Datasets:

delen

/

vistext

like 0

{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "multiColumn/data/682.tsv"}, "mark": "area", "encoding": {"color": {"value": "#7fc97f"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Migration balance"}, "field": "Migration balance"}}, "title": ["Immigration , emigration , and migration", "balance in the Netherlands from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Migration balance Dec 31, 2008 34481 Dec 31, 2009 33081 Dec 31, 2010 29768 Dec 31, 2011 13883 Dec 31, 2012 19103 Dec 31, 2013 35087 Dec 31, 2014 55106 Dec 31, 2015 79194 Dec 31, 2016 80665 Dec 31, 2017 86371 Dec 31, 2018 108035

1. between 2009 and 2012 immigration fell by 200002. from 2012 to 2019 immigration increased year on year reaching 109000 in 2019. 3. the rate of increase between 2016 and 2018 decreased compared to preceeding and proceeding years during which it remained fairly consistant.

{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "multiColumn/data/6830.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#f4cae4"}, "x": {"type": "quantitative", "axis": {"title": "Feb 3 - Mar 1"}, "field": "Feb 3 - Mar 1"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": " leading news and information websites"}}, "title": ["Change in the number of users of leading", "news and information websites due to coronavirus", "(COVID-19) in Poland in March 2020 (in million", "users)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Feb 3 - Mar 1 leading news and information websites 5.01 onet.pl 5.03 wp.pl 3.34 tvn24.pl 2.8 gazeta.pl 3.99 fakt.pl 2.66 interia.pl 1.75 rmf24.pl 2.21 wyborcza.pl 2.67 radiozet.pl 2.07 natemat.pl

Overall all leading news sites in Poland experienced an increase in the number of users between Feb 3 - Mar 1. Onet.pl and wp.pl both experienced the equal highest increase in the number of users during the period.

{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "multiColumn/data/6830.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b79a20"}, "x": {"type": "quantitative", "axis": {"title": "Mar 9 - Mar 15"}, "field": "Mar 9 - Mar 15"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": " leading news and information websites"}}, "title": ["Change in the number of users of leading", "news and information websites due to coronavirus", "(COVID-19) in Poland in March 2020 (in million", "users)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Mar 9 - Mar 15 leading news and information websites 8.7 onet.pl 7.2 wp.pl 5.68 tvn24.pl 5.03 gazeta.pl 4.72 fakt.pl 4.08 interia.pl 3.97 rmf24.pl 3.86 wyborcza.pl 3.36 radiozet.pl 3.04 natemat.pl

Onet leading at 9 followed by wp at 7. Natemat and radiozet lowest.

{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "multiColumn/data/6830.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b79a20"}, "x": {"type": "quantitative", "axis": {"title": "Mar 9 - Mar 15"}, "field": "Mar 9 - Mar 15"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": " leading news and information websites"}}, "title": ["Change in the number of users of leading", "news and information websites due to coronavirus", "(COVID-19) in Poland in March 2020 (in million", "users)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Mar 9 - Mar 15 leading news and information websites 8.7 onet.pl 7.2 wp.pl 5.68 tvn24.pl 5.03 gazeta.pl 4.72 fakt.pl 4.08 interia.pl 3.97 rmf24.pl 3.86 wyborcza.pl 3.36 radiozet.pl 3.04 natemat.pl

All 10 of the leading news and information websites depicted experienced an increase in users in the March 9 - 15th period. onet.pl had the largest increase, at 9 million users. natemat.pl had the smallest increase, at 3 million users.

{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "multiColumn/data/6830.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b79a20"}, "x": {"type": "quantitative", "axis": {"title": "Mar 9 - Mar 15"}, "field": "Mar 9 - Mar 15"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": " leading news and information websites"}}, "title": ["Change in the number of users of leading", "news and information websites due to coronavirus", "(COVID-19) in Poland in March 2020 (in million", "users)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Mar 9 - Mar 15 leading news and information websites 8.7 onet.pl 7.2 wp.pl 5.68 tvn24.pl 5.03 gazeta.pl 4.72 fakt.pl 4.08 interia.pl 3.97 rmf24.pl 3.86 wyborcza.pl 3.36 radiozet.pl 3.04 natemat.pl

Onet.pl and wo.lp have the most views. Natemat.pl has the least. This will sunset it is the least trusted source of information.

{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "multiColumn/data/6831.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#3580b1"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "2020"}, "field": "2020"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "region of Poland"}}, "title": ["Number of beds in infectious diseases wards", "and in isolation hospitals during the coronavirus", "(COVID-19) outbreak in Poland in 2018 and", "March 2020 , by region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

2020 region of Poland 137 Dolnośląskie 181 Kujawsko-pomorskie 184 Lubelskie 421 Lubuskie 157 Łódzkie 300 Małopolskie 278 Mazowieckie 41 Opolskie 167 Podkarpackie 219 Podlaskie 290 Pomorskie 193 Śląskie 76 Świętokrzystkie 87 Warmińsko-mazurskie 126 Wielkopolskie 69 Zachodniopomorskie

There are no trends visible from this graph as only the numbers from 2020 are logged, no data is placed from 2018.

{"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": "multiColumn/data/6839.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c4c4c4"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Did not purchase"}, "field": "Did not purchase"}}, "title": ["If and when did you purchase airline ancillary", "services ?"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Did not purchase Checked bagge 0.42 Advance seat selection 0.67 In-flight meals/snacks 0.72 In-flight movies/entertainment 0.85 Additional frequent flyer program miles 0.86 Preferred seating/extra legroom 0.87 Priority boarding 0.87 In-flight wireless Internet access 0.9 Airport lounge access 0.91 Discounted flight change fee 0.92

Most people purchased the checked bag. The fewest people purchased the discounted flight fee.

{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "multiColumn/data/6849.tsv"}, "mark": "area", "encoding": {"color": {"value": "#b3e2cd"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Engaged"}, "field": "Engaged"}}, "title": ["United States : Employee engagement 2002", "to 2012"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Engaged Dec 31, 2001 0.3 Dec 31, 2002 0.28 Dec 31, 2003 0.29 Dec 31, 2004 0.26 Dec 31, 2005 0.3 Dec 31, 2006 0.3 Dec 31, 2007 0.29 Dec 31, 2008 0.28 Dec 31, 2009 0.28 Dec 31, 2010 0.29 Dec 31, 2011 0.3

Overall employee engagement in the United States between 2002 and 2012 has been of a relatively high level with some fluctuations across the years. The graph demonstrates in 2005 engagement levels were lowest on record at around 0.26. However, engagement sharply increased and by 2006 levels were at approximately 0.30. Engagement levels have experienced a slight dip since 2006 but gradually increased again to 0.30 in 2012.

{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "multiColumn/data/6849.tsv"}, "mark": "area", "encoding": {"color": {"value": "#b3e2cd"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Engaged"}, "field": "Engaged"}}, "title": ["United States : Employee engagement 2002", "to 2012"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Engaged Dec 31, 2001 0.3 Dec 31, 2002 0.28 Dec 31, 2003 0.29 Dec 31, 2004 0.26 Dec 31, 2005 0.3 Dec 31, 2006 0.3 Dec 31, 2007 0.29 Dec 31, 2008 0.28 Dec 31, 2009 0.28 Dec 31, 2010 0.29 Dec 31, 2011 0.3

The engagement has been steadily between 0.25 and 0.30 between the 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": "multiColumn/data/6858.tsv"}, "mark": "area", "encoding": {"color": {"value": "#3690c0"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Hazardous waste"}, "field": "Hazardous waste"}}, "title": ["Number of enterprises in the collection", "of hazardous and non-hazardous waste in Sweden", "from 2008 to 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Hazardous waste Dec 31, 2007 28 Dec 31, 2008 24 Dec 31, 2009 20 Dec 31, 2010 23 Dec 31, 2011 24 Dec 31, 2012 29 Dec 31, 2013 28 Dec 31, 2014 25 Dec 31, 2015 25 Dec 31, 2016 29

The number of enterprises involved in the collection of waste varies between 20 and 28 between 2008 and 2017. The numbers involved fluctuated each year, with no clear trend or pattern. Over the period, there is only a very small increase.

{"config": {"background": "#fff", "area": {"fill": "#3e5c69"}, "line": {"stroke": "#3e5c69"}, "rect": {"fill": "#3e5c69"}, "bar": {"fill": "#3e5c69"}, "point": {"color": "#3e5c69"}, "axis": {"domainWidth": 0.5, "grid": true, "labelPadding": 2, "tickSize": 5, "tickWidth": 0.5, "titleFontWeight": "normal"}, "axisBand": {"grid": false}, "axisX": {"gridWidth": 0.2}, "axisY": {"gridDash": [3], "gridWidth": 0.4}, "legend": {"labelFontSize": 11, "padding": 1, "symbolType": "square"}, "range": {"category": ["#3e5c69", "#6793a6", "#182429", "#0570b0", "#3690c0", "#74a9cf", "#a6bddb", "#e2ddf2"]}}, "data": {"url": "multiColumn/data/6858.tsv"}, "mark": "area", "encoding": {"color": {"value": "#3690c0"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Hazardous waste"}, "field": "Hazardous waste"}}, "title": ["Number of enterprises in the collection", "of hazardous and non-hazardous waste in Sweden", "from 2008 to 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Hazardous waste Dec 31, 2007 28 Dec 31, 2008 24 Dec 31, 2009 20 Dec 31, 2010 23 Dec 31, 2011 24 Dec 31, 2012 29 Dec 31, 2013 28 Dec 31, 2014 25 Dec 31, 2015 25 Dec 31, 2016 29

There are the same number of enterprises in the collection of hazardous and non-hazardous waste in Sweden in 2017 as 2013, which is the highest.

{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "multiColumn/data/6858.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d95f02"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Non-hazardous waste"}, "field": "Non-hazardous waste"}}, "title": ["Number of enterprises in the collection", "of hazardous and non-hazardous waste in Sweden", "from 2008 to 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Non-hazardous waste Dec 31, 2007 251 Dec 31, 2008 243 Dec 31, 2009 247 Dec 31, 2010 254 Dec 31, 2011 256 Dec 31, 2012 259 Dec 31, 2013 262 Dec 31, 2014 260 Dec 31, 2015 261 Dec 31, 2016 276

The number of enterprises in Sweden in non hazardous waste between 2008 to 2017 has pretty much remained static.

{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "multiColumn/data/6858.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d95f02"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Non-hazardous waste"}, "field": "Non-hazardous waste"}}, "title": ["Number of enterprises in the collection", "of hazardous and non-hazardous waste in Sweden", "from 2008 to 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Non-hazardous waste Dec 31, 2007 251 Dec 31, 2008 243 Dec 31, 2009 247 Dec 31, 2010 254 Dec 31, 2011 256 Dec 31, 2012 259 Dec 31, 2013 262 Dec 31, 2014 260 Dec 31, 2015 261 Dec 31, 2016 276

The number of enterprises has trended upwards from 2008 to 2016. The highest number of enterprises is around 275 which was in 2017. The lowest was just under 250 which was in 2009.

{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "multiColumn/data/6858.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d95f02"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Non-hazardous waste"}, "field": "Non-hazardous waste"}}, "title": ["Number of enterprises in the collection", "of hazardous and non-hazardous waste in Sweden", "from 2008 to 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Non-hazardous waste Dec 31, 2007 251 Dec 31, 2008 243 Dec 31, 2009 247 Dec 31, 2010 254 Dec 31, 2011 256 Dec 31, 2012 259 Dec 31, 2013 262 Dec 31, 2014 260 Dec 31, 2015 261 Dec 31, 2016 276

The number of entreprises in the collection of waste has been slightly increasing between 2008 and 2017, with the exeption of 2009 where just under 250 entreprises in this activity were recorded. Overall, the number slowly progressed from the initial 250 entreprises in 2008 to just under 300 in 2017.

{"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": "multiColumn/data/685.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ab5787"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Subscription and streaming**"}, "field": "Subscription and streaming**"}}, "title": ["Digital music revenue in the United States", "from 2008 to 2019 , by type (in million U.S.", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Subscription and streaming** Dec 31, 2007 0 Dec 31, 2008 0 Dec 31, 2009 0 Dec 31, 2010 651.2 Dec 31, 2011 1032.8 Dec 31, 2012 1449.6 Dec 31, 2013 1868.3 Dec 31, 2014 2406.6 Dec 31, 2015 3962.1 Dec 31, 2016 5664.5 Dec 31, 2017 7336.8 Dec 31, 2018 8831.3

Since 2015 there has been a sharp rise in the digital music industry revenue.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "multiColumn/data/685.tsv"}, "mark": "line", "encoding": {"color": {"value": "#D9B300"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Album downloads"}, "field": "Album downloads"}}, "title": ["Digital music revenue in the United States", "from 2008 to 2019 , by type (in million U.S.", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Album downloads Dec 31, 2007 635.3 Dec 31, 2008 763.4 Dec 31, 2009 872.4 Dec 31, 2010 1070.8 Dec 31, 2011 1204.8 Dec 31, 2012 1232.1 Dec 31, 2013 1150.9 Dec 31, 2014 1090.7 Dec 31, 2015 818.8 Dec 31, 2016 668.5 Dec 31, 2017 499.7 Dec 31, 2018 394.5

Album downloads seem to have peaked and are now in decline. This has had a similar affect on revenue. The curve is very uniform suggesting that average revenue per download has remained consistent.

{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "multiColumn/data/6862.tsv"}, "mark": "line", "encoding": {"color": {"value": "#666666"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Synthetic resins and rubbers (exports)"}, "field": "Synthetic resins and rubbers (exports)"}}, "title": ["Synthetic resins , rubbers , and fibers", "import and export value from Canada from", "2009 to 2019 (in million Canadian dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Synthetic resins and rubbers (exports) Dec 31, 2008 4782 Dec 31, 2009 5540 Dec 31, 2010 6476 Dec 31, 2011 6333 Dec 31, 2012 7073 Dec 31, 2013 7946 Dec 31, 2014 8143 Dec 31, 2015 7932 Dec 31, 2016 7626 Dec 31, 2017 8515 Dec 31, 2018 7712

2009 was the lowest recorded number of exports during this period with only 5000. This then steadily rose until 2011 where there was a very tiny dip in the numbers for that year only. Between 2012 and 2014 there was a very steep increase where the number of exports rose by over 2000. It then remained fairly steady, at around 8,000 with a very slight dip in 2016. 2018 saw a spike in the numbers to 8500, then there was a small dip before recording finished in 2019 on 7500.

{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "multiColumn/data/6862.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d6616b"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Synthetic fibrers (exports)"}, "field": "Synthetic fibrers (exports)"}}, "title": ["Synthetic resins , rubbers , and fibers", "import and export value from Canada from", "2009 to 2019 (in million Canadian dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Synthetic fibrers (exports) Dec 31, 2008 403 Dec 31, 2009 457 Dec 31, 2010 472 Dec 31, 2011 436 Dec 31, 2012 400 Dec 31, 2013 367 Dec 31, 2014 391 Dec 31, 2015 370 Dec 31, 2016 316 Dec 31, 2017 301 Dec 31, 2018 307

between the years of 2010 and 2018, the export value of synthetic fibres has gone down. There was a brief increase in value between 2010 and 2011 and then the trend went downwards. The value always remained between 300 and 500, going up to about 475 in 2011, and hovering above 300 in 2018.

{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "multiColumn/data/6862.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d6616b"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Synthetic fibrers (exports)"}, "field": "Synthetic fibrers (exports)"}}, "title": ["Synthetic resins , rubbers , and fibers", "import and export value from Canada from", "2009 to 2019 (in million Canadian dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Synthetic fibrers (exports) Dec 31, 2008 403 Dec 31, 2009 457 Dec 31, 2010 472 Dec 31, 2011 436 Dec 31, 2012 400 Dec 31, 2013 367 Dec 31, 2014 391 Dec 31, 2015 370 Dec 31, 2016 316 Dec 31, 2017 301 Dec 31, 2018 307

Sharp increase of sales between 2009-2010 then a steady decline till 2018.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/6863.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#C9C9C9"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Month"}, "y": {"type": "quantitative", "axis": {"title": "2018"}, "field": "2018"}}, "title": ["Monthly import value of animal feed in Canada", "from 2015 to 2020 (in million Canadian dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Month 2018 De 150.9; Month: Dec No 143.8; Month: Nov Oc 146.1; Month: Oct Se 152.9; Month: Sep Au 141.7; Month: Aug Ju 140.6; Month: Jul Ju 141.3; Month: Jun Ma 142.6; Month: May Ap 144; Month: Apr Ma 146.8; Month: Mar Fe 146.4; Month: Feb Ja 134.4; Month: Jan

The Monthly import value of animal feed in Canada from 2015 to 2020 is stable. January is the lowest month.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/6863.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#C9C9C9"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Month"}, "y": {"type": "quantitative", "axis": {"title": "2018"}, "field": "2018"}}, "title": ["Monthly import value of animal feed in Canada", "from 2015 to 2020 (in million Canadian dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Month 2018 De 150.9; Month: Dec No 143.8; Month: Nov Oc 146.1; Month: Oct Se 152.9; Month: Sep Au 141.7; Month: Aug Ju 140.6; Month: Jul Ju 141.3; Month: Jun Ma 142.6; Month: May Ap 144; Month: Apr Ma 146.8; Month: Mar Fe 146.4; Month: Feb Ja 134.4; Month: Jan

In the graph, you can see a jump in december, I think this is due to people getting animals for christmas, I also think it’s interesting that the graph isn’t in order; the months are scattered around which makes it hard to decipher data.

{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "multiColumn/data/6864.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d6616b"}, "x": {"type": "quantitative", "axis": {"title": "Oil"}, "field": "Oil"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}}, "title": ["Major countries receiving subsidies for", "fossil fuels worldwide in 2016 , by fuel", "type (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Oil Country 16074.96 Iran 19407.2 Saudi Arabia 11919.63 Venezuela 12662.32 China 10240.27 India 1426.57 Algeria 232.09 United Arab Emirates 5840.13 Egypt 4337.68 Indonesia

Saudi Arabia have received the biggest subsidiary in oil compared to United Arab Emirates.

{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "multiColumn/data/6869.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#000000"}, "x": {"type": "quantitative", "axis": {"title": "2019"}, "field": "2019"}, "y": {"type": "nominal", "axis": {"labelAngle": 0}, "bin": false, "field": "state of Germany"}}, "title": ["Number of camping sites in Germany in 2018", "and 2019 , by state"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

2019 state of Germany 447 Bavaria 393 Lower Saxony 371 Baden-Württemberg 318 North Rhine-Westphalia 276 Rhineland-Palatinate 275 Schleswig-Holstein 233 Hesse 212 Mecklenburg-Western Pomerania 172 Brandenburg 102 Saxony 80 Thuringia 80 Saxony-Anhalt 31 Saarland 11 Berlin 7 Hamburg 3 Bremen

Bremen had the lowest number of camping sites. Bavaria had the highest number of camping sites. Most states had at least 100 camping sites.

{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "multiColumn/data/6869.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#000000"}, "x": {"type": "quantitative", "axis": {"title": "2019"}, "field": "2019"}, "y": {"type": "nominal", "axis": {"labelAngle": 0}, "bin": false, "field": "state of Germany"}}, "title": ["Number of camping sites in Germany in 2018", "and 2019 , by state"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

2019 state of Germany 447 Bavaria 393 Lower Saxony 371 Baden-Württemberg 318 North Rhine-Westphalia 276 Rhineland-Palatinate 275 Schleswig-Holstein 233 Hesse 212 Mecklenburg-Western Pomerania 172 Brandenburg 102 Saxony 80 Thuringia 80 Saxony-Anhalt 31 Saarland 11 Berlin 7 Hamburg 3 Bremen

There is a wide variety in the number of camp sites in each state. The state with the highest number of camp sites is Bavaria and the state with the lowest number of camp sites is Breman.

{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "multiColumn/data/6879.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#DB4437"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "1990"}, "field": "1990"}}, "title": ["Final energy consumption for space cooling", "in buildings worldwide in 1990 and 2016 ,", "by region (in terawatt hours)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Country 1990 United State 339; Country: United States Chin 7; Country: China European Unio 63; Country: European Union Middle Eas 26; Country: Middle East Japa 48; Country: Japan Indi 6; Country: India Kore 4; Country: Korea Mexic 7; Country: Mexico Brazi 10; Country: Brazil Indonesi 2; Country: Indonesia South Afric 4; Country: South Africa

The more developed countries have used significantly more energy than those countries which are less industrialised. The usage by the United States far exceeds the total used by al” other countries shown.

{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "multiColumn/data/6879.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#fff2ae"}, "x": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"title": "2016"}, "field": "2016"}}, "title": ["Final energy consumption for space cooling", "in buildings worldwide in 1990 and 2016 ,", "by region (in terawatt hours)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Country 2016 United State 616; Country: United States Chin 450; Country: China European Unio 152; Country: European Union Middle Eas 129; Country: Middle East Japa 107; Country: Japan Indi 91; Country: India Kore 41; Country: Korea Mexic 37; Country: Mexico Brazi 32; Country: Brazil Indonesi 25; Country: Indonesia South Afric 8; Country: South Africa

Some countries for example China and the USA use a lot more power to heat rooms than more primitive or warmer countries.

{"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": "multiColumn/data/6880.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#00609f"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "region of Russia"}, "y": {"type": "quantitative", "axis": {"title": "May 2020"}, "field": "May 2020"}}, "title": ["Number of heavy commercial vehicles (HCV)", "in Russia from May 2019 to May 2020 , by", "region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

region of Russia May 2020 Moscow 445 Nizhny Novgorod Region 291 Khanty-Mansi Autonomous Okrug 265 Moscow region 241 Republic of Tatarstan 177 Irkutsk region 152 Saint Petersburg 151 Smara region 129 Rostov oblast 120 Krasnodar Krai 116

Generally companies in russia use between 100 and 450 heavy commerce vehicles in 2019 - 2020.

{"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": "multiColumn/data/6880.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#00609f"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "region of Russia"}, "y": {"type": "quantitative", "axis": {"title": "May 2020"}, "field": "May 2020"}}, "title": ["Number of heavy commercial vehicles (HCV)", "in Russia from May 2019 to May 2020 , by", "region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

region of Russia May 2020 Moscow 445 Nizhny Novgorod Region 291 Khanty-Mansi Autonomous Okrug 265 Moscow region 241 Republic of Tatarstan 177 Irkutsk region 152 Saint Petersburg 151 Smara region 129 Rostov oblast 120 Krasnodar Krai 116

In Moscow there is the highest number of heavy commercial vehicles, and there is a significant difference between Moscow and other regions of Russia, the lowest number of vehicles being in Kasnodar Krai and Rostov oblast.

{"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": "multiColumn/data/6880.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#00609f"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "region of Russia"}, "y": {"type": "quantitative", "axis": {"title": "May 2020"}, "field": "May 2020"}}, "title": ["Number of heavy commercial vehicles (HCV)", "in Russia from May 2019 to May 2020 , by", "region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

region of Russia May 2020 Moscow 445 Nizhny Novgorod Region 291 Khanty-Mansi Autonomous Okrug 265 Moscow region 241 Republic of Tatarstan 177 Irkutsk region 152 Saint Petersburg 151 Smara region 129 Rostov oblast 120 Krasnodar Krai 116

In May 2020 Moscow had the largest number of HCVs and Krasnodar Krai and the Rostov Oblast had the fewest. All regions had over 100 HCVs with four regions had over 200.

{"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": "multiColumn/data/6880.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#00609f"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "region of Russia"}, "y": {"type": "quantitative", "axis": {"title": "May 2020"}, "field": "May 2020"}}, "title": ["Number of heavy commercial vehicles (HCV)", "in Russia from May 2019 to May 2020 , by", "region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

region of Russia May 2020 Moscow 445 Nizhny Novgorod Region 291 Khanty-Mansi Autonomous Okrug 265 Moscow region 241 Republic of Tatarstan 177 Irkutsk region 152 Saint Petersburg 151 Smara region 129 Rostov oblast 120 Krasnodar Krai 116

Moscow has significantly more heavy commercial vehicles. The majority of regions have between 100 and 250 HMVs.

{"config": {"background": "#fff", "area": {"fill": "#3e5c69"}, "line": {"stroke": "#3e5c69"}, "rect": {"fill": "#3e5c69"}, "bar": {"fill": "#3e5c69"}, "point": {"color": "#3e5c69"}, "axis": {"domainWidth": 0.5, "grid": true, "labelPadding": 2, "tickSize": 5, "tickWidth": 0.5, "titleFontWeight": "normal"}, "axisBand": {"grid": false}, "axisX": {"gridWidth": 0.2}, "axisY": {"gridDash": [3], "gridWidth": 0.4}, "legend": {"labelFontSize": 11, "padding": 1, "symbolType": "square"}, "range": {"category": ["#3e5c69", "#6793a6", "#182429", "#0570b0", "#3690c0", "#74a9cf", "#a6bddb", "#e2ddf2"]}}, "data": {"url": "multiColumn/data/6889.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#6793a6"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "I'm not sure"}, "field": "I\\'m not sure"}}, "title": ["Global CIOs and IT executives ' economic", "recovery expectations for COVID-19 , by industry"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response I'm not sure Financials/Insurance 0.16 IT/TelCo 0.12 Services/Consulting 0.1 Retail/Consumer 0.07 Indus/Mat/Manu 0.08 Health/Pharma 0.13 Education 0.14 Energy/Utilities 0.08 Other (Airlines/Transportation) 0.05 All Respondents 0.11

The sector with the highest response of 'I'm not sure' is the financial/ insurance sector at 0.15. Other (airlines/ transportation) reported the lowest incidence of responding 'I'm not sure', at 0.05. The average score across all respondents was 0.11. Education, Financials/ Insurance, Health/ Pharma and IT/ Tel/ Co all report higher levels of uncertainty than the survey average.

{"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": "multiColumn/data/688.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d365ba"}, "x": {"type": "quantitative", "axis": {"title": "Owned"}, "field": "Owned"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "leading container ship operators"}}, "title": ["Owned and chartered TEUs of the world 's", "leading container ship operators as of October", "12 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Owned leading container ship operators 2327592 APM-Maersk 965533 Mediterranean Shg Co 1551249 COSCO Group 970019 CMA CGM Group 1052321 Hapag-Lloyd 513955 ONE (Ocean Network Express) 600514 Evergreen Line 417054 HMM Co Ltd 182053 Yang Ming Marine Transport Corp. 167277 Wan Hai Lines 6126 Zim 131701 PIL (Pacific Int. Line) 94041 Zhonggu Logistics Corp. 93674 IRISL Group 75099 KMTC

The majority of the operators are below the 1,000000 mark, APM Maersk are way ahead of the rest of the operators, only 2 owe more than 1,000000.

{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "multiColumn/data/689.tsv"}, "mark": "line", "encoding": {"color": {"value": "#843c39"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Services"}, "field": "Services"}}, "title": ["Sweden : Distribution of employment by economic", "sector from 2010 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Services Dec 31, 2009 0.7802 Dec 31, 2010 0.7808 Dec 31, 2011 0.7833 Dec 31, 2012 0.7881 Dec 31, 2013 0.7942 Dec 31, 2014 0.7968 Dec 31, 2015 0.7992 Dec 31, 2016 0.8001 Dec 31, 2017 0.8015 Dec 31, 2018 0.8044 Dec 31, 2019 0.8069

The distribution of services seems to remain fairly static at approx 0.8 between 2010 and 2018. There is very little variation in the trend over this time period.

{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "multiColumn/data/6903.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#9e765f"}, "x": {"type": "quantitative", "axis": {"title": "2016/17"}, "field": "2016/17"}, "y": {"type": "nominal", "axis": {"labelAngle": 0}, "bin": false, "field": "BBC radio stations"}}, "title": ["Cost to deliver BBC radio services to individual", "users per user hour in the United Kingdom", "(UK) from 2015/16 to 2019/20 , by station", "(in pence)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

2016/17 BBC radio stations 5.7 BBC Radio 3 3.4 BBC Asian Network 2.8 BBC 1Xtra 2.3 BBC Radio 5 1.3 BBC Radio 4 0.5 BBC Radio 4 extra 0.9 BBC 6 Music 1.2 BBC Radio 1 1.5 BBC Radio 5 live sports extra 0.5 BBC Radio 2

BBC radio 3 was the most expensive station to deliver per individual user per hour at between 5 and 6 pence. BBC radio 4extra, bbc radio 2 and bbc 6 music were cheapest at less than 1 pence per hour.

{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "multiColumn/data/6909.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e5d8bd"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Hospitals"}, "field": "Hospitals"}}, "title": ["Private sector health expenditure on professional", "care in Canada by type from 2010 to 2019", "(in million Canadian dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Hospitals 2019* 6884 2018* 6696.3 2017 6489.5 2016 6640.1 2015 6459.1 2014 6130.2 2013 5891.9 2012 5658.4 2011 5549.2 2010 5293.2

The graph shows an overall steady increase in private sector health expenditure on professional care in Canada between 2010 and 2019. The graph shows that in 2010 approximately 5,200 hospitals allocated expenditure to professional care. The number of hospitals increased gradually up until 2016 when there was a total of approximately 6,600 hospitals. The graph shows a slight dip in numbers in 2017, reducing to a total of 6,400 hospitals. But numbers have been gradually increasing again since and forecasted numbers for 2019 are expected to be 6,900 hospitals.

{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "multiColumn/data/6909.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e5d8bd"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Hospitals"}, "field": "Hospitals"}}, "title": ["Private sector health expenditure on professional", "care in Canada by type from 2010 to 2019", "(in million Canadian dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Hospitals 2019* 6884 2018* 6696.3 2017 6489.5 2016 6640.1 2015 6459.1 2014 6130.2 2013 5891.9 2012 5658.4 2011 5549.2 2010 5293.2

I can see from the graph that there was a spiking numbers in 2016. Than after 2026 there was a small decline before numbers started rising again.

{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "multiColumn/data/6909.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e5d8bd"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Hospitals"}, "field": "Hospitals"}}, "title": ["Private sector health expenditure on professional", "care in Canada by type from 2010 to 2019", "(in million Canadian dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Hospitals 2019* 6884 2018* 6696.3 2017 6489.5 2016 6640.1 2015 6459.1 2014 6130.2 2013 5891.9 2012 5658.4 2011 5549.2 2010 5293.2

As the number of hospitals increases, the expenditure grows year on year until 2017 when it slightly drops. As the years go on, Canadian spending on professional care increases quite steadily.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/6909.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#999999"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Dental services"}, "field": "Dental services"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Private sector health expenditure on professional", "care in Canada by type from 2010 to 2019", "(in million Canadian dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Dental services Year 15825.9 2019* 15176.1 2018* 14609.1 2017 14290.2 2016 13124.9 2015 12326.2 2014 12001.2 2013 11701.2 2012 11172 2011 11205.9 2010

Expenditure has grown year on year from 2011. The expenditure from 2010-2011 was the same.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/6909.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#999999"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Dental services"}, "field": "Dental services"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Private sector health expenditure on professional", "care in Canada by type from 2010 to 2019", "(in million Canadian dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Dental services Year 15825.9 2019* 15176.1 2018* 14609.1 2017 14290.2 2016 13124.9 2015 12326.2 2014 12001.2 2013 11701.2 2012 11172 2011 11205.9 2010

There has been an increase in spending on private dental services from 2010 to 2019. This increase was fairly gradual between 2010 to 2014. We see a greater increase from 2014 to 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": "multiColumn/data/690.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#E66C37"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "March to April"}, "field": "March to April"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "retail sector"}}, "title": ["Impact of coronavirus (COVID-19) on monthly", "retail sales development in the United States", "2020 , by retail sector"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

March to April retail sector −0.123 Motor vehicle & parts dealers −0.488 Furniture & home furniture stores −0.432 Electronics & appliance stores −0.024 Building material & garden equipment & supplies dealers −0.128 Food & beverage stores −0.148 Health & personal care stores −0.244 Gasoline stations −0.752 Clothing & clothing accessories stores −0.337 Sporting goods, hobby, musical instrument, & book stores −0.136 General merchandise stores −0.259 Miscellaneous store retailers 0.095 Nonstore retailers −0.147 Total retail

Coronavirus had the most negative impact on the retail sector of clothing and clothing accessories between March and April 2020 in the United States.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "multiColumn/data/690.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#E66C37"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "March to April"}, "field": "March to April"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "retail sector"}}, "title": ["Impact of coronavirus (COVID-19) on monthly", "retail sales development in the United States", "2020 , by retail sector"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

March to April retail sector −0.123 Motor vehicle & parts dealers −0.488 Furniture & home furniture stores −0.432 Electronics & appliance stores −0.024 Building material & garden equipment & supplies dealers −0.128 Food & beverage stores −0.148 Health & personal care stores −0.244 Gasoline stations −0.752 Clothing & clothing accessories stores −0.337 Sporting goods, hobby, musical instrument, & book stores −0.136 General merchandise stores −0.259 Miscellaneous store retailers 0.095 Nonstore retailers −0.147 Total retail

Sales decreased in all sectors from March to April with the clothing and clothing accessories sector being the worst hit. However non store retailers went against the trend and had an increase in sales.

{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "multiColumn/data/690.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec008b"}, "x": {"type": "quantitative", "axis": {"labelAngle": -90, "title": "June to July"}, "field": "June to July"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "retail sector"}}, "title": ["Impact of coronavirus (COVID-19) on monthly", "retail sales development in the United States", "2020 , by retail sector"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

June to July retail sector −0.012 Motor vehicle & parts dealers 0 Furniture & home furniture stores 0.229 Electronics & appliance stores −0.029 Building material & garden equipment & supplies dealers 0.002 Food & beverage stores 0.036 Health & personal care stores 0.062 Gasoline stations 0.057 Clothing & clothing accessories stores −0.05 Sporting goods, hobby, musical instrument, & book stores −0.002 General merchandise stores 0.062 Miscellaneous store retailers 0.007 Nonstore retailers 0.012 Total retail

Sales of building materials and gardening equipment, and sporting goods were adversely affected by Covid. Sales of electronics increased more than any other category as people bought goods to keep them occupied during lockdown.

{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "multiColumn/data/690.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec008b"}, "x": {"type": "quantitative", "axis": {"labelAngle": -90, "title": "June to July"}, "field": "June to July"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "retail sector"}}, "title": ["Impact of coronavirus (COVID-19) on monthly", "retail sales development in the United States", "2020 , by retail sector"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

June to July retail sector −0.012 Motor vehicle & parts dealers 0 Furniture & home furniture stores 0.229 Electronics & appliance stores −0.029 Building material & garden equipment & supplies dealers 0.002 Food & beverage stores 0.036 Health & personal care stores 0.062 Gasoline stations 0.057 Clothing & clothing accessories stores −0.05 Sporting goods, hobby, musical instrument, & book stores −0.002 General merchandise stores 0.062 Miscellaneous store retailers 0.007 Nonstore retailers 0.012 Total retail

electronics had the biggest growth due to coronavirus, whereas sporting goods had the worst performance.

{"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": "multiColumn/data/690.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#00609f"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "retail sector"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "September to October"}, "field": "September to October"}}, "title": ["Impact of coronavirus (COVID-19) on monthly", "retail sales development in the United States", "2020 , by retail sector"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

retail sector September to October Motor vehicle & parts dealers 0.004 Furniture & home furniture stores −0.004 Electronics & appliance stores 0.012 Building material & garden equipment & supplies dealers 0.009 Food & beverage stores −0.002 Health & personal care stores −0.001 Gasoline stations 0.004 Clothing & clothing accessories stores −0.042 Sporting goods, hobby, musical instrument, & book stores −0.042 General merchandise stores −0.011 Miscellaneous store retailers −0.009 Nonstore retailers 0.031 Total retail 0.003

Covid has impacted the sports clothing and clothing industry the most. Covid has had little effect on health stores. Nonstore retailers have benefitted from covid.

{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "multiColumn/data/690.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#00609f"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "retail sector"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "September to October"}, "field": "September to October"}}, "title": ["Impact of coronavirus (COVID-19) on monthly", "retail sales development in the United States", "2020 , by retail sector"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

retail sector September to October Motor vehicle & parts dealers 0.004 Furniture & home furniture stores −0.004 Electronics & appliance stores 0.012 Building material & garden equipment & supplies dealers 0.009 Food & beverage stores −0.002 Health & personal care stores −0.001 Gasoline stations 0.004 Clothing & clothing accessories stores −0.042 Sporting goods, hobby, musical instrument, & book stores −0.042 General merchandise stores −0.011 Miscellaneous store retailers −0.009 Nonstore retailers 0.031 Total retail 0.003

The non-store retail sector saw the biggest improvement, whereas clothing and sporting goods sectors experienced the biggest loss.

{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "multiColumn/data/6910.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d2d2d2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "International"}, "field": "International"}}, "title": ["Distribution of the 50 most sold Spanish", "and international albums in Spain between", "2004 and 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year International Dec 31, 2003 9 Dec 31, 2004 16 Dec 31, 2005 19 Dec 31, 2006 16 Dec 31, 2007 17 Dec 31, 2008 17 Dec 31, 2009 21 Dec 31, 2010 20 Dec 31, 2011 16 Dec 31, 2012 22 Dec 31, 2013 13 Dec 31, 2014 11 Dec 31, 2015 10 Dec 31, 2016 10 Dec 31, 2017 8 Dec 31, 2018 9

The chart shows that the highest number of Spanish and International albums sold in Spain occurred around 2013. This may be due to a prevalent artist having a release during this 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": "multiColumn/data/6914.tsv"}, "mark": "line", "encoding": {"color": {"value": "#ce6dbd"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Industry"}, "field": "Industry"}}, "title": ["North Macedonia : Distribution of employment", "by economic sector from 2010 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Industry Dec 31, 2009 0.296 Dec 31, 2010 0.3 Dec 31, 2011 0.2987 Dec 31, 2012 0.3038 Dec 31, 2013 0.3036 Dec 31, 2014 0.305 Dec 31, 2015 0.3031 Dec 31, 2016 0.3055 Dec 31, 2017 0.3136 Dec 31, 2018 0.314 Dec 31, 2019 0.3144

Industrial distribution in North Macedonia showed a small increase in 2013 and another greater increase in 2018. There have been no times of decline.

{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "multiColumn/data/6920.tsv"}, "mark": "area", "encoding": {"color": {"value": "#7570b3"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Threat to the economy"}, "field": "Threat to the economy"}}, "title": ["What do you think foreign trade means for", "America ?"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Threat to the economy Dec 31, 1991 0.48 Dec 31, 1993 0.38 Dec 31, 1999 0.35 Dec 31, 2000 0.37 Dec 31, 2001 0.39 Dec 31, 2002 0.41 Dec 31, 2004 0.46 Dec 31, 2005 0.48 Dec 31, 2007 0.52 Dec 31, 2008 0.47 Dec 31, 2010 0.45 Dec 31, 2011 0.46 Dec 31, 2012 0.35 Dec 31, 2013 0.38 Dec 31, 2014 0.33 Dec 31, 2015 0.34 Dec 31, 2016 0.23 Dec 31, 2017 0.25 Dec 31, 2018 0.21 Dec 31, 2019 0.18

The threat to the economy has fallen since 2008. The threat to the economy was lower in 2015 than it was in 1995.

{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "multiColumn/data/692.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Confirmed cases"}, "field": "Confirmed cases"}}, "title": ["Cumulative number of coronavirus (COVID-19)", "cases , recoveries , deaths in Moscow , Russia", "as of December 26 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Confirmed cases Mar 01, 2020 1 Mar 02, 2020 1 Mar 03, 2020 1 Mar 04, 2020 1 Mar 05, 2020 6 Mar 06, 2020 6 Mar 07, 2020 6 Mar 08, 2020 9 Mar 09, 2020 9 Mar 10, 2020 15 Mar 11, 2020 19 Mar 12, 2020 24 Mar 13, 2020 33 Mar 14, 2020 33 Mar 15, 2020 53 Mar 16, 2020 56 Mar 17, 2020 86 Mar 18, 2020 98 Mar 19, 2020 131 Mar 20, 2020 137 Mar 21, 2020 191 Mar 22, 2020 262 Mar 23, 2020 290 Mar 24, 2020 410 Mar 25, 2020 546 Mar 26, 2020 703 Mar 27, 2020 817 Mar 28, 2020 1014 Mar 29, 2020 1226 Mar 30, 2020 1613 Mar 31, 2020 1880 Apr 01, 2020 2475 Apr 02, 2020 2923 Apr 03, 2020 3357 Apr 04, 2020 3893 Apr 05, 2020 4484 Apr 06, 2020 5181 Apr 07, 2020 5841 Apr 08, 2020 6698 Apr 09, 2020 7822 Apr 10, 2020 8852 Apr 11, 2020 10158 Apr 12, 2020 11513 Apr 13, 2020 13002 Apr 14, 2020 14776 Apr 15, 2020 16146 Apr 16, 2020 18105 Apr 17, 2020 20754 Apr 18, 2020 24324 Apr 19, 2020 26350 Apr 20, 2020 29433 Apr 21, 2020 31981 Apr 22, 2020 33940 Apr 23, 2020 36897 Apr 24, 2020 39509 Apr 25, 2020 42480 Apr 26, 2020 45351 Apr 27, 2020 48426 Apr 28, 2020 50646 Apr 29, 2020 53739 Apr 30, 2020 57300 May 01, 2020 62658 May 02, 2020 68606 May 03, 2020 74401 May 04, 2020 80115 May 05, 2020 85973 May 06, 2020 92676 May 07, 2020 98522 May 08, 2020 104189 May 09, 2020 109740 May 10, 2020 115909 May 11, 2020 121301 May 12, 2020 126004 May 13, 2020 130716 May 14, 2020 135464 May 15, 2020 138969 May 16, 2020 142824 May 17, 2020 146062 May 18, 2020 149607 May 19, 2020 152306 May 20, 2020 155219 May 21, 2020 158207 May 22, 2020 161397 May 23, 2020 163913 May 24, 2020 166473 May 25, 2020 169303 May 26, 2020 171443 May 27, 2020 173497 May 28, 2020 175829 May 29, 2020 178196 May 30, 2020 180791 May 31, 2020 183088 Jun 01, 2020 185374 Jun 02, 2020 187216 Jun 03, 2020 189214 Jun 04, 2020 191069 Jun 05, 2020 193061 Jun 06, 2020 195017 Jun 07, 2020 197018 Jun 08, 2020 198590 Jun 09, 2020 199785 Jun 10, 2020 201221 Jun 11, 2020 202935 Jun 12, 2020 204428 Jun 13, 2020 205905 Jun 14, 2020 207264 Jun 15, 2020 208680 Jun 16, 2020 209745 Jun 17, 2020 210785 Jun 18, 2020 211921 Jun 19, 2020 212978 Jun 20, 2020 213946 Jun 21, 2020 215014 Jun 22, 2020 216095 Jun 23, 2020 216906 Jun 24, 2020 217791 Jun 25, 2020 218604 Jun 26, 2020 219354 Jun 27, 2020 220071 Jun 28, 2020 220853 Jun 29, 2020 221598 Jun 30, 2020 222209 Jul 01, 2020 222871 Jul 02, 2020 223530 Jul 03, 2020 224210 Jul 04, 2020 224860 Jul 05, 2020 225545 Jul 06, 2020 226174 Jul 07, 2020 226795 Jul 08, 2020 227363 Jul 09, 2020 228000 Jul 10, 2020 228678 Jul 11, 2020 229357 Jul 12, 2020 230029 Jul 13, 2020 230642 Jul 14, 2020 231270 Jul 15, 2020 231801 Jul 16, 2020 232376 Jul 17, 2020 232954 Jul 18, 2020 233545 Jul 19, 2020 234123 Jul 20, 2020 234725 Jul 21, 2020 235363 Jul 22, 2020 235971 Jul 23, 2020 236616 Jul 24, 2020 237264 Jul 25, 2020 237947 Jul 26, 2020 238641 Jul 27, 2020 239315 Jul 28, 2020 239986 Jul 29, 2020 240664 Jul 30, 2020 241359 Jul 31, 2020 242049 Aug 01, 2020 242713 Aug 02, 2020 243406 Aug 03, 2020 244097 Aug 04, 2020 244784 Aug 05, 2020 245468 Aug 06, 2020 246154 Aug 07, 2020 246845 Aug 08, 2020 247534 Aug 09, 2020 248228 Aug 10, 2020 248922 Aug 11, 2020 249611 Aug 12, 2020 250303 Aug 13, 2020 250991 Aug 14, 2020 251686 Aug 15, 2020 252374 Aug 16, 2020 253064 Aug 17, 2020 253757 Aug 18, 2020 254448 Aug 19, 2020 255136 Aug 20, 2020 255826 Aug 21, 2020 256513 Aug 22, 2020 257124 Aug 23, 2020 257749 Aug 24, 2020 258430 Aug 25, 2020 259070 Aug 26, 2020 259707 Aug 27, 2020 260361 Aug 28, 2020 261038 Aug 29, 2020 261733 Aug 30, 2020 262418 Aug 31, 2020 263059 Sep 01, 2020 263684 Sep 02, 2020 264374 Sep 03, 2020 265066 Sep 04, 2020 265737 Sep 05, 2020 266357 Sep 06, 2020 267047 Sep 07, 2020 267742 Sep 08, 2020 268384 Sep 09, 2020 269079 Sep 10, 2020 269777 Sep 11, 2020 270447 Sep 12, 2020 271097 Sep 13, 2020 271793 Sep 14, 2020 272523 Sep 15, 2020 273273 Sep 16, 2020 274003 Sep 17, 2020 274808 Sep 18, 2020 275633 Sep 19, 2020 276493 Sep 20, 2020 277408 Sep 21, 2020 278388 Sep 22, 2020 279358 Sep 23, 2020 280408 Sep 24, 2020 281968 Sep 25, 2020 283760 Sep 26, 2020 285776 Sep 27, 2020 287993 Sep 28, 2020 290293 Sep 29, 2020 292601 Sep 30, 2020 295025 Oct 01, 2020 297729 Oct 02, 2020 300613 Oct 03, 2020 303940 Oct 04, 2020 307477 Oct 05, 2020 311559 Oct 06, 2020 314788 Oct 07, 2020 318111 Oct 08, 2020 321812 Oct 09, 2020 325917 Oct 10, 2020 330418 Oct 11, 2020 334813 Oct 12, 2020 339431 Oct 13, 2020 344004 Oct 14, 2020 347946 Oct 15, 2020 352995 Oct 16, 2020 357643 Oct 17, 2020 362253 Oct 18, 2020 367629 Oct 19, 2020 372628 Oct 20, 2020 377017 Oct 21, 2020 381430 Oct 22, 2020 386908 Oct 23, 2020 391361 Oct 24, 2020 395816 Oct 25, 2020 401040 Oct 26, 2020 405352 Oct 27, 2020 409022 Oct 28, 2020 413928 Oct 29, 2020 419196 Oct 30, 2020 424148 Oct 31, 2020 429409 Nov 01, 2020 434205 Nov 02, 2020 439355 Nov 03, 2020 445181 Nov 04, 2020 450436 Nov 05, 2020 456689 Nov 06, 2020 462518 Nov 07, 2020 468269 Nov 08, 2020 475166 Nov 09, 2020 481068 Nov 10, 2020 485545 Nov 11, 2020 491542 Nov 12, 2020 497516 Nov 13, 2020 503943 Nov 14, 2020 510214 Nov 15, 2020 516574 Nov 16, 2020 522456 Nov 17, 2020 526630 Nov 18, 2020 533068 Nov 19, 2020 539970 Nov 20, 2020 547138 Nov 21, 2020 553713 Nov 22, 2020 560579 Nov 23, 2020 566417 Nov 24, 2020 571102 Nov 25, 2020 577177 Nov 26, 2020 585095 Nov 27, 2020 592415 Nov 28, 2020 599213 Nov 29, 2020 605724 Nov 30, 2020 612248 Dec 01, 2020 617439 Dec 02, 2020 625189 Dec 03, 2020 632057 Dec 04, 2020 640050 Dec 05, 2020 647562 Dec 06, 2020 654841 Dec 07, 2020 660073 Dec 08, 2020 665218 Dec 09, 2020 671948 Dec 10, 2020 679163 Dec 11, 2020 685785 Dec 12, 2020 692210 Dec 13, 2020 698084 Dec 14, 2020 703502 Dec 15, 2020 708530 Dec 16, 2020 715241 Dec 17, 2020 722178 Dec 18, 2020 728637 Dec 19, 2020 735900 Dec 20, 2020 743697 Dec 21, 2020 750934 Dec 22, 2020 756586 Dec 23, 2020 764789 Dec 24, 2020 772104 Dec 25, 2020 779584

The confirmed cases are drastically increasing. In April 2021 there were 0 cases. In July 2021, they went up to over 200.000 cases. After October they increased even sharper, reaching almost 800.000.

{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "multiColumn/data/692.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Confirmed cases"}, "field": "Confirmed cases"}}, "title": ["Cumulative number of coronavirus (COVID-19)", "cases , recoveries , deaths in Moscow , Russia", "as of December 26 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Confirmed cases Mar 01, 2020 1 Mar 02, 2020 1 Mar 03, 2020 1 Mar 04, 2020 1 Mar 05, 2020 6 Mar 06, 2020 6 Mar 07, 2020 6 Mar 08, 2020 9 Mar 09, 2020 9 Mar 10, 2020 15 Mar 11, 2020 19 Mar 12, 2020 24 Mar 13, 2020 33 Mar 14, 2020 33 Mar 15, 2020 53 Mar 16, 2020 56 Mar 17, 2020 86 Mar 18, 2020 98 Mar 19, 2020 131 Mar 20, 2020 137 Mar 21, 2020 191 Mar 22, 2020 262 Mar 23, 2020 290 Mar 24, 2020 410 Mar 25, 2020 546 Mar 26, 2020 703 Mar 27, 2020 817 Mar 28, 2020 1014 Mar 29, 2020 1226 Mar 30, 2020 1613 Mar 31, 2020 1880 Apr 01, 2020 2475 Apr 02, 2020 2923 Apr 03, 2020 3357 Apr 04, 2020 3893 Apr 05, 2020 4484 Apr 06, 2020 5181 Apr 07, 2020 5841 Apr 08, 2020 6698 Apr 09, 2020 7822 Apr 10, 2020 8852 Apr 11, 2020 10158 Apr 12, 2020 11513 Apr 13, 2020 13002 Apr 14, 2020 14776 Apr 15, 2020 16146 Apr 16, 2020 18105 Apr 17, 2020 20754 Apr 18, 2020 24324 Apr 19, 2020 26350 Apr 20, 2020 29433 Apr 21, 2020 31981 Apr 22, 2020 33940 Apr 23, 2020 36897 Apr 24, 2020 39509 Apr 25, 2020 42480 Apr 26, 2020 45351 Apr 27, 2020 48426 Apr 28, 2020 50646 Apr 29, 2020 53739 Apr 30, 2020 57300 May 01, 2020 62658 May 02, 2020 68606 May 03, 2020 74401 May 04, 2020 80115 May 05, 2020 85973 May 06, 2020 92676 May 07, 2020 98522 May 08, 2020 104189 May 09, 2020 109740 May 10, 2020 115909 May 11, 2020 121301 May 12, 2020 126004 May 13, 2020 130716 May 14, 2020 135464 May 15, 2020 138969 May 16, 2020 142824 May 17, 2020 146062 May 18, 2020 149607 May 19, 2020 152306 May 20, 2020 155219 May 21, 2020 158207 May 22, 2020 161397 May 23, 2020 163913 May 24, 2020 166473 May 25, 2020 169303 May 26, 2020 171443 May 27, 2020 173497 May 28, 2020 175829 May 29, 2020 178196 May 30, 2020 180791 May 31, 2020 183088 Jun 01, 2020 185374 Jun 02, 2020 187216 Jun 03, 2020 189214 Jun 04, 2020 191069 Jun 05, 2020 193061 Jun 06, 2020 195017 Jun 07, 2020 197018 Jun 08, 2020 198590 Jun 09, 2020 199785 Jun 10, 2020 201221 Jun 11, 2020 202935 Jun 12, 2020 204428 Jun 13, 2020 205905 Jun 14, 2020 207264 Jun 15, 2020 208680 Jun 16, 2020 209745 Jun 17, 2020 210785 Jun 18, 2020 211921 Jun 19, 2020 212978 Jun 20, 2020 213946 Jun 21, 2020 215014 Jun 22, 2020 216095 Jun 23, 2020 216906 Jun 24, 2020 217791 Jun 25, 2020 218604 Jun 26, 2020 219354 Jun 27, 2020 220071 Jun 28, 2020 220853 Jun 29, 2020 221598 Jun 30, 2020 222209 Jul 01, 2020 222871 Jul 02, 2020 223530 Jul 03, 2020 224210 Jul 04, 2020 224860 Jul 05, 2020 225545 Jul 06, 2020 226174 Jul 07, 2020 226795 Jul 08, 2020 227363 Jul 09, 2020 228000 Jul 10, 2020 228678 Jul 11, 2020 229357 Jul 12, 2020 230029 Jul 13, 2020 230642 Jul 14, 2020 231270 Jul 15, 2020 231801 Jul 16, 2020 232376 Jul 17, 2020 232954 Jul 18, 2020 233545 Jul 19, 2020 234123 Jul 20, 2020 234725 Jul 21, 2020 235363 Jul 22, 2020 235971 Jul 23, 2020 236616 Jul 24, 2020 237264 Jul 25, 2020 237947 Jul 26, 2020 238641 Jul 27, 2020 239315 Jul 28, 2020 239986 Jul 29, 2020 240664 Jul 30, 2020 241359 Jul 31, 2020 242049 Aug 01, 2020 242713 Aug 02, 2020 243406 Aug 03, 2020 244097 Aug 04, 2020 244784 Aug 05, 2020 245468 Aug 06, 2020 246154 Aug 07, 2020 246845 Aug 08, 2020 247534 Aug 09, 2020 248228 Aug 10, 2020 248922 Aug 11, 2020 249611 Aug 12, 2020 250303 Aug 13, 2020 250991 Aug 14, 2020 251686 Aug 15, 2020 252374 Aug 16, 2020 253064 Aug 17, 2020 253757 Aug 18, 2020 254448 Aug 19, 2020 255136 Aug 20, 2020 255826 Aug 21, 2020 256513 Aug 22, 2020 257124 Aug 23, 2020 257749 Aug 24, 2020 258430 Aug 25, 2020 259070 Aug 26, 2020 259707 Aug 27, 2020 260361 Aug 28, 2020 261038 Aug 29, 2020 261733 Aug 30, 2020 262418 Aug 31, 2020 263059 Sep 01, 2020 263684 Sep 02, 2020 264374 Sep 03, 2020 265066 Sep 04, 2020 265737 Sep 05, 2020 266357 Sep 06, 2020 267047 Sep 07, 2020 267742 Sep 08, 2020 268384 Sep 09, 2020 269079 Sep 10, 2020 269777 Sep 11, 2020 270447 Sep 12, 2020 271097 Sep 13, 2020 271793 Sep 14, 2020 272523 Sep 15, 2020 273273 Sep 16, 2020 274003 Sep 17, 2020 274808 Sep 18, 2020 275633 Sep 19, 2020 276493 Sep 20, 2020 277408 Sep 21, 2020 278388 Sep 22, 2020 279358 Sep 23, 2020 280408 Sep 24, 2020 281968 Sep 25, 2020 283760 Sep 26, 2020 285776 Sep 27, 2020 287993 Sep 28, 2020 290293 Sep 29, 2020 292601 Sep 30, 2020 295025 Oct 01, 2020 297729 Oct 02, 2020 300613 Oct 03, 2020 303940 Oct 04, 2020 307477 Oct 05, 2020 311559 Oct 06, 2020 314788 Oct 07, 2020 318111 Oct 08, 2020 321812 Oct 09, 2020 325917 Oct 10, 2020 330418 Oct 11, 2020 334813 Oct 12, 2020 339431 Oct 13, 2020 344004 Oct 14, 2020 347946 Oct 15, 2020 352995 Oct 16, 2020 357643 Oct 17, 2020 362253 Oct 18, 2020 367629 Oct 19, 2020 372628 Oct 20, 2020 377017 Oct 21, 2020 381430 Oct 22, 2020 386908 Oct 23, 2020 391361 Oct 24, 2020 395816 Oct 25, 2020 401040 Oct 26, 2020 405352 Oct 27, 2020 409022 Oct 28, 2020 413928 Oct 29, 2020 419196 Oct 30, 2020 424148 Oct 31, 2020 429409 Nov 01, 2020 434205 Nov 02, 2020 439355 Nov 03, 2020 445181 Nov 04, 2020 450436 Nov 05, 2020 456689 Nov 06, 2020 462518 Nov 07, 2020 468269 Nov 08, 2020 475166 Nov 09, 2020 481068 Nov 10, 2020 485545 Nov 11, 2020 491542 Nov 12, 2020 497516 Nov 13, 2020 503943 Nov 14, 2020 510214 Nov 15, 2020 516574 Nov 16, 2020 522456 Nov 17, 2020 526630 Nov 18, 2020 533068 Nov 19, 2020 539970 Nov 20, 2020 547138 Nov 21, 2020 553713 Nov 22, 2020 560579 Nov 23, 2020 566417 Nov 24, 2020 571102 Nov 25, 2020 577177 Nov 26, 2020 585095 Nov 27, 2020 592415 Nov 28, 2020 599213 Nov 29, 2020 605724 Nov 30, 2020 612248 Dec 01, 2020 617439 Dec 02, 2020 625189 Dec 03, 2020 632057 Dec 04, 2020 640050 Dec 05, 2020 647562 Dec 06, 2020 654841 Dec 07, 2020 660073 Dec 08, 2020 665218 Dec 09, 2020 671948 Dec 10, 2020 679163 Dec 11, 2020 685785 Dec 12, 2020 692210 Dec 13, 2020 698084 Dec 14, 2020 703502 Dec 15, 2020 708530 Dec 16, 2020 715241 Dec 17, 2020 722178 Dec 18, 2020 728637 Dec 19, 2020 735900 Dec 20, 2020 743697 Dec 21, 2020 750934 Dec 22, 2020 756586 Dec 23, 2020 764789 Dec 24, 2020 772104 Dec 25, 2020 779584

As it got cooler in the country the cases rose, shown in the sharp increase around October.

{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "multiColumn/data/692.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Confirmed cases"}, "field": "Confirmed cases"}}, "title": ["Cumulative number of coronavirus (COVID-19)", "cases , recoveries , deaths in Moscow , Russia", "as of December 26 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Confirmed cases Mar 01, 2020 1 Mar 02, 2020 1 Mar 03, 2020 1 Mar 04, 2020 1 Mar 05, 2020 6 Mar 06, 2020 6 Mar 07, 2020 6 Mar 08, 2020 9 Mar 09, 2020 9 Mar 10, 2020 15 Mar 11, 2020 19 Mar 12, 2020 24 Mar 13, 2020 33 Mar 14, 2020 33 Mar 15, 2020 53 Mar 16, 2020 56 Mar 17, 2020 86 Mar 18, 2020 98 Mar 19, 2020 131 Mar 20, 2020 137 Mar 21, 2020 191 Mar 22, 2020 262 Mar 23, 2020 290 Mar 24, 2020 410 Mar 25, 2020 546 Mar 26, 2020 703 Mar 27, 2020 817 Mar 28, 2020 1014 Mar 29, 2020 1226 Mar 30, 2020 1613 Mar 31, 2020 1880 Apr 01, 2020 2475 Apr 02, 2020 2923 Apr 03, 2020 3357 Apr 04, 2020 3893 Apr 05, 2020 4484 Apr 06, 2020 5181 Apr 07, 2020 5841 Apr 08, 2020 6698 Apr 09, 2020 7822 Apr 10, 2020 8852 Apr 11, 2020 10158 Apr 12, 2020 11513 Apr 13, 2020 13002 Apr 14, 2020 14776 Apr 15, 2020 16146 Apr 16, 2020 18105 Apr 17, 2020 20754 Apr 18, 2020 24324 Apr 19, 2020 26350 Apr 20, 2020 29433 Apr 21, 2020 31981 Apr 22, 2020 33940 Apr 23, 2020 36897 Apr 24, 2020 39509 Apr 25, 2020 42480 Apr 26, 2020 45351 Apr 27, 2020 48426 Apr 28, 2020 50646 Apr 29, 2020 53739 Apr 30, 2020 57300 May 01, 2020 62658 May 02, 2020 68606 May 03, 2020 74401 May 04, 2020 80115 May 05, 2020 85973 May 06, 2020 92676 May 07, 2020 98522 May 08, 2020 104189 May 09, 2020 109740 May 10, 2020 115909 May 11, 2020 121301 May 12, 2020 126004 May 13, 2020 130716 May 14, 2020 135464 May 15, 2020 138969 May 16, 2020 142824 May 17, 2020 146062 May 18, 2020 149607 May 19, 2020 152306 May 20, 2020 155219 May 21, 2020 158207 May 22, 2020 161397 May 23, 2020 163913 May 24, 2020 166473 May 25, 2020 169303 May 26, 2020 171443 May 27, 2020 173497 May 28, 2020 175829 May 29, 2020 178196 May 30, 2020 180791 May 31, 2020 183088 Jun 01, 2020 185374 Jun 02, 2020 187216 Jun 03, 2020 189214 Jun 04, 2020 191069 Jun 05, 2020 193061 Jun 06, 2020 195017 Jun 07, 2020 197018 Jun 08, 2020 198590 Jun 09, 2020 199785 Jun 10, 2020 201221 Jun 11, 2020 202935 Jun 12, 2020 204428 Jun 13, 2020 205905 Jun 14, 2020 207264 Jun 15, 2020 208680 Jun 16, 2020 209745 Jun 17, 2020 210785 Jun 18, 2020 211921 Jun 19, 2020 212978 Jun 20, 2020 213946 Jun 21, 2020 215014 Jun 22, 2020 216095 Jun 23, 2020 216906 Jun 24, 2020 217791 Jun 25, 2020 218604 Jun 26, 2020 219354 Jun 27, 2020 220071 Jun 28, 2020 220853 Jun 29, 2020 221598 Jun 30, 2020 222209 Jul 01, 2020 222871 Jul 02, 2020 223530 Jul 03, 2020 224210 Jul 04, 2020 224860 Jul 05, 2020 225545 Jul 06, 2020 226174 Jul 07, 2020 226795 Jul 08, 2020 227363 Jul 09, 2020 228000 Jul 10, 2020 228678 Jul 11, 2020 229357 Jul 12, 2020 230029 Jul 13, 2020 230642 Jul 14, 2020 231270 Jul 15, 2020 231801 Jul 16, 2020 232376 Jul 17, 2020 232954 Jul 18, 2020 233545 Jul 19, 2020 234123 Jul 20, 2020 234725 Jul 21, 2020 235363 Jul 22, 2020 235971 Jul 23, 2020 236616 Jul 24, 2020 237264 Jul 25, 2020 237947 Jul 26, 2020 238641 Jul 27, 2020 239315 Jul 28, 2020 239986 Jul 29, 2020 240664 Jul 30, 2020 241359 Jul 31, 2020 242049 Aug 01, 2020 242713 Aug 02, 2020 243406 Aug 03, 2020 244097 Aug 04, 2020 244784 Aug 05, 2020 245468 Aug 06, 2020 246154 Aug 07, 2020 246845 Aug 08, 2020 247534 Aug 09, 2020 248228 Aug 10, 2020 248922 Aug 11, 2020 249611 Aug 12, 2020 250303 Aug 13, 2020 250991 Aug 14, 2020 251686 Aug 15, 2020 252374 Aug 16, 2020 253064 Aug 17, 2020 253757 Aug 18, 2020 254448 Aug 19, 2020 255136 Aug 20, 2020 255826 Aug 21, 2020 256513 Aug 22, 2020 257124 Aug 23, 2020 257749 Aug 24, 2020 258430 Aug 25, 2020 259070 Aug 26, 2020 259707 Aug 27, 2020 260361 Aug 28, 2020 261038 Aug 29, 2020 261733 Aug 30, 2020 262418 Aug 31, 2020 263059 Sep 01, 2020 263684 Sep 02, 2020 264374 Sep 03, 2020 265066 Sep 04, 2020 265737 Sep 05, 2020 266357 Sep 06, 2020 267047 Sep 07, 2020 267742 Sep 08, 2020 268384 Sep 09, 2020 269079 Sep 10, 2020 269777 Sep 11, 2020 270447 Sep 12, 2020 271097 Sep 13, 2020 271793 Sep 14, 2020 272523 Sep 15, 2020 273273 Sep 16, 2020 274003 Sep 17, 2020 274808 Sep 18, 2020 275633 Sep 19, 2020 276493 Sep 20, 2020 277408 Sep 21, 2020 278388 Sep 22, 2020 279358 Sep 23, 2020 280408 Sep 24, 2020 281968 Sep 25, 2020 283760 Sep 26, 2020 285776 Sep 27, 2020 287993 Sep 28, 2020 290293 Sep 29, 2020 292601 Sep 30, 2020 295025 Oct 01, 2020 297729 Oct 02, 2020 300613 Oct 03, 2020 303940 Oct 04, 2020 307477 Oct 05, 2020 311559 Oct 06, 2020 314788 Oct 07, 2020 318111 Oct 08, 2020 321812 Oct 09, 2020 325917 Oct 10, 2020 330418 Oct 11, 2020 334813 Oct 12, 2020 339431 Oct 13, 2020 344004 Oct 14, 2020 347946 Oct 15, 2020 352995 Oct 16, 2020 357643 Oct 17, 2020 362253 Oct 18, 2020 367629 Oct 19, 2020 372628 Oct 20, 2020 377017 Oct 21, 2020 381430 Oct 22, 2020 386908 Oct 23, 2020 391361 Oct 24, 2020 395816 Oct 25, 2020 401040 Oct 26, 2020 405352 Oct 27, 2020 409022 Oct 28, 2020 413928 Oct 29, 2020 419196 Oct 30, 2020 424148 Oct 31, 2020 429409 Nov 01, 2020 434205 Nov 02, 2020 439355 Nov 03, 2020 445181 Nov 04, 2020 450436 Nov 05, 2020 456689 Nov 06, 2020 462518 Nov 07, 2020 468269 Nov 08, 2020 475166 Nov 09, 2020 481068 Nov 10, 2020 485545 Nov 11, 2020 491542 Nov 12, 2020 497516 Nov 13, 2020 503943 Nov 14, 2020 510214 Nov 15, 2020 516574 Nov 16, 2020 522456 Nov 17, 2020 526630 Nov 18, 2020 533068 Nov 19, 2020 539970 Nov 20, 2020 547138 Nov 21, 2020 553713 Nov 22, 2020 560579 Nov 23, 2020 566417 Nov 24, 2020 571102 Nov 25, 2020 577177 Nov 26, 2020 585095 Nov 27, 2020 592415 Nov 28, 2020 599213 Nov 29, 2020 605724 Nov 30, 2020 612248 Dec 01, 2020 617439 Dec 02, 2020 625189 Dec 03, 2020 632057 Dec 04, 2020 640050 Dec 05, 2020 647562 Dec 06, 2020 654841 Dec 07, 2020 660073 Dec 08, 2020 665218 Dec 09, 2020 671948 Dec 10, 2020 679163 Dec 11, 2020 685785 Dec 12, 2020 692210 Dec 13, 2020 698084 Dec 14, 2020 703502 Dec 15, 2020 708530 Dec 16, 2020 715241 Dec 17, 2020 722178 Dec 18, 2020 728637 Dec 19, 2020 735900 Dec 20, 2020 743697 Dec 21, 2020 750934 Dec 22, 2020 756586 Dec 23, 2020 764789 Dec 24, 2020 772104 Dec 25, 2020 779584

Between April and December, there were almost 800,000 cases of coronavirus in Moscow. This amount greatly increased between April and May/June. After July the cases of covid slowed down a lot, until October when the cases greatly increased again.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/692.tsv"}, "mark": "line", "encoding": {"color": {"value": "#4D4D4D"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Deaths"}, "field": "Deaths"}}, "title": ["Cumulative number of coronavirus (COVID-19)", "cases , recoveries , deaths in Moscow , Russia", "as of December 26 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Deaths Mar 01, 2020 0 Mar 02, 2020 0 Mar 03, 2020 0 Mar 04, 2020 0 Mar 05, 2020 0 Mar 06, 2020 0 Mar 07, 2020 0 Mar 08, 2020 0 Mar 09, 2020 0 Mar 10, 2020 0 Mar 11, 2020 0 Mar 12, 2020 0 Mar 13, 2020 0 Mar 14, 2020 0 Mar 15, 2020 0 Mar 16, 2020 0 Mar 17, 2020 0 Mar 18, 2020 0 Mar 19, 2020 0 Mar 20, 2020 0 Mar 21, 2020 0 Mar 22, 2020 0 Mar 23, 2020 0 Mar 24, 2020 2 Mar 25, 2020 2 Mar 26, 2020 3 Mar 27, 2020 4 Mar 28, 2020 5 Mar 29, 2020 10 Mar 30, 2020 15 Mar 31, 2020 15 Apr 01, 2020 19 Apr 02, 2020 24 Apr 03, 2020 27 Apr 04, 2020 29 Apr 05, 2020 31 Apr 06, 2020 31 Apr 07, 2020 38 Apr 08, 2020 38 Apr 09, 2020 50 Apr 10, 2020 58 Apr 11, 2020 72 Apr 12, 2020 82 Apr 13, 2020 95 Apr 14, 2020 106 Apr 15, 2020 113 Apr 16, 2020 127 Apr 17, 2020 148 Apr 18, 2020 176 Apr 19, 2020 204 Apr 20, 2020 233 Apr 21, 2020 261 Apr 22, 2020 288 Apr 23, 2020 325 Apr 24, 2020 366 Apr 25, 2020 404 Apr 26, 2020 435 Apr 27, 2020 479 Apr 28, 2020 546 Apr 29, 2020 611 Apr 30, 2020 658 May 01, 2020 695 May 02, 2020 729 May 03, 2020 764 May 04, 2020 816 May 05, 2020 866 May 06, 2020 905 May 07, 2020 956 May 08, 2020 1010 May 09, 2020 1068 May 10, 2020 1124 May 11, 2020 1179 May 12, 2020 1232 May 13, 2020 1290 May 14, 2020 1358 May 15, 2020 1432 May 16, 2020 1503 May 17, 2020 1580 May 18, 2020 1651 May 19, 2020 1726 May 20, 2020 1794 May 21, 2020 1867 May 22, 2020 1934 May 23, 2020 1993 May 24, 2020 2034 May 25, 2020 2110 May 26, 2020 2183 May 27, 2020 2254 May 28, 2020 2330 May 29, 2020 2408 May 30, 2020 2477 May 31, 2020 2553 Jun 01, 2020 2624 Jun 02, 2020 2685 Jun 03, 2020 2749 Jun 04, 2020 2806 Jun 05, 2020 2864 Jun 06, 2020 2919 Jun 07, 2020 2970 Jun 08, 2020 3029 Jun 09, 2020 3085 Jun 10, 2020 3138 Jun 11, 2020 3187 Jun 12, 2020 3231 Jun 13, 2020 3281 Jun 14, 2020 3334 Jun 15, 2020 3386 Jun 16, 2020 3434 Jun 17, 2020 3483 Jun 18, 2020 3531 Jun 19, 2020 3565 Jun 20, 2020 3597 Jun 21, 2020 3617 Jun 22, 2020 3643 Jun 23, 2020 3657 Jun 24, 2020 3669 Jun 25, 2020 3694 Jun 26, 2020 3714 Jun 27, 2020 3738 Jun 28, 2020 3761 Jun 29, 2020 3796 Jun 30, 2020 3831 Jul 01, 2020 3870 Jul 02, 2020 3904 Jul 03, 2020 3929 Jul 04, 2020 3953 Jul 05, 2020 3975 Jul 06, 2020 3999 Jul 07, 2020 4027 Jul 08, 2020 4059 Jul 09, 2020 4087 Jul 10, 2020 4116 Jul 11, 2020 4143 Jul 12, 2020 4168 Jul 13, 2020 4205 Jul 14, 2020 4234 Jul 15, 2020 4258 Jul 16, 2020 4271 Jul 17, 2020 4285 Jul 18, 2020 4299 Jul 19, 2020 4314 Jul 20, 2020 4331 Jul 21, 2020 4350 Jul 22, 2020 4364 Jul 23, 2020 4375 Jul 24, 2020 4389 Jul 25, 2020 4398 Jul 26, 2020 4411 Jul 27, 2020 4421 Jul 28, 2020 4434 Jul 29, 2020 4446 Jul 30, 2020 4460 Jul 31, 2020 4473 Aug 01, 2020 4485 Aug 02, 2020 4498 Aug 03, 2020 4510 Aug 04, 2020 4521 Aug 05, 2020 4534 Aug 06, 2020 4546 Aug 07, 2020 4560 Aug 08, 2020 4572 Aug 09, 2020 4585 Aug 10, 2020 4599 Aug 11, 2020 4611 Aug 12, 2020 4622 Aug 13, 2020 4633 Aug 14, 2020 4645 Aug 15, 2020 4656 Aug 16, 2020 4666 Aug 17, 2020 4677 Aug 18, 2020 4687 Aug 19, 2020 4698 Aug 20, 2020 4710 Aug 21, 2020 4720 Aug 22, 2020 4731 Aug 23, 2020 4741 Aug 24, 2020 4753 Aug 25, 2020 4764 Aug 26, 2020 4776 Aug 27, 2020 4786 Aug 28, 2020 4798 Aug 29, 2020 4809 Aug 30, 2020 4821 Aug 31, 2020 4832 Sep 01, 2020 4844 Sep 02, 2020 4857 Sep 03, 2020 4867 Sep 04, 2020 4878 Sep 05, 2020 4891 Sep 06, 2020 4905 Sep 07, 2020 4921 Sep 08, 2020 4933 Sep 09, 2020 4947 Sep 10, 2020 4956 Sep 11, 2020 4968 Sep 12, 2020 4982 Sep 13, 2020 4993 Sep 14, 2020 5006 Sep 15, 2020 5016 Sep 16, 2020 5025 Sep 17, 2020 5033 Sep 18, 2020 5044 Sep 19, 2020 5057 Sep 20, 2020 5069 Sep 21, 2020 5084 Sep 22, 2020 5100 Sep 23, 2020 5115 Sep 24, 2020 5129 Sep 25, 2020 5146 Sep 26, 2020 5164 Sep 27, 2020 5180 Sep 28, 2020 5203 Sep 29, 2020 5230 Sep 30, 2020 5254 Oct 01, 2020 5282 Oct 02, 2020 5314 Oct 03, 2020 5343 Oct 04, 2020 5370 Oct 05, 2020 5401 Oct 06, 2020 5442 Oct 07, 2020 5497 Oct 08, 2020 5530 Oct 09, 2020 5560 Oct 10, 2020 5595 Oct 11, 2020 5629 Oct 12, 2020 5687 Oct 13, 2020 5739 Oct 14, 2020 5796 Oct 15, 2020 5850 Oct 16, 2020 5906 Oct 17, 2020 5958 Oct 18, 2020 6009 Oct 19, 2020 6058 Oct 20, 2020 6121 Oct 21, 2020 6187 Oct 22, 2020 6249 Oct 23, 2020 6312 Oct 24, 2020 6380 Oct 25, 2020 6442 Oct 26, 2020 6503 Oct 27, 2020 6578 Oct 28, 2020 6644 Oct 29, 2020 6713 Oct 30, 2020 6768 Oct 31, 2020 6820 Nov 01, 2020 6873 Nov 02, 2020 6936 Nov 03, 2020 7004 Nov 04, 2020 7071 Nov 05, 2020 7140 Nov 06, 2020 7215 Nov 07, 2020 7289 Nov 08, 2020 7361 Nov 09, 2020 7429 Nov 10, 2020 7502 Nov 11, 2020 7573 Nov 12, 2020 7643 Nov 13, 2020 7712 Nov 14, 2020 7787 Nov 15, 2020 7859 Nov 16, 2020 7933 Nov 17, 2020 8009 Nov 18, 2020 8082 Nov 19, 2020 8159 Nov 20, 2020 8233 Nov 21, 2020 8308 Nov 22, 2020 8379 Nov 23, 2020 8455 Nov 24, 2020 8530 Nov 25, 2020 8603 Nov 26, 2020 8680 Nov 27, 2020 8756 Nov 28, 2020 8828 Nov 29, 2020 8902 Nov 30, 2020 8978 Dec 01, 2020 9053 Dec 02, 2020 9126 Dec 03, 2020 9203 Dec 04, 2020 9277 Dec 05, 2020 9349 Dec 06, 2020 9425 Dec 07, 2020 9496 Dec 08, 2020 9571 Dec 09, 2020 9645 Dec 10, 2020 9722 Dec 11, 2020 9798 Dec 12, 2020 9870 Dec 13, 2020 9945 Dec 14, 2020 10022 Dec 15, 2020 10095 Dec 16, 2020 10171 Dec 17, 2020 10243 Dec 18, 2020 10317 Dec 19, 2020 10394 Dec 20, 2020 10469 Dec 21, 2020 10540 Dec 22, 2020 10613 Dec 23, 2020 10689 Dec 24, 2020 10763 Dec 25, 2020 10840

it is difficult to read the graph if it contains cases and recoveries. it appears to show that deaths steadied in the summer then increased massively before and after suggesting two clear surges.

{"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": "multiColumn/data/6930.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Less likely"}, "field": "Less likely"}}, "title": ["Percentage of U.S. adults likely to change", "select behaviors if coronavirus (COVID-19)", "were to spread to their community as of March", "1 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Less likely Go to a concert 0.63 Use public transport 0.63 Go to a movie theater 0.62 Go to an amusement park 0.62 Go to a theater performance 0.62 Go to sporting events 0.61 Go to a museum 0.61 Go to a shopping mall 0.6 Go to a party or social event 0.59 Go to the gym 0.59 Go to a political rally 0.58 Use a ride-hailing service 0.57 Go out to eat in a restaurant or cafe 0.56 Take a vacation 0.51 Go to a work conference 0.51 Go to a religious gathering or meeting 0.5 Go to the grocery store 0.4 Invest in the stock market 0.37 Vote in a political election 0.33

There aren't any clear patterns to be observed from this visualization.

{"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": "multiColumn/data/6930.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Less likely"}, "field": "Less likely"}}, "title": ["Percentage of U.S. adults likely to change", "select behaviors if coronavirus (COVID-19)", "were to spread to their community as of March", "1 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Response Less likely Go to a concert 0.63 Use public transport 0.63 Go to a movie theater 0.62 Go to an amusement park 0.62 Go to a theater performance 0.62 Go to sporting events 0.61 Go to a museum 0.61 Go to a shopping mall 0.6 Go to a party or social event 0.59 Go to the gym 0.59 Go to a political rally 0.58 Use a ride-hailing service 0.57 Go out to eat in a restaurant or cafe 0.56 Take a vacation 0.51 Go to a work conference 0.51 Go to a religious gathering or meeting 0.5 Go to the grocery store 0.4 Invest in the stock market 0.37 Vote in a political election 0.33

Percentage of U.S. adults likely to change select behaviors if coronavirus (COVID-19) were to spread to their community is less likely for going to a concert and using public transportation. Percentage of U.S. adults likely to change select behaviors if coronavirus (COVID-19) were to spread to their community is more likely for voting and invest to stock market.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "multiColumn/data/6930.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#E66C37"}, "x": {"type": "quantitative", "axis": {"title": "Don't know/no opinion"}, "field": "Don\\'t know/no opinion"}, "y": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Response"}}, "title": ["Percentage of U.S. adults likely to change", "select behaviors if coronavirus (COVID-19)", "were to spread to their community as of March", "1 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Don't know/no opinion Response 0.1 Go to a concert 0.11 Use public transport 0.09 Go to a movie theater 0.1 Go to an amusement park 0.1 Go to a theater performance 0.11 Go to sporting events 0.11 Go to a museum 0.08 Go to a shopping mall 0.09 Go to a party or social event 0.11 Go to the gym 0.12 Go to a political rally 0.12 Use a ride-hailing service 0.07 Go out to eat in a restaurant or cafe 0.09 Take a vacation 0.16 Go to a work conference 0.11 Go to a religious gathering or meeting 0.07 Go to the grocery store 0.18 Invest in the stock market 0.11 Vote in a political election

the chart isnt laid out very well I understand the figures to read between 0.% and 0.15% This is a very low percentage. This shows that very few people are prepared to change to the imminance of covid covid.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "multiColumn/data/6930.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#12239E"}, "x": {"type": "quantitative", "axis": {"title": "More likely"}, "field": "More likely"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Response"}}, "title": ["Percentage of U.S. adults likely to change", "select behaviors if coronavirus (COVID-19)", "were to spread to their community as of March", "1 , 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

More likely Response 0.06 Go to a concert 0.05 Use public transport 0.06 Go to a movie theater 0.06 Go to an amusement park 0.05 Go to a theater performance 0.05 Go to sporting events 0.05 Go to a museum 0.07 Go to a shopping mall 0.05 Go to a party or social event 0.05 Go to the gym 0.05 Go to a political rally 0.06 Use a ride-hailing service 0.09 Go out to eat in a restaurant or cafe 0.11 Take a vacation 0.04 Go to a work conference 0.08 Go to a religious gathering or meeting 0.12 Go to the grocery store 0.07 Invest in the stock market 0.15 Vote in a political election

The most likely behavioural change would be to vote in a political election, where 15% of adults would be more likely to change this behaviour. The least likely change is to go to a work conference, where only 4% would change this behaviour. All the other behaviours asked about would result in at least 5% of adults likely to change their behaviour. Apart from voting in an election, only take a vacation and go to the grocery resulted in over 10% of adults likely to change this behaviour.

{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "multiColumn/data/6934.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#3580b1"}, "x": {"type": "quantitative", "axis": {"title": "Total"}, "field": "Total"}, "y": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}}, "title": ["U.S. e-commerce value of computers , computer", "equipment and software wholesale trade from", "2007 to 2018 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Total Year 272967 2018 252585 2017 242659 2016 243427 2015 241009 2014 237247 2013 232087 2012* 237215 2011* 244991 2010 226254 2009 247650 2008 253295 2007

There is a slight curve in numbers shown, starting with an initial peak of numbers that decreases slightly before increasing again. 2018 surpasses the initial peak.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/6934.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#333333"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "E-commerce"}, "field": "E-commerce"}}, "title": ["U.S. e-commerce value of computers , computer", "equipment and software wholesale trade from", "2007 to 2018 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year E-commerce 2018 66396 2017 65643 2016 62607 2015 62243 2014 62754 2013 63243 2010 68344 2009 62704 2008 66265 2007 67489

All years had ecommerce valued between 60,000 and 70,000. 2020 was the year with the highest ecommerce value at about 69,000.

{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "multiColumn/data/6938.tsv"}, "mark": "line", "encoding": {"color": {"value": "#DB4437"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Electric power sector"}, "field": "Electric power sector"}}, "title": ["Consumption of coal in the United States", "from 1990 to 2019 , by sector (in trillion", "British thermal units)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Electric power sector Dec 31, 1989 16261 Dec 31, 1994 17466 Dec 31, 1999 20220 Dec 31, 2004 20737 Dec 31, 2005 20462 Dec 31, 2006 20808 Dec 31, 2007 20513 Dec 31, 2008 18225 Dec 31, 2009 19133 Dec 31, 2010 18035 Dec 31, 2011 15821 Dec 31, 2012 16451 Dec 31, 2013 16427 Dec 31, 2014 14138 Dec 31, 2015 12996 Dec 31, 2016 12622 Dec 31, 2017 12053 Dec 31, 2018 10203

Consumption of coal increased between 1990 and 2005. Since 2005 the trend has broadly been for a reduction in the use of coal. The amount of coal used in 2020 is half of that used in 2005.

{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "multiColumn/data/6938.tsv"}, "mark": "line", "encoding": {"color": {"value": "#4285F4"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Industrial sector"}, "field": "Industrial sector"}}, "title": ["Consumption of coal in the United States", "from 1990 to 2019 , by sector (in trillion", "British thermal units)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Industrial sector Dec 31, 1989 2756 Dec 31, 1994 2488 Dec 31, 1999 2256 Dec 31, 2004 1954 Dec 31, 2005 1914 Dec 31, 2006 1865 Dec 31, 2007 1796 Dec 31, 2008 1392 Dec 31, 2009 1631 Dec 31, 2010 1561 Dec 31, 2011 1513 Dec 31, 2012 1546 Dec 31, 2013 1530 Dec 31, 2014 1380 Dec 31, 2015 1205 Dec 31, 2016 1195 Dec 31, 2017 1180 Dec 31, 2018 1186

Even steady decline from 1990 until roughly 2008. Steep drop in 2009 before a steady increase in 2010. Overall consumption is significantly lower by 2019.

{"config": {"background": "#fff", "area": {"fill": "#3e5c69"}, "line": {"stroke": "#3e5c69"}, "rect": {"fill": "#3e5c69"}, "bar": {"fill": "#3e5c69"}, "point": {"color": "#3e5c69"}, "axis": {"domainWidth": 0.5, "grid": true, "labelPadding": 2, "tickSize": 5, "tickWidth": 0.5, "titleFontWeight": "normal"}, "axisBand": {"grid": false}, "axisX": {"gridWidth": 0.2}, "axisY": {"gridDash": [3], "gridWidth": 0.4}, "legend": {"labelFontSize": 11, "padding": 1, "symbolType": "square"}, "range": {"category": ["#3e5c69", "#6793a6", "#182429", "#0570b0", "#3690c0", "#74a9cf", "#a6bddb", "#e2ddf2"]}}, "data": {"url": "multiColumn/data/6938.tsv"}, "mark": "area", "encoding": {"color": {"value": "#0570b0"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Commercial sector"}, "field": "Commercial sector"}}, "title": ["Consumption of coal in the United States", "from 1990 to 2019 , by sector (in trillion", "British thermal units)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Commercial sector Dec 31, 1989 124 Dec 31, 1994 117 Dec 31, 1999 92 Dec 31, 2004 97 Dec 31, 2005 65 Dec 31, 2006 70 Dec 31, 2007 81 Dec 31, 2008 73 Dec 31, 2009 70 Dec 31, 2010 62 Dec 31, 2011 44 Dec 31, 2012 41 Dec 31, 2013 40 Dec 31, 2014 31 Dec 31, 2015 24 Dec 31, 2016 21 Dec 31, 2017 19 Dec 31, 2018 17

There's a decrease in consumption from the year 1990 to about 2000, then suddenly increases and drops in about 2005 and then there's an increase after that and continues with a decreasing pattern.

{"config": {"background": "#fff", "area": {"fill": "#3e5c69"}, "line": {"stroke": "#3e5c69"}, "rect": {"fill": "#3e5c69"}, "bar": {"fill": "#3e5c69"}, "point": {"color": "#3e5c69"}, "axis": {"domainWidth": 0.5, "grid": true, "labelPadding": 2, "tickSize": 5, "tickWidth": 0.5, "titleFontWeight": "normal"}, "axisBand": {"grid": false}, "axisX": {"gridWidth": 0.2}, "axisY": {"gridDash": [3], "gridWidth": 0.4}, "legend": {"labelFontSize": 11, "padding": 1, "symbolType": "square"}, "range": {"category": ["#3e5c69", "#6793a6", "#182429", "#0570b0", "#3690c0", "#74a9cf", "#a6bddb", "#e2ddf2"]}}, "data": {"url": "multiColumn/data/6942.tsv"}, "mark": "line", "encoding": {"color": {"value": "#3690c0"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Short-term assets"}, "field": "Short-term assets"}}, "title": ["Net investment of insurance companies ,", "pension funds and trusts in the United Kingdom", "(UK) from 2007 to 2018 , by asset type (in", "billion GBP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Short-term assets Dec 31, 2006 41.1 Dec 31, 2007 −4.8 Dec 31, 2008 −4.2 Dec 31, 2009 −7.6 Dec 31, 2010 10.9 Dec 31, 2011 15 Dec 31, 2012 24.9 Dec 31, 2013 5.9 Dec 31, 2014 4.5 Dec 31, 2015 −8.5 Dec 31, 2016 23.7 Dec 31, 2017 −22.1

The highest net investment of insurance companies was in 2007, with £40 billion short term assets. The lowest net investment of insurance companies was in 2018 with £-20 billion short term assets.

{"config": {"background": "#fff", "area": {"fill": "#3e5c69"}, "line": {"stroke": "#3e5c69"}, "rect": {"fill": "#3e5c69"}, "bar": {"fill": "#3e5c69"}, "point": {"color": "#3e5c69"}, "axis": {"domainWidth": 0.5, "grid": true, "labelPadding": 2, "tickSize": 5, "tickWidth": 0.5, "titleFontWeight": "normal"}, "axisBand": {"grid": false}, "axisX": {"gridWidth": 0.2}, "axisY": {"gridDash": [3], "gridWidth": 0.4}, "legend": {"labelFontSize": 11, "padding": 1, "symbolType": "square"}, "range": {"category": ["#3e5c69", "#6793a6", "#182429", "#0570b0", "#3690c0", "#74a9cf", "#a6bddb", "#e2ddf2"]}}, "data": {"url": "multiColumn/data/6942.tsv"}, "mark": "line", "encoding": {"color": {"value": "#3690c0"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Short-term assets"}, "field": "Short-term assets"}}, "title": ["Net investment of insurance companies ,", "pension funds and trusts in the United Kingdom", "(UK) from 2007 to 2018 , by asset type (in", "billion GBP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Short-term assets Dec 31, 2006 41.1 Dec 31, 2007 −4.8 Dec 31, 2008 −4.2 Dec 31, 2009 −7.6 Dec 31, 2010 10.9 Dec 31, 2011 15 Dec 31, 2012 24.9 Dec 31, 2013 5.9 Dec 31, 2014 4.5 Dec 31, 2015 −8.5 Dec 31, 2016 23.7 Dec 31, 2017 −22.1

Since 2007 the highest investment by insurance companies in pension funds and trusts was in 2007 at over £40bn. In 2018 the investment by insurance companies in Pension Funds and Trusts was less than £-20bn.

{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "multiColumn/data/6942.tsv"}, "mark": "line", "encoding": {"color": {"value": "#ec8431"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Gilts*"}, "field": "Gilts*"}}, "title": ["Net investment of insurance companies ,", "pension funds and trusts in the United Kingdom", "(UK) from 2007 to 2018 , by asset type (in", "billion GBP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Gilts* Dec 31, 2006 −0.4 Dec 31, 2007 −19.6 Dec 31, 2008 13.9 Dec 31, 2009 29.2 Dec 31, 2010 −0.8 Dec 31, 2011 −10.2 Dec 31, 2012 12.6 Dec 31, 2013 10.2 Dec 31, 2014 0.8 Dec 31, 2015 37.8 Dec 31, 2016 18.5 Dec 31, 2017 15

Net investments peak at 2016 and lowest point is at 2008. Each 2 years the data increases and decreases consistently.

{"config": {"background": "#fff", "area": {"fill": "#4572a7"}, "line": {"stroke": "#4572a7", "strokeWidth": 2}, "rect": {"fill": "#4572a7"}, "bar": {"fill": "#4572a7"}, "point": {"color": "#4572a7", "strokeWidth": 1.5, "size": 50}, "axis": {"bandPosition": 0.5, "grid": true, "gridColor": "#000000", "gridOpacity": 1, "gridWidth": 0.5, "labelPadding": 10, "tickSize": 5, "tickWidth": 0.5}, "axisBand": {"grid": false, "tickExtra": true}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 50, "symbolType": "square"}, "range": {"category": ["#4572a7", "#aa4643", "#8aa453", "#71598e", "#4598ae", "#d98445", "#94aace", "#d09393", "#b9cc98", "#a99cbc"]}}, "data": {"url": "multiColumn/data/6942.tsv"}, "mark": "line", "encoding": {"color": {"value": "#a99cbc"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Overseas securities"}, "field": "Overseas securities"}}, "title": ["Net investment of insurance companies ,", "pension funds and trusts in the United Kingdom", "(UK) from 2007 to 2018 , by asset type (in", "billion GBP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Overseas securities Dec 31, 2006 44.2 Dec 31, 2007 15.3 Dec 31, 2008 43.3 Dec 31, 2009 24.8 Dec 31, 2010 13.3 Dec 31, 2011 46.5 Dec 31, 2012 18.1 Dec 31, 2013 −0.7 Dec 31, 2014 18.8 Dec 31, 2015 −31.9 Dec 31, 2016 1.3 Dec 31, 2017 −49.7

The years differentiates for how many net investments of insurance companies, pension funds and trusts in the United Kingdom.

{"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": "multiColumn/data/697.tsv"}, "mark": "line", "encoding": {"color": {"value": "#666666"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Agriculture"}, "field": "Agriculture"}}, "title": ["Norway : Distribution of employment by economic", "sector from 2010 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Agriculture Dec 31, 2009 0.0255 Dec 31, 2010 0.0237 Dec 31, 2011 0.0223 Dec 31, 2012 0.0221 Dec 31, 2013 0.0225 Dec 31, 2014 0.0201 Dec 31, 2015 0.0209 Dec 31, 2016 0.0206 Dec 31, 2017 0.0211 Dec 31, 2018 0.0206 Dec 31, 2019 0.0201

As time increases, employment steadily decreases in the agriculture sector.

{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "multiColumn/data/697.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Industry"}, "field": "Industry"}}, "title": ["Norway : Distribution of employment by economic", "sector from 2010 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Industry Dec 31, 2009 0.197 Dec 31, 2010 0.2025 Dec 31, 2011 0.2023 Dec 31, 2012 0.2032 Dec 31, 2013 0.2044 Dec 31, 2014 0.2012 Dec 31, 2015 0.1947 Dec 31, 2016 0.1941 Dec 31, 2017 0.1947 Dec 31, 2018 0.1928 Dec 31, 2019 0.191

The distribution of employment in Norway has changed little in recent 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": "multiColumn/data/697.tsv"}, "mark": "area", "encoding": {"color": {"value": "#182429"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Services"}, "field": "Services"}}, "title": ["Norway : Distribution of employment by economic", "sector from 2010 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Services Dec 31, 2009 0.7776 Dec 31, 2010 0.7738 Dec 31, 2011 0.7754 Dec 31, 2012 0.7748 Dec 31, 2013 0.7731 Dec 31, 2014 0.7787 Dec 31, 2015 0.7844 Dec 31, 2016 0.7853 Dec 31, 2017 0.7843 Dec 31, 2018 0.7867 Dec 31, 2019 0.7889

The number of services has been maintained over the last ten years.

{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "multiColumn/data/69.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#8ca252"}, "x": {"type": "quantitative", "axis": {"title": "Government Laboratories"}, "field": "Government Laboratories"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "state of India"}}, "title": ["Number of government and private testing", "centers for the coronavirus (COVID-19) across", "India as of December 20 , 2020 , by state"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Government Laboratories state of India 1196 Total 124 Uttar Pradesh 94 Maharashtra 78 Andhra Pradesh 77 Madhya Pradesh 74 West Bengal 67 Tamil Nadu 60 Karnataka 53 Uttarakhand 48 Bihar 43 Odisha 42 Kerala 42 Gujarat 39 Rajasthan 37 Punjab 36 Chhattisgarh 33 Jharkhand 30 Himachal Pradesh 28 Delhi 25 Jammu & Kashmir 23 Haryana 21 Telangana 21 Arunachal Pradesh 16 Assam 15 Meghalaya 14 Nagaland 11 Mizoram 7 Manipur 7 Chandigarh 7 Goa 6 Puducherry 5 Andaman & Nicobar islands 4 Tripura 4 Leh-Ladakh 2 Sikkim 2 Lakshadweep 1 Dadra & Nagar Haveli 0 Mobile testing labs

Uttar Pradesh has the most government and private testing facilities for covid in India.

{"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": "multiColumn/data/704.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#00609f"}, "x": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "region of United Kingdom"}, "y": {"type": "quantitative", "axis": {"title": "Dec '19"}, "field": "Dec \\'19"}}, "title": ["Ratio of household rent to income in the", "United Kingdom (UK) December 2018 and December", "2019 , by region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

region of United Kingdom Dec '19 London 0.359 South West 0.325 South East 0.318 UK Average 0.311 East England 0.307 UK Average (Excluding London) 0.297 Wales 0.287 East Midlands 0.288 North West 0.287 West Midlands 0.296 Scotland 0.27 Northern Ireland 0.277 Yorkshire & Humber 0.267 North East 0.244

London has the highest ratio of household rent to income with the North east being the lowest.

{"config": {"background": "#fff", "area": {"fill": "#4572a7"}, "line": {"stroke": "#4572a7", "strokeWidth": 2}, "rect": {"fill": "#4572a7"}, "bar": {"fill": "#4572a7"}, "point": {"color": "#4572a7", "strokeWidth": 1.5, "size": 50}, "axis": {"bandPosition": 0.5, "grid": true, "gridColor": "#000000", "gridOpacity": 1, "gridWidth": 0.5, "labelPadding": 10, "tickSize": 5, "tickWidth": 0.5}, "axisBand": {"grid": false, "tickExtra": true}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 50, "symbolType": "square"}, "range": {"category": ["#4572a7", "#aa4643", "#8aa453", "#71598e", "#4598ae", "#d98445", "#94aace", "#d09393", "#b9cc98", "#a99cbc"]}}, "data": {"url": "multiColumn/data/706.tsv"}, "mark": "area", "encoding": {"color": {"value": "#4572a7"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Winning team"}, "field": "Winning team"}}, "title": ["Amount a player receives for playing in", "the NFL Super Bowl 2020 - 2030 (in 1,000", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Winning team Dec 31, 2019 130 Dec 31, 2020 150 Dec 31, 2021 157 Dec 31, 2022 164 Dec 31, 2023 171 Dec 31, 2024 178 Dec 31, 2025 188 Dec 31, 2026 198 Dec 31, 2027 208 Dec 31, 2028 218 Dec 31, 2029 228

Winning NFL teams progressively earn significantly more money in USD every year.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "multiColumn/data/708.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#6B007B"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Basketball fund"}, "y": {"type": "quantitative", "axis": {"title": "Academic Enhancement fund"}, "field": "Academic Enhancement fund"}}, "title": ["NCAA revenue returned to Division I conferences", "and member institutions from 2010/11 to 2019/20", "(in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Basketball fund Academic Enhancement fund 167.6 49.04 168.5 49.22 164.94 48.02 159.71 46.73 205 26.54 199.2 26.92 193.58 25.1 188.31 24.41 202 24.6 193.9 22.4

Basketball fund increases when the academic enhancement fund decreases.

{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "multiColumn/data/708.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ffff99"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Basketball fund"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Special Assistance Fund"}, "field": "Special Assistance Fund"}}, "title": ["NCAA revenue returned to Division I conferences", "and member institutions from 2010/11 to 2019/20", "(in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Basketball fund Special Assistance Fund 167.6 18.75 168.5 18.63 164.94 18.18 159.71 17.69

The bar graph is confusing and does not present the data well. The highest amount from the basketball fund is 168.5. The highest amount from the Special Assistance Fund is 17.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/708.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "quantitative", "axis": {"title": "Conference Grant*"}, "field": "Conference Grant*"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}}, "title": ["NCAA revenue returned to Division I conferences", "and member institutions from 2010/11 to 2019/20", "(in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Conference Grant* Year 9.93 2019/20 9.97 2018/19 9.72 2017/18 9.46 2016/17 9.2 2015/16 8.96 2014/15 8.7 2013/14 8.47 2012/13 8.3 2011/12

As the years increase, so does the conference grant. however there was a dip in the conference grant in the year 2019/2020.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/709.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#999999"}, "x": {"type": "quantitative", "axis": {"labelAngle": -90, "title": "2020*"}, "field": "2020*"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "United States federal executive department"}}, "title": ["Proposed federal spending by the U.S. government", "on cyber security for selected government", "agencies from FY 2020 to FY 2021 (in million", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

2020* United States federal executive department 231.2 Department of Agriculture 514.3 Department of Commerce 10075 Department of Defense 166.2 Department of Education 550.4 Department of Energy 475.7 Department of Health & Human Services 2574.1 Department of Homeland Security 68.2 Department of Housing & Urban Development 900.5 Department of Justice 92.2 Department of Labor 405.8 Department of State 121.4 Department of the Interior 588.4 Department of the Treasury 262.1 Department of Transportation 524.6 Department of Veterans Affairs 32.5 Environmental Protection Agency 82.4 General Services Administration 166.6 National Aeronautics & Space Administration 226.3 National Science Foundation 27.5 Nuclear Regulatory Commission 47.1 Office of Personnel Management 15.7 Small Business Administration 207.6 Social Security Administration 42.5 U.S. Agency for International Developmen 393.6 Non-CFO Act Agencies

The visulazation shows that the department that has the most Proposed federal spending by the U.S. governmenton cyber security for selected governmentagencies from FY 2020 to FY 2021 was the department of defense. Numerous other departments received no proposed funding for cyber security.

{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "multiColumn/data/70.tsv"}, "mark": "line", "encoding": {"color": {"value": "#f2cf5b"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "65 years +"}, "field": "65 years +"}}, "title": ["India : Age distribution from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year 65 years + Dec 31, 2008 0.0502 Dec 31, 2009 0.0508 Dec 31, 2010 0.0517 Dec 31, 2011 0.0527 Dec 31, 2012 0.0536 Dec 31, 2013 0.0548 Dec 31, 2014 0.0561 Dec 31, 2015 0.0579 Dec 31, 2016 0.0598 Dec 31, 2017 0.0618 Dec 31, 2018 0.0638

India 65+ year old population increased .013 somethings in 10 years.

{"config": {"background": "#fff", "area": {"fill": "#4572a7"}, "line": {"stroke": "#4572a7", "strokeWidth": 2}, "rect": {"fill": "#4572a7"}, "bar": {"fill": "#4572a7"}, "point": {"color": "#4572a7", "strokeWidth": 1.5, "size": 50}, "axis": {"bandPosition": 0.5, "grid": true, "gridColor": "#000000", "gridOpacity": 1, "gridWidth": 0.5, "labelPadding": 10, "tickSize": 5, "tickWidth": 0.5}, "axisBand": {"grid": false, "tickExtra": true}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 50, "symbolType": "square"}, "range": {"category": ["#4572a7", "#aa4643", "#8aa453", "#71598e", "#4598ae", "#d98445", "#94aace", "#d09393", "#b9cc98", "#a99cbc"]}}, "data": {"url": "multiColumn/data/712.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#a99cbc"}, "x": {"type": "quantitative", "axis": {"title": "World"}, "field": "World"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}}, "title": ["Percentage of global population accessing", "the internet from 2005 to 2019 , by market", "maturity"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

World Year 0.536 2019* 0.514 2018 0.49 2017 0.448 2016 0.415 2015 0.391 2014 0.37 2013 0.348 2012 0.318 2011 0.293 2010 0.258 2009 0.231 2008 0.206 2007 0.184 2006 0.168 2005

The number of the population accessing the internet increased every year. The largest increase of population accessing the internet was in 2017.

{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "multiColumn/data/712.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1696d2"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "LDCs"}, "field": "LDCs"}}, "title": ["Percentage of global population accessing", "the internet from 2005 to 2019 , by market", "maturity"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year LDCs 2019* 0.191 2018 0.176 2017 0.161 2016 0.143 2015 0.124 2014 0.108 2013 0.093 2012 0.081 2011 0.066 2010 0.055 2009 0.043 2008 0.031 2007 0.02 2006 0.016 2005 0.014

The global population access to internet increases steadily over the researches period proportionally to the increase of LDC.

{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "multiColumn/data/715.tsv"}, "mark": "area", "encoding": {"color": {"value": "#5254a3"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Agriculture"}, "field": "Agriculture"}}, "title": ["France : Distribution of the workforce across", "economic sectors from 2010 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Agriculture Dec 31, 2009 0.0291 Dec 31, 2010 0.0291 Dec 31, 2011 0.0292 Dec 31, 2012 0.0308 Dec 31, 2013 0.0285 Dec 31, 2014 0.0275 Dec 31, 2015 0.0287 Dec 31, 2016 0.0263 Dec 31, 2017 0.0251 Dec 31, 2018 0.0244 Dec 31, 2019 0.0238

Between 2010 and 2012, the distribution of workers in the agricultural sector remained the same at 0.028. This amount then rose steadily to around 0.032 in 2013 which was the highest year recorded. The agricultural workforce then dropped steadily until 2015, rose a little in 2016 but then continued to drop until 2018 where only 0.023 was recorded.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/715.tsv"}, "mark": "area", "encoding": {"color": {"value": "#DCDCDC"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Industry"}, "field": "Industry"}}, "title": ["France : Distribution of the workforce across", "economic sectors from 2010 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Industry Dec 31, 2009 0.2225 Dec 31, 2010 0.2217 Dec 31, 2011 0.2176 Dec 31, 2012 0.2131 Dec 31, 2013 0.2053 Dec 31, 2014 0.2038 Dec 31, 2015 0.2029 Dec 31, 2016 0.2049 Dec 31, 2017 0.2029 Dec 31, 2018 0.2009 Dec 31, 2019 0.1989

2010 saw the highest distribution of economic sectors with a value of 0.225. Since then, distribution has steadily dropped to just under 0.20 in 2020.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/715.tsv"}, "mark": "area", "encoding": {"color": {"value": "#DCDCDC"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Industry"}, "field": "Industry"}}, "title": ["France : Distribution of the workforce across", "economic sectors from 2010 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Industry Dec 31, 2009 0.2225 Dec 31, 2010 0.2217 Dec 31, 2011 0.2176 Dec 31, 2012 0.2131 Dec 31, 2013 0.2053 Dec 31, 2014 0.2038 Dec 31, 2015 0.2029 Dec 31, 2016 0.2049 Dec 31, 2017 0.2029 Dec 31, 2018 0.2009 Dec 31, 2019 0.1989

The distribution of the workforce in the industry sector has steadily declined since 2010, with the exception of a very small increase in 2016.

{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "multiColumn/data/716.tsv"}, "mark": "area", "encoding": {"color": {"value": "#d67195"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "15-64 years"}, "field": "15-64 years"}}, "title": ["Hong Kong : Age distribution from 2009 to", "2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year 15-64 years Dec 31, 2008 0.75 Dec 31, 2009 0.7512 Dec 31, 2010 0.7511 Dec 31, 2011 0.75 Dec 31, 2012 0.7473 Dec 31, 2013 0.7428 Dec 31, 2014 0.7365 Dec 31, 2015 0.7305 Dec 31, 2016 0.7222 Dec 31, 2017 0.7122 Dec 31, 2018 0.7018

The chart shows that the population of Hong Kong is gradually aging as more people are living outside of the 15-64 boundary. This can suggest an increase in life expectancy and can also suggest that infant mortality rates are decreasing as there are more people under the age of 15 as well as more people above the age of 64.

{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "multiColumn/data/716.tsv"}, "mark": "line", "encoding": {"color": {"value": "#6d904f"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "65 years +"}, "field": "65 years +"}}, "title": ["Hong Kong : Age distribution from 2009 to", "2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year 65 years + Dec 31, 2008 0.1269 Dec 31, 2009 0.1295 Dec 31, 2010 0.1324 Dec 31, 2011 0.1365 Dec 31, 2012 0.1415 Dec 31, 2013 0.1467 Dec 31, 2014 0.1519 Dec 31, 2015 0.1575 Dec 31, 2016 0.1631 Dec 31, 2017 0.1688 Dec 31, 2018 0.175

Age distribution from 0.12 in 2009. Gradually increased to 0.19 in 2019.

{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "multiColumn/data/717.tsv"}, "mark": "area", "encoding": {"color": {"value": "#e45756"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Services"}, "field": "Services"}}, "title": ["Canada : Distribution of the workforce across", "economic sectors from 2010 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Services Dec 31, 2009 0.7804 Dec 31, 2010 0.7793 Dec 31, 2011 0.7779 Dec 31, 2012 0.779 Dec 31, 2013 0.7811 Dec 31, 2014 0.7843 Dec 31, 2015 0.7871 Dec 31, 2016 0.7896 Dec 31, 2017 0.7894 Dec 31, 2018 0.7907 Dec 31, 2019 0.7918

the distribution of the workforce has remained pretty constant with a slight increase from 2010 to 2018.

{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "multiColumn/data/721.tsv"}, "mark": "line", "encoding": {"color": {"value": "#f0027f"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Middle East & Africa"}, "field": "Middle East & Africa"}}, "title": ["Revenue per available room (RevPAR) of Marriott", "International hotels worldwide from 2010", "to 2019 , by region (in U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Middle East & Africa Dec 31, 2009 93.86 Dec 31, 2010 83.11 Dec 31, 2011 82.25 Dec 31, 2012 82.22 Dec 31, 2013 114.47 Dec 31, 2014 110.85 Dec 31, 2015 106.49 Dec 31, 2016 106.33 Dec 31, 2017 102.39 Dec 31, 2018 107.2

The revenue was at a steady level of 80, reaching a peak of 118 in 2014. There has been a slow decline until 2018 when the figures begin to increase again.

{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "multiColumn/data/721.tsv"}, "mark": "line", "encoding": {"color": {"value": "#52d2ca"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Overall worldwide"}, "field": "Overall worldwide"}}, "title": ["Revenue per available room (RevPAR) of Marriott", "International hotels worldwide from 2010", "to 2019 , by region (in U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Overall worldwide Dec 31, 2009 103.3 Dec 31, 2010 129.96 Dec 31, 2011 115.91 Dec 31, 2012 122.32 Dec 31, 2013 131.83 Dec 31, 2014 132.3 Dec 31, 2015 128.37 Dec 31, 2016 131.14 Dec 31, 2017 134.58 Dec 31, 2018 134.6

The RevPAR of Marriott International hotels worldwide has increased by a small percentage overall between 2010 and 2019.

{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "multiColumn/data/722.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "nominal", "axis": {"labelAngle": 90}, "bin": false, "field": "Leading footwear retailers"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "2012*"}, "field": "2012*"}}, "title": ["Leading footwear retailers , ranked by market", "share in the United Kingdom (UK) in 2007", "and 2012"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Leading footwear retailers 2012* C&J Clark 0.111 Sports Direct 0.079 Marks & Spencer 0.071 JD Sports Fashion 0.066 Next 0.049 Kurt Geiger 0.042 Primark 0.039 New Look 0.038 Office 0.034 Schuh 0.032 Shoe Zone Group 0.03 River Island 0.029 Asda 0.028 Barratt Priceless 0.025 Dune Group 0.022 Brantano 0.019 Debenhams 0.018 Tesco 0.017

C&J Clark were the biggest retailer of 2012. Tesco had the lowest market share.

{"config": {"background": "#fff", "area": {"fill": "#3e5c69"}, "line": {"stroke": "#3e5c69"}, "rect": {"fill": "#3e5c69"}, "bar": {"fill": "#3e5c69"}, "point": {"color": "#3e5c69"}, "axis": {"domainWidth": 0.5, "grid": true, "labelPadding": 2, "tickSize": 5, "tickWidth": 0.5, "titleFontWeight": "normal"}, "axisBand": {"grid": false}, "axisX": {"gridWidth": 0.2}, "axisY": {"gridDash": [3], "gridWidth": 0.4}, "legend": {"labelFontSize": 11, "padding": 1, "symbolType": "square"}, "range": {"category": ["#3e5c69", "#6793a6", "#182429", "#0570b0", "#3690c0", "#74a9cf", "#a6bddb", "#e2ddf2"]}}, "data": {"url": "multiColumn/data/725.tsv"}, "mark": "area", "encoding": {"color": {"value": "#74a9cf"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Rest of the World"}, "field": "Rest of the World"}}, "title": ["Internet penetration rate in the Middle", "East compared to the global internet penetration", "rate from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Rest of the World Dec 31, 2008 0.266 Dec 31, 2009 0.287 Dec 31, 2010 0.326 Dec 31, 2011 0.341 Dec 31, 2013 0.421 Dec 31, 2014 0.462 Dec 31, 2015 0.493 Dec 31, 2016 0.489 Dec 31, 2017 0.541 Dec 31, 2018 0.568

There has been an increase in the internet penetration rate across the ten year period, although there was a slight drop around 2017. The biggest increase came between 2012 and 2016.

{"config": {"background": "#fff", "area": {"fill": "#4572a7"}, "line": {"stroke": "#4572a7", "strokeWidth": 2}, "rect": {"fill": "#4572a7"}, "bar": {"fill": "#4572a7"}, "point": {"color": "#4572a7", "strokeWidth": 1.5, "size": 50}, "axis": {"bandPosition": 0.5, "grid": true, "gridColor": "#000000", "gridOpacity": 1, "gridWidth": 0.5, "labelPadding": 10, "tickSize": 5, "tickWidth": 0.5}, "axisBand": {"grid": false, "tickExtra": true}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 50, "symbolType": "square"}, "range": {"category": ["#4572a7", "#aa4643", "#8aa453", "#71598e", "#4598ae", "#d98445", "#94aace", "#d09393", "#b9cc98", "#a99cbc"]}}, "data": {"url": "multiColumn/data/731.tsv"}, "mark": "area", "encoding": {"color": {"value": "#aa4643"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Broadcasting"}, "field": "Broadcasting"}}, "title": ["Manchester United revenue by segment from", "2009 to 2020 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Broadcasting Dec 31, 2008 128.9 Dec 31, 2009 135.83 Dec 31, 2010 154.2 Dec 31, 2011 136.76 Dec 31, 2012 133.66 Dec 31, 2013 178.6 Dec 31, 2014 141.64 Dec 31, 2015 184.7 Dec 31, 2016 255.27 Dec 31, 2017 268.47 Dec 31, 2018 306.32 Dec 31, 2019 173.41

Manchester United revenue has fluctuated between 2009 and 2020, but there is higher in 2020 than it was in 2009.

{"config": {"background": "#fff", "area": {"fill": "#4572a7"}, "line": {"stroke": "#4572a7", "strokeWidth": 2}, "rect": {"fill": "#4572a7"}, "bar": {"fill": "#4572a7"}, "point": {"color": "#4572a7", "strokeWidth": 1.5, "size": 50}, "axis": {"bandPosition": 0.5, "grid": true, "gridColor": "#000000", "gridOpacity": 1, "gridWidth": 0.5, "labelPadding": 10, "tickSize": 5, "tickWidth": 0.5}, "axisBand": {"grid": false, "tickExtra": true}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 50, "symbolType": "square"}, "range": {"category": ["#4572a7", "#aa4643", "#8aa453", "#71598e", "#4598ae", "#d98445", "#94aace", "#d09393", "#b9cc98", "#a99cbc"]}}, "data": {"url": "multiColumn/data/731.tsv"}, "mark": "area", "encoding": {"color": {"value": "#aa4643"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Broadcasting"}, "field": "Broadcasting"}}, "title": ["Manchester United revenue by segment from", "2009 to 2020 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Broadcasting Dec 31, 2008 128.9 Dec 31, 2009 135.83 Dec 31, 2010 154.2 Dec 31, 2011 136.76 Dec 31, 2012 133.66 Dec 31, 2013 178.6 Dec 31, 2014 141.64 Dec 31, 2015 184.7 Dec 31, 2016 255.27 Dec 31, 2017 268.47 Dec 31, 2018 306.32 Dec 31, 2019 173.41

The Line graph illustrates that Manchester United revenue (in million U.S. dollars) peaked in 2018 at around 300 millions. The worst year for revenue was 2009 where revenue fell to under 150 million.

{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "multiColumn/data/742.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#bf5b17"}, "x": {"type": "nominal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Deaths"}, "field": "Deaths"}}, "title": ["Patients profile of coronavirus disease", "(COVID-19) cases in Japan as of May 7 , 2020", ", by age group and state of health"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Deaths 9 years and younger 0 10 to 19 years 0 20 to 29 years 0 30 to 39 years 2 40 to 49 years 8 50 to 59 years 16 60 to 69 years 44 70 to 79 years 102 80 years and older 228 Age unknown 3

The rate of deaths for those diagnosed with covid 19 rose exponentially with ago.

{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "multiColumn/data/743.tsv"}, "mark": "area", "encoding": {"color": {"value": "#56cc60"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "0-14 years"}, "field": "0-14 years"}}, "title": ["Ghana : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year 0-14 years Dec 31, 2008 0.396 Dec 31, 2009 0.3929 Dec 31, 2010 0.3908 Dec 31, 2011 0.3887 Dec 31, 2012 0.3865 Dec 31, 2013 0.3842 Dec 31, 2014 0.3819 Dec 31, 2015 0.3802 Dec 31, 2016 0.3782 Dec 31, 2017 0.376 Dec 31, 2018 0.3736

The age structure of Ghana has decreased at a steady rate from 2009 to 2019. It reduces from 0.4 to approximately 0.375, but the scale is not detailed enough to show the exact number.

{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "multiColumn/data/743.tsv"}, "mark": "area", "encoding": {"color": {"value": "#56cc60"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "0-14 years"}, "field": "0-14 years"}}, "title": ["Ghana : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year 0-14 years Dec 31, 2008 0.396 Dec 31, 2009 0.3929 Dec 31, 2010 0.3908 Dec 31, 2011 0.3887 Dec 31, 2012 0.3865 Dec 31, 2013 0.3842 Dec 31, 2014 0.3819 Dec 31, 2015 0.3802 Dec 31, 2016 0.3782 Dec 31, 2017 0.376 Dec 31, 2018 0.3736

In Ghana, the birth rate is steadily decreasing over the past 10 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": "multiColumn/data/743.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fddaec"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "15-64 years"}, "field": "15-64 years"}}, "title": ["Ghana : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year 15-64 years Dec 31, 2008 0.5757 Dec 31, 2009 0.5788 Dec 31, 2010 0.5804 Dec 31, 2011 0.5822 Dec 31, 2012 0.584 Dec 31, 2013 0.5859 Dec 31, 2014 0.588 Dec 31, 2015 0.5895 Dec 31, 2016 0.5913 Dec 31, 2017 0.5934 Dec 31, 2018 0.5954

The age structure reached its highest peak value of 0.6 in 2019.

{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "multiColumn/data/743.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ffbf79"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "65 years and older"}, "field": "65 years and older"}}, "title": ["Ghana : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year 65 years and older Dec 31, 2008 0.0283 Dec 31, 2009 0.0284 Dec 31, 2010 0.0287 Dec 31, 2011 0.0291 Dec 31, 2012 0.0295 Dec 31, 2013 0.0298 Dec 31, 2014 0.03 Dec 31, 2015 0.0303 Dec 31, 2016 0.0305 Dec 31, 2017 0.0307 Dec 31, 2018 0.031

Over the years the amount of citizens in Ghana over 65 has been gradually increasing.

{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "multiColumn/data/745.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "nominal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "United Kingdom"}, "field": "United Kingdom"}}, "title": ["Number of high net worth individuals in", "the United Kingdom (UK) and Europe from 2009", "to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year United Kingdom 2019 591 2018 556 2017* 575 2016 568.3 2015 552.8 2014 549.6 2013 527.5 2012 465.1 2011 441.3 2010 454.3 2009 448.1

The number of high net individuals has slowly increased as the years have gone on, with a slight dip in 2018 and then a peak at its highest yet in 2019.

{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "multiColumn/data/746.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ec8431"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "0-14 years"}, "field": "0-14 years"}}, "title": ["Indonesia : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year 0-14 years Dec 31, 2008 0.2891 Dec 31, 2009 0.2883 Dec 31, 2010 0.2852 Dec 31, 2011 0.2827 Dec 31, 2012 0.2804 Dec 31, 2013 0.2777 Dec 31, 2014 0.2745 Dec 31, 2015 0.2723 Dec 31, 2016 0.2691 Dec 31, 2017 0.2655 Dec 31, 2018 0.2622

There is a decreasing number of 0-14 year olds between 2009 and 2019 The decrease is less than 0.05 over 10 years There was the least decrease between 2009-2010.

{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "multiColumn/data/746.tsv"}, "mark": "line", "encoding": {"color": {"value": "#bab0ac"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "65 years and older"}, "field": "65 years and older"}}, "title": ["Indonesia : Age structure from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year 65 years and older Dec 31, 2008 0.0497 Dec 31, 2009 0.0496 Dec 31, 2010 0.0504 Dec 31, 2011 0.0512 Dec 31, 2012 0.0519 Dec 31, 2013 0.0528 Dec 31, 2014 0.0538 Dec 31, 2015 0.0552 Dec 31, 2016 0.0568 Dec 31, 2017 0.0586 Dec 31, 2018 0.0605

The percentage of people in Indonesia over 65 has increased over the 10 years from 5% to 6%. This was a gradual increase with no apparent decreases over the years. The number of people over 65 years old still makes up only a small part of the population of Indonesia.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/748.tsv"}, "mark": "line", "encoding": {"color": {"value": "#999999"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Non-Athlete"}, "field": "Non-Athlete"}}, "title": ["USA Swimming number of members from 2000", "to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Non-Athlete Dec 31, 1999 25732 Dec 31, 2000 26151 Dec 31, 2001 26322 Dec 31, 2002 27652 Dec 31, 2003 27314 Dec 31, 2004 27847 Dec 31, 2005 28416 Dec 31, 2006 28695 Dec 31, 2007 28447 Dec 31, 2008 29557 Dec 31, 2009 30554 Dec 31, 2010 31154 Dec 31, 2011 32013 Dec 31, 2012 34480 Dec 31, 2013 36521 Dec 31, 2014 37399 Dec 31, 2015 38375 Dec 31, 2016 39154 Dec 31, 2017 39637 Dec 31, 2018 40273

Non-athlete numbers increased steadily from 2000 to 2020.

{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "multiColumn/data/748.tsv"}, "mark": "line", "encoding": {"color": {"value": "#999999"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Non-Athlete"}, "field": "Non-Athlete"}}, "title": ["USA Swimming number of members from 2000", "to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}

Year Non-Athlete Dec 31, 1999 25732 Dec 31, 2000 26151 Dec 31, 2001 26322 Dec 31, 2002 27652 Dec 31, 2003 27314 Dec 31, 2004 27847 Dec 31, 2005 28416 Dec 31, 2006 28695 Dec 31, 2007 28447 Dec 31, 2008 29557 Dec 31, 2009 30554 Dec 31, 2010 31154 Dec 31, 2011 32013 Dec 31, 2012 34480 Dec 31, 2013 36521 Dec 31, 2014 37399 Dec 31, 2015 38375 Dec 31, 2016 39154 Dec 31, 2017 39637 Dec 31, 2018 40273

Between 2000 to 2019 the number of non-athlete members of USA swimming has risen from around 25,000 to just above 40,000. The rise in membership was steady between 2000 to 2012; but thereafter saw a sharper rise before returning to previous rate around 2014.