delen/vistext huggingface dataset:
Chart description: There is a relatively steady increase in the amount of GDP in dollars from the year 2000 to 2015.

Chart data: Year Real GDP in billion U.S. dollars Dec 31, 1999 85.27 Dec 31, 2000 88.51 Dec 31, 2001 91.2 Dec 31, 2002 93.03 Dec 31, 2003 97.8 Dec 31, 2004 99.95 Dec 31, 2005 100.24 Dec 31, 2006 102.96 Dec 31, 2007 106.75 Dec 31, 2008 106.35 Dec 31, 2009 109.96 Dec 31, 2010 112.01 Dec 31, 2011 112.74 Dec 31, 2012 112.68 Dec 31, 2013 114.91 Dec 31, 2014 117.24 Dec 31, 2015 119.57 Dec 31, 2016 121.01 Dec 31, 2017 123.98 Dec 31, 2018 125.94

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/6301.tsv"}, "mark": "area", "encoding": {"color": {"value": "#d190b6"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Real GDP in billion U.S. dollars"}, "field": "Real GDP in billion U\\.S\\. dollars"}}, "title": ["Real Gross Domestic Product (GDP) of the", "District of Columbia from 2000 to 2019 (in", "billion U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Revenue created rose from just over 1,000 million U.S. dollars in 2010 to over 2,500 million U.S. dollars in 2019.

Chart data: Year Revenue in million U.S. dollars Dec 31, 2000 114 Dec 31, 2001 108 Dec 31, 2002 117 Dec 31, 2003 126 Dec 31, 2004 146 Dec 31, 2005 170 Dec 31, 2006 173 Dec 31, 2007 186 Dec 31, 2008 188 Dec 31, 2009 192 Dec 31, 2010 217 Dec 31, 2011 238 Dec 31, 2012 262 Dec 31, 2013 254 Dec 31, 2014 268 Dec 31, 2015 275 Dec 31, 2016 277 Dec 31, 2017 276 Dec 31, 2018 276

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/6303.tsv"}, "mark": "area", "encoding": {"color": {"value": "#829eb1"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Revenue in million U.S. dollars"}, "field": "Revenue in million U\\.S\\. dollars"}}, "title": ["Revenue of the Detroit Tigers (MLB) from", "2001 to 2019 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Revenue for detroit tigers has increased massively from 2001 to 2019. In that time revenue has increased by over 150 million.

Chart data: Year Revenue in million U.S. dollars Dec 31, 2000 114 Dec 31, 2001 108 Dec 31, 2002 117 Dec 31, 2003 126 Dec 31, 2004 146 Dec 31, 2005 170 Dec 31, 2006 173 Dec 31, 2007 186 Dec 31, 2008 188 Dec 31, 2009 192 Dec 31, 2010 217 Dec 31, 2011 238 Dec 31, 2012 262 Dec 31, 2013 254 Dec 31, 2014 268 Dec 31, 2015 275 Dec 31, 2016 277 Dec 31, 2017 276 Dec 31, 2018 276

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/6303.tsv"}, "mark": "area", "encoding": {"color": {"value": "#829eb1"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Revenue in million U.S. dollars"}, "field": "Revenue in million U\\.S\\. dollars"}}, "title": ["Revenue of the Detroit Tigers (MLB) from", "2001 to 2019 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Quality of labor is the highest ranked for small business issues.

Chart data: Response Share of respondents Quality of labor 0.24 Taxes 0.2 Government regulations & red tape 0.14 Poor sales 0.13 Competition from large businesses 0.09 Cost/availability of insurance 0.07 Cost of labor 0.06 Other 0.04 Inflation 0.02 Finance & interest rates 0.01

Vega-Lite specification:
{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "data/6313.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec008b"}, "x": {"type": "nominal", "axis": {"labelAngle": 90}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Single most important problem for small", "businesses in the United States in November", "2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The highest share of respondents attributed quality of labour as the single most important problem. Finances and interest rates was sighted the least by the respondents as a problem. Taxes was chosen by the second highest share of respondents.

Chart data: Response Share of respondents Quality of labor 0.24 Taxes 0.2 Government regulations & red tape 0.14 Poor sales 0.13 Competition from large businesses 0.09 Cost/availability of insurance 0.07 Cost of labor 0.06 Other 0.04 Inflation 0.02 Finance & interest rates 0.01

Vega-Lite specification:
{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "data/6313.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec008b"}, "x": {"type": "nominal", "axis": {"labelAngle": 90}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Single most important problem for small", "businesses in the United States in November", "2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The average age for people in UK working in hospitality or tourism  in 2011 was between 26 and 49. The oldest age average is from people working in Management roles.

Chart data: Age in years profession 49 Hotel and accommodation managers and propirieters 47 Housekeepers and related occupations 45 Cooks 44 Catering and bar managers 39 Restaurant and catering establishment managers and proprieters 38 Publicans and managers of licensed premises 37 Conference and exhibition managers 36 Chefs 35 Kitchen and catering assistants 29 Leisure and theme park attendants 28 Bar staff 26 Waiting staff

Vega-Lite specification:
{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "data/6314.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#cccccc"}, "x": {"type": "quantitative", "axis": {"title": "Age in years"}, "field": "Age in years"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "profession"}}, "title": ["Average age of hospitality and tourism sector", "employees in the United Kingdom (UK) in 2011", ", by occupation"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: India's infant morality rate between 2009 and 2019 shows a steady decline from around 46,000 to 28,000.

Chart data: Year Deaths per 1,000 live births Dec 31, 2008 47.3 Dec 31, 2009 45.1 Dec 31, 2010 43 Dec 31, 2011 40.9 Dec 31, 2012 38.8 Dec 31, 2013 36.9 Dec 31, 2014 34.9 Dec 31, 2015 33.1 Dec 31, 2016 31.4 Dec 31, 2017 29.7 Dec 31, 2018 28.3

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/631.tsv"}, "mark": "area", "encoding": {"color": {"value": "#545454"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Deaths per 1,000 live births"}, "field": "Deaths per 1,000 live births"}}, "title": ["India : Infant mortality rate from 2009", "to 2019 (in deaths per 1,000 live births)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In 2010 the revenue dropped to the lowest it bad been in the 12years recorded.

Chart data: Year Revenue in billion Japanese yen Dec 31, 2007 4025.08 Dec 31, 2008 3142.67 Dec 31, 2009 2976.71 Dec 31, 2010 3131.46 Dec 31, 2011 3154.63 Dec 31, 2012 3580.92 Dec 31, 2013 4094.96 Dec 31, 2014 4309.79 Dec 31, 2015 4524.52 Dec 31, 2016 4527.15 Dec 31, 2017 5108.29 Dec 31, 2018 5362.77 Dec 31, 2019 5153.48

Vega-Lite specification:
{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "data/6325.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fed9a6"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Revenue in billion Japanese yen"}, "field": "Revenue in billion Japanese yen"}}, "title": ["Denso 's global revenue from FY 2008 to", "FY 2020 (in billion Japanese yen)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: That the unemployment rate in Lithuania dropped dramatically between 2005 and 2010 and rose dramatically to it's highest in ,2910, dropping in 2920 to just above pre 3010 levels.

Chart data: Year Unemployment rate Dec 31, 1998 0.1339 Dec 31, 1999 0.1593 Dec 31, 2000 0.1684 Dec 31, 2001 0.1301 Dec 31, 2002 0.1288 Dec 31, 2003 0.1068 Dec 31, 2004 0.0833 Dec 31, 2005 0.0578 Dec 31, 2006 0.0425 Dec 31, 2007 0.0583 Dec 31, 2008 0.1379 Dec 31, 2009 0.1781 Dec 31, 2010 0.1539 Dec 31, 2011 0.1337 Dec 31, 2012 0.1177 Dec 31, 2013 0.107 Dec 31, 2014 0.0912 Dec 31, 2015 0.0786 Dec 31, 2016 0.0707 Dec 31, 2017 0.0615 Dec 31, 2018 0.0635 Dec 31, 2019 0.0626

Vega-Lite specification:
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/6328.tsv"}, "mark": "line", "encoding": {"color": {"value": "#1b9e77"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Unemployment rate"}, "field": "Unemployment rate"}}, "title": ["Lithuania : Unemployment rate from 1999", "to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: This chart shows a significant drop in unemployment in Lithuania from the years 2005-2010. It also shows a significant rise again in unemployment in 2010 which drops between 2010 and 2015 and then levels out.

Chart data: Year Unemployment rate Dec 31, 1998 0.1339 Dec 31, 1999 0.1593 Dec 31, 2000 0.1684 Dec 31, 2001 0.1301 Dec 31, 2002 0.1288 Dec 31, 2003 0.1068 Dec 31, 2004 0.0833 Dec 31, 2005 0.0578 Dec 31, 2006 0.0425 Dec 31, 2007 0.0583 Dec 31, 2008 0.1379 Dec 31, 2009 0.1781 Dec 31, 2010 0.1539 Dec 31, 2011 0.1337 Dec 31, 2012 0.1177 Dec 31, 2013 0.107 Dec 31, 2014 0.0912 Dec 31, 2015 0.0786 Dec 31, 2016 0.0707 Dec 31, 2017 0.0615 Dec 31, 2018 0.0635 Dec 31, 2019 0.0626

Vega-Lite specification:
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/6328.tsv"}, "mark": "line", "encoding": {"color": {"value": "#1b9e77"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Unemployment rate"}, "field": "Unemployment rate"}}, "title": ["Lithuania : Unemployment rate from 1999", "to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The employment rate in Lithuania dropped significantly from 2010 to 2016.

Chart data: Year Unemployment rate Dec 31, 1998 0.1339 Dec 31, 1999 0.1593 Dec 31, 2000 0.1684 Dec 31, 2001 0.1301 Dec 31, 2002 0.1288 Dec 31, 2003 0.1068 Dec 31, 2004 0.0833 Dec 31, 2005 0.0578 Dec 31, 2006 0.0425 Dec 31, 2007 0.0583 Dec 31, 2008 0.1379 Dec 31, 2009 0.1781 Dec 31, 2010 0.1539 Dec 31, 2011 0.1337 Dec 31, 2012 0.1177 Dec 31, 2013 0.107 Dec 31, 2014 0.0912 Dec 31, 2015 0.0786 Dec 31, 2016 0.0707 Dec 31, 2017 0.0615 Dec 31, 2018 0.0635 Dec 31, 2019 0.0626

Vega-Lite specification:
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/6328.tsv"}, "mark": "line", "encoding": {"color": {"value": "#1b9e77"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Unemployment rate"}, "field": "Unemployment rate"}}, "title": ["Lithuania : Unemployment rate from 1999", "to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: From 2000 to 2013, the number of red lobster restaurants in the USA and Canada only has a very slight increase, hovering just under 700 in 2000, and slightly over 700 in 2013.

Chart data: Year Number of restaurants Dec 31, 1999 654 Dec 31, 2000 661 Dec 31, 2001 667 Dec 31, 2002 673 Dec 31, 2003 680 Dec 31, 2004 679 Dec 31, 2005 682 Dec 31, 2006 680 Dec 31, 2007 680 Dec 31, 2008 690 Dec 31, 2009 694 Dec 31, 2010 698 Dec 31, 2011 704 Dec 31, 2012 705

Vega-Lite specification:
{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/6332.tsv"}, "mark": "area", "encoding": {"color": {"value": "#6B007B"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of restaurants"}, "field": "Number of restaurants"}}, "title": ["Number of Red Lobster restaurants in the", "U.S. and Canada from 2000 to 2013"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Madrid is by far the greatest population at over 3 million, followed by Barcelona at 1.7 million.  The remaining cities are all below 1 million and are ranked similarly.

Chart data: city Residents in million Madrid 3.14 Barcelona 1.6 Valencia 0.79 Seville 0.69 Zaragoza 0.66 Málaga 0.57 Murcia 0.44 Palma, Majorca 0.4 Las Palmas 0.38 Bilbao 0.35

Vega-Lite specification:
{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/6333.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#12239E"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "city"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Residents in million"}, "field": "Residents in million"}}, "title": ["Spain : The largest cities in 2015 (in million", "residents)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: largest city is barcelona and the smallest city is bilbilo.

Chart data: city Residents in million Madrid 3.14 Barcelona 1.6 Valencia 0.79 Seville 0.69 Zaragoza 0.66 Málaga 0.57 Murcia 0.44 Palma, Majorca 0.4 Las Palmas 0.38 Bilbao 0.35

Vega-Lite specification:
{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/6333.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#12239E"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "city"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Residents in million"}, "field": "Residents in million"}}, "title": ["Spain : The largest cities in 2015 (in million", "residents)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The largest brand value change in the timeframe measured was for Moutai. The second largest brand value change was that of Instagram. Estée Lauder and Tencent experienced the lowest level of change.

Chart data: Brand value change (2020 vs. 2019) Platform 0.58 Moutai 0.47 Instagram 0.4 lululemon 0.35 Costco 0.34 Netflix 0.32 Amazon 0.31 Linkedin 0.3 Microsoft 0.29 Adobe 0.27 Meituan 0.27 Target 0.24 Walmart 0.24 JD.com 0.22 Tesla 0.18 Xbox 0.18 Mastercard 0.17 Intel 0.16 Alibaba 0.15 Estée Lauder 0.15 Tencent

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/6346.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d190b6"}, "x": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Brand value change (2020 vs. 2019)"}, "field": "Brand value change (2020 vs\\. 2019)"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Platform"}}, "title": ["Largest brand value change of the leading", "brands in the world in 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Moutai had the largest brand value change. Tencent had the lowest brand value change.

Chart data: Brand value change (2020 vs. 2019) Platform 0.58 Moutai 0.47 Instagram 0.4 lululemon 0.35 Costco 0.34 Netflix 0.32 Amazon 0.31 Linkedin 0.3 Microsoft 0.29 Adobe 0.27 Meituan 0.27 Target 0.24 Walmart 0.24 JD.com 0.22 Tesla 0.18 Xbox 0.18 Mastercard 0.17 Intel 0.16 Alibaba 0.15 Estée Lauder 0.15 Tencent

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/6346.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d190b6"}, "x": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Brand value change (2020 vs. 2019)"}, "field": "Brand value change (2020 vs\\. 2019)"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Platform"}}, "title": ["Largest brand value change of the leading", "brands in the world in 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The graph shows trends in the largest brand value change of the leading brands in the world in 2020with Moutai recording the highest figure at 0.575. The chart also shows that all brands featured showed value change between 0.15 - 0.575.

Chart data: Brand value change (2020 vs. 2019) Platform 0.58 Moutai 0.47 Instagram 0.4 lululemon 0.35 Costco 0.34 Netflix 0.32 Amazon 0.31 Linkedin 0.3 Microsoft 0.29 Adobe 0.27 Meituan 0.27 Target 0.24 Walmart 0.24 JD.com 0.22 Tesla 0.18 Xbox 0.18 Mastercard 0.17 Intel 0.16 Alibaba 0.15 Estée Lauder 0.15 Tencent

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/6346.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d190b6"}, "x": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Brand value change (2020 vs. 2019)"}, "field": "Brand value change (2020 vs\\. 2019)"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Platform"}}, "title": ["Largest brand value change of the leading", "brands in the world in 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Estimated share of e-commerce in total sales increased as time goes on from 2010 to 2019.

Chart data: Estimated share of e-commerce in total sales Quarter 0.115 Q4 '19* 0.101 Q4 '18 0.091 Q4 '17 0.082 Q4 '16 0.075 Q4 '15 0.066 Q4 '14 0.061 Q4 '13 0.055 Q4 '12 0.051 Q4 '11 0.046 Q4 '10

Vega-Lite specification:
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/6348.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#FF7043"}, "x": {"type": "quantitative", "axis": {"title": "Estimated share of e-commerce in total sales"}, "field": "Estimated share of e-commerce in total sales"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Quarter"}}, "title": ["Percentage of e-commerce in total U.S. retail", "sales in 4th quarter from 2010 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There is a consistent and steady increase in the estimated share of E-commerce between the 4th quarter of 2010 and the 4th quarter of 2019. In 2010 the estimated share in the 4th quarter was just over 0.04 and the estimated share in the 4th quarter of 2019 was just under 0.12.

Chart data: Estimated share of e-commerce in total sales Quarter 0.115 Q4 '19* 0.101 Q4 '18 0.091 Q4 '17 0.082 Q4 '16 0.075 Q4 '15 0.066 Q4 '14 0.061 Q4 '13 0.055 Q4 '12 0.051 Q4 '11 0.046 Q4 '10

Vega-Lite specification:
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/6348.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#FF7043"}, "x": {"type": "quantitative", "axis": {"title": "Estimated share of e-commerce in total sales"}, "field": "Estimated share of e-commerce in total sales"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Quarter"}}, "title": ["Percentage of e-commerce in total U.S. retail", "sales in 4th quarter from 2010 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The estimated shares of e commerce in total sales was highest in the 4th quarter in 2019 whereas it was the lowest in 2010.

Chart data: Estimated share of e-commerce in total sales Quarter 0.115 Q4 '19* 0.101 Q4 '18 0.091 Q4 '17 0.082 Q4 '16 0.075 Q4 '15 0.066 Q4 '14 0.061 Q4 '13 0.055 Q4 '12 0.051 Q4 '11 0.046 Q4 '10

Vega-Lite specification:
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/6348.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#FF7043"}, "x": {"type": "quantitative", "axis": {"title": "Estimated share of e-commerce in total sales"}, "field": "Estimated share of e-commerce in total sales"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Quarter"}}, "title": ["Percentage of e-commerce in total U.S. retail", "sales in 4th quarter from 2010 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Haiti had the higher number of cases by a significant margin, twice the number of cases of DC Congo that had the second highest number of cases. Mozambique, the Congo  and the Dominic Republic all had the same lowest cases numbers.

Chart data: Country Number of reported cases Haiti 58809 DC Congo 29644 Sierra Leone 23124 Somalia 6864 Angola 6655 Nigeria 6600 India 6008 Dominican Republic 1954 Mozambique 1869 Congo 1624

Vega-Lite specification:
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/6349.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Number of reported cases"}, "field": "Number of reported cases"}}, "title": ["Countries with the highest number of reported", "cases of cholera in 2013"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: many of the influencers on twitter shared a similar score, only some scoring slightly higher scores.

Chart data: person Index score @taylorswift13 97 @realdonaldtrump 97 @narendramodi 97 @katyperry 96 @elonmusk 96 @Cristiano 95 @ladygaga 95 @TheEllenShow 95 @ArianaGrande 95 @KimKardashian 95 @BillGates 95 @JLo 95 @BarackObama 94 @justinbieber 94 @rihanna 94 @jtimberlake 94 @selenagomez 94 @shakira 94 @jimmyfallon 94 @MileyCyrus 94

Vega-Lite specification:
{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/6351.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#12239E"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "person"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Index score"}, "field": "Index score"}}, "title": ["Most influential people on Twitter worldwide", "in 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Manchester city had the most players at the 2018 Fifa cup. No teams had less than 9 players.

Chart data: Number of players association football club 16 Manchester City 15 Real Madrid 14 FC Barcelona 12 Tottenham Hotspur 12 FC Paris Saint-Germain 12 Chelsea FC 11 FC Bayern München 11 Manchester United 11 Juventus 9 Atlético Madrid 9 Al-Hilal Riad 9 Al-Ahli Dschidda

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/635.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#51b2e5"}, "x": {"type": "quantitative", "axis": {"title": "Number of players"}, "field": "Number of players"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "association football club"}}, "title": ["Club teams with the most players at the", "2018 FIFA World Cup in Russia"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The later the year the bigger the capacity factor gets.

Chart data: Capacity factor Year 0.935 2019** 0.925 2018 0.923 2017 0.923 2016 0.923 2015 0.917 2014 0.899 2013 0.861 2012 0.891 2011 0.911 2010 0.893 2005 0.881 2000 0.774 1995 0.66 1990 0.58 1985 0.563 1980 0.559 1975

Vega-Lite specification:
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/6364.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#7570b3"}, "x": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Capacity factor"}, "field": "Capacity factor"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Capacity factor of nuclear power plants", "in the United States from 1975 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: 2012 is the highest year hitting over 12 closely followed by 2018.

Chart data: Year Number of shutdowns Dec 31, 2004 2 Dec 31, 2005 8 Dec 31, 2006 1 Dec 31, 2007 2 Dec 31, 2008 3 Dec 31, 2009 1 Dec 31, 2010 13 Dec 31, 2011 3 Dec 31, 2012 6 Dec 31, 2013 1 Dec 31, 2014 7 Dec 31, 2015 4 Dec 31, 2016 5 Dec 31, 2017 7 Dec 31, 2018 11 Dec 31, 2019 3

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/6374.tsv"}, "mark": "area", "encoding": {"color": {"value": "#b79a20"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of shutdowns"}, "field": "Number of shutdowns"}}, "title": ["Global number of permanent nuclear reactor", "shutdowns from 2005 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: the number of shut downs seem to reach peak about every 4 years and the other years not so many shutdowns occur.

Chart data: Year Number of shutdowns Dec 31, 2004 2 Dec 31, 2005 8 Dec 31, 2006 1 Dec 31, 2007 2 Dec 31, 2008 3 Dec 31, 2009 1 Dec 31, 2010 13 Dec 31, 2011 3 Dec 31, 2012 6 Dec 31, 2013 1 Dec 31, 2014 7 Dec 31, 2015 4 Dec 31, 2016 5 Dec 31, 2017 7 Dec 31, 2018 11 Dec 31, 2019 3

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/6374.tsv"}, "mark": "area", "encoding": {"color": {"value": "#b79a20"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of shutdowns"}, "field": "Number of shutdowns"}}, "title": ["Global number of permanent nuclear reactor", "shutdowns from 2005 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}