delen/vistext huggingface dataset:
Chart description: Stanley Johnson has the highest number of receiving yards at over 10,000.

Chart data: Receiving yards player 10352 Stanley Morgan 7861 Rob Gronkowski 7459 Wes Welker 6822 Julian Edelman 6366 Troy Brown 5726 Irving Fryar 5471 Ben Coates 5001 Jim Colclough 4669 Terry Glenn 4589 Gino Cappelletti 4297 Deion Branch 3904 Randy Moss 3701 Kevin Faulk 3162 Harold Jackson 3157 Russ Francis 3142 Vincent Brisby 3107 Art Graham 3081 Shawn Jefferson 3055 Randy Vataha 2980 James White

Vega-Lite specification:
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Chart description: The highest number of points scored was by Wayne Gretzky, with around 2800, and the lowest was a tie between Dale Hawerchuk and Doug Gilmore, with fewer than 1500 points each.  14 players scored 1500 or more points each.

Chart data: Number of points scored player 2857 Wayne Gretzky 1921 Jaromir Jagr 1887 Mark Messier 1850 Gordie Howe 1798 Ron Francis 1771 Marcel Dionne 1755 Steve Yzerman 1723 Mario Lemieux 1641 Joe Sakic 1590 Phil Esposito 1579 Ray Bourque 1533 Mark Recchi 1531 Paul Coffey 1509 Joe Thornton 1467 Stan Mikita 1457 Teemu Selanne 1425 Bryan Trottier 1420 Adam Oates 1414 Doug Gilmour 1409 Dale Hawerchuk

Vega-Lite specification:
{"config": {"background": "#fff", "area": {"fill": "#3e5c69"}, "line": {"stroke": "#3e5c69"}, "rect": {"fill": "#3e5c69"}, "bar": {"fill": "#3e5c69"}, "point": {"color": "#3e5c69"}, "axis": {"domainWidth": 0.5, "grid": true, "labelPadding": 2, "tickSize": 5, "tickWidth": 0.5, "titleFontWeight": "normal"}, "axisBand": {"grid": false}, "axisX": {"gridWidth": 0.2}, "axisY": {"gridDash": [3], "gridWidth": 0.4}, "legend": {"labelFontSize": 11, "padding": 1, "symbolType": "square"}, "range": {"category": ["#3e5c69", "#6793a6", "#182429", "#0570b0", "#3690c0", "#74a9cf", "#a6bddb", "#e2ddf2"]}}, "data": {"url": "data/4318.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e2ddf2"}, "x": {"type": "quantitative", "axis": {"title": "Number of points scored"}, "field": "Number of points scored"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "player"}}, "title": ["National Hockey League all-time leaders", "of regular season points scored as of October", "23 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The national debt to GDP ratio was gradually decreasing between 2015-2019. In 2020 it saw a sharp increase and then has steadily decreased since.

Chart data: National debt to GDP ratio Year 1.0624 2025* 1.1002 2024* 1.1283 2023* 1.1467 2022* 1.1497 2021* 1.1465 2020* 0.8862 2019 0.8975 2018 0.9055 2017 0.9173 2016 0.9119 2015

Vega-Lite specification:
{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "data/4331.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d67195"}, "x": {"type": "quantitative", "axis": {"title": "National debt to GDP ratio"}, "field": "National debt to GDP ratio"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Canada : National debt from 2015 to 2025", "in relation to gross domestic product (GDP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The US generated the largest revenue for tinder in the third quarter - almost three times more than the next highest generating country Germany.

Chart data: Country Revenue in million U.S. dollars United States 11.07 Germany 4.2 Brazil 3.45 Netherlands 2.26 United Kingdom 2.01 Canada 1.95 Australia 1.21 Switzerland 1.09 France 1.06 UAE 1.05

Vega-Lite specification:
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Chart description: The graph suggest a that Russia and North Korea have the most Magnesite reserves. The graph suggests that Spain and the United States have the least Magnesite reserves. The graph suggests that Russia and North Korea are in a better situation and could import some of their Magnesite reserves. This suggests that Russia and North Korea are more powerful.

Chart data: Country Reserves in thousand metric tons Russia 2300000 North Korea 2300000 China 1000000 Brazil 390000 Australia 320000 Greece 280000 Turkey 230000 Slovakia 120000 India 82000 Austria 50000 Spain 35000 United States 35000

Vega-Lite specification:
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Chart description: North Korea and Russia show as having by far the largest stores of Magnesite, with each having over 2,00,000 thousand metric tonnes. The next largest country, China, has 1,000,000 thousand metric tons. Austria, Spain and United States have the lowest reserves of Magnesite.

Chart data: Country Reserves in thousand metric tons Russia 2300000 North Korea 2300000 China 1000000 Brazil 390000 Australia 320000 Greece 280000 Turkey 230000 Slovakia 120000 India 82000 Austria 50000 Spain 35000 United States 35000

Vega-Lite specification:
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Chart description: Net income of Chuck E. Cheese 's worldwide peaked in 2009 at just over 60 million dollars. Steady declines in income continued reaching its bottom in 2014 at negative 60 million dollars. In 2017 income returned to 50 million dollars before returning to a lower figure of -25 million in 2019.

Chart data: Year Net income in million U.S. dollars Dec 31, 2006 55.92 Dec 31, 2007 56.49 Dec 31, 2008 61.19 Dec 31, 2009 54.03 Dec 31, 2010 54.96 Dec 31, 2011 43.59 Dec 31, 2012 47.82 Dec 31, 2013 −61.38 Dec 31, 2014 −12.51 Dec 31, 2015 −3.67 Dec 31, 2016 53.07 Dec 31, 2017 −20.46 Dec 31, 2018 −28.92

Vega-Lite specification:
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Chart description: Unemployment in Peru remains very low.  And has fallen on average of the current century.

Chart data: Year Unemployment rate Dec 31, 1998 0.057 Dec 31, 1999 0.057 Dec 31, 2000 0.0567 Dec 31, 2001 0.058 Dec 31, 2002 0.0479 Dec 31, 2003 0.049 Dec 31, 2004 0.0486 Dec 31, 2005 0.0426 Dec 31, 2006 0.0419 Dec 31, 2007 0.0406 Dec 31, 2008 0.039 Dec 31, 2009 0.0348 Dec 31, 2010 0.0344 Dec 31, 2011 0.0311 Dec 31, 2012 0.0324 Dec 31, 2013 0.0296 Dec 31, 2014 0.03 Dec 31, 2015 0.0354 Dec 31, 2016 0.0346 Dec 31, 2017 0.0339 Dec 31, 2018 0.0331 Dec 31, 2019 0.032

Vega-Lite specification:
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Chart description: The unemployment rate in Peru has decreased between 1999 and 2020, dropping from just under 6% to about 3%. Unemployment reached a peak in around 2002 before sharply dropping. Unemployment was at the lowest point in around 2014, before slightly increasing until 2015. Between 2015 and 2020 unemployment has been steadily falling.

Chart data: Year Unemployment rate Dec 31, 1998 0.057 Dec 31, 1999 0.057 Dec 31, 2000 0.0567 Dec 31, 2001 0.058 Dec 31, 2002 0.0479 Dec 31, 2003 0.049 Dec 31, 2004 0.0486 Dec 31, 2005 0.0426 Dec 31, 2006 0.0419 Dec 31, 2007 0.0406 Dec 31, 2008 0.039 Dec 31, 2009 0.0348 Dec 31, 2010 0.0344 Dec 31, 2011 0.0311 Dec 31, 2012 0.0324 Dec 31, 2013 0.0296 Dec 31, 2014 0.03 Dec 31, 2015 0.0354 Dec 31, 2016 0.0346 Dec 31, 2017 0.0339 Dec 31, 2018 0.0331 Dec 31, 2019 0.032

Vega-Lite specification:
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Chart description: On the Y-axis, total assets of The Royal Bank of Scotland increases in increments of 500 billion GBP. On the X-axis the year increases in increments of 2. The total assets peaked just before 2008 at around 2375 billion GBP. From 2008 to 2018, there has been a general decline in assets from around 2375 billion GBP to around 750 billion GPB. The lowest total assets of The Royal Bank of Scotland is around 750 billion GPB in 2018.

Chart data: Year Total assets in billion GBP Dec 31, 2006 1840.8 Dec 31, 2007 2401.7 Dec 31, 2008 1696.5 Dec 31, 2009 1453.6 Dec 31, 2010 1506.9 Dec 31, 2011 1312.3 Dec 31, 2012 1027.9 Dec 31, 2013 1051.02 Dec 31, 2014 815.4 Dec 31, 2015 798.66 Dec 31, 2016 738.06 Dec 31, 2017 694.24 Dec 31, 2018 723.04

Vega-Lite specification:
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Chart description: The attendance is fairly constant throught the years.

Chart data: Year Average attendance Dec 31, 2007 62890 Dec 31, 2008 63485 Dec 31, 2009 63083 Dec 31, 2010 63034 Dec 31, 2011 61141 Dec 31, 2012 57311 Dec 31, 2013 62226 Dec 31, 2014 64356 Dec 31, 2015 64312 Dec 31, 2016 62471 Dec 31, 2017 63456 Dec 31, 2018 62237

Vega-Lite specification:
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Chart description: There is a small decline in arrests over the period (apart from two spikes). This does NOT mean the crime rate has fallen.

Chart data: Year Number of arrests Dec 31, 1989 14195100 Dec 31, 1990 14211900 Dec 31, 1991 14075100 Dec 31, 1992 14036300 Dec 31, 1993 14648700 Dec 31, 1994 15119800 Dec 31, 1995 15168100 Dec 31, 1996 15284300 Dec 31, 1997 14528300 Dec 31, 1998 14031100 Dec 31, 1999 13980300 Dec 31, 2000 13699300 Dec 31, 2001 13741400 Dec 31, 2002 13639500 Dec 31, 2003 13938071 Dec 31, 2004 14094186 Dec 31, 2005 14380370 Dec 31, 2006 14209365 Dec 31, 2007 14005615 Dec 31, 2008 13687241 Dec 31, 2009 13120947 Dec 31, 2010 12408899 Dec 31, 2011 12196959 Dec 31, 2012 11302102 Dec 31, 2013 11205833 Dec 31, 2014 10797088 Dec 31, 2015 10662252 Dec 31, 2016 10554985 Dec 31, 2017 10310960 Dec 31, 2018 10085207

Vega-Lite specification:
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Chart description: The highest number of arrests (15,000,000+) took place between 1995-2000. The lowest number of arrests took place in 2019, around 10,000,000).

Chart data: Year Number of arrests Dec 31, 1989 14195100 Dec 31, 1990 14211900 Dec 31, 1991 14075100 Dec 31, 1992 14036300 Dec 31, 1993 14648700 Dec 31, 1994 15119800 Dec 31, 1995 15168100 Dec 31, 1996 15284300 Dec 31, 1997 14528300 Dec 31, 1998 14031100 Dec 31, 1999 13980300 Dec 31, 2000 13699300 Dec 31, 2001 13741400 Dec 31, 2002 13639500 Dec 31, 2003 13938071 Dec 31, 2004 14094186 Dec 31, 2005 14380370 Dec 31, 2006 14209365 Dec 31, 2007 14005615 Dec 31, 2008 13687241 Dec 31, 2009 13120947 Dec 31, 2010 12408899 Dec 31, 2011 12196959 Dec 31, 2012 11302102 Dec 31, 2013 11205833 Dec 31, 2014 10797088 Dec 31, 2015 10662252 Dec 31, 2016 10554985 Dec 31, 2017 10310960 Dec 31, 2018 10085207

Vega-Lite specification:
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Chart description: Smart phone penetration in South Korea continued to increase from 2015 to date, rising from approximately 85% of the population to just under 100%. From 2021 to 2025, penetration is predicted to remain stable just below 100% of the population.

Chart data: Response Share of population with a smartphone 2025* 0.974 2024* 0.974 2023* 0.974 2022* 0.974 2021* 0.9713 2020* 0.9542 2019 0.9363 2018 0.9175 2017 0.898 2016 0.8777 2015 0.8567

Vega-Lite specification:
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Chart description: The share of population with a smart phone has increased over the years, lowest at 2015 with a predicted higher rate up to 2025. Gradually increasing each year.

Chart data: Response Share of population with a smartphone 2025* 0.974 2024* 0.974 2023* 0.974 2022* 0.974 2021* 0.9713 2020* 0.9542 2019 0.9363 2018 0.9175 2017 0.898 2016 0.8777 2015 0.8567

Vega-Lite specification:
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Chart description: The amount of revenue in US dollars increased over a 10 year period in major league baseball sponsorship.

Chart data: Year Revenue in million U.S. dollars 2020* 1050 2019 994 2018 938 2017 892 2016 827 2015 778 2014 695 2013 663 2012 626 2011 585 2010 548

Vega-Lite specification:
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Chart description: From this bar chart I can see that sponsorship revenue in Major League Baseball has doubled in 10 years. Over the 10 years between 2010 and 2020 the revenue was steadily increasing.

Chart data: Year Revenue in million U.S. dollars 2020* 1050 2019 994 2018 938 2017 892 2016 827 2015 778 2014 695 2013 663 2012 626 2011 585 2010 548

Vega-Lite specification:
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Chart description: The chart shows that as we have progressed through time more youth in China are unemployed.

Chart data: Year Youth unemployment rate Dec 31, 1998 0.068 Dec 31, 1999 0.0687 Dec 31, 2000 0.0806 Dec 31, 2001 0.0902 Dec 31, 2002 0.0975 Dec 31, 2003 0.0957 Dec 31, 2004 0.0962 Dec 31, 2005 0.0946 Dec 31, 2006 0.093 Dec 31, 2007 0.0984 Dec 31, 2008 0.1018 Dec 31, 2009 0.0986 Dec 31, 2010 0.1002 Dec 31, 2011 0.1022 Dec 31, 2012 0.1044 Dec 31, 2013 0.1065 Dec 31, 2014 0.1083 Dec 31, 2015 0.1071 Dec 31, 2016 0.1056 Dec 31, 2017 0.1022 Dec 31, 2018 0.1033 Dec 31, 2019 0.1052

Vega-Lite specification:
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Chart description: node.js is the most used library among developers world wide. chef is the least used library.

Chart data: Response Share of respondents Node.js 0.514 .NET 0.351 .NET Core 0.267 Pandas 0.155 TensorFlow 0.115 React Native 0.115 Unity 3D 0.109 Ansible 0.073 Flutter 0.072 Keras 0.062 Teraform 0.062 Cordova 0.06 Xamarin 0.058 Apache Spark 0.052 Torch/PyTorch 0.046 Hadoop 0.045 Unreal Engine 0.033 Puppet 0.023 Chef 0.018

Vega-Lite specification:
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Chart description: NB: The graph is a little hard to read due to the low contrast in colours used. Between 1999 and 2020, the highest youth unemployment rate in Bangladesh was around 0.125 in 2017. The lowest rate was around 0.065 in 2010. Overall, in the period 1999-2020 youth unemployment in Bangladesh is trending up. However, from 2017 to 2020 it started to trend down. Youth unemployment was roughly the same in 2005 and 2009. It dropped in 2006, before rising back up again over the next three years.

Chart data: Year Youth unemployment rate Dec 31, 1998 0.0884 Dec 31, 1999 0.0972 Dec 31, 2000 0.0925 Dec 31, 2001 0.0824 Dec 31, 2002 0.0664 Dec 31, 2003 0.0776 Dec 31, 2004 0.0886 Dec 31, 2005 0.0733 Dec 31, 2006 0.0768 Dec 31, 2007 0.0815 Dec 31, 2008 0.0907 Dec 31, 2009 0.064 Dec 31, 2010 0.0746 Dec 31, 2011 0.0871 Dec 31, 2012 0.1013 Dec 31, 2013 0.1046 Dec 31, 2014 0.108 Dec 31, 2015 0.1112 Dec 31, 2016 0.123 Dec 31, 2017 0.1215 Dec 31, 2018 0.1187 Dec 31, 2019 0.1156

Vega-Lite specification:
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Chart description: 2004 and 2010 have the biggest drops in youth unemployment rate.

Chart data: Year Youth unemployment rate Dec 31, 1998 0.0884 Dec 31, 1999 0.0972 Dec 31, 2000 0.0925 Dec 31, 2001 0.0824 Dec 31, 2002 0.0664 Dec 31, 2003 0.0776 Dec 31, 2004 0.0886 Dec 31, 2005 0.0733 Dec 31, 2006 0.0768 Dec 31, 2007 0.0815 Dec 31, 2008 0.0907 Dec 31, 2009 0.064 Dec 31, 2010 0.0746 Dec 31, 2011 0.0871 Dec 31, 2012 0.1013 Dec 31, 2013 0.1046 Dec 31, 2014 0.108 Dec 31, 2015 0.1112 Dec 31, 2016 0.123 Dec 31, 2017 0.1215 Dec 31, 2018 0.1187 Dec 31, 2019 0.1156

Vega-Lite specification:
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Chart description: Since 2010, youth unemployment has been steadily increasing until approximately 2017.

Chart data: Year Youth unemployment rate Dec 31, 1998 0.0884 Dec 31, 1999 0.0972 Dec 31, 2000 0.0925 Dec 31, 2001 0.0824 Dec 31, 2002 0.0664 Dec 31, 2003 0.0776 Dec 31, 2004 0.0886 Dec 31, 2005 0.0733 Dec 31, 2006 0.0768 Dec 31, 2007 0.0815 Dec 31, 2008 0.0907 Dec 31, 2009 0.064 Dec 31, 2010 0.0746 Dec 31, 2011 0.0871 Dec 31, 2012 0.1013 Dec 31, 2013 0.1046 Dec 31, 2014 0.108 Dec 31, 2015 0.1112 Dec 31, 2016 0.123 Dec 31, 2017 0.1215 Dec 31, 2018 0.1187 Dec 31, 2019 0.1156

Vega-Lite specification:
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Chart description: China has the highest import value at around 450 billion U.S dollars. Malaysia has the lowest import value at around 50 billion U.S dollars. Canada, China and Mexico have a considerably higher import value than all 12 other countries depicted in this chart.

Chart data: Country Import value in billion U.S. dollars China 452.2 Mexico 358.1 Canada 319.7 Japan 143.6 Germany 127.5 South Korea 77.5 Vietnam 66.7 United Kingdom 63.2 Ireland 61.8 India 57.7 France 57.4 Italy 57.2 Taiwan 54.3 Switzerland 44.6 Malaysia 40.6

Vega-Lite specification:
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Chart description: The most popular leisure activities for baby boomers tends to be reading and watching TV. Concerts/Listening to music and working out seems to be amongst the least popular leisure activities amongst baby boomers.

Chart data: Response Share of respondents Watch TV 0.42 Reading 0.4 Computer/internet 0.21 Spending time with family and friends 0.17 Walking/running/jogging 0.11 Gardening 0.11 Watching/going to the movies 0.1 Playing video games and computer/internet games 0.08 Exercise/working out 0.07 Concerts/listening to/playing music 0.07

Vega-Lite specification:
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Chart description: SW Bates has the highest number of runs scored at 3,250 compared to the lowest SJ Taylor with 2,250, 1,000 runs less. 3 players scored 2,250 and 3 others also scored 2,750.

Chart data: player Number of runs scored SW Bates (NZ) 3243 SR Taylor (WI) 2984 MM Lanning (AUS) 2788 CM Edwards (ENG) 2605 SFM Devine (NZ) 2384 DJS Dottin (WI) 2380 M Raj (IND) 2364 Bismah Maroof (PAK) 2225 H Kaur (IND) 2186 SJ Taylor (ENG) 2177

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