delen/vistext huggingface dataset:
Chart description: The chart shows a clear preference towards the more general / common courses to study in further education, being health, social studies, business studies and nursing. These are not just common for females only but would indicate studies in nursing, care, marketing etc which are common with females are the same for the females in Canada.

Chart data: Women Field of study 214998 Business, management and public administration 48075 Architecture, engineering and related technologies 165216 Humanities 190701 Social and behavioural sciences and law 96720 Physical and life sciences and technologies 197151 Health and related fields 24126 Mathematics, computer and information sciences 48222 Visual and performing arts and communications technologies 32688 Other instructional programs 20388 Personal, protective and transportation services 68142 Education 17562 Agriculture, natural resources and conservation 13272 Personal improvement and leisure

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/1054.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#E044A7"}, "x": {"type": "quantitative", "axis": {"title": "Women"}, "field": "Women"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Field of study"}}, "title": ["Number of students enrolled in postsecondary", "institutions in Canada in 2017/18 , by gender", "and field of study"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Canada provides the highest amount of tea by far, followed by Argentina and China.

Chart data: 2009 Country 42265 Argentinia 58436 Canada 24596 China 12329 India 5417 Vietnam 3327 Sri Lanka 2801 Malawi 8146 Germany** 6899 Indonesia 9814 Mexico

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/1058.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#79706e"}, "x": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "2009"}, "field": "2009"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}}, "title": ["Amount of U.S. tea imports from selected", "countries of origin in 2018 (in 1,000 metric", "tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The US imports most of its tea from Canada (from the list of countries here). The US gets more tea imported from Canada than it does from Veitnam, Sri Lanka, Mexico, Malawi, Indonesia, India and Germany combined.

Chart data: 2010 Country 49388 Argentinia 60660 Canada 30091 China 14307 India 5037 Vietnam 4028 Sri Lanka 2928 Malawi 9480 Germany** 6401 Indonesia 4303 Mexico

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/1058.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#DB4437"}, "x": {"type": "quantitative", "axis": {"title": "2010"}, "field": "2010"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}}, "title": ["Amount of U.S. tea imports from selected", "countries of origin in 2018 (in 1,000 metric", "tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: It shows that the US import more tea from Canada than anywhere else on the chart. The least amount of tea is imported from Malawi.

Chart data: 2010 Country 49388 Argentinia 60660 Canada 30091 China 14307 India 5037 Vietnam 4028 Sri Lanka 2928 Malawi 9480 Germany** 6401 Indonesia 4303 Mexico

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/1058.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#DB4437"}, "x": {"type": "quantitative", "axis": {"title": "2010"}, "field": "2010"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}}, "title": ["Amount of U.S. tea imports from selected", "countries of origin in 2018 (in 1,000 metric", "tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In 2018 the US imported tea from a number of countries, importing the most from Canada, shortly followed by Argentina. In 2018 the US imported the least amount of tea from Malawi, Indonesia and Sri Lanka.

Chart data: Country 2012 Argentini  50205; Country: Argentinia Canad  55723; Country: Canada Chin  29527; Country: China Indi  13906; Country: India Vietna  8164; Country: Vietnam Sri Lank  4151; Country: Sri Lanka Malaw  3729; Country: Malawi Germany*  7160; Country: Germany** Indonesi  4435; Country: Indonesia Mexic  9225; Country: Mexico

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/1058.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#4D4D4D"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"title": "2012"}, "field": "2012"}}, "title": ["Amount of U.S. tea imports from selected", "countries of origin in 2018 (in 1,000 metric", "tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In 2018, the highest number of US tea imports occurred from Canada, at approximately 55,000 metric tons of tea. The smallest number of tea imports were from Malawi, at approximately 3,000 metric tons.

Chart data: Country 2012 Argentini  50205; Country: Argentinia Canad  55723; Country: Canada Chin  29527; Country: China Indi  13906; Country: India Vietna  8164; Country: Vietnam Sri Lank  4151; Country: Sri Lanka Malaw  3729; Country: Malawi Germany*  7160; Country: Germany** Indonesi  4435; Country: Indonesia Mexic  9225; Country: Mexico

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/1058.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#4D4D4D"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"title": "2012"}, "field": "2012"}}, "title": ["Amount of U.S. tea imports from selected", "countries of origin in 2018 (in 1,000 metric", "tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Canada has imported the most tea in 2018, whereas Malawi has imported the least.

Chart data: Country 2012 Argentini  50205; Country: Argentinia Canad  55723; Country: Canada Chin  29527; Country: China Indi  13906; Country: India Vietna  8164; Country: Vietnam Sri Lank  4151; Country: Sri Lanka Malaw  3729; Country: Malawi Germany*  7160; Country: Germany** Indonesi  4435; Country: Indonesia Mexic  9225; Country: Mexico

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/1058.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#4D4D4D"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"title": "2012"}, "field": "2012"}}, "title": ["Amount of U.S. tea imports from selected", "countries of origin in 2018 (in 1,000 metric", "tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The greatest import of tea into the US came from Canada, closely followed by Argentina. The country providing the least import of tea into the US is Indonesia.

Chart data: 2016 Country 54061 Argentinia 54586 Canada 23762 China 17265 India 9224 Vietnam 5635 Sri Lanka 9224 Malawi 6582 Germany** 4084 Indonesia 6581 Mexico

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/1058.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#4285F4"}, "x": {"type": "quantitative", "axis": {"title": "2016"}, "field": "2016"}, "y": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Country"}}, "title": ["Amount of U.S. tea imports from selected", "countries of origin in 2018 (in 1,000 metric", "tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The us imported most of their tea from Argentina and Canada, these countries providing more than the rest put together.

Chart data: 2016 Country 54061 Argentinia 54586 Canada 23762 China 17265 India 9224 Vietnam 5635 Sri Lanka 9224 Malawi 6582 Germany** 4084 Indonesia 6581 Mexico

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/1058.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#4285F4"}, "x": {"type": "quantitative", "axis": {"title": "2016"}, "field": "2016"}, "y": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Country"}}, "title": ["Amount of U.S. tea imports from selected", "countries of origin in 2018 (in 1,000 metric", "tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: USA import most of his tea from Argentina and Canada.

Chart data: 2018 Country 49990 Argentinia 48994 Canada 21186 China 14610 India 6602 Vietnam 6149 Sri Lanka 5671 Malawi 4939 Germany** 3358 Indonesia 3094 Mexico

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/1058.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#843c39"}, "x": {"type": "quantitative", "axis": {"title": "2018"}, "field": "2018"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Country"}}, "title": ["Amount of U.S. tea imports from selected", "countries of origin in 2018 (in 1,000 metric", "tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: This shows the difference in costs of building across canada. It appears to show ambulatory care building a are the most expensive.

Chart data: Minimum building type 2670 Health care - residential care 4780 Health care - ambulatory care 2530 High-rise residential - market units** 1720 Low- & mid-rise apartments - market units** 1500 Townhouses (wood frame) - market units** 2420 Enclosed mall 1790 Supermarket 2250 Office - 5-10 stories 2330 Office - 10-20 stories 2340 Secondary schools 2760 Higher education 1090 Warehouse 2090 Hotels - low rise

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/1060.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#B0B0B0"}, "x": {"type": "quantitative", "axis": {"title": "Minimum"}, "field": "Minimum"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "building type"}}, "title": ["Construction costs of buildings in Alberta", ", Canada in 2019 , by select building type", "(in Canadian dollars per square meter)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The most expensive buildings were health care - ambulatory care at just over 5000 dollars per square meters. The cheapest were warehouses which cost around 1500 dollars per square meter. The remaining buildings were similar prices with the higher education being one of the outliers.

Chart data: building type Maximum Health care - residential care 3210 Health care - ambulatory care 5220 High-rise residential - market units** 3190 Low- & mid-rise apartments - market units** 2290 Townhouses (wood frame) - market units** 1660 Enclosed mall 3160 Supermarket 2240 Office - 5-10 stories 2880 Office - 10-20 stories 3010 Secondary schools 3030 Higher education 3870 Warehouse 1540 Hotels - low rise 2520

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/1060.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#cedb9c"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "building type"}, "y": {"type": "quantitative", "axis": {"title": "Maximum"}, "field": "Maximum"}}, "title": ["Construction costs of buildings in Alberta", ", Canada in 2019 , by select building type", "(in Canadian dollars per square meter)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Airline insurance peaked in 2010 to around 2100 million dollars then the lowest been at 1200 in 2016 then started to rise again in 2018 to around 1300.

Chart data: Year Written premium Dec 31, 2006 1575 Dec 31, 2007 1660 Dec 31, 2008 1960 Dec 31, 2009 2100 Dec 31, 2010 2050 Dec 31, 2011 1825 Dec 31, 2012 1600 Dec 31, 2013 1650 Dec 31, 2014 1300 Dec 31, 2015 1150 Dec 31, 2016 1150 Dec 31, 2017 1325

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/1066.tsv"}, "mark": "area", "encoding": {"color": {"value": "#b96db8"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Written premium"}, "field": "Written premium"}}, "title": ["Airline insurance costs in worldwide commercial", "aviation between 2007 and 2018 , by type", "(in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: airline insurance prices dipped in the late 2008s before jumping rapidly in 2010 this saw a huge decline again in 2012 to new lows followed by another recovery to another even steeper low. Some of these factors are due to world events.

Chart data: Year Liability cost Dec 31, 2006 665 Dec 31, 2007 321 Dec 31, 2008 930 Dec 31, 2009 361 Dec 31, 2010 88 Dec 31, 2011 102 Dec 31, 2012 422 Dec 31, 2013 734 Dec 31, 2014 425 Dec 31, 2015 172 Dec 31, 2016 7 Dec 31, 2017 153

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/1066.tsv"}, "mark": "area", "encoding": {"color": {"value": "#C2185B"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Liability cost"}, "field": "Liability cost"}}, "title": ["Airline insurance costs in worldwide commercial", "aviation between 2007 and 2018 , by type", "(in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The lowest number of conducted examinations was on February 12 with 500. The highest number of conducted examinations was on February 28 and March 2 with marginally over 4,000. The five highest figures for number of conducted examinations were all after February 21. The number of conducted examinations rose consistently until March 2. On March 6 the number of conducted examinations declined for the first time since February 12.

Chart data: Month Number of conducted examinations March 6 3618 March 2 4061 February 28 4061 February 25 3894 February 21 3063 February 17 1723 February 12 492

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/106.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#386cb0"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Month"}, "y": {"type": "quantitative", "axis": {"title": "Number of conducted examinations"}, "field": "Number of conducted examinations"}}, "title": ["Cumulative number of coronavirus-positive", "(COVID-19) patients confirmed on Diamond", "Princess cruise ship docked in Japan as of", "April 16 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: On April 16th, there were the most deaths whereas March 6th had fewer deaths compared to the rest of the days.

Chart data: Number of deaths Month 13 April 16 12 April 11 11 April 1 10 March 23 7 March 18 7 March 15 7 March 11 6 March 6

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/106.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e6ab02"}, "x": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of deaths"}, "field": "Number of deaths"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Month"}}, "title": ["Cumulative number of coronavirus-positive", "(COVID-19) patients confirmed on Diamond", "Princess cruise ship docked in Japan as of", "April 16 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The gdp of Austria has hardly changed between 2010 and 2018.

Chart data: Year Industry Dec 31, 2008 0.2579 Dec 31, 2009 0.2553 Dec 31, 2010 0.2545 Dec 31, 2011 0.2563 Dec 31, 2012 0.2552 Dec 31, 2013 0.2536 Dec 31, 2014 0.2517 Dec 31, 2015 0.2542 Dec 31, 2016 0.2547 Dec 31, 2017 0.2575 Dec 31, 2018 0.2571

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/1070.tsv"}, "mark": "area", "encoding": {"color": {"value": "#3580b1"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Industry"}, "field": "Industry"}}, "title": ["Austria : Distribution of gross domestic", "product (GDP) across economic sectors from", "2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: This graph shows a stable line with small dips but very little and creating a curve which peaks in 2018.

Chart data: Year Ages 0-14 Dec 31, 2008 0.2433 Dec 31, 2009 0.2404 Dec 31, 2010 0.2381 Dec 31, 2011 0.237 Dec 31, 2012 0.2368 Dec 31, 2013 0.2375 Dec 31, 2014 0.2388 Dec 31, 2015 0.2403 Dec 31, 2016 0.2425 Dec 31, 2017 0.2448 Dec 31, 2018 0.2465

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/1074.tsv"}, "mark": "line", "encoding": {"color": {"value": "#7b4173"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Ages 0-14"}, "field": "Ages 0-14"}}, "title": ["Iran : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The age structure of 10 to 14 year olds in Iran in the 10 year period from 2009 does not vary greatly. There is a small increase in numbers of children in 2018 compared to 2009. There is a small and gradual increase in this age group from 20014 to 2019.

Chart data: Year Ages 0-14 Dec 31, 2008 0.2433 Dec 31, 2009 0.2404 Dec 31, 2010 0.2381 Dec 31, 2011 0.237 Dec 31, 2012 0.2368 Dec 31, 2013 0.2375 Dec 31, 2014 0.2388 Dec 31, 2015 0.2403 Dec 31, 2016 0.2425 Dec 31, 2017 0.2448 Dec 31, 2018 0.2465

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/1074.tsv"}, "mark": "line", "encoding": {"color": {"value": "#7b4173"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Ages 0-14"}, "field": "Ages 0-14"}}, "title": ["Iran : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Something happened in 2009 and 2014 that resulted in a change in the age structure which seems to have resolved from 2014 onwards.

Chart data: Year Ages 0-14 Dec 31, 2008 0.2433 Dec 31, 2009 0.2404 Dec 31, 2010 0.2381 Dec 31, 2011 0.237 Dec 31, 2012 0.2368 Dec 31, 2013 0.2375 Dec 31, 2014 0.2388 Dec 31, 2015 0.2403 Dec 31, 2016 0.2425 Dec 31, 2017 0.2448 Dec 31, 2018 0.2465

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/1074.tsv"}, "mark": "line", "encoding": {"color": {"value": "#7b4173"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Ages 0-14"}, "field": "Ages 0-14"}}, "title": ["Iran : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: That's the age of the population  between these ages hasn't really increased or decreased.

Chart data: Year Ages 15-64 Dec 31, 2008 0.7062 Dec 31, 2009 0.7081 Dec 31, 2010 0.7093 Dec 31, 2011 0.7093 Dec 31, 2012 0.7082 Dec 31, 2013 0.7061 Dec 31, 2014 0.7031 Dec 31, 2015 0.7005 Dec 31, 2016 0.6971 Dec 31, 2017 0.6934 Dec 31, 2018 0.6899

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/1074.tsv"}, "mark": "area", "encoding": {"color": {"value": "#DB4437"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Ages 15-64"}, "field": "Ages 15-64"}}, "title": ["Iran : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The age structure for Iran for 15-64 years old never dips below 0.6 but does trend slightly downward from 2014-2018 in a linear trend suggesting a drop in this demographic overtime.

Chart data: Year Ages 15-64 Dec 31, 2008 0.7062 Dec 31, 2009 0.7081 Dec 31, 2010 0.7093 Dec 31, 2011 0.7093 Dec 31, 2012 0.7082 Dec 31, 2013 0.7061 Dec 31, 2014 0.7031 Dec 31, 2015 0.7005 Dec 31, 2016 0.6971 Dec 31, 2017 0.6934 Dec 31, 2018 0.6899

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/1074.tsv"}, "mark": "area", "encoding": {"color": {"value": "#DB4437"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Ages 15-64"}, "field": "Ages 15-64"}}, "title": ["Iran : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The age structure of those aged 15-64 in Iran has remained fairly stable from 2009-2019, with a slight decrease in the number of those aged between 15-64 noted in 2019.

Chart data: Year Ages 15-64 Dec 31, 2008 0.7062 Dec 31, 2009 0.7081 Dec 31, 2010 0.7093 Dec 31, 2011 0.7093 Dec 31, 2012 0.7082 Dec 31, 2013 0.7061 Dec 31, 2014 0.7031 Dec 31, 2015 0.7005 Dec 31, 2016 0.6971 Dec 31, 2017 0.6934 Dec 31, 2018 0.6899

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/1074.tsv"}, "mark": "area", "encoding": {"color": {"value": "#DB4437"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Ages 15-64"}, "field": "Ages 15-64"}}, "title": ["Iran : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There is an overall decreasing trend in agriculture between 2009 and 2019. There was a spike in agriculture in 2013.

Chart data: Year Agriculture Dec 31, 2008 0.1302 Dec 31, 2009 0.1294 Dec 31, 2010 0.1312 Dec 31, 2011 0.1233 Dec 31, 2012 0.1339 Dec 31, 2013 0.1166 Dec 31, 2014 0.1263 Dec 31, 2015 0.12 Dec 31, 2016 0.1236 Dec 31, 2017 0.1226 Dec 31, 2018 0.1138

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/1083.tsv"}, "mark": "area", "encoding": {"color": {"value": "#74a9cf"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Agriculture"}, "field": "Agriculture"}}, "title": ["Morocco : Distribution of gross domestic", "product (GDP) across economic sectors from", "2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The GDP of Morocco increased between 2009 and 2011 then stayed consisted for the rest of the measured timescale.

Chart data: Year Industry Dec 31, 2008 0.2424 Dec 31, 2009 0.2566 Dec 31, 2010 0.2661 Dec 31, 2011 0.2641 Dec 31, 2012 0.2617 Dec 31, 2013 0.2649 Dec 31, 2014 0.2609 Dec 31, 2015 0.2589 Dec 31, 2016 0.2615 Dec 31, 2017 0.2592 Dec 31, 2018 0.2603

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/1083.tsv"}, "mark": "area", "encoding": {"color": {"value": "#545454"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Industry"}, "field": "Industry"}}, "title": ["Morocco : Distribution of gross domestic", "product (GDP) across economic sectors from", "2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The revenue of Mattel has dropped steadily from 2013 after rising from 2010 to 2013.

Chart data: Year International Dec 31, 2009 2679.27 Dec 31, 2010 3001.71 Dec 31, 2011 3126.09 Dec 31, 2012 3277.8 Dec 31, 2013 3061.51 Dec 31, 2014 2603.54 Dec 31, 2015 2447.62 Dec 31, 2016 2503.53 Dec 31, 2017 2312.23 Dec 31, 2018 2363.79

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/1088.tsv"}, "mark": "area", "encoding": {"color": {"value": "#8aa453"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "International"}, "field": "International"}}, "title": ["Revenue of Mattel by segment worldwide from", "2010 to 2019 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Between 2012 and 2014 American Girl saw the highest revenue, after which revenues always decreased leading to their lowest level after 2018.  The data is trending downwards after the record high.

Chart data: Year American Girl Dec 31, 2009 517.03 Dec 31, 2010 542.39 Dec 31, 2011 596.3 Dec 31, 2012 658.8 Dec 31, 2013 645.31 Dec 31, 2014 596.22 Dec 31, 2015 589.92 Dec 31, 2016 473.94 Dec 31, 2017 341.19 Dec 31, 2018 268.49

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/1088.tsv"}, "mark": "area", "encoding": {"color": {"value": "#f2cf5b"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "American Girl"}, "field": "American Girl"}}, "title": ["Revenue of Mattel by segment worldwide from", "2010 to 2019 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: 20127 had the highest foreign citizenship, and 21528 had the lowest.

Chart data: Foreign citizenship Danish citizenship 54470 20127 47736 20909 43745 21006 39534 21544 34509 21831 37536 21690 34073 22343 32559 22348 32918 22864 30226 21871 30460 21528

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/1092.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#12239E"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Foreign citizenship"}, "field": "Foreign citizenship"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Danish citizenship"}}, "title": ["Number people emigrating from Denmark from", "2009 to 2019 , by citizenship"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The number of people emigrating from Denmark trended upwards from 2009 to 2019. More than 50000 people emigrated in 2019. The fewest people emigrated in 2010.

Chart data: Year Foreign citizenship Dec 31, 2008 30460 Dec 31, 2009 30226 Dec 31, 2010 32918 Dec 31, 2011 32559 Dec 31, 2012 34073 Dec 31, 2013 37536 Dec 31, 2014 34509 Dec 31, 2015 39534 Dec 31, 2016 43745 Dec 31, 2017 47736 Dec 31, 2018 54470

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/1092.tsv"}, "mark": "line", "encoding": {"color": {"value": "#83bcb6"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Foreign citizenship"}, "field": "Foreign citizenship"}}, "title": ["Number people emigrating from Denmark from", "2009 to 2019 , by citizenship"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: A downward trend from 2009 to 2011. An upward trend from 2011 to 2013. Small fluctuations after 2013, with little range of variation. Agricultural ratio minimum approx. 0.018 and maximum approx. 0.021.

Chart data: Year Agriculture Dec 31, 2008 0.0195 Dec 31, 2009 0.0194 Dec 31, 2010 0.0183 Dec 31, 2011 0.0192 Dec 31, 2012 0.021 Dec 31, 2013 0.0208 Dec 31, 2014 0.021 Dec 31, 2015 0.0207 Dec 31, 2016 0.021 Dec 31, 2017 0.0205 Dec 31, 2018 0.0207

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/1097.tsv"}, "mark": "area", "encoding": {"color": {"value": "#4598ae"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Agriculture"}, "field": "Agriculture"}}, "title": ["Portugal : Share of economic sectors in", "gross domestic product (GDP) from 2009 to", "2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}