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/1078.tsv"}, "mark": "line", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of fatalities"}, "field": "Number of fatalities"}}, "title": ["Number of worldwide air traffic fatalities", "from 2006 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of fatalities Dec 31, 2005 905 Dec 31, 2006 774 Dec 31, 2007 595 Dec 31, 2008 763 Dec 31, 2009 943 Dec 31, 2010 525 Dec 31, 2011 477 Dec 31, 2012 232 Dec 31, 2013 692 Dec 31, 2014 186 Dec 31, 2015 258 Dec 31, 2016 59 Dec 31, 2017 561 Dec 31, 2018 287

Generally, the number of worldwide air traffic fatalities has declined between 2006 and 2019. However looking in more detail at the year on year changes shows variation. A low was reached in 2017. A high was reached in 2010.

{"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/10791.tsv"}, "mark": "area", "encoding": {"color": {"value": "#7F7F7F"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Percentage of population"}, "field": "Percentage of population"}}, "title": ["Poverty rate in Hawaii from 2000 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Percentage of population Dec 31, 1999 0.088 Dec 31, 2000 0.104 Dec 31, 2001 0.101 Dec 31, 2002 0.109 Dec 31, 2003 0.106 Dec 31, 2004 0.098 Dec 31, 2005 0.093 Dec 31, 2006 0.08 Dec 31, 2007 0.091 Dec 31, 2008 0.104 Dec 31, 2009 0.107 Dec 31, 2010 0.12 Dec 31, 2011 0.116 Dec 31, 2012 0.108 Dec 31, 2013 0.114 Dec 31, 2014 0.106 Dec 31, 2015 0.093 Dec 31, 2016 0.095 Dec 31, 2017 0.088 Dec 31, 2018 0.093

2011 has the highest poverty rate at 0.12%. 2007 has the lowest poverty rate at 0.08%.

{"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/10792.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#545454"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Revenue in million U.S. dollars"}, "field": "Revenue in million U\\.S\\. dollars"}}, "title": ["United Kingdom casino gaming market revenue", "from 2006 to 2015 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Revenue in million U.S. dollars 2015* 1353 2014* 1291 2013* 1237 2012* 1198 2011* 1191 2010 1209 2009 1202 2008 1160 2007 1260 2006 1245

The chart shows there to be a similar trend across the years but a small increase in 2015 shows this is within an upward trend.

{"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/10792.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#545454"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Revenue in million U.S. dollars"}, "field": "Revenue in million U\\.S\\. dollars"}}, "title": ["United Kingdom casino gaming market revenue", "from 2006 to 2015 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Revenue in million U.S. dollars 2015* 1353 2014* 1291 2013* 1237 2012* 1198 2011* 1191 2010 1209 2009 1202 2008 1160 2007 1260 2006 1245

In 2008, there was a noticeable drop in revenue. The revenue from 2009 to 2012 was approximately 1,200 million dollars. From 2013 to 2015, the revenue was increasing.

{"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/10798.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec8431"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Index score"}, "field": "Index score"}}, "title": ["Corruption perception index in the ASEAN", "region in 2019 , by country"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Country Index score Singapore 85 Malaysia 53 Indonesia 40 Timor-Leste 38 Vietnam 37 Thailand 36 Philippines 34 Myanmar 29 Laos 29 Cambodia 20

From the bar chart I conclude that Singapore has the highest number of corruption perception index in the ASEAN region in 2019 at 86. Cambodia has the lowest figure of 20. The next highest country is Malaysia at 55 followed by Indonesia at 40. The other countries shown on the chart range between 25 and 38.

{"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/10806.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#F4B400"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Share of voluntary work in the sports sector", "in the last year in England from 2005/06", "to 2018/19"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Share of respondents 2018/19 0.207 2017/18 0.208 2016/17* 0.2 2015/16 0.193 2014/15 0.209 2013/14 0.191 2012/13 0.219 2011/12 0.175 2010/11 0.207 2008/09 0.213 2007/08 0.196 2006/07 0.194 2005/06 0.192

I can see that 2005 until 2019 the volunteer work has almost his the 0.20 mark. In the years 2011 until 2012 is the lowest time in the years studied. There isn’t much variation between all of the years.

{"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/10807.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#829eb1"}, "x": {"type": "quantitative", "axis": {"title": "Number of combined likes and comments"}, "field": "Number of combined likes and comments"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "beauty and fashion brand"}}, "title": ["Leading beauty and fashion brands ranked", "by Instagram user engagement as of May 2017", "(in million interactions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Number of combined likes and comments beauty and fashion brand 134.37 hypebeast 127.82 anastasiabeverlyhills 67.9 maccosmetics 62.53 urbanoutfitters 57.75 toofaced 37.28 gucci 37.27 vspink 37.11 dior 33.87 maybelline 33.4 primark

Hypebeast has the most likes and comments of these brands. Maybelline and primark jointly have the lowest number of likes and comments.

{"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/10807.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#829eb1"}, "x": {"type": "quantitative", "axis": {"title": "Number of combined likes and comments"}, "field": "Number of combined likes and comments"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "beauty and fashion brand"}}, "title": ["Leading beauty and fashion brands ranked", "by Instagram user engagement as of May 2017", "(in million interactions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Number of combined likes and comments beauty and fashion brand 134.37 hypebeast 127.82 anastasiabeverlyhills 67.9 maccosmetics 62.53 urbanoutfitters 57.75 toofaced 37.28 gucci 37.27 vspink 37.11 dior 33.87 maybelline 33.4 primark

Anastasiabeveryhills and hypebeast have the highest number of interactions. All other brands are averaging between 30M and 70M interaction. Maybelene and primakr have the lest number of interactions around 30M.

{"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/10808.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#6B007B"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "New York Mets all-time games played leader"}, "y": {"type": "quantitative", "axis": {"title": "Number of games played"}, "field": "Number of games played"}}, "title": ["New York Mets all-time games played leaders", "as of October 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

New York Mets all-time games played leader Number of games played Ed Kranepool 1853 David Wright 1585 Jose Reyes 1365 Bud Harrelson 1322 Jerry Grote 1235 Cleon Jones 1201 Howard Johnson 1154 Mookie Wilson 1116 Darryl Strawberry 1109 Edgardo Alfonzo 1086

The majority of players have competed in roughly the same amount of games apart from David Wright and Ed Kranepool who have done more.

{"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/10808.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#6B007B"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "New York Mets all-time games played leader"}, "y": {"type": "quantitative", "axis": {"title": "Number of games played"}, "field": "Number of games played"}}, "title": ["New York Mets all-time games played leaders", "as of October 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

New York Mets all-time games played leader Number of games played Ed Kranepool 1853 David Wright 1585 Jose Reyes 1365 Bud Harrelson 1322 Jerry Grote 1235 Cleon Jones 1201 Howard Johnson 1154 Mookie Wilson 1116 Darryl Strawberry 1109 Edgardo Alfonzo 1086

Number of games played is between 1100 and 1800 for each player. 8 out of 10 players have played between 1100 and 1400 games. the two players who have played between 1600 and 1800 games are more unusual/extreme/outliers.

{"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/10808.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#6B007B"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "New York Mets all-time games played leader"}, "y": {"type": "quantitative", "axis": {"title": "Number of games played"}, "field": "Number of games played"}}, "title": ["New York Mets all-time games played leaders", "as of October 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

New York Mets all-time games played leader Number of games played Ed Kranepool 1853 David Wright 1585 Jose Reyes 1365 Bud Harrelson 1322 Jerry Grote 1235 Cleon Jones 1201 Howard Johnson 1154 Mookie Wilson 1116 Darryl Strawberry 1109 Edgardo Alfonzo 1086

Ed Kranepool led a large number a large number of New York games than the other players at around 1800, with David Wright closely behind at 1600. The lowest led games were by Edgardo Alfonzo at about 1100/.

{"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/10813.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#fed9a6"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": " matter of expense"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Costs in million U.S. dollars"}, "field": "Costs in million U\\.S\\. dollars"}}, "title": ["Budget of London 2012 Summer Olympic Games", ", by matter of expense (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

matter of expense Costs in million U.S. dollars Venues 4607 Olympic Village and media centers 1919 Counter-terrorism operations 1776 Other Olympic Park projects 1421 Transport projects 1392 Other anticipated final costs 1311 Legacy projects 1298 Purchase of Olympic parkland 1204 Police 943 Venue security 869 Creation of government bodies to oversee Olympics 696 Contingency fund 157 Ceremonies 126

Venues spent 3 times more than any other dept in the 2012 Olympics while cermonies spent the least.

{"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/10817.tsv"}, "mark": "area", "encoding": {"color": {"value": "#88d27a"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Real GDP in billion U.S. dollars"}, "field": "Real GDP in billion U\\.S\\. dollars"}}, "title": ["Real Gross Domestic Product (GDP) of the", "federal state of Texas from 2000 to 2019", "(in billion U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Real GDP in billion U.S. dollars Dec 31, 1999 998.3 Dec 31, 2000 1021.89 Dec 31, 2001 1041.81 Dec 31, 2002 1048.3 Dec 31, 2003 1102.7 Dec 31, 2004 1132.8 Dec 31, 2005 1210.29 Dec 31, 2006 1274.3 Dec 31, 2007 1275.77 Dec 31, 2008 1271.44 Dec 31, 2009 1301.73 Dec 31, 2010 1343.79 Dec 31, 2011 1411.38 Dec 31, 2012 1472.1 Dec 31, 2013 1523.06 Dec 31, 2014 1596.36 Dec 31, 2015 1600.26 Dec 31, 2016 1646.26 Dec 31, 2017 1712.76 Dec 31, 2018 1788.53

The GDP of Texas increased steadily by approx. 75% in the years between 2000 and 2019. It stagnated briefly at some points but never declined.

{"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/10818.tsv"}, "mark": "line", "encoding": {"color": {"value": "#f1e2cc"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Number of doping cases"}, "field": "Number of doping cases"}}, "title": ["Number of doping cases worldwide in cycling", "between 2000 and 2010"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of doping cases Dec 31, 1999 6 Dec 31, 2000 9 Dec 31, 2001 14 Dec 31, 2002 7 Dec 31, 2003 28 Dec 31, 2004 20 Dec 31, 2005 44 Dec 31, 2006 32 Dec 31, 2007 28 Dec 31, 2008 15 Dec 31, 2009 24

The chart shows there was a significant rise in doping cases in 2006. It also shows a significant drop in doping cases between 2006 and 2009. It is a very clear easy to read linear chart.

{"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/10827.tsv"}, "mark": "line", "encoding": {"color": {"value": "#829eb1"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of cards in millions"}, "field": "Number of cards in millions"}}, "title": ["Total number of payment cards issued in", "France from 2010 to 2019 (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of cards in millions Dec 31, 2009 84.86 Dec 31, 2010 83.01 Dec 31, 2011 82.31 Dec 31, 2012 82.22 Dec 31, 2013 81.04 Dec 31, 2014 77.69 Dec 31, 2015 78.87 Dec 31, 2016 79.86 Dec 31, 2017 81.84 Dec 31, 2018 84.49

Number of cards issued in France is fairly stable from 2010-2014 where it then drops sharply. It begins to rise again through 2016 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/10840.tsv"}, "mark": "line", "encoding": {"color": {"value": "#6793a6"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Production in million heads"}, "field": "Production in million heads"}}, "title": ["Cattle production in Indonesia from 2005", "to 2018 (in million heads)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Production in million heads Dec 31, 2004 10.57 Dec 31, 2005 10.88 Dec 31, 2006 11.51 Dec 31, 2007 12.26 Dec 31, 2008 12.76 Dec 31, 2009 13.58 Dec 31, 2010 14.82 Dec 31, 2011 15.98 Dec 31, 2012 12.69 Dec 31, 2013 14.73 Dec 31, 2014 15.42 Dec 31, 2015 16 Dec 31, 2016 16.6 Dec 31, 2017 16.43

Cattle production was 11m in 2005 and rose steadily until 2012 when it peaked at 16m. It then dropped over the next three years, decreasing to 13m by 2013. Since then it has risen each year, growing significant between 2014 and 2017, eventually reaching a peak of 17m and dropping slightly by 2018.

{"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/10840.tsv"}, "mark": "line", "encoding": {"color": {"value": "#6793a6"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Production in million heads"}, "field": "Production in million heads"}}, "title": ["Cattle production in Indonesia from 2005", "to 2018 (in million heads)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Production in million heads Dec 31, 2004 10.57 Dec 31, 2005 10.88 Dec 31, 2006 11.51 Dec 31, 2007 12.26 Dec 31, 2008 12.76 Dec 31, 2009 13.58 Dec 31, 2010 14.82 Dec 31, 2011 15.98 Dec 31, 2012 12.69 Dec 31, 2013 14.73 Dec 31, 2014 15.42 Dec 31, 2015 16 Dec 31, 2016 16.6 Dec 31, 2017 16.43

Cattle production decreased suddenly in 2012 but gradually made an up.

{"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/10840.tsv"}, "mark": "line", "encoding": {"color": {"value": "#6793a6"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Production in million heads"}, "field": "Production in million heads"}}, "title": ["Cattle production in Indonesia from 2005", "to 2018 (in million heads)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Production in million heads Dec 31, 2004 10.57 Dec 31, 2005 10.88 Dec 31, 2006 11.51 Dec 31, 2007 12.26 Dec 31, 2008 12.76 Dec 31, 2009 13.58 Dec 31, 2010 14.82 Dec 31, 2011 15.98 Dec 31, 2012 12.69 Dec 31, 2013 14.73 Dec 31, 2014 15.42 Dec 31, 2015 16 Dec 31, 2016 16.6 Dec 31, 2017 16.43

There was a steady increase of production between 2006 to 2012. There was a steep decline of production between 2012 to 2013Production rose steadily after 2013.

{"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/10841.tsv"}, "mark": "line", "encoding": {"color": {"value": "#88d27a"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Population density in people per square kilometer"}, "field": "Population density in people per square kilometer"}}, "title": ["Population density of Sri Lanka from 2009", "to 2018 (in people per square kilometer)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Population density in people per square kilometer Dec 31, 2008 320.13 Dec 31, 2009 322.09 Dec 31, 2010 323.95 Dec 31, 2011 325.71 Dec 31, 2012 328.26 Dec 31, 2013 331.22 Dec 31, 2014 334.33 Dec 31, 2015 338.11 Dec 31, 2016 341.96 Dec 31, 2017 345.56

The population has slowly risen over the 7 years plotted.

{"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/10841.tsv"}, "mark": "line", "encoding": {"color": {"value": "#88d27a"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Population density in people per square kilometer"}, "field": "Population density in people per square kilometer"}}, "title": ["Population density of Sri Lanka from 2009", "to 2018 (in people per square kilometer)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Population density in people per square kilometer Dec 31, 2008 320.13 Dec 31, 2009 322.09 Dec 31, 2010 323.95 Dec 31, 2011 325.71 Dec 31, 2012 328.26 Dec 31, 2013 331.22 Dec 31, 2014 334.33 Dec 31, 2015 338.11 Dec 31, 2016 341.96 Dec 31, 2017 345.56

Sri Lankas population density rose from 325 people per square kilometer in 2009 to 350 people per square kilometer in 2018. As each year passed the population density in Square kilometers in Sri Lanka gradually increased.

{"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/10842.tsv"}, "mark": "line", "encoding": {"color": {"value": "#cbd5e8"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Unemployment rate"}, "field": "Unemployment rate"}}, "title": ["Tajikistan : Unemployment rate from 1999", "to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Unemployment rate Dec 31, 1998 0.154 Dec 31, 1999 0.1496 Dec 31, 2000 0.1457 Dec 31, 2001 0.143 Dec 31, 2002 0.1393 Dec 31, 2003 0.1345 Dec 31, 2004 0.1296 Dec 31, 2005 0.1236 Dec 31, 2006 0.118 Dec 31, 2007 0.1139 Dec 31, 2008 0.115 Dec 31, 2009 0.1161 Dec 31, 2010 0.1161 Dec 31, 2011 0.1164 Dec 31, 2012 0.1167 Dec 31, 2013 0.1157 Dec 31, 2014 0.115 Dec 31, 2015 0.1142 Dec 31, 2016 0.1127 Dec 31, 2017 0.1113 Dec 31, 2018 0.1102 Dec 31, 2019 0.1099

The Tajikistan unemployment rate drops steadily from just over 0.15 in 1999, to around 0.12 in 2008. The rate then remains relatively stable from 2008 to 2020. There is a slight rise from 2008 to 2013 before trending down up to 2020, but the rate remains between around 0.12 and 0.13 during this time.

{"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/10842.tsv"}, "mark": "line", "encoding": {"color": {"value": "#cbd5e8"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Unemployment rate"}, "field": "Unemployment rate"}}, "title": ["Tajikistan : Unemployment rate from 1999", "to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Unemployment rate Dec 31, 1998 0.154 Dec 31, 1999 0.1496 Dec 31, 2000 0.1457 Dec 31, 2001 0.143 Dec 31, 2002 0.1393 Dec 31, 2003 0.1345 Dec 31, 2004 0.1296 Dec 31, 2005 0.1236 Dec 31, 2006 0.118 Dec 31, 2007 0.1139 Dec 31, 2008 0.115 Dec 31, 2009 0.1161 Dec 31, 2010 0.1161 Dec 31, 2011 0.1164 Dec 31, 2012 0.1167 Dec 31, 2013 0.1157 Dec 31, 2014 0.115 Dec 31, 2015 0.1142 Dec 31, 2016 0.1127 Dec 31, 2017 0.1113 Dec 31, 2018 0.1102 Dec 31, 2019 0.1099

The unemployment level in Tajikistan dropped steadily from 1999-2007 and has since remained fairly constant with a slight decrease in the next 13 years.

{"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/10846.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#decbe4"}, "x": {"type": "quantitative", "axis": {"title": "Share of e-commerce sales"}, "field": "Share of e-commerce sales"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["E-commerce as percentage of total electronic", "shopping sales in the United States in 2018", ", by merchandise line"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Share of e-commerce sales Response 0.96 Computer software (includes video game software) 0.944 Furniture and home furnishings 0.938 Toys, hobby goods, and games 0.93 Electronics and appliances 0.93 Audio and video recordings (includes purchased downloads) 0.908 Books (includes audio books and e-books) 0.905 Clothing and clothing accessories (includes footwear) 0.9 Computer and peripheral equipment, communications equipment, and related products (includes cellular phones) 0.879 Food, beer, and wine 0.875 Sporting goods 0.865 Office equipment and supplies 0.823 Other merchandise* 0.819 Nonmerchandise receipts** 0.791 Jewelry 0.738 Total Electronic Shopping and Mail-Order Houses (NAICS 4541) 0.257 Drugs, health aids, and beauty aids

computer software has the highest sales, drugs beauty aids and heath aids is the lowest sales catogory.

{"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/10848.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b279a2"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "weather conditions and storms"}, "y": {"type": "quantitative", "axis": {"title": "Number of fatalities"}, "field": "Number of fatalities"}}, "title": ["Number of deaths due to weather conditions", "and storms in the U.S. in 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

weather conditions and storms Number of fatalities Rip current 72 Extreme heat 63 River flood 49 Flash flood 43 Tornado 42 Thunderstorm wind 38 Extreme cold 35 Lightning strike 20 Avalanche 14 Winter storm 13 High Wind 13 Fire Weather 3 Miscellaneous 2 Mud slide 1

Rip currents caused the most deaths due to weather conditions and mud slides caused the least.

{"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/10848.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b279a2"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "weather conditions and storms"}, "y": {"type": "quantitative", "axis": {"title": "Number of fatalities"}, "field": "Number of fatalities"}}, "title": ["Number of deaths due to weather conditions", "and storms in the U.S. in 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

weather conditions and storms Number of fatalities Rip current 72 Extreme heat 63 River flood 49 Flash flood 43 Tornado 42 Thunderstorm wind 38 Extreme cold 35 Lightning strike 20 Avalanche 14 Winter storm 13 High Wind 13 Fire Weather 3 Miscellaneous 2 Mud slide 1

Rip currents along with extreme heat are two of the most dangerous weather conditions. Very few people die from mudslides.

{"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/10849.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#6793a6"}, "x": {"type": "quantitative", "axis": {"title": "Percentage of GDP"}, "field": "Percentage of GDP"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Size of the untaxed `` shadow '' economy", "in selected countries 2010 , as a share of", "GDP"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Percentage of GDP Response 0.44 Russia 0.39 Brazil 0.36 Pakistan 0.35 Egypt 0.31 Turkey 0.28 Greece 0.27 Italy 0.22 India 0.16 Canada 0.13 China 0.09 U.S.

Russia has the largest untaxed ‘shadow’ economy and the US has the least, from the responses given. The difference between Russia and the US in terms of the percentage of GDP the ‘shadow’ economy takes up is about 0.4.

{"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/10849.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#6793a6"}, "x": {"type": "quantitative", "axis": {"title": "Percentage of GDP"}, "field": "Percentage of GDP"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Size of the untaxed `` shadow '' economy", "in selected countries 2010 , as a share of", "GDP"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Percentage of GDP Response 0.44 Russia 0.39 Brazil 0.36 Pakistan 0.35 Egypt 0.31 Turkey 0.28 Greece 0.27 Italy 0.22 India 0.16 Canada 0.13 China 0.09 U.S.

Russia has the largest shadow economy, followed closely by Turkey. The US has the smallest shadows economy of the group.

{"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/10849.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#6793a6"}, "x": {"type": "quantitative", "axis": {"title": "Percentage of GDP"}, "field": "Percentage of GDP"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Size of the untaxed `` shadow '' economy", "in selected countries 2010 , as a share of", "GDP"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Percentage of GDP Response 0.44 Russia 0.39 Brazil 0.36 Pakistan 0.35 Egypt 0.31 Turkey 0.28 Greece 0.27 Italy 0.22 India 0.16 Canada 0.13 China 0.09 U.S.

The countries with the largest untaxed shadow economy are Russia and Brazil. The smallest are the USA and Russia. Greece and Italy are very similar with almost 0.3.

{"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/10849.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#6793a6"}, "x": {"type": "quantitative", "axis": {"title": "Percentage of GDP"}, "field": "Percentage of GDP"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Size of the untaxed `` shadow '' economy", "in selected countries 2010 , as a share of", "GDP"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Percentage of GDP Response 0.44 Russia 0.39 Brazil 0.36 Pakistan 0.35 Egypt 0.31 Turkey 0.28 Greece 0.27 Italy 0.22 India 0.16 Canada 0.13 China 0.09 U.S.

The untaxed shadow economy represented over 0.4% of Russia's GDP in 2010. Among the countries shown in this graph, Russia is the leader, followed closely by Brazil. US has the lowest level among the countries represented, where the untaxed economy represents less than 0.1% of its GDP.

{"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/10849.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#6793a6"}, "x": {"type": "quantitative", "axis": {"title": "Percentage of GDP"}, "field": "Percentage of GDP"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Size of the untaxed `` shadow '' economy", "in selected countries 2010 , as a share of", "GDP"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Percentage of GDP Response 0.44 Russia 0.39 Brazil 0.36 Pakistan 0.35 Egypt 0.31 Turkey 0.28 Greece 0.27 Italy 0.22 India 0.16 Canada 0.13 China 0.09 U.S.

Russia has the biggest shadow economy in proportion to its GDP at over 0.4%, whilst the USA has the least at under 0.1%.

{"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/10852.tsv"}, "mark": "area", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Share of urban population in total population"}, "field": "Share of urban population in total population"}}, "title": ["Azerbaijan : 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 0.5319 Dec 31, 2009 0.5341 Dec 31, 2010 0.5364 Dec 31, 2011 0.5388 Dec 31, 2012 0.5415 Dec 31, 2013 0.5442 Dec 31, 2014 0.5471 Dec 31, 2015 0.5502 Dec 31, 2016 0.5534 Dec 31, 2017 0.5568 Dec 31, 2018 0.5603

Urbanisation in Azerbaijan has seen a very steady increase between 2009 and 2019.

{"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/10852.tsv"}, "mark": "area", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Share of urban population in total population"}, "field": "Share of urban population in total population"}}, "title": ["Azerbaijan : 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 0.5319 Dec 31, 2009 0.5341 Dec 31, 2010 0.5364 Dec 31, 2011 0.5388 Dec 31, 2012 0.5415 Dec 31, 2013 0.5442 Dec 31, 2014 0.5471 Dec 31, 2015 0.5502 Dec 31, 2016 0.5534 Dec 31, 2017 0.5568 Dec 31, 2018 0.5603

From 2010 to 2018 there has been an increase in the share of urban population in the total population. Each year has seen a steady increase.

{"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/10855.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#cedb9c"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Most popular services for online payments", "in Russia in 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Share of respondents Sberbank Online 0.83 Yandex Money 0.55 QIWI 0.4 WebMoney 0.35 Google Pay 0.29 Tinkoff Bank 0.26 VTB 0.26 Alpha Bank 0.25 Apple Pay 0.2 Samsung Pay 0.17 MTS-Money (wallet) 0.17 VK Pay 0.16 Raiffeisenbank 0.12 Pocket Bank 0.07 Russian Standard Bank 0.05 Garmin Pay 0.01

One company seems to dominate the market. Yandex, whilst in second place, does not seem to be much of a threat. The rest vary from almost no share to quite a sunstantial share, but in no way do they approach the leader.

{"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/10855.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#cedb9c"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Most popular services for online payments", "in Russia in 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Share of respondents Sberbank Online 0.83 Yandex Money 0.55 QIWI 0.4 WebMoney 0.35 Google Pay 0.29 Tinkoff Bank 0.26 VTB 0.26 Alpha Bank 0.25 Apple Pay 0.2 Samsung Pay 0.17 MTS-Money (wallet) 0.17 VK Pay 0.16 Raiffeisenbank 0.12 Pocket Bank 0.07 Russian Standard Bank 0.05 Garmin Pay 0.01

I cannot see any trends of patterns. Sberbank online is clearly the best. Almost no one uses Garmin Pay.

{"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/10858.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#adc839"}, "x": {"type": "quantitative", "axis": {"title": "Chartered TEUs"}, "field": "Chartered TEUs"}, "y": {"type": "nominal", "axis": {"labelAngle": 0}, "bin": false, "field": "container ship operators"}}, "title": ["The world 's leading container ship operators", "as of October 12 , 2020 , based on chartered", "TEUs"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Chartered TEUs container ship operators 2876506 Mediterranean Shg Co 1914767 CMA CGM Group 1791792 APM-Maersk 1472957 COSCO Group 1044888 ONE (Ocean Network Express) 660300 Evergreen Line 654312 Hapag-Lloyd 441210 Yang Ming Marine Transport Corp. 305642 Zim 293263 HMM Co Ltd

There is one clear winning operator Mediterranean shipping co.

{"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/10859.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Production in thousand units"}, "field": "Production in thousand units"}}, "title": ["Number of motor vehicles produced by PSA", "Peugeot Citroen worldwide from 2006 to 2016", "(in 1,000 units)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Production in thousand units Dec 31, 2005 3356.86 Dec 31, 2006 3457.39 Dec 31, 2007 3325.41 Dec 31, 2008 3042.31 Dec 31, 2009 3605.52 Dec 31, 2010 3582.41 Dec 31, 2011 2911.76 Dec 31, 2012 2833.78 Dec 31, 2013 2917.05 Dec 31, 2014 2982.04 Dec 31, 2015 3152.79

The number of motor vehicles produced by PSA Peugeot shows that in the year 2010 more vehicles where produced compared to other years.

{"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/10868.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#a6bddb"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "State"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Number of players"}, "field": "Number of players"}}, "title": ["Number of active NFL players by state of", "birth (as of October 6 , 2011)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

State Number of players California 225 Texas 192 Florida 180 Ohio 94 Georgia 85 Louisiana 81 New York 62 New Jersey 61 Michigan 60 Pennsylvania 60 South Carolina 55 Virginia 53 Alabama 51 Illinois 51 North Carolina 50

No trends can be determined, as it only shows one date. California, Florida and Texas seem to provide almost 50% of all active NFL players.

{"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/1086.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ccebc5"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Number of tests in thousands"}, "field": "Number of tests in thousands"}}, "title": ["Number of coronavirus (COVID-19) tests taken", "in Romania as of January 7 , 2021 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of tests in thousands Jul 03, 2020 759.04 Jul 07, 2020 796.48 Jul 11, 2020 847.59 Jul 15, 2020 906.02 Jul 19, 2020 967.19 Jul 23, 2020 1053.66 Jul 27, 2020 1131.7 Jul 31, 2020 1226.06 Aug 04, 2020 1295.22 Aug 08, 2020 1378.73 Aug 12, 2020 1450.27 Aug 15, 2020 1513.89 Aug 19, 2020 1591.02 Aug 22, 2020 1652.64 Aug 26, 2020 1730.42 Aug 30, 2020 1802.95 Sep 03, 2020 1901.24 Sep 07, 2020 1966.29 Sep 11, 2020 2066.86 Sep 15, 2020 2136.54 Sep 19, 2020 2223.46 Sep 23, 2020 2302.83 Sep 27, 2020 2372.34 Oct 01, 2020 2471.5 Oct 05, 2020 2540.22 Oct 08, 2020 2627.54 Oct 12, 2020 2709.31 Oct 16, 2020 2832.68 Oct 19, 2020 2890.07 Oct 22, 2020 3002.8 Oct 25, 2020 3064.7 Oct 28, 2020 3169.98 Oct 31, 2020 3264.3 Nov 03, 2020 3341.87 Nov 06, 2020 3455.09 Nov 09, 2020 3519.1 Nov 12, 2020 3630.86 Nov 15, 2020 3702.33 Nov 18, 2020 3808.28 Nov 21, 2020 3898.74 Nov 24, 2020 3975 Nov 27, 2020 4081.56 Nov 30, 2020 4122.19 Dec 03, 2020 4205.48 Dec 06, 2020 4264.27 Dec 09, 2020 4356.57 Dec 12, 2020 4428.07 Dec 15, 2020 4505.92 Dec 18, 2020 4587.55 Dec 21, 2020 4630.87 Dec 24, 2020 4706.49 Dec 27, 2020 4726.47 Dec 30, 2020 4797.45 Jan 02, 2021 4827.67 Jan 04, 2021 4868.38 Jan 06, 2021 4934.12

the number of coronavirus tests taken in romania has increased steadily from July 2020 to Jan 2021.

{"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/10876.tsv"}, "mark": "line", "encoding": {"color": {"value": "#c4c4c4"}, "x": {"type": "temporal", "axis": {}, "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": ["Madagascar : 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 0.313 Dec 31, 2009 0.3194 Dec 31, 2010 0.3258 Dec 31, 2011 0.3323 Dec 31, 2012 0.3388 Dec 31, 2013 0.3453 Dec 31, 2014 0.3519 Dec 31, 2015 0.3586 Dec 31, 2016 0.3652 Dec 31, 2017 0.3719 Dec 31, 2018 0.3786

Urbanisation has risen slowly over a ten year period, starting at 0.31 and rising to 0.38 by 2019. Although the increase has been slow, it’s also been steady with no decreases throughout the decade. If this trend continues then urbanisation can be expected to further increase over the next decade.

{"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/10876.tsv"}, "mark": "line", "encoding": {"color": {"value": "#c4c4c4"}, "x": {"type": "temporal", "axis": {}, "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": ["Madagascar : 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 0.313 Dec 31, 2009 0.3194 Dec 31, 2010 0.3258 Dec 31, 2011 0.3323 Dec 31, 2012 0.3388 Dec 31, 2013 0.3453 Dec 31, 2014 0.3519 Dec 31, 2015 0.3586 Dec 31, 2016 0.3652 Dec 31, 2017 0.3719 Dec 31, 2018 0.3786

The graph showing Madagascar’s Urbanization from 2009 to 2019 shows a very slow positive trend. Over 10 years it did not go up more than 0.1.

{"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/10878.tsv"}, "mark": "line", "encoding": {"color": {"value": "#e2ddf2"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of live births in thousands"}, "field": "Number of live births in thousands"}}, "title": ["Number of live births in Russia from 2000", "to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of live births in thousands Dec 31, 1999 1266.8 Dec 31, 2004 1457.4 Dec 31, 2009 1788.9 Dec 31, 2010 1796.6 Dec 31, 2011 1902.1 Dec 31, 2012 1895.8 Dec 31, 2013 1942.7 Dec 31, 2014 1940.6 Dec 31, 2015 1888.7 Dec 31, 2016 1690.3 Dec 31, 2017 1604.3 Dec 31, 2018 1481.07

The number of live births rose steadily until 2015. The number of live births has been declining since 2015.

{"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/1087.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#00ACC1"}, "x": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Income in million U.S. dollars"}, "field": "Income in million U\\.S\\. dollars"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Celebrity"}}, "title": ["Annual incomes of highest-paid film , TV", ", music and sports celebrities in 2019 (in", "million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Income in million U.S. dollars Celebrity 185 Taylor Swift 170 Kylie Jenner 150 Kanye West 127 Lionel Messi 110 Ed Sheeran 109 Cristiano Ronaldo 105 Neymar 100 The Eagles 95 Dr. Phil McGraw 94 Canelo Alvarez

The bottom 6 highest paid people shown in that graph do not have much difference between them. The 4 highest earners shown in the graph earn significantly different amounts than each other.

{"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/10881.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b3e2cd"}, "x": {"type": "quantitative", "axis": {"title": "GDP growth rate"}, "field": "GDP growth rate"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}}, "title": ["African countries with the highest Gross", "Domestic Product (GDP) growth rate in 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

GDP growth rate Country 0.09 Ethiopia* 0.084 Rwanda 0.079 Ghana 0.075 Benin 0.075 Ivory Coast 0.068 Tanzania 0.067 Mauritania 0.063 Guinea 0.06 Djibouti 0.06 Niger 0.058 South Sudan 0.057 Cape Verde 0.056 Egypt 0.056 Uganda 0.055 Kenya 0.054 Gambia 0.054 Guinea Bissau 0.054 Mali 0.053 Togo 0.052 Madagascar

The graph shows that Ethiopia and Rwanda have the highest GDP growth rate in 2019 whereas Togo, Madagascar and Gambia have the lowest.

{"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/10883.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#fddaec"}, "x": {"type": "quantitative", "axis": {"title": "Units sold"}, "field": "Units sold"}, "y": {"type": "nominal", "axis": {"labelAngle": 0}, "bin": false, "field": "Toyota and Lexus hybrid car"}}, "title": ["Number of Toyota and Lexus hybrid cars sold", "in the United Kingdom (UK) in 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Units sold Toyota and Lexus hybrid car 17001 Yaris Hybrid 11972 Auris Hybrid (incl. TS) 6918 RAV4 Hybrid 4835 Lexus NX 4187 Prius 2846 Lexus CT 2204 Lexus RX 1572 Lexus IS 978 Prius + 771 Prius Plug-in 274 Lexus GS 76 Lexus LS

The Yaris hybrid had the highest sales for Toyota/Lexus cars in the U.K in 2018. The least sold car was a Lexus GS.Hybrid cars tend to sell better than others.

{"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/10884.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1A1A1A"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "advertising"}, "y": {"type": "quantitative", "axis": {"title": "Advertising spendings in million SEK"}, "field": "Advertising spendings in million SEK"}}, "title": ["Cumulated advertising spending in Sweden", "from January to November 2020 , by media", "(in million SEK)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

advertising Advertising spendings in million SEK Total 11498.74 TV 4152.07 Online display 1838.35 Social media 1059.51 Online video 1029.92 Outdoor advertising 1012.55 Radio 536.47 Search 524.42 Regional newspapers 431.26 City newspapers 309.09 Direct ads/ supplements 206.31 Consumer magazines 110.33 Trade journals 43.27 Evening newspapers 37.84 Cinema 29.01 Others* 178.33

It is clear that the organisation focuses on advertising on the TV the most, as it takes up just over a quarter of their budget. The second most popular ways the organisation decided to use are online display and online video. It can be clearly spotted that the graph represents the massive difference between the high use of technological ways rather than using newspapers, magazines or journals.

{"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/10884.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1A1A1A"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "advertising"}, "y": {"type": "quantitative", "axis": {"title": "Advertising spendings in million SEK"}, "field": "Advertising spendings in million SEK"}}, "title": ["Cumulated advertising spending in Sweden", "from January to November 2020 , by media", "(in million SEK)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

advertising Advertising spendings in million SEK Total 11498.74 TV 4152.07 Online display 1838.35 Social media 1059.51 Online video 1029.92 Outdoor advertising 1012.55 Radio 536.47 Search 524.42 Regional newspapers 431.26 City newspapers 309.09 Direct ads/ supplements 206.31 Consumer magazines 110.33 Trade journals 43.27 Evening newspapers 37.84 Cinema 29.01 Others* 178.33

Cumulated advertising spending in Sweden from January to November 2020 , by the media totalled 11,800 million SEK. Of this, TV had by far the most spending at 4100 million SEK followed by online display at 1900. Trade journals, cinemas and evening newspapers had the least spending.

{"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/10888.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#4D4D4D"}, "x": {"type": "quantitative", "axis": {"title": "Brand value in billion euros"}, "field": "Brand value in billion euros"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "corporate brand"}}, "title": ["Value of leading corporate brands in the", "Asia Pacific region in 2019 (in billion euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Brand value in billion euros corporate brand 53.73 Alibaba Group 49.83 China Mobile 45.4 Tencent/QQ 36.72 Samsung 33.97 Toyota 29.84 ICBC 29.55 China Construction Bank 28.54 Pingan Insurance 25.1 Agricultural Bank of China 23.94 Bank of China 23.43 Huwawei 22.62 China Life Insurance 18.43 Tata 16.85 NTT Group 16.51 Baidu 16.26 PetroChina 15.04 Honda 15.01 SoftBank 14.57 China Telecom

The most valuable brand is Alibaba Group at £50+ million. The lowest is China telecom at £11+ 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/10901.tsv"}, "mark": "line", "encoding": {"color": {"value": "#ab5787"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Electricity generation in terawatt hours"}, "field": "Electricity generation in terawatt hours"}}, "title": ["Electricity generation worldwide from 1990", "to 2018 (in terawatt hours)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Electricity generation in terawatt hours Dec 31, 1989 11897 Dec 31, 1990 12175 Dec 31, 1991 12286 Dec 31, 1992 12576 Dec 31, 1993 12884 Dec 31, 1994 13324 Dec 31, 1995 13751 Dec 31, 1996 14043 Dec 31, 1997 14403 Dec 31, 1998 14811 Dec 31, 1999 15510 Dec 31, 2000 15628 Dec 31, 2001 16242 Dec 31, 2002 16834 Dec 31, 2003 17606 Dec 31, 2004 18368 Dec 31, 2005 19083 Dec 31, 2006 19916 Dec 31, 2007 20291 Dec 31, 2008 20221 Dec 31, 2009 21611 Dec 31, 2010 22296 Dec 31, 2011 22819 Dec 31, 2012 23501 Dec 31, 2013 23931 Dec 31, 2014 24368 Dec 31, 2015 25076 Dec 31, 2016 25727 Dec 31, 2017 26730

Electricity generation has increased steadily. The generation plateaued in 2008-2009The x-axis is measured in 5 year intervals.

{"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/10905.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#D9B300"}, "x": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Compound annual growth rate (CAGR)"}, "field": "Compound annual growth rate (CAGR)"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Key product sectors"}}, "title": ["Compound annual growth rate (CAGR) of the", "retail sector in Germany between 2013 and", "2016 , by key product sector"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Compound annual growth rate (CAGR) Key product sectors 0.0507 Electrical and electronics 0.0363 Furniture and floor coverings 0.0335 Clothing 0.0333 Sports and leisure equipment 0.0239 Footwear 0.0238 Personal care 0.0233 Total retail 0.0172 Food and grocery 0.0155 Jewellery, watches and accessories 0.0148 Luggage and leather goods 0.0128 Music, video and games 0.0108 Home and garden products 0.0039 Books, news and stationery

Electrical and electronics have the largest annual growth rate in the retail sector in Germany between 2013and 2016. Books, news and stationary had the smallest growth rate in Germany between 2013 and 2016. Electrical and electronics had over 0.04 growth rate between 2013 and 2016. Furniture and floor coverings had the second largest annual growth rate of just under 0.04 between 2013 and 2016. Clothing was the 3rd largest growth rate between 2013-2016 .

{"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/10909.tsv"}, "mark": "line", "encoding": {"color": {"value": "#C9C9C9"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Cases of murder and manslaughter"}, "field": "Cases of murder and manslaughter"}}, "title": ["Registered cases of murder and manslaughter", "in Belgium from 2008 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Cases of murder and manslaughter Dec 31, 2007 886 Dec 31, 2008 935 Dec 31, 2009 951 Dec 31, 2010 1072 Dec 31, 2011 1042 Dec 31, 2012 1013 Dec 31, 2013 1059 Dec 31, 2014 1111 Dec 31, 2015 1056 Dec 31, 2016 1168 Dec 31, 2017 1216 Dec 31, 2018 1146

Cases of murder and manslaughter show a general upward trend from 2008-2019. 2018 shows the highest number of cases during the time period plotted. There is a difference of over 300 cases between the lowest and highest number of cases recorded. There is a downward direction of cases during 2019, the last year plotted.

{"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/10918.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#703c5c"}, "x": {"type": "nominal", "axis": {"labelAngle": -90}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Share of Indonesian population who wore", "face masks in public places during COVID-19", "outbreak from February to September 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Share of respondents Sep 14 0.86 Aug 31 0.82 Aug 17 0.82 Aug 3 0.83 Jul 20 0.85 Jul 6 0.85 Jun 22 0.85 Jun 8 0.84 May 22 0.82 May 4 0.84 Apr 27 0.79 Apr 20 0.81 Apr 13 0.79 Apr 6 0.7 Mar 30 0.71 Mar 23 0.59 Mar 16 0.48 Mar 9 0.47 Mar 2 0.61 Feb 24 0.54

Sept 14 showed the highest number of respondents wearing a facemask was approx 0.9. The lowest number of respondents wearing a facemask was Mar 9 and the figure was approx 0.4.

{"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/10921.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ff9d98"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Spending in million euros"}, "field": "Spending in million euros"}}, "title": ["Beiersdorf AG 's spending on research and", "development from 2001 to 2019 (in million", "euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Spending in million euros 2019 236 2018 211 2017 196 2016 188 2015 183 2014* 168 2013* 154 2012* 159 2011* 163 2010* 152 2009* 149 2008* 149 2007* 127 2006* 118 2005 109 2004 101 2003 97 2002 93 2001 92

Between 2001 and 2019 the rate of spending generally increased year on 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/10922.tsv"}, "mark": "line", "encoding": {"color": {"value": "#bf5b17"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Average ticket price in U.S. dollars"}, "field": "Average ticket price in U\\.S\\. dollars"}}, "title": ["Average ticket price at NCAA college basketball", "tournament games from 1980 to 2013 (in U.S.", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Average ticket price in U.S. dollars Dec 31, 1979 9.69 Dec 31, 1980 10.85 Dec 31, 1981 11.67 Dec 31, 1982 12.3 Dec 31, 1983 14.26 Dec 31, 1984 13.84 Dec 31, 1985 14.77 Dec 31, 1986 16.23 Dec 31, 1987 16.38 Dec 31, 1988 17.8 Dec 31, 1989 18.33 Dec 31, 1990 21.63 Dec 31, 1991 21.31 Dec 31, 1992 22.35 Dec 31, 1993 23.96 Dec 31, 1994 26.13 Dec 31, 1995 27.64 Dec 31, 1996 33.2 Dec 31, 1997 37.2 Dec 31, 1998 37.01 Dec 31, 1999 40.14 Dec 31, 2000 44.93 Dec 31, 2001 48.91 Dec 31, 2002 54.26 Dec 31, 2003 64.53 Dec 31, 2004 63.28 Dec 31, 2005 71.7 Dec 31, 2006 67.47 Dec 31, 2007 77.92 Dec 31, 2008 89.88 Dec 31, 2009 88.69 Dec 31, 2010 96.49 Dec 31, 2011 86.12 Dec 31, 2012 89.81

The price of tickets to the basketball games increased steadily between 1983 and 2010.

{"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/10924.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b5cf6b"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "stadium"}, "y": {"type": "quantitative", "axis": {"title": "Construction costs in million U.S. dollars"}, "field": "Construction costs in million U\\.S\\. dollars"}}, "title": ["Sports stadiums worldwide ranked by total", "construction costs as of May 2014 (in million", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

stadium Construction costs in million U.S. dollars MetLife Stadium New Jersey, United States 1600 Yankees Stadium New York, United States 1500 Olympic Stadium Montreal, Canada 1470 AT&T Stadium Dallas, United States 1400 Wembley Stadium London, England 1350 Madison Square Garden New York, United States 1100 Nissan Stadium Yokohama, Japan 990 Stade de France Saint Denis, France 974 Rogers Centre Toronto, Canada 930 Jamsil Olympic Stadium Seoul, South Korea 923

All of the stadiums constructed in the United States cost over 1,000 million dollars. Almost half of the stadiums in the chart (4) are in the US. Over half of the stadiums shown (6) are in North America.

{"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/10924.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b5cf6b"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "stadium"}, "y": {"type": "quantitative", "axis": {"title": "Construction costs in million U.S. dollars"}, "field": "Construction costs in million U\\.S\\. dollars"}}, "title": ["Sports stadiums worldwide ranked by total", "construction costs as of May 2014 (in million", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

stadium Construction costs in million U.S. dollars MetLife Stadium New Jersey, United States 1600 Yankees Stadium New York, United States 1500 Olympic Stadium Montreal, Canada 1470 AT&T Stadium Dallas, United States 1400 Wembley Stadium London, England 1350 Madison Square Garden New York, United States 1100 Nissan Stadium Yokohama, Japan 990 Stade de France Saint Denis, France 974 Rogers Centre Toronto, Canada 930 Jamsil Olympic Stadium Seoul, South Korea 923

The most expensive statium in case of construction costs is Metlie Stadium in New Jersey with cost of more than 1500mln of dollars followed with Yankees Stadium in new York.The less expensive stadiums were Jamsil Olympic in Seoul and Rogers Centre in Toronto Canada with approximate cost of 900 mln dollars each.

{"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/10924.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b5cf6b"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "stadium"}, "y": {"type": "quantitative", "axis": {"title": "Construction costs in million U.S. dollars"}, "field": "Construction costs in million U\\.S\\. dollars"}}, "title": ["Sports stadiums worldwide ranked by total", "construction costs as of May 2014 (in million", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

stadium Construction costs in million U.S. dollars MetLife Stadium New Jersey, United States 1600 Yankees Stadium New York, United States 1500 Olympic Stadium Montreal, Canada 1470 AT&T Stadium Dallas, United States 1400 Wembley Stadium London, England 1350 Madison Square Garden New York, United States 1100 Nissan Stadium Yokohama, Japan 990 Stade de France Saint Denis, France 974 Rogers Centre Toronto, Canada 930 Jamsil Olympic Stadium Seoul, South Korea 923

Only 2 of the stadiums cost over 1,500 million dollars. 50% of The stadiums were approximately 1000 million dollars. The 2 most expensive stadiums were in the USA.

{"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/10926.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#C2185B"}, "x": {"type": "quantitative", "axis": {"title": "GERD as percentage of GDP"}, "field": "GERD as percentage of GDP"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Gross domestic expenditure on research and", "development (GERD) as a percentage of GDP", "in Italy from 2000 to 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

GERD as percentage of GDP Year 0.0135 2017* 0.0137 2016* 0.0134 2015 0.0138 2014 0.0131 2013 0.0127 2012 0.0121 2011 0.0122 2010 0.0122 2009 0.0116 2008 0.0113 2007 0.0109 2006 0.0105 2005 0.0105 2004 0.0106 2003 0.0108 2002 0.0104 2001 0.0101 2000

The is a general increase in the gross domestic expenditure on GERD as a percentage of GDP in Italy between the years 2000-2017. The peak in the gross domestic expenditure on GERD as a percentage of GDP in Italy during the year 2014, however there is a decline in 2015 and 2017.

{"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/10927.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#a99cbc"}, "x": {"type": "quantitative", "axis": {"title": "Data volume in megabytes per month"}, "field": "Data volume in megabytes per month"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Average monthly data volume per mobile internet", "subscription in Germany from 2009 to 2020", "(in megabytes)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Data volume in megabytes per month Year 3000 2020** 2060 2019 1600 2018 870 2017 591 2016 404 2015 289 2014 195 2013 114 2012 76 2011 52 2010 27 2009

The chart clearly shows a steady growth year on year of data volume used on average per mobile internet subscription in GermanyIn 10 years it has grown from almost nothing to a huge volume of 3000 megabytes each month on average.

{"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/10927.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#a99cbc"}, "x": {"type": "quantitative", "axis": {"title": "Data volume in megabytes per month"}, "field": "Data volume in megabytes per month"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Average monthly data volume per mobile internet", "subscription in Germany from 2009 to 2020", "(in megabytes)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Data volume in megabytes per month Year 3000 2020** 2060 2019 1600 2018 870 2017 591 2016 404 2015 289 2014 195 2013 114 2012 76 2011 52 2010 27 2009

A lot of population was starting to use internet ,more and more every year.

{"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/10929.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fff2ae"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Expenditure in billion NOK"}, "field": "Expenditure in billion NOK"}}, "title": ["Budget expenditure of the central government", "in Norway from 2010 to 2020 (in billion NOK)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Expenditure in billion NOK Dec 31, 2009 891 Dec 31, 2010 960 Dec 31, 2011 1006 Dec 31, 2012 1065 Dec 31, 2013 1116 Dec 31, 2014 1202 Dec 31, 2015 1257 Dec 31, 2016 1302 Dec 31, 2017 1379 Dec 31, 2018 1377 Dec 31, 2019 1443

It has grown steadily over the 8 years but has increased from 800b to over 1.9b b expenditure budget.

{"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/10935.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ffbf79"}, "x": {"type": "nominal", "axis": {}, "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 Buffalo Sabres 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 2018/19 135 2017/18 128 2016/17 120 2015/16 116 2014/15 113 2013/14 103 2012/13* 76 2011/12 95 2010/11 87 2009/10 81 2008/09 79 2007/08 76 2006/07 74 2005/06 70

This bar graph represents revenue of the buffalo sabres and shows a steady incline of revenue from 2005-2019. The graph has an average increase per year of about 2-3 million.

{"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/10937.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#393b79"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Dividend per share in euros"}, "field": "Dividend per share in euros"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}}, "title": ["Dividend per share of chemical and gas company", "Air Liquide from 2002 to 2019 (in euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Dividend per share in euros Year 2.7 2019 2.65 2018 2.65 2017 2.36 2016** 2.3 2015** 2.25 2014** 2.04 2013 2.5 2012 2.5 2011 2.35 2010 2.25 2009 2.25 2008 2.25 2007 4 2006 3.85 2005 3.5 2004* 3.2 2003 3.2 2002

There is an increase in the dividend per share between 2002 and 2006, peaking with a high of 4 euros in 2006. After this, the share value stablises just above 2 euros, with a low of roughly 2 euros in 2013.

{"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/10947.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#adc839"}, "x": {"type": "quantitative", "axis": {"title": "Employment in millions"}, "field": "Employment in millions"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Taiwan : Employment from 2010 to 2020 (in", "millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Employment in millions Year 11.54 2020* 11.49 2019* 11.43 2018 11.35 2017 11.27 2016 11.2 2015 11.08 2014 10.97 2013 10.86 2012 10.71 2011 10.49 2010

Employment in Taiwan has been holding steady every year with a slight increase in employment.

{"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/10950.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#74a9cf"}, "x": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Production in million bushels"}, "field": "Production in million bushels"}}, "title": ["U.S. wheat supply projection from 2010/2011", "to 2027/2028 (in million bushels)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Production in million bushels 27/28* 3076 26/27* 3060 25/26* 3045 24/25* 3028 23/24* 3010 22/23* 2991 21/22* 2980 20/21* 2976 19/20* 2984 18/19* 2990 17/18 3070 16/17 3402 15/16 2927 14/15 2768 13/14 3025 12/13 3119 11/12 2969 10/11 3236

Wheat supply is irregular up to 17/18 where there is a significant raise. This supply will drop and slowly rise until 27/28, yet not reaching again the rise observerd in 17/18.

{"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/10952.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#6793a6"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Operating income in million U.S. dollars"}, "field": "Operating income in million U\\.S\\. dollars"}}, "title": ["Los Angeles Lakers operating income from", "2001/02 to 2018/19 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Operating income in million U.S. dollars 18/19 178 17/18 147 16/17 136 15/16 119.2 14/15 133.4 13/14 104.1 12/13 66.4 11/12 47.6 10/11 24.3 09/10 33.4 08/09 51.1 07/08 47.9 06/07 31.8 05/06 33.3 04/05 35.8 03/04 38.2 02/03 22.8 01/02 44.1

The Lakers income has gone up consecutively since 2016. The Lakers worst year was in 2002. The Lakers income declined between 2003 and 2007.

{"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/10966.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#C9C9C9"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "company"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Turnover in million SEK"}, "field": "Turnover in million SEK"}}, "title": ["Leading companies in Uppsala county , Sweden", "as of December 2018 , by turnover (in million", "SEK)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

company Turnover in million SEK GE Healthcare Bio-Sciences AB 15104 GE Healthcare BioProcess R&D AB 6334 Upplands Motor Holding AB 5123 Beijer Alma AB 4409 Fresenius Kabi AB 4064 Phadia AB 3783 Jötagruppen AB 3140 Uppsala Stadshus AB 3047 Stora Enso Pulp AB 2103 Erasteel Kloster AB 1373 Lars Svensson Holding AB 1289 Q-Med AB 1070 Didner & Gerge Fonder AB 957 Biotage AB 911 Orexo AB 783 Madhat AB 633 Sallén Förvaltning AB 628 Relita Industri & Skadeservice AB 591 Angel Fall AB 589 Mellansvenska Logistiktransporter AB 568

GE HEALTHCARE bio has the largest turnover in millions sek compared to the other companies. The remaining companies are all quite equal.

{"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/10966.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#C9C9C9"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "company"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Turnover in million SEK"}, "field": "Turnover in million SEK"}}, "title": ["Leading companies in Uppsala county , Sweden", "as of December 2018 , by turnover (in million", "SEK)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

company Turnover in million SEK GE Healthcare Bio-Sciences AB 15104 GE Healthcare BioProcess R&D AB 6334 Upplands Motor Holding AB 5123 Beijer Alma AB 4409 Fresenius Kabi AB 4064 Phadia AB 3783 Jötagruppen AB 3140 Uppsala Stadshus AB 3047 Stora Enso Pulp AB 2103 Erasteel Kloster AB 1373 Lars Svensson Holding AB 1289 Q-Med AB 1070 Didner & Gerge Fonder AB 957 Biotage AB 911 Orexo AB 783 Madhat AB 633 Sallén Förvaltning AB 628 Relita Industri & Skadeservice AB 591 Angel Fall AB 589 Mellansvenska Logistiktransporter AB 568

The two highest turnover businesses are in the healthcare sector.

{"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/10966.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#C9C9C9"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "company"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Turnover in million SEK"}, "field": "Turnover in million SEK"}}, "title": ["Leading companies in Uppsala county , Sweden", "as of December 2018 , by turnover (in million", "SEK)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

company Turnover in million SEK GE Healthcare Bio-Sciences AB 15104 GE Healthcare BioProcess R&D AB 6334 Upplands Motor Holding AB 5123 Beijer Alma AB 4409 Fresenius Kabi AB 4064 Phadia AB 3783 Jötagruppen AB 3140 Uppsala Stadshus AB 3047 Stora Enso Pulp AB 2103 Erasteel Kloster AB 1373 Lars Svensson Holding AB 1289 Q-Med AB 1070 Didner & Gerge Fonder AB 957 Biotage AB 911 Orexo AB 783 Madhat AB 633 Sallén Förvaltning AB 628 Relita Industri & Skadeservice AB 591 Angel Fall AB 589 Mellansvenska Logistiktransporter AB 568

The leading company was GE Healthcare-Sciences AB, turning over 15,000 million. The second and third were Upplands Motor Holding AB and GE Healthcare BioProcess R&D AB, but they both only turned over 5,000 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/10967.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Number of retail chains"}, "field": "Number of retail chains"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "retail chain"}}, "title": ["Number of retail chains in Lithuania in", "2020 , by sector"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Number of retail chains retail chain 13 Fashion & Clothing 10 Consumer Electronics 10 Food 10 Personal Care 7 Footwear & Leather 5 Petrol 4 Furniture & Decoration 4 Home Ware 3 DIY & Gardening 1 Baby Ware 1 Jewelry & Watches 1 Books & Magazines 0 Car Parts & Accessories 0 Toys & Games 0 Optical 0 Pet Care 0 Telecom 0 Sport & Leisure

The largest number of retail chains in Lithuania is in the 'Fashion & Clothing' sector. There are zero retail chains in Lithuania in the 'Car Parts & Accessories', 'Optical', 'Pet Care', 'Sport & Leisure', 'Telecom', and 'Toys & Games' sectors.

{"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/1096.tsv"}, "mark": "line", "encoding": {"color": {"value": "#8b8b8b"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Number of prescriptions filled in billions"}, "field": "Number of prescriptions filled in billions"}}, "title": ["Total number of retail prescriptions filled", "annually in the United States from 2013 to", "2025 (in billions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of prescriptions filled in billions Dec 31, 2012 4.24 Dec 31, 2013 4.33 Dec 31, 2014 4.4 Dec 31, 2015 4.07 Dec 31, 2016 4.1 Dec 31, 2017 4.21 Dec 31, 2018 4.38 Dec 31, 2019 4.55 Dec 31, 2020 4.69 Dec 31, 2021 4.76 Dec 31, 2022 4.83 Dec 31, 2023 4.9 Dec 31, 2024 4.98

The number of prescriptions increases as the year increases, with the exception of the year between 2015 and 2016 when the number of prescriptions decreases.

{"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/1096.tsv"}, "mark": "line", "encoding": {"color": {"value": "#8b8b8b"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Number of prescriptions filled in billions"}, "field": "Number of prescriptions filled in billions"}}, "title": ["Total number of retail prescriptions filled", "annually in the United States from 2013 to", "2025 (in billions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of prescriptions filled in billions Dec 31, 2012 4.24 Dec 31, 2013 4.33 Dec 31, 2014 4.4 Dec 31, 2015 4.07 Dec 31, 2016 4.1 Dec 31, 2017 4.21 Dec 31, 2018 4.38 Dec 31, 2019 4.55 Dec 31, 2020 4.69 Dec 31, 2021 4.76 Dec 31, 2022 4.83 Dec 31, 2023 4.9 Dec 31, 2024 4.98

The increases of retail prescriptions is a slight upward trend. It has a small dip in 2016 but continues upwards in 2016 and beyond.

{"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/1096.tsv"}, "mark": "line", "encoding": {"color": {"value": "#8b8b8b"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Number of prescriptions filled in billions"}, "field": "Number of prescriptions filled in billions"}}, "title": ["Total number of retail prescriptions filled", "annually in the United States from 2013 to", "2025 (in billions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of prescriptions filled in billions Dec 31, 2012 4.24 Dec 31, 2013 4.33 Dec 31, 2014 4.4 Dec 31, 2015 4.07 Dec 31, 2016 4.1 Dec 31, 2017 4.21 Dec 31, 2018 4.38 Dec 31, 2019 4.55 Dec 31, 2020 4.69 Dec 31, 2021 4.76 Dec 31, 2022 4.83 Dec 31, 2023 4.9 Dec 31, 2024 4.98

In 2013 the number of prescriptions filled annually in the US was just over 4 billion increasing to 4.5in 2015. This dropped to 4 billion in 2016 and then rose steadily year on year rising to 5 billion in 2025.

{"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/10970.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d365ba"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Deliveries in units"}, "field": "Deliveries in units"}}, "title": ["Embraer 's aircraft deliveries from FY 2005", "to FY 2016 (in units)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Deliveries in units Dec 31, 2004 141 Dec 31, 2005 130 Dec 31, 2006 169 Dec 31, 2007 204 Dec 31, 2008 244 Dec 31, 2009 281 Dec 31, 2010 258 Dec 31, 2011 283 Dec 31, 2012 275 Dec 31, 2013 253 Dec 31, 2014 260 Dec 31, 2015 244

Between 2006 and 2010 the number of deliveries almost doubled.

{"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/1097.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Age in years"}, "field": "Age in years"}}, "title": ["Median age of the resident population of", "Canada in 2020 , by province"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Country Age in years Newfoundland and Labrador 47.4 New Brunswick 46.1 Nova Scotia 45 Prince Edward Island 42.9 Quebec 42.7 British Columbia 42.2 Canada 40.9 Ontario 40.4 Yukon 39.4 Saskatchewan 37.8 Manitoba 37.6 Alberta 37.5 Northwest Territories 35.5 Nunavut 26.2

The average median age of the resident population for each Canadian province is around 40. The only significant outlier to this is Nunavut, with a relatively young median age of around 25. This is followed by the Northwest Territories, with an average median age of around 35. Newfoundland and Labrador has the oldest median age of around 47. This is followed by New Brunswick, with a median age of around 46.

{"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/1097.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Age in years"}, "field": "Age in years"}}, "title": ["Median age of the resident population of", "Canada in 2020 , by province"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Country Age in years Newfoundland and Labrador 47.4 New Brunswick 46.1 Nova Scotia 45 Prince Edward Island 42.9 Quebec 42.7 British Columbia 42.2 Canada 40.9 Ontario 40.4 Yukon 39.4 Saskatchewan 37.8 Manitoba 37.6 Alberta 37.5 Northwest Territories 35.5 Nunavut 26.2

The median age of the population by province in Canada is a fairly even spread across the majority of provinces. most fall into the 40-50 age group, with a few in the late 30s. one obvious outlier is the province of nunavut with a median age of approximately 26 years old.

{"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/10980.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#829eb1"}, "x": {"type": "quantitative", "axis": {"title": "Number of facebook fans"}, "field": "Number of facebook fans"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Bundesliga football club"}}, "title": ["Bundesliga football clubs ranked by number", "of fans on Facebook in Germany in September", "2015"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Number of facebook fans Bundesliga football club 32150908 Bayern Munich 13241976 Borussia Dortmund 2676313 FC Schalke 04 1528178 Bayer 04 Leverkusen 860278 Werder Bremen 785941 Borussia Mönchengladbach 749106 Hamburger SV 662047 1. FC Köln 599966 VfL Wolfsburg 484366 VfB Stuttgart 421861 Eintracht Frankfurt 313489 Hannover 96 278081 Hertha BSC 198316 FC Augsburg 192134 TSG Hoffenheim 171677 1. FSV Mainz 05 111159 SV Darmstadt 98 56836 FC Ingolstadt 04

Bayern Munich has the most Facebook followers. Borussia dortmund has the second most Facebook followers.

{"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/10982.tsv"}, "mark": "area", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Percentage of physicians"}, "field": "Percentage of physicians"}}, "title": ["Usage of electronic medical records (EMR)", "among primary care physicians in Canada from", "2004 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Percentage of physicians Dec 31, 2003 0.16 Dec 31, 2005 0.23 Dec 31, 2006 0.24 Dec 31, 2008 0.37 Dec 31, 2009 0.41 Dec 31, 2011 0.56 Dec 31, 2012 0.64 Dec 31, 2013 0.77 Dec 31, 2014 0.73 Dec 31, 2016 0.85 Dec 31, 2017 0.84

There has been a 4 fold increase in usage of EMR among physicians from 2004 to 2016, increasing from just below 0.2 to just above 0.8 in 2016.

{"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/10982.tsv"}, "mark": "area", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Percentage of physicians"}, "field": "Percentage of physicians"}}, "title": ["Usage of electronic medical records (EMR)", "among primary care physicians in Canada from", "2004 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Percentage of physicians Dec 31, 2003 0.16 Dec 31, 2005 0.23 Dec 31, 2006 0.24 Dec 31, 2008 0.37 Dec 31, 2009 0.41 Dec 31, 2011 0.56 Dec 31, 2012 0.64 Dec 31, 2013 0.77 Dec 31, 2014 0.73 Dec 31, 2016 0.85 Dec 31, 2017 0.84

the use of EMR among primary care physicians in Canada from 2004 to 2018 shows a positive correlation.

{"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/11003.tsv"}, "mark": "area", "encoding": {"color": {"value": "#e5d8bd"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of employees"}, "field": "Number of employees"}}, "title": ["Number of employees of Church & Dwight worldwide", "from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of employees Dec 31, 2008 3664 Dec 31, 2009 3543 Dec 31, 2010 3457 Dec 31, 2011 4354 Dec 31, 2012 4177 Dec 31, 2013 4145 Dec 31, 2014 4406 Dec 31, 2015 4500 Dec 31, 2016 4700 Dec 31, 2017 4700 Dec 31, 2018 4800

There were more employees in 2018 than in 2010There is a positive correlation between number of employees and the years. There was a decrease in employees between 2013 and 2014.

{"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/11019.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#DB4437"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Arizona Diamondbacks all-time home run leader"}, "y": {"type": "quantitative", "axis": {"title": "Number of home runs"}, "field": "Number of home runs"}}, "title": ["Arizona Diamondbacks all-time home run leaders", "as of October 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Arizona Diamondbacks all-time home run leader Number of home runs Luis Gonzalez 224 Paul Goldschmidt 209 Steve Finley 153 Chris Young 132 Mark Reynolds 121 Justin Upton 108 Matt Williams 99 Miguel Montero 97 Jay Bell 91 David Peralta 90

Luis has the most home runs (225) Jay and David both have approximately the same number of home runs.

{"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/11028.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#0570b0"}, "x": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Passenger numbers in thousands"}, "field": "Passenger numbers in thousands"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Mediterranean cruise port"}}, "title": ["Leading Mediterranean cruise ports by passenger", "numbers in 2019 (in 1,000)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Passenger numbers in thousands Mediterranean cruise port 3138 Barcelona 2658 Balearic Islands 2652 Civitavecchia 2019 Genoa/Savona 1866 Marseille 1611 Venice 1454 Naples /Salerno 1098 Piraeus 1067 Tenerife ports 902 Valletta

the number of passengers at each destinationthe ports visited the most popular.

{"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/11028.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#0570b0"}, "x": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Passenger numbers in thousands"}, "field": "Passenger numbers in thousands"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Mediterranean cruise port"}}, "title": ["Leading Mediterranean cruise ports by passenger", "numbers in 2019 (in 1,000)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Passenger numbers in thousands Mediterranean cruise port 3138 Barcelona 2658 Balearic Islands 2652 Civitavecchia 2019 Genoa/Savona 1866 Marseille 1611 Venice 1454 Naples /Salerno 1098 Piraeus 1067 Tenerife ports 902 Valletta

The most popular cruise port is in Barcelona and the least popular is Valletta.

{"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/11028.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#0570b0"}, "x": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Passenger numbers in thousands"}, "field": "Passenger numbers in thousands"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Mediterranean cruise port"}}, "title": ["Leading Mediterranean cruise ports by passenger", "numbers in 2019 (in 1,000)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Passenger numbers in thousands Mediterranean cruise port 3138 Barcelona 2658 Balearic Islands 2652 Civitavecchia 2019 Genoa/Savona 1866 Marseille 1611 Venice 1454 Naples /Salerno 1098 Piraeus 1067 Tenerife ports 902 Valletta

Barcelona is the most popular cruise port. Valletta is the least popular. Balearic Islands and Civitavecchia have the same popularity, also making them the second popular in the chart.

{"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/1102.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdcdac"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Number of passengers uplifted in millions"}, "field": "Number of passengers uplifted in millions"}}, "title": ["Number of passengers uplifted by British", "Airways plc from 2008 to 2019 (in millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Number of passengers uplifted in millions Dec 31, 2007 31.62 Dec 31, 2008 31.44 Dec 31, 2009 29.73 Dec 31, 2010 33.01 Dec 31, 2011 35.63 Dec 31, 2012 38.41 Dec 31, 2013 39.64 Dec 31, 2014 41.26 Dec 31, 2015 42.14 Dec 31, 2016 42.78 Dec 31, 2017 44.14 Dec 31, 2018 44.56

The chart shows that typically the British Airways passengers have increased year on year from 2008 to 2018. There is a general upwards trend.

{"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/11035.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#a99cbc"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Therapeutic areas"}, "y": {"type": "quantitative", "axis": {"title": "Sales in billion U.S. dollars"}, "field": "Sales in billion U\\.S\\. dollars"}}, "title": ["Pharmaceutical sales by top 10 therapeutic", "areas worldwide in 2009 (in billion U.S.", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Therapeutic areas Sales in billion U.S. dollars Oncologics 52.37 Lipid Regulators 35.28 Respiratory Agents 33.6 Antidiabetics 30.41 Anti-ulcerants 29.61 Antiotensin II Antagonists 25.21 Antipsychotics 23.25 Antidepressants 19.42 AutoImmune agents 17.96 Platelet Aggr. Inhibitors 14.6

The graph shows that the Oncologics unit had the most sales and the Platelet Inhibitors had the least sales.

{"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/11038.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#74a9cf"}, "x": {"type": "quantitative", "axis": {"title": "Ratio of asset value to GDP value"}, "field": "Ratio of asset value to GDP value"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Country"}}, "title": ["Ratio of investment funds assets under management", "in Europe to the country 's GDP at the end", "of 2018 , by country"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Ratio of asset value to GDP value Country 1.84 Switzerland 1.4 United Kingdom 1.07 Netherlands 0.98 France 0.77 Denmark 0.57 Germany 0.34 Austria 0.31 Belgium 0.25 Italy 0.25 Spain 0.14 Hungary 0.12 Poland 0.11 Portugal 0.06 Croatia 0.04 Slovenia 0.03 Greece 0.03 Turkey 0.01 Bulgaria 0.72 Total for Europe

Switzerland has the highest ratio of investment funds to GDP at around 1.8, followed by the UK at around 1.4. The lowest ratios were Turkey and Greece at around 0.005.

{"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/11042.tsv"}, "mark": "area", "encoding": {"color": {"value": "#999999"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Revenue in trillion Japanese yen"}, "field": "Revenue in trillion Japanese yen"}}, "title": ["Operating revenue of the East Japan Railway", "Company (JR East) from 2011 to 2020 (in trillion", "Japanese yen)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Revenue in trillion Japanese yen Dec 31, 2010 2.54 Dec 31, 2011 2.53 Dec 31, 2012 2.67 Dec 31, 2013 2.7 Dec 31, 2014 2.76 Dec 31, 2015 2.87 Dec 31, 2016 2.88 Dec 31, 2017 2.95 Dec 31, 2018 3 Dec 31, 2019 2.95

Whilst consistently high, there is a trend of increasing revenue year on year with it slightly decreasing in the most recent years shown.

{"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/11043.tsv"}, "mark": "area", "encoding": {"color": {"value": "#000000"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Youth unemployment rate"}, "field": "Youth unemployment rate"}}, "title": ["Nepal : 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 0.0293 Dec 31, 1999 0.0284 Dec 31, 2000 0.0281 Dec 31, 2001 0.0289 Dec 31, 2002 0.0281 Dec 31, 2003 0.0272 Dec 31, 2004 0.0263 Dec 31, 2005 0.0246 Dec 31, 2006 0.0231 Dec 31, 2007 0.0219 Dec 31, 2008 0.0246 Dec 31, 2009 0.0253 Dec 31, 2010 0.0254 Dec 31, 2011 0.0253 Dec 31, 2012 0.0254 Dec 31, 2013 0.0245 Dec 31, 2014 0.0243 Dec 31, 2015 0.0241 Dec 31, 2016 0.0225 Dec 31, 2017 0.022 Dec 31, 2018 0.023 Dec 31, 2019 0.0242

From 2000 youth unemployment has dropped until rising back up around 2007 and gradually decreasing again until 2017 ish.

{"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/11045.tsv"}, "mark": "line", "encoding": {"color": {"value": "#ff9d98"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Production in tons"}, "field": "Production in tons"}}, "title": ["U.S. production of tomatoes for processing", "from 2000 to 2018 (in tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Production in tons Dec 31, 1999 10858240 Dec 31, 2000 9248720 Dec 31, 2001 11670820 Dec 31, 2002 9819710 Dec 31, 2003 12266410 Dec 31, 2004 10193120 Dec 31, 2005 10611820 Dec 31, 2006 12659890 Dec 31, 2007 12305820 Dec 31, 2008 13970560 Dec 31, 2009 12776280 Dec 31, 2010 12396150 Dec 31, 2011 13178750 Dec 31, 2012 12631500 Dec 31, 2013 14637300 Dec 31, 2014 14754350 Dec 31, 2015 13226865 Dec 31, 2016 11005854 Dec 31, 2018 12792655

The production of tomatoes in the US fluctuates year on year but overall there has been a slight increase over the period 2000 to 2018. 2015 saw the most tomatoes produced with almost 15 million tonnes ready for processing.

{"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/11045.tsv"}, "mark": "line", "encoding": {"color": {"value": "#ff9d98"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Production in tons"}, "field": "Production in tons"}}, "title": ["U.S. production of tomatoes for processing", "from 2000 to 2018 (in tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Production in tons Dec 31, 1999 10858240 Dec 31, 2000 9248720 Dec 31, 2001 11670820 Dec 31, 2002 9819710 Dec 31, 2003 12266410 Dec 31, 2004 10193120 Dec 31, 2005 10611820 Dec 31, 2006 12659890 Dec 31, 2007 12305820 Dec 31, 2008 13970560 Dec 31, 2009 12776280 Dec 31, 2010 12396150 Dec 31, 2011 13178750 Dec 31, 2012 12631500 Dec 31, 2013 14637300 Dec 31, 2014 14754350 Dec 31, 2015 13226865 Dec 31, 2016 11005854 Dec 31, 2018 12792655

The overall production of tomatoes in tonnes has increased by around 2m since 2000. The increase is marked by large fluctuations in production. The production decreased sharply from its historic highest point in 2015 but has since begun increasing again in 2016/2017.

{"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/11052.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#f2cf5b"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Debt in million SEK"}, "field": "Debt in million SEK"}}, "title": ["Central government debt in Sweden from 2010", "to 2019 , with a forecast to 2021 (in million", "SEK)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Debt in million SEK 2021* 1556150 2020* 1488222 2019 1112796 2018 1262090 2017 1327927 2016 1347253 2015 1403421 2014 1394314 2013 1277100 2012 1146165 2011 1150767 2010 1179347

The central government debt reduced in 2011 and 2012 it rose for the next 3 years and gradually went down to its lowest in 2019 it then went up to its highest in 2020.

{"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/11052.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#f2cf5b"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Debt in million SEK"}, "field": "Debt in million SEK"}}, "title": ["Central government debt in Sweden from 2010", "to 2019 , with a forecast to 2021 (in million", "SEK)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Debt in million SEK 2021* 1556150 2020* 1488222 2019 1112796 2018 1262090 2017 1327927 2016 1347253 2015 1403421 2014 1394314 2013 1277100 2012 1146165 2011 1150767 2010 1179347

In 2021, the debt reached a maximum of 1500000. Debt has not been less than 1000000.

{"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/11062.tsv"}, "mark": "area", "encoding": {"color": {"value": "#de9ed6"}, "x": {"type": "temporal", "axis": {}, "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": ["South Sudan : 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 0.177 Dec 31, 2009 0.1786 Dec 31, 2010 0.1803 Dec 31, 2011 0.1822 Dec 31, 2012 0.1842 Dec 31, 2013 0.1863 Dec 31, 2014 0.1885 Dec 31, 2015 0.1909 Dec 31, 2016 0.1935 Dec 31, 2017 0.1962 Dec 31, 2018 0.199

The share of urban population in the total population in South Sudan has increased between 2009 and 2019. This appears to be a steady increase year on year.

{"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/11062.tsv"}, "mark": "area", "encoding": {"color": {"value": "#de9ed6"}, "x": {"type": "temporal", "axis": {}, "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": ["South Sudan : 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 0.177 Dec 31, 2009 0.1786 Dec 31, 2010 0.1803 Dec 31, 2011 0.1822 Dec 31, 2012 0.1842 Dec 31, 2013 0.1863 Dec 31, 2014 0.1885 Dec 31, 2015 0.1909 Dec 31, 2016 0.1935 Dec 31, 2017 0.1962 Dec 31, 2018 0.199

There has been a consistent increase in urbanisation over the last 10 years.

{"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/11066.tsv"}, "mark": "line", "encoding": {"color": {"value": "#7fc97f"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Fertility rate"}, "field": "Fertility rate"}}, "title": ["Total fertility rate in France from 1800", "to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Fertility rate Dec 31, 1799 4.41 Dec 31, 1804 4.26 Dec 31, 1809 4 Dec 31, 1814 3.88 Dec 31, 1819 3.85 Dec 31, 1824 3.8 Dec 31, 1829 3.74 Dec 31, 1834 3.67 Dec 31, 1839 3.59 Dec 31, 1844 3.53 Dec 31, 1849 3.5 Dec 31, 1854 3.36 Dec 31, 1859 3.45 Dec 31, 1864 3.51 Dec 31, 1869 3.5 Dec 31, 1874 3.44 Dec 31, 1879 3.46 Dec 31, 1884 3.38 Dec 31, 1889 3.14 Dec 31, 1894 2.96 Dec 31, 1899 2.89 Dec 31, 1904 2.8 Dec 31, 1909 2.61 Dec 31, 1914 2.26 Dec 31, 1919 1.68 Dec 31, 1924 2.44 Dec 31, 1929 2.3 Dec 31, 1934 2.16 Dec 31, 1939 2.1 Dec 31, 1944 2.13 Dec 31, 1949 2.98 Dec 31, 1954 2.76 Dec 31, 1959 2.7 Dec 31, 1964 2.85 Dec 31, 1969 2.65 Dec 31, 1974 2.31 Dec 31, 1979 1.86 Dec 31, 1984 1.86 Dec 31, 1989 1.8 Dec 31, 1994 1.7 Dec 31, 1999 1.76 Dec 31, 2004 1.88 Dec 31, 2009 1.98 Dec 31, 2014 1.98 Dec 31, 2019 1.85

The fertility rate in Year 1800 is more than twice the fertility rate in Year 2000. The fertility rate was higher in Year 1800. The fertility rate was lowest between Year 1900 and 1950.

{"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/1106.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "State"}, "y": {"type": "quantitative", "axis": {"title": "Points on the Global Financial Centres Index"}, "field": "Points on the Global Financial Centres Index"}}, "title": ["Leading financial centers globally as of", "March 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

State Points on the Global Financial Centres Index New York 769 London 742 Tokyo 741 Shanghai 740 Singapore 738 Hong Kong 737 Beijing 734 San Francisco 732 Geneva 729 Los Angeles 723 Shenzhen 722 Dubai 721 Frankfurt 720 Zurich 719 Paris 718 Chicago 717 Edinburgh 716 Luxembourg 715 Guangzhou 714 Sydney 713

No significant trend other than New York has slightly more points.

{"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/1106.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "State"}, "y": {"type": "quantitative", "axis": {"title": "Points on the Global Financial Centres Index"}, "field": "Points on the Global Financial Centres Index"}}, "title": ["Leading financial centers globally as of", "March 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

State Points on the Global Financial Centres Index New York 769 London 742 Tokyo 741 Shanghai 740 Singapore 738 Hong Kong 737 Beijing 734 San Francisco 732 Geneva 729 Los Angeles 723 Shenzhen 722 Dubai 721 Frankfurt 720 Zurich 719 Paris 718 Chicago 717 Edinburgh 716 Luxembourg 715 Guangzhou 714 Sydney 713

There is a similarity across the chart with very little differentiation across all of the financial centres, this represents a range of around 500 points across all the centres. New York is the best performing city at around 775.

{"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/1106.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "State"}, "y": {"type": "quantitative", "axis": {"title": "Points on the Global Financial Centres Index"}, "field": "Points on the Global Financial Centres Index"}}, "title": ["Leading financial centers globally as of", "March 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

State Points on the Global Financial Centres Index New York 769 London 742 Tokyo 741 Shanghai 740 Singapore 738 Hong Kong 737 Beijing 734 San Francisco 732 Geneva 729 Los Angeles 723 Shenzhen 722 Dubai 721 Frankfurt 720 Zurich 719 Paris 718 Chicago 717 Edinburgh 716 Luxembourg 715 Guangzhou 714 Sydney 713

Most of the states have around the same number of points on the Global Financial Centres Index, with New York having the most at around 780. The lowest is Sydney with about 710.

{"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/11090.tsv"}, "mark": "area", "encoding": {"color": {"value": "#D9B300"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Births per 1,000 Hispanic women"}, "field": "Births per 1,000 Hispanic women"}}, "title": ["Hispanic fertility rate in the United States", "from 1990 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Births per 1,000 Hispanic women Dec 31, 1989 107.7 Dec 31, 1990 106.9 Dec 31, 1991 106.1 Dec 31, 1992 103.3 Dec 31, 1993 100.7 Dec 31, 1994 98.8 Dec 31, 1995 97.5 Dec 31, 1996 94.2 Dec 31, 1997 93.2 Dec 31, 1998 93 Dec 31, 1999 95.9 Dec 31, 2000 95.4 Dec 31, 2001 94.7 Dec 31, 2002 95.2 Dec 31, 2003 95.7 Dec 31, 2004 96.4 Dec 31, 2005 98.3 Dec 31, 2006 97.4 Dec 31, 2007 92.7 Dec 31, 2008 86.5 Dec 31, 2009 80.2 Dec 31, 2010 76.2 Dec 31, 2011 74.4 Dec 31, 2012 72.9 Dec 31, 2013 72.1 Dec 31, 2014 71.7 Dec 31, 2015 70.6 Dec 31, 2016 67.6 Dec 31, 2017 65.9

The graph shows that from 1990, the fertility rate has decreased. Around the year 2000, it slightly peaks before suddenly decreasing again around 2005.

{"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/11094.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Deaths per 1,000 live births"}, "field": "Deaths per 1,000 live births"}}, "title": ["Cambodia : 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 40.3 Dec 31, 2009 37.4 Dec 31, 2010 34.7 Dec 31, 2011 32.3 Dec 31, 2012 30.3 Dec 31, 2013 28.6 Dec 31, 2014 27.2 Dec 31, 2015 25.9 Dec 31, 2016 24.8 Dec 31, 2017 23.7 Dec 31, 2018 22.8

The infant mortality rate in Cambodia has fallen over recent years.