delen/vistext huggingface dataset:
Chart description: Charley Taylor has scored the most touchdowns for the Washington Football team, from 1932-2020. He had over 80 touchdowns, with the second place touchdown getter, John Riggins, also having over 80 touchdowns. No other player for the Washington Football team had over 80 touchdowns. In third place was Art Monk, who had just over 60 touchdowns.

Chart data: Touchdowns scored leader 90 Charley Taylor 85 John Riggins 65 Art Monk 60 Jerry Smith 58 Gary Clark 58 Hugh Taylor 55 Larry Brown 53 Bobby Mitchell 49 Clinton Portis 48 Stephen Davis 48 Santana Moss 39 Terry Allen 37 Ricky Sanders 35 Andy Farkas 34 Dick Todd 33 Chris Cooley 31 Cliff Battles 31 George Rogers 30 Dick James 29 Earnest Byner

Vega-Lite specification:
{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "data/20609.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#999999"}, "x": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Touchdowns scored"}, "field": "Touchdowns scored"}, "y": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "leader"}}, "title": ["Washington Football Team all-time leaders", "number of touchdowns scored from 1932 to", "2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Between 1932 and 1920  only three all time touchdown leaders of Washington Football Team had scored more than 60.

Chart data: Touchdowns scored leader 90 Charley Taylor 85 John Riggins 65 Art Monk 60 Jerry Smith 58 Gary Clark 58 Hugh Taylor 55 Larry Brown 53 Bobby Mitchell 49 Clinton Portis 48 Stephen Davis 48 Santana Moss 39 Terry Allen 37 Ricky Sanders 35 Andy Farkas 34 Dick Todd 33 Chris Cooley 31 Cliff Battles 31 George Rogers 30 Dick James 29 Earnest Byner

Vega-Lite specification:
{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "data/20609.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#999999"}, "x": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Touchdowns scored"}, "field": "Touchdowns scored"}, "y": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "leader"}}, "title": ["Washington Football Team all-time leaders", "number of touchdowns scored from 1932 to", "2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: increase from around 250 million metric tons in 2001 to just over 350 million metrc tons in 2020.

Chart data: Year Domestic use in million metric tons 2020** 360.7 2019* 349.6 2018 353 2017 354.6 2016 359.3 2015 345.2 2014 348.8 2013 345.3 2012 318.7 2011 326.3 2010 333.1 2009 331.1 2008 314.4 2007 307.2 2006 277.8 2005 280.2 2004 275.9 2003 275.8 2002 248.7 2001 253.1

Vega-Lite specification:
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/20629.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#adc839"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Domestic use in million metric tons"}, "field": "Domestic use in million metric tons"}}, "title": ["Total U.S. domestic grain use from 2001", "to 2020 (in million metric tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Domestic grain use has continuously risen on average from 2001 to 2020. Since 2007 over 300 metric tonnes has been used every year. 2002 had the lowest grain use of any year. 2020 had the highest grain use of any year.

Chart data: Year Domestic use in million metric tons 2020** 360.7 2019* 349.6 2018 353 2017 354.6 2016 359.3 2015 345.2 2014 348.8 2013 345.3 2012 318.7 2011 326.3 2010 333.1 2009 331.1 2008 314.4 2007 307.2 2006 277.8 2005 280.2 2004 275.9 2003 275.8 2002 248.7 2001 253.1

Vega-Lite specification:
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/20629.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#adc839"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Domestic use in million metric tons"}, "field": "Domestic use in million metric tons"}}, "title": ["Total U.S. domestic grain use from 2001", "to 2020 (in million metric tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Generally, media entrepreneurs at the highest level have similar net worths, aside from Bloomberg, who also makes a large amount from his market tools and activities.

Chart data: Net worth in billion U.S. dollars Entrepreneur (source of wealth) 31 Michael Bloomberg(Bloomberg L.P.) 13.5 Anne Cox Chambers(Cox Enterprises) 13.4 Rupert Murdoch(News Corp.) 8.9 Samuel Newhouse, Jr.(Advance Publications) 8.2 Donald Newhouse(Advance Publications) 6.7 Jim Kennedy(Cox Enterprises) 6.7 John Malone(Liberty Media) 6.7 Blair Parry-Okeden(Cox Enterprises) 6 David Geffen(films and music) 5.8 Sumner Redstone(Viacom/CBS) 5.1 Patrick McGovern(International Data Group) 4.2 George Lucas(films) 3.4 Martha Ingram(books) 3.4 Haim Saban(TV) 3.3 Charles Dolan(Cablevision)

Vega-Lite specification:
{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "data/20634.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#52d2ca"}, "x": {"type": "quantitative", "axis": {"title": "Net worth in billion U.S. dollars"}, "field": "Net worth in billion U\\.S\\. dollars"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Entrepreneur (source of wealth)"}}, "title": ["Estimated net worth of the wealthiest media", "entrepreneurs in the United States in 2013", "(in billion U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: michael bloomberg is by far the wealthiest entrepeneur in the united states in terms of their net worth. male entrepeneurs dominate women by a margin of 4 to 1.

Chart data: Net worth in billion U.S. dollars Entrepreneur (source of wealth) 31 Michael Bloomberg(Bloomberg L.P.) 13.5 Anne Cox Chambers(Cox Enterprises) 13.4 Rupert Murdoch(News Corp.) 8.9 Samuel Newhouse, Jr.(Advance Publications) 8.2 Donald Newhouse(Advance Publications) 6.7 Jim Kennedy(Cox Enterprises) 6.7 John Malone(Liberty Media) 6.7 Blair Parry-Okeden(Cox Enterprises) 6 David Geffen(films and music) 5.8 Sumner Redstone(Viacom/CBS) 5.1 Patrick McGovern(International Data Group) 4.2 George Lucas(films) 3.4 Martha Ingram(books) 3.4 Haim Saban(TV) 3.3 Charles Dolan(Cablevision)

Vega-Lite specification:
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Chart description: Female internet users in the United States are mostly on their phones to stay up to date with emails, news and researching. Female users do however, use their phones for a number of other reasons.

Chart data: Share of internet users Response 0.45 Buy a product online 0.2 Buy or make a reservation for travel 0.55 Check weather reports and forecasts online 0.55 Do any banking online 0.66 Get news online 0.42 Look for news and information about politics 0.14 Participate in an online auction 0.35 Send instant messages 0.82 Send or read e-mail 0.7 Use a search engine to find information 0.56 Use a social networking site 0.15 Use classified ads sites like Craigslist 0.4 Watch a video on a video-sharing site 0.01 None of the above 0 I don’t use the Internet

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/20638.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "quantitative", "axis": {"title": "Share of internet users"}, "field": "Share of internet users"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Most popular daily activities of female", "internet users in the United States as of", "February 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: All of the women in the study used the internet in some wayEmailing was the most popular activity.

Chart data: Share of internet users Response 0.45 Buy a product online 0.2 Buy or make a reservation for travel 0.55 Check weather reports and forecasts online 0.55 Do any banking online 0.66 Get news online 0.42 Look for news and information about politics 0.14 Participate in an online auction 0.35 Send instant messages 0.82 Send or read e-mail 0.7 Use a search engine to find information 0.56 Use a social networking site 0.15 Use classified ads sites like Craigslist 0.4 Watch a video on a video-sharing site 0.01 None of the above 0 I don’t use the Internet

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/20638.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "quantitative", "axis": {"title": "Share of internet users"}, "field": "Share of internet users"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Most popular daily activities of female", "internet users in the United States as of", "February 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: China was by far the greatest exporter of US soya beans in 2018 at over 8million metric tons and Mexico was the second at over 4 million while all the other countries were below 4 million metric tons.

Chart data: Country Exports in million metric tons China 8.34 Mexico 4.57 Netherlands 3.78 Egypt 3.19 Indonesia 2.64 Japan 2.34 Taiwan 2.19 Argentina 2.05 Pakistan 1.93 Spain 1.81

Vega-Lite specification:
{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "data/2063.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#C9C9C9"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Exports in million metric tons"}, "field": "Exports in million metric tons"}}, "title": ["Major countries of destination for U.S.", "soybean exports in 2018 (in million metric", "tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: During this timescale dependency rate was at it's highest in 2010. The variation of dependency is only 0.1 through the time period.

Chart data: Year Energy imports dependency rate Dec 31, 2007 0.9754 Dec 31, 2008 0.9634 Dec 31, 2009 1.0078 Dec 31, 2010 0.9243 Dec 31, 2011 0.9698 Dec 31, 2012 0.963 Dec 31, 2013 0.9325 Dec 31, 2014 0.9769 Dec 31, 2015 0.9618 Dec 31, 2016 0.9633

Vega-Lite specification:
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Chart description: Cyprus’ dependency on energy imports has remained relatively stable over the last 15 years.

Chart data: Year Energy imports dependency rate Dec 31, 2007 0.9754 Dec 31, 2008 0.9634 Dec 31, 2009 1.0078 Dec 31, 2010 0.9243 Dec 31, 2011 0.9698 Dec 31, 2012 0.963 Dec 31, 2013 0.9325 Dec 31, 2014 0.9769 Dec 31, 2015 0.9618 Dec 31, 2016 0.9633

Vega-Lite specification:
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Chart description: There is a gradual increase of urbanization in Vietnam.

Chart data: Year Share of total population Dec 31, 2008 0.2976 Dec 31, 2009 0.3042 Dec 31, 2010 0.3108 Dec 31, 2011 0.3175 Dec 31, 2012 0.3243 Dec 31, 2013 0.3312 Dec 31, 2014 0.3381 Dec 31, 2015 0.3451 Dec 31, 2016 0.3521 Dec 31, 2017 0.3592 Dec 31, 2018 0.3663

Vega-Lite specification:
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Chart description: The data values increase over time. From 2009 to 2018, the population increased by approximately .03.

Chart data: Response Share of population Dec 31, 2008 0.287 Dec 31, 2009 0.2889 Dec 31, 2010 0.2908 Dec 31, 2011 0.2927 Dec 31, 2012 0.2946 Dec 31, 2013 0.2965 Dec 31, 2014 0.2986 Dec 31, 2015 0.3008 Dec 31, 2016 0.3032 Dec 31, 2017 0.3058

Vega-Lite specification:
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Chart description: The share of urban population in Myanmar steadily increases from 2009 to 2018.

Chart data: Response Share of population Dec 31, 2008 0.287 Dec 31, 2009 0.2889 Dec 31, 2010 0.2908 Dec 31, 2011 0.2927 Dec 31, 2012 0.2946 Dec 31, 2013 0.2965 Dec 31, 2014 0.2986 Dec 31, 2015 0.3008 Dec 31, 2016 0.3032 Dec 31, 2017 0.3058

Vega-Lite specification:
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Chart description: The mean number of games played is around 1,000. The median number of games played is around 1,100. The number of games played by Todd Helton is likely to be a statistical anomaly.

Chart data: Number of games played leader 2247 Todd Helton 1247 Carlos Gonzalez 1170 Larry Walker 1119 Charlie Blackmon 1098 Vinny Castilla 1048 Troy Tulowitzki 1031 Nolan Arenado 1018 Dante Bichette 918 DJ LeMahieu 816 Brad Hawpe

Vega-Lite specification:
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Chart description: Spain had the highest share of respondents who accessed ebooks of audiobooks through illegal channels. Japan, Germany and Sweden showed the lowest rate of respondents accessing ebooks of audiobooks through illegal channels.

Chart data: Response Share of respondents Spain 0.21 Thailand 0.14 Hong Kong 0.14 Brazil 0.13 Canada 0.12 Poland 0.12 United Kingdom 0.1 Indonesia 0.1 France 0.08 The Netherlands 0.08 Sweden 0.07 Germany 0.07 Japan 0.06 Total 0.13

Vega-Lite specification:
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Chart description: Mobile internet user penetration has risen a lot from 2015 to 2020, from 2020 onwards, it doesn’t increase by much.

Chart data: Share of population Response 0.622 2025* 0.612 2024* 0.599 2023* 0.583 2022* 0.561 2021* 0.528 2020 0.478 2019 0.41 2018 0.334 2017 0.262 2016 0.201 2015

Vega-Lite specification:
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Chart description: Mobile internet user penetration has increased and will continue to increase in the future.

Chart data: Share of population Response 0.622 2025* 0.612 2024* 0.599 2023* 0.583 2022* 0.561 2021* 0.528 2020 0.478 2019 0.41 2018 0.334 2017 0.262 2016 0.201 2015

Vega-Lite specification:
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Chart description: The usage of mobile internet has increased clear on year.

Chart data: Share of population Response 0.622 2025* 0.612 2024* 0.599 2023* 0.583 2022* 0.561 2021* 0.528 2020 0.478 2019 0.41 2018 0.334 2017 0.262 2016 0.201 2015

Vega-Lite specification:
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Chart description: Since 1990 to 2018, the U.S. producer price index of plumbing fixtures and fittings has steadily increased. The quickest increase in price index came between 2005 and 20132015 to 2016 saw the only stagnant price index since 1990.

Chart data: Year Price index Dec 31, 1989 144.3 Dec 31, 1994 166 Dec 31, 1999 180.4 Dec 31, 2004 197.6 Dec 31, 2009 231.4 Dec 31, 2010 237 Dec 31, 2011 241.5 Dec 31, 2012 245.7 Dec 31, 2013 252.6 Dec 31, 2014 257.1 Dec 31, 2015 257.8 Dec 31, 2016 262.4 Dec 31, 2017 270.2

Vega-Lite specification:
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Chart description: Over time you can see the number of shopping malls increases.

Chart data: Year Number of shopping malls Dec 31, 1985 331 Dec 31, 1986 339 Dec 31, 1987 347 Dec 31, 1988 355 Dec 31, 1989 366 Dec 31, 1990 371 Dec 31, 1991 380 Dec 31, 1992 385 Dec 31, 1993 388 Dec 31, 1994 394 Dec 31, 1995 403 Dec 31, 1996 410 Dec 31, 1997 416 Dec 31, 1998 430 Dec 31, 1999 440 Dec 31, 2000 452 Dec 31, 2001 460 Dec 31, 2002 464 Dec 31, 2003 474 Dec 31, 2004 486 Dec 31, 2005 493 Dec 31, 2006 497 Dec 31, 2007 501 Dec 31, 2008 504 Dec 31, 2009 505

Vega-Lite specification:
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Chart description: The number of shopping malls in the United States with more than 1 million sq . feet of gross leasable area increases (upward trend) from 1986 to 2010. The least number of shopping malls with more than 1 million sq . feet of gross leasable area occurs in 1986.

Chart data: Year Number of shopping malls Dec 31, 1985 331 Dec 31, 1986 339 Dec 31, 1987 347 Dec 31, 1988 355 Dec 31, 1989 366 Dec 31, 1990 371 Dec 31, 1991 380 Dec 31, 1992 385 Dec 31, 1993 388 Dec 31, 1994 394 Dec 31, 1995 403 Dec 31, 1996 410 Dec 31, 1997 416 Dec 31, 1998 430 Dec 31, 1999 440 Dec 31, 2000 452 Dec 31, 2001 460 Dec 31, 2002 464 Dec 31, 2003 474 Dec 31, 2004 486 Dec 31, 2005 493 Dec 31, 2006 497 Dec 31, 2007 501 Dec 31, 2008 504 Dec 31, 2009 505

Vega-Lite specification:
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Chart description: The  number of malls increased at a steady rate until approx 2008. After this, rate of increase has flattened with the number of malls over 1 million sq ft has stabilised at approx 500.

Chart data: Year Number of shopping malls Dec 31, 1985 331 Dec 31, 1986 339 Dec 31, 1987 347 Dec 31, 1988 355 Dec 31, 1989 366 Dec 31, 1990 371 Dec 31, 1991 380 Dec 31, 1992 385 Dec 31, 1993 388 Dec 31, 1994 394 Dec 31, 1995 403 Dec 31, 1996 410 Dec 31, 1997 416 Dec 31, 1998 430 Dec 31, 1999 440 Dec 31, 2000 452 Dec 31, 2001 460 Dec 31, 2002 464 Dec 31, 2003 474 Dec 31, 2004 486 Dec 31, 2005 493 Dec 31, 2006 497 Dec 31, 2007 501 Dec 31, 2008 504 Dec 31, 2009 505

Vega-Lite specification:
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Chart description: Florida has the highest number of HIV diagnoses in 2018, with over 4,000 diagnosed. Louisiana has the lowest number of HIV diagnoses in 2018, with 1,000 diagnosed.

Chart data: Number of diagnoses State 4683 Florida 4483 Texas 4398 California 2552 Georgia 2470 New York 1352 Illinois 1200 North Carolina 1044 New Jersey 1002 Pennsylvania 986 Louisiana

Vega-Lite specification:
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Chart description: Operating income dramatically increased in 2011. Highest income was in year 2016. The trend still fluctuates up and down but at much higher numbers from 2011.

Chart data: Year Operating income in million U.S. dollars Dec 31, 2000 26 Dec 31, 2001 44.9 Dec 31, 2002 17.3 Dec 31, 2003 24.3 Dec 31, 2004 20.7 Dec 31, 2005 2.5 Dec 31, 2006 22.3 Dec 31, 2007 22.9 Dec 31, 2008 15 Dec 31, 2009 32.2 Dec 31, 2010 11.5 Dec 31, 2011 28.9 Dec 31, 2012 55.6 Dec 31, 2013 77.8 Dec 31, 2014 53 Dec 31, 2015 102 Dec 31, 2016 62 Dec 31, 2017 78 Dec 31, 2018 96

Vega-Lite specification:
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Chart description: Carolina Panthers income started with a sharp rise up to 50 million dollars before crashing down to almost zero in 2005. The income then starts to increase steadily to 100 million with a couple of sharp drops - one around 2011, one around 2015 and one around 2017. The highest operating income on the chart is  just over 100 million around 2016.

Chart data: Year Operating income in million U.S. dollars Dec 31, 2000 26 Dec 31, 2001 44.9 Dec 31, 2002 17.3 Dec 31, 2003 24.3 Dec 31, 2004 20.7 Dec 31, 2005 2.5 Dec 31, 2006 22.3 Dec 31, 2007 22.9 Dec 31, 2008 15 Dec 31, 2009 32.2 Dec 31, 2010 11.5 Dec 31, 2011 28.9 Dec 31, 2012 55.6 Dec 31, 2013 77.8 Dec 31, 2014 53 Dec 31, 2015 102 Dec 31, 2016 62 Dec 31, 2017 78 Dec 31, 2018 96

Vega-Lite specification:
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