delen/vistext huggingface dataset:
Chart description: The majority of states on this chart are affiliated with the Republican party.

Chart data: Republican State 1 Alabama 2 Alaska 1 Arizona 2 Arkansas 0 California 1 Colorado 0 Connecticut 0 Delaware 2 Florida 2 Georgia 0 Hawaii 2 Idaho 0 Illinois 2 Indiana 2 Iowa 2 Kansas 2 Kentucky 2 Louisiana 1 Maine 0 Maryland 0 Massachusetts 0 Michigan 0 Minnesota 2 Mississippi 2 Missouri 1 Montana 2 Nebraska 2 Nevada 0 New Hampshire 0 New Jersey 0 New Mexico 0 New York 2 North Carolina 1 North Dakota 1 Ohio 2 Oklahoma 0 Oregon 1 Pennsylvania 0 Rhode Island 2 South Carolina 2 South Dakota 2 Tennessee 2 Texas 2 Utah 0 Vermont 0 Virginia 0 Washington 1 West Virginia 1 Wisconsin 2 Wyoming

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/3173.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#bf5b17"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Republican"}, "field": "Republican"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "State"}}, "title": ["Composition of the U.S. Senate by political", "party affiliation for the 116th Congress", "in 2019 , by state"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There is more republican representation in the senate from Wyoming than Wisconsin. There is at least some republican representation in the senate from 32 states. There is no republican representation in the senate from 18 states.

Chart data: Republican State 1 Alabama 2 Alaska 1 Arizona 2 Arkansas 0 California 1 Colorado 0 Connecticut 0 Delaware 2 Florida 2 Georgia 0 Hawaii 2 Idaho 0 Illinois 2 Indiana 2 Iowa 2 Kansas 2 Kentucky 2 Louisiana 1 Maine 0 Maryland 0 Massachusetts 0 Michigan 0 Minnesota 2 Mississippi 2 Missouri 1 Montana 2 Nebraska 2 Nevada 0 New Hampshire 0 New Jersey 0 New Mexico 0 New York 2 North Carolina 1 North Dakota 1 Ohio 2 Oklahoma 0 Oregon 1 Pennsylvania 0 Rhode Island 2 South Carolina 2 South Dakota 2 Tennessee 2 Texas 2 Utah 0 Vermont 0 Virginia 0 Washington 1 West Virginia 1 Wisconsin 2 Wyoming

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/3173.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#bf5b17"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Republican"}, "field": "Republican"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "State"}}, "title": ["Composition of the U.S. Senate by political", "party affiliation for the 116th Congress", "in 2019 , by state"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Licensing income dipped in 2008 to below $100 million. It then rose each year until 2013. Licensing income in 2014 is at its highest point at over $150 million.

Chart data: Year Licensing Dec 31, 2006 141.6 Dec 31, 2007 96.7 Dec 31, 2008 103.6 Dec 31, 2009 107.4 Dec 31, 2010 108.3 Dec 31, 2011 129 Dec 31, 2012 152 Dec 31, 2013 150 Dec 31, 2014 152 Dec 31, 2015 155

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/3177.tsv"}, "mark": "area", "encoding": {"color": {"value": "#71598e"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Licensing"}, "field": "Licensing"}}, "title": ["Income of Polo Ralph Lauren worldwide from", "2007 to 2016 , by business segment (in million", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Polo Ralph Lauren's income was lowest in 2008, when licensing was down to 100. It has since been going up. It reached its maximum in beyond 2014, with licensing crossing 150.

Chart data: Year Licensing Dec 31, 2006 141.6 Dec 31, 2007 96.7 Dec 31, 2008 103.6 Dec 31, 2009 107.4 Dec 31, 2010 108.3 Dec 31, 2011 129 Dec 31, 2012 152 Dec 31, 2013 150 Dec 31, 2014 152 Dec 31, 2015 155

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/3177.tsv"}, "mark": "area", "encoding": {"color": {"value": "#71598e"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Licensing"}, "field": "Licensing"}}, "title": ["Income of Polo Ralph Lauren worldwide from", "2007 to 2016 , by business segment (in million", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Berlin has the most kareoke bars with over 20 and Munich has the least with 4.

Chart data: German cities Kareoke bars Cologne 16 Berlin 22 Frankfurt 12 Leverkusen 12 Solingen 12 Mülheim 8 Wuppertal 8 Düsseldorf 9 Essen 7 Hamburg 7 Munich 4 Bonn 7 Duisburg 7 Lübeck 6 Bochum 14 Hagen 18 Krefeld 7 Gelsenkirchen 14 Stuttgart 4 Potsdam 10

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/3182.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#74a9cf"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "German cities"}, "y": {"type": "quantitative", "axis": {"title": "Kareoke bars"}, "field": "Kareoke bars"}}, "title": ["Number of bars , dance clubs and music venues", "in selected German cities in 2016 , by type"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The city with the most piano bars in Germany in 2016 was Munich with a total of 6. The city with the lowest amount of piano bars in Germany on the same year was Bochum with 1.

Chart data: Piano bars German cities 2 Cologne 4 Berlin 3 Frankfurt 2 Leverkusen 2 Solingen 2 Mülheim 2 Wuppertal 2 Düsseldorf 2 Essen 0 Hamburg 6 Munich 2 Bonn 2 Duisburg 0 Lübeck 1 Bochum 2 Hagen 2 Krefeld 0 Gelsenkirchen 3 Stuttgart 4 Potsdam

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/3182.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#f1e2cc"}, "x": {"type": "quantitative", "axis": {"title": "Piano bars"}, "field": "Piano bars"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "German cities"}}, "title": ["Number of bars , dance clubs and music venues", "in selected German cities in 2016 , by type"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: This chart shows the fall of two parent households over a period of 49 years.

Chart data: Year Two parents Dec 31, 1969 58939 Dec 31, 1974 53072 Dec 31, 1979 48624 Dec 31, 1984 46149 Dec 31, 1989 46503 Dec 31, 1994 48276 Dec 31, 1999 49795 Dec 31, 2004 49481 Dec 31, 2009 51823 Dec 31, 2011 50267 Dec 31, 2012 50646 Dec 31, 2013 50602 Dec 31, 2014 50966 Dec 31, 2015 50679 Dec 31, 2016 50827 Dec 31, 2017 50953 Dec 31, 2018 51561

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/3186.tsv"}, "mark": "area", "encoding": {"color": {"value": "#aa4643"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Two parents"}, "field": "Two parents"}}, "title": ["Living arrangements of children under 18", "years old in the United States from 1970", "to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There has been a steady decline in the share of gross domestic product since 2010.

Chart data: Year Agriculture Dec 31, 2008 0.1454 Dec 31, 2009 0.1375 Dec 31, 2010 0.141 Dec 31, 2011 0.131 Dec 31, 2012 0.1247 Dec 31, 2013 0.1227 Dec 31, 2014 0.11 Dec 31, 2015 0.1021 Dec 31, 2016 0.1018 Dec 31, 2017 0.0965 Dec 31, 2018 0.0882

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/318.tsv"}, "mark": "line", "encoding": {"color": {"value": "#DCDCDC"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Agriculture"}, "field": "Agriculture"}}, "title": ["Philippines : 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: Share of economic sectors in the gross domestic product peaked around 2010. Since 2010 there has been a slight, steady decline, until it appears to start flattening out around 2017.

Chart data: Year Industry Dec 31, 2008 0.3152 Dec 31, 2009 0.3234 Dec 31, 2010 0.3134 Dec 31, 2011 0.314 Dec 31, 2012 0.3078 Dec 31, 2013 0.3105 Dec 31, 2014 0.3048 Dec 31, 2015 0.3029 Dec 31, 2016 0.3013 Dec 31, 2017 0.3056 Dec 31, 2018 0.3017

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/318.tsv"}, "mark": "area", "encoding": {"color": {"value": "#55b748"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Industry"}, "field": "Industry"}}, "title": ["Philippines : 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: the last 10 years has shown slow but steady growth in the philippines service industry.

Chart data: Year Services Dec 31, 2008 0.5394 Dec 31, 2009 0.5391 Dec 31, 2010 0.5457 Dec 31, 2011 0.555 Dec 31, 2012 0.5675 Dec 31, 2013 0.5668 Dec 31, 2014 0.5852 Dec 31, 2015 0.5951 Dec 31, 2016 0.5969 Dec 31, 2017 0.5979 Dec 31, 2018 0.61

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/318.tsv"}, "mark": "area", "encoding": {"color": {"value": "#118DFF"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Services"}, "field": "Services"}}, "title": ["Philippines : 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: Marine systems increased sales by nearly double between 2005 and 2019 which indicated steady growth and with a few steady years.

Chart data: Year Marine Systems 2019* 9183 2018* 8502 2017 8004 2016 8072 2015 8032 2014 7312 2013 6712 2012 6592 2011 6631 2010 6677 2009 6363 2008 5556 2007 4993 2006 4940 2005 4695

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/3198.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#94aace"}, "x": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Marine Systems"}, "field": "Marine Systems"}}, "title": ["Revenue of the defense technology supplier", "General Dynamics from 2005 to 2019 , by product", "group (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: As the years increase the marine systems increase. By 2019 the figure had almost double in figure.

Chart data: Year Marine Systems 2019* 9183 2018* 8502 2017 8004 2016 8072 2015 8032 2014 7312 2013 6712 2012 6592 2011 6631 2010 6677 2009 6363 2008 5556 2007 4993 2006 4940 2005 4695

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/3198.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#94aace"}, "x": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Marine Systems"}, "field": "Marine Systems"}}, "title": ["Revenue of the defense technology supplier", "General Dynamics from 2005 to 2019 , by product", "group (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Puerto Rico's distribution of gross domestic product (GDP) across economic sectors from 2008 to 2018 remains a steady correlation with a semi-relaxed line from 2008-18. It only dips under the 0.5 line in 2013 however rises again over this the following year. Heading towards 2018 the GDP does appear to begin declining slightly however this is not a major drop.

Chart data: Year Industry Dec 31, 2007 0.474 Dec 31, 2008 0.494 Dec 31, 2009 0.509 Dec 31, 2010 0.4978 Dec 31, 2011 0.4964 Dec 31, 2012 0.4926 Dec 31, 2013 0.4993 Dec 31, 2014 0.5094 Dec 31, 2015 0.5115 Dec 31, 2016 0.5163 Dec 31, 2017 0.5021

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/3201.tsv"}, "mark": "line", "encoding": {"color": {"value": "#30a2da"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Industry"}, "field": "Industry"}}, "title": ["Puerto Rico : Distribution of gross domestic", "product (GDP) across economic sectors from", "2008 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Number of people 65 and over was more or less stable until 2014 which after that, the number has begun an upward trend.

Chart data: Year 65 years and older Dec 31, 2008 0.0699 Dec 31, 2009 0.0681 Dec 31, 2010 0.0681 Dec 31, 2011 0.0681 Dec 31, 2012 0.0678 Dec 31, 2013 0.0676 Dec 31, 2014 0.0676 Dec 31, 2015 0.0692 Dec 31, 2016 0.0714 Dec 31, 2017 0.0739 Dec 31, 2018 0.0765

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/3205.tsv"}, "mark": "line", "encoding": {"color": {"value": "#0F9D58"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "65 years and older"}, "field": "65 years and older"}}, "title": ["Kazakhstan : Age structure from 2009 to", "2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There was a notable increase in femicides in January however the chart is not in chronological month order which makes it harder to see the trend. By May 2020 femicides were at the lowest point at 21.

Chart data: Month 2020 Jun  21; Month: June Ma  21; Month: May Apri  30; Month: April Marc  28; Month: March Februar  28; Month: February Januar  34; Month: January

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/3207.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b9cc98"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Month"}, "y": {"type": "quantitative", "axis": {"title": "2020"}, "field": "2020"}}, "title": ["Number of femicides in Argentina from January", "2019 to May 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: most regions had below 10000 cases 5 regions had below 5000.

Chart data: Cases region of Kazakhstan 18088 East Kazakhstan Region 17432 Nur-Sultan 17344 Almaty 12343 Atyrau Region 11602 Karaganda Region 8708 West Kazakhstan Region 8071 Pavlodar Region 7773 North Kazakhstan Region 6677 Kostanay Region 6456 Akmola Region 6405 Almaty Region 5449 Shymkent 4820 Jambyl Region 3604 Aktobe Region 3564 Turkistan Region 3539 Mangystau Region 3338 Kyzylorda Region

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/320.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#a6bddb"}, "x": {"type": "quantitative", "axis": {"title": "Cases"}, "field": "Cases"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "region of Kazakhstan"}}, "title": ["Coronavirus (COVID-19) confirmed cases ,", "recoveries , and deaths in Kazakhstan as", "of December 17 , 2020 , by region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The top three regions all have roughly the same number of cases.

Chart data: Cases region of Kazakhstan 18088 East Kazakhstan Region 17432 Nur-Sultan 17344 Almaty 12343 Atyrau Region 11602 Karaganda Region 8708 West Kazakhstan Region 8071 Pavlodar Region 7773 North Kazakhstan Region 6677 Kostanay Region 6456 Akmola Region 6405 Almaty Region 5449 Shymkent 4820 Jambyl Region 3604 Aktobe Region 3564 Turkistan Region 3539 Mangystau Region 3338 Kyzylorda Region

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/320.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#a6bddb"}, "x": {"type": "quantitative", "axis": {"title": "Cases"}, "field": "Cases"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "region of Kazakhstan"}}, "title": ["Coronavirus (COVID-19) confirmed cases ,", "recoveries , and deaths in Kazakhstan as", "of December 17 , 2020 , by region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The Almaty and Nur-Sultan regions have had the most recoveries from Covid in Kazakhstan, over 15,000. The worst hit places for recover have been the Aktobe region, Kyzylorda region and Turkistan region with under approximately 4,000.

Chart data: region of Kazakhstan Recoveries East Kazakhstan Region 13443 Nur-Sultan 16033 Almaty 16089 Atyrau Region 11647 Karaganda Region 10930 West Kazakhstan Region 7670 Pavlodar Region 6335 North Kazakhstan Region 6882 Kostanay Region 6374 Akmola Region 5288 Almaty Region 5559 Shymkent 5285 Jambyl Region 4376 Aktobe Region 3450 Turkistan Region 3439 Mangystau Region 3444 Kyzylorda Region 3281

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/320.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c4c4c4"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "region of Kazakhstan"}, "y": {"type": "quantitative", "axis": {"title": "Recoveries"}, "field": "Recoveries"}}, "title": ["Coronavirus (COVID-19) confirmed cases ,", "recoveries , and deaths in Kazakhstan as", "of December 17 , 2020 , by region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Almaty and Nur Sultan had the highest amount of cases.

Chart data: region of Kazakhstan Recoveries East Kazakhstan Region 13443 Nur-Sultan 16033 Almaty 16089 Atyrau Region 11647 Karaganda Region 10930 West Kazakhstan Region 7670 Pavlodar Region 6335 North Kazakhstan Region 6882 Kostanay Region 6374 Akmola Region 5288 Almaty Region 5559 Shymkent 5285 Jambyl Region 4376 Aktobe Region 3450 Turkistan Region 3439 Mangystau Region 3444 Kyzylorda Region 3281

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/320.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c4c4c4"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "region of Kazakhstan"}, "y": {"type": "quantitative", "axis": {"title": "Recoveries"}, "field": "Recoveries"}}, "title": ["Coronavirus (COVID-19) confirmed cases ,", "recoveries , and deaths in Kazakhstan as", "of December 17 , 2020 , by region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: this graph shows that the gender pay gap is steadily decreasing between 1997 to 2020.

Chart data: Year All-employees Dec 31, 1996 0.275 Dec 31, 1997 0.273 Dec 31, 1998 0.269 Dec 31, 1999 0.267 Dec 31, 2000 0.263 Dec 31, 2001 0.269 Dec 31, 2002 0.251 Dec 31, 2003 0.247 Dec 31, 2004 0.226 Dec 31, 2005 0.222 Dec 31, 2006 0.219 Dec 31, 2007 0.225 Dec 31, 2008 0.22 Dec 31, 2009 0.198 Dec 31, 2010 0.202 Dec 31, 2011 0.196 Dec 31, 2012 0.198 Dec 31, 2013 0.192 Dec 31, 2014 0.193 Dec 31, 2015 0.182 Dec 31, 2016 0.184 Dec 31, 2017 0.178 Dec 31, 2018 0.174 Dec 31, 2019 0.155

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/3218.tsv"}, "mark": "line", "encoding": {"color": {"value": "#f0027f"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "All-employees"}, "field": "All-employees"}}, "title": ["Gender pay gap for median gross hourly earnings", "in the United Kingdom from 1997 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The gender pay gap for median gross hourly earnings in the UK has steadily decreased since 1997, with the exception of a minor spike in 2002/2003 and 2007/2008. Although the trajectory indicates the gender pay gap is narrowing, it still very much exists and affects some 15% of all British workers.

Chart data: Year All-employees Dec 31, 1996 0.275 Dec 31, 1997 0.273 Dec 31, 1998 0.269 Dec 31, 1999 0.267 Dec 31, 2000 0.263 Dec 31, 2001 0.269 Dec 31, 2002 0.251 Dec 31, 2003 0.247 Dec 31, 2004 0.226 Dec 31, 2005 0.222 Dec 31, 2006 0.219 Dec 31, 2007 0.225 Dec 31, 2008 0.22 Dec 31, 2009 0.198 Dec 31, 2010 0.202 Dec 31, 2011 0.196 Dec 31, 2012 0.198 Dec 31, 2013 0.192 Dec 31, 2014 0.193 Dec 31, 2015 0.182 Dec 31, 2016 0.184 Dec 31, 2017 0.178 Dec 31, 2018 0.174 Dec 31, 2019 0.155

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/3218.tsv"}, "mark": "line", "encoding": {"color": {"value": "#f0027f"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "All-employees"}, "field": "All-employees"}}, "title": ["Gender pay gap for median gross hourly earnings", "in the United Kingdom from 1997 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There are 16 countries in total along the y-axis and are listed in alphabetical order. The lowest share is France with 0.2 share and the highest is China with approximately 0.75.  The increments on the y-axis are in .2 with no other indicators to show levels inbetween the main markers. Only 4 countries are above 0.6 with 7 falling between 0.2 and 0.4, 5 falling between 0.4-0.6.

Chart data: Country Have paid for additional tutoring China 0.74 India 0.71 Indonesia 0.71 Malaysia 0.63 Taiwan 0.59 Singapore 0.59 Turkey 0.57 Hong Kong 0.56 United Arab Emirates 0.51 Brazil 0.35 Mexico 0.26 United States 0.26 United Kingdom 0.23 Canada 0.23 Australia 0.21 France 0.2

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/3221.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#FF7043"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Have paid for additional tutoring"}, "field": "Have paid for additional tutoring"}}, "title": ["Share of parents who have paid or would", "pay for tutoring for their child in selected", "countries in 2015"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: It would appear from the bar chart that countries in the east, More specifically those in asia value Tutoring over those in western countires. China has the high number of tutored children.

Chart data: Country Have paid for additional tutoring China 0.74 India 0.71 Indonesia 0.71 Malaysia 0.63 Taiwan 0.59 Singapore 0.59 Turkey 0.57 Hong Kong 0.56 United Arab Emirates 0.51 Brazil 0.35 Mexico 0.26 United States 0.26 United Kingdom 0.23 Canada 0.23 Australia 0.21 France 0.2

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/3221.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#FF7043"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Have paid for additional tutoring"}, "field": "Have paid for additional tutoring"}}, "title": ["Share of parents who have paid or would", "pay for tutoring for their child in selected", "countries in 2015"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: I can see that the highest rate of 'have not paid, but would consider paying' data, is with Australia, France and Mexico, with the values at or just below 0.5. I can see that the countries with the lowest rates are China, India and Taiwan, as these are all below 0.2.

Chart data: Country Have not paid, but would consider paying China 0.19 India 0.18 Indonesia 0.21 Malaysia 0.26 Taiwan 0.19 Singapore 0.27 Turkey 0.2 Hong Kong 0.22 United Arab Emirates 0.23 Brazil 0.35 Mexico 0.49 United States 0.43 United Kingdom 0.42 Canada 0.44 Australia 0.5 France 0.49

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/3221.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e5ae38"}, "x": {"type": "nominal", "axis": {"labelAngle": -90}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Have not paid, but would consider paying"}, "field": "Have not paid, but would consider paying"}}, "title": ["Share of parents who have paid or would", "pay for tutoring for their child in selected", "countries in 2015"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The overall trend is an increase however in 2007 there was a significant decrease which then significantly rose back up 2 years later.

Chart data: Year Impressionist and Modern Dec 31, 1999 0.51 Dec 31, 2000 0.58 Dec 31, 2001 0.65 Dec 31, 2002 0.62 Dec 31, 2003 1.17 Dec 31, 2004 1.06 Dec 31, 2005 1.5 Dec 31, 2006 2.21 Dec 31, 2007 2.3 Dec 31, 2008 0.05 Dec 31, 2009 2.19 Dec 31, 2010 1.74 Dec 31, 2011 1.89 Dec 31, 2012 2.07 Dec 31, 2013 2.4 Dec 31, 2014 2.74 Dec 31, 2015 1.54 Dec 31, 2016 2.41

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/3226.tsv"}, "mark": "area", "encoding": {"color": {"value": "#4D4D4D"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Impressionist and Modern"}, "field": "Impressionist and Modern"}}, "title": ["Christie 's and Sotheby 's auction sales", "worldwide from 2000 to 2017 , by category", "(in billion U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The number of male inhabitants of Jamaica has increased steadily between 2008 and 2016. By 2016 the number was just below 1.5 million.

Chart data: Year Male Dec 31, 2007 1.38 Dec 31, 2008 1.39 Dec 31, 2009 1.4 Dec 31, 2010 1.41 Dec 31, 2011 1.41 Dec 31, 2012 1.42 Dec 31, 2013 1.43 Dec 31, 2014 1.44 Dec 31, 2015 1.44 Dec 31, 2016 1.45 Dec 31, 2017 1.46

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/3233.tsv"}, "mark": "line", "encoding": {"color": {"value": "#f58518"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Male"}, "field": "Male"}}, "title": ["Total population of Jamaica from 2008 to", "2018 , by gender (in million inhabitants)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The total male population of Jamaica from 2008-18 has been on a gradual incline. It was lowest in 2008 and highest in 2018. In 2008 it was around 1.4 and in 2018 it was nearly at 1.5.

Chart data: Year Male Dec 31, 2007 1.38 Dec 31, 2008 1.39 Dec 31, 2009 1.4 Dec 31, 2010 1.41 Dec 31, 2011 1.41 Dec 31, 2012 1.42 Dec 31, 2013 1.43 Dec 31, 2014 1.44 Dec 31, 2015 1.44 Dec 31, 2016 1.45 Dec 31, 2017 1.46

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/3233.tsv"}, "mark": "line", "encoding": {"color": {"value": "#f58518"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Male"}, "field": "Male"}}, "title": ["Total population of Jamaica from 2008 to", "2018 , by gender (in million inhabitants)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In 10 years the population of one gender in Jamaica increase by approximately 0.2 million. This took place gradually over the years, and there was no overall decrease at any point. The total will soon hit 3 million if the trend continues.

Chart data: Year Total Dec 31, 2007 2.78 Dec 31, 2008 2.8 Dec 31, 2009 2.81 Dec 31, 2010 2.83 Dec 31, 2011 2.84 Dec 31, 2012 2.86 Dec 31, 2013 2.88 Dec 31, 2014 2.89 Dec 31, 2015 2.91 Dec 31, 2016 2.92 Dec 31, 2017 2.93

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/3233.tsv"}, "mark": "line", "encoding": {"color": {"value": "#a6761d"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Total"}, "field": "Total"}}, "title": ["Total population of Jamaica from 2008 to", "2018 , by gender (in million inhabitants)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}