Datasets:

delen

/

vistext

like 0

{"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/10400.tsv"}, "mark": "area", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Import value in thousand GBP"}, "field": "Import value in thousand GBP"}}, "title": ["Value of onions , shallots , garlic and", "leeks imported to the United Kingdom (UK)", "from 2001 to 2019 (in 1,000 GBP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Import value in thousand GBP Dec 31, 2000 76130 Dec 31, 2001 91347 Dec 31, 2002 96607 Dec 31, 2003 97379 Dec 31, 2004 97035 Dec 31, 2005 119177 Dec 31, 2006 161808 Dec 31, 2007 153968 Dec 31, 2008 147950 Dec 31, 2009 199329 Dec 31, 2010 203775 Dec 31, 2011 149087 Dec 31, 2012 214449 Dec 31, 2013 188428 Dec 31, 2014 188444 Dec 31, 2015 228030 Dec 31, 2016 197835 Dec 31, 2017 225513 Dec 31, 2018 293448

The value appears to have peaks and troughs throughout the years.

{"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/10401.tsv"}, "mark": "line", "encoding": {"color": {"value": "#AB47BC"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Number of born children per woman"}, "field": "Number of born children per woman"}}, "title": ["Philippines : Fertility rate from 2008 to", "2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of born children per woman Dec 31, 2007 3.29 Dec 31, 2008 3.24 Dec 31, 2009 3.18 Dec 31, 2010 3.12 Dec 31, 2011 3.06 Dec 31, 2012 2.98 Dec 31, 2013 2.89 Dec 31, 2014 2.81 Dec 31, 2015 2.72 Dec 31, 2016 2.64 Dec 31, 2017 2.58

There has been a general decline in number of children born per woman between 2008 to 2018. As of 2018, the number of children born per woman is at the lowest it has been within the last decade.

{"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/10404.tsv"}, "mark": "area", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Deaths per 1,000 live births"}, "field": "Deaths per 1,000 live births"}}, "title": ["Libya : Infant mortality rate from 2009", "to 2019 (in deaths per 1,000 live births)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Deaths per 1,000 live births Dec 31, 2008 15.1 Dec 31, 2009 14.2 Dec 31, 2010 13.9 Dec 31, 2011 12.9 Dec 31, 2012 12.4 Dec 31, 2013 11.8 Dec 31, 2014 11.4 Dec 31, 2015 11 Dec 31, 2016 10.6 Dec 31, 2017 10.2 Dec 31, 2018 9.9

In the last 10 years Libya had seen the number of child deaths decreases by a 3rd over the 10 year period down here.

{"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/10404.tsv"}, "mark": "area", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Deaths per 1,000 live births"}, "field": "Deaths per 1,000 live births"}}, "title": ["Libya : Infant mortality rate from 2009", "to 2019 (in deaths per 1,000 live births)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Deaths per 1,000 live births Dec 31, 2008 15.1 Dec 31, 2009 14.2 Dec 31, 2010 13.9 Dec 31, 2011 12.9 Dec 31, 2012 12.4 Dec 31, 2013 11.8 Dec 31, 2014 11.4 Dec 31, 2015 11 Dec 31, 2016 10.6 Dec 31, 2017 10.2 Dec 31, 2018 9.9

Libya’s infant mortality rate has been steadily declining between 2010 and 2018. The decline in Libya’s infant mortality rate within that time is around 5 live births out of 1000 no longer resulting in death. There was a slight plateau in 2011, with a sharp decline into 2012 of infant mortality rate in Libya.

{"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/10405.tsv"}, "mark": "area", "encoding": {"color": {"value": "#4D4D4D"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Deaths per 1,000 live births"}, "field": "Deaths per 1,000 live births"}}, "title": ["Nicaragua : Infant mortality rate from 2009", "to 2019 (in deaths per 1,000 live births)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Deaths per 1,000 live births Dec 31, 2008 21.1 Dec 31, 2009 20.2 Dec 31, 2010 19.5 Dec 31, 2011 18.7 Dec 31, 2012 18 Dec 31, 2013 17.3 Dec 31, 2014 16.6 Dec 31, 2015 16 Dec 31, 2016 15.4 Dec 31, 2017 14.9 Dec 31, 2018 14.3

There was a higher number of deaths per 1,000 live births in 2010, than there was in 2018.

{"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/10405.tsv"}, "mark": "area", "encoding": {"color": {"value": "#4D4D4D"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Deaths per 1,000 live births"}, "field": "Deaths per 1,000 live births"}}, "title": ["Nicaragua : Infant mortality rate from 2009", "to 2019 (in deaths per 1,000 live births)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Deaths per 1,000 live births Dec 31, 2008 21.1 Dec 31, 2009 20.2 Dec 31, 2010 19.5 Dec 31, 2011 18.7 Dec 31, 2012 18 Dec 31, 2013 17.3 Dec 31, 2014 16.6 Dec 31, 2015 16 Dec 31, 2016 15.4 Dec 31, 2017 14.9 Dec 31, 2018 14.3

This area diagram shows a steady decrease in infant deaths in Nicaragua over ten years.

{"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/10415.tsv"}, "mark": "area", "encoding": {"color": {"value": "#d365ba"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Youth unemployment rate"}, "field": "Youth unemployment rate"}}, "title": ["Barbados : Youth unemployment rate from", "1999 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Youth unemployment rate Dec 31, 1998 0.2252 Dec 31, 1999 0.2091 Dec 31, 2000 0.2302 Dec 31, 2001 0.2306 Dec 31, 2002 0.2594 Dec 31, 2003 0.2301 Dec 31, 2004 0.2187 Dec 31, 2005 0.2106 Dec 31, 2006 0.1865 Dec 31, 2007 0.2014 Dec 31, 2008 0.242 Dec 31, 2009 0.2583 Dec 31, 2010 0.2698 Dec 31, 2011 0.2878 Dec 31, 2012 0.2928 Dec 31, 2013 0.3331 Dec 31, 2014 0.3206 Dec 31, 2015 0.2888 Dec 31, 2016 0.2924 Dec 31, 2017 0.2844 Dec 31, 2018 0.3022 Dec 31, 2019 0.3158

the area graph shows that youth unemployment has been going up and down between 1999 and 2020. It hit a low in around 2007, and since then has been steadily increasing, hitting a peak in approximately 2014 before going down again.

{"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/10418.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "quantitative", "axis": {"labelAngle": 90, "title": "Number of followers in millions"}, "field": "Number of followers in millions"}, "y": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Wikimedia list article"}}, "title": ["Most popular influencers on TikTok from", "the United States as of July 2020 , by number", "of followers (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Number of followers in millions Wikimedia list article 72.5 @charlidamelio 51.2 @addisonre 45.9 @zachking 45.6 @lorengray 38.6 @spencerx 34.4 @babyariel 34.5 @justmaiko 30.9 @willsmith 30.6 @brentrivera 29.2 @jasonderulo

'@Charlidamelio has the most followers on TikTok. The most popular influences all have over 20million subscribers.

{"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/10418.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "quantitative", "axis": {"labelAngle": 90, "title": "Number of followers in millions"}, "field": "Number of followers in millions"}, "y": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Wikimedia list article"}}, "title": ["Most popular influencers on TikTok from", "the United States as of July 2020 , by number", "of followers (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Number of followers in millions Wikimedia list article 72.5 @charlidamelio 51.2 @addisonre 45.9 @zachking 45.6 @lorengray 38.6 @spencerx 34.4 @babyariel 34.5 @justmaiko 30.9 @willsmith 30.6 @brentrivera 29.2 @jasonderulo

There is a clear favourite which is charlidamello. After them there are three below who are fairly equal. The remainder are below and also at a fairly equal number.

{"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/10419.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fcbfd2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Revenue in billion U.S. dollars"}, "field": "Revenue in billion U\\.S\\. dollars"}}, "title": ["Revenue of the Indian gaming industry in", "the United States from 2006 to 2018 (in billion", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Revenue in billion U.S. dollars Dec 31, 2005 24.89 Dec 31, 2006 26.14 Dec 31, 2007 26.74 Dec 31, 2008 26.48 Dec 31, 2009 26.5 Dec 31, 2010 27.15 Dec 31, 2011 27.9 Dec 31, 2012 28 Dec 31, 2013 28.46 Dec 31, 2014 29.88 Dec 31, 2015 31.2 Dec 31, 2016 32.4 Dec 31, 2017 33.7

Looking at the Line chart I can see that there is an upwards trend in revenue from 2006 to 2018. I can clearly see that from 2014 revenue started to sore but were steadily rising every year.

{"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/10426.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ab7fb4"}, "x": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Market cap in million U.S. dollars"}, "field": "Market cap in million U\\.S\\. dollars"}, "y": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "business"}}, "title": ["Market cap of leading online marketplaces", "worldwide as of September 2020 (in million", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Market cap in million U.S. dollars business 63936 Uber (United States) 53810 Mercado Libre (Argentina) 22946 Delivery Hero (Germany) 16725 Takeaway.com (Netherlands) 15488 Carvana (United States) 14514 Etsy (United States) 8598 Lyft (United States) 6674 GrubHub (United States) 5401 Kakaku.com (Japan) 2444 CarGurus (United States) 1854 Moneysupermarket.com (United Kingdom) 1047 Gruppo MutuiOnline (Italy) 794 Redbubble (Australia) 540 TrueCar (United States)

Uber, Lyft and Delivery Hero are the only companies to have a market cap over 20,000 million dollars. Of these, Uber and Lyft have the greatest market caps at over 50,000 million dollars and over 60,000 million dollars respectively. Redbubble, Truecar and Gruppo MutuiOnline have the lowest market caps with only a tiny bar showing on the graph, reaching just a tiny amount above the 0 baseline.

{"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/10427.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#4D4D4D"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "ethnic group"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Mean age"}, "field": "Mean age"}}, "title": ["Mean age of mothers at first birth in the", "United States in 2018 , by Hispanic origin"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

ethnic group Mean age Asian, non-Hispanic 30.5 White, non-Hispanics 27.7 Cuban-Hispanics 27.7 All origins 26.9 Central and South American-Hispanics 26.5 Dominican-Hispanic 25.8 Black, non-Hispanics 25.1 All Hispanic 25 Puerto Rican-Hispanics 24.8 Native Hawaiian or Pacific Islander, non-Hispanic 24.7 Other and unknown Hispanics 24.7 Mexican-Hispanics 24.4 American Indian or Alaska Native, non-Hispanic 23.5

American Indian or Alaska Native has the youngest mean age and Asian, Non Hispanic has the highest mean age.

{"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": "data/10428.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#52689e"}, "x": {"type": "quantitative", "axis": {"title": "Percentage of population aged 15 and over"}, "field": "Percentage of population aged 15 and over"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Countries with the lowest literacy rates", "worldwide as of 2012"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Percentage of population aged 15 and over Response 0.29 Burkina Faso 0.33 Mali 0.35 Chad 0.39 Ethiopia 0.41 Guinea 0.42 Benin 0.43 Sierra Leone 0.49 Haiti (2006-2013) 0.5 Senegal 0.51 Gambia 0.55 Guinea-Bissau 0.56 Mozambique 0.57 Central African Republic 0.57 Nepal

Burkina Faso is the country with the lowest literacy rate with just under 30% of the over 15 population able to read.

{"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/10432.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d67195"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "human"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Points scored"}, "field": "Points scored"}}, "title": ["Los Angeles Clippers all-time points leaders", "from 1970 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

human Points scored Randy Smith 12735 Blake Griffin 10863 Bob McAdoo 9434 Elton Brand 9336 Corey Maggette 8835 Chris Paul 7674 Danny Manning 7120 DeAndre Jordan 7078 Loy Vaught 6614 Ken Norman 6432

The Los Angeles Clippers highest point leader is Randy Smith at 12.5,000points. Blake griffin was the next highest point leader at 11,000 points. The lowest point leader is Ken Norman.

{"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/10432.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d67195"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "human"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Points scored"}, "field": "Points scored"}}, "title": ["Los Angeles Clippers all-time points leaders", "from 1970 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

human Points scored Randy Smith 12735 Blake Griffin 10863 Bob McAdoo 9434 Elton Brand 9336 Corey Maggette 8835 Chris Paul 7674 Danny Manning 7120 DeAndre Jordan 7078 Loy Vaught 6614 Ken Norman 6432

LA Clippers all time point leader is Randy Smith at around 12,200. Randy Smith is around 1,800 points higher than the next person down - Blake Griffin.

{"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/10436.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Silver consumption in million ounces"}, "field": "Silver consumption in million ounces"}, "y": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Country"}}, "title": ["Leading silver consumers worldwide in 2010", ", by country (in million ounces)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Silver consumption in million ounces Country 189.6 United States 127.2 China 102.1 Japan 94.1 India 39.6 Germany 35.2 Italy 30.7 Thailand 29.9 South Korea 20.7 UK and Ireland 17.7 Belgium

The United States is the leading silver consumer, at around 185 million ounces. This is followed by China (125 million), Japan (100 million) and India (95 million).

{"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/10457.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#7570b3"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Platform"}, "y": {"type": "quantitative", "axis": {"title": "Uninstall rate"}, "field": "Uninstall rate"}}, "title": ["Leading Android apps with the lowest uninstall", "rate worldwide as of August 2015"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Platform Uninstall rate WhatsApp 0.062 Yandex Search 0.077 Melon 0.087 BBM 0.096 VK 0.103 Mnet 0.104 Naver Cloud 0.112 Yahoo Mail 0.114 Xender 0.117 Pandora Radio 0.131

The WhatsApp application has the lowest uninstall rate worldwide compared to other leading Android apps.

{"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/10463.tsv"}, "mark": "area", "encoding": {"color": {"value": "#00ACC1"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of new cases"}, "field": "Number of new cases"}}, "title": ["Number of new cases of diphtheria in the", "U.S. from 1950 to 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of new cases Dec 31, 1949 5796 Dec 31, 1959 918 Dec 31, 1969 435 Dec 31, 1979 3 Dec 31, 1984 3 Dec 31, 1987 2 Dec 31, 1988 3 Dec 31, 1989 4 Dec 31, 1990 5 Dec 31, 1991 4 Dec 31, 1992 0 Dec 31, 1993 2 Dec 31, 1994 0 Dec 31, 1995 2 Dec 31, 1996 4 Dec 31, 1997 1 Dec 31, 1998 1 Dec 31, 1999 1 Dec 31, 2000 2 Dec 31, 2001 1 Dec 31, 2002 0 Dec 31, 2003 0 Dec 31, 2004 0 Dec 31, 2005 0 Dec 31, 2006 0 Dec 31, 2007 0 Dec 31, 2008 0 Dec 31, 2009 0 Dec 31, 2010 0 Dec 31, 2011 1 Dec 31, 2012 0 Dec 31, 2013 1 Dec 31, 2014 0 Dec 31, 2015 0 Dec 31, 2016 0

Number of new cases of diphtheria in the U.S. strictly dropped from 1950s to 1960s. Further slower drop seen from 1960s to 1980s.

{"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/10470.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec8431"}, "x": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Share of respondents"}, "field": "Share of respondents"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Which of the following , if any , would", "persuade you to book a vacation during the", "coronavirus outbreak ?"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Share of respondents Response 0.2 A travel insurance policy that covered disruption caused by the virus 0.2 Flight discounts 0.16 Flexibility 0.16 Accommodation discounts 0.16 Taking a trip within my own country instead 0.08 If the situation in the UK became worse 0.14 No change fees 0.09 Attraction discounts 0.08 Being able to speak to an agent about safest/most effective travel plans 0.48 I could not be persuaded to book a vacation during this time

Most respondents could.not be persuaded to book a vacation during the corona virus outbreakThe second most likely response is that they would book a vacation but only with travel insurance.

{"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/10470.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec8431"}, "x": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Share of respondents"}, "field": "Share of respondents"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Which of the following , if any , would", "persuade you to book a vacation during the", "coronavirus outbreak ?"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Share of respondents Response 0.2 A travel insurance policy that covered disruption caused by the virus 0.2 Flight discounts 0.16 Flexibility 0.16 Accommodation discounts 0.16 Taking a trip within my own country instead 0.08 If the situation in the UK became worse 0.14 No change fees 0.09 Attraction discounts 0.08 Being able to speak to an agent about safest/most effective travel plans 0.48 I could not be persuaded to book a vacation during this time

The highest proportion of people could not be persuaded to go. People that could be persuaded to go would be looking for good travel insurance and flight discounts.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/10472.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "city of the United States"}, "y": {"type": "quantitative", "axis": {"title": "Average wind speed (MPH)"}, "field": "Average wind speed (MPH)"}}, "title": ["Major U.S. cities with the highest average", "wind speeds (in MPH) , data through 2011"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

city of the United States Average wind speed (MPH) Boston, Massachusetts 12.3 Oklahoma City, Oklahoma 12.2 Buffalo, New York 11.8 Milwaukee, Wisconsin 11.5 Dallas, Texas 10.7 San Francisco, California 10.6 Kansas City, Missouri 10.6 Virginia Beach, Virginia 10.5 Minneapolis, Minnesota 10.5 Cleveland, Ohio 10.5 Providence, Rhode Island 10.4 Chicago, Illinois 10.3 Detroit, Michigan 10.2

All cities appear to have fairly similar wind speeds. But the top two appear to be Boston and Oklahoma city. Both have an average wind speed over 10 MPH.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/10472.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "city of the United States"}, "y": {"type": "quantitative", "axis": {"title": "Average wind speed (MPH)"}, "field": "Average wind speed (MPH)"}}, "title": ["Major U.S. cities with the highest average", "wind speeds (in MPH) , data through 2011"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

city of the United States Average wind speed (MPH) Boston, Massachusetts 12.3 Oklahoma City, Oklahoma 12.2 Buffalo, New York 11.8 Milwaukee, Wisconsin 11.5 Dallas, Texas 10.7 San Francisco, California 10.6 Kansas City, Missouri 10.6 Virginia Beach, Virginia 10.5 Minneapolis, Minnesota 10.5 Cleveland, Ohio 10.5 Providence, Rhode Island 10.4 Chicago, Illinois 10.3 Detroit, Michigan 10.2

Boston, Massachusetts and Oklahoma City, Oklahoma have the highest average wind speed. All cities seem to have an average of 10 or over.

{"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/1048.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#0570b0"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "National debt in billion U.S. dollars"}, "field": "National debt in billion U\\.S\\. dollars"}}, "title": ["Ireland : National debt from 2014 to 2024", "(in billion U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year National debt in billion U.S. dollars 2024* 224.31 2023* 228.22 2022* 230.52 2021* 231.85 2020* 227.68 2019* 229.83 2018 226.92 2017 221.59 2016 220.87 2015 221.87 2014 223.78

The bar chart shows the National debt of approximately 225 billion dollars slowly and slightly increasing and peaking to approximately 230 billion dollars in 2021 and levelling back down to 225 billion in 2024.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/10492.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#D64550"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Volume in thousand hectoliters"}, "field": "Volume in thousand hectoliters"}}, "title": ["Volume of wine (excluding juice and musts)", "produced in Greece from 2010 to 2019 (in", "1,000 hectoliters)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Volume in thousand hectoliters 2019** 2 2018* 2.2 2017 2.6 2016 2.5 2015 2.5 2014 2.8 2013 3.3 2012 3.1 2011 2.8 2010 3

Volume has decreased over the last few years. The volume peaked in 2013.

{"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": "data/10494.tsv"}, "mark": "area", "encoding": {"color": {"value": "#7fc97f"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Unnamed: 1"}, "field": "Unnamed: 1"}}, "title": ["Georgia : Urbanization from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Unnamed: 1 Dec 31, 2008 0.5515 Dec 31, 2009 0.5554 Dec 31, 2010 0.5592 Dec 31, 2011 0.563 Dec 31, 2012 0.5669 Dec 31, 2013 0.5707 Dec 31, 2014 0.5745 Dec 31, 2015 0.5784 Dec 31, 2016 0.5823 Dec 31, 2017 0.5863 Dec 31, 2018 0.5904

Urbanisation in Georgia has been on the increase since 2009.

{"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": "data/104.tsv"}, "mark": "area", "encoding": {"color": {"value": "#d09393"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Month"}, "y": {"type": "quantitative", "axis": {"title": "Number of players in thousands"}, "field": "Number of players in thousands"}}, "title": ["Number of peak concurrent players of PlayerUnknown\u00e2\u20ac\u2122s", "Battlegrounds (PUBG) on Steam worldwide from", "April 2017 to November 2020 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Month Number of players in thousands Mar 31, 2017 140.1 Apr 30, 2017 189.46 May 31, 2017 267.19 Jun 30, 2017 481.29 Jul 31, 2017 874.17 Aug 31, 2017 1550.46 Sep 30, 2017 2390.95 Oct 31, 2017 2915.72 Nov 30, 2017 3080.77 Dec 31, 2017 3236.03 Jan 31, 2018 2934.76 Feb 28, 2018 2799.27 Mar 31, 2018 2456.36 Apr 30, 2018 2175.7 May 31, 2018 1750.22 Jun 30, 2018 1350.46 Jul 31, 2018 1260.89 Aug 31, 2018 1125.23 Sep 30, 2018 1048.66 Oct 31, 2018 895.65 Nov 30, 2018 1109.77 Dec 31, 2018 1084.61 Jan 31, 2019 931.75 Feb 28, 2019 931.41 Mar 31, 2019 886.26 Apr 30, 2019 817.6 May 31, 2019 745.96 Jun 30, 2019 745.22 Jul 31, 2019 750.94 Aug 31, 2019 660.86 Sep 30, 2019 637.87 Oct 31, 2019 695.92 Nov 30, 2019 686.24 Dec 31, 2019 645.41 Jan 31, 2020 606.69 Feb 29, 2020 562.03 Mar 31, 2020 913.07 Apr 30, 2020 553.12 May 31, 2020 546.36 Jun 30, 2020 469.57 Jul 31, 2020 443.39 Aug 31, 2020 403.69 Sep 30, 2020 381.08 Oct 31, 2020 438.92

Number of peak concurrent players peaked in January 2018, following a steep rise. Popularity then steeply declined in the early months of 2018, then more gradually throughout 2019 and 2020.

{"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/10501.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#333333"}, "x": {"type": "quantitative", "axis": {"title": "Output in thousand barrels per day"}, "field": "Output in thousand barrels per day"}, "y": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Country"}}, "title": ["Leading countries in crude oil production", "in Latin America and the Caribbean in 2019", "(in 1,000 barrels per day)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Output in thousand barrels per day Country 2788 Brazil 1704 Mexico 886 Colombia 877 Venezuela 531 Ecuador 507 Argentina 60 Bolivia 59 Trinidad and Tobago 53 Peru 50 Cuba 14 Suriname 9.5 Guatemala 3.2 Chile 2 Belize 1 Barbados

Brazil is the leading country in crude oil production in Latin America and the Caribbean in 2019.

{"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/10503.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#F4B400"}, "x": {"type": "quantitative", "axis": {"title": "Miles of freight railroad"}, "field": "Miles of freight railroad"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "State"}}, "title": ["Miles of freight railroad operated by U.S.", "Class I railroads in 2015 , by state"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Miles of freight railroad State 12092 Texas 7877 Illinois 5241 California 4366 Minnesota 4131 Missouri 4051 Ohio 3810 Kansas 3623 Pennsylvania 3333 Georgia 3318 Iowa 3265 Indiana 3231 Wisconsin 3046 Virginia 2844 Colorado 2750 Montana 2616 Louisiana 2616 Alabama 2611 Oklahoma 2599 New York 2552 Nebraska 2518 Arkansas 2314 North Carolina 2313 Kentucky 2227 North Dakota 2133 Tennessee 2119 West Virginia 2069 South Carolina 2048 New Mexico 2048 Michigan 1996 Washington 1991 Nevada 1840 Wyoming 1800 Florida 1777 Mississippi 1695 Utah 1573 New Jersey 1481 Oregon 1350 Arizona 1019 South Dakota 953 Idaho 816 Maryland 399 Massachusetts 247 Delaware 68 Connecticut 33 District of Columbia

Texas and Illinois is like like are the most operated road from usa.

{"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/1050.tsv"}, "mark": "line", "encoding": {"color": {"value": "#ec008b"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Online banking penetration in Great Britain", "from 2007 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Share of respondents Dec 31, 2006 0.3 Dec 31, 2007 0.35 Dec 31, 2008 0.41 Dec 31, 2009 0.42 Dec 31, 2010 0.44 Dec 31, 2011 0.47 Dec 31, 2012 0.5 Dec 31, 2013 0.53 Dec 31, 2014 0.55 Dec 31, 2015 0.6 Dec 31, 2016 0.63 Dec 31, 2017 0.69 Dec 31, 2018 0.73 Dec 31, 2019 0.76

The line shows an upward trend. There is a very sharp increase between 2008 and 2010.

{"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/10512.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec8431"}, "x": {"type": "quantitative", "axis": {"labelAngle": 90, "title": "Sales in million US dollars"}, "field": "Sales in million US dollars"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Value of edible grocery sales for Aldi in", "the United Kingdom (UK) from 2010 forecast", "to 2020 (in million US dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Sales in million US dollars Year 12636.82 2020** 11973.74 2019** 11241.92 2018** 10249.41 2017** 9222.13 2016** 8076.71 2015* 7307.41 2014 5373.41 2013 4017.08 2012 2785.51 2011 2059.8 2010

Sales have increased every year, since 2010, by similar amounts.

{"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/10521.tsv"}, "mark": "area", "encoding": {"color": {"value": "#DCDCDC"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Deaths per 1,000 live births"}, "field": "Deaths per 1,000 live births"}}, "title": ["Malaysia : Infant mortality rate from 2009", "to 2019 (in deaths per 1,000 live births)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Deaths per 1,000 live births Dec 31, 2008 6.9 Dec 31, 2009 6.9 Dec 31, 2010 6.8 Dec 31, 2011 6.8 Dec 31, 2012 6.8 Dec 31, 2013 6.8 Dec 31, 2014 6.9 Dec 31, 2015 7 Dec 31, 2016 7.1 Dec 31, 2017 7.2 Dec 31, 2018 7.3

The chart shows that the infant mortality rate in Malaysia during the period 2009-2014 was on a declining trend. From 2014 to 2019 however this trend is reversed and the infant mortality rate is shown to be increasing in this period.

{"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": "data/10524.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#30a2da"}, "x": {"type": "quantitative", "axis": {"labelAngle": -90, "title": "Share of smartphone using population"}, "field": "Share of smartphone using population"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "Response"}}, "title": ["Adoption of government endorsed COVID-19", "contact tracing apps in selected countries", "as of July 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Share of smartphone using population Response 0.216 Australia 0.173 Turkey 0.144 Germany 0.125 India 0.072 Italy 0.068 Peru 0.05 Japan 0.049 Saudi Arabia 0.031 France 0.023 Indonesia 0.007 Thailand 0.004 Vietnam 0.002 Philippines

Looking at the bar chart the Philippines didn’t do hardly any tracing form the app, where as Australia did the most.

{"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/10533.tsv"}, "mark": "line", "encoding": {"color": {"value": "#829eb1"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Real interest rates"}, "field": "Real interest rates"}}, "title": ["Real interest rates in Vietnam from 2010", "to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Real interest rates Dec 31, 2009 0.0095 Dec 31, 2010 −0.0355 Dec 31, 2011 0.0229 Dec 31, 2012 0.0536 Dec 31, 2013 0.0483 Dec 31, 2014 0.0732 Dec 31, 2015 0.0579 Dec 31, 2016 0.0286 Dec 31, 2017 0.0384 Dec 31, 2018 0.0581

Biggest increase of interest rates was between 2011 and 2013.

{"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/10534.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e6ab02"}, "x": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of hits"}, "field": "Number of hits"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Boston Red Sox all-time hits leaders"}}, "title": ["Boston Red Sox all-time hits leaders as", "of October 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Number of hits Boston Red Sox all-time hits leaders 3419 Carl Yastrzemski 2654 Ted Williams 2452 Jim Rice 2373 Dwight Evans 2098 Wade Boggs 2079 David Ortiz 2042 Bobby Doerr 1805 Dustin Pedroia 1707 Harry Hooper 1680 Dom DiMaggio

Everyone on the chart has hit over 1500. The leader with the most hits is Carl Yastrzemski who hit near 3500.

{"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/10536.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#4D4D4D"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"title": "New installed capacity in megawatts"}, "field": "New installed capacity in megawatts"}}, "title": ["Leading countries in new installed wind", "power capacity in 2019 (in megawatts)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Country New installed capacity in megawatts China 26155 United States 9143 United Kingdom 2393 India 2377 Germany 2189 Sweden 1588 France 1336 Mexico 1281 Argentina 931 Australia 837 Brazil 745 Turkey 686 Canada 597 Chile 526 Denmark 374

The trends of leading countries installing wind power capacity in 2019 show that China is by far the leading country with an output of over 25,000 megawatts with the USA in second place, India in third place, Germany in fourth place, Sweden in fifth place and France in sixth place.

{"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/10537.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fddaec"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Net income/loss in million U.S. dollars"}, "field": "Net income/loss in million U\\.S\\. dollars"}}, "title": ["Live Nation Entertainment net income/loss", "from 2006 to 2019 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Net income/loss in million U.S. dollars Dec 31, 2005 −19.23 Dec 31, 2006 −7.32 Dec 31, 2007 −237.83 Dec 31, 2008 −60.18 Dec 31, 2009 −228.39 Dec 31, 2010 −83.02 Dec 31, 2011 −163.23 Dec 31, 2012 −43.38 Dec 31, 2013 −90.81 Dec 31, 2014 −32.51 Dec 31, 2015 2.94 Dec 31, 2016 −6.02 Dec 31, 2017 60.25 Dec 31, 2018 69.89

Live Nation Entertainment was struggling for the first 8 years to make a profit but is now trending upwards, and has peaked at over $50 Million in 2019.

{"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/10537.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fddaec"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Net income/loss in million U.S. dollars"}, "field": "Net income/loss in million U\\.S\\. dollars"}}, "title": ["Live Nation Entertainment net income/loss", "from 2006 to 2019 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Net income/loss in million U.S. dollars Dec 31, 2005 −19.23 Dec 31, 2006 −7.32 Dec 31, 2007 −237.83 Dec 31, 2008 −60.18 Dec 31, 2009 −228.39 Dec 31, 2010 −83.02 Dec 31, 2011 −163.23 Dec 31, 2012 −43.38 Dec 31, 2013 −90.81 Dec 31, 2014 −32.51 Dec 31, 2015 2.94 Dec 31, 2016 −6.02 Dec 31, 2017 60.25 Dec 31, 2018 69.89

Live Nation had a period of eight years where there was a rollercoaster of profit and loss. From 2014 there has been a greater period of profit. 2007 saw the greatest period of loss but was followed in 2008 with the greatest rise in profts.

{"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/10537.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fddaec"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Net income/loss in million U.S. dollars"}, "field": "Net income/loss in million U\\.S\\. dollars"}}, "title": ["Live Nation Entertainment net income/loss", "from 2006 to 2019 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Net income/loss in million U.S. dollars Dec 31, 2005 −19.23 Dec 31, 2006 −7.32 Dec 31, 2007 −237.83 Dec 31, 2008 −60.18 Dec 31, 2009 −228.39 Dec 31, 2010 −83.02 Dec 31, 2011 −163.23 Dec 31, 2012 −43.38 Dec 31, 2013 −90.81 Dec 31, 2014 −32.51 Dec 31, 2015 2.94 Dec 31, 2016 −6.02 Dec 31, 2017 60.25 Dec 31, 2018 69.89

After an initial slight rise during 2007, income plunged in 2008. There followed 6 years of quite strong fluctuation, albeit decreasing in magnitude and trending upwards, to a steady and more stable climb from 2014 onwards.

{"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/10538.tsv"}, "mark": "line", "encoding": {"color": {"value": "#3690c0"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Sales in million U.S. dollars"}, "field": "Sales in million U\\.S\\. dollars"}}, "title": ["Faurecia 's global OEM automotive parts", "sales from 2001 to 2019 (in million U.S.", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Sales in million U.S. dollars Dec 31, 2000 8600 Dec 31, 2001 10000 Dec 31, 2002 12700 Dec 31, 2003 13327 Dec 31, 2004 14000 Dec 31, 2005 15000 Dec 31, 2006 17400 Dec 31, 2007 17656 Dec 31, 2008 13000 Dec 31, 2010 22500 Dec 31, 2011 22500 Dec 31, 2012 23950 Dec 31, 2013 25043 Dec 31, 2014 22967 Dec 31, 2015 20700 Dec 31, 2016 19170 Dec 31, 2017 20667 Dec 31, 2018 19900

In general the parts progressively increased in price between 2001 and 2014, the exception being a big drop in price in 2009 and no increase or decrease in 2011/12.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/10543.tsv"}, "mark": "area", "encoding": {"color": {"value": "#E044A7"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Share of households"}, "field": "Share of households"}}, "title": ["Share of households with internet access", "in Norway from 2007 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Share of households Dec 31, 2006 0.78 Dec 31, 2007 0.84 Dec 31, 2008 0.86 Dec 31, 2009 0.9 Dec 31, 2010 0.92 Dec 31, 2011 0.93 Dec 31, 2012 0.94 Dec 31, 2013 0.93 Dec 31, 2014 0.97 Dec 31, 2015 0.97 Dec 31, 2016 0.97 Dec 31, 2017 0.96 Dec 31, 2018 0.98

The increase from 2008 - 2012 is higher than the increase from 2012 onwards. There was a decrease between 2012-2013 then a sharp increase.

{"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": "data/10546.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#f0027f"}, "x": {"type": "quantitative", "axis": {"title": "Millions of units"}, "field": "Millions of units"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}}, "title": ["PC , tablet , and internet/cloud-computing", "systems unit shipments worldwide from 2002", "to 2018 (in million units)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Millions of units Year 450 2018** 443 2017** 452 2016 487 2015 532 2014 521 2013 466 2012 418 2011 366 2010 310 2009 290 2008 263 2007 234 2006 211 2005 183 2004 158 2003 142 2002

The bar graph shows that the number of shipments increased year on year until 2014. The drop in shipments since then shows that people are not purchasing as many as they were, as they do not need replacing as often.

{"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": "data/10547.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d98445"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Average daily hotel room rate in U.S. dollars"}, "field": "Average daily hotel room rate in U\\.S\\. dollars"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "city of the United States"}}, "title": ["Average daily rate at the most popular destinations", "for spring break in the United States as", "of January 2015 (in U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Average daily hotel room rate in U.S. dollars city of the United States 213 Miami, FL 195 New York City, NY 176 New Orleans, LA 170 Washington D.C. 166 San Francisco, CA 162 Fort Lauderdale, FL 153 Phoenix, AZ 149 Chicago, IL 144 Los Angeles, CA 132 San Diego, CA 125 Orlando, FL 121 Orange County, CA 120 San Antonio, TX 106 Las Vegas, NV 99 Dallas,TX

The most expensive room rates occur in Miami followed by New York. There is over €100 difference between the most expensive room rate (Miami) and the least expensive (Dallas, TX).

{"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": "data/10551.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#52689e"}, "x": {"type": "nominal", "axis": {"labelAngle": -90}, "bin": false, "field": "baby names"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Number of individuals"}, "field": "Number of individuals"}}, "title": ["Most common baby names of boys in Norway", "in 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

baby names Number of individuals Jakob/Jacob 423 Lucas/Lukas 392 Filip/Fillip/Philip/Phillip 387 Oskar/Oscar 358 Oliver 353 Emil 347 Henrik 339 William 333 Noah/Noa 314 Aksel/Axel 311

The most common boys name in Norway 2019 is Jacob/Jakob and the least common names are Noah/Noa and Axel/Aksel.

{"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": "data/10556.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ff9e27"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Most common reasons for U.S. citizens to", "support the death penalty in 2014"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Share of respondents An eye for an eye/ they took a life/ fits the crime 0.35 Save taxpayers money/ cost associated with prison 0.14 They deserve it 0.14 They will repeat crime/ keep them from repeating it 0.07 Deterrent for potential crimes/ set an example 0.06 Depends on the type of crime they commit 0.05 Fair punishment 0.04 Serve justice 0.04 If there's no doubt the person committed the crime 0.03 Support/ believe in death penalty 0.03 Don't believe they can be rehabiliateted 0.03 Biblical reasons 0.03 Life sentences don't always mean life in prison 0.02 Relieves prison overcrowding 0.02 Would help/ benefit families of victims 0.01 Other 0.01 No opinion 0.04

"An eye for an eye" had the highest responses from US citizens with 0.35.

{"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": "data/10556.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ff9e27"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Most common reasons for U.S. citizens to", "support the death penalty in 2014"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Share of respondents An eye for an eye/ they took a life/ fits the crime 0.35 Save taxpayers money/ cost associated with prison 0.14 They deserve it 0.14 They will repeat crime/ keep them from repeating it 0.07 Deterrent for potential crimes/ set an example 0.06 Depends on the type of crime they commit 0.05 Fair punishment 0.04 Serve justice 0.04 If there's no doubt the person committed the crime 0.03 Support/ believe in death penalty 0.03 Don't believe they can be rehabiliateted 0.03 Biblical reasons 0.03 Life sentences don't always mean life in prison 0.02 Relieves prison overcrowding 0.02 Would help/ benefit families of victims 0.01 Other 0.01 No opinion 0.04

The majority of respondents who supported the death penalty cited the reason as the taking of a life or because the crime deserved "an eye for an eye" response. the second most common reasons came in jointly as saving the tax payer money and because the respondents felt that the crime deserved the death penalty. It is worth noting that only a minority of respondents felt that it would help the families of the victims.

{"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/10560.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#f4cae4"}, "x": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Annual salaries in millions of euros"}, "field": "Annual salaries in millions of euros"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "best-paid French sportsmen"}}, "title": ["Ranking of the ten best-paid French sportsmen", "in 2018 (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Annual salaries in millions of euros best-paid French sportsmen 33 Antoine Griezmann 24.7 Kylian Mbappé (football) 22.1 Paul Pogba (football) 22 Karim Benzema (football) 20.9 Nicolas Batum (basket) 20.6 Rudy Gobert (basket) 19.1 Ousmane Dembélé (football) 17.5 Joakim Noah (basket) 15.2 Evan Fournier (basket) 14.3 Franck Ribéry (football)

The best paid French sportsmen all drew a similar salary of around €17 million. The highest paid sportsman, Antoine Griezmann earns significantly more than the others at around €33 million.

{"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/10571.tsv"}, "mark": "line", "encoding": {"color": {"value": "#e2ddf2"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Average age in years"}, "field": "Average age in years"}}, "title": ["Average age of the father at the birth of", "the first child in the Netherlands from 2009", "to 2019 (in years)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Average age in years Dec 31, 2008 32.4 Dec 31, 2009 32.4 Dec 31, 2010 32.4 Dec 31, 2011 32.4 Dec 31, 2012 32.4 Dec 31, 2013 32.5 Dec 31, 2014 32.5 Dec 31, 2015 32.6 Dec 31, 2016 32.7 Dec 31, 2017 32.7 Dec 31, 2018 32.8

Between 2009 and 2019 the average age of becoming a father increased very slightly.

{"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/10571.tsv"}, "mark": "line", "encoding": {"color": {"value": "#e2ddf2"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Average age in years"}, "field": "Average age in years"}}, "title": ["Average age of the father at the birth of", "the first child in the Netherlands from 2009", "to 2019 (in years)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Average age in years Dec 31, 2008 32.4 Dec 31, 2009 32.4 Dec 31, 2010 32.4 Dec 31, 2011 32.4 Dec 31, 2012 32.4 Dec 31, 2013 32.5 Dec 31, 2014 32.5 Dec 31, 2015 32.6 Dec 31, 2016 32.7 Dec 31, 2017 32.7 Dec 31, 2018 32.8

The average age of the father at birth in the Netherlands has stayed the same from 2009 to 2019.

{"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/10579.tsv"}, "mark": "line", "encoding": {"color": {"value": "#e7298a"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Unnamed: 1"}, "field": "Unnamed: 1"}}, "title": ["Oman : Urbanization from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

year Unnamed: 1 Dec 31, 2008 0.7462 Dec 31, 2009 0.7516 Dec 31, 2010 0.7618 Dec 31, 2011 0.7756 Dec 31, 2012 0.7888 Dec 31, 2013 0.8015 Dec 31, 2014 0.8135 Dec 31, 2015 0.825 Dec 31, 2016 0.8356 Dec 31, 2017 0.8454 Dec 31, 2018 0.8544

There is an increase in urbanisation observed in Oman between 2009 and 2019.

{"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/10583.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d95f02"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "state of India"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Number of teachers"}, "field": "Number of teachers"}}, "title": ["Number of secondary school teachers in India", "in FY 2014 , by state and union territory"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

state of India Number of teachers Maharastra 172046 Karnataka 127898 Telangana 120017 Andhra Pradesh 117220 Rajasthan 114947 Tamil Nadu 83095 Assam 82335 Odisha 82252 Punjab 59967 Madhya Pradesh 51155 Uttar Pradesh 48979 Jammu & Kashmir 42233 West Bengal 41759 Bihar 40095 Haryana 37902 Kerala 35461 Jharkhand 28095 Gujarat 23446 Chhattisgarh 19989 Manipur 14678 Himachal Pradesh 14418 Tripura 10456 Uttarakhand 9949 Delhi 9301 Meghalaya 9002 Nagaland 8994 Goa 6896 Mizoram 4394 Arunachal Pradesh 3898 Puducherry 3487 Sikkim 2915 Chandigarh 2824 Andaman & Nicobar Islands 992 Dadra & Nagar Haveli 256 Daman & Diu 217 Lakshadweep 23

Maharastra, Kamataka, Andhar,Rajasthan & Telenghan are the states in India that exceed 100k teachers in India while the majority are below 50 thousand To a few with either 0 or close zero teachers whatsoever.

{"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": "data/10584.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#beaed4"}, "x": {"type": "quantitative", "axis": {"title": "Number of retail chains"}, "field": "Number of retail chains"}, "y": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "sector"}}, "title": ["Number of retail chains in Austria in 2020", ", by sector"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Number of retail chains sector 44 Fashion & Clothing 21 Consumer Electronics 17 Sport & Leisure 17 Furniture & Decoration 16 Car Parts & Accessories 15 DIY & Gardening 15 Personal Care 14 Pet Care 14 Food 13 Footwear & Leather 12 Toys & Games 11 Home Ware 11 Books & Magazines 10 Petrol 9 Optical 8 Jewelry & Watches 8 Baby Ware 7 Telecom

There is significantly more fashion and clothing sectors than any other.

{"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/10585.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#000000"}, "x": {"type": "quantitative", "axis": {"title": "Receiving yards"}, "field": "Receiving yards"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Kansas City Chiefs"}}, "title": ["Kansas City Chiefs all-time receiving leaders", "from 1960 to 2020 (in yards)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Receiving yards Kansas City Chiefs 10940 Tony Gonzalez 7306 Otis Taylor 7155 Dwayne Bowe 7075 Travis Kelce 6545 Henry Marshall 6360 Carlos Carson 6341 Stephone Paige 5505 Chris Burford 5230 Eddie Kennison 4652 Tyreek Hill 3685 Derrick Alexander 3101 Fred Arbanas 3014 Willie Davis 2829 Kimble Anders 2819 J.J. Birden 2739 Abner Haynes 2516 Frank Jackson 2457 Jamaal Charles 2456 Ed Podolak 2396 Walter White

Tony Gonzalez had the most receiving yards with over 10,000 yards.

{"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/10585.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#000000"}, "x": {"type": "quantitative", "axis": {"title": "Receiving yards"}, "field": "Receiving yards"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Kansas City Chiefs"}}, "title": ["Kansas City Chiefs all-time receiving leaders", "from 1960 to 2020 (in yards)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Receiving yards Kansas City Chiefs 10940 Tony Gonzalez 7306 Otis Taylor 7155 Dwayne Bowe 7075 Travis Kelce 6545 Henry Marshall 6360 Carlos Carson 6341 Stephone Paige 5505 Chris Burford 5230 Eddie Kennison 4652 Tyreek Hill 3685 Derrick Alexander 3101 Fred Arbanas 3014 Willie Davis 2829 Kimble Anders 2819 J.J. Birden 2739 Abner Haynes 2516 Frank Jackson 2457 Jamaal Charles 2456 Ed Podolak 2396 Walter White

Tony Gonzalez has the most receiving yards. Jamaal Charles has the least receiving yards.

{"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/10585.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#000000"}, "x": {"type": "quantitative", "axis": {"title": "Receiving yards"}, "field": "Receiving yards"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Kansas City Chiefs"}}, "title": ["Kansas City Chiefs all-time receiving leaders", "from 1960 to 2020 (in yards)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Receiving yards Kansas City Chiefs 10940 Tony Gonzalez 7306 Otis Taylor 7155 Dwayne Bowe 7075 Travis Kelce 6545 Henry Marshall 6360 Carlos Carson 6341 Stephone Paige 5505 Chris Burford 5230 Eddie Kennison 4652 Tyreek Hill 3685 Derrick Alexander 3101 Fred Arbanas 3014 Willie Davis 2829 Kimble Anders 2819 J.J. Birden 2739 Abner Haynes 2516 Frank Jackson 2457 Jamaal Charles 2456 Ed Podolak 2396 Walter White

Tony Gonzalez has the longest receiving yards, over 10,000 yards. Tony Gonzalez is the only receiving leader between 1960-2000 to receive a ball over 10,000 yards away.

{"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/10585.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#000000"}, "x": {"type": "quantitative", "axis": {"title": "Receiving yards"}, "field": "Receiving yards"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Kansas City Chiefs"}}, "title": ["Kansas City Chiefs all-time receiving leaders", "from 1960 to 2020 (in yards)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Receiving yards Kansas City Chiefs 10940 Tony Gonzalez 7306 Otis Taylor 7155 Dwayne Bowe 7075 Travis Kelce 6545 Henry Marshall 6360 Carlos Carson 6341 Stephone Paige 5505 Chris Burford 5230 Eddie Kennison 4652 Tyreek Hill 3685 Derrick Alexander 3101 Fred Arbanas 3014 Willie Davis 2829 Kimble Anders 2819 J.J. Birden 2739 Abner Haynes 2516 Frank Jackson 2457 Jamaal Charles 2456 Ed Podolak 2396 Walter White

The Kansas city chief with the most receiving years was Tony Gonzalez while the least was Walter White. Most chiefs were within the range of 2000 to 4000 yards, while 6 were within the range of 6000 to 8000 yards.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/1058.tsv"}, "mark": "area", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Index value in points"}, "field": "Index value in points"}}, "title": ["Value of the Dow Jones Composite Index from", "2000 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Index value in points Dec 31, 1999 3317.61 Dec 31, 2000 2892.23 Dec 31, 2001 2374.96 Dec 31, 2002 3000.75 Dec 31, 2003 3395.82 Dec 31, 2004 3638.06 Dec 31, 2005 4120.96 Dec 31, 2006 4394.05 Dec 31, 2007 3086.07 Dec 31, 2008 3566.68 Dec 31, 2009 4033.19 Dec 31, 2010 4232.17 Dec 31, 2011 4442.07 Dec 31, 2012 5641.55 Dec 31, 2013 6473.6 Dec 31, 2014 5978.34 Dec 31, 2015 6902.45 Dec 31, 2016 8310.35 Dec 31, 2017 7710.44 Dec 31, 2018 9386.48 Dec 31, 2019 10108.71

The chart shows a steady increase in the value of points of the Dow Jones composite index. A few stumbles around 2000 and 2008 but overall a reasonably steady increase.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/1058.tsv"}, "mark": "area", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Index value in points"}, "field": "Index value in points"}}, "title": ["Value of the Dow Jones Composite Index from", "2000 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Index value in points Dec 31, 1999 3317.61 Dec 31, 2000 2892.23 Dec 31, 2001 2374.96 Dec 31, 2002 3000.75 Dec 31, 2003 3395.82 Dec 31, 2004 3638.06 Dec 31, 2005 4120.96 Dec 31, 2006 4394.05 Dec 31, 2007 3086.07 Dec 31, 2008 3566.68 Dec 31, 2009 4033.19 Dec 31, 2010 4232.17 Dec 31, 2011 4442.07 Dec 31, 2012 5641.55 Dec 31, 2013 6473.6 Dec 31, 2014 5978.34 Dec 31, 2015 6902.45 Dec 31, 2016 8310.35 Dec 31, 2017 7710.44 Dec 31, 2018 9386.48 Dec 31, 2019 10108.71

The value of the dow jones has more than doubled over the last 20 years. Small blips would always be followed by large increases.

{"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/10594.tsv"}, "mark": "area", "encoding": {"color": {"value": "#3e5c69"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Gross private savings in trillion U.S. dollars"}, "field": "Gross private savings in trillion U\\.S\\. dollars"}}, "title": ["Value of gross private savings in the United", "States from 1960 to 2019 (in trillion U.S.", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Gross private savings in trillion U.S. dollars Dec 31, 1959 0.1 Dec 31, 1969 0.23 Dec 31, 1979 0.67 Dec 31, 1989 1.14 Dec 31, 1999 1.65 Dec 31, 2000 1.81 Dec 31, 2001 2.14 Dec 31, 2002 2.24 Dec 31, 2003 2.3 Dec 31, 2004 2.45 Dec 31, 2005 2.52 Dec 31, 2006 2.41 Dec 31, 2007 2.67 Dec 31, 2008 3.28 Dec 31, 2009 3.56 Dec 31, 2010 3.73 Dec 31, 2011 3.94 Dec 31, 2012 3.62 Dec 31, 2013 3.98 Dec 31, 2014 3.8 Dec 31, 2015 3.91 Dec 31, 2016 4.19 Dec 31, 2017 4.64 Dec 31, 2018 4.75

Overall the gross private savings have increased from 1960-2010There have been a few dips inbetween.

{"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/10595.tsv"}, "mark": "line", "encoding": {"color": {"value": "#1696d2"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of visitors in millions"}, "field": "Number of visitors in millions"}}, "title": ["Number of visitors to Indiana Dunes National", "Lakeshore in the United States from 2008", "to 2019 (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of visitors in millions Dec 31, 2007 1.83 Dec 31, 2008 1.94 Dec 31, 2009 2.15 Dec 31, 2010 1.84 Dec 31, 2011 1.89 Dec 31, 2012 1.68 Dec 31, 2013 1.55 Dec 31, 2014 1.64 Dec 31, 2015 1.7 Dec 31, 2016 2.16 Dec 31, 2017 1.76 Dec 31, 2018 2.13

Visitor numbers dropped between 2012 & 2016. Number of visitors tend to increase back to pre 2012 figures after 2016.

{"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/10596.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1b9e77"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "National debt to GDP ratio"}, "field": "National debt to GDP ratio"}}, "title": ["Portugal : National debt from 2015 to 2025", "in relation to gross domestic product (GDP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year National debt to GDP ratio 2025* 1.159 2024* 1.1779 2023* 1.1961 2022* 1.2406 2021* 1.3005 2020* 1.3724 2019 1.1774 2018 1.22 2017 1.2614 2016 1.3151 2015 1.3118

2020 showed the most national debt, whereas 2019, the year before shows the less recorded.

{"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/10597.tsv"}, "mark": "line", "encoding": {"color": {"value": "#8c6d31"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of households"}, "field": "Share of households"}}, "title": ["Share of households with internet access", "in Poland from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Share of households Dec 31, 2008 0.59 Dec 31, 2009 0.63 Dec 31, 2010 0.67 Dec 31, 2011 0.7 Dec 31, 2012 0.72 Dec 31, 2013 0.75 Dec 31, 2014 0.76 Dec 31, 2015 0.8 Dec 31, 2016 0.82 Dec 31, 2017 0.84 Dec 31, 2018 0.87

The share of household with internet access in poland are on the continuous rise from 2009 to 2019.

{"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": "data/10602.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#30a2da"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "business"}, "y": {"type": "quantitative", "axis": {"title": "Revenue in billion U.S. dollars"}, "field": "Revenue in billion U\\.S\\. dollars"}}, "title": ["Leading privately owned retailing companies", "in the United States in 2019 , by revenue", "(in billion U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

business Revenue in billion U.S. dollars Staples* 14 Menards* 10 Bass Pro Shops* 8 Petsmart* 6 84 Lumber 5.6 Academy Sports + Outdoors* 5 Hobby Lobby Stores* 5 Discount Tire 4.8 Neiman Marcus Group* 4.7 New Balance 4.1 Petco Animal Supplies* 4 Belk* 3.6 Follett 3.2 Forever 21* 2.7 Micro Electronics* 2.6 Rooms To Go* 2.5 J. Crew 2.5 Jo-Ann Stores* 2.4 Fry's Electronics* 2.3

The business Staples holds the highest revenue at 14 billion US dollars whilst Fry's Electronics holds the smallest revenue across all the businesses shown on the bar chart.

{"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/10604.tsv"}, "mark": "area", "encoding": {"color": {"value": "#9ecae9"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Household income in current U.S. dollars"}, "field": "Household income in current U\\.S\\. dollars"}}, "title": ["Median household income in Arizona from", "1990 to 2019 (in current U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Household income in current U.S. dollars Dec 31, 1989 29224 Dec 31, 1990 30737 Dec 31, 1991 29358 Dec 31, 1992 30510 Dec 31, 1993 31293 Dec 31, 1994 30863 Dec 31, 1995 31637 Dec 31, 1996 32740 Dec 31, 1997 37090 Dec 31, 1998 36995 Dec 31, 1999 39783 Dec 31, 2000 42704 Dec 31, 2001 39734 Dec 31, 2002 41166 Dec 31, 2003 43846 Dec 31, 2004 45245 Dec 31, 2005 46657 Dec 31, 2006 47215 Dec 31, 2007 46914 Dec 31, 2008 45739 Dec 31, 2009 46896 Dec 31, 2010 48621 Dec 31, 2011 47044 Dec 31, 2012 50602 Dec 31, 2013 49254 Dec 31, 2014 52248 Dec 31, 2015 57100 Dec 31, 2016 61125 Dec 31, 2017 62283 Dec 31, 2018 70674

Average household income has been increasing consecutively year upon year. 2019 was 70k and 1990 30k, it has more than doubled.

{"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/10604.tsv"}, "mark": "area", "encoding": {"color": {"value": "#9ecae9"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Household income in current U.S. dollars"}, "field": "Household income in current U\\.S\\. dollars"}}, "title": ["Median household income in Arizona from", "1990 to 2019 (in current U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Household income in current U.S. dollars Dec 31, 1989 29224 Dec 31, 1990 30737 Dec 31, 1991 29358 Dec 31, 1992 30510 Dec 31, 1993 31293 Dec 31, 1994 30863 Dec 31, 1995 31637 Dec 31, 1996 32740 Dec 31, 1997 37090 Dec 31, 1998 36995 Dec 31, 1999 39783 Dec 31, 2000 42704 Dec 31, 2001 39734 Dec 31, 2002 41166 Dec 31, 2003 43846 Dec 31, 2004 45245 Dec 31, 2005 46657 Dec 31, 2006 47215 Dec 31, 2007 46914 Dec 31, 2008 45739 Dec 31, 2009 46896 Dec 31, 2010 48621 Dec 31, 2011 47044 Dec 31, 2012 50602 Dec 31, 2013 49254 Dec 31, 2014 52248 Dec 31, 2015 57100 Dec 31, 2016 61125 Dec 31, 2017 62283 Dec 31, 2018 70674

Household income has gradually increased throughout the years as described in the graph, this is what you would like to see when you take into account inflation.

{"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/10622.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d2d2d2"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "city of Canada"}, "y": {"type": "quantitative", "axis": {"title": "Snowfall (in centimeters)"}, "field": "Snowfall (in centimeters)"}}, "title": ["Average annual snowfall in Canada from1971", "to 2000 , by city (in centimeters)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

city of Canada Snowfall (in centimeters) St. John's 322.3 Québec 315.9 Charlottetown 311.9 Fredericton 276.5 Ottawa 235.7 Halifax 230.5 Montréal 217.5 Yellowknife 151.8 Whitehorse 145 Calgary 126.7 Edmonton 121.4 Toronto 115.4 Winnipeg 110.6 Regina 105.9 Vancouver 48.2 Victoria 43.8

The annual snowfall bar chart of Canadian cities, 1971 - 2000, observes that St John's has the highest average snowfall over 300cms annually, closely followed by Charlottestown and Regina. The remaining cities vary from 50cm to just over 150 cm annually.

{"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/10622.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d2d2d2"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "city of Canada"}, "y": {"type": "quantitative", "axis": {"title": "Snowfall (in centimeters)"}, "field": "Snowfall (in centimeters)"}}, "title": ["Average annual snowfall in Canada from1971", "to 2000 , by city (in centimeters)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

city of Canada Snowfall (in centimeters) St. John's 322.3 Québec 315.9 Charlottetown 311.9 Fredericton 276.5 Ottawa 235.7 Halifax 230.5 Montréal 217.5 Yellowknife 151.8 Whitehorse 145 Calgary 126.7 Edmonton 121.4 Toronto 115.4 Winnipeg 110.6 Regina 105.9 Vancouver 48.2 Victoria 43.8

st john's has the highest annual snowfall in canada, at just under 325cm. victoria has the lowest annual snowfall in canada, at under 50cm. no two cities in this graph have the same snowfall values. both victoria and vancouver have below 50cm of snow per year. st johns, quebec, and charlottetown all have above 300cm of snowfall per year.

{"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": "data/10627.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#fdc086"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Revenue in million U.S. dollars"}, "field": "Revenue in million U\\.S\\. dollars"}}, "title": ["Revenue of the San Jose Sharks from 2005/06", "to 2018/19 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Revenue in million U.S. dollars 2018/19 164 2017/18 148 2016/17 137 2015/16 141 2014/15 126 2013/14 117 2012/13* 84 2011/12 101 2010/11 96 2009/10 88 2008/09 84 2007/08 85 2006/07 72 2005/06 69

The trend that mostly stands out to me is that there is a constant incline in revenue over the course of 4 years. However there are three instances as you can see in 2008/2009, 2012/2013 and 2016/2017 that shows also every 4 years they go into a decline in revenue.

{"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/1062.tsv"}, "mark": "line", "encoding": {"color": {"value": "#55b748"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Prices in U.S. dollars per ton"}, "field": "Prices in U\\.S\\. dollars per ton"}}, "title": ["Average bentonite price in the U.S. from", "2007 to 2019 (in U.S. dollars per ton)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Prices in U.S. dollars per ton Dec 31, 2006 52 Dec 31, 2007 49 Dec 31, 2008 57 Dec 31, 2009 58 Dec 31, 2010 61 Dec 31, 2011 62 Dec 31, 2012 65 Dec 31, 2013 67 Dec 31, 2014 98 Dec 31, 2015 99 Dec 31, 2016 99 Dec 31, 2017 99 Dec 31, 2018 100

1. between 2008 and 2019 the price of bentonite has increased relatively steadily from 45 to 100 dollars per ton. 2. 2018/19 however saw a sharp increase of approximately 35 dollars per ton in one year.

{"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/10633.tsv"}, "mark": "area", "encoding": {"color": {"value": "#d95f02"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of nights in millions"}, "field": "Number of nights in millions"}}, "title": ["Number of overnight stays in travel accommodation", "in Spain between 2007 and 2018 (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of nights in millions Dec 31, 2006 381.91 Dec 31, 2007 375.69 Dec 31, 2008 348.55 Dec 31, 2009 364.86 Dec 31, 2010 389.86 Dec 31, 2011 382.67 Dec 31, 2012 389.21 Dec 31, 2013 403.96 Dec 31, 2014 422.23 Dec 31, 2015 454.96 Dec 31, 2016 471.2 Dec 31, 2017 466.94

The number of overnight stays has increased over the years. The number of stays ranged between around 350-580million overnight stays. There was a dip in stays between 2008-2010.

{"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/1063.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#000000"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Soccer player"}, "y": {"type": "quantitative", "axis": {"title": "Facebook fans in millions"}, "field": "Facebook fans in millions"}}, "title": ["Soccer players with the most Facebook fans", "as of June 2020 (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Soccer player Facebook fans in millions Cristiano Ronaldo 122.28 Lionel Messi 90.16 Neymar Jr. 59.86 David Beckham 51.69 Ronaldinho Gaúcho 34.16 Mesut Özil 31.02 Kaká 30.38 Gareth Bale 27.9 Andrés Iniesta 26.26 Zlatan Ibrahimović 26.14

Ronaldo is far and away the highest scorer with over 120 goals, Messi comes second but with a wide divide at around 90 goals.Although Beckham and Neymar, outclass the rest scoring 50 & 60 those below score only 30 or less.

{"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/10659.tsv"}, "mark": "line", "encoding": {"color": {"value": "#666666"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Production index (2012 = 100)"}, "field": "Production index (2012 = 100)"}}, "title": ["United States chemical industry production", "index from 2001 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Production index (2012 = 100) Dec 31, 2000 80.5 Dec 31, 2001 85.1 Dec 31, 2002 86.5 Dec 31, 2003 90 Dec 31, 2004 109.3 Dec 31, 2005 111.9 Dec 31, 2006 117.5 Dec 31, 2007 108.6 Dec 31, 2008 98.1 Dec 31, 2009 101.4 Dec 31, 2010 101.3 Dec 31, 2011 100 Dec 31, 2012 99.3 Dec 31, 2013 98.5 Dec 31, 2014 95.6 Dec 31, 2015 95.7 Dec 31, 2016 99.4 Dec 31, 2017 103.4 Dec 31, 2018 103.3

US chemical industry production has been between 80 and 120 since 2000. The peak was in 2007, at just under 120. Since 2009 production has been stable and between 95 and 105.

{"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/10660.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#51b2e5"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Type of product"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Number of recalls in millions"}, "field": "Number of recalls in millions"}}, "title": ["The largest car recalls from 1971 to 2010", ", by manufacturer (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Type of product Number of recalls in millions Ford (1981) 21 Ford (1999-2009) 15 Ford (1996) 7.9 General Motors (1971) 6.7 General Motors (1981) 5.8 Toyota (2007-2010) 5.7 Ford (1972) 4.1 Honda (1995) 3.7 Volkswagen (1972) 3.7 General Motors (1973) 3.7

From 1971 to 2010, Ford (1981) had the most car recalls, with over 20 million. Ford (1972), General Motors (1973), Honda (1995) and Volkswagen (1972) had the least with under 5 million.

{"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/10660.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#51b2e5"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Type of product"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Number of recalls in millions"}, "field": "Number of recalls in millions"}}, "title": ["The largest car recalls from 1971 to 2010", ", by manufacturer (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Type of product Number of recalls in millions Ford (1981) 21 Ford (1999-2009) 15 Ford (1996) 7.9 General Motors (1971) 6.7 General Motors (1981) 5.8 Toyota (2007-2010) 5.7 Ford (1972) 4.1 Honda (1995) 3.7 Volkswagen (1972) 3.7 General Motors (1973) 3.7

Ford had the two largest recalls in 1981 and 1999-2009. The least amount of recalls were General Motors 1973, Honda 1995 and Volkswagen 1972.

{"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/10661.tsv"}, "mark": "area", "encoding": {"color": {"value": "#0F9D58"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Total direct premiums in billion euros*"}, "field": "Total direct premiums in billion euros*"}}, "title": ["Total direct premiums of the insurance industry", "in the United Kingdom (UK) from 2004 to 2018", "(in billion euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Total direct premiums in billion euros* Dec 31, 2003 236.87 Dec 31, 2004 266.03 Dec 31, 2005 298.75 Dec 31, 2006 342.07 Dec 31, 2007 206.99 Dec 31, 2008 205.02 Dec 31, 2009 205.31 Dec 31, 2010 218.15 Dec 31, 2011 230.97 Dec 31, 2012 225.91 Dec 31, 2013 239.39 Dec 31, 2014 270.06 Dec 31, 2015 251.08 Dec 31, 2016 297.21 Dec 31, 2017 341.61

Premiums rose in the three years from 2004 to a peak of around 340 billion Euros in 2007. They then fell sharply to just above 200 billion Euros in 2008. After that there was a generally steady growth back to around 340 billion Euros in 2018.

{"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/10661.tsv"}, "mark": "area", "encoding": {"color": {"value": "#0F9D58"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Total direct premiums in billion euros*"}, "field": "Total direct premiums in billion euros*"}}, "title": ["Total direct premiums of the insurance industry", "in the United Kingdom (UK) from 2004 to 2018", "(in billion euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Total direct premiums in billion euros* Dec 31, 2003 236.87 Dec 31, 2004 266.03 Dec 31, 2005 298.75 Dec 31, 2006 342.07 Dec 31, 2007 206.99 Dec 31, 2008 205.02 Dec 31, 2009 205.31 Dec 31, 2010 218.15 Dec 31, 2011 230.97 Dec 31, 2012 225.91 Dec 31, 2013 239.39 Dec 31, 2014 270.06 Dec 31, 2015 251.08 Dec 31, 2016 297.21 Dec 31, 2017 341.61

The total direct premiums of the insurance industry in the UK from 2004 to 2018 varied a lot. It was at it's highest amount in 2020 and 2007.

{"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/10661.tsv"}, "mark": "area", "encoding": {"color": {"value": "#0F9D58"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Total direct premiums in billion euros*"}, "field": "Total direct premiums in billion euros*"}}, "title": ["Total direct premiums of the insurance industry", "in the United Kingdom (UK) from 2004 to 2018", "(in billion euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Total direct premiums in billion euros* Dec 31, 2003 236.87 Dec 31, 2004 266.03 Dec 31, 2005 298.75 Dec 31, 2006 342.07 Dec 31, 2007 206.99 Dec 31, 2008 205.02 Dec 31, 2009 205.31 Dec 31, 2010 218.15 Dec 31, 2011 230.97 Dec 31, 2012 225.91 Dec 31, 2013 239.39 Dec 31, 2014 270.06 Dec 31, 2015 251.08 Dec 31, 2016 297.21 Dec 31, 2017 341.61

The total direct premiums of the insurance industry in the United Kingdom rose sharply in 2007 to around 240 billion Euros before falling again in 2008. The total direct premiums of the insurance industry in the United Kingdom from 2004 to 2018 peaked again in 2018 at around 240 billion Euros. The total direct premiums of the insurance industry in the United Kingdom from 2004 to 2018 remained fairly static between 2008 and 2010.

{"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/10661.tsv"}, "mark": "area", "encoding": {"color": {"value": "#0F9D58"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Total direct premiums in billion euros*"}, "field": "Total direct premiums in billion euros*"}}, "title": ["Total direct premiums of the insurance industry", "in the United Kingdom (UK) from 2004 to 2018", "(in billion euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Total direct premiums in billion euros* Dec 31, 2003 236.87 Dec 31, 2004 266.03 Dec 31, 2005 298.75 Dec 31, 2006 342.07 Dec 31, 2007 206.99 Dec 31, 2008 205.02 Dec 31, 2009 205.31 Dec 31, 2010 218.15 Dec 31, 2011 230.97 Dec 31, 2012 225.91 Dec 31, 2013 239.39 Dec 31, 2014 270.06 Dec 31, 2015 251.08 Dec 31, 2016 297.21 Dec 31, 2017 341.61

The overall trend appears to be upwards but with a big dip in 2008.

{"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/10666.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ff9d98"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Brand value in million U.S. dollars"}, "field": "Brand value in million U\\.S\\. dollars"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "utilities brand"}}, "title": ["Brand value of most valuable utilities brands", "worldwide in 2020 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Brand value in million U.S. dollars utilities brand 56965 State Grid(China) 11907 EDF(France) 11079 Engie(France) 10799 Enel(Italy) 7469 Korea Electric Power Corporation(South Korea) 4788 Iberdrola(Spain) 4456 Innogy(Germany) 3925 Duke Energy(United States) 3759 Exelon(United States) 3637 TEPCO(Japan)

State Grid’s brand value is the highest. France and United States have two utilities brands within this graph.

{"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": "data/10684.tsv"}, "mark": "area", "encoding": {"color": {"value": "#e5ae38"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Number of offences"}, "field": "Number of offences"}}, "title": ["Number of drug-trafficking , production", "or distribution offences in Canada from 2000", "to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of offences Dec 31, 1999 33276 Dec 31, 2000 31543 Dec 31, 2001 32166 Dec 31, 2002 33513 Dec 31, 2003 34476 Dec 31, 2004 32699 Dec 31, 2005 32353 Dec 31, 2006 32843 Dec 31, 2007 32260 Dec 31, 2008 32974 Dec 31, 2009 35283 Dec 31, 2010 33871 Dec 31, 2011 33722 Dec 31, 2012 30897 Dec 31, 2013 25923 Dec 31, 2014 27184 Dec 31, 2015 26531 Dec 31, 2016 27184 Dec 31, 2017 28300 Dec 31, 2018 22989

there was a peak in drug trafficking in 2010 then a drop in approximately 2012.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/1069.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#E044A7"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "County in the United States"}, "y": {"type": "quantitative", "axis": {"title": "Change in population"}, "field": "Change in population"}}, "title": ["The 25 fastest growing counties in the United", "States in 2018 , by change in population", "from 2010 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

County in the United States Change in population Williams County, North Dakota 0.678 Hays County, Texas 0.465 Wasatch County, Utah 0.449 Comal County, Texas 0.439 Kendall County, Texas 0.421 Sumter County, Florida 0.417 Dallas County, Iowa 0.413 Osceola County, Florida 0.398 Williamson County, Texas 0.398 St. Johns County, Florida 0.393 Forsyth County, Georgia 0.392 Fort Bend County, Texas 0.388 Lincoln County, South Dakota 0.364 Long County, Georgia 0.364 Walton County, Florida 0.346 Rockwall County, Texas 0.339 Denton County, Texas 0.339 Montgomery County, Texas 0.333 Brunswick County, North Carolina 0.329 Collin County, Texas 0.324 Loudoun County, Virginia 0.324 Kaufman County, Texas 0.317 St. Bernard Parish, Louisiana 0.316 Horry County, South Carolina 0.316 Bryan County, Georgia 0.312

The fastest growing county is Williams county which has a change in population over 0.6. The slowest growing counties are Bryan County, Horry County, Kaufman County and St Bernard County with a change in population of approximately 0.33.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/10710.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#118DFF"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Type"}, "y": {"type": "quantitative", "axis": {"title": "Number of trials"}, "field": "Number of trials"}}, "title": ["Number of coronavirus (COVID-19) clinical", "trials for drugs and vaccines worldwide as", "of January 7 , 2021 , by type"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Type Number of trials Treatment 1787 COVID-19 complication support 1057 COVID-19 pneumonia 951 Severe COVID-19 904 Moderate COVID-19 810 Mild COVID-19 635 Exposure prophylaxis (PEP/PrEP) 361 Critical COVID-19 352 COVID-19 vaccines 331 Healthy subjects 281 Asymptomatic COVID-19 136 Post-COVID syndrome 28 Unspecified 107

The greatest number of clinical trials are for patients who currently have or recently had covid.

{"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/10711.tsv"}, "mark": "area", "encoding": {"color": {"value": "#666666"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Gross output in million U.S. dollars"}, "field": "Gross output in million U\\.S\\. dollars"}}, "title": ["Tire manufacturing gross output in the United", "States from 1998 to 2018 (in million U.S.", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Gross output in million U.S. dollars Dec 31, 1997 15.5 Dec 31, 1998 15.2 Dec 31, 1999 15.8 Dec 31, 2000 14.7 Dec 31, 2001 14.7 Dec 31, 2002 15.3 Dec 31, 2003 15.9 Dec 31, 2004 16.3 Dec 31, 2005 16.5 Dec 31, 2006 17.8 Dec 31, 2007 16.9 Dec 31, 2008 15.7 Dec 31, 2009 17.2 Dec 31, 2010 21.5 Dec 31, 2011 21.1 Dec 31, 2012 20.1 Dec 31, 2013 20.1 Dec 31, 2014 20.5 Dec 31, 2015 18.4 Dec 31, 2016 18.9 Dec 31, 2017 18.7

The general trend of gross output is upwards. There are some dips in the output in 2001 and 2009 but the chart is not specific enough in years so be certain that these are the years as the linear scale goes up in 5s. There is a major upward trend of output between 2009 and 2011 but there is no way of knowing why this is from the chart. Although the overall trend is upwards between 2015 and 2017 the output has reduced.

{"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/10712.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "quantitative", "axis": {"title": "Income in U.S. dollars"}, "field": "Income in U\\.S\\. dollars"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "year"}}, "title": ["Per capita personal income in New Jersey", "from 2000 to 2019 (in current U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Income in U.S. dollars year 70471 '19 67845 '18 64983 '17 62842 '16 61153 '15 58670 '14 56054 '13 55587 '12 53694 '11 51420 '10 50149 '09 52005 '08 50723 '07 48015 '06 44775 '05 43208 '04 41425 '03 40626 '02 40306 '01 39257 '00

The per capita income in New Jersey from 2000 and to 2019 has year on year ear grown steadily and people have better incomes now than 2000.

{"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/10718.tsv"}, "mark": "line", "encoding": {"color": {"value": "#7F7F7F"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of shares in millions"}, "field": "Number of shares in millions"}}, "title": ["Number of Merck & Co. 's common shares outstanding", "from 2009 to 2019 (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of shares in millions Dec 31, 2008 2268 Dec 31, 2009 3095 Dec 31, 2010 3071 Dec 31, 2011 3041 Dec 31, 2012 2963 Dec 31, 2013 2894 Dec 31, 2014 2816 Dec 31, 2015 2766 Dec 31, 2016 2730 Dec 31, 2017 2664 Dec 31, 2018 2565

There is a steady increase in shares per million from 2, 400 in 2009 to 2,100 in 2010. They remained fairly steady up to 2012 where a downard trend can be observed. By 2019 shares were going down to 2,500.

{"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/10718.tsv"}, "mark": "line", "encoding": {"color": {"value": "#7F7F7F"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of shares in millions"}, "field": "Number of shares in millions"}}, "title": ["Number of Merck & Co. 's common shares outstanding", "from 2009 to 2019 (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of shares in millions Dec 31, 2008 2268 Dec 31, 2009 3095 Dec 31, 2010 3071 Dec 31, 2011 3041 Dec 31, 2012 2963 Dec 31, 2013 2894 Dec 31, 2014 2816 Dec 31, 2015 2766 Dec 31, 2016 2730 Dec 31, 2017 2664 Dec 31, 2018 2565

There was a steep share prise rise from 2300 to 3100 approximately in the short time from the six months prior to 2010. A consistent fall in share price started from the end of 2010 until the middle of 2018. This share price fall was almost entirely regular and consistent, there were no noticeable rises in price.

{"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": "data/10723.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#52689e"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "region of Finland"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Number of inhabitants"}, "field": "Number of inhabitants"}}, "title": ["Population of Finland in 2019 , by region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

region of Finland Number of inhabitants Uusimaa 1689725 Pirkanmaa 517666 Southwest Finland 479341 North Ostrobothnia 412830 Central Finland 275104 North Savo 244236 Satakunta 216752 Päijät-Häme 199604 South Ostrobothnia 188685 Ostrobothnia 180445 Lapland 177161 Kymenlaakso 171167 Kanta-Häme 170925 North Karelia 161211 South Savo 142335 South Karelia 127757 Kainuu 72306 Central Ostrobothnia 68158 Åland 29884

The region of Finland with the highest population in 2019 was Uusimaa with with approximately 1,200,000 inhabitants. The region of Finland with the lowest population in 2019 was Aland with less than 100,000 inhabitants. Most regions have a population between 100,000 and 500,000 inhabitants.

{"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/10725.tsv"}, "mark": "area", "encoding": {"color": {"value": "#54a24b"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of urban population in total population"}, "field": "Share of urban population in total population"}}, "title": ["Dominican Republic : Urbanization from 2009", "to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Share of urban population in total population Dec 31, 2008 0.7254 Dec 31, 2009 0.7375 Dec 31, 2010 0.7483 Dec 31, 2011 0.758 Dec 31, 2012 0.7675 Dec 31, 2013 0.7767 Dec 31, 2014 0.7857 Dec 31, 2015 0.7944 Dec 31, 2016 0.8028 Dec 31, 2017 0.8107 Dec 31, 2018 0.8183

The share of urban population compared to totalPopulation has increased steadily since 2010. Over the last 8 years the share has increased from 0.75 to 0.81. This growth has been steady with no significant jumps or declines.

{"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/1072.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d8b5a5"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of gross domestic product"}, "field": "Share of gross domestic product"}}, "title": ["Energy-related industry value added as a", "percentage of GDP in 2015 , by select country"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Share of gross domestic product Canada* 0.0715 Australia 0.0457 United States 0.0297 United Kingdom 0.0253 South Korea* 0.0183 Mexico* 0.0174 Japan 0.0164 France 0.0149 Germany 0.0149 Turkey 0.007

Turkey had the least amount of shares and Canada had the most.

{"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/10738.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#0570b0"}, "x": {"type": "quantitative", "axis": {"title": "Share of respondents*"}, "field": "Share of respondents*"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Thinking about the effects of technology", "on relationships with friends and family", ", please indicate to what extent do you agree", "or disagree with each of the following statements", "?"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Share of respondents* Response 0.8 Technology makes getting in touch with family members easier 0.7 Communication technology enables constant dialogue wth family and friends 0.66 Technology devices make one feel more connected 0.64 Technology makes it easier to arrange meetings with family members 0.61 Mobile technology enhances interactions with immediate family 0.57 Internet encourages contact with family or friends 0.55 Social networks are a great way to feel close to family and friends 0.54 Technology has little impact on one's frequency of communication with friends or family 0.47 Uncomfortable with finding out about life of family members via social network 0.33 Social media helps understanding family members better 0.33 Uncomfortable knowing family members can monitor one's activity on social media 0.31 Technology has led to less face-to-face interactions among families 0.28 Playing online games with family members helps connect with them 0.24 Uncomfortable with family members being able to get in touch at any time

Overall, you can tell that technology has impacted people's relationships with friends and family in a positive and negative way. The most agreed upon statement was that technology makes getting in touch...(im assuming easier).

{"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/10739.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ce6dbd"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Number of students"}, "field": "Number of students"}}, "title": ["Number of students enrolled in colleges", "in Canada from 2000 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of students Dec 31, 1999 497250 Dec 31, 2000 589752 Dec 31, 2001 616059 Dec 31, 2002 626784 Dec 31, 2003 661137 Dec 31, 2004 660471 Dec 31, 2005 658626 Dec 31, 2006 664821 Dec 31, 2007 693081 Dec 31, 2008 691323 Dec 31, 2009 706722 Dec 31, 2010 730500 Dec 31, 2011 738003 Dec 31, 2012 742668 Dec 31, 2013 754692 Dec 31, 2014 747645 Dec 31, 2015 748017 Dec 31, 2016 755883 Dec 31, 2017 774654

The number of students enrolled in Canadian colleges has risen steadily in the years between 2000 and 2018 by around 250000 overall. The sharpest rise was between 2000-2001 and then this was followed by a steady increase over the next 17 years.

{"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/10745.tsv"}, "mark": "area", "encoding": {"color": {"value": "#f2cf5b"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of shipments in thousands"}, "field": "Number of shipments in thousands"}}, "title": ["Number of wholesale shipments of motorhomes", "in the United States from 2007 to 2019 (in", "thousands)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of shipments in thousands Dec 31, 2006 55.4 Dec 31, 2007 28.3 Dec 31, 2008 13.2 Dec 31, 2009 25.2 Dec 31, 2010 24.8 Dec 31, 2011 28.3 Dec 31, 2012 38.3 Dec 31, 2013 43.9 Dec 31, 2014 47.31 Dec 31, 2015 54.74 Dec 31, 2016 62.64 Dec 31, 2017 57.59 Dec 31, 2018 46.63

From 2007 to 2009 the wholesale shipments of motorhomes in the USA fell from 55 thousand to just over 10 thousand. It then increased quite steadily to over 60 thousand, peaking in 2017. It has been decreasing ever since to around 45 thousand in 2019.

{"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/10756.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ab7fb4"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Expenses in million U.S. dollars"}, "field": "Expenses in million U\\.S\\. dollars"}}, "title": ["General Dynamics Corporation 's company-sponsored", "research and development expenses from FY", "2010 to FY 2019 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Expenses in million U.S. dollars Dec 31, 2009 325 Dec 31, 2010 372 Dec 31, 2011 374 Dec 31, 2012 310 Dec 31, 2013 358 Dec 31, 2014 395 Dec 31, 2015 418 Dec 31, 2016 521 Dec 31, 2017 502 Dec 31, 2018 466

Expenses ranged from $3.2 million to $5.2 million. Expenses generally went in an upward trend although dipped in 2013 and after 2017.

{"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/10759.tsv"}, "mark": "area", "encoding": {"color": {"value": "#666666"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of hospital beds"}, "field": "Number of hospital beds"}}, "title": ["Number of hospital beds in Austria from", "2000 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of hospital beds Dec 31, 1999 63674 Dec 31, 2000 63114 Dec 31, 2001 63092 Dec 31, 2002 62806 Dec 31, 2003 63206 Dec 31, 2004 63248 Dec 31, 2005 63354 Dec 31, 2006 64307 Dec 31, 2007 64018 Dec 31, 2008 64069 Dec 31, 2009 64008 Dec 31, 2010 64417 Dec 31, 2011 64691 Dec 31, 2012 64825 Dec 31, 2013 64815 Dec 31, 2014 65138 Dec 31, 2015 64838 Dec 31, 2016 64805 Dec 31, 2017 64285

Number of hospital beds remains stable year on year with no significant change from year 2000.

{"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/10761.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#8c6d31"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Sales in billion U.S. dollars"}, "field": "Sales in billion U\\.S\\. dollars"}}, "title": ["Sales of power transistors worldwide from", "2008 to 2024 (in billion U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Sales in billion U.S. dollars 2008 10 2009 8.4 2010 12 2011 13.5 2012 12.4 2013 11.6 2014 13.2 2015 12.3 2016 12.9 2017* 13.6 2018 16.3 2019 17.1 2020* 15.9 2021* 16.9 2022* 17.7 2024* 18.6

The amount has nearly double in the space of 15 years2020 took a dip, but has increased each year since.

{"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/10769.tsv"}, "mark": "line", "encoding": {"color": {"value": "#f2f2f2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of hospitals"}, "field": "Number of hospitals"}}, "title": ["Number of hospitals in Hungary from 2000", "to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of hospitals Dec 31, 1999 175 Dec 31, 2000 176 Dec 31, 2001 177 Dec 31, 2002 177 Dec 31, 2003 181 Dec 31, 2004 182 Dec 31, 2005 182 Dec 31, 2006 179 Dec 31, 2007 176 Dec 31, 2008 175 Dec 31, 2009 175 Dec 31, 2010 173 Dec 31, 2011 176 Dec 31, 2012 173 Dec 31, 2013 174 Dec 31, 2014 168 Dec 31, 2015 168 Dec 31, 2016 165 Dec 31, 2017 165

The number of hospitals in Hungary has remained relatively stable between the year 2000 and 2020. However, between 2005 and 2000 there was a gradual decline, from a peak in 2005 of around 180 hospitals to around 165 in 2000.

{"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/10769.tsv"}, "mark": "line", "encoding": {"color": {"value": "#f2f2f2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of hospitals"}, "field": "Number of hospitals"}}, "title": ["Number of hospitals in Hungary from 2000", "to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of hospitals Dec 31, 1999 175 Dec 31, 2000 176 Dec 31, 2001 177 Dec 31, 2002 177 Dec 31, 2003 181 Dec 31, 2004 182 Dec 31, 2005 182 Dec 31, 2006 179 Dec 31, 2007 176 Dec 31, 2008 175 Dec 31, 2009 175 Dec 31, 2010 173 Dec 31, 2011 176 Dec 31, 2012 173 Dec 31, 2013 174 Dec 31, 2014 168 Dec 31, 2015 168 Dec 31, 2016 165 Dec 31, 2017 165

Over the years the number of hospitals had slightly decreased.

{"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/10774.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ec8431"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of retail locations"}, "field": "Number of retail locations"}}, "title": ["Number of specialty bicycle retail locations", "in the U.S. from 2000 to 2015"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of retail locations Dec 31, 1999 6195 Dec 31, 2000 6259 Dec 31, 2001 5505 Dec 31, 2002 5358 Dec 31, 2003 4982 Dec 31, 2004 4704 Dec 31, 2005 4600 Dec 31, 2006 4451 Dec 31, 2007 4349 Dec 31, 2008 4319 Dec 31, 2009 4256 Dec 31, 2010 4178 Dec 31, 2011 4089 Dec 31, 2012 4055 Dec 31, 2013 3950 Dec 31, 2014 3790

There has been a decline in the number of specialty bicycle retail locations from 2000 to 2015. There was a more significant decline between 2000 and 2002. 2002 to 2003 the decrease in bicycle retail locations slowed a little.

{"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/10776.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#182429"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Pharmaceutical company"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Indicator score from 1 (lowest) to 5 (highest)"}, "field": "Indicator score from 1 (lowest) to 5 (highest)"}}, "title": ["Pharmaceutical company ranking based on", "product donations in developing countries", "in 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Pharmaceutical company Indicator score from 1 (lowest) to 5 (highest) Sanofi 5 GlaxoSmithKline plc 4.54 Novo Nordisk A/S 4.54 Novartis AG 4.43 Eisai Co. Ltd. 4.04 Merck KGaA 3.91 Pfizer Inc. 3.62 Merck & Co. Inc. 3.58 Bayer AG 3.33 Johnson & Johnson 3.1 Bristol-Myers Squibb Co. 3.05 AstraZeneca plc 2.87 Gilead Sciences Inc. 2.87 Roche Holding Ltd. 2.87 Eli Lilly & Co. 2.83 AbbVie Inc. 2.6 Takeda Pharamceutical Co. 2.43 Boehringer-Ingelheim GmbH 2.02 Astellas Pharma Inc. 1.27 Daiichi Sankyo Co. Ltd. 1.21

Few companies have indicator scores on the extreme ends of the scale, while most fall somewhere in between. More companies are above the middle indicator score of 3 than below it.

{"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/10782.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fddaec"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Net income in million U.S. dollars"}, "field": "Net income in million U\\.S\\. dollars"}}, "title": ["McKesson Corporation 's net income from", "2007 to 2020 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Net income in million U.S. dollars Dec 31, 2006 913 Dec 31, 2007 990 Dec 31, 2008 823 Dec 31, 2009 1263 Dec 31, 2010 1202 Dec 31, 2011 1403 Dec 31, 2012 1338 Dec 31, 2013 1258 Dec 31, 2014 1543 Dec 31, 2015 2310 Dec 31, 2016 5153 Dec 31, 2017 297 Dec 31, 2018 255 Dec 31, 2019 1120

Overall the income rises gradually from 2007-2015. Between 2015-2017 it rises very steeply, peaking in 2017. In 2017 the income value then plummets.

{"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/10789.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#55b748"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"title": "Foreign debt as share of GDP"}, "field": "Foreign debt as share of GDP"}}, "title": ["External debt as percentage of gross domestic", "product in Latin America in 2018 , by country"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Country Foreign debt as share of GDP Argentina 0.418 El Salvador 0.332 Ecuador 0.326 Nicaragua 0.326 Panama 0.316 Uruguay 0.314 Honduras 0.296 Dominican Republic 0.266 Bolivia 0.226 Haiti 0.215 Colombia 0.168 Costa Rica 0.148 Paraguay 0.14 Guatemala 0.108 Brazil 0.098 Mexico 0.081 Peru 0.075 Chile 0.053

Chile, Mexico and Peru were in the lowest debt, whilst Argentina was the highest by quite a large amount.