delen/vistext huggingface dataset:
Chart description: Agriculture's share of the GDP has been steadily falling since its peak in 2011.

Chart data: Year Agriculture Dec 31, 2008 0.0224 Dec 31, 2009 0.0214 Dec 31, 2010 0.0221 Dec 31, 2011 0.0219 Dec 31, 2012 0.021 Dec 31, 2013 0.0206 Dec 31, 2014 0.02 Dec 31, 2015 0.0186 Dec 31, 2016 0.0185 Dec 31, 2017 0.0182 Dec 31, 2018 0.0169

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/376.tsv"}, "mark": "area", "encoding": {"color": {"value": "#d2d2d2"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Agriculture"}, "field": "Agriculture"}}, "title": ["South Korea : 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 Industry GDP was fairly flat from 2010 to 2018 but has recently dipped.

Chart data: Year Industry Dec 31, 2008 0.3291 Dec 31, 2009 0.3412 Dec 31, 2010 0.3445 Dec 31, 2011 0.3413 Dec 31, 2012 0.3445 Dec 31, 2013 0.3409 Dec 31, 2014 0.3415 Dec 31, 2015 0.343 Dec 31, 2016 0.3477 Dec 31, 2017 0.3412 Dec 31, 2018 0.3297

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/376.tsv"}, "mark": "line", "encoding": {"color": {"value": "#5254a3"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Industry"}, "field": "Industry"}}, "title": ["South Korea : 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 chart shows that the Asian population of most cities in America are quite low except for two cities where the population is far greater.

Chart data: largest metropolitan areas in the U\.S\. Asian alone ew York-Northern New Jersey-Long Island, NY-NJ-PA 1829097 os Angeles-Long Beach-Santa Ana, CA 1820552 hicago-Naperville-Joliet, IL-IN-WI 519852 allas-Fort Worth-Arlington, TX 317311 ouston-Sugar Land-Baytown, TX 352808 hiladelphia-Camden-Wilmington, PA-NJ-DE-MD 271746 iami-Fort Lauderdale-Pompano Beach, FL 130269 oston-Cambridge-Quincy, MA-NH 282035 ashington-Arlington-Alexandria, DC-VA-MD-WV 482463 tlanta-Sandy Springs-Marietta, GA 235823

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/3771.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#9fe4f8"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "largest metropolitan areas in the U\\.S\\."}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Asian alone"}, "field": "Asian alone"}}, "title": ["Racial structure of the population in the", "largest metropolitan areas in the U.S. 2009"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Los Angeles-Long Beach has the largest number of Native Hawaiian and Other Pacific Islander residents. New York-Northern New Jersey has the second highest number of Native Hawaiian and Other Pacific Islander residents. The other areas are broadly similar in their poulations of Native Hawaiian and Other Pacific Islander.

Chart data: Native Hawaiian and Other Pacific Islander alone largest metropolitan areas in the U\.S\. 23606 r alone: 23606; largest metropolitan areas in the U.S.: New York-Northern New Jersey-Long Island, NY-NJ-PA 45139 r alone: 45139; largest metropolitan areas in the U.S.: Los Angeles-Long Beach-Santa Ana, CA 8480 r alone: 8480; largest metropolitan areas in the U.S.: Chicago-Naperville-Joliet, IL-IN-WI 9871 r alone: 9871; largest metropolitan areas in the U.S.: Dallas-Fort Worth-Arlington, TX 7691 r alone: 7691; largest metropolitan areas in the U.S.: Houston-Sugar Land-Baytown, TX 4038 r alone: 4038; largest metropolitan areas in the U.S.: Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 7284 r alone: 7284; largest metropolitan areas in the U.S.: Miami-Fort Lauderdale-Pompano Beach, FL 3552 r alone: 3552; largest metropolitan areas in the U.S.: Boston-Cambridge-Quincy, MA-NH 6542 r alone: 6542; largest metropolitan areas in the U.S.: Washington-Arlington-Alexandria, DC-VA-MD-WV 4836 r alone: 4836; largest metropolitan areas in the U.S.: Atlanta-Sandy Springs-Marietta, GA

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/3771.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1A1A1A"}, "x": {"type": "quantitative", "axis": {"title": "Native Hawaiian and Other Pacific Islander alone"}, "field": "Native Hawaiian and Other Pacific Islander alone"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "largest metropolitan areas in the U\\.S\\."}}, "title": ["Racial structure of the population in the", "largest metropolitan areas in the U.S. 2009"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: LA shows the longest bar, while the two lowest are Boston and Philadelphia.

Chart data: Native Hawaiian and Other Pacific Islander alone largest metropolitan areas in the U\.S\. 23606 r alone: 23606; largest metropolitan areas in the U.S.: New York-Northern New Jersey-Long Island, NY-NJ-PA 45139 r alone: 45139; largest metropolitan areas in the U.S.: Los Angeles-Long Beach-Santa Ana, CA 8480 r alone: 8480; largest metropolitan areas in the U.S.: Chicago-Naperville-Joliet, IL-IN-WI 9871 r alone: 9871; largest metropolitan areas in the U.S.: Dallas-Fort Worth-Arlington, TX 7691 r alone: 7691; largest metropolitan areas in the U.S.: Houston-Sugar Land-Baytown, TX 4038 r alone: 4038; largest metropolitan areas in the U.S.: Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 7284 r alone: 7284; largest metropolitan areas in the U.S.: Miami-Fort Lauderdale-Pompano Beach, FL 3552 r alone: 3552; largest metropolitan areas in the U.S.: Boston-Cambridge-Quincy, MA-NH 6542 r alone: 6542; largest metropolitan areas in the U.S.: Washington-Arlington-Alexandria, DC-VA-MD-WV 4836 r alone: 4836; largest metropolitan areas in the U.S.: Atlanta-Sandy Springs-Marietta, GA

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/3771.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1A1A1A"}, "x": {"type": "quantitative", "axis": {"title": "Native Hawaiian and Other Pacific Islander alone"}, "field": "Native Hawaiian and Other Pacific Islander alone"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "largest metropolitan areas in the U\\.S\\."}}, "title": ["Racial structure of the population in the", "largest metropolitan areas in the U.S. 2009"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The bar chart shows that Los angeles is a popular and frequent city for hawaiian residents to live as the racial demographics are very high for hawaiians in Los angeles in comparison to the other cities that are charted. The chart also shows that there is few Hawaiians and islanders living in BostonA pattern i have noticed is that there are more Hawaiians and islanders taking residency in southern states and cities rather than northern areas.

Chart data: Native Hawaiian and Other Pacific Islander alone largest metropolitan areas in the U\.S\. 23606 r alone: 23606; largest metropolitan areas in the U.S.: New York-Northern New Jersey-Long Island, NY-NJ-PA 45139 r alone: 45139; largest metropolitan areas in the U.S.: Los Angeles-Long Beach-Santa Ana, CA 8480 r alone: 8480; largest metropolitan areas in the U.S.: Chicago-Naperville-Joliet, IL-IN-WI 9871 r alone: 9871; largest metropolitan areas in the U.S.: Dallas-Fort Worth-Arlington, TX 7691 r alone: 7691; largest metropolitan areas in the U.S.: Houston-Sugar Land-Baytown, TX 4038 r alone: 4038; largest metropolitan areas in the U.S.: Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 7284 r alone: 7284; largest metropolitan areas in the U.S.: Miami-Fort Lauderdale-Pompano Beach, FL 3552 r alone: 3552; largest metropolitan areas in the U.S.: Boston-Cambridge-Quincy, MA-NH 6542 r alone: 6542; largest metropolitan areas in the U.S.: Washington-Arlington-Alexandria, DC-VA-MD-WV 4836 r alone: 4836; largest metropolitan areas in the U.S.: Atlanta-Sandy Springs-Marietta, GA

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/3771.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1A1A1A"}, "x": {"type": "quantitative", "axis": {"title": "Native Hawaiian and Other Pacific Islander alone"}, "field": "Native Hawaiian and Other Pacific Islander alone"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "largest metropolitan areas in the U\\.S\\."}}, "title": ["Racial structure of the population in the", "largest metropolitan areas in the U.S. 2009"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Between 2005 and 2010 there was a large decrease in truck sales falling from 50 thousand to nearly 10 thousand. In the following years it slowly rose, finishing at 20,000 in 2015-20.

Chart data: Year Class 4 Dec 31, 2000 52 Dec 31, 2001 38 Dec 31, 2002 40 Dec 31, 2003 47 Dec 31, 2004 49 Dec 31, 2005 50 Dec 31, 2006 51 Dec 31, 2007 36 Dec 31, 2008 20 Dec 31, 2009 12 Dec 31, 2010 10 Dec 31, 2011 9 Dec 31, 2012 12 Dec 31, 2013 13 Dec 31, 2014 14 Dec 31, 2015 14 Dec 31, 2016 19 Dec 31, 2017 21 Dec 31, 2018 22

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/377.tsv"}, "mark": "area", "encoding": {"color": {"value": "#f0027f"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Class 4"}, "field": "Class 4"}}, "title": ["Class 3 - 8 truck sales in the United States", "from 2001 to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There was a peak of Class 4 trucks sales of 50 thousand in 2007.

Chart data: Year Class 4 Dec 31, 2000 52 Dec 31, 2001 38 Dec 31, 2002 40 Dec 31, 2003 47 Dec 31, 2004 49 Dec 31, 2005 50 Dec 31, 2006 51 Dec 31, 2007 36 Dec 31, 2008 20 Dec 31, 2009 12 Dec 31, 2010 10 Dec 31, 2011 9 Dec 31, 2012 12 Dec 31, 2013 13 Dec 31, 2014 14 Dec 31, 2015 14 Dec 31, 2016 19 Dec 31, 2017 21 Dec 31, 2018 22

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/377.tsv"}, "mark": "area", "encoding": {"color": {"value": "#f0027f"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Class 4"}, "field": "Class 4"}}, "title": ["Class 3 - 8 truck sales in the United States", "from 2001 to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In 2006 truck sales reched an all time high followed by a big slump in sales over the next four years. there is only a slight increase in sales over the next ten years.

Chart data: Year Class 7 Dec 31, 2000 92 Dec 31, 2001 69 Dec 31, 2002 67 Dec 31, 2003 75 Dec 31, 2004 89 Dec 31, 2005 91 Dec 31, 2006 70 Dec 31, 2007 49 Dec 31, 2008 39 Dec 31, 2009 38 Dec 31, 2010 41 Dec 31, 2011 47 Dec 31, 2012 48 Dec 31, 2013 54 Dec 31, 2014 59 Dec 31, 2015 60 Dec 31, 2016 62 Dec 31, 2017 64 Dec 31, 2018 66

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/377.tsv"}, "mark": "line", "encoding": {"color": {"value": "#bf5b17"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Class 7"}, "field": "Class 7"}}, "title": ["Class 3 - 8 truck sales in the United States", "from 2001 to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The number of class 7 trucks peaked on 2006 and then decreased to its lowest number in 2010. Since then there has been a gradual increase in sales.

Chart data: Year Class 7 Dec 31, 2000 92 Dec 31, 2001 69 Dec 31, 2002 67 Dec 31, 2003 75 Dec 31, 2004 89 Dec 31, 2005 91 Dec 31, 2006 70 Dec 31, 2007 49 Dec 31, 2008 39 Dec 31, 2009 38 Dec 31, 2010 41 Dec 31, 2011 47 Dec 31, 2012 48 Dec 31, 2013 54 Dec 31, 2014 59 Dec 31, 2015 60 Dec 31, 2016 62 Dec 31, 2017 64 Dec 31, 2018 66

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/377.tsv"}, "mark": "line", "encoding": {"color": {"value": "#bf5b17"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Class 7"}, "field": "Class 7"}}, "title": ["Class 3 - 8 truck sales in the United States", "from 2001 to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Class 7's truck sale dipped lowest to 39/40 in 2010 but rose to 48 in 2020.

Chart data: Year Class 7 Dec 31, 2000 92 Dec 31, 2001 69 Dec 31, 2002 67 Dec 31, 2003 75 Dec 31, 2004 89 Dec 31, 2005 91 Dec 31, 2006 70 Dec 31, 2007 49 Dec 31, 2008 39 Dec 31, 2009 38 Dec 31, 2010 41 Dec 31, 2011 47 Dec 31, 2012 48 Dec 31, 2013 54 Dec 31, 2014 59 Dec 31, 2015 60 Dec 31, 2016 62 Dec 31, 2017 64 Dec 31, 2018 66

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/377.tsv"}, "mark": "line", "encoding": {"color": {"value": "#bf5b17"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Class 7"}, "field": "Class 7"}}, "title": ["Class 3 - 8 truck sales in the United States", "from 2001 to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Class 3-8 truck sales peaked once in around 2006 and the again in 2019. Sales dipped hugely in around 2008 before climbing back up again.

Chart data: Year Class 8 Dec 31, 2000 140 Dec 31, 2001 146 Dec 31, 2002 142 Dec 31, 2003 203 Dec 31, 2004 253 Dec 31, 2005 284 Dec 31, 2006 151 Dec 31, 2007 133 Dec 31, 2008 95 Dec 31, 2009 107 Dec 31, 2010 171 Dec 31, 2011 195 Dec 31, 2012 185 Dec 31, 2013 220 Dec 31, 2014 249 Dec 31, 2015 193 Dec 31, 2016 192 Dec 31, 2017 251 Dec 31, 2018 276

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/377.tsv"}, "mark": "area", "encoding": {"color": {"value": "#4c78a8"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Class 8"}, "field": "Class 8"}}, "title": ["Class 3 - 8 truck sales in the United States", "from 2001 to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The lowest truck sales were seen between 2009/2010.

Chart data: Year Class 8 Dec 31, 2000 140 Dec 31, 2001 146 Dec 31, 2002 142 Dec 31, 2003 203 Dec 31, 2004 253 Dec 31, 2005 284 Dec 31, 2006 151 Dec 31, 2007 133 Dec 31, 2008 95 Dec 31, 2009 107 Dec 31, 2010 171 Dec 31, 2011 195 Dec 31, 2012 185 Dec 31, 2013 220 Dec 31, 2014 249 Dec 31, 2015 193 Dec 31, 2016 192 Dec 31, 2017 251 Dec 31, 2018 276

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/377.tsv"}, "mark": "area", "encoding": {"color": {"value": "#4c78a8"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Class 8"}, "field": "Class 8"}}, "title": ["Class 3 - 8 truck sales in the United States", "from 2001 to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: EU production has dropped steadily year-on-year, from over 0.9 to around 0.85 in 2012.

Chart data: Year EU production Dec 31, 2002 0.92 Dec 31, 2003 0.91 Dec 31, 2004 0.89 Dec 31, 2005 0.89 Dec 31, 2006 0.87 Dec 31, 2007 0.87 Dec 31, 2008 0.87 Dec 31, 2009 0.85 Dec 31, 2010 0.86 Dec 31, 2011 0.85

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/3786.tsv"}, "mark": "area", "encoding": {"color": {"value": "#52689e"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "EU production"}, "field": "EU production"}}, "title": ["Distribution of furniture consumption in", "the European Union (28 countries) from 2003", "to 2012 , by EU and extra-EU production"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In terms of immigration in Norway where people were born with at least one foreign parent has grown steadily from 2010 to 2020 where the population of immigrants has risen from just over 30000 to just under 40000.

Chart data: Year Foreign born with one parent born in Norway Dec 31, 2009 30766 Dec 31, 2010 31540 Dec 31, 2011 32233 Dec 31, 2012 32994 Dec 31, 2013 33692 Dec 31, 2014 34513 Dec 31, 2015 35444 Dec 31, 2016 36125 Dec 31, 2017 36836 Dec 31, 2018 37495 Dec 31, 2019 38195

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/3788.tsv"}, "mark": "area", "encoding": {"color": {"value": "#d09393"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Foreign born with one parent born in Norway"}, "field": "Foreign born with one parent born in Norway"}}, "title": ["Foreign population in Norway from 2010 to", "2020 , by immigration category"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: the foreign population in norway has gradually increased from 2010 to 2020. in 2010 there was around 37,000 people born abroad to parents born in norway compared to around 39,000 in 2020.

Chart data: Year Persons born abroad to parents born in Norway Dec 31, 2009 36688 Dec 31, 2010 37056 Dec 31, 2011 37321 Dec 31, 2012 37555 Dec 31, 2013 37709 Dec 31, 2014 37919 Dec 31, 2015 38483 Dec 31, 2016 38685 Dec 31, 2017 38863 Dec 31, 2018 38977 Dec 31, 2019 39086

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/3788.tsv"}, "mark": "area", "encoding": {"color": {"value": "#e5ae38"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Persons born abroad to parents born in Norway"}, "field": "Persons born abroad to parents born in Norway"}}, "title": ["Foreign population in Norway from 2010 to", "2020 , by immigration category"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There is a great difference between the brands included with sprite having the largest penetration and big and lilt are showing nothing. There seems to be no correlation to comment upon from the graph shown.

Chart data: Brand 2012 Sprit  0.48; Brand: Sprite 7-U  0.25; Brand: 7-Up Fant  0.26; Brand: Fanta Mirind  0.33; Brand: Mirinda Limc  0.21; Brand: Limca Coca-Col  0.28; Brand: Coca-Cola Thums u  0.42; Brand: Thums up Pepsi Col  0.25; Brand: Pepsi Cola Mountain De  0.08; Brand: Mountain Dew Lil  0; Brand: Lilt Appletise  0.01; Brand: Appletiser Dr Peppe  0.01; Brand: Dr Pepper Lucozad  0.01; Brand: Lucozade Bi  0; Brand: Big

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/3793.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#9ecae9"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Brand"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "2012"}, "field": "2012"}}, "title": ["Average penetration rate of soft drink brand", "in India in 2012 and 2013"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Sprite was the most popular brand of drink in India between 2012 & 2013.

Chart data: Brand 2012 Sprit  0.48; Brand: Sprite 7-U  0.25; Brand: 7-Up Fant  0.26; Brand: Fanta Mirind  0.33; Brand: Mirinda Limc  0.21; Brand: Limca Coca-Col  0.28; Brand: Coca-Cola Thums u  0.42; Brand: Thums up Pepsi Col  0.25; Brand: Pepsi Cola Mountain De  0.08; Brand: Mountain Dew Lil  0; Brand: Lilt Appletise  0.01; Brand: Appletiser Dr Peppe  0.01; Brand: Dr Pepper Lucozad  0.01; Brand: Lucozade Bi  0; Brand: Big

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/3793.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#9ecae9"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Brand"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "2012"}, "field": "2012"}}, "title": ["Average penetration rate of soft drink brand", "in India in 2012 and 2013"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The line/area graph shows that from 2009 to 2018 the number of Nike retail stores in the US have been increasing. However at 2018 the number of retail stores peaked and have started to decrease. In 2013 there was briefly an abnormal spike in the number of retail stores which corrected itself back to following the trend.

Chart data: Year NIKE factory stores Dec 31, 2008 140 Dec 31, 2009 145 Dec 31, 2010 150 Dec 31, 2011 156 Dec 31, 2012 171 Dec 31, 2013 176 Dec 31, 2014 185 Dec 31, 2015 196 Dec 31, 2016 209 Dec 31, 2017 220 Dec 31, 2018 217 Dec 31, 2019 212

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/379.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdcdac"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "NIKE factory stores"}, "field": "NIKE factory stores"}}, "title": ["Number of Nike 's retail stores in the United", "States from 2009 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The biggest spend by far was on advertising. Mail and fundraising follow this in nearly equal amounts.  The lowest spend was in polling.

Chart data: Romney spending major category 356.8 Advertising 123.9 Mail 48 Payroll 90.7 Fundraising 20.8 Administration 31.4 Travel 11.9 Polling 21.1 Consult. 9 Lists 5.6 Events

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/3807.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#8b8b8b"}, "x": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Romney"}, "field": "Romney"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "spending major category"}}, "title": ["Total spending of Barack Obama and Mitt", "Romney in the 2012 elections as of October", "2012 , by major category (in million U.S.", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The leading music television channel in Poland is Polo TV, the least popular are Eska TV Extra, Power TV and Vox Music TV.

Chart data: 2020 Leading music television channels 0.0045 Polo TV 0.0031 Eska TV 0.0011 Disco Polo Music 0.0007 4FUN DANCE 0.0006 4fun.tv 0.0006 Stars.tv 0.0006 Kino Polska Muzyka 0.0005 Power TV 0.0005 Vox Music TV 0.0005 Eska TV Extra

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/3818.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#66a61e"}, "x": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "2020"}, "field": "2020"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Leading music television channels"}}, "title": ["Leading music television channels in Poland", "from 2019 to 2020 , by audience share (SHR)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The study in coronavirus deaths in women from Spain in as of 18May 2020 was almost zero between the ages of 30 to 39 and just slightly higher at the 40 to 49 age group steadily rizing to 0.04 between the 50 to 59 to just over 0.1for 60 to 69 age group then considerably rising to just under 0.3 between the 70 to 79 age group to a high of 0.4 between the 80 to 89 age group with the 89 and above age group just over 0.1.

Chart data: Years Woman 0-9 0 10-19 0 20-29 0.001 30-39 0.004 40-49 0.012 50-59 0.04 60-69 0.109 70-79 0.289 80-89 0.399 > 89 0.145

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/3819.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#54a24b"}, "x": {"type": "nominal", "axis": {"labelAngle": 90}, "bin": false, "field": "Years"}, "y": {"type": "quantitative", "axis": {"title": "Woman"}, "field": "Woman"}}, "title": ["Percentage distribution of coronavirus deaths", "in Spain as of May 18 , 2020 , by age and", "gender"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There is comparably few deaths from Covid-19  between the years 0 and 39 to the figures recorded for deaths from Covid-19 in Woman in Spain than there is between the years 40 and 89 (and up). However, from 30 years and up, woman in Spain can be said to be twice as likely to die from Covid-19 as their younger age-group contemporaries. The exception to this doubling-rate can be observed in the 89 years and up category, which has declined by more than 50% in deaths recorded than the 80-89 years age group.

Chart data: Years Woman 0-9 0 10-19 0 20-29 0.001 30-39 0.004 40-49 0.012 50-59 0.04 60-69 0.109 70-79 0.289 80-89 0.399 > 89 0.145

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/3819.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#54a24b"}, "x": {"type": "nominal", "axis": {"labelAngle": 90}, "bin": false, "field": "Years"}, "y": {"type": "quantitative", "axis": {"title": "Woman"}, "field": "Woman"}}, "title": ["Percentage distribution of coronavirus deaths", "in Spain as of May 18 , 2020 , by age and", "gender"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The highest corona virus deaths in woman in Spain were between the ages 80 and 89. The second highest were women with ages between 70 and 79. There were no deaths in women between the ages 0 and 19.

Chart data: Years Woman 0-9 0 10-19 0 20-29 0.001 30-39 0.004 40-49 0.012 50-59 0.04 60-69 0.109 70-79 0.289 80-89 0.399 > 89 0.145

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/3819.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#54a24b"}, "x": {"type": "nominal", "axis": {"labelAngle": 90}, "bin": false, "field": "Years"}, "y": {"type": "quantitative", "axis": {"title": "Woman"}, "field": "Woman"}}, "title": ["Percentage distribution of coronavirus deaths", "in Spain as of May 18 , 2020 , by age and", "gender"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Between 2008 and 2018, 2009 was the year with the highest share of economic sectors in the gross domestic product, while the year 2018 was the lowest.

Chart data: Year Agriculture Dec 31, 2007 0.0744 Dec 31, 2008 0.0827 Dec 31, 2009 0.0768 Dec 31, 2010 0.0807 Dec 31, 2011 0.0745 Dec 31, 2012 0.0803 Dec 31, 2013 0.081 Dec 31, 2014 0.0806 Dec 31, 2015 0.0747 Dec 31, 2016 0.0685 Dec 31, 2017 0.0673

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/3821.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdcdac"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Agriculture"}, "field": "Agriculture"}}, "title": ["Montenegro : Share of economic sectors in", "gross domestic product (GDP) from 2008 to", "2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The highest share of GDP is in 2018 at an industry level of around 0.19.  The lowest point comes between 2014 and 2015 where the industry level is at around 0.14.  The industry level remains between around 0.14 and 0.17 from 2011 to 2018.

Chart data: Year Industry Dec 31, 2007 0.1769 Dec 31, 2008 0.1735 Dec 31, 2009 0.1715 Dec 31, 2010 0.1509 Dec 31, 2011 0.1489 Dec 31, 2012 0.1546 Dec 31, 2013 0.143 Dec 31, 2014 0.1435 Dec 31, 2015 0.1565 Dec 31, 2016 0.1486 Dec 31, 2017 0.1593

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/3821.tsv"}, "mark": "line", "encoding": {"color": {"value": "#a6761d"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Industry"}, "field": "Industry"}}, "title": ["Montenegro : Share of economic sectors in", "gross domestic product (GDP) from 2008 to", "2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: 2008-2012 there was a steady increase in services sector then from 2012 it has been around the same mark at 0.6 .if anything the latest results show that this trend could be about to fall and start to see lower results going forward.

Chart data: Year Services Dec 31, 2007 0.5524 Dec 31, 2008 0.574 Dec 31, 2009 0.5864 Dec 31, 2010 0.6062 Dec 31, 2011 0.6152 Dec 31, 2012 0.5857 Dec 31, 2013 0.5855 Dec 31, 2014 0.6028 Dec 31, 2015 0.596 Dec 31, 2016 0.6013 Dec 31, 2017 0.5917

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/3821.tsv"}, "mark": "line", "encoding": {"color": {"value": "#54a24b"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Services"}, "field": "Services"}}, "title": ["Montenegro : Share of economic sectors in", "gross domestic product (GDP) from 2008 to", "2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There seems to be an emphasis on the use of apps for work or educational purposes such as managing projects or information gathering. Some of the reasons seen to overlap such as communicating with friends and staying connected with friends.

Chart data: Pinterest Response 0.83 Collecting ideas for projects 0.75 Discovering new interests 0.73 Pursuing your hobbies 0.72 Seeking inspiration 0.7 Planning for holidays or parties 0.69 Learning more about something that interests you 0.67 Expressing your creativity 0.63 Planning daily meals 0.55 Finding fitness tips 0.48 Relaxing 0.44 Finding/taking time for yourself 0.43 Relieving stress 0.36 Planning a vacation 0.32 Shopping 0.27 Managing work/school projects 0.09 Staying connected with friends/family 0.09 Keeping up-to-date on my favourite celebrities 0.08 Making professional connections 0.08 Communicating with friends and family 0.07 Keeping up to date with news, sports, or politics

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/3826.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e5ae38"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Pinterest"}, "field": "Pinterest"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Usage of selected social media apps during", "the planning of everyday activities according", "to users in the United States as of October", "2015"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Twitter users used the app the most for keeping up to date on their friends or family ( more than 0.5) in October 2015 in the United States. Keeping tabs on the news was the second most common usage (0.5). Communicating and staying connected with friends were the next two most popular uses of the app (both slightly above 0.3). The app was least used for actual planning activities such as planning everyday meals ( just above 0.1), planning a vacation and managing school/work projects (about 0.12).

Chart data: Response Twitter Collecting ideas for projects 0.18 Discovering new interests 0.26 Pursuing your hobbies 0.21 Seeking inspiration 0.22 Planning for holidays or parties 0.15 Learning more about something that interests you 0.26 Expressing your creativity 0.25 Planning daily meals 0.11 Finding fitness tips 0.21 Relaxing 0.27 Finding/taking time for yourself 0.23 Relieving stress 0.27 Planning a vacation 0.13 Shopping 0.13 Managing work/school projects 0.13 Staying connected with friends/family 0.32 Keeping up-to-date on my favourite celebrities 0.55 Making professional connections 0.23 Communicating with friends and family 0.31 Keeping up to date with news, sports, or politics 0.5

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/3826.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#0F9D58"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Twitter"}, "field": "Twitter"}}, "title": ["Usage of selected social media apps during", "the planning of everyday activities according", "to users in the United States as of October", "2015"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}