delen/vistext huggingface dataset:
Chart description: There are no tends on show, I would argue that it is dependant on the movies released on an articulate year as to how financially successful a particular year is. 2005 is the highest, perhaps due to this being being home streaming.

Chart data: Year India Dec 31, 2000 0.012 Dec 31, 2001 0.01 Dec 31, 2002 0.01 Dec 31, 2003 0.011 Dec 31, 2004 0.015 Dec 31, 2005 0.018 Dec 31, 2006 0.016 Dec 31, 2007 0.014 Dec 31, 2008 0.01 Dec 31, 2009 0.013 Dec 31, 2010 0.01 Dec 31, 2011 0.012 Dec 31, 2012 0.014 Dec 31, 2013 0.015 Dec 31, 2014 0.013 Dec 31, 2015 0.012 Dec 31, 2016 0.011 Dec 31, 2017 0.012 Dec 31, 2018 0.012

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": "multiColumn/data/3891.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "India"}, "field": "India"}}, "title": ["Market share of cinema box office revenue", "in the United Kingdom (UK) from 2001 to 2019", ", by film country of origin"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Netflix.com has the highest streaming service with Apple.com having about a third less and the other services are way behind.

Chart data: Nov '19 Platform 1017.49 netflix.com 628.13 apple.com 141.42 hulu.com 85.78 cbc.ca 59.75 primevideo.com 16.68 hbo.com 4.72 crave.ca 5.08 disneyplus.com 1.43 kanopy.com 135.57 Facebook

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": "multiColumn/data/3900.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#9fe4f8"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Nov '19"}, "field": "Nov \\'19"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "Platform"}}, "title": ["Most popular streaming services worldwide", "from November 2019 to March 2020 , by number", "of monthly visits (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Netflix was the most popular streaming service with over 1,000 million visits. Netflix was visited almost twice as much as the 2nd most popular service. The most popular streaming service was visited more than all other services combined.

Chart data: Platform Feb '20 netflix.com 1045.88 apple.com 548.91 hulu.com 139.51 cbc.ca 76.59 primevideo.com 68.98 hbo.com 14.14 crave.ca 4.54 disneyplus.com 3.62 kanopy.com 1.7

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": "multiColumn/data/3900.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ab7fb4"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Platform"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Feb '20"}, "field": "Feb \\'20"}}, "title": ["Most popular streaming services worldwide", "from November 2019 to March 2020 , by number", "of monthly visits (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The chart shows that the strongest streaming network platform goes to Netflix with over a thousand people using it and apple coming in at a strong second , Hulu third, cbc fourth, prime TV fifth and crave / Disney plus last with figures under 100 people.

Chart data: Platform Feb '20 netflix.com 1045.88 apple.com 548.91 hulu.com 139.51 cbc.ca 76.59 primevideo.com 68.98 hbo.com 14.14 crave.ca 4.54 disneyplus.com 3.62 kanopy.com 1.7

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": "multiColumn/data/3900.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ab7fb4"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Platform"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Feb '20"}, "field": "Feb \\'20"}}, "title": ["Most popular streaming services worldwide", "from November 2019 to March 2020 , by number", "of monthly visits (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Energy consumption and projections have more than doubled between between 1990 and 2030.

Chart data: Year Hydroelectricity Dec 31, 1989 487.5 Dec 31, 1994 563.19 Dec 31, 1999 600.75 Dec 31, 2004 660.29 Dec 31, 2009 777.46 Dec 31, 2014 880.53 Dec 31, 2016 918.65 Dec 31, 2019 993.04 Dec 31, 2024 1101.26 Dec 31, 2029 1164.49 Dec 31, 2034 1204.89 Dec 31, 2039 1245.29

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": "multiColumn/data/390.tsv"}, "mark": "area", "encoding": {"color": {"value": "#d8b5a5"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Hydroelectricity"}, "field": "Hydroelectricity"}}, "title": ["Projected energy consumption worldwide from", "1990 to 2040 , by energy source (in million", "metric tons of oil equivalent)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The predicted energy increase is approximately a third from 1990 to 2040. The energy consumption never falls it always increases.

Chart data: Year Natural gas Dec 31, 1989 1767.44 Dec 31, 1994 1816.15 Dec 31, 1999 2065.34 Dec 31, 2004 2367.76 Dec 31, 2009 2730.79 Dec 31, 2014 2987.26 Dec 31, 2016 3155.97 Dec 31, 2019 3381.84 Dec 31, 2024 3736.11 Dec 31, 2029 4041.3 Dec 31, 2034 4343.72 Dec 31, 2039 4617.1

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": "multiColumn/data/390.tsv"}, "mark": "line", "encoding": {"color": {"value": "#b3cde3"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Natural gas"}, "field": "Natural gas"}}, "title": ["Projected energy consumption worldwide from", "1990 to 2040 , by energy source (in million", "metric tons of oil equivalent)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In 1990 energy consumption was around 3.2bn metric tonnes.  Consumption rose quite rapidly reaching 4bn MT by 2005.  The rate of increase slowed slightly until 2010 reaching 4.1bn MT.  Between 2010 and 2020 the rate of increase was greater growing to 4.7bn MT.  After 2020 the projected rate of increase reduces significantly with only a small increase in consumption to 4.9bn MT predicted by 2040.

Chart data: Year Total liquids* Dec 31, 1989 3152.5 Dec 31, 1994 3391.29 Dec 31, 1999 3690.61 Dec 31, 2004 4029.85 Dec 31, 2009 4145.01 Dec 31, 2014 4395.91 Dec 31, 2016 4537.74 Dec 31, 2019 4674.96 Dec 31, 2024 4789.79 Dec 31, 2029 4829.29 Dec 31, 2034 4875.69 Dec 31, 2039 4860.35

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": "multiColumn/data/390.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdcdac"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Total liquids*"}, "field": "Total liquids*"}}, "title": ["Projected energy consumption worldwide from", "1990 to 2040 , by energy source (in million", "metric tons of oil equivalent)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The consumption increases by almost 2,000 million metric tons of oil equivalent between 1990 and 2020. After this, consumption does not increase significantly, remaining at just below 5,000 million metric tons until the end of the graph.

Chart data: Year Total liquids* Dec 31, 1989 3152.5 Dec 31, 1994 3391.29 Dec 31, 1999 3690.61 Dec 31, 2004 4029.85 Dec 31, 2009 4145.01 Dec 31, 2014 4395.91 Dec 31, 2016 4537.74 Dec 31, 2019 4674.96 Dec 31, 2024 4789.79 Dec 31, 2029 4829.29 Dec 31, 2034 4875.69 Dec 31, 2039 4860.35

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": "multiColumn/data/390.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdcdac"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Total liquids*"}, "field": "Total liquids*"}}, "title": ["Projected energy consumption worldwide from", "1990 to 2040 , by energy source (in million", "metric tons of oil equivalent)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Germany (big four) achieved the highest value in investment in first to third quarter of 2018-2019. Warsaw achieved the lowest value.

Chart data: city Q1-Q3 2018 Germany (big four)* 2179 Central Paris 1177 Milan 220 Central London 778 Madrid 725 Dublin 243 Brussels 868 Lisbon 267 Warsaw 17 Amsterdam 75 Prague 89

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": "multiColumn/data/3910.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "city"}, "y": {"type": "quantitative", "axis": {"title": "Q1-Q3 2018"}, "field": "Q1-Q3 2018"}}, "title": ["Total value of retail real estate investment", "in Europe from first to third quarter of", "2018 and 2019 , by city (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Germany's big 4 have outperformed all other towns, could be down to showing a value for 4 cities instead of one on all the other citiesBrussels, London, Paris and Madrid have invested the most money.

Chart data: city Q1-Q3 2018 Germany (big four)* 2179 Central Paris 1177 Milan 220 Central London 778 Madrid 725 Dublin 243 Brussels 868 Lisbon 267 Warsaw 17 Amsterdam 75 Prague 89

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": "multiColumn/data/3910.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "city"}, "y": {"type": "quantitative", "axis": {"title": "Q1-Q3 2018"}, "field": "Q1-Q3 2018"}}, "title": ["Total value of retail real estate investment", "in Europe from first to third quarter of", "2018 and 2019 , by city (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The chart shows information for eleven cities. The greatest investment by far came from Germany (big four) at around 2200 million Euros. This was followed by Central Paris at around 1200 million Euros. Brussels, Central London and Madrid were all between 500 and 1000 million Euros, with the remaining cities all less than 500 million Euros.

Chart data: city Q1-Q3 2018 Germany (big four)* 2179 Central Paris 1177 Milan 220 Central London 778 Madrid 725 Dublin 243 Brussels 868 Lisbon 267 Warsaw 17 Amsterdam 75 Prague 89

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": "multiColumn/data/3910.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "city"}, "y": {"type": "quantitative", "axis": {"title": "Q1-Q3 2018"}, "field": "Q1-Q3 2018"}}, "title": ["Total value of retail real estate investment", "in Europe from first to third quarter of", "2018 and 2019 , by city (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The number of people aged 65 years and above showed a slight decline from the year 2009 to 2014. It was less than 0.15 but more than 0.10 during this time. From 2014 onwards, the numbers started climbing gradually, reaching above 0.15 by the year 2019.

Chart data: Year 65 years and older Dec 31, 2008 0.1434 Dec 31, 2009 0.1424 Dec 31, 2010 0.1419 Dec 31, 2011 0.1418 Dec 31, 2012 0.1421 Dec 31, 2013 0.1427 Dec 31, 2014 0.1435 Dec 31, 2015 0.145 Dec 31, 2016 0.1467 Dec 31, 2017 0.1487 Dec 31, 2018 0.1506

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": "multiColumn/data/3916.tsv"}, "mark": "area", "encoding": {"color": {"value": "#000000"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "65 years and older"}, "field": "65 years and older"}}, "title": ["Georgia : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Tourism payments were highest in August, July and September. The lowest payment was in May.

Chart data: Month Payments Nov 2029 Oct 2315 Sep 2899 Aug 2938 Jul 3038 Jun 2135 May 1362 Apr 1624 Mar 1573 Feb 1439 Jan 1522

Vega-Lite specification:
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "multiColumn/data/3918.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#7fc97f"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Month"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Payments"}, "field": "Payments"}}, "title": ["Monthly balance of payments in the tourism", "sector in Spain from January to November", "2019 (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The age structure has reduced from 2009 onwards by around 0.05.

Chart data: Year 0-14 years Dec 31, 2008 0.2104 Dec 31, 2009 0.2063 Dec 31, 2010 0.2027 Dec 31, 2011 0.1989 Dec 31, 2012 0.1949 Dec 31, 2013 0.1907 Dec 31, 2014 0.1859 Dec 31, 2015 0.1801 Dec 31, 2016 0.1743 Dec 31, 2017 0.1684 Dec 31, 2018 0.1626

Vega-Lite specification:
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "multiColumn/data/3921.tsv"}, "mark": "line", "encoding": {"color": {"value": "#f0027f"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "0-14 years"}, "field": "0-14 years"}}, "title": ["Puerto Rico : Age structure from 2009 to", "2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: This does not show much of a difference in age structure in Puerto Rico it decreases slightly from 2014.

Chart data: Year 15-64 years Dec 31, 2008 0.6605 Dec 31, 2009 0.6626 Dec 31, 2010 0.6611 Dec 31, 2011 0.6593 Dec 31, 2012 0.6571 Dec 31, 2013 0.6551 Dec 31, 2014 0.6537 Dec 31, 2015 0.6512 Dec 31, 2016 0.6483 Dec 31, 2017 0.6449 Dec 31, 2018 0.6404

Vega-Lite specification:
{"config": {"background": "#fff", "area": {"fill": "#4572a7"}, "line": {"stroke": "#4572a7", "strokeWidth": 2}, "rect": {"fill": "#4572a7"}, "bar": {"fill": "#4572a7"}, "point": {"color": "#4572a7", "strokeWidth": 1.5, "size": 50}, "axis": {"bandPosition": 0.5, "grid": true, "gridColor": "#000000", "gridOpacity": 1, "gridWidth": 0.5, "labelPadding": 10, "tickSize": 5, "tickWidth": 0.5}, "axisBand": {"grid": false, "tickExtra": true}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 50, "symbolType": "square"}, "range": {"category": ["#4572a7", "#aa4643", "#8aa453", "#71598e", "#4598ae", "#d98445", "#94aace", "#d09393", "#b9cc98", "#a99cbc"]}}, "data": {"url": "multiColumn/data/3921.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d09393"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "15-64 years"}, "field": "15-64 years"}}, "title": ["Puerto Rico : Age structure from 2009 to", "2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Age structure has gradually decreased from 2010 to 2018.

Chart data: Year 15-64 years Dec 31, 2008 0.6605 Dec 31, 2009 0.6626 Dec 31, 2010 0.6611 Dec 31, 2011 0.6593 Dec 31, 2012 0.6571 Dec 31, 2013 0.6551 Dec 31, 2014 0.6537 Dec 31, 2015 0.6512 Dec 31, 2016 0.6483 Dec 31, 2017 0.6449 Dec 31, 2018 0.6404

Vega-Lite specification:
{"config": {"background": "#fff", "area": {"fill": "#4572a7"}, "line": {"stroke": "#4572a7", "strokeWidth": 2}, "rect": {"fill": "#4572a7"}, "bar": {"fill": "#4572a7"}, "point": {"color": "#4572a7", "strokeWidth": 1.5, "size": 50}, "axis": {"bandPosition": 0.5, "grid": true, "gridColor": "#000000", "gridOpacity": 1, "gridWidth": 0.5, "labelPadding": 10, "tickSize": 5, "tickWidth": 0.5}, "axisBand": {"grid": false, "tickExtra": true}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 50, "symbolType": "square"}, "range": {"category": ["#4572a7", "#aa4643", "#8aa453", "#71598e", "#4598ae", "#d98445", "#94aace", "#d09393", "#b9cc98", "#a99cbc"]}}, "data": {"url": "multiColumn/data/3921.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d09393"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "15-64 years"}, "field": "15-64 years"}}, "title": ["Puerto Rico : Age structure from 2009 to", "2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The industry has slowly declined over the 5 years.

Chart data: Year Industry Dec 31, 2008 0.4765 Dec 31, 2009 0.4278 Dec 31, 2010 0.4391 Dec 31, 2011 0.4359 Dec 31, 2012 0.4264 Dec 31, 2013 0.4193 Dec 31, 2014 0.4005 Dec 31, 2015 0.3931 Dec 31, 2016 0.3938 Dec 31, 2017 0.3973 Dec 31, 2018 0.3895

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": "multiColumn/data/393.tsv"}, "mark": "area", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Industry"}, "field": "Industry"}}, "title": ["Indonesia : Share of economic sectors in", "the gross domestic product (GDP) from 2009", "to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Services reduced over time from 0.6 to closer to 0.5 from years 2009 to 2019.

Chart data: Year Services Dec 31, 2008 0.5962 Dec 31, 2009 0.5455 Dec 31, 2010 0.5237 Dec 31, 2011 0.5328 Dec 31, 2012 0.5508 Dec 31, 2013 0.5297 Dec 31, 2014 0.5038 Dec 31, 2015 0.5198 Dec 31, 2016 0.5336 Dec 31, 2017 0.5128 Dec 31, 2018 0.5145

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": "multiColumn/data/3944.tsv"}, "mark": "line", "encoding": {"color": {"value": "#7570b3"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Services"}, "field": "Services"}}, "title": ["Lesotho : Distribution of gross domestic", "product (GDP) across economic sectors from", "2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The revenue of HarmÃ " s worldwide is on a steady increase from 2010 to 2015, stays on the same level for one year and then starts increasing again.

Chart data: Year Ready-to-wear & accessories Dec 31, 2009 445 Dec 31, 2010 576 Dec 31, 2011 746 Dec 31, 2012 843 Dec 31, 2013 934 Dec 31, 2014 1101 Dec 31, 2015 1099 Dec 31, 2016 1181 Dec 31, 2017 1310 Dec 31, 2018 1574

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": "multiColumn/data/3948.tsv"}, "mark": "area", "encoding": {"color": {"value": "#C2185B"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Ready-to-wear & accessories"}, "field": "Ready-to-wear & accessories"}}, "title": ["Revenue of Herm\u00c3\u00a8s worldwide from 2010 to", "2019 , by segment (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Hermas revenue has increased year on year from 2010 until 2019 and has now almost doubled since the recording within the graph began.

Chart data: Year Silk & textiles Dec 31, 2009 284 Dec 31, 2010 347 Dec 31, 2011 425 Dec 31, 2012 454 Dec 31, 2013 482 Dec 31, 2014 521 Dec 31, 2015 515 Dec 31, 2016 534 Dec 31, 2017 537 Dec 31, 2018 592

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": "multiColumn/data/3948.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fc4f30"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Silk & textiles"}, "field": "Silk & textiles"}}, "title": ["Revenue of Herm\u00c3\u00a8s worldwide from 2010 to", "2019 , by segment (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The revenue of HermA"s worldwide from 2010 to 2019 has increased significantly with minor fluctuations along the way.

Chart data: Year Silk & textiles Dec 31, 2009 284 Dec 31, 2010 347 Dec 31, 2011 425 Dec 31, 2012 454 Dec 31, 2013 482 Dec 31, 2014 521 Dec 31, 2015 515 Dec 31, 2016 534 Dec 31, 2017 537 Dec 31, 2018 592

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": "multiColumn/data/3948.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fc4f30"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Silk & textiles"}, "field": "Silk & textiles"}}, "title": ["Revenue of Herm\u00c3\u00a8s worldwide from 2010 to", "2019 , by segment (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Between 2010 and 2019 there has been a steady increase in perfume revenue growing from approximately 140 million euros to 325 million euros. There was a slight plateau in 2014-2015.

Chart data: Year Perfumes Dec 31, 2009 138 Dec 31, 2010 159 Dec 31, 2011 184 Dec 31, 2012 210 Dec 31, 2013 231 Dec 31, 2014 241 Dec 31, 2015 262 Dec 31, 2016 288 Dec 31, 2017 312 Dec 31, 2018 326

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": "multiColumn/data/3948.tsv"}, "mark": "line", "encoding": {"color": {"value": "#56cc60"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Perfumes"}, "field": "Perfumes"}}, "title": ["Revenue of Herm\u00c3\u00a8s worldwide from 2010 to", "2019 , by segment (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: All regions apart from Moscow do not go above 3500 on the chart. Moscow is the region with the highest number of new vehicles.

Chart data: region of Russian May 2020 Moscow city 6194 Saint Petersburg 3381 Moscow region 3136 Republic of Tatarstan 2816 Samara Region 2495 Sverdlovsk region 2424 Republic of Bashkortostan 2307 Chelyabinsk region 2009 Krasnodar region 1942

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": "multiColumn/data/3953.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#DB4437"}, "x": {"type": "nominal", "axis": {"labelAngle": -90}, "bin": false, "field": "region of Russian"}, "y": {"type": "quantitative", "axis": {"title": "May 2020"}, "field": "May 2020"}}, "title": ["Total number of new passenger vehicles in", "the Russian car market in May 2019 and May", "2020 , by region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Ashleigh Barty has the highest income from prize money and endorsements. 9 of the 10 highest earners were tennis players.

Chart data: Prize money Female athletes with the highest income 3.4 Naomi Osaka (tennis) 4 Serena Williams (tennis) 2.1 Garbine Muguruza (tennis) 0.4 Alex Morgan (soccer) 4.9 Bianca Andreescu (tennis) 1.3 Angelique Kerber (tennis) 6.9 Simona Halep (tennis) 10.1 Ashleigh Barty (tennis) 5.4 Elina Svitolina (tennis) 4.8 Sofia Kenin (tennis)

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": "multiColumn/data/395.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#182429"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Prize money"}, "field": "Prize money"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Female athletes with the highest income"}}, "title": ["Female athletes with the highest income", "from prize money and endorsements in 2020", "(in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Ashleigh Barty who is a tennis player had the highest income from prize money and endorsements in 2020 among female athletes, with female tennis players had the highest income from prize money and endorsements in 2020.

Chart data: Prize money Female athletes with the highest income 3.4 Naomi Osaka (tennis) 4 Serena Williams (tennis) 2.1 Garbine Muguruza (tennis) 0.4 Alex Morgan (soccer) 4.9 Bianca Andreescu (tennis) 1.3 Angelique Kerber (tennis) 6.9 Simona Halep (tennis) 10.1 Ashleigh Barty (tennis) 5.4 Elina Svitolina (tennis) 4.8 Sofia Kenin (tennis)

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": "multiColumn/data/395.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#182429"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Prize money"}, "field": "Prize money"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Female athletes with the highest income"}}, "title": ["Female athletes with the highest income", "from prize money and endorsements in 2020", "(in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: 9 out of 10 of these female athletes are tennis players while there is a single soccer player. The female athlete with the highest income is Ashleigh Barty (tennis) at 10 million U.S. dollars. Alex Morgan (soccer) came as the lowest with under 1 million U.S. dollars in prize money and endorsements for 2020.

Chart data: Prize money Female athletes with the highest income 3.4 Naomi Osaka (tennis) 4 Serena Williams (tennis) 2.1 Garbine Muguruza (tennis) 0.4 Alex Morgan (soccer) 4.9 Bianca Andreescu (tennis) 1.3 Angelique Kerber (tennis) 6.9 Simona Halep (tennis) 10.1 Ashleigh Barty (tennis) 5.4 Elina Svitolina (tennis) 4.8 Sofia Kenin (tennis)

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": "multiColumn/data/395.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#182429"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Prize money"}, "field": "Prize money"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Female athletes with the highest income"}}, "title": ["Female athletes with the highest income", "from prize money and endorsements in 2020", "(in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Naomi Osaka and Serena Williams are significantly ahead of all other athletes in the chartAlex Morgan is the only soccer player within the stats.

Chart data: Endorsements Female athletes with the highest income 34 Naomi Osaka (tennis) 32 Serena Williams (tennis) 4.5 Garbine Muguruza (tennis) 4.2 Alex Morgan (soccer) 4 Bianca Andreescu (tennis) 4 Angelique Kerber (tennis) 4 Simona Halep (tennis) 3 Ashleigh Barty (tennis) 1 Elina Svitolina (tennis) 1 Sofia Kenin (tennis)

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": "multiColumn/data/395.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b79a20"}, "x": {"type": "quantitative", "axis": {"title": "Endorsements"}, "field": "Endorsements"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Female athletes with the highest income"}}, "title": ["Female athletes with the highest income", "from prize money and endorsements in 2020", "(in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}