delen/vistext huggingface dataset:
Chart description: The most malicious file extensions come from office or archive showing a group of 0.3-0-4 with the likes of PDF and others showing between 0.0 and 0.2.scripts, image, android and apple show no data.

Chart data: Share of detections Response 0.38 Office 0.37 Archive 0.14 PDF 0.06 Other extension 0.04 Binaries 0.01 XML/HTML/JS 0 Apple 0 Image 0 Scripts 0 Android

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": "data/26022.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#adc839"}, "x": {"type": "quantitative", "axis": {"title": "Share of detections"}, "field": "Share of detections"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Response"}}, "title": ["Most commonly detected malicious file extensions", "worldwide from January to September 2017", ", by detection share"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Apple and office show the greatest number of detections.

Chart data: Share of detections Response 0.38 Office 0.37 Archive 0.14 PDF 0.06 Other extension 0.04 Binaries 0.01 XML/HTML/JS 0 Apple 0 Image 0 Scripts 0 Android

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": "data/26022.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#adc839"}, "x": {"type": "quantitative", "axis": {"title": "Share of detections"}, "field": "Share of detections"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Response"}}, "title": ["Most commonly detected malicious file extensions", "worldwide from January to September 2017", ", by detection share"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Military spending has directly affected the GDp over the year.

Chart data: Year Ratio of military spending to GDP Dec 31, 2008 0.0327 Dec 31, 2009 0.0342 Dec 31, 2010 0.0329 Dec 31, 2011 0.0348 Dec 31, 2012 0.0347 Dec 31, 2013 0.0348 Dec 31, 2014 0.0355 Dec 31, 2015 0.0359 Dec 31, 2016 0.0377 Dec 31, 2017 0.0405 Dec 31, 2018 0.0398

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": "data/26024.tsv"}, "mark": "area", "encoding": {"color": {"value": "#e7969c"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Ratio of military spending to GDP"}, "field": "Ratio of military spending to GDP"}}, "title": ["Pakistan : Ratio of military spending to", "gross domestic product (GDP) from 2009 to", "2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In 2015 there was the greatest number of out-of-pocket price. It declined rapidly in 2014 and then began to rise again in 2017. In 2006, there was a drop in the out-of-pocket price.

Chart data: Year Out-of-pocket price in U.S. dollars Dec 31, 2003 11.48 Dec 31, 2004 11.25 Dec 31, 2005 9.53 Dec 31, 2006 10.15 Dec 31, 2007 11.72 Dec 31, 2008 13.95 Dec 31, 2009 11.91 Dec 31, 2010 14.08 Dec 31, 2011 16.23 Dec 31, 2012 16.57 Dec 31, 2013 13.99 Dec 31, 2014 13.81 Dec 31, 2015 13.77 Dec 31, 2016 14.84

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/26026.tsv"}, "mark": "area", "encoding": {"color": {"value": "#168dd9"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Out-of-pocket price in U.S. dollars"}, "field": "Out-of-pocket price in U\\.S\\. dollars"}}, "title": ["Albuterol out-of-pocket cost in the U.S.", "from 2004 to 2017 (in U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Out of pocket price was the highest in 2013 and at its lowest in 2006. As the years progressed, the out of pocket price had a lot of peaks and troughs. At 2016, it is higher than it was in 2004.

Chart data: Year Out-of-pocket price in U.S. dollars Dec 31, 2003 11.48 Dec 31, 2004 11.25 Dec 31, 2005 9.53 Dec 31, 2006 10.15 Dec 31, 2007 11.72 Dec 31, 2008 13.95 Dec 31, 2009 11.91 Dec 31, 2010 14.08 Dec 31, 2011 16.23 Dec 31, 2012 16.57 Dec 31, 2013 13.99 Dec 31, 2014 13.81 Dec 31, 2015 13.77 Dec 31, 2016 14.84

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/26026.tsv"}, "mark": "area", "encoding": {"color": {"value": "#168dd9"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Out-of-pocket price in U.S. dollars"}, "field": "Out-of-pocket price in U\\.S\\. dollars"}}, "title": ["Albuterol out-of-pocket cost in the U.S.", "from 2004 to 2017 (in U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Albuterol out of pocket costs were at it's highest in around 2013. Albuterol out of pocket costs were at its lowest in around 2005. Albuterol out of pockets costs are rising on average since 2005.

Chart data: Year Out-of-pocket price in U.S. dollars Dec 31, 2003 11.48 Dec 31, 2004 11.25 Dec 31, 2005 9.53 Dec 31, 2006 10.15 Dec 31, 2007 11.72 Dec 31, 2008 13.95 Dec 31, 2009 11.91 Dec 31, 2010 14.08 Dec 31, 2011 16.23 Dec 31, 2012 16.57 Dec 31, 2013 13.99 Dec 31, 2014 13.81 Dec 31, 2015 13.77 Dec 31, 2016 14.84

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/26026.tsv"}, "mark": "area", "encoding": {"color": {"value": "#168dd9"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Out-of-pocket price in U.S. dollars"}, "field": "Out-of-pocket price in U\\.S\\. dollars"}}, "title": ["Albuterol out-of-pocket cost in the U.S.", "from 2004 to 2017 (in U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Milano is by far the highest with Roma torena and Firenza also being above the average.

Chart data: CPC in euros city of Italy 0.35 Milano 0.28 Roma 0.24 Torino 0.22 Firenze 0.18 Bologna 0.18 Genova 0.18 Napoli 0.17 Palermo 0.13 Venezia 0.12 Bari

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": "data/26032.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e6ab02"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "CPC in euros"}, "field": "CPC in euros"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "city of Italy"}}, "title": ["Average value of CPC (cost per click) of", "online advertising campaigns in the real", "estate sector in Italy in 2018 , by city", "(in euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Milan has the highest CPC in Italy in 2018. Bari has the lowest CPC in Italy in 2018. All cities shown are above 0.10 Euros CPC.

Chart data: CPC in euros city of Italy 0.35 Milano 0.28 Roma 0.24 Torino 0.22 Firenze 0.18 Bologna 0.18 Genova 0.18 Napoli 0.17 Palermo 0.13 Venezia 0.12 Bari

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": "data/26032.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e6ab02"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "CPC in euros"}, "field": "CPC in euros"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "city of Italy"}}, "title": ["Average value of CPC (cost per click) of", "online advertising campaigns in the real", "estate sector in Italy in 2018 , by city", "(in euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In the real estate sector in 2018 in Italy, Milano takes the lead regarding cost per click of online advertising campaign.

Chart data: CPC in euros city of Italy 0.35 Milano 0.28 Roma 0.24 Torino 0.22 Firenze 0.18 Bologna 0.18 Genova 0.18 Napoli 0.17 Palermo 0.13 Venezia 0.12 Bari

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": "data/26032.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e6ab02"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "CPC in euros"}, "field": "CPC in euros"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "city of Italy"}}, "title": ["Average value of CPC (cost per click) of", "online advertising campaigns in the real", "estate sector in Italy in 2018 , by city", "(in euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Milano shows the most exspensive cost to click on the grid topping out at 0.35 euros per click. Venezia and Bari show the lowest with around 0.13 per click.

Chart data: CPC in euros city of Italy 0.35 Milano 0.28 Roma 0.24 Torino 0.22 Firenze 0.18 Bologna 0.18 Genova 0.18 Napoli 0.17 Palermo 0.13 Venezia 0.12 Bari

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": "data/26032.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e6ab02"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "CPC in euros"}, "field": "CPC in euros"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "city of Italy"}}, "title": ["Average value of CPC (cost per click) of", "online advertising campaigns in the real", "estate sector in Italy in 2018 , by city", "(in euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Germany 7, UK and France 6 are European countries with the highest number of leading banks in 2017. Austria, Finland and Norway have only one leading bank.

Chart data: Total assets in billion euros Country 7 Germany 6 United Kingdom 6 France 5 Spain 5 Italy 4 Switzerland 4 Sweden 3 Netherlands 3 Belgium 2 Russia 2 Denmark 1 Austria 1 Norway 1 Finland

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/26036.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d365ba"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Total assets in billion euros"}, "field": "Total assets in billion euros"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}}, "title": ["European countries with the highest number", "of leading banks in 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The graph indicates that Germany has the highest number of leading banks in Europe, representing total assets of 7 billion Euros. This is followed by France and the United Kingdom (tied at second place with 6 billion Euros in assets) and Italy and Spain (tied at third place with 5 billion Euros in assets). Austria, Finland, and Norway are the countries will the lowest number of leading banks in Europe, each of which only has approximately 1 billion Euros in total assets.

Chart data: Total assets in billion euros Country 7 Germany 6 United Kingdom 6 France 5 Spain 5 Italy 4 Switzerland 4 Sweden 3 Netherlands 3 Belgium 2 Russia 2 Denmark 1 Austria 1 Norway 1 Finland

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/26036.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d365ba"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Total assets in billion euros"}, "field": "Total assets in billion euros"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}}, "title": ["European countries with the highest number", "of leading banks in 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Germany has the highest assets, at 7 billion euros. France and the United Kingdom have the second highest assets, with 6 billion euros each. Italy and Spain have the 3rd highest assets, with 5 billion euros each. Sweden and Switzerland have the 4th highest total assets, with 4 billion euros each. Belgium and the Netherlands have the 5th highest total assets, with 3 billion euros each. Denmark and Russia have the 2nd lowest total assets with 2 billion euros each. Austria, Norway and Finland have the lowest total assets with 1 billion each.

Chart data: Total assets in billion euros Country 7 Germany 6 United Kingdom 6 France 5 Spain 5 Italy 4 Switzerland 4 Sweden 3 Netherlands 3 Belgium 2 Russia 2 Denmark 1 Austria 1 Norway 1 Finland

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/26036.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d365ba"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Total assets in billion euros"}, "field": "Total assets in billion euros"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}}, "title": ["European countries with the highest number", "of leading banks in 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There is only one mosque that has more than 6000 capacity, and only 9 others over 4000 capacity.  There is a vast difference between the lowest (2750) and highest (8000) capacities.

Chart data: Capacity by dedicated prayer space mosque 8000 Al -Jamia Suffa-Tul-Islam Grand Mosque (Bradford) 6000 Central Jamia Mosque Ghamkol Sharif (Birmingham) 6000 Birmingham Central Mosque (Birmingham) 5400 Islamic Cultural Centre (London) 5200 Jamiyat Tabligh-ul-Islam (Bradford) 5000 East London Mosque & London Muslim Centre (London) 4700 Leeds Islamic Centre (Leeds) 4700 Jamia al-Akbaria (Luton) 4500 Masjid-e-Jamia Al Madina (Middlesbrough) 4500 Jamia Masjid Ghousia (Luton) 4000 Markazi Masjid  (Dewsbury) 4000 Madni Jamia Masjid  (Bradford) 4000 Jamia Masjid Ghosia  (London) 4000 Islamic Da'wah Academy  (Leicester) 4000 Husseini Islamic Centre  (London) 3800 Jamiyat Tabligh-ul-Islam Mosque (Bradford) 3600 Wolverhampton Mosque Trust (Wolverhampton) 3500 Makki Masjid (Blackburn) 3500 Green Lane Masjid & Community Centre (Birmingham) 3400 Al-Madina Mosque (London)

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": "data/2603.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#f58518"}, "x": {"type": "quantitative", "axis": {"title": "Capacity by dedicated prayer space"}, "field": "Capacity by dedicated prayer space"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "mosque"}}, "title": ["Biggest masjids in the United Kingdom in", "2017 , by dedicated prayer space capacity"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Overall the export value tends to increase from 2003 to 2018. 2003 has the lowest export value at about 75 billion US dollars. 2018 has the highest export value at about 190 billion US dollars. The only years where there is a decrease from the previous year are 2009, 2015 and 2016. 2009 has the greatest  decrease in a year of about 25 billion dollars.

Chart data: Year Export value in billion U.S. dollars Dec 31, 2002 76.1 Dec 31, 2003 89.8 Dec 31, 2004 97.6 Dec 31, 2005 108.4 Dec 31, 2006 127 Dec 31, 2007 136.6 Dec 31, 2008 112.6 Dec 31, 2009 129.1 Dec 31, 2010 145.9 Dec 31, 2011 156.6 Dec 31, 2012 166.3 Dec 31, 2013 173.8 Dec 31, 2014 167.1 Dec 31, 2015 165.6 Dec 31, 2016 175 Dec 31, 2017 189.3

Vega-Lite specification:
{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "data/26050.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d6a5c9"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Export value in billion U.S. dollars"}, "field": "Export value in billion U\\.S\\. dollars"}}, "title": ["Export value of furniture worldwide from", "2003 to 2018 (in billion U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Palmers had the highest number if soccer clubs in 2018.

Chart data: team Revenue in million Brazilian reals Palmeiras 653.9 Flamengo 542.8 Corinthians 469.9 São Paulo 424.5 Grêmio 420.3 Cruzeiro 386.8 Fluminense 297.4 Internacional 293.3 Vasco da Gama 260.9 Atlético-MG 258 Santos 217.8 Athletico-PR 196.8 Botafogo 183 Bahia 136.1 Sport 104.1 Coritiba 102.9 Vitória 88.2 Goiás 80.8 Chapecoense 80.1 Ceará 64.8

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": "data/26056.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#fcbfd2"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "team"}, "y": {"type": "quantitative", "axis": {"title": "Revenue in million Brazilian reals"}, "field": "Revenue in million Brazilian reals"}}, "title": ["Revenue of the S\u00c3\u00a9rie A soccer clubs in", "Brazil in 2018 , by team (in million Brazilian", "reals)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Pakistan and the United States are the leaders in importing tea worldwide.

Chart data: Trade value in million U.S. dollars Country 496.02 Pakistan 488.41 United States 425.91 Russian Federation 356.1 United Kingdom 276.49 Egypt 236.63 Saudi Arabia 231.91 Morocco 227.92 Germany 187.32 China 186.43 China, Hong Kong SAR 179.79 Japan 167.38 France 138.81 Canada

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": "data/2605.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#74a9cf"}, "x": {"type": "quantitative", "axis": {"labelAngle": 90, "title": "Trade value in million U.S. dollars"}, "field": "Trade value in million U\\.S\\. dollars"}, "y": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Country"}}, "title": ["Leading tea importing countries worldwide", "in 2019 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Japan has the lowest proportion of women on corporate boards, whereas Norway has the highest at almost 50%. European countries generally have higher proportions of women on their corporate boards than Asia, however far more European countries are listed than any other continent.

Chart data: Proportion of women Country 0.34 Norway 0.27 Sweden 0.27 France 0.2 Denmark* 0.19 Germany 0.18 Belgium 0.17 United Kingdom 0.16 United States 0.15 Italy 0.08 China* 0.06 Brazil 0.05 India* 0.02 Japan*

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": "data/26079.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#333333"}, "x": {"type": "quantitative", "axis": {"title": "Proportion of women"}, "field": "Proportion of women"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}}, "title": ["Percentage of women in corporate boards", "worldwide in 2013"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Since 01/02, player expenses have more than doubled to the year 18/19. There has been a large change since 14/15 where expenses were at 60 million US dollars and in 18/19 this rose to 608f27fad7aa0d739c14a740almost 120 million US dollars.

Chart data: Player expenses in million U.S. dollars Year 117 18/19 105 17/18 89 16/17 73 15/16 60 14/15 60 13/14 83 12/13 60 11/12 73 10/11 69 09/10 70 08/09 78 07/08 75 06/07 78 05/06 70 04/05 63 03/04 61 02/03 53 01/02

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": "data/26080.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#74a9cf"}, "x": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Player expenses in million U.S. dollars"}, "field": "Player expenses in million U\\.S\\. dollars"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Philadelphia 76ers player expenses (payroll)", "from 2001/02 to 2018/19 (in million U.S.", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: expenditure decreased from just over 500 to around 200 between 2009/10 and 2011/12. expenditure drops to 0 in 2012/13. from 2015/16, expenditure continues to rise, peaking at just under 3000 in 2018/19 before falling to around 2300 in 2019/20.

Chart data: Year Expenditure in million GBP 2019/20 2179 2018/19 2870 2017/18 2222 2016/17 858 2015/16 168 2014/15 39 2013/14 273 2012/13 −12 2011/12 142 2010/11 239 2009/10 597

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": "data/26087.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Expenditure in million GBP"}, "field": "Expenditure in million GBP"}}, "title": ["Public sector expenditure on mining , manufacturing", "and construction in the United Kingdom (UK)", "from 2009/10 to 2019/20 (in million GBP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The number of intentional homicides in Paraguay was at its highest between 2000 and 2004, dropping sharply in 2005. Other than a peak in around 2007 it has slowly declined.

Chart data: Year Number of homicides Dec 31, 1999 995 Dec 31, 2000 1314 Dec 31, 2001 1372 Dec 31, 2002 1285 Dec 31, 2003 1209 Dec 31, 2004 887 Dec 31, 2005 742 Dec 31, 2006 783 Dec 31, 2007 833 Dec 31, 2008 821 Dec 31, 2009 741 Dec 31, 2010 657 Dec 31, 2011 649 Dec 31, 2012 604 Dec 31, 2013 578 Dec 31, 2014 617 Dec 31, 2015 599

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/26094.tsv"}, "mark": "area", "encoding": {"color": {"value": "#00609f"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of homicides"}, "field": "Number of homicides"}}, "title": ["Number of victims of intentional homicide", "in Paraguay from 2000 to 2016"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Overall general increase in life expectancy in Sweden between the year 2008 and 2018 with an increase of about 1 year.

Chart data: Year Life expectancy at birth in years Dec 31, 2007 81.1 Dec 31, 2008 81.35 Dec 31, 2009 81.45 Dec 31, 2010 81.8 Dec 31, 2011 81.7 Dec 31, 2012 81.96 Dec 31, 2013 82.25 Dec 31, 2014 82.2 Dec 31, 2015 82.31 Dec 31, 2016 82.41 Dec 31, 2017 82.56

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": "data/26101.tsv"}, "mark": "line", "encoding": {"color": {"value": "#C9C9C9"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Life expectancy at birth in years"}, "field": "Life expectancy at birth in years"}}, "title": ["Sweden : Life expectancy at birth from 2008", "to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: this chart shows life expectancy increasing from 2008 at just over 80 years to approx. 84 years in 2016 onwards, this is a continual steady increase every two years.

Chart data: Year Life expectancy at birth in years Dec 31, 2007 81.1 Dec 31, 2008 81.35 Dec 31, 2009 81.45 Dec 31, 2010 81.8 Dec 31, 2011 81.7 Dec 31, 2012 81.96 Dec 31, 2013 82.25 Dec 31, 2014 82.2 Dec 31, 2015 82.31 Dec 31, 2016 82.41 Dec 31, 2017 82.56

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": "data/26101.tsv"}, "mark": "line", "encoding": {"color": {"value": "#C9C9C9"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Life expectancy at birth in years"}, "field": "Life expectancy at birth in years"}}, "title": ["Sweden : Life expectancy at birth from 2008", "to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Cars being sold declined for years before beginning to rise again.

Chart data: Year Units sold Dec 31, 2008 89258 Dec 31, 2009 94877 Dec 31, 2010 94102 Dec 31, 2011 71666 Dec 31, 2012 59620 Dec 31, 2013 61246 Dec 31, 2014 64170 Dec 31, 2015 68280 Dec 31, 2016 45548 Dec 31, 2017 71619

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": "data/26109.tsv"}, "mark": "line", "encoding": {"color": {"value": "#74a9cf"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Units sold"}, "field": "Units sold"}}, "title": ["Number of Opel cars sold in France from", "2009 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The parent of the victim had the highest number of perpetrators in the United States in 2018 by over 400,000. A relative to the victim had the second highest number of perpetrators in the United States in 2018 as less than 100,000.

Chart data: relationship to victim Number of perpetrators Parent 417512 Relative 34513 Multiple relationships 22762 Other 20103 Unmarried partner of parent 18960 Unknown 12200 Friend and neighbor 5482 Child daycare provider 1659 Legal guardian 1640 Foster parent 1423 Other professional 1127 Group home and residential facility staff 1001

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": "data/26110.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#fff2ae"}, "x": {"type": "nominal", "axis": {"labelAngle": 90}, "bin": false, "field": "relationship to victim"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Number of perpetrators"}, "field": "Number of perpetrators"}}, "title": ["Number of perpetrators in child abuse cases", "in the United States in 2018 , by relationship", "to victim"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Remains 1.5, we can see a small increase but over the last decade, it has not reached 2 children per woman.

Chart data: Year Number of children born per woman Dec 31, 2007 1.71 Dec 31, 2008 1.72 Dec 31, 2009 1.72 Dec 31, 2010 1.72 Dec 31, 2011 1.73 Dec 31, 2012 1.73 Dec 31, 2013 1.73 Dec 31, 2014 1.74 Dec 31, 2015 1.74 Dec 31, 2016 1.75 Dec 31, 2017 1.76

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": "data/26114.tsv"}, "mark": "area", "encoding": {"color": {"value": "#cccccc"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of children born per woman"}, "field": "Number of children born per woman"}}, "title": ["Armenia : Fertility rate from 2008 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Initially, fertility decreased from 2008 to 2009. From 2009 it then gradually increased to a peak of 2 children per woman in 2012. This then decreased gently to 1.9 in 2016, where it then fell rapidly as it approached 2018.

Chart data: Year Number of children born per woman Dec 31, 2007 1.9 Dec 31, 2008 1.82 Dec 31, 2009 1.92 Dec 31, 2010 1.96 Dec 31, 2011 2 Dec 31, 2012 1.98 Dec 31, 2013 1.97 Dec 31, 2014 1.94 Dec 31, 2015 1.9 Dec 31, 2016 1.75 Dec 31, 2017 1.73

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": "data/26118.tsv"}, "mark": "line", "encoding": {"color": {"value": "#AB47BC"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of children born per woman"}, "field": "Number of children born per woman"}}, "title": ["Azerbaijan : Fertility rate from 2008 to", "2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: between 2014 and 2016, the value had a sharp increase. the lowest value reached was approximately 5,500,000. the highest value reached was approximately 6,600,000.

Chart data: Year Export value in thousand SEK Dec 31, 2007 6416730 Dec 31, 2008 6063440 Dec 31, 2009 5848984 Dec 31, 2010 5623475 Dec 31, 2011 5921751 Dec 31, 2012 5773276 Dec 31, 2013 5819856 Dec 31, 2014 6572597 Dec 31, 2015 6510902 Dec 31, 2016 6454379 Dec 31, 2017 6588168 Dec 31, 2018 6573043

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": "data/26119.tsv"}, "mark": "line", "encoding": {"color": {"value": "#55b748"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Export value in thousand SEK"}, "field": "Export value in thousand SEK"}}, "title": ["Value of alcoholic beverage exports from", "Sweden from 2009 to 2019 (in 1,000 SEK)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There is an initial dip from 2008 to mid 2011 then an increase from 2014 followed by a stable section.

Chart data: Year Export value in thousand SEK Dec 31, 2007 6416730 Dec 31, 2008 6063440 Dec 31, 2009 5848984 Dec 31, 2010 5623475 Dec 31, 2011 5921751 Dec 31, 2012 5773276 Dec 31, 2013 5819856 Dec 31, 2014 6572597 Dec 31, 2015 6510902 Dec 31, 2016 6454379 Dec 31, 2017 6588168 Dec 31, 2018 6573043

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": "data/26119.tsv"}, "mark": "line", "encoding": {"color": {"value": "#55b748"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Export value in thousand SEK"}, "field": "Export value in thousand SEK"}}, "title": ["Value of alcoholic beverage exports from", "Sweden from 2009 to 2019 (in 1,000 SEK)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}