delen/vistext huggingface dataset:
Chart description: The number of stores peaked at over 4,000 in 2019. The number of stores rapidly increased in 2018 and 2019.

Chart data: Year company-operated stores 2019 4123 2018 3521 2017 1540 2016 1272 2015 1026 2014 823 2013 614 2012 408 2011 278 2010** 220 2009 191 2008 178 2007* 141 2006 38 2005 24

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": "multiColumn/data/134.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ab5787"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "company-operated stores"}, "field": "company-operated stores"}}, "title": ["Number of Starbucks stores in China from", "2005 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: While stores have been steadily climbing, its only from 2017 that the Number of Starbucks stores in China has risen dramatically.

Chart data: Year company-operated stores 2019 4123 2018 3521 2017 1540 2016 1272 2015 1026 2014 823 2013 614 2012 408 2011 278 2010** 220 2009 191 2008 178 2007* 141 2006 38 2005 24

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": "multiColumn/data/134.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ab5787"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "company-operated stores"}, "field": "company-operated stores"}}, "title": ["Number of Starbucks stores in China from", "2005 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The graph shows that Starbucks had a slow start at expanding in china for the first 5 years but has consistently expanded since 2013. In 2017 there were at least 6 times as many Starbucks as there were in 2005.

Chart data: licensed stores Year 0 2019 0 2018 1396 2017 1110 2016 785 2015 544 2014 403 2013 292 2012 218 2011 250 2010** 283 2009 269 2008 212 2007* 223 2006 185 2005

Vega-Lite specification:
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "multiColumn/data/134.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d6616b"}, "x": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "licensed stores"}, "field": "licensed stores"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Number of Starbucks stores in China from", "2005 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There has been a decline over the years taken into consideration by this chart. From 2010 to 2012 there is a steady growth. The growth stops and start to decline from 0.35 to 0.27 approx in industry between 2012 and 2016 and it kept declining steadily until 2019.

Chart data: Year Industry Dec 31, 2008 0.3125 Dec 31, 2009 0.3137 Dec 31, 2010 0.333 Dec 31, 2011 0.3341 Dec 31, 2012 0.3275 Dec 31, 2013 0.3099 Dec 31, 2014 0.2859 Dec 31, 2015 0.2769 Dec 31, 2016 0.2681 Dec 31, 2017 0.2693 Dec 31, 2018 0.2627

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/1359.tsv"}, "mark": "line", "encoding": {"color": {"value": "#6d904f"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Industry"}, "field": "Industry"}}, "title": ["Colombia : 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: Overall from the years 2010 to 2020, services stayed consistent at approximately 0.6, however during this time the number did increase from just below 0.6 in 2010 to just above in 2020.

Chart data: Year Services Dec 31, 2009 0.5891 Dec 31, 2010 0.5855 Dec 31, 2011 0.5883 Dec 31, 2012 0.5946 Dec 31, 2013 0.5921 Dec 31, 2014 0.5905 Dec 31, 2015 0.5903 Dec 31, 2016 0.5914 Dec 31, 2017 0.597 Dec 31, 2018 0.5999 Dec 31, 2019 0.6024

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/1362.tsv"}, "mark": "line", "encoding": {"color": {"value": "#439894"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Services"}, "field": "Services"}}, "title": ["Czech Republic : Distribution of employment", "by economic sector from 2010 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: It would appear that the age structure decreases very slightly over the period from 2010 to 2018. The peak in age structure was just before 2010.

Chart data: Year 15-64 years Dec 31, 2008 0.6599 Dec 31, 2009 0.6588 Dec 31, 2010 0.6579 Dec 31, 2011 0.6575 Dec 31, 2012 0.6573 Dec 31, 2013 0.6568 Dec 31, 2014 0.6556 Dec 31, 2015 0.654 Dec 31, 2016 0.6518 Dec 31, 2017 0.6492 Dec 31, 2018 0.6464

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/1364.tsv"}, "mark": "area", "encoding": {"color": {"value": "#f2cf5b"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "15-64 years"}, "field": "15-64 years"}}, "title": ["Germany : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: this shows the age structure has decreased subtley between 2009 and 2014 at 0.66 down to 0.65 but appears to have decreased at a faster rate between 2014 and 2019 from 0.65 to 0.63 . which would indicate it has doubled rate.

Chart data: Year 15-64 years Dec 31, 2008 0.6599 Dec 31, 2009 0.6588 Dec 31, 2010 0.6579 Dec 31, 2011 0.6575 Dec 31, 2012 0.6573 Dec 31, 2013 0.6568 Dec 31, 2014 0.6556 Dec 31, 2015 0.654 Dec 31, 2016 0.6518 Dec 31, 2017 0.6492 Dec 31, 2018 0.6464

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/1364.tsv"}, "mark": "area", "encoding": {"color": {"value": "#f2cf5b"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "15-64 years"}, "field": "15-64 years"}}, "title": ["Germany : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: between 2009 and 2019 the share of gross domestic product has remained relatively stable with small positive and negative fluctuations over the period. An increase of 0.05 has been achieved over the period.

Chart data: Year Services Dec 31, 2008 0.6919 Dec 31, 2009 0.7186 Dec 31, 2010 0.7354 Dec 31, 2011 0.7297 Dec 31, 2012 0.7209 Dec 31, 2013 0.7376 Dec 31, 2014 0.7369 Dec 31, 2015 0.7403 Dec 31, 2016 0.7518 Dec 31, 2017 0.7667 Dec 31, 2018 0.7591

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/1374.tsv"}, "mark": "area", "encoding": {"color": {"value": "#f2cf5b"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Services"}, "field": "Services"}}, "title": ["Lebanon : 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: Enterprise value has increased over the years, slight dip after 2018 but pulled it back.

Chart data: Year Enterprise value (KPMG) Dec 31, 2010 0 Dec 31, 2011 0 Dec 31, 2012 0 Dec 31, 2013 0 Dec 31, 2014 0 Dec 31, 2015 905 Dec 31, 2016 1017 Dec 31, 2017 1277 Dec 31, 2018 1236 Dec 31, 2019 1434

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/1377.tsv"}, "mark": "line", "encoding": {"color": {"value": "#000000"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Enterprise value (KPMG)"}, "field": "Enterprise value (KPMG)"}}, "title": ["Borussia Dortmund brand/team value from", "2011 to 2020 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The number of children in the 0-14 age range in Argentina has decreased consistently from 2010 to 2018.

Chart data: Year 0-14 years Dec 31, 2008 0.2626 Dec 31, 2009 0.2604 Dec 31, 2010 0.2582 Dec 31, 2011 0.2565 Dec 31, 2012 0.2551 Dec 31, 2013 0.2537 Dec 31, 2014 0.2521 Dec 31, 2015 0.2508 Dec 31, 2016 0.2493 Dec 31, 2017 0.2476 Dec 31, 2018 0.246

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/1379.tsv"}, "mark": "line", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "0-14 years"}, "field": "0-14 years"}}, "title": ["Argentina : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The line showing 15-64 years olds increased very slightly and gradually between 2009 and 2019. It didn't fluctuate. The line was over 0.6.

Chart data: Year 15-64 years Dec 31, 2008 0.6362 Dec 31, 2009 0.6377 Dec 31, 2010 0.6388 Dec 31, 2011 0.6395 Dec 31, 2012 0.6398 Dec 31, 2013 0.6402 Dec 31, 2014 0.6406 Dec 31, 2015 0.6406 Dec 31, 2016 0.6408 Dec 31, 2017 0.6412 Dec 31, 2018 0.6416

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/1379.tsv"}, "mark": "line", "encoding": {"color": {"value": "#F4B400"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "15-64 years"}, "field": "15-64 years"}}, "title": ["Argentina : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: it was most expensive to buy fuel in apr 2016 than any other month. the best time to buy fuel in 2016 was during february.

Chart data: 2016 Month 1.18 Dec 1.24 Nov 1.25 Oct 1.19 Sep 1.16 Aug 1.21 Jul 1.21 Jun 1.07 May 1.28 Apr 1.13 Mar 1.08 Feb 1.1 Jan

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/1381.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#bf5b17"}, "x": {"type": "quantitative", "axis": {"title": "2016"}, "field": "2016"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Month"}}, "title": ["Average price of diesel fuel in France between", "January 2013 and December 2018 (in euros", "per liter)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: This graph shows the age structure of Syria has increased from 2009 to 2019. It started just below 0.6 in 2009 and has raised to about 0.65 in 2019.

Chart data: Year 15-64 years Dec 31, 2008 0.5864 Dec 31, 2009 0.592 Dec 31, 2010 0.5967 Dec 31, 2011 0.6037 Dec 31, 2012 0.6127 Dec 31, 2013 0.6219 Dec 31, 2014 0.6295 Dec 31, 2015 0.6349 Dec 31, 2016 0.6385 Dec 31, 2017 0.6408 Dec 31, 2018 0.6425

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/1382.tsv"}, "mark": "line", "encoding": {"color": {"value": "#000000"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "15-64 years"}, "field": "15-64 years"}}, "title": ["Syria : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In this area chart it shows that in 2010 in Syria the amount of 65+ citizens was only just above 0.03. This amount has gradually and steadily increased to just below 0.05 from 2010 to 2019.

Chart data: Year 65 years and older Dec 31, 2008 0.0339 Dec 31, 2009 0.0341 Dec 31, 2010 0.0351 Dec 31, 2011 0.036 Dec 31, 2012 0.0371 Dec 31, 2013 0.0384 Dec 31, 2014 0.0399 Dec 31, 2015 0.0415 Dec 31, 2016 0.0432 Dec 31, 2017 0.045 Dec 31, 2018 0.0469

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/1382.tsv"}, "mark": "area", "encoding": {"color": {"value": "#adc839"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "65 years and older"}, "field": "65 years and older"}}, "title": ["Syria : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Clothing/clothing accessories and gasoline stations were the two sectors most negatively affected. In comparison, nonstore retailers and sporting goods saw the most benefit.

Chart data: retail sectors July Total retail 0.027 Nonstore retailers 0.247 Miscellaneous store retailers 0.045 General merchandise stores 0.011 Sporting goods, hobby, musical instrument, & book stores 0.178 Clothing & clothing accessories  stores −0.209 Gasoline stations −0.156 Health & personal care stores 0.032 Food & beverage stores 0.111 Building material & garden equipment & supplies dealers 0.148 Electronics & appliance stores −0.028 Furniture & home furniture stores −0.007 Motor vehicle & parts dealers 0.061

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/1386.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#545454"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "retail sectors"}, "y": {"type": "quantitative", "axis": {"title": "July"}, "field": "July"}}, "title": ["Year over year monthly retail sales comparison", "of the impact of coronavirus (COVID-19) in", "the United States from 2019 to 2020 , by", "retail sector"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The average monthly temperatures range from about 6 degrees Celsius in February to 19 degrees Celsius in August. The visualisation description is incorrect in labelling the x axis as "2020", the correct X axis label is "temperature in degrees Celsius". The y category axis has ordered the months in alphabetical order thus stymieing any attempts at discerning any trends through time, although the writer notes that it appears that as the months progress from January through to August, the average monthly temperatures increases.

Chart data: 2020 Month 17.2 Aug 15.7 Jul 15.1 Jun 12.6 May 10.3 Apr 6.8 Mar 6.4 Feb 6.7 Jan

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/13.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#386cb0"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "2020"}, "field": "2020"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Month"}}, "title": ["Monthly average daily temperatures in the", "United Kingdom (UK) from 2015 to 2020 (in", "degrees Celsius)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: it's much hotter in august, than june or july and cooler in january, february and march.

Chart data: 2020 Month 17.2 Aug 15.7 Jul 15.1 Jun 12.6 May 10.3 Apr 6.8 Mar 6.4 Feb 6.7 Jan

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/13.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#386cb0"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "2020"}, "field": "2020"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Month"}}, "title": ["Monthly average daily temperatures in the", "United Kingdom (UK) from 2015 to 2020 (in", "degrees Celsius)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Twitter's data licensing steadily and continuously increases over the years, from 2010 to 2018, where it hits the highest point of 500 million dollars. Since 2010, Twitter's data licensing has never decreased, following a positive trend of continuous growth.

Chart data: Year Data licensing Dec 31, 2009 20.96 Dec 31, 2010 28.6 Dec 31, 2011 47.51 Dec 31, 2012 70.34 Dec 31, 2013 147.31 Dec 31, 2014 234 Dec 31, 2015 281.57 Dec 31, 2016 333.31 Dec 31, 2017 424.96 Dec 31, 2018 465.94

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": "multiColumn/data/1401.tsv"}, "mark": "line", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Data licensing"}, "field": "Data licensing"}}, "title": ["Annual revenue of Twitter from 2010 to 2019", ", by segment (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: People in the United States have decreased their spending on many things as a result of COVID-19. The biggest fall was in the amount spent on going out. Some things remained at a similar, albeit lowered level of spending, such as cleaning products, medicine, and housing costs.

Chart data: United States Response 0.77 Going out (e.g. restaurants, cinemas, pubs / bars) 0.7 Travel (e.g. public transport, vacations) 0.69 Services (e.g. hairdresser) 0.49 Clothing 0.34 Consumer electronics, household appliances, furniture 0.26 Hobbies 0.26 Investments 0.23 Food and drinks (e.g. shopping, takeaways, delivery) 0.22 Childcare 0.19 Home entertainment (e.g. video, games, books) 0.19 Insurance 0.13 Housing, energy, maintenance (e.g. electricity, waste disposal, DIY) 0.08 Health and hygiene (e.g. medicine, hand sanitizer) 0.08 Household cleaning products

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/1408.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1696d2"}, "x": {"type": "quantitative", "axis": {"labelAngle": 90, "title": "United States"}, "field": "United States"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Products and services people spend less", "than usual on due to the COVID-19 / coronavirus", "pandemic in the United States , United Kingdom", "and Germany 2020 (as of May 31)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Finding and picking up a pen is the most religiously affiliated superstition. Stepping on a crack superstition is the least religiously affiliated. All other superstitions (bar the stepping on a crack) are above 0.1 catholic affiliation.

Chart data: Catholic Response 0.4 Finding and picking up a penny is good luck 0.31 It’s bad luck for the groom to see the bridebefore the wedding 0.26 Seven is a lucky number 0.27 Knocking on wood prevents bad luck 0.26 It’s unlucky to walk under a ladder 0.18 Opening an umbrella indoors is bad luck 0.18 Friday the 13th is an unlucky day 0.21 Breaking a mirror brings seven years bad luck 0.18 Throwing spilled salt over the left shoulderprevents bad luck 0.17 A black cat crossing your path is bad luck 0.15 13 is an unlucky number 0.08 Stepping on a crack is bad luck

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/1416.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Catholic"}, "field": "Catholic"}, "y": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Response"}}, "title": ["Superstitions held by Americans in 2014", ", by religious affiliation"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: January and February had the highest sales with over 2 billion, June, July and August next with over 1 and a half billion, followed by March with over 1 billion then May with over half a billion then April having the lowest sales with under half a billion.  The majority of months made over 1 and a half billion.

Chart data: Month 2020 Au  1.98; Month: Aug Ju  1.99; Month: Jul Ju  1.75; Month: Jun Ma  0.71; Month: May Ap  0.34; Month: Apr Ma  1.13; Month: Mar Fe  2.33; Month: Feb Ja  2.34; Month: Jan

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/1422.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#66a61e"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Month"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "2020"}, "field": "2020"}}, "title": ["Monthly retail sales of clothing stores", "in Canada from 2015 to 2020 (in billion Canadian", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There has been an increase in the amount of corn consumed in the US over the 20-year period. The rate of increase was steepest between 2005 and 2009. There was a dip in overall usage between 2010-2012. Since 2012, the general trend has been for a much smaller year on year increase in corn use than in the previous period. 2017 marked the peak usage and there has been a small decline to 2019. Over the 20 years, usage has increased from 2,000,000,000 bushels to a peak of around 7,000,000,000 before falling back very slightly.

Chart data: Year Food, seed and industrial Dec 31, 2000 2046 Dec 31, 2001 2340 Dec 31, 2002 2537 Dec 31, 2003 2687 Dec 31, 2004 3019 Dec 31, 2005 3541 Dec 31, 2006 4442 Dec 31, 2007 5025 Dec 31, 2008 5961 Dec 31, 2009 6426 Dec 31, 2010 6424 Dec 31, 2011 6038 Dec 31, 2012 6493 Dec 31, 2013 6601 Dec 31, 2014 6648 Dec 31, 2015 6885 Dec 31, 2016 7056 Dec 31, 2017 6791 Dec 31, 2018 6790

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/1426.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fed9a6"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Food, seed and industrial"}, "field": "Food, seed and industrial"}}, "title": ["Total U.S. domestic use of corn from 2001", "to 2019 (in million bushels)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Mobility plummets quickly in March 2020. The lowest mobility was in late April. After late April/May, mobility rose very gradually overall up until December.

Chart data: Response Sao Paolo Mar 02, 2020 1.16 Mar 09, 2020 1.17 Mar 16, 2020 0.45 Mar 23, 2020 0.11 Mar 30, 2020 0.14 Apr 06, 2020 0.12 Apr 13, 2020 0.11 Apr 20, 2020 0.07 Apr 27, 2020 0.11 May 04, 2020 0.11 May 11, 2020 0.13 May 18, 2020 0.11 May 25, 2020 0.12 Jun 01, 2020 0.13 Jun 08, 2020 0.15 Jun 15, 2020 0.17 Jun 22, 2020 0.16 Jun 29, 2020 0.18 Jul 06, 2020 0.18 Jul 13, 2020 0.19 Jul 20, 2020 0.19 Jul 27, 2020 0.22 Aug 03, 2020 0.22 Aug 10, 2020 0.22 Aug 17, 2020 0.21 Aug 24, 2020 0.24 Aug 31, 2020 0.25 Sep 07, 2020 0.27 Sep 14, 2020 0.26 Sep 21, 2020 0.24 Sep 28, 2020 0.27 Oct 05, 2020 0.29 Oct 12, 2020 0.29 Oct 19, 2020 0.29 Oct 26, 2020 0.29 Nov 02, 2020 0.3 Nov 09, 2020 0.3 Nov 16, 2020 0.28 Nov 23, 2020 0.3 Nov 30, 2020 0.28 Dec 07, 2020 0.3

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": "multiColumn/data/1427.tsv"}, "mark": "area", "encoding": {"color": {"value": "#4c78a8"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Sao Paolo"}, "field": "Sao Paolo"}}, "title": ["Mobility in selected cities between March", "3 , 2020 and December 7 , 2020 , compared", "with movement prior to the coronavirus outbreak"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: April to July shows the lowest amount of usage. July to October show travel spike again. We see a drop from October onwards.

Chart data: Response Moscow Mar 02, 2020 1.17 Mar 09, 2020 1.06 Mar 16, 2020 0.82 Mar 23, 2020 0.57 Mar 30, 2020 0.17 Apr 06, 2020 0.17 Apr 13, 2020 0.17 Apr 20, 2020 0.17 Apr 27, 2020 0.17 May 04, 2020 0.14 May 11, 2020 0.18 May 18, 2020 0.2 May 25, 2020 0.25 Jun 01, 2020 0.28 Jun 08, 2020 0.46 Jun 15, 2020 0.63 Jun 22, 2020 0.68 Jun 29, 2020 0.68 Jul 06, 2020 0.66 Jul 13, 2020 0.65 Jul 20, 2020 0.67 Jul 27, 2020 0.68 Aug 03, 2020 0.73 Aug 10, 2020 0.74 Aug 17, 2020 0.79 Aug 24, 2020 0.8 Aug 31, 2020 0.88 Sep 07, 2020 0.82 Sep 14, 2020 0.83 Sep 21, 2020 0.86 Sep 28, 2020 0.78 Oct 05, 2020 0.74 Oct 12, 2020 0.6 Oct 19, 2020 0.56 Oct 26, 2020 0.59 Nov 02, 2020 0.53 Nov 09, 2020 0.51 Nov 16, 2020 0.5 Nov 23, 2020 0.5 Nov 30, 2020 0.48 Dec 07, 2020 0.53

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/1427.tsv"}, "mark": "line", "encoding": {"color": {"value": "#FF7043"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Moscow"}, "field": "Moscow"}}, "title": ["Mobility in selected cities between March", "3 , 2020 and December 7 , 2020 , compared", "with movement prior to the coronavirus outbreak"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There was a drop in mobility between March and April before an increase towards the summer months.

Chart data: Response Moscow Mar 02, 2020 1.17 Mar 09, 2020 1.06 Mar 16, 2020 0.82 Mar 23, 2020 0.57 Mar 30, 2020 0.17 Apr 06, 2020 0.17 Apr 13, 2020 0.17 Apr 20, 2020 0.17 Apr 27, 2020 0.17 May 04, 2020 0.14 May 11, 2020 0.18 May 18, 2020 0.2 May 25, 2020 0.25 Jun 01, 2020 0.28 Jun 08, 2020 0.46 Jun 15, 2020 0.63 Jun 22, 2020 0.68 Jun 29, 2020 0.68 Jul 06, 2020 0.66 Jul 13, 2020 0.65 Jul 20, 2020 0.67 Jul 27, 2020 0.68 Aug 03, 2020 0.73 Aug 10, 2020 0.74 Aug 17, 2020 0.79 Aug 24, 2020 0.8 Aug 31, 2020 0.88 Sep 07, 2020 0.82 Sep 14, 2020 0.83 Sep 21, 2020 0.86 Sep 28, 2020 0.78 Oct 05, 2020 0.74 Oct 12, 2020 0.6 Oct 19, 2020 0.56 Oct 26, 2020 0.59 Nov 02, 2020 0.53 Nov 09, 2020 0.51 Nov 16, 2020 0.5 Nov 23, 2020 0.5 Nov 30, 2020 0.48 Dec 07, 2020 0.53

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/1427.tsv"}, "mark": "line", "encoding": {"color": {"value": "#FF7043"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Moscow"}, "field": "Moscow"}}, "title": ["Mobility in selected cities between March", "3 , 2020 and December 7 , 2020 , compared", "with movement prior to the coronavirus outbreak"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: this chart shows that mobility was restricted due to lockdown restrictions and that as they were gradually lifted, people's mobility wasn't as curtailed as during lockdown.

Chart data: Response Moscow Mar 02, 2020 1.17 Mar 09, 2020 1.06 Mar 16, 2020 0.82 Mar 23, 2020 0.57 Mar 30, 2020 0.17 Apr 06, 2020 0.17 Apr 13, 2020 0.17 Apr 20, 2020 0.17 Apr 27, 2020 0.17 May 04, 2020 0.14 May 11, 2020 0.18 May 18, 2020 0.2 May 25, 2020 0.25 Jun 01, 2020 0.28 Jun 08, 2020 0.46 Jun 15, 2020 0.63 Jun 22, 2020 0.68 Jun 29, 2020 0.68 Jul 06, 2020 0.66 Jul 13, 2020 0.65 Jul 20, 2020 0.67 Jul 27, 2020 0.68 Aug 03, 2020 0.73 Aug 10, 2020 0.74 Aug 17, 2020 0.79 Aug 24, 2020 0.8 Aug 31, 2020 0.88 Sep 07, 2020 0.82 Sep 14, 2020 0.83 Sep 21, 2020 0.86 Sep 28, 2020 0.78 Oct 05, 2020 0.74 Oct 12, 2020 0.6 Oct 19, 2020 0.56 Oct 26, 2020 0.59 Nov 02, 2020 0.53 Nov 09, 2020 0.51 Nov 16, 2020 0.5 Nov 23, 2020 0.5 Nov 30, 2020 0.48 Dec 07, 2020 0.53

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/1427.tsv"}, "mark": "line", "encoding": {"color": {"value": "#FF7043"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Moscow"}, "field": "Moscow"}}, "title": ["Mobility in selected cities between March", "3 , 2020 and December 7 , 2020 , compared", "with movement prior to the coronavirus outbreak"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}