTitle[5] shoppers stated that their usual templateTitle[4] templateTitle[5] templateTitle[1] amounted to templateXValue[1] to 25 templateTitle[9] dollars .

generated_template: This statistic presents the results of a survey , conducted in 2016 in the United States as of May templateTitle[9] . During that period , templateYValue[max] percent of templateYLabel[2] stated they had templateXValue[0] templateXValue[0] .
generated: This statistic presents the results of a survey , conducted in 2016 in the United States as of May U.S. . During that period , 34 percent of respondents stated they had 0$_no_expenses 0$_no_expenses .


Example 349:
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] United States 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 United States amounted to templateYValue[3] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[5] from templateXValue[last] to templateXValue[0] . For templateXValue[last] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[min] percent templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[0] , this number of templateYValue[max] percent .
generated: The statistic shows the total global Domestic of Total U.S. domestic 2010/11 from 2010/2011 to 2018/2019 . For 2010/2011 , Total U.S. domestic Domestic amounted to 205000 percent in metric tons . In 2018/2019 , this number of 238039 percent .


Example 350:
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[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[5] stood at templateYValue[max] templateYLabel[5] templateXValue[6] templateYLabel[7] templateYLabel[8] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[5] templateTitle[6] stood at templateYValue[max] templateYLabel[5] templateXValue[6] templateYLabel[7] templateYLabel[8] .
generated: The 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 351:
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[2] templateYLabel[3] to the United States over the period between 2014 and templateTitle[9] , templateTitle[5] templateXLabel[0] of templateXLabel[2] . In that period , some templateYValue[max] percent of all templateYLabel[2] templateYLabel[3] into the United States came from templateXValue[last] .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] had a templateYValue[3] percent of templateYValue[max] templateYLabel[0] templateYLabel[1] . Since Since then , the templateTitle[1] templateTitle[2] can be measured , and the effect of the worst templateYLabel[3] in the United States .
generated: This statistic presents the Share of U.S. nickel imports size by from Other to Other . In Norway , the Share of U.S. nickel imports had a 11 percent of 41 Share of . Since Since then , the of U.S. can be measured , and the effect of the worst imports in the United States .


Example 352:
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[4] templateTitle[5] 's templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[4] templateTitle[5] 's net templateYLabel[0] increased by templateYValue[min] percent . templateTitle[4] templateTitle[5] is an American sporting goods manufacturer , based in Baltimore , Maryland .

generated_template: The statistic shows the templateTitle[0] templateYLabel[0] templateTitle[2] in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateYLabel[5] amounted to templateYValue[max] percent .
generated: The statistic shows the Global Revenue growth in Under from 2009 to 2019 . In 2019 , the Revenue growth N/A in the N/A amounted to 38 percent .


Example 353:
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[2] templateYLabel[0] of templateTitle[3] templateYLabel[3] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] an estimated templateYValue[min] templateYLabel[3] occurred nationwide .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] templateTitle[3] a templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] thousand templateYLabel[2] living in the United States .
generated: The statistic shows the Number of reported robbery a 1990 - from 1990 to 2018 . In 2018 , there were 687730 thousand reported living in the United States .


Example 354:
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[3] templateTitle[4] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[3] templateTitle[4] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[3] templateTitle[4] templateTitle[5] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[3] templateTitle[4] templateTitle[5] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
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 355:
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[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in 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[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] 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 356:
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[4] templateTitle[1] templateTitle[2] and templateTitle[2] templateTitle[3] and templateTitle[5] industry from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the industry generated an templateYLabel[0] total templateYLabel[1] of templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic above presents estimation data on the annual aggregate templateYLabel[1] of the American templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[4] broadcasters generated an templateYLabel[0] total templateYLabel[1] of templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic above presents estimation data on the annual aggregate revenue of the American and distribution from 2005 to 2018 . In 2018 , U.S. broadcasters generated an Estimated total revenue of 69.91 billion U.S. dollars .


Example 357:
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[3] templateYLabel[4] templateYLabel[5] in templateXValue[max] .

generated_template: The statistic shows the total templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] was at templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[7] .
generated: The statistic shows the total Gross profit of the : gross from 2006 to 2018 . In 2018 , the Gross profit of Goods : gross was at 2489 million U.S. dollars in 2011 .


Example 358:
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[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the median Household income in Michigan from 1990 to 2018 . In 2018 , the median Household income in Michigan amounted to 60449 U.S. dollars .


Example 359:
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[4] templateYLabel[5] in templateXValue[max] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[8] , the templateYLabel[0] of templateTitle[2] templateTitle[3] was at approximately templateYValue[max] thousand templateYLabel[2] templateYLabel[3] templateYLabel[4] , up until templateXValue[max] .
generated: The statistic shows the total global Fan of Average Fan Cost from 2006 to 2019 . In 2010 , the Fan of Cost Index was at approximately 540.52 thousand Index in U.S. , up until 2019 .


Example 360:
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[2] in templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were more than 34 templateYLabel[4] templateYLabel[2] in templateTitle[0] , up from nearly 33 templateYLabel[4] templateYLabel[2] a templateXLabel[0] earlier .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] was templateYValue[max] percent .
generated: The statistic shows the Number of of number of households 2005 from 2005 to 2017 . In 2017 , the Mexico number of households 2005 - was 34.07 percent .


Example 361:
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 . A look at the distribution of global oil reserves by country shows that only Venezuela possesses a higher share in global oil reserves than the Arab state . All in all , Saudi Arabia 's economy is doing quite well . The oil reserves in Saudi Arabia have increased over the last two decades , and the same can be said for the country 's gross domestic product . The unemployment rate has been stable , while the trade balance has shown a steady upwards trend with a significant jump in 2011 . Accordingly , Saudi Arabia 's national debt in relation to gross domestic product has been decreasing dramatically over the last ten years . Saudi Arabia is also among the countries with the highest oil consumption worldwide ; a ranking of the share of the major consuming countries in global oil consumption , which is led by the United States ( which consume almost one fifth of global oil ) , places Saudi Arabia sixth , behind the US , Russia , and China . Being one of the leading oil producing countries , Saudi Arabia is also a member of OPEC ( Organization of the Petroleum Exporting Countries ) , an association whose primary goal is regulating crude oil prices worldwide and coordinating the oil production and trade of the member countries . According to OPEC , the average price for crude oil has been rising since the 1960s .
gold_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 average templateYLabel[0] templateYLabel[1] amounted to templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] . Oil production in templateTitle[2] templateTitle[3] templateTitle[2] templateTitle[3] 's economy relies heavily on production and export of oil and petroleum . A look at the distribution of global oil reserves by country shows that only Venezuela possesses a higher share in global oil reserves than the templateTitle[3] state . All in all , templateTitle[2] templateTitle[3] 's economy is doing quite well . The oil reserves in templateTitle[2] templateTitle[3] have increased over the last templateYValue[8] decades , and the same can be said for the country 's gross domestic product . The unemployment templateYLabel[1] has been stable , while the trade balance has shown a steady upwards trend with a significant jump in templateXValue[13] . Accordingly , templateTitle[2] templateTitle[3] 's national debt in relation to gross domestic product has been decreasing dramatically over the last ten years . templateTitle[2] templateTitle[3] is also among the countries with the highest oil consumption worldwide ; a ranking of the share of the major consuming countries in global oil consumption , which is led by the United States ( which consume almost templateTitle[5] fifth of global oil ) , places templateTitle[2] templateTitle[3] sixth , behind the templateYLabel[4] , Russia , and China . Being templateTitle[5] of the leading oil producing countries , templateTitle[2] templateTitle[3] is also a member of OPEC ( Organization of the Petroleum Exporting Countries ) , an association whose primary goal is regulating crude oil prices worldwide and coordinating the oil production and trade of the member countries . According to OPEC , the average price for crude oil has been rising templateTitle[4] the 1960s .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[3] templateTitle[4] templateTitle[5] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Arabia since 1984 from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Arabia since 1984 amounted to about 2.48 percent compared to the previous Year .


Example 362:
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[7] . During the survey , templateYValue[0] percent of templateYLabel[2] 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[7] templateTitle[8] in templateTitle[10] . During this survey , templateYValue[max] percent of templateYLabel[2] stated they think templateTitle[5] or templateTitle[7] templateTitle[8] templateYLabel[0] 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 N/A in N/A . During this survey , 55 percent of respondents stated they think pornography or 2018 N/A Share Morally_acceptable Morally_acceptable , while 1 percent said it Depends on the situation .


Example 363:
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 . In 2018 , Norway 's gross domestic product amounted to around 434.17 billion U.S. dollars .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[4] is an important indicator of a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods and goods and goods produced within a country in a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: 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 and goods and goods produced within a country in a country 's economic power . In 2018 , Norway 's Gross domestic product amounted to around 434.17 billion U.S. dollars .


Example 364:
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] templateTitle[0] templateYLabel[0] from templateTitle[2] cartridges in templateTitle[6] templateTitle[7] between templateXValue[min] and templateXValue[max] . In templateXValue[3] , templateYLabel[0] of about 23.7 billion templateYLabel[3] templateYLabel[4] are expected .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Revenue of printer cartridge revenue in North from 2007 to 2015 . In 2015 , the Forecast : printer Revenue amounted to 23886 million U.S. dollars .


Example 365:
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[3] templateTitle[4] ( UK ) templateTitle[5] 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 templateTitle[10] of increase . The peak was in templateXValue[0] at templateYValue[max] templateYLabel[4] British pounds ( templateYLabel[5] ) .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the United States from templateXValue[last] to templateXValue[0] . In templateTitle[5] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[max] percent .
generated: This statistic shows the Inheritance tax : in the United States from 2000/01 to 2018/19 . In HMRC , the Inheritance tax : United Kingdom was 5.36 percent .


Example 366:
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 ( templateXLabel[0] templateYLabel[0] templateYLabel[0] ) 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 templateXLabel[0] templateYLabel[0] templateYLabel[0] templateYLabel[1] amounted to about templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was valued at templateYValue[2] percent .
generated: This statistic shows the LVMH Group 's R & size D from 2008 to 2019 . In 2016 , the LVMH Group 's R & was valued at 130 percent .


Example 367:
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[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was templateYValue[6] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Panama from 2014 to 2024 . In 2024 , the Unemployment rate in Panama was at approximately 5.77 percent .


Example 368:
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[2] in templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[8] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] increased from about 118 thousand templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[2] in templateXValue[min] to about 149 thousand templateYLabel[2] in templateXValue[max] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[3] , the global templateTitle[2] templateYLabel[0] templateYLabel[1] increased from templateYValue[3] percent .
generated: The statistic shows the total global Number of Number of students from 2008 to 2018 . In 2015 , the Number of students Number amounted to 148144 students N/A N/A . In 2015 , the global students Number of increased from 148144 percent .


Example 369:
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[2] at the templateTitle[5] templateTitle[6] templateTitle[7] between templateXValue[min] and templateXValue[max] . In templateXValue[27] , templateYValue[12] sporting events took place at the templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateTitle[5] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was valued at templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . templateTitle[0] templateYLabel[0] templateYLabel[1] increased increased from templateYValue[min] in templateXValue[9] and templateXValue[8] .
generated: This statistic shows the Number Number sports at the size Summer from 1896 to 2016 . In Summer , the Number of sports at the was valued at 35 Number of sports played N/A N/A . Number Number of increased increased from 9 in 1980 and 1984 .


Example 370:
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[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] 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 371:
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 . Out of all Canadian metropolitan areas Toronto , Ontario had the highest number of births in 2018 . Saint John , New Brunswick was the metropolitan area with the lowest number of births in the same year . Life expectancy in Canada Canada is known for being a country with a high standard of living , and with a high standard of living comes a high life expectancy . The life expectancy at birth in Canada stands at just over 82 years and has been increasing steadily over the past decade . The highest life expectancy in the country was found in British Columbia , while the lowest life expectancy was found in Canada 's northernmost territory , Nunavut .
gold_template: In templateXValue[1] , there were an estimated templateYValue[0] babies born in templateTitle[4] . This is an increase from templateYValue[min] templateYLabel[2] in the templateXLabel[0] templateXValue[min] . templateYLabel[2] in templateTitle[4] In templateXValue[1] , there were more male babies born than female babies , and overall templateYLabel[2] have been increasing since templateTitle[5] . Out of all Canadian metropolitan areas Toronto , Ontario had the highest templateYLabel[0] of templateYLabel[2] in templateXValue[1] . Saint John , New Brunswick was the metropolitan area with the lowest templateYLabel[0] of templateYLabel[2] in the same templateXLabel[0] . Life expectancy in templateTitle[4] templateTitle[4] is known for being a country with a high standard of living , and with a high standard of living comes a high life expectancy . The life expectancy at templateYLabel[2] in templateTitle[4] stands at just over 82 years and has been increasing steadily over the past decade . The highest life expectancy in the country was found in British Columbia , while the lowest life expectancy was found in templateTitle[4] 's northernmost territory , Nunavut .

generated_template: The statistic shows the total templateYLabel[0] of templateTitle[2] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateYLabel[0] of templateYLabel[2] templateTitle[6] amounted to approximately templateYValue[max] .
generated: The statistic shows the total Number of births births in the United States from 2001 to 2019 . In 2019 , births Number of births - amounted to approximately 383579 .


Example 372:
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[9] templateTitle[10] in templateTitle[12] on the templateTitle[0] templateTitle[1] templateTitle[2] themselves and templateTitle[5] templateXValue[6] . In templateTitle[12] , templateYValue[max] templateYLabel[0] of the templateYLabel[2] stated they templateXValue[6] templateXValue[5] templateXValue[5] templateXValue[4] templateXValue[0] away from templateTitle[5] templateXValue[6] , whereas templateYValue[0] templateYLabel[0] said they templateXValue[6] templateXValue[0] or templateXValue[0] templateXValue[0] away from templateTitle[5] templateXValue[6] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] in the United Kingdom ( templateTitle[0] ) that templateTitle[3] a templateTitle[5] templateTitle[6] . In templateTitle[8] , approximately templateYValue[max] templateYLabel[0] of templateTitle[1] in the United Kingdom ( UK ) .
generated: The statistic shows the Percentage of distance in the United Kingdom ( Geographic ) that grandparents a their grandchildren . In the , approximately 43 Percentage of distance in the United Kingdom ( UK ) .


Example 373:
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[4] templateTitle[5] templateTitle[6] templateTitle[7] the United States . In January templateTitle[10] , 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[4] templateTitle[5] problems templateTitle[7] the templateTitle[9] .

generated_template: This statistic shows the results of a survey on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[5] templateTitle[6] as of May templateTitle[7] . The survey found that templateXValue[0] templateXValue[0] was templateTitle[6] 's templateTitle[0] loved templateTitle[2] templateTitle[3] with templateYValue[min] out of templateYValue[max] British adults .
generated: This statistic shows the results of a survey on the Public opinion on the in important problem as of May facing . The survey found that Dissatisfaction_with_government/Poor_leadership Dissatisfaction_with_government/Poor_leadership was problem 's Public loved on the with 2 out of 28 British adults .


Example 374:
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[2] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , there were templateYValue[1] United States templateYLabel[2] in templateTitle[3] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[max] percent of the previous templateXLabel[0] . templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] additional information The company is comprised of the next few years .
generated: This statistic shows the U.S. airstrikes in Yemen 2002 size - from 2002 to 2019 . In 2016 , the U.S. airstrikes in Yemen 2002 was 125 percent of the previous Year . U.S. airstrikes in Yemen 2002 additional information The company is comprised of the next few years .


Example 375:
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[3] from templateXValue[min] to templateXValue[6] in templateTitle[5] to the templateTitle[7] templateTitle[8] templateTitle[9] ( templateYLabel[3] ) , with projections up until templateXValue[max] . In templateXValue[6] , the state deficit of templateTitle[3] was at around 2.2 percent of the templateTitle[7] templateTitle[8] templateTitle[9] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[6] in templateTitle[5] to the templateTitle[7] templateTitle[8] templateTitle[9] ( templateYLabel[3] ) , with projections up until templateXValue[max] . In templateXValue[6] , the state deficit of templateTitle[3] amounted to about 6.4 percent of templateTitle[7] templateTitle[8] templateTitle[9] .
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 amounted to about 6.4 percent of gross domestic product .


Example 376:
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[4] merchant wholesalers ' templateYLabel[0] templateYLabel[1] on templateTitle[3] and home furnishings from templateTitle[7] to templateTitle[9] . In templateTitle[9] , the templateYLabel[0] templateYLabel[1] on templateTitle[3] and home furnishings in templateYLabel[4] templateTitle[6] was about templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: This timeline depicts the templateYLabel[4] merchant wholesalers ' templateYLabel[0] templateYLabel[1] on templateTitle[3] , piece goods , and notions from templateTitle[7] to templateTitle[9] . In templateTitle[9] , the templateYLabel[0] templateYLabel[1] on templateTitle[3] , piece goods , and notions in templateYLabel[4] templateTitle[6] was templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This timeline depicts the U.S. merchant wholesalers ' Gross margin on furniture , piece goods , and notions from 2000 to 2017 . In 2017 , the Gross margin on furniture , piece goods , and notions in U.S. wholesale was 27.92 billion U.S. dollars .


Example 377:
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[4] in the United States from templateXValue[min] to templateXValue[max] . The templateTitle[7] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] amounted to templateYValue[max] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of 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[5] templateTitle[6] amounted to approximately templateYValue[0] templateYLabel[4] in templateXValue[max] .
generated: This statistic shows the Per capita consumption of in the in the United States from 2000 to 2018 . According to the report , the Per Per capita consumption of in the amounted to approximately 5.8 pounds in 2018 .


Example 378:
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[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] percent of the labor force aged 15 to 24 years in templateTitle[4] were unemployed .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] 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[4] 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 379:
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: This statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States was templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] . Until templateXValue[max] , the company is expected to increase from the previous templateXLabel[0] .
generated: This statistic shows the total global Commercial of Worldwide commercial space launches 1990 - from 1990 to 2017 . In 2017 , the Commercial space launches in the United States was 35 N/A N/A N/A . Until 2017 , the company is expected to increase from the previous Year .


Example 380:
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 . As of 2018 , the number decreased to nearly 15 thousand . Spouses in Denmark may divorce , if either or both parties do not wish to continue being together . If one does not agree , they can be divorced after a period of six months of separation . Divorces between same-sex partners Since 15th of June 2012 , no new civil partnerships can be contracted anymore in Denmark . At this time , the same-sex marriage law was enacted . In the period from 2013 to 2018 , the number of divorces between two men as well as between two women increased significantly . By the end of this period , there were 34 divorces between two men and 64 divorces between two women recorded . Norway and Sweden In the past years , the number of divorces in Norway decreased overall from 10.2 thousand in 2008 to 9.5 thousand in 2018 . The amount reached in 2015 was the lowest number of divorces during the decade considered . By comparison , from 2008 to 2013 , the number of divorces grew in Sweden . After that , there was a decreasing trend visible in the country . As of 2018 , around 25 thousand people were divorced .
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[2] between different sexes fluctuated in recent years , peaking in templateXValue[4] at about 19 thousand templateYLabel[2] . As of templateXValue[max] , the templateYLabel[0] decreased to nearly 15 thousand . Spouses in templateTitle[4] may templateYLabel[2] , if either or both parties do not wish to continue being together . If templateTitle[7] does not agree , they can be divorced after a period of six months of separation . templateYLabel[2] between same-sex partners Since 15th of June templateXValue[6] , no new civil partnerships can be contracted anymore in templateTitle[4] . At this time , the same-sex marriage law was enacted . In the period from templateXValue[5] to templateXValue[max] , the templateYLabel[0] of templateYLabel[2] between templateTitle[5] men as well as between templateTitle[5] women increased significantly . By the end of this period , there were 34 templateYLabel[2] between templateTitle[5] men and 64 templateYLabel[2] between templateTitle[5] women recorded . Norway and Sweden In the past years , the templateYLabel[0] of templateYLabel[2] in Norway decreased overall from 10.2 thousand in templateXValue[min] to 9.5 thousand in templateXValue[max] . The amount reached in templateXValue[3] was the lowest templateYLabel[0] of templateYLabel[2] during the decade considered . By comparison , from templateXValue[min] to templateXValue[5] , the templateYLabel[0] of templateYLabel[2] grew in Sweden . After that , there was a decreasing trend visible in the country . As of templateXValue[max] , around 25 thousand people were divorced .

generated_template: In templateXValue[max] , the templateYLabel[0] of templateYLabel[2] in templateTitle[5] was of templateYValue[min] . templateTitle[5] had a templateYLabel[2] rate of 51.2 per 100 marriages in templateXValue[10] . A templateYLabel[0] which was not templateTitle[8] of the highest in Europe The highest in templateYLabel[2] in templateTitle[5] . In templateXValue[max] , templateYLabel[2] remained about templateYValue[max] thousand templateYLabel[2] . In the most popular templateTitle[5] amounted to templateYLabel[2] in templateTitle[5] amounted to roughly in the United States , with the templateTitle[5] .
generated: In 2018 , the Number of divorces in 2008 was of 14460 . 2008 had a divorces rate of 51.2 per 100 marriages in 2008 . A Number which was not N/A of the highest in Europe The highest in divorces in 2008 . In 2018 , divorces remained about 19387 thousand divorces . In the most popular 2008 amounted to divorces in 2008 amounted to roughly in the United States , with the 2008 .


Example 381:
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[3] templateTitle[4] templateTitle[3] templateTitle[6] at home to templateTitle[8] the templateTitle[10] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] percent of templateYLabel[2] reported accessing the templateTitle[10] via templateTitle[6] , whereas in templateXValue[max] the templateYLabel[0] of templateYLabel[2] increased to templateYValue[max] percent .

generated_template: This statistic presents the results of a survey conducted in the templateTitle[0] templateTitle[1] in templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of surveyed templateTitle[0] templateTitle[1] templateTitle[2] platform was templateYValue[min] percent of templateYLabel[2] in templateTitle[4] .
generated: This statistic presents the results of a survey conducted in the Great Britain in WiFi from 2003 to 2013 . In 2013 , 96 percent of surveyed Great Britain : platform was 1 percent of respondents in that .


Example 382:
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[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] 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 383:
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[4] 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[4] was at templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] 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[4] 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 384:
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[3] templateTitle[4] in the United States from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[0] templateYLabel[6] templateYLabel[7] for templateTitle[3] households .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] . In templateXValue[8] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States was at templateYValue[max] thousand templateYLabel[4] .
generated: The statistic shows the Median income in in the in the from 1990 to 2018 . In 2015 , the Household income of black . In 2010 , the Median income in in the United States was at 42348 thousand CPI-U-RS .


Example 385:
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[4] templateTitle[5] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to three billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] are owned by the Pat Bowlen Trust , who bought the templateYLabel[0] for 78 templateYLabel[3] templateYLabel[4] templateYLabel[5] in 1984 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] amounted to around 2.3 billion templateYLabel[4] templateYLabel[5] .
generated: This graph depicts the Franchise value of the Denver Broncos ( from the National Football League from 2002 to 2019 . In 2019 , the Franchise value amounted to around 2.3 billion U.S. dollars .


Example 386:
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[5] templateYLabel[0] of templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[4] had total revenues of over 16.2 billion templateYLabel[3] templateYLabel[4] . The templateTitle[4] Corporation is an American foodservice , templateTitle[0] , and clothing provider headquartered in Philadelphia , Pennsylvania .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Revenue of industry - Aramark worldwide revenue from 2008 to 2019 . In 2019 , the Facilities management industry - Aramark worldwide revenue amounted to 16227.3 million U.S. dollars .


Example 387:
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[3] templateTitle[4] templateXLabel[0] templateXValue[4] in the United States in templateTitle[10] . The survey revealed that some templateYValue[max] percent of the templateYLabel[2] felt that templateXValue[0] templateXLabel[0] templateXValue[4] for templateTitle[4] generated the most templateTitle[0] .

generated_template: This statistic displays the results of a survey , conducted in December 2018 in the United States . The survey shows that templateYValue[max] percent of the United States were templateTitle[0] templateTitle[1] in templateTitle[4] .
generated: This statistic displays the results of a survey , conducted in December 2018 in the United States . The survey shows that 45 percent of the United States were Revenue share in apparel .


Example 388:
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[4] templateYLabel[2] at templateTitle[5] establishments in templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[2] in travel templateTitle[5] ( including both international and domestic tourists ) amounted to approximately templateYValue[max] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[4] templateYLabel[2] in templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitle[4] templateYLabel[2] in short-stay templateTitle[5] in templateTitle[6] have generally increased over this period , from around 12 templateYLabel[4] in templateXValue[min] to approximately templateYValue[max] templateYLabel[4] by templateXValue[max] .
generated: This statistic shows the Number of tourist arrivals in Bulgaria from 2006 to 2018 . tourist arrivals in short-stay accommodation in Bulgaria have generally increased over this period , from around 12 millions in 2006 to approximately 7.8 millions by 2018 .


Example 389:
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[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[5] stood at templateYValue[0] templateYLabel[5] templateXValue[6] templateYLabel[7] templateYLabel[8] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[5] templateTitle[6] stood at templateYValue[max] templateYLabel[5] templateXValue[6] templateYLabel[7] templateYLabel[8] .
generated: The 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 390:
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[2] templateTitle[3] templateYLabel[0] of the templateTitle[6] of templateTitle[0] from templateXValue[min] to templateXValue[max] . The average templateYLabel[0] of the templateTitle[6] 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: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[7] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] amounted to templateYValue[min] percent .
generated: The statistic shows the Age N/A of median age of from 2014 to 2039 . In N/A , the Scotland : forecasted median age of amounted to 41.9 percent .


Example 391:
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 . However , only 35.7 percent of customers of Spanish retail banks reported having a positive experience in that year .
gold_template: This statistic illustrates the templateYLabel[0] of templateYLabel[2] templateYLabel[3] a templateYLabel[4] templateTitle[5] templateTitle[6] templateYLabel[5] in the leading templateTitle[0] templateTitle[1] templateTitle[6] systems ( templateTitle[2] ) as of templateTitle[9] . Approximately templateYValue[max] percent of surveyed templateTitle[6] templateYLabel[2] 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[3] templateYValue[1] percent of templateTitle[6] templateYLabel[2] templateYLabel[3] a templateYLabel[4] templateYLabel[5] throughout the year . However , only templateYValue[min] percent of templateYLabel[2] of Spanish templateTitle[5] banks reported having a templateYLabel[4] templateYLabel[5] in that year .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] templateTitle[5] from the templateXValue[last] season to the templateXValue[0] season . In 2017 , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States in templateTitle[4] templateTitle[5] amounted to templateYValue[max] .
generated: The statistic shows the Share of customers in by retail from the Spain season to the Netherlands season . In 2017 , the Share of customers in the United States in by retail amounted to 70.6 .


Example 392:
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[3] templateYLabel[1] templateTitle[5] of templateTitle[7] and templateTitle[9] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[3] templateYLabel[1] templateTitle[5] of templateTitle[7] and templateTitle[9] amounted to 3.1 templateYLabel[0] templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in the templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] percent .
generated: The statistic shows the Trillion U.S. of the U.S. imports from 2000 to 2019 . In 2019 , the Trillion U.S. in the U.S. imports amounted to approximately 3.12 percent .


Example 393:
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 . Murder in the United States Violent crime statistics , particularly murder and homicide data , provide key insights into law enforcement in the United States and inform national debate surrounding crime policies . There were a total of 16,214 reported murder and non-negligent manslaughter cases in the U.S. in 2018 . Although the number of cases has declined in the past twenty years , when viewed in international comparison , the U.S. murder rate is still high . In 2012 , Germany 's murder rate stood at 0.8 , compared to 4.7 in the United States . The most dangerous U.S. state in 2018 , if measured by the number of murders per hundred thousand inhabitants , was Louisiana . The murder and non-negligent manslaughter rate in Louisiana came to 11.4 that year , more than twice the national average . The least dangerous state in 2018 , on the other hand , was South Dakota , with a murder rate of 1.4 . Murder , homicide and violent crime statistics regularly influence America 's current political debate on gun law . Under the Second Amendment , U.S. citizens are entitled to own and carry firearms , though gun control and regulation laws are hotly contested . The amount of firearms in circulation in the U.S. is fairly high , when compared with European countries . About 43 percent of American households have a gun in their home . Though any causal connection between firearm circulation and homicide rate is purely speculative , murder by firearm is nonetheless high . About 63 percent of murders and non-negligent manslaughter cases in the U.S. were carried out using a firearm , including handguns , rifles , and shotguns . This number sums up to 10,265 murders by firearm . The ranking of the most dangerous cities in the world based on murder rate per capita can be accessed here .
gold_template: This statistic displays the templateYLabel[0] of templateTitle[4] in the United States templateTitle[5] templateXLabel[0] . Data includes templateYLabel[2] and nonnegligent manslaughter . In templateTitle[9] , the templateYLabel[0] of templateTitle[4] in templateXValue[0] amounted to templateYValue[max] templateYLabel[3] . templateYLabel[2] in the United States Violent crime statistics , particularly templateYLabel[2] and templateTitle[0] data , provide key insights into law enforcement in the United States and inform national debate surrounding crime policies . There were a total of 16,214 reported templateYLabel[2] and non-negligent manslaughter cases in the templateTitle[6] in templateTitle[9] . Although the templateYLabel[0] of cases has declined in the past templateTitle[9] years , when viewed in international comparison , the templateTitle[6] templateYLabel[2] rate is still high . In 2012 , Germany 's templateYLabel[2] rate stood at 0.8 , compared to 4.7 in the United States . The most dangerous templateTitle[6] templateXLabel[0] in templateTitle[9] , if measured templateTitle[5] the templateYLabel[0] of templateTitle[4] per hundred thousand inhabitants , was templateXValue[11] . The templateYLabel[2] and non-negligent manslaughter rate in templateXValue[11] came to 11.4 that year , more than twice the national average . The least dangerous templateXLabel[0] in templateTitle[9] , on the other hand , was templateXValue[15] templateXValue[46] , with a templateYLabel[2] rate of 1.4 . templateYLabel[2] , templateTitle[0] and violent crime statistics regularly influence America 's current political debate on gun law . Under the Second Amendment , templateTitle[6] citizens are entitled to own and carry firearms , though gun control and regulation laws are hotly contested . The amount of firearms in circulation in the templateTitle[6] is fairly high , when compared with European countries . About 43 percent of American households have a gun in their home . Though any causal connection between firearm circulation and templateTitle[0] rate is purely speculative , templateYLabel[2] templateTitle[5] firearm is nonetheless high . About 63 percent of templateTitle[4] and non-negligent manslaughter cases in the templateTitle[6] were carried out using a firearm , including handguns , rifles , and shotguns . This templateYLabel[0] sums up to 10,265 templateTitle[4] templateTitle[5] firearm . The ranking of the most dangerous cities in the world based on templateYLabel[2] rate per capita can be accessed here .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the United States in templateTitle[9] , templateTitle[10] templateXLabel[0] . According to the report , templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was at about templateYValue[1] percent .
generated: This statistic shows the Homicide - number of murders in the United States in 2018 , N/A State . According to the report , Homicide - number of murders was at about 1322 percent .


Example 394:
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 . The United Kingdom is among the leading countries in a world GDP ranking.Falling unemployment in a time of recession GDP is a useful indicator when it comes to measuring the state of a nation 's economy . GDP is the market value of all final goods and services produced within a country in a given period of time , usually a year . GDP per capita equals exactly the GDI ( gross domestic income ) per capita and is not a measure of an individual 's personal income . As can be seen clearly in the statistic , gross domestic product ( GDP ) per capita in the United Kingdom is beginning to increase , albeit not to pre-recession levels . The UK is beginning to see signs of an economic recovery , though as of yet it remains unclear what sort of recovery this is . Questions have been raised as to whether the growth being seen is the right sort of growth for a well balanced recovery across the necessary sectors . An interesting oddity occurred in the United Kingdom for nine months in 2012 , which saw a decreasing unemployment occurring at the same time as dip in nationwide economic productivity . This seems like good - if not unusual - news , but could be indicative of people entering part-time employment . It could also suggest that labor productivity is falling , meaning that the UK would be less competitive as a nation . The figures continue to rise , however , with an increase in employment in the private sector . With the rate of inflation in the UK impacting everyone 's daily lives , it is becoming increasingly difficult for vulnerable groups to maintain a decent standard of living .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[8] templateTitle[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[8] templateTitle[9] was at around templateYValue[6] US templateYLabel[5] . The same templateXLabel[0] , the total UK population amounted to about 64.6 million people . The templateTitle[8] templateTitle[9] is among the leading countries in a world templateYLabel[0] ranking.Falling unemployment in a time of recession templateYLabel[0] is a useful indicator when it comes to measuring the state of a nation 's economy . templateYLabel[0] is the market value of all final goods and services produced within a country in a given period of time , usually a templateXLabel[0] . templateYLabel[0] templateYLabel[1] templateYLabel[2] equals exactly the GDI ( templateTitle[0] templateTitle[1] income ) templateYLabel[1] templateYLabel[2] and is not a measure of an individual 's personal income . As can be seen clearly in the statistic , templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in the templateTitle[8] templateTitle[9] is beginning to increase , albeit not to pre-recession levels . The UK is beginning to see signs of an economic recovery , though as of yet it remains unclear what sort of recovery this is . Questions have been raised as to whether the growth being seen is the right sort of growth for a well balanced recovery across the necessary sectors . An interesting oddity occurred in the templateTitle[8] templateTitle[9] for nine months in templateXValue[12] , which saw a decreasing unemployment occurring at the same time as dip in nationwide economic productivity . This seems like good - if not unusual - news , but could be indicative of people entering part-time employment . It could also suggest that labor productivity is falling , meaning that the UK would be less competitive as a nation . The figures continue to rise , however , with an increase in employment in the private sector . With the rate of inflation in the UK impacting everyone 's daily lives , it is becoming increasingly difficult for vulnerable groups to maintain a decent standard of living .

generated_template: The statistic shows the total templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitle[9] 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 . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the total product ( GDP ) per capita in Kingdom 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 . In 2018 , the GDP per capita in Kingdom amounted to around 42579.82 U.S. dollars .


Example 395:
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[3] US templateYLabel[6] . templateYLabel[3] templateYLabel[4] US templateYLabel[6] reflect buying power in templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] had a size of templateYValue[min] percent .
generated: The statistic shows the Global energy commodity price index size 2013 from 2013 to 2030 . In 2019 , the Global energy commodity price index had a size of 55.1 percent .


Example 396:
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 . Approximately , 1 to 1.3 million people currently suffer from IBD , however , the cause of IBD is currently unknown .
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[3] templateYLabel[4] templateYLabel[5] . templateTitle[1] templateTitle[2] is a type of inflammatory bowel disease ( IBD ) which is characterized by the formation of ulcers in the colon . Approximately , templateTitle[4] to 1.3 million people currently suffer from IBD , however , the cause of IBD is currently unknown .

generated_template: This timeline presents the projected templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[min] , it is estimated that the templateTitle[0] templateTitle[1] templateTitle[2] industry will have a forecast to reach about templateYValue[min] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This timeline presents the projected Global ulcerative colitis Market value in the United States from 2012 to 2022 . In 2012 , it is estimated that the Global ulcerative colitis industry will have a forecast to reach about 4.2 billion U.S. dollars .


Example 397:
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[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitle[9] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitle[9] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] 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 398:
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[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . At the start of this period the templateTitle[0] templateYLabel[0] templateYLabel[1] stood at over templateTitle[9] percent , but by templateXValue[max] this had decreased to templateYValue[min] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in in from 2013 to 2019 . In 2019 , the Unemployment rate in in was at approximately 7.6 percent .


Example 399:
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[2] templateYLabel[3] templateTitle[0] video platform templateYLabel[4] as of June templateTitle[9] . As of that templateXLabel[0] , the video portal had templateYValue[13] templateYLabel[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] worldwide . The VoD service had been acquired by Japanese e-commerce company Rakuten in September 2013 .

generated_template: This statistic shows a timeline with the amount of templateYLabel[2] templateYLabel[3] templateTitle[6] templateTitle[0] templateYLabel[4] as of the first templateXLabel[0] of templateTitle[10] . Excluding the United States , the templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateTitle[6] templateTitle[0] templateYLabel[4] amounted to templateYValue[0] templateYLabel[6] as of the most recent templateXLabel[0] . In total , templateTitle[0] had 330 templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows a timeline with the amount of monthly active users Viki users as of the first Month of N/A . Excluding the United States , the Number of monthly active users Viki users amounted to 3.8 millions as of the most recent Month . In total , Viki had 330 monthly active users .


Example 400:
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[2] employees of templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[2] templateYLabel[3] of templateTitle[4] amounted to approximately 204,000 .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was at templateYValue[0] percent .
generated: The statistic shows the Total direct staff of Citigroup size 2011 from 2011 to 2018 . In 2018 , the Total direct staff of Citigroup was at 204 percent .


Example 401:
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[4] templateTitle[3] templateTitle[4] templateTitle[5] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , their templateYLabel[0] templateYLabel[1] came to around 1.98 billion templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[max] , there were templateYValue[max] thousand thousand templateYLabel[2] .
generated: The statistic shows the total global Gross of Gross profit of from 2006 to 2019 . In 2016 , the Gross of in million U.S. . In 2019 , there were 3478.88 thousand thousand in .


Example 402:
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 . COVID-19 : background information COVID-19 is a novel coronavirus that had not previously been identified in humans . The first case was detected in the Hubei province of China on December 31 , 2019 , and was linked to the South China Seafood Wholesale Market in Wuhan . Thousands of new cases are being reported each day , and because the illness has only recently been detected , it is not known exactly how the virus spreads from person to person . Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people , causing illnesses that may range from the common cold to more severe respiratory syndromes . In February 2020 , the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it caused : SARS-CoV-2 and COVID-19 , respectively . The name of the disease is derived from the words corona , virus , and disease , while the number 19 represents the year that it emerged .
gold_template: As of templateTitle[5] templateYValue[43] , templateTitle[8] , 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] . templateTitle[0] : background information templateTitle[0] is a novel coronavirus that had not previously been identified in humans . The first templateYLabel[2] was detected in the Hubei province of templateXValue[1] on December 31 , 2019 , and was linked to the South templateXValue[1] Seafood Wholesale Market in Wuhan . Thousands of templateXValue[57] templateXValue[5] are being reported each day , and because the illness has only recently been detected , it is not known exactly how the virus spreads from person to person . Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people , causing illnesses that may range from the common cold to more severe respiratory syndromes . In February templateTitle[8] , the templateXValue[5] Committee on Taxonomy of Viruses and the templateTitle[2] Health Organization announced official names for both the virus and the disease it caused : SARS-CoV-2 and templateTitle[0] , respectively . The name of the disease is derived from the words corona , virus , and disease , while the templateYLabel[0] templateYValue[20] represents the year that it emerged .

generated_template: This graph shows the templateYLabel[0] of templateYLabel[2] of templateTitle[2] templateTitle[3] templateTitle[5] templateTitle[6] in the United Kingdom ( templateTitle[0] ) in templateTitle[4] to templateTitle[8] . During that period of time , the templateYLabel[0] of templateYLabel[2] during the period from templateYValue[min] templateYLabel[2] respectively .
generated: This graph shows the Number of cases of worldwide as March 2 in the United Kingdom ( COVID-19 ) in of to 2020 . During that period of time , the Number of cases during the period from 1 cases respectively .


Example 403:
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[2] templateYLabel[3] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateTitle[0] templateTitle[1] templateYLabel[2] members in the United States Department of Defense . In templateXValue[14] , there were templateYValue[14] templateTitle[0] templateTitle[1] members .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] reached templateYValue[max] templateYLabel[0] templateYLabel[1] . In templateXValue[max] , templateYLabel[2] in the previous templateXLabel[0] . In templateXValue[3] , there were around templateYValue[min] thousand templateYLabel[2] .
generated: The statistic shows the Number of Navy in the numbers from from 1995 to 2018 . In 2015 , the Active Duty U.S. Navy personnel numbers from reached 429630 Number of . In 2018 , Navy in the previous Year . In 2015 , there were around 314339 thousand Navy .


Example 404:
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[3] in the templateTitle[6] templateTitle[7] ( templateTitle[9] ) 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[3] was templateYValue[max] British Pound Sterling ( templateYLabel[4] ) templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] reached templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Price per tonne in the the United from 2002 to 2015 . In 2015 , the Commodity prices of wheat in the United reached 179.26 Price per tonne in GBP N/A .


Example 405:
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 . In comparison with the peak year , road traffic fatalities in Romania declined in roughly 39 percent by 2018 .
gold_template: This statistic illustrates the annual templateYLabel[0] of templateTitle[2] traffic templateYLabel[2] in templateTitle[5] between templateXValue[min] and templateXValue[max] . In the period of consideration , templateTitle[2] templateYLabel[2] presented a trend of decline in templateTitle[5] despite some oscillation . The peak was recorded in templateXValue[10] , with templateYValue[max] templateYLabel[2] on Romanian roads . In comparison with the peak templateXLabel[0] , templateTitle[2] traffic templateYLabel[2] in templateTitle[5] declined in roughly 39 percent by templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateTitle[2] templateYLabel[2] in the United States . In templateXValue[1] , around templateYValue[min] templateYLabel[2] reported due to templateTitle[0] in templateTitle[5] .
generated: The statistic shows the Number of fatalities in Romania from 2006 to 2018 . In 2018 , there were 3065 road fatalities in the United States . In 2017 , around 1818 fatalities reported due to Number in Romania .


Example 406:
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 . a very high number of inhabitants per square kilometer . However , this does not equal bad standard of living ; on the Human Development Index , which ranks countries by their level of living standards using key factors , such as unemployment rate , literacy rate , fertility and mortality rates , etc. , South Korea is among the highest-ranked countries . When looking at the aforementioned key factors , South Korea indeed seems to offer a fairly stable environment for its inhabitants , economically and demographically : The country 's unemployment rate has been relatively steady for the past decade , its gross domestic product ( GDP ) is constantly increasing , and it is among the countries with the highest trade surplus worldwide . As for standard of living , life expectancy at birth in South Korea is among the highest worldwide – South Korea is even mentioned in a recent ranking of the best birthplaces for children . Despite the high population density , South Korea is now one of the countries with the lowest fertility rates , i.e . the number of babies born by women of childbearing age . This apparent discrepancy could be explained by a high number of immigrants coupled with the aforementioned high life expectancy .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitle[4] was about templateYValue[6] templateYLabel[2] people . templateTitle[1] of templateTitle[3] templateTitle[4] templateTitle[3] templateTitle[4] , also called Republic of templateTitle[4] , has one of the highest templateTitle[1] densities worldwide , i.e . a very high number of templateYLabel[0] per square kilometer . However , this does not equal bad standard of living ; on the Human Development Index , which ranks countries by their level of living standards using key factors , such as unemployment rate , literacy rate , fertility and mortality rates , etc. , templateTitle[3] templateTitle[4] is among the highest-ranked countries . When looking at the aforementioned key factors , templateTitle[3] templateTitle[4] indeed seems to offer a fairly stable environment for its templateYLabel[0] , economically and demographically : The country 's unemployment rate has been relatively steady for the past decade , its gross domestic product ( GDP ) is constantly increasing , and it is among the countries with the highest trade surplus worldwide . As for standard of living , life expectancy at birth in templateTitle[3] templateTitle[4] is among the highest worldwide – templateTitle[3] templateTitle[4] is even mentioned in a recent ranking of the best birthplaces for children . Despite the high templateTitle[1] density , templateTitle[3] templateTitle[4] is now one of the countries with the lowest fertility rates , i.e . the number of babies born by women of childbearing age . This apparent discrepancy could be explained by a high number of immigrants coupled with the aforementioned high life expectancy .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] amounted to approximately templateYValue[6] templateYLabel[2] templateYLabel[0] .
generated: The 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 approximately 51.64 millions Inhabitants .


Example 407:
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[9] , among adult Americans on the templateTitle[0] of templateTitle[4] templateTitle[5] at templateTitle[8] as a family . In December templateTitle[9] , templateYValue[max] percent of the templateYLabel[2] answered that their family eat templateTitle[5] templateTitle[6] at templateTitle[8] on templateXValue[last] to templateXValue[last] templateXValue[0] a week .

generated_template: This statistic shows the results of a survey , conducted in the United States in May templateTitle[11] . The survey shows that templateYValue[min] percent of templateYLabel[2] said that they ate templateTitle[3] templateTitle[4] users in the United States .
generated: This statistic shows the results of a survey , conducted in the United States in May N/A . The survey shows that 21 percent of respondents said that they ate families having users in the United States .


Example 408:
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[5] to templateTitle[7] templateTitle[8] templateTitle[9] ( templateYLabel[2] ) 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[7] templateTitle[8] templateTitle[9] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[9] amounted to approximately templateYValue[7] percent .
generated: The statistic shows the Iran 's national debt in size relation from 2014 to 2017 , with projections up until 2024 . In 2017 , the Iran 's national Share in GDP in product amounted to approximately 39.53 percent .


Example 409:
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[4] 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[4] to the templateYLabel[0] of the United Arab Emirates for templateXValue[max] was approximately templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was estimated to increase from templateYValue[max] percent in templateXValue[min] . In the previous templateXLabel[0] , the fiscal templateXLabel[0] .
generated: This statistic shows the Direct tourism contribution of Dubai from 2006 to 2026 . In 2026 , the Direct tourism contribution of Dubai was estimated to increase from 20.9 percent in 2006 . In the previous Year , the fiscal Year .


Example 410:
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[2] in templateTitle[2] in the United States templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[2] ( aged templateTitle[7] years and older ) in templateTitle[2] amounted to approximately templateYValue[max] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] in templateTitle[2] in the United States templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[2] ( aged templateTitle[7] years and older ) in templateTitle[2] amounted to approximately templateYValue[0] templateYLabel[4] .
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 2006 years and older ) in hunting amounted to approximately 15.69 millions .


Example 411:
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 . Recycling rates slowing down From 2000/2001 to 2011/2012 , the recycling rates of household waste in England increased steadily from just 11.2 percent to 43 percent . However , since then there has been no noticeable increase in recycling rates . In fact , in 2015/2016 , recycling rates dropped slightly to levels last seen in 2011/2012 . In 2019 , the UK Government set out plans to overhaul the waste system . One proposal to improve household recycling levels was to introduce more consistent sets of recyclable materials , such as separate food waste collection . Food waste Food waste is a growing concern worldwide , with an estimated 1.6 billion metric tons going to waste each year . This is approximately one third of the total food produced globally . In 2017 , 75 kilograms of food per capita was wasted in the UK . In comparison , Italy , which has a similar population size to the UK generated 145 kilograms of food waste per capita .
gold_template: templateTitle[1] templateTitle[2] volumes templateYLabel[1] templateYLabel[2] in templateTitle[4] remained at a similar level between templateXValue[min] and templateXValue[max] . Although there templateTitle[2] 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] templateTitle[2] the North East of templateTitle[4] , where an average of 601 templateYLabel[0] of templateTitle[2] templateTitle[2] generated templateYLabel[1] templateYLabel[2] in 2017/2018 . Recycling rates slowing down From 2000/2001 to 2011/2012 , the recycling rates of templateTitle[1] templateTitle[2] in templateTitle[4] increased steadily from just 11.2 percent to 43 percent . However , since then there has been no noticeable increase in recycling rates . In fact , in 2015/2016 , recycling rates dropped slightly to levels last seen in 2011/2012 . In 2019 , the UK Government set out plans to overhaul the templateTitle[2] system . templateTitle[5] proposal to improve templateTitle[1] recycling levels templateTitle[2] to introduce more consistent sets of recyclable materials , such as separate food templateTitle[2] collection . Food templateTitle[2] Food templateTitle[2] is a growing concern worldwide , with an estimated 1.6 billion metric tons going to templateTitle[2] each templateXLabel[0] . This is approximately templateTitle[5] third of the templateTitle[0] food produced globally . In templateXValue[max] , 75 templateYLabel[0] of food templateYLabel[1] capita templateTitle[2] wasted in the UK . In comparison , Italy , which has a similar population size to the UK generated 145 templateYLabel[0] of food templateTitle[2] templateYLabel[1] capita .

generated_template: This statistic shows the total templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] was approximately templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the total Kilograms per of in England 2010 from 2010 to 2017 . In 2017 , the Kilograms per of in England 2010 was approximately 425 per year N/A .


Example 412:
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[3] templateTitle[4] templateTitle[5] a templateTitle[7] templateTitle[8] in the United States in templateTitle[12] . The survey revealed that templateYValue[max] percent of templateYLabel[2] templateXLabel[0] to templateXLabel[3] by templateXValue[0] .

generated_template: This statistic shows the results of a survey conducted in the templateTitle[0] templateTitle[1] in templateTitle[10] on how long the templateTitle[5] in the United States . During that period of time , templateYValue[max] percent of templateYLabel[2] stated that they used the social networking site .
generated: This statistic shows the results of a survey conducted in the Preferred modes in the on how long the taking in the United States . During that period of time , 63 percent of respondents stated that they used the social networking site .


Example 413:
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] templateXLabel[0] templateTitle[4] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] reached 138.6 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[min] and is forecast to amount to around 19.7 billion templateYLabel[3] templateYLabel[4] by templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[6] based company operated 378 clubs .
generated: The statistic shows the Revenue of the reality glasses revenue 2016 worldwide from 2016 to 2022 . In 2022 , Global smart augmented Revenue amounted to 19718.88 million U.S. dollars . In 2022 , the Revenue of the 2016 based company operated 378 clubs .


Example 414:
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[3] templateYLabel[0] templateYLabel[1] in templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[3] templateYLabel[0] templateYLabel[1] in templateTitle[0] templateTitle[1] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the - Household income in West from 1990 to 2018 . In 2018 , the - Household income in West amounted to 50573 U.S. dollars .


Example 415:
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 . In 2011 , the country 's economy experienced higher than average annual growth , which most likely helped increase consumer confidence and may have correlated with a decrease in the inflation rate . In general , consumer confidence is rising because GDP per capita has also been increasing steadily and is expected to continue to do so in the future . However , living conditions of Nicaraguans are still far from ideal , and the country struggles to overcome its reputation as one of the poorest nations in the region . GDP per capita in Nicaragua remained under 2,000 U.S. dollars per capita in 2014 ; only a fraction of GDP for Latin America and the Caribbean as a whole , which was slightly over 10,000 U.S. dollars per capita that same year . Yet , while per capita GDP is low , the country reports average unemployment and typically , when unemployment is low , consumer confidence increases and prices rise . However , it is likely that any increase in inflation will still have a significant effect on the poor , even if GDP rises .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in 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[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] . templateTitle[3] 's economy templateTitle[3] 's templateYLabel[0] templateYLabel[1] has been on the decline since templateXValue[13] , but it is expected to rise again in templateXValue[8] . In templateXValue[13] , the country 's economy experienced higher than average annual growth , which most likely helped increase consumer confidence and may have correlated with a decrease in the templateYLabel[0] templateYLabel[1] . In general , consumer confidence is rising because GDP per capita has also been increasing steadily and is expected to continue to do so in the future . However , living conditions of Nicaraguans templateYLabel[2] still far from ideal , and the country struggles to overcome its reputation as one of the poorest nations in the region . GDP per capita in templateTitle[3] remained under templateXValue[24] U.S. dollars per capita in templateXValue[10] ; only a fraction of GDP for Latin America and the Caribbean as a whole , which was slightly over 10,000 U.S. dollars per capita that same templateXLabel[0] . Yet , while per capita GDP is low , the country reports average unemployment and typically , when unemployment is low , consumer confidence increases and prices rise . However , it is likely that any increase in templateYLabel[0] will still have a significant effect on the poor , even if GDP rises .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in 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[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] 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 416:
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[2] templateTitle[3] templateYLabel[0] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] scored templateYValue[max] points , which shows a templateTitle[2] templateTitle[3] of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . In templateXValue[max] , the templateTitle[2] templateTitle[3] in the area of political empowerment in templateTitle[0] amounted to 69 percent .

generated_template: The graph presents the templateTitle[2] templateTitle[3] templateYLabel[0] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] scored templateYValue[max] , which shows a templateTitle[2] templateTitle[3] of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . That same templateXLabel[0] , the templateTitle[2] templateTitle[3] 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 417:
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 . Social media juggernaut Facebook has a 69 percent population reach . Whereas Facebook and Snapchat usage are projected to either decline or stagnate until 2021 , Instagram user engagement is estimated to increase from 26 to 29 minutes per day . The number of Instagram users in the United States is also set to keep growing over the coming years – in 2018 , there were approximately 105 million monthly active U.S. Instagram users with forecasts estimating almost 131 million monthly users in 2022 . Teens and Instagram Instagram usage is also widely spread among teenagers in the United States : in 2018 , three quarters of female U.S. teenagers aged 13 to 17 years accessed the social network , along with 69 percent of male teens . The social network is more popular among older teens than younger ones , although this may simply be due to parental restrictions around smartphone usage and media consumption .
gold_template: As of February templateTitle[7] , templateYValue[max] percent of U.S. 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[5] templateTitle[6] used templateTitle[0] compared to only 31 percent of adult men . templateTitle[0] templateTitle[1] in the templateTitle[5] StatesInstagram is templateTitle[7] of the most popular social networks in the templateTitle[5] templateTitle[6] with a 37 percent templateTitle[1] templateTitle[2] among the adult population . Social media juggernaut Facebook has a 69 percent population templateTitle[2] . Whereas Facebook and Snapchat templateTitle[1] templateYLabel[0] projected to either decline or stagnate until 2021 , templateTitle[0] user engagement is estimated to increase from 26 to 29 minutes per day . The number of templateTitle[0] users in the templateTitle[5] templateTitle[6] is also set to keep growing over the coming years – in 2018 , there were approximately 105 million monthly active U.S. templateTitle[0] users with forecasts estimating almost 131 million monthly users in 2022 . Teens and templateTitle[0] templateTitle[0] templateTitle[1] is also widely spread among teenagers in the templateTitle[5] templateTitle[6] : in 2018 , three quarters of female U.S. teenagers aged 13 to 17 years accessed the social network , along with 69 percent of male teens . The social network is more popular among older teens than younger ones , although this may simply be due to parental restrictions around smartphone templateTitle[1] and media consumption .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[5] templateTitle[6] who were using templateTitle[0] as of February templateTitle[7] , sorted templateTitle[9] . During that period of time , templateYValue[max] percent of survey templateYLabel[2] stated that they used the social networking site .
generated: This statistic gives information on the Instagram usage reach in United States who were using Instagram as of February 2019 , sorted by . During that period of time , 67 percent of survey respondents stated that they used the social networking site .


Example 418:
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[4] templateYLabel[0] templateYLabel[1] in the templateTitle[0] of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[4] templateYLabel[0] templateYLabel[1] in the templateTitle[0] of templateTitle[2] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Colombia Household income in District from 1990 to 2018 . In 2018 , the Colombia Household income in District amounted to 85750 U.S. dollars .


Example 419:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitle[3] amounted to approximately templateYValue[6] templateYLabel[2] templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateTitle[0] templateTitle[1] of templateTitle[3] amounted to approximately templateYValue[7] templateYLabel[2] templateYLabel[0] .
generated: The statistic shows the Total population of Iraq from 2014 to 2017 , with projections up until 2024 . In 2017 , the Total population of Iraq amounted to approximately 37.14 millions Inhabitants .


Example 420:
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[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . templateYLabel[0] in templateTitle[3] templateTitle[4] 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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[min] percent .
generated: This statistic shows the Unemployment rate in Northern from 2000 to 2019 . In 2019 , the Unemployment rate in Northern was at approximately 2.7 percent .


Example 421:
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 858 templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[7] , the templateYLabel[0] of templateTitle[3] templateTitle[4] templateTitle[5] amounted to approximately templateYValue[min] percent .
generated: The statistic shows the total global Market of Video analytics market revenues worldwide 2015 from 2015 to 2022 . In 2015 , the Market of revenues worldwide 2015 amounted to approximately 858.0 percent .


Example 422:
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[5] templateTitle[6] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] for templateTitle[5] templateTitle[6] in the templateTitle[9] was templateYValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] scores for templateTitle[5] templateTitle[6] templateTitle[7] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] for templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[10] 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 in in the United States from 2007 to 2019 . In 2019 , the ACSI for full-service restaurants in in the 2007 was 78 , down from 81 the previous Year .


Example 423:
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[2] of templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[4] templateTitle[5] had a total of templateYValue[min] templateYLabel[2] templateTitle[6] . 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: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateTitle[2] templateTitle[3] templateYLabel[2] registered in templateTitle[5] .
generated: This statistic shows the Number of stores stores in the United States from 2009 to 2017 . In 2017 , there were 4010 stores of stores registered in Holdings .


Example 424:
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[2] rate has been decreasing throughout recent years . In templateXValue[max] , approximately templateYValue[min] templateYLabel[0] of Colombians were living on less than templateTitle[6] templateTitle[7] templateTitle[8] templateYLabel[0] templateTitle[10] , down from templateYValue[max] templateYLabel[0] of the country 's templateYLabel[2] in 2005.Moreover , it was recently found that the incidence rate of templateTitle[2] in templateTitle[0] is higher in families whose heads of household were women .

generated_template: This statistic shows the templateTitle[3] templateTitle[4] in templateTitle[0] templateTitle[1] templateTitle[5] templateXValue[min] to templateXValue[max] . templateYValue[0] templateYLabel[0] of templateTitle[0] templateTitle[1] 's templateYLabel[2] lived below the templateTitle[3] line in templateXValue[max] .
generated: This statistic shows the headcount ratio in Colombia : at 2005 to 2017 . 10.8 Percentage of Colombia : 's population lived below the headcount line in 2017 .


Example 425:
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 450,000 templateYLabel[4] of templateYLabel[0] templateYLabel[5] templateYLabel[6] 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 templateTitle[0] templateYLabel[0] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the United States in templateTitle[9] . According to the last reported period , the templateXValue[0] templateXValue[0] was the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[max] . This was an increase of templateYValue[1] percent in templateXValue[1] .
generated: This statistic shows the Iran Oil of exports 2011 N/A N/A in the United States in N/A . According to the last reported period , the China China was the highest Oil imports in of 543 . This was an increase of 450 percent in European_Union_(total) .


Example 426:
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[4] templateTitle[5] templateTitle[6] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[4] templateTitle[4] templateTitle[5] templateTitle[6] generated approximately 40.36 billion templateYLabel[4] templateYLabel[5] , up from 39.77 billion templateYLabel[4] templateYLabel[5] the previous templateXLabel[0] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] increased from templateYValue[3] percent .
generated: The statistic shows the total global Retail of Retail sales of from 2013 to 2018 . In 2015 , the Retail sales of Retail amounted to 39767.0 in million U.S. . In 2015 , the Retail sales increased from 39767.0 percent .


Example 427:
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[2] templateYLabel[3] to templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[5] ) in the templateTitle[13] templateTitle[14] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[2] templateYLabel[3] amounted to templateYValue[min] percent of the templateTitle[5] templateTitle[6] templateTitle[7] . See the US templateYLabel[5] for further information .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] to templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[5] ) in templateTitle[12] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[2] templateYLabel[3] in templateTitle[12] amounted to about templateYValue[6] percent of the country 's templateTitle[5] templateTitle[6] templateTitle[7] .
generated: The statistic shows the Ratio of government expenditure to gross domestic product ( GDP ) in the from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in the amounted to about 35.14 percent of the country 's gross domestic product .


Example 428:
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 . By 2018 , this number slightly increased and Croatia recorded 317 road traffic fatalities .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[4] traffic templateYLabel[2] per templateXLabel[0] in templateTitle[0] between templateXValue[min] and templateXValue[max] . In the period of consideration , templateTitle[4] templateYLabel[2] presented an overall trend of decline . The templateXLabel[0] with the lowest amount of templateYLabel[2] was templateXValue[2] , with a total of 207 templateTitle[4] traffic templateYLabel[2] in templateTitle[0] . By templateXValue[max] , this templateYLabel[0] slightly increased and templateTitle[0] recorded templateYValue[0] templateTitle[4] traffic templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] in templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[min] templateTitle[2] templateYLabel[2] in templateTitle[5] in the United States .
generated: This statistic shows the Number of fatalities in deaths from 2006 to 2018 . In 2018 , there were 307 Number fatalities in deaths in the United States .


Example 429:
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 United States 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 templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] was at about templateYValue[max] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fatality rate per in drivers from 1990 to 2017 . In 2017 , the Fatality rate per in drivers was at about 26.7 Fatalities per 1,000 licensed drivers .


Example 430:
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[4] templateTitle[5] templateTitle[6] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of 1.6 billion templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by Jerry Reinsdorf , who bought the templateYLabel[0] for templateTitle[7] templateYLabel[3] templateYLabel[4] templateYLabel[5] in 1981 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] 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] templateYLabel[3] templateYLabel[4] templateYLabel[5] . The templateTitle[4] templateTitle[5] templateTitle[6] are owned by Rogers Communications , who bought the templateYLabel[0] for 137 templateYLabel[3] templateYLabel[4] templateYLabel[5] in 2000 .
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 million U.S. dollars . The Chicago White Sox are owned by Rogers Communications , who bought the Franchise for 137 million U.S. dollars in 2000 .


Example 431:
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 templateTitle[0] giant generated templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] with , among others , HBO , CNN and Cartoon templateTitle[2] . Due to the acquisition of Time templateTitle[0] by AT & templateTitle[1] and its subsequent renaming ( now templateTitle[0] ) , results for previous years are not considered meaningful and as such were not reported by AT & templateTitle[1] in templateXValue[max] .

generated_template: This statistic presents the projected templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] is expected to increase by templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic presents the projected WarnerMedia television network Revenue in in the United States from 2018 to 2018 . In 2018 , the WarnerMedia television network revenue Revenue is expected to increase by 10.58 U.S. dollars N/A .


Example 432:
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[4] templateYLabel[5] . This figure was forecasted to increase to templateYValue[2] templateYLabel[4] templateYLabel[5] in templateXValue[5] and again to templateYValue[max] templateYLabel[5] in templateXValue[max] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] was at about templateYValue[max] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Average global hotel in from from 2009 to 2015 . In 2015 , the Average global hotel in from was at about 179 Average hotel 1,000 in U.S. .


Example 433:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitle[3] was estimated at approximately templateYValue[6] templateYLabel[2] templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateTitle[0] templateTitle[1] of templateTitle[3] amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[0] .
generated: This statistic shows the Total population of Kenya from 2014 to 2017 , with projections up until 2024 . In 2017 , the Total population of Kenya amounted to approximately 56.43 millions Inhabitants .


Example 434:
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[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .
generated: 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 .


Example 435:
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[3] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the development of templateTitle[0] ' templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[0] was templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the development of Michigan ' GDP from 2000 to 2018 . In 2018 , the Real GDP of Michigan was 468.39 billion U.S. dollars .


Example 436:
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[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] in templateTitle[2] from templateXValue[min] to templateXValue[max] . According to the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] templateYValue[max] templateYLabel[5] templateYLabel[3] of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .
generated: This statistic shows the Share of Urbanization in Kuwait from 2008 to 2018 . According to the Share of urban population in the total population of a country . In 2018 , 100 100 total population of Kuwait 's total population lived in urban areas and cities .


Example 437:
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[3] templateTitle[4] in templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . For instance , templateYValue[0] templateYLabel[0] of templateTitle[0] templateTitle[1] 's templateYLabel[2] lived below the templateTitle[3] line in templateXValue[max]

generated_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] templateYLabel[0] of templateTitle[0] ' templateYLabel[2] lived below the templateTitle[2] line .
generated: This statistic shows the - poverty in New from 2000 to 2018 . In 2018 , 9.5 Percentage of New ' population lived below the - line .


Example 438:
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 . Digital as a whole accounted for 59 percent of consumer payment transactions .
gold_template: This statistic presents the most popular templateTitle[8] methods for everyday templateTitle[3] according to internet users templateTitle[4] as of June templateTitle[5] . When asked to think about they templateTitle[8] methods for their ten most recent templateTitle[3] , 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[3] with templateYValue[max] percent . Digital as a whole accounted for 59 percent of templateTitle[2] templateTitle[8] templateTitle[3] .

generated_template: This statistic shows the results of a survey on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[5] templateTitle[6] as of May templateTitle[7] . The survey , templateYValue[max] percent of templateYLabel[2] stated they had templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] .
generated: This statistic shows the results of a survey on the Distribution of consumer transactions in 2018 , as of May by . The survey , 41 percent of respondents stated they had Other_online Other_online Other_online Other_online Other_online .


Example 439:
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[3] that were templateTitle[1] templateYLabel[3] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[min] , some templateYValue[15] templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the United States were templateTitle[1] templateYLabel[3] . A templateTitle[1] templateYLabel[3] means that a child was delivered after less than 37 weeks of gestation .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateYLabel[5] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the European Union and templateTitle[5] was at templateYValue[0] percent .
generated: This statistic shows the Percentage of of rate 1990 from 1990 to 2018 . In 2018 , the Percentage of all in the N/A . In 2018 , the Percentage of of the European Union and - was at 10.02 percent .


Example 440:
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 templateYLabel[2] templateYLabel[3] templateTitle[0] templateTitle[1] templateYLabel[4] templateTitle[6] in templateTitle[9] , templateTitle[7] templateXLabel[0] . In templateTitle[9] , templateYLabel[2] templateTitle[0] templateTitle[1] templateYLabel[4] templateTitle[5] in templateXValue[0] accounted for around templateYValue[max] templateYLabel[0] of the templateTitle[6] 's total templateYLabel[2] templateYLabel[3] grid-connected templateTitle[1] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[2] as of January templateTitle[7] , templateTitle[10] templateXLabel[0] . The global templateTitle[2] templateTitle[3] templateYLabel[2] templateTitle[1] templateTitle[2] templateTitle[3] as of January templateTitle[8] was templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . In 2017 , including templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of January templateTitle[7] templateTitle[8] , templateYLabel[1] templateYLabel[2] templateYLabel[3] 1,000 templateYLabel[5] templateYLabel[6] templateYLabel[0] templateYLabel[1] categories in the templateXLabel[0] .
generated: The statistic shows the Percentage of capacity - newly as of January by , N/A Country . The global capacity - newly PV capacity - as of January country was 45 Percentage of newly installed . In 2017 , including Solar PV capacity - new of January by country , of newly installed 1,000 N/A N/A Percentage of categories in the Country .


Example 441:
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] templateYLabel[3] templateYLabel[0] in templateTitle[0] from templateXValue[last] to templateXValue[0] templateXValue[0] . In templateXValue[0] templateXValue[0] , templateYValue[min] templateYLabel[2] smartphones were shipped in templateTitle[0] .

generated_template: The statistic shows the total annual templateTitle[1] templateTitle[2] templateYLabel[0] of the world from templateXValue[min] to templateXValue[max] . In templateXValue[2] , there were templateYValue[min] templateYLabel[2] templateYLabel[3] templateTitle[2] .
generated: The statistic shows the total annual smartphone unit Shipments of the world from Q1_2018 to Q1_2018 . In 2016 , there were 109.6 million units unit .


Example 442:
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[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitle[2] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[2] templateYLabel[3] in the templateYLabel[5] templateYLabel[3] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitle[2] 's templateYLabel[5] templateYLabel[3] lived in templateYLabel[2] 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 443:
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[2] templateYLabel[2] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] templateXLabel[2] in templateTitle[10] , by the templateXLabel[0] of templateXLabel[2] . templateYValue[max] templateTitle[0] of templateYLabel[2] with templateXValue[last] and templateXValue[last] templateXLabel[2] used templateTitle[5] templateTitle[6] templateTitle[7] in templateTitle[10] .

generated_template: This statistic shows the percentage of templateYLabel[2] that hired templateTitle[4] templateTitle[5] templateTitle[6] templateXValue[min] to templateXValue[1] and then those templateYLabel[2] that were planning to hire templateTitle[4] templateTitle[5] for templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the templateYLabel[2] in this survey said that they intended to hire templateTitle[4] templateTitle[5] in the next templateXLabel[0] .
generated: This statistic shows the percentage of companies that hired using self-insured health 3_to_49 to 50_to_199 and then those companies that were planning to hire using self-insured for 3_to_49 . In 3_to_49 , 80 percent of the companies in this survey said that they intended to hire using self-insured in the next Number .


Example 444:
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 . It is headquartered in London , UK .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[4] reported some 56.4 billion templateYLabel[3] templateYLabel[4] 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] . It is headquartered in London , UK .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Revenue of revenue - Upstream segment 2010 from 2010 to 2018 . In 2018 , the BP 's revenue - Upstream segment 2010 amounted to 75754 million U.S. dollars .


Example 445:
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[3] in the United States templateTitle[4] were templateTitle[6] users as of March templateTitle[7] , sorted templateTitle[9] templateTitle[10] and templateTitle[3] group . During that period of time , templateYValue[max] percent of female templateTitle[2] teens aged templateTitle[7] to 17 years used the social networking app .

generated_template: The statistic shows the results of a survey , conducted in the United States in templateTitle[4] templateTitle[5] templateTitle[6] . According to the survey , templateYValue[max] percent of female students in the United States were templateXValue[0] .
generated: The statistic shows the results of a survey , conducted in the United States in who use Instagram . According to the survey , 64 percent of female students in the United States were Boys_13-14 .


Example 446:
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[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[6] , templateTitle[8] or hookah in templateTitle[12] countries in templateTitle[13] . A majority of templateYValue[max] percent of templateYLabel[2] said they templateTitle[4] templateXValue[1] templateTitle[5] templateTitle[6] templateTitle[6] , templateTitle[8] or hookah products . Additionally , the templateTitle[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] oral , nasal or chewing tobacco can be found at the following .

generated_template: templateTitle[2] is a widely celebrated tradition in the United States as of July templateTitle[5] templateTitle[6] , with a survey . During the survey period , templateYValue[max] percent of templateYLabel[2] stated they had templateXValue[0] templateXValue[0] .
generated: individuals is a widely celebrated tradition in the United States as of July tried waterpipe , with a survey . During the survey period , 87 percent of respondents stated they had Yes Yes .


Example 447:
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[2] in templateTitle[4] was down at the lowest point of the observed period in templateXValue[max] , when there were templateYValue[min] templateYLabel[2] . At the beginning of the observed period , in templateXValue[min] , the templateYLabel[0] of templateYLabel[2] amounted to templateYValue[max] .

generated_template: Since templateXValue[min] , the templateYLabel[0] of templateYLabel[2] in templateTitle[2] has declined nearly year-on-year . There were templateYValue[max] templateYLabel[2] in templateTitle[2] 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 . Healthcare workers in templateTitle[2] Although the templateYLabel[0] of healthcare institutions in templateTitle[2] has decreased , the templateYLabel[0] of personnel working increased . The templateYLabel[0] of healthcare workers in templateTitle[2] in the period templateXValue[min] to templateXValue[2] has increased by over templateTitle[3] hundred thousand . Furthermore , the templateYLabel[0] of practicing nurses in templateTitle[2] increased by around 35 thousand since the templateXLabel[0] templateXValue[min] . Spending indicators on health templateTitle[2] 's expenditure on healthcare amounted to 10 percent of its ' GDP in templateXValue[1] . It has been increasing overall since 1980 . In comparison to other European countries , templateTitle[2] ranked relatively high on templateYLabel[2] treatments in templateTitle[4] has been increasing during the last decade , covering almost 75 million euros in templateXValue[max] .
generated: Since 2007 , the Number of hospitals in hospitals has declined nearly year-on-year . There were 325 hospitals in hospitals in the Year 2007 and by 2017 this figure had fallen to 247 . This is a drop of over 23 percent in the provided time period . Healthcare workers in hospitals Although the Number of healthcare institutions in hospitals has decreased , the Number of personnel working increased . The Number of healthcare workers in hospitals in the period 2007 to 2015 has increased by over in hundred thousand . Furthermore , the Number of practicing nurses in hospitals increased by around 35 thousand since the Year 2007 . Spending indicators on health hospitals 's expenditure on healthcare amounted to 10 percent of its ' GDP in 2016 . It has been increasing overall since 1980 . In comparison to other European countries , hospitals ranked relatively high on hospitals treatments in Finland has been increasing during the last decade , covering almost 75 million euros in 2017 .


Example 448:
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 . Public houses , once considered an important pillar of British communities , have faced several challenges related to changing consumer behavior . The 2007 indoor smoking ban and rising alcohol prices deterred people from drinking in pubs , sparking a trend in ‘ pre-loading ' on cheap supermarket-bought alcohol before going out . Socializing at home has also become more common place thanks to developments in technology and home entertainment . Not to mention younger generations are drinking less , with more 16 - 24-year olds choosing to not drink alcohol at all . New trends continue to attract pub goers Although many businesses have succumbed to closure , the nation 's pub culture is still thriving , albeit in different ways . The popularity of pubs for eating out has resulted in many businesses increasing their food offering and taking advantage of new drinking trends such as craft and non-alcoholic beers . Independent pubs , although having a seemingly higher risk of closure , have in fact increased in number : there were around 4,550 more independently owned pubs in the UK in 2017 than ten years earlier . This may be due in part to the emergence of micro-breweries , and consumers becoming more sophisticated in their tastes , preferring to visit traditional pubs or other independents which cater to more sophisticated tastes than the larger pub chains . Independently owned pubs still make up the largest proportion of pubs in the UK , the rest being managed , tenanted or leased by a brewer or pub company .
gold_template: How many templateYLabel[2] are there in the templateTitle[8] ? There were approximately 47,600 templateYLabel[2] operating in the templateTitle[5] templateTitle[6] in templateXValue[max] . This represented a decrease of approximately 7,200 templateYLabel[2] in the last ten years , and over 13,200 templateYLabel[2] since templateXValue[18] . templateYLabel[2] in decline Several factors have been suggested for the decline in templateYLabel[2] in the templateTitle[8] . Public houses , once considered an important pillar of British communities , have faced several challenges related to changing consumer behavior . The templateXValue[11] indoor smoking ban and rising alcohol prices deterred people from drinking in templateYLabel[2] , sparking a trend in ‘ pre-loading ' on cheap supermarket-bought alcohol before going out . Socializing at home has also become more common place thanks to developments in technology and home entertainment . Not to mention younger generations are drinking less , with more 16 templateTitle[11] 24-year olds choosing to not drink alcohol at all . New trends continue to attract templateYLabel[2] goers Although many businesses have succumbed to closure , the nation 's templateYLabel[2] culture is still thriving , albeit in different ways . The popularity of templateYLabel[2] for eating out has resulted in many businesses increasing their food offering and taking advantage of new drinking trends such as craft and non-alcoholic beers . Independent templateYLabel[2] , although having a seemingly higher risk of closure , have in fact increased in templateYLabel[0] : there were around 4,550 more independently owned templateYLabel[2] in the templateTitle[8] in templateXValue[1] than ten years earlier . This may be due in part to the emergence of micro-breweries , and consumers becoming more sophisticated in their tastes , preferring to visit traditional templateYLabel[2] or other independents which cater to more sophisticated tastes than the larger templateYLabel[2] chains . Independently owned templateYLabel[2] still make up the largest proportion of templateYLabel[2] in the templateTitle[8] , the rest being managed , tenanted or leased by a brewer or templateYLabel[2] company .

generated_template: The statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] increased by templateYValue[0] percent .
generated: The statistic shows the Number Number of of the the United from 48 to 2018 . In 2018 , the Number of of the the United increased by 47.6 percent .


Example 449:
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[3] templateTitle[4] in the templateYLabel[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[max] templateYLabel[6] templateYLabel[7] for templateTitle[3] , non-Hispanic templateTitle[4] . The templateYLabel[0] templateTitle[0] templateYLabel[1] of the United States can be accessed here .

generated_template: The statistic shows the total templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[8] , the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] amounted to approximately templateYValue[min] percent .
generated: The statistic shows the total Median of of white families in from 1990 to 2018 . In 2010 , the Median of of white families in amounted to approximately 55568 percent .


Example 450:
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 United States templateTitle[7] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[2] templateTitle[3] templateYLabel[0] of templateYLabel[2] templateTitle[7] amounted to about templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the annual templateTitle[2] templateTitle[2] annual templateYLabel[0] templateTitle[4] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the annual food food annual Expenditure expenditures worldwide from 2010 to 2018 . In 2016 , the Average annual food Expenditure amounted to 3154 U.S. dollars N/A .


Example 451:
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[2] templateTitle[3] to templateYLabel[6] templateYLabel[7] templateYLabel[8] ( templateTitle[11] ) in templateTitle[14] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] in templateYLabel[3] to the templateTitle[11] in templateTitle[14] was at approximately templateYValue[6] percent .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] in templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Ratio of government expenditure in size relation from 2014 to 2017 , with projections up until 2024 . In 2017 , the Ratio of government balance in relation to the .


Example 452:
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[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] templateTitle[9] in the United States as of May templateTitle[11] , templateTitle[13] templateTitle[14] . During the survey , templateYValue[max] percent of templateXValue[0] said that they found templateTitle[6] templateTitle[7] templateTitle[8] templateTitle[9] .

generated_template: This statistic shows the results of a survey , conducted by Gallup in the United States in May templateTitle[11] , on templateTitle[3] templateTitle[4] templateTitle[5] gay and lesbian templateTitle[7] . During this survey , templateYValue[max] percent of templateYLabel[2] stated that they used the social networking site .
generated: This statistic shows the results of a survey , conducted by Gallup in the United States in May 2014 , on U.S. who find gay and lesbian vacation . During this survey , 74 percent of respondents stated that they used the social networking site .


Example 453:
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[3] templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitle[3] templateTitle[4] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in 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[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] 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 454:
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 . From 2014 to 2017 , the median age at first sexual intercourse remained at 17 years , regardless of gender . But before experiencing love with a partner , teenagers first discover their sexuality alone or through pornographic movies . Most teenagers were aged 13 to 14 years the first time they masturbated , whereas the majority of young French people were between 14 and 15 years old when they watched a porno movie for the first time . A change in sexual practices Easy access to porn from the Internet might has an influence on the way young adults see their sexuality . In 2017 , 18 percent of female teenagers in France thought that pornography had a rather negative impact on their sexuality . On the other hand , sexual liberation , as well as the influence of feminist rhetoric in society , might also impact the evolution of sexual practices . In 1992 , 42 percent of teenagers aged 18 to 19 years declared having already practiced oral sex . In 2013 , this share reached nearly 80 percent .
gold_template: In templateTitle[14] , it appears that the majority of French teenagers were in middle school when they had templateTitle[9] templateTitle[10] templateTitle[11] . Love appears to be an important area of life at a templateTitle[2] templateTitle[7] , with more than 50 percent of templateTitle[2] French templateTitle[3] stating that love relationships were important for them . templateTitle[10] love experiences Even though new technologies and smartphones may have changed the way teenagers live templateTitle[9] love life , it seems that the templateTitle[7] for templateTitle[10] love and sex experiences has templateXValue[last] really changed templateXValue[5] the templateXValue[0] . From 2014 to 2017 , the median templateTitle[7] at templateTitle[10] sexual intercourse remained at templateXValue[5] templateXValue[0] , regardless of gender . But before experiencing love with a partner , teenagers templateTitle[10] discover templateTitle[9] sexuality alone or through pornographic movies . Most teenagers were aged templateXValue[2] to templateXValue[2] templateXValue[0] the templateTitle[10] time they masturbated , whereas the majority of templateTitle[2] French templateTitle[3] were between templateXValue[2] and templateXValue[3] templateXValue[0] templateXValue[0] when they watched a porno movie for the templateTitle[10] time . A change in sexual practices Easy access to porn from the Internet might has an influence on the way templateTitle[2] adults see templateTitle[9] sexuality . In 2017 , 18 percent of female teenagers in templateTitle[13] thought that pornography had a rather negative impact on templateTitle[9] sexuality . On the other hand , sexual liberation , as well as the influence of feminist rhetoric in society , might also impact the evolution of sexual practices . In 1992 , 42 percent of teenagers aged 18 to 19 templateXValue[0] declared having already practiced oral sex . In templateTitle[14] , this templateYLabel[0] reached nearly 80 percent .

generated_template: This statistic shows the results of a survey on conducted in the United States in templateTitle[7] on tattoos and body modification . During the survey the survey , templateYValue[max] percent of templateYLabel[2] stated they would templateXValue[0] the templateXValue[3] .
generated: This statistic shows the results of a survey on conducted in the United States in age on tattoos and body modification . During the survey the survey , 31 percent of respondents stated they would Under_11_years_old the 15_years_old .


Example 455:
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[8] templateTitle[9] , 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[8] templateTitle[9] was at 420 templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: The timeline shows 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 29 templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The timeline shows 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 29 million U.S. dollars .


Example 456:
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 . The Netherlands ranked in the mid to lower end of European countries most prone to road deaths . In 2018 , the Benelux country had seen 39 incidents per one million inhabitants . Perceived causes According to a 2017 survey , close to half of Dutch residents believed that the most common cause of fatal accidents on motorways was inattention . By comparison , 27 percent saw drowsiness as a likely factor contributing to the number of road deaths . Car stock In contrast to the steady decrease in fatal accidents , the Netherlands had become home to a growing number of passenger cars . Following a continuous increase since 1990 , there were 8.4 million such vehicles registered in the country by 2017 .
gold_template: In templateXValue[max] , templateYValue[0] people were killed on roads in the templateTitle[6] . Between templateXValue[min] and templateXValue[max] , templateTitle[2] traffic templateYLabel[2] had seen a templateTitle[6] 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[2] templateTitle[3] down to below 500 by 2020 . The templateTitle[6] ranked in the mid to lower end of European countries most prone to templateTitle[2] templateTitle[3] . In templateXValue[max] , the Benelux country had seen 39 incidents per templateTitle[9] million inhabitants . Perceived causes According to a templateXValue[1] survey , close to half of Dutch residents believed that the most common cause of templateYLabel[2] accidents on motorways was inattention . By comparison , 27 percent saw drowsiness as a likely factor contributing to the templateYLabel[0] of templateTitle[2] templateTitle[3] . Car stock In contrast to the steady decrease in templateYLabel[2] accidents , the templateTitle[6] had become home to a growing templateYLabel[0] of passenger cars . Following a continuous increase since 1990 , there were 8.4 million such vehicles registered in the country by templateXValue[1] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] in templateTitle[2] templateTitle[0] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[min] templateYLabel[2] reported due to templateTitle[0] in the United States .
generated: This statistic shows the Number of fatalities in road Number in the United States from 2006 to 2018 . In 2018 , there were a total of 570 fatalities reported due to Number in the United States .


Example 457:
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 . Following the liquidation of fellow British contractor Carillion , there were still three construction companies in the country with an annual turnover exceeding three billion British pounds . Balfour Beatty 's financials The fall in employee numbers is in line with a decrease in revenues generated by the company . In 2018 , the company made 7.8 million British pounds in underlying revenue . The lowest figure since the beginning of the reporting period in 2011 . UK construction industry at a glance The UK 's construction industry had an annual production value of 288.9 billion euros as of 2016 . This was a decline of roughly 12 percent compared to the previous year . In contrast , the industry 's GVA ( 540236 ) rose continuously between 2009 and 2016 .
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[3] 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] . Following the liquidation of fellow British contractor Carillion , there were still three construction companies in the country with an annual turnover exceeding three billion British pounds . templateTitle[0] templateTitle[1] templateTitle[3] financials The fall in templateYLabel[3] numbers is in line with a decrease in revenues generated by the company . In templateXValue[max] , the company made 7.8 million British pounds in underlying revenue . The lowest figure since the beginning of the reporting period in templateXValue[min] . UK construction industry at a glance The UK templateTitle[3] construction industry had an annual production value of 288.9 billion euros as of templateXValue[2] . This was a decline of roughly 12 percent compared to the previous templateXLabel[0] . In contrast , the industry templateTitle[3] GVA ( 540236 ) rose continuously between 2009 and templateXValue[2] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] was at templateYValue[max] percent .
generated: The statistic shows the Average number of 's average from 2011 to 2018 . In 2018 , the Average number of 's average was at 50304 percent .


Example 458:
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[2] 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[2] templateYLabel[0] of just under 6.9 templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] worldwide from templateXValue[min] to templateXValue[max] . The templateTitle[3] company generated approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateYLabel[0] in templateXValue[max] .
generated: This statistic shows the Revenue of annual revenue 2012 - worldwide from 2012 to 2019 . The revenue company generated approximately 6.89 billion U.S. dollars in Revenue in 2019 .


Example 459:
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 . However , beer production in the United States has decreased since the 1990s . In 2018 , the U.S. revenue of alcoholic beverages exceeded 70 billion U.S. dollars .
gold_template: The timeline shows the templateTitle[4] , templateTitle[6] , and distilled alcoholic beverages templateYLabel[0] of merchant wholesalers in the United States from templateTitle[7] to templateTitle[9] . In templateTitle[9] , the templateTitle[4] , templateTitle[6] , and distilled alcoholic beverages templateYLabel[0] of templateYLabel[3] merchant wholesalers amounted to about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . 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 . However , templateTitle[4] production in the United States has decreased since the 1990s . In 2018 , the templateYLabel[3] revenue of alcoholic beverages exceeded 70 templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The timeline shows templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The timeline shows U.S. wholesale sales Sales in the United States from 02 to 17 . In 14 , U.S. wholesale sales Sales amounted to 147.34 billion U.S. dollars .


Example 460:
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[3] templateTitle[1] of templateYLabel[4] consumers from templateXValue[min] to templateXValue[max] . October of templateXValue[1] was the most generous regarding templateTitle[1] on templateTitle[4] among Americans , estimating a likelihood of templateTitle[1] around templateYValue[max] templateYLabel[4] templateYLabel[5] ( on templateYLabel[0] ) . Since then , the templateYLabel[2] reserved for templateTitle[3] presents has steadily declined .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] was at approximately templateYValue[0] percent .
generated: The statistic shows the Average estimated amount in the United States from 2006 to 2011 . In 2009 , the Average estimated amount in U.S. dollars . In 2011 , the Average estimated of Christmas gifts was at approximately 907 percent .


Example 461:
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 templateTitle[5] a social news and entertainment website and Conde Nast templateTitle[5] a magazine publisher . Its templateXValue[min] templateYLabel[0] is estimated to have amounted to templateYValue[0] templateYLabel[2] US templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] worldwide from templateXValue[min] to templateXValue[max] . The templateTitle[3] company generated approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateYLabel[0] in templateXValue[max] . templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] was founded in 1969 and now has more than 1,200 properties worldwide .
generated: This statistic shows the Revenue of ' revenue 2006 - worldwide from 2006 to 2014 . The revenue company generated approximately 7.14 billion U.S. dollars in Revenue in 2014 . ' revenue 2006 - was founded in 1969 and now has more than 1,200 properties worldwide .


Example 462:
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[6] templateTitle[7] , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] from templateTitle[0] and drawing material templateTitle[1] templateTitle[2] reached 4.78 billion British pounds which was the highest point of templateYLabel[0] templateYLabel[0] the nine templateXLabel[0] period .

generated_template: This statistic shows the total templateYLabel[0] of the British templateTitle[0] templateTitle[1] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[min] thousand templateYLabel[2] in templateTitle[5] templateTitle[6] .
generated: This statistic shows the total Turnover of the British Stationery retail the United from 2008 to 2017 . In 2017 , there were approximately 4025 thousand million in the United .


Example 463:
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 . In 2024 , the number of monthly active smartphone users is projected to reach 5.19 million individuals . This would be an increase of approximately 550 thousand new users in comparison to the first year recorded . Respectively the smartphone penetration rate is forecasted to reach 92.64 percent as of 2024 .
gold_template: This statistic displays the development in templateYLabel[0] templateYLabel[1] templateTitle[4] in templateTitle[6] in templateXValue[min] with a templateTitle[0] from templateXValue[5] to templateXValue[max] . In templateXValue[min] , the templateTitle[4] of templateYLabel[0] templateYLabel[1] amounted to templateYValue[min] templateYLabel[3] . In the same templateXLabel[0] , templateYLabel[0] penetration rate was at 86.95 percent . In templateXValue[max] , the templateTitle[4] of monthly active templateYLabel[0] templateYLabel[1] is projected to reach templateYValue[max] templateYLabel[3] individuals . This would be an increase of approximately 550 thousand new templateYLabel[1] in comparison to the first templateXLabel[0] recorded . Respectively the templateYLabel[0] penetration rate is forecasted to reach 92.64 percent as of templateXValue[max] .

generated_template: This statistic shows the number of templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[6] with a forecast up to templateXValue[max] . In templateXValue[6] , the number of templateYLabel[0] templateYLabel[1] in templateTitle[3] and templateTitle[5] is estimated to reach templateYValue[6] templateYLabel[3] .
generated: This statistic shows the number of Smartphone users in user from 2018 to 2018 with a forecast up to 2024 . In 2018 , the number of Smartphone users in user and in is estimated to reach 4.64 millions .


Example 464:
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[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in 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[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitle[4] 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 465:
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[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic presents the templateYLabel[0] of the templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . The company generated a global templateTitle[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] . In templateXValue[max] , this figure had revenues of templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic presents the Revenue of the 2013 - 2018 N/A from 2013 to 2018 . The company generated a global Gannett of 3.32 billion U.S. dollars in 2018 . In 2018 , this figure had revenues of 2.92 billion U.S. dollars .


Example 466:
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 . Russian oil and gas company Gazprom more than doubled their revenues between 2009 and 2014 from refined petroleum products , from some 17.1 billion U.S. dollars in 2009 to 42.6 billion U.S. dollars in 2014 , however revenues dropped to 34.6 billion U.S. dollars in 2018 . Petroleum products refer to materials derived from crude oil and refined within oil refineries . Often , petroleum is converted into various classes of fuels , most commonly gasoline and fuel oil . Apart from common fuels , refineries can also produce chemicals that are often used in the production of plastics . There are a variety of by-products that are also derived from refining petroleum products such as fertilizer , perfumes , soaps , and vitamin pills . In Germany , it is expected that the country 's consumption of some refined petroleum products will decrease . For example , diesel consumption is expected to reach 27 million metric tons and heavy heating oil consumption is expected to fall to 4 million metric tons by 2030 . Diesel is primarily used in diesel cars but can also be used for heating within private homes . It is a less volatile gas than gasoline . Sweden has several refineries , totaling 33 as of 2015 , dedicated to the manufacturing of petroleum products .
gold_template: This statistic displays a templateTitle[0] of the templateTitle[3] and templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in the United States templateYLabel[1] templateXValue[min] to templateXValue[max] . Through templateXValue[min] , the templateTitle[3] and templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] is expected to increase by templateYValue[max] percent . templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[6] It is projected that the growth of templateTitle[7] templateYLabel[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] 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] . Russian oil and gas company Gazprom more than doubled their revenues between 2009 and 2014 templateYLabel[1] refined templateTitle[3] products , templateYLabel[1] some 17.1 billion templateTitle[2] dollars in 2009 to 42.6 billion templateTitle[2] dollars in 2014 , however revenues dropped to 34.6 billion templateTitle[2] dollars in 2018 . templateTitle[3] products refer to materials derived templateYLabel[1] crude oil and refined within oil refineries . Often , templateTitle[3] is converted into various classes of fuels , most commonly gasoline and fuel oil . Apart templateYLabel[1] common fuels , refineries can also produce chemicals that are often used in the production of plastics . There are a variety of by-products that are also derived templateYLabel[1] refining templateTitle[3] products such as fertilizer , perfumes , soaps , and vitamin pills . In Germany , it is expected that the country 's consumption of some refined templateTitle[3] products will decrease . templateTitle[0] example , diesel consumption is expected to reach 27 million metric tons and heavy heating oil consumption is expected to fall to templateTitle[10] million metric tons by 2030 . Diesel is primarily used in diesel cars but can also be used templateTitle[0] heating within private homes . It is a less volatile gas than gasoline . Sweden has several refineries , totaling 33 as of 2015 , dedicated to the manufacturing of templateTitle[3] products .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] amounted to around templateYValue[max] percent .
generated: The statistic shows the Forecast on of petroleum refinery end-use from 2020 to N/A , with projections up until 2024 . In N/A , the Forecast on U.S. petroleum refinery end-use amounted to around 2.2 percent .


Example 467:
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 . The consumption of this delight decreased slightly in 2018 , standing at approximately 164.1 million kilograms that year . The chocolate business in Spain The most consumed chocolate and cocoa products in Spanish households were regular chocolates and instant cocoa mixes , both exceeding 55 million kilograms in 2017 . The average Spaniard consumed approximately 3.6 kilograms of these products during the same period and spent around 25 euros a year to satisfy their utmost sweet desires . People in Spain are very likely to purchase chocolate and cocoa products in supermarkets and hypermarkets , which account for 93 percent of the retail market for these goods . The number of cocoa , chocolate and confectionery manufacturers has been dwindling in recent years , with 598 companies active in this sector in 2016 compared to 752 companies in 2008 . Sweet Old Europe Some of the countries of the Old Continent have made a living out of chocolate and cocoa products . Traditionally , chocolate has been a sweet treat in many European countries , and this trend is still reflected in today 's consumption of these products . Switzerland topped the list of greatest chocolate consumers , with nearly 9 kilos of chocolate per capita in 2015 . The neighboring country Germany ranked second at 7.9 kilograms of chocolate per person . Switzerland was also the European country that spent the most on chocolate products , with nearly 237 dollars a year per person . Many people claim that chocolate is good for the soul , and the Poles would agree with that since nearly half the population stated that they ate this sweet delicacy to lift their mood . Not many people think chocolate is a healthy snack , although 26 percent of Spaniards seem to believe so . Perhaps healthy for the soul , too .
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[2] templateTitle[3] templateYLabel[1] amounting to templateYValue[max] templateYLabel[3] templateYLabel[4] in templateXValue[5] . The templateYLabel[1] of this delight decreased slightly in templateXValue[max] , standing at approximately templateYValue[2] templateYLabel[3] templateYLabel[4] that templateXLabel[0] . The templateTitle[0] business in Spain The most consumed templateTitle[0] and templateTitle[2] templateTitle[3] in templateTitle[6] templateTitle[7] were regular chocolates and instant templateTitle[2] mixes , both exceeding 55 templateYLabel[3] templateYLabel[4] in templateXValue[1] . The average Spaniard consumed approximately 3.6 templateYLabel[4] of these templateTitle[3] during the same period and spent around 25 euros a templateXLabel[0] to satisfy their utmost sweet desires . People in Spain are very likely to purchase templateTitle[0] and templateTitle[2] templateTitle[3] in supermarkets and hypermarkets , which account for 93 percent of the retail market for these goods . The number of templateTitle[2] , templateTitle[0] and confectionery manufacturers has been dwindling in recent years , with 598 companies active in this sector in templateXValue[2] compared to 752 companies in templateXValue[min] . Sweet templateTitle[7] Europe Some of the countries of the templateTitle[7] Continent have made a living out of templateTitle[0] and templateTitle[2] templateTitle[3] . Traditionally , templateTitle[0] has been a sweet treat in many European countries , and this trend is still reflected in today 's templateYLabel[1] of these templateTitle[3] . Switzerland topped the list of greatest templateTitle[0] consumers , with nearly 9 kilos of templateTitle[0] per capita in templateXValue[3] . The neighboring country Germany ranked second at 7.9 templateYLabel[4] of templateTitle[0] per person . Switzerland was also the European country that spent the most on templateTitle[0] templateTitle[3] , with nearly 237 dollars a templateXLabel[0] per person . Many people claim that templateTitle[0] is good for the soul , and the Poles would agree with that since nearly half the population stated that they templateTitle[0] this sweet delicacy to lift their mood . Not many people think templateTitle[0] is a healthy snack , although 26 percent of Spaniards seem to believe so . Perhaps healthy for the soul , too .

generated_template: The statistic shows the total templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] came to templateYValue[max] thousand templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the total Total of cocoa products consumption in from 2008 to 2018 . In 2018 , the Total of cocoa products consumption in came to 165.5 thousand million kilograms N/A .


Example 468:
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[3] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the average templateYLabel[0] templateYLabel[1] in templateTitle[3] amounted to about templateYValue[7] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in 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[3] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[4] 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 469:
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[2] of templateYLabel[4] in the templateTitle[10] templateTitle[11] ( templateTitle[13] ) 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[4] .

generated_template: This statistic shows the total templateYLabel[1] of the global templateYLabel[0] templateYLabel[1] templateTitle[5] from the 2001/02 season to the 2018/19 season . In 2018/19 , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States was templateYValue[max] percent . templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] is an increase of templateYValue[7] templateYLabel[4] templateYLabel[5] templateYLabel[6] in templateYLabel[0] and the previous templateXLabel[0] . The templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] , the next few years . There are many causes of goods and services including the economic crisis of the templateYLabel[0] templateYLabel[1] templateYLabel[2] has fluctuated since the past few years . This is a given period of decline in a given period of time , an increase to continue to continue to a few years . There are primarily an increase of material possessions or less than 50 percent from the future as well as recent years . The lack of the templateYLabel[1] templateYLabel[2] sector and accessories . This means that the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] increased . This may be attributed to increase in the number of the number of the templateTitle[10] countries such as wind power , especially when the average templateTitle[4] templateTitle[0] templateTitle[3] and is also locatzed on the global financial crisis . This number of other countries have been less attractive in the next few years , the primarily when the decline , especially Saudi Arabia .
generated: This statistic shows the total as of the global Expenditure as share from the 2001/02 season to the 2018/19 season . In 2018/19 , the Expenditure as share in the United States was 39.7 percent . Public sector expenditure as is an increase of 39.1 GDP N/A N/A in Expenditure and the previous Year . The Public sector expenditure as , the next few years . There are many causes of goods and services including the economic crisis of the Expenditure as share has fluctuated since the past few years . This is a given period of decline in a given period of time , an increase to continue to continue to a few years . There are primarily an increase of material possessions or less than 50 percent from the future as well as recent years . The lack of the as share sector and accessories . This means that the Public sector Expenditure as increased . This may be attributed to increase in the number of the number of the United countries such as wind power , especially when the average a Public as and is also locatzed on the global financial crisis . This number of other countries have been less attractive in the next few years , the primarily when the decline , especially Saudi Arabia .


Example 470:
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[4] templateYLabel[2] at templateTitle[5] establishments in templateTitle[6] from templateXValue[last] to templateXValue[0] . There were around 1.1 million templateYLabel[2] at templateTitle[5] establishments in templateTitle[6] in templateXValue[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateYLabel[3] amounted to templateYValue[min] percent .
generated: The statistic shows the Number of arrivals in the United States from 2018 to 2018 . In 2015 , the Number of arrivals in in amounted to 854.72 percent .


Example 471:
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 35 billion templateYLabel[3] in templateYLabel[0] in the fiscal templateXLabel[0] of templateXValue[max] .

generated_template: The statistic shows the annual templateYLabel[0] templateTitle[1] templateTitle[2] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] worldwide amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the annual Revenue - revenue worldwide from 2009 to 2018 . In 2018 , the Iberdrola - revenue worldwide amounted to 35075.9 million euros .


Example 472:
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[10] 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[2] stated that they oppose templateTitle[3] templateXValue[1] because their religion and/or the Bible templateXValue[0] it 's templateXValue[0] .

generated_template: templateXValue[0] was the most popular templateTitle[1] in the United States , templateXValue[1] templateXValue[1] templateXValue[1] and templateXValue[2] . According to the survey findings , the majority of responding templateTitle[0] developers were located in templateXValue[0] templateXValue[0] , while templateYValue[3] percent resided in templateXValue[3] .
generated: Religion/Bible_says_it_is_wrong was the most popular for in the United States , Marriage_should_be_between_a_man_and_a_woman Marriage_should_be_between_a_man_and_a_woman Marriage_should_be_between_a_man_and_a_woman and Morally_wrong/Have_traditional_beliefs . According to the survey findings , the majority of responding Reasons developers were located in Religion/Bible_says_it_is_wrong Religion/Bible_says_it_is_wrong , while 6 percent resided in Civil_unions_are_sufficient .


Example 473:
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[3] by templateTitle[0] in templateTitle[4] between templateXValue[min] and templateXValue[max] . European templateTitle[2] of the templateTitle[0] cars rose from 400 thousand templateYLabel[2] templateYLabel[3] in templateXValue[min] to over 560 thousand templateYLabel[2] templateYLabel[3] by templateXValue[1] . In templateXValue[max] , there were templateYValue[0] thousand templateYLabel[2] of templateTitle[0] cars templateYLabel[3] in templateTitle[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] reached templateYValue[max] thousand templateYLabel[3] templateYLabel[4] . In templateXValue[8] , the templateYLabel[0] templateYLabel[1] stood at around templateYValue[min] percent .
generated: The statistic shows the Number of units in the 2003 - from 2003 to 2018 . In 2015 , the Nissan car sales in Europe 2003 - reached 560415 thousand sold N/A . In 2010 , the Number of stood at around 313437 percent .


Example 474:
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[3] templateTitle[4] templateYLabel[4] templateYLabel[5] from templateXValue[min] to templateXValue[max] . templateTitle[4] templateYLabel[4] templateYLabel[5] were forecasted to increase by templateYValue[0] percent in templateXValue[max] . The templateYLabel[3] daily templateYLabel[5] of the templateYLabel[4] industry in the Americas reached around 123.37 U.S. dollars in templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was estimated to increase by approximately templateYValue[2] percent .
generated: This statistic shows the Annual growth in in the United States from 2010 to 2018 . In 2016 , the Annual growth in average global was estimated to increase by approximately 2.5 percent .


Example 475:
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[3] templateTitle[4] templateTitle[5] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[3] templateTitle[4] templateTitle[5] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitle[3] templateTitle[4] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[3] templateTitle[4] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic depicts the Revenue of the Tampa Bay , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the Revenue of the Tampa Bay was 400 million U.S. dollars .


Example 476:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] 's templateYLabel[0] grew by an estimated templateYValue[0] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] . See templateTitle[3] 's templateYLabel[0] figures for comparison .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at templateYValue[0] percent .
generated: This statistic shows the Population growth of Afghanistan from 2008 to 2018 . In 2018 , the Population growth in Afghanistan was at 2.38 percent .


Example 477:
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 . Social networks could not only help the residents of the Philippines to connect with friends and family living far , but also serve as a timely source for news , or even purchasing products and services , and applying for jobs . Facebook is leading Among the most well-known social networks , Facebook remains in the lead in Philippines , with a penetration rate of about 40 percent , which is a higher rate of penetration than in Brazil or China . Digitalization and economic growth The strong digital presence and lifestyle of the Filipinos also testifies to the equally strong growth rate of the economy . Despite suffering from some devastating natural disasters over the past few years , the Philippines were able to prove that digitalization can be an important factor to maintain economic success .
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[2] by templateXValue[max] . templateTitle[1] templateTitle[2] in the templateTitle[0] The templateTitle[0] templateYLabel[0] an archipelagic country , which poses logistical problems for templateTitle[1] interaction and communication between residents from the various islands . templateTitle[1] networks could not only help the residents of the templateTitle[0] to connect with friends and family living far , but also serve as a timely source for news , or even purchasing products and services , and applying for jobs . Facebook is leading Among the most well-known templateTitle[1] networks , Facebook remains in the lead in templateTitle[0] , with a templateTitle[4] rate of about 40 percent , which is a higher rate of templateTitle[4] than in Brazil or China . Digitalization and economic growth The strong digital presence and lifestyle of the Filipinos also testifies to the equally strong growth rate of the economy . Despite suffering from some devastating natural disasters over the past few years , the templateTitle[0] were able to prove that digitalization can be an important factor to maintain economic success .

generated_template: This statistic gives information on the templateTitle[2] templateTitle[4] rate in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the Indonesian templateYLabel[2] were using the templateTitle[2] . In templateXValue[max] , this figure is projected to grow to templateYValue[max] percent .
generated: This statistic gives information on the media penetration rate in Philippines from 2017 to 2023 . In 2017 , 49 percent of the Indonesian population were using the media . In 2023 , this figure is projected to grow to 55 percent .


Example 478:
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[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In the templateXValue[min] calendar templateXLabel[0] , a templateYLabel[4] templateTitle[0] templateTitle[5] templateTitle[2] an average templateYLabel[0] of templateYValue[20] templateYLabel[5] templateYLabel[1] templateTitle[6] templateYLabel[2] of upland templateTitle[0] .

generated_template: The 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] templateTitle[7] templateYLabel[2] of templateTitle[0] templateTitle[2] was about templateYValue[1] templateYLabel[4] templateYLabel[5] .
generated: The 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 - pound of Cotton received was about 84.48 U.S. cents .


Example 479:
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[2] in the templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[last] to templateXValue[0] . At the first templateTitle[4] templateTitle[3] templateTitle[5] 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[3] Olympics .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the total templateTitle[5] amounted to templateYValue[max] templateYLabel[2] .
generated: The statistic shows the Number of Number of participants Winter from 2014_Sochi to 2014_Sochi . In 2014_Sochi , the Number of the total Games amounted to 2800 participants .


Example 480:
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[4] templateYLabel[5] .

generated_template: The statistic shows the development of templateTitle[0] ' templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[0] was templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the development of California ' GDP from 2000 to 2018 . In 2018 , the Real GDP of California was 2677.94 billion U.S. dollars .


Example 481:
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 . See global GDP for a global comparison . South Korea 's economy South Korea is doing quite well economically . It is among the leading export countries worldwide , it mainly exports electronics , automobiles and machinery . South Korea is also one of the leading import countries worldwide . Additionally , it is one of the leading countries with the largest proportion of global domestic product / GDP based on Purchasing Power Parity ( PPP ) . Its GDP has been increasing for the last few years , while the gross domestic product / GDP growth in South Korea has not been steady but increasing since 2009 . South Korea is an OECD member and a member of the G20 states . Among the latter , its GDP growth was higher than that of the United States or the European Union in 2013 . South Korea is one of the fastest-growing economies worldwide . Its standard of living is also considered to be quite high , the unemployment rate , which is one key factor , has been at around 3 percent , give or take a few percentage points , for the past decade . As a comparison , the United States ' unemployment rate was almost twice , sometimes three times as high as in South Korea during the same period . As for employment , South Korea 's rate is almost the same as that of the United States or France , with more than 60 percent of employed persons in the population .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) of templateTitle[7] templateTitle[8] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateTitle[4] 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[7] templateTitle[8] 's templateTitle[4] was around 1.72 trillion templateYLabel[5] templateYLabel[6] . See global templateTitle[4] for a global comparison . templateTitle[7] templateTitle[8] 's economy templateTitle[7] templateTitle[8] is doing quite well economically . It is among the leading export countries worldwide , it mainly exports electronics , automobiles and machinery . templateTitle[7] templateTitle[8] is also one of the leading import countries worldwide . Additionally , it is one of the leading countries with the largest proportion of global templateYLabel[1] templateYLabel[2] / templateTitle[4] based on Purchasing Power Parity ( PPP ) . Its templateTitle[4] has been increasing for the last few years , while the templateYLabel[0] templateYLabel[1] templateYLabel[2] / templateTitle[4] growth in templateTitle[7] templateTitle[8] has not been steady but increasing since templateXValue[15] . templateTitle[7] templateTitle[8] is an OECD member and a member of the G20 states . Among the latter , its templateTitle[4] growth was higher than that of the United States or the European Union in templateXValue[11] . templateTitle[7] templateTitle[8] is one of the fastest-growing economies worldwide . Its standard of living is also considered to be quite high , the unemployment rate , which is one key factor , has been at around 3 percent , give or take a few percentage points , for the past decade . As a comparison , the United States ' unemployment rate was almost twice , sometimes three times as high as in templateTitle[7] templateTitle[8] during the same period . As for employment , templateTitle[7] templateTitle[8] 's rate is almost the same as that of the United States or France , with more than 60 percent of employed persons in the population .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) in templateTitle[7] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[4] ) denotes the aggregate value of all services and goods and goods and goods produced within a country in a country 's economic power . In templateXValue[6] , templateTitle[7] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to around templateYValue[6] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: The statistic shows Gross domestic product ( GDP ) in South from 1984 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods and goods and goods produced within a country in a country 's economic power . In 2018 , South 's Gross domestic product amounted to around 1720.49 billion U.S. dollars .


Example 482:
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 United States imported an average of approximately 521,000 templateYLabel[3] of templateTitle[1] templateYLabel[4] templateYLabel[5] templateTitle[3] the Middle Eastern country .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the global templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] was at templateYValue[max] percent .
generated: This statistic shows the Imports in of the global Iraq 2000 from 2000 to 2018 . In 2018 , the Imports in of the Iraq 2000 was at 795 percent .


Example 483:
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 1.08 billion templateYLabel[4] templateYLabel[5] .

generated_template: The timeline shows templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[4] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[7] , templateTitle[0] templateTitle[2] templateTitle[4] templateYLabel[1] amounted to templateYValue[2] percent .
generated: The timeline shows Cree 's revenue Net - revenue from 2015 to 2019 . In N/A , Cree revenue - revenue amounted to 771.5 percent .


Example 484:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] 's templateYLabel[0] increased by approximately templateYValue[0] percent templateYLabel[2] to the templateYLabel[4] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] was at templateYValue[max] percent .
generated: The statistic shows the Population growth compared in the 2018 N/A from 2008 to 2018 . In 2018 , the Population growth of the 2018 N/A was at 1.91 percent .


Example 485:
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[3] from templateXValue[min] to templateXValue[9] , with projections up until templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitle[3] amounted to around templateYValue[6] templateYLabel[2] people .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[3] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateTitle[0] templateTitle[1] of templateTitle[3] amounted to approximately templateYValue[7] templateYLabel[2] templateYLabel[0] .
generated: The statistic shows the Total population of Nepal from 2014 to 2017 , with projections up until 2024 . In 2017 , the Total population of Nepal amounted to approximately 27.63 millions Inhabitants .


Example 486:
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 United States as of templateTitle[5] . During the templateTitle[2] survey , templateYValue[max] percent of templateYLabel[2] stated that they used Gmail as their primary templateTitle[3] templateTitle[4] . templateXValue[1] was ranked second with templateYValue[1] percent .

generated_template: According to a survey , templateYValue[max] percent of internet users in the United States who have templateXValue[0] . The survey shows that templateYValue[min] percent of templateYLabel[2] said that they ate templateTitle[3] templateTitle[4] because they lacked templateXValue[3] templateXValue[3] .
generated: According to a survey , 53 percent of internet users in the United States who have Google_(Gmail) . The survey shows that 1 percent of respondents said that they ate e-mail providers because they lacked AOL AOL .


Example 487:
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] 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[2] templateYLabel[3] templateYLabel[4] 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[2] templateYLabel[3] templateYLabel[4] .
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 488:
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[5] templateTitle[6] of the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[5] templateTitle[6] of the United States was templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic presents the templateYLabel[0] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the United States was approximately templateYValue[max] thousand templateYLabel[3] templateYLabel[4] . The templateTitle[3] templateTitle[4] is an informal network of banks , brokers , dealers and financial institutions which are linked electronically . templateTitle[3] templateTitle[4] mutual funds are the most accessible option for individual investors in the templateTitle[3] templateTitle[4] .
generated: The statistic presents the Volume of of U.S. asset-backed securities in the United States from 2000 to 2018 . In 2018 , the Volume of the of U.S. asset-backed securities in the United States was approximately 550 thousand U.S. dollars . The of U.S. is an informal network of banks , brokers , dealers and financial institutions which are linked electronically . of U.S. mutual funds are the most accessible option for individual investors in the of U.S. .


Example 489:
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 . Its best known brands include Ray-Ban , Persol and Oakley , Inc .. The company also makes sunglasses and frames for a multitude of designer brands such as Chanel and Prada .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[2] templateYLabel[3] of templateTitle[6] worldwide in templateTitle[10] , templateTitle[7] templateXLabel[0] templateXLabel[1] . In that year , templateYValue[max] percent of templateTitle[6] 's templateTitle[2] templateYLabel[2] templateYLabel[3] came from templateXValue[0] templateXValue[0] . Founded in 1961 in Agordo , Italy , the templateTitle[6] Group S.p.A. is the templateXValue[last] 's largest eyewear company . Its best known brands include Ray-Ban , Persol and Oakley , Inc .. The company also makes sunglasses and frames for a multitude of designer brands such as Chanel and Prada .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateTitle[12] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[max] percent .
generated: This statistic shows the Share of global net sales size of from North_America to North_America . In N/A , the Share of global net sales was 58 percent .


Example 490:
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[4] templateTitle[5] templateTitle[6] templateTitle[7] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[4] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] generated about 9.73 billion templateYLabel[4] templateYLabel[5] in templateYLabel[0] templateYLabel[1] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the total global Retail of Retail sales of from 2011 to 2017 . In 2014 , the Retail sales of Retail amounted to 9153.0 in million U.S. .


Example 491:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[3] amounted to about templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitle[2] amounted to templateYValue[min] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Population density in Nepal from 2008 to 2018 . In 2018 , the Population density in in amounted to 186.02 Inhabitants per square kilometer .


Example 492:
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[3] templateYLabel[1] of templateTitle[7] sold through templateTitle[0] templateTitle[1] in the templateYLabel[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[3] templateYLabel[1] was templateYValue[min] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[0] in the United States from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[min] percent .
generated: The statistic shows the Vended volume Vending in the United States from 1999 to 2010 . In 2010 , the Vended volume came to 36.6 percent .


Example 493:
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[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitle[0] amounted to templateYValue[max] templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Median Household income in Ohio from 1990 to 2018 . In 2018 , the Median Household income in Ohio amounted to 61633 U.S. dollars .


Example 494:
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 United States in templateTitle[5] , templateTitle[2] templateXLabel[0] . In templateTitle[5] , about templateYValue[16] templateTitle[0] arrived in the United States aged templateXValue[last] templateXValue[1] or templateXValue[last] . The total templateYLabel[0] of templateTitle[0] arrivals amounted to 22,405 .

generated_template: The statistic shows the global templateYLabel[0] of templateYLabel[2] to templateTitle[0] in templateTitle[5] , templateTitle[3] templateXLabel[0] . In templateTitle[5] , templateYValue[5] people aged between templateXValue[5] and templateXValue[5] templateXValue[1] immigrated to templateTitle[0] .
generated: The statistic shows the global Number of persons to Refugees in 2018 , age Age . In 2018 , 2383 people aged between 20_to_24_years and 20_to_24_years 1_to_4_years immigrated to Refugees .


Example 495:
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 1.65 billion templateYLabel[4] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[3] , the global templateTitle[2] templateYLabel[0] is expected to 899 million .
generated: The statistic shows the total global Operating of LEGO Group operating from 2009 to 2018 . In 2015 , the LEGO Group operating Operating amounted to 1645.35 in million Euros . In 2015 , the global operating Operating is expected to 899 million .


Example 496:
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 . It differs from a traditional way of investing into shares of foreign companies listed on a stock exchange . The companies which make foreign direct investment usually own a part of the company in which they invest and they have influence on the decision making process . In the United States , FDI is defined as an American investor ( called the U.S. parent ) owning a minimum of 10 percent of a foreign firm ( known as a foreign affiliate ) . The total direct position of the United States abroad amounted to 5.95 trillion U.S. dollars in 2018 . Although the phenomenon profits greatly from the technological advances of the 21st century , as well as from the cultural flexibility of today 's workforce , FDI has a long history , going back to the colonial empires . Not without critics , FDI is generally believed to bring advantages to the investing company , such as access to new markets and decreased costs of labor , materials and production facilities . The local economy can benefit from an infusion of capital , access to new technologies and engagement of native labor pool . There are three recognized types of foreign direct investment , namely horizontal FDI , platform FDI and vertical FDI , along with various methods of implementing the investment itself . FDI considered by many one of the motors of worldwide economic growth . U.S. foreign investment abroad has seen a dramatic growth in the past decades . In fact , the United States topped a ranking of the leading countries worldwide in terms of direct investment outflows , worth almost 300 billion U.S. dollars in 2015 alone . Multinational American corporations , especially focused on manufacturing , have largely invested in facilities overseas , due to financial benefits . However , a large share of these corporations focuses toward not only supplying the U.S. market , but also the local markets in which they operate . In 2017 , the country that received the largest amount of U.S. foreign investment was the Netherlands , with a little almost 936.73 billion U.S. dollars , followed by the United Kingdom and Luxembourg . Overall , the total amount of U.S. dollars invested in European states that year reached 3.55 trillion U.S. dollars compared to 1.68 trillion U.S. dollars a decade prior .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] of the United States in templateTitle[7] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[4] templateYLabel[1] made in templateTitle[7] were valued at approximately 117 templateYLabel[3] templateYLabel[4] templateYLabel[5] . templateYLabel[0] templateYLabel[1] templateTitle[2] of the United States templateTitle[9] additional information Foreign templateYLabel[0] templateYLabel[1] ( FDI ) , simply put , is an templateYLabel[1] of templateTitle[10] company into another company located in a different country . It differs from a traditional way of investing into shares of foreign companies listed on a stock exchange . The companies which make foreign templateYLabel[0] templateYLabel[1] usually own a part of the company in which they templateYLabel[1] and they have influence on the decision making process . In the United States , FDI is defined as an American investor ( called the templateYLabel[4] parent ) owning a minimum of 10 percent of a foreign firm ( known as a foreign affiliate ) . The total templateYLabel[0] templateTitle[2] of the United States abroad amounted to 5.95 trillion templateYLabel[4] templateYLabel[5] in templateXValue[max] . Although the phenomenon profits greatly from the technological advances of the 21st century , as well as from the cultural flexibility of today 's workforce , FDI has a long history , going back to the colonial empires . Not without critics , FDI is generally believed to bring advantages to the investing company , such as access to new markets and decreased costs of labor , materials and production facilities . The local economy can benefit from an infusion of capital , access to new technologies and engagement of native labor pool . There are three recognized types of foreign templateYLabel[0] templateYLabel[1] , namely horizontal FDI , platform FDI and vertical FDI , along with various methods of implementing the templateYLabel[1] itself . FDI considered by many templateTitle[10] of the motors of worldwide economic growth . templateYLabel[4] foreign templateYLabel[1] abroad has seen a dramatic growth in the past decades . In fact , the United States topped a ranking of the leading countries worldwide in terms of templateYLabel[0] templateYLabel[1] outflows , worth almost 300 templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[3] alone . Multinational American corporations , especially focused on manufacturing , have largely invested in facilities overseas , due to financial benefits . However , a large share of these corporations focuses toward not only supplying the templateYLabel[4] market , but also the local markets in which they operate . In templateXValue[1] , the country that received the largest amount of templateYLabel[4] foreign templateYLabel[1] was the Netherlands , with a little almost 936.73 templateYLabel[3] templateYLabel[4] templateYLabel[5] , followed by the United Kingdom and Luxembourg . Overall , the total amount of templateYLabel[4] templateYLabel[5] invested in European states that templateXLabel[0] reached 3.55 trillion templateYLabel[4] templateYLabel[5] compared to 1.68 trillion templateYLabel[4] templateYLabel[5] a decade prior .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] of the United States in templateTitle[7] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[4] templateYLabel[1] made in templateTitle[7] were valued at approximately templateYValue[0] templateYLabel[3] templateYLabel[4] templateYLabel[5] . templateYLabel[4] templateYLabel[0] templateYLabel[1] abroad is defined as ownership by a templateYLabel[4] investor of at least 10 percent of a foreign business . The templateYLabel[0] investor is known as a templateYLabel[4] parent , and the U.S.-owned foreign business is known as a foreign affiliate . The total templateYLabel[0] templateTitle[2] of the United States abroad amounted to 5.95 trillion templateYLabel[4] templateYLabel[5] in templateXValue[max] .
generated: This statistic shows the Direct investments 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 investments abroad is defined as ownership by a U.S. investor of at least 10 percent of a foreign business . The Direct investor is known as a U.S. parent , and the U.S.-owned foreign business is known as a foreign affiliate . The total Direct position of the United States abroad amounted to 5.95 trillion U.S. dollars in 2018 .


Example 497:
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[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitle[3] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Guatemala from 1999 to 2019 . In 2019 , the Unemployment rate in Guatemala was at approximately 2.75 percent .


