Response
stringlengths
613
2.3k
Input
stringlengths
30
7.14k
Instruction
stringlengths
22
601
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12194.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#f0027f"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Brand"}, "y": {"type": "quantitative", "axis": {"title": "Percent change in sales"}, "field": "Percent change in sales"}}, "title": ["Sales growth of leading 20 ethnic hair care", "brands in the United States in 2014"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Brand Percent change in sales Organic Root Stimulator βˆ’0.116 SoftSheen-Carson Dark & Lovely βˆ’0.112 Africa's Best βˆ’0.093 Luster's Pink Smooth Touch βˆ’0.084 African Pride Olive Miracle 0.154 SoftSheen-Carson Optimum Care βˆ’0.195 Ultra Sheen Supreme βˆ’0.157 Soft & Beautiful Just for Me βˆ’0.215 Luster's S-Curl βˆ’0.054 Africa's Best Organic βˆ’0.086 Luster's Pink 0.049 Motions βˆ’0.192 Pro-Line Comb-Thru βˆ’0.052 Dr. Miracle's βˆ’0.346 Dr. Miracle's Thermaceutical βˆ’0.201 Smart Perm 0.1 Soft and Beautiful Botanicals βˆ’0.29 Luster's Pink ShortLooks 0.137 Pro-Line Soft & Beautiful βˆ’0.314
This bar chart shows the increase or decrease in sales of ethnic hair care brands in America. From this chart you can see that the vast majority of these brands have had a decrease in sales while only a few have a positive percentage change.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12194.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#f0027f"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Brand"}, "y": {"type": "quantitative", "axis": {"title": "Percent change in sales"}, "field": "Percent change in sales"}}, "title": ["Sales growth of leading 20 ethnic hair care", "brands in the United States in 2014"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Brand Percent change in sales Organic Root Stimulator βˆ’0.116 SoftSheen-Carson Dark & Lovely βˆ’0.112 Africa's Best βˆ’0.093 Luster's Pink Smooth Touch βˆ’0.084 African Pride Olive Miracle 0.154 SoftSheen-Carson Optimum Care βˆ’0.195 Ultra Sheen Supreme βˆ’0.157 Soft & Beautiful Just for Me βˆ’0.215 Luster's S-Curl βˆ’0.054 Africa's Best Organic βˆ’0.086 Luster's Pink 0.049 Motions βˆ’0.192 Pro-Line Comb-Thru βˆ’0.052 Dr. Miracle's βˆ’0.346 Dr. Miracle's Thermaceutical βˆ’0.201 Smart Perm 0.1 Soft and Beautiful Botanicals βˆ’0.29 Luster's Pink ShortLooks 0.137 Pro-Line Soft & Beautiful βˆ’0.314
African Pride Olive Miracle, Luster's Pink, Luster's Pink Short Cuts and Smart Perm have had increases in sales in 2014. The remaining 14 brands had decreases.
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/12195.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#bd9e39"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Inhabitants in millions"}, "field": "Inhabitants in millions"}}, "title": ["Colombia : Total population from 2015 to", "2025 (in million inhabitants)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Inhabitants in millions 2025* 53.26 2024* 52.79 2023* 52.32 2022* 51.85 2021* 51.39 2020* 50.88 2019 50.37 2018 49.83 2017 49.29 2016 48.75 2015 48.2
There is a year on year increase which is predicted to continue to rise at the same rate as it currently is. In 2018 it reached a total of 50 million inhabitants.
{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "data/12209.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#000000"}, "x": {"type": "quantitative", "axis": {"title": "Occupancy level"}, "field": "Occupancy level"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Country"}}, "title": ["Countries with the highest prison occupancy", "level as of June 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Occupancy level Country 6.169 Republic of Congo 4.636 Philippines 4.544 Haiti 3.72 Guatemala 3.639 Bolivia 3.192 Uganda 3.03 Zambia 2.866 Lebanon 2.57 Burundi 2.553 Sudan 2.413 Peru 2.374 Madagascar 2.338 Grenada 2.323 Chad 2.233 Mali 2.221 Liberia 2.202 Sierra Leone 2.152 El Salvador 2.151 Bangladesh 2.107 Mozambique
The DROC has the highest prison occupancy level (it is not stated whether this is per capita or based on raw numbers). Closely behind are Haiti, Bolivia, and Phillipines. China, Russia, and the U.S. are not represented in the graph.
{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "data/12212.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#bab0ac"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "State"}, "y": {"type": "quantitative", "axis": {"title": "Number of presidents"}, "field": "Number of presidents"}}, "title": ["Number of U.S. presidents born in each state", ", from those elected between 1789 and 2021"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
State Number of presidents Virginia 8 Ohio 7 New York 5 Massachusetts 4 North Carolina 2 Pennsylvania 2 Texas 2 Vermont 2 Arkansas 1 California 1 Connecticut 1 Georgia 1 Hawaii 1 Illinois 1 Iowa 1 Kentucky 1 Missouri 1 Nebraska 1 New Hampshire 1 New Jersey 1 South Carolina* 1
The greatest number of Presidents elected between 1789 and 2021 from one state, originated from Virginia. Thirteen states were responsible for just one elected US President between 1789 and 2021.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12213.tsv"}, "mark": "area", "encoding": {"color": {"value": "#adc839"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Month"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Number of viewers in thousands"}, "field": "Number of viewers in thousands"}}, "title": ["Average viewers of Call of Duty : Warzone", "on Twitch worldwide from September 2019 to", "December 2020 (in 1000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Month Number of viewers in thousands Jun 30, 2019 0.73 Jul 31, 2019 4.23 Aug 31, 2019 16.92 Sep 30, 2019 31.13 Oct 31, 2019 37.75 Nov 30, 2019 23.8 Dec 31, 2019 12.13 Jan 31, 2020 12.93 Feb 29, 2020 129.59 Mar 31, 2020 111.43 Apr 30, 2020 123.03 May 31, 2020 120.54 Jun 30, 2020 118.19 Jul 31, 2020 113.27 Aug 31, 2020 102.65 Sep 30, 2020 102.44 Oct 31, 2020 69.79 Nov 30, 2020 88.47
The number of viewers increased from zero in July 2019 to nearly 40,000 by November 2019. This then declined to just over 10,000 in January 2020. In February 2020, viewers increased rapidly to 120,000. Since February 2020, numbers have decreased, but have not fallen below 70,000 at any point.
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/12215.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1b9e77"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Month"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Growth rate of HICP"}, "field": "Growth rate of HICP"}}, "title": ["Monthly inflation rate in the European Union", "28 from January to December 2018 (HICP compared", "to same month in the previous year)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Month Growth rate of HICP Dec '18 0.017 Nov '18 0.02 Oct '18 0.022 Sep '18 0.021 Aug '18 0.022 Jul '18 0.022 Jun '18 0.021 May '18 0.02 Apr '18 0.015 Mar '18 0.015 Feb '18 0.014 Jan '18 0.016
The growth rate of HICP April to September is above 0.010. In August, July and October the HICP growth rate were the highest.
{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "data/1221.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fc4f30"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Median income in Canadian dollars"}, "field": "Median income in Canadian dollars"}}, "title": ["Median annual family income in Canada from", "2000 to 2018 (in Canadian dollar)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Median income in Canadian dollars Dec 31, 1999 50800 Dec 31, 2000 53500 Dec 31, 2001 55000 Dec 31, 2002 56000 Dec 31, 2003 58100 Dec 31, 2004 60600 Dec 31, 2005 63600 Dec 31, 2006 66550 Dec 31, 2007 68860 Dec 31, 2008 68410 Dec 31, 2009 69860 Dec 31, 2010 72240 Dec 31, 2011 74540 Dec 31, 2012 76550 Dec 31, 2013 78870 Dec 31, 2014 80940 Dec 31, 2015 82110 Dec 31, 2016 84950 Dec 31, 2017 87930
from 2000 the median income in Canada has almost doubled (although there are dips), from 45,000 to over 80,000.
{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "data/12223.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#e6f5c9"}, "x": {"type": "quantitative", "axis": {"title": "Number of floors"}, "field": "Number of floors"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Building (city)"}}, "title": ["Ranking of skyscrapers in the U.S. as of", "2019 , by number of floors"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Number of floors Building (city) 108 Willis Tower(Chicago) 104 One World Trade Center(New York City) 102 Empire State Building(New York City) 100 875 North Michigan Avenue(Chicago) 98 Trump International Hotel & Tower(Chicago) 96 432 Park Avenue(New York City) 83 Aon Center(Chicago) 77 Chrysler Building(New York City) 73 53W53(New York) 73 Wilshire Grand Center (Los Angeles) 73 30 Hudson Yards(New York)
In 2019, the Willis Tower in Chicago had the most floors of any skyscraper in the usa, with almost 120 floors. The majority of the largest u.s skyscrapers in 2019 were located in New York.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12225.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#beaed4"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Employment in millions"}, "field": "Employment in millions"}}, "title": ["Italy : Employment from 2010 to 2020 (in", "millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Employment in millions 2020* 23.35 2019* 23.31 2018 23.22 2017 23.02 2016 22.76 2015 22.47 2014 22.28 2013 22.19 2012 22.57 2011 22.6 2010 22.53
Levels of employment in Italy have remained fairly steady over the entire time period show in the chart. However, there has been a slight steady increase in employment levels in Italy throughout the years 2012-2020.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12225.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#beaed4"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Employment in millions"}, "field": "Employment in millions"}}, "title": ["Italy : Employment from 2010 to 2020 (in", "millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Employment in millions 2020* 23.35 2019* 23.31 2018 23.22 2017 23.02 2016 22.76 2015 22.47 2014 22.28 2013 22.19 2012 22.57 2011 22.6 2010 22.53
Employment in 2020 is higher than it was in 2010. There is a slight dip in employment in the years 2013 to 2015 but then a small but steady increase up to 2018. Between 2018 and 2020 employment was just about the same rate.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12225.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#beaed4"}, "x": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Employment in millions"}, "field": "Employment in millions"}}, "title": ["Italy : Employment from 2010 to 2020 (in", "millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Employment in millions 2020* 23.35 2019* 23.31 2018 23.22 2017 23.02 2016 22.76 2015 22.47 2014 22.28 2013 22.19 2012 22.57 2011 22.6 2010 22.53
Employment has remained largely steady in Italy over ten years between 2010 and 2020. There was a slight decline in employment in 2013, but this steadily increased over the coming years. The highest rates of employment can be seen in 2019 and 2020.
{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "data/12226.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b79a20"}, "x": {"type": "quantitative", "axis": {"title": "Number of followers in thousands"}, "field": "Number of followers in thousands"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "esports team"}}, "title": ["Leading Overwatch League teams on Twitter", "worldwide as of February 2018 , by number", "of followers (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Number of followers in thousands esports team 112 San Francisco Shock 78.2 Dallas Fuel 75.2 Houston Outlaws 63.5 Seoul Dynasty 53.4 London Spitfire 51.2 Los Angeles Valiant 44.3 New York Exclesior 42.5 Los Angeles Gladiators 41.2 Boston Uprising 40.7 Philadelphia Fusion 36 Shanghai Dragons 33.4 Florida Mayhem
The team with the most amount of followers is San Francisco shock with 110,000 followers. The team with the least followers is florida mayhem. Most of the other teams have around 40,000 followers.
{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "data/12226.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b79a20"}, "x": {"type": "quantitative", "axis": {"title": "Number of followers in thousands"}, "field": "Number of followers in thousands"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "esports team"}}, "title": ["Leading Overwatch League teams on Twitter", "worldwide as of February 2018 , by number", "of followers (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Number of followers in thousands esports team 112 San Francisco Shock 78.2 Dallas Fuel 75.2 Houston Outlaws 63.5 Seoul Dynasty 53.4 London Spitfire 51.2 Los Angeles Valiant 44.3 New York Exclesior 42.5 Los Angeles Gladiators 41.2 Boston Uprising 40.7 Philadelphia Fusion 36 Shanghai Dragons 33.4 Florida Mayhem
The San sanfracisco shock has 110000 followers that is the highest followers.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12235.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#bf5b17"}, "x": {"type": "quantitative", "axis": {"title": "Complete University Guide score"}, "field": "Complete University Guide score"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "higher education institution"}}, "title": ["The Complete University Guide 's top twenty", "universities in the United Kingdom in 2021", ", by overall score"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Complete University Guide score higher education institution 1 University of Cambridge 0.99 University of Oxford 0.96 University of St Andrews 0.94 London School of Economics 0.91 Imperial College London 0.9 Loughborough University 0.9 Durham University 0.89 Lancaster University 0.87 University of Bath 0.86 University College London 0.85 University of Warwick 0.83 University of Exeter 0.81 University of Birmingham 0.81 University of Bristol 0.81 University of Edinburgh 0.8 University of Leeds 0.8 University of Manchester 0.8 University of Southampton 0.79 University of Glasgow 0.77 King's College London
All universities scored above 0.7. Cambridge and Oxford both score very highly.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12235.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#bf5b17"}, "x": {"type": "quantitative", "axis": {"title": "Complete University Guide score"}, "field": "Complete University Guide score"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "higher education institution"}}, "title": ["The Complete University Guide 's top twenty", "universities in the United Kingdom in 2021", ", by overall score"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Complete University Guide score higher education institution 1 University of Cambridge 0.99 University of Oxford 0.96 University of St Andrews 0.94 London School of Economics 0.91 Imperial College London 0.9 Loughborough University 0.9 Durham University 0.89 Lancaster University 0.87 University of Bath 0.86 University College London 0.85 University of Warwick 0.83 University of Exeter 0.81 University of Birmingham 0.81 University of Bristol 0.81 University of Edinburgh 0.8 University of Leeds 0.8 University of Manchester 0.8 University of Southampton 0.79 University of Glasgow 0.77 King's College London
University of Cambridge has the highest complete iniversity guide score for 2021 of 1.0 in comparison to Kings college London which has a score of around 0.7.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12235.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#bf5b17"}, "x": {"type": "quantitative", "axis": {"title": "Complete University Guide score"}, "field": "Complete University Guide score"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "higher education institution"}}, "title": ["The Complete University Guide 's top twenty", "universities in the United Kingdom in 2021", ", by overall score"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Complete University Guide score higher education institution 1 University of Cambridge 0.99 University of Oxford 0.96 University of St Andrews 0.94 London School of Economics 0.91 Imperial College London 0.9 Loughborough University 0.9 Durham University 0.89 Lancaster University 0.87 University of Bath 0.86 University College London 0.85 University of Warwick 0.83 University of Exeter 0.81 University of Birmingham 0.81 University of Bristol 0.81 University of Edinburgh 0.8 University of Leeds 0.8 University of Manchester 0.8 University of Southampton 0.79 University of Glasgow 0.77 King's College London
The chart shows that University of Cambridge is the highest scoring top 20 university, closely followed by University of Oxford. These are the only unis to be close to the maximum score, while most of the others hover at or around 80%.
{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "data/12237.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#55b748"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": " leading artists worldwide"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Auction sales in million U.S. dollars"}, "field": "Auction sales in million U\\.S\\. dollars"}}, "title": ["Leading artists worldwide in 2019 , by auction", "revenue (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
leading artists worldwide Auction sales in million U.S. dollars Pablo Picasso 346.22 Claude Monet 298.41 Zao Wou-Ki 237.85 Andy Warhol 228.33 Zhang Daqian 177.81 Wu Guanzhong 148.72 Gerhard Richter 130.53 David Hockney 130.51 Qi Baishi 129.75 Jean-Michel Basquiat 128.79 RenΓ© Magritte 127.8 Ed Ruscha 116.81 Francis Bacon 115.32 Li Keran 112.14 Jeff Koons 111.1 San Yu 109.32 Kaws 108.45 Mark Rothko 106.46 Roy Lichtenstein 102.35 Paul CΓ©zane 100.87
Pablo PIcasso has the highest auction sales at $350 million (USD), followed by Claude Monet at $300 million (USD) and Zao Wou-Ki at just under $280 million (USD).
{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "data/12239.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fc4f30"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Percentage of employees"}, "field": "Percentage of employees"}}, "title": ["Trade union density : Proportion of employees", "that were members of a trade union in accommodation", "and food service activities in the United", "Kingdom from 1995 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Response Percentage of employees Dec 31, 1994 0.079 Dec 31, 1995 0.07 Dec 31, 1996 0.069 Dec 31, 1997 0.067 Dec 31, 1998 0.061 Dec 31, 1999 0.06 Dec 31, 2000 0.057 Dec 31, 2001 0.063 Dec 31, 2002 0.055 Dec 31, 2003 0.05 Dec 31, 2004 0.042 Dec 31, 2005 0.056 Dec 31, 2006 0.049 Dec 31, 2007 0.054 Dec 31, 2008 0.039 Dec 31, 2009 0.038 Dec 31, 2010 0.036 Dec 31, 2011 0.035 Dec 31, 2012 0.042 Dec 31, 2013 0.035 Dec 31, 2014 0.035 Dec 31, 2015 0.025 Dec 31, 2016 0.029 Dec 31, 2017 0.033
Overall, as the years passed from 1995 to 2018, the number of employees that were members of a trade union in accommodation and food service activities in the United Kingdom has decreased by over half.
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/12252.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ce6dbd"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Share of respondents owning selected devices", "in the United Kingdom (UK) in 2012"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Response Share of respondents Laptop (including Mac laptops) 0.59 Desktop PC / Mac 0.45 A standard mobile phone without internet 0.36 PVR box (e.g. Sky+) or Freeview box 0.35 Games console with internet 0.3 Android smartphone 0.25 Games console without internet 0.2 iPhone 0.16 Handheld gaming device e.g. Nintendo DS, PSP/ VITA with internet access 0.14 Handheld gaming device e.g. Nintendo DS, PSP/ VITA without internet access 0.13 iPad 0.13 Blackberry 0.12 Other smartphone 0.08 Other tablet 0.07 Windows smartphone 0.03 PDA 0.02 None of these 0.05
The most popular owned device in the UK in 2012 was a laptop (including mac laptops). A laptop was owned by almost 60% of respondents. The next most owned devices were a desktop PC followed by a PVR/Freeview box. The least owned device was a PDA.
{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "data/12263.tsv"}, "mark": "area", "encoding": {"color": {"value": "#d2d2d2"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Home attendance"}, "field": "Home attendance"}}, "title": ["Total regular season home attendance of", "the NFL Carolina Panthers franchise from", "2006 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Home attendance Dec 31, 2005 588536 Dec 31, 2006 587216 Dec 31, 2007 585680 Dec 31, 2008 586312 Dec 31, 2009 580960 Dec 31, 2010 578342 Dec 31, 2011 586347 Dec 31, 2012 587544 Dec 31, 2013 588861 Dec 31, 2014 592454 Dec 31, 2015 590343 Dec 31, 2016 588942 Dec 31, 2017 590182 Dec 31, 2018 577765
attendance is a regular pattern at just under 600,00 every year. there is a slight dip in attendance between in 2011 there is a slight falling off in attendance in 2018.
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/12271.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d95f02"}, "x": {"type": "quantitative", "axis": {"labelAngle": 90, "title": "Number of Facebook interactions in millions"}, "field": "Number of Facebook interactions in millions"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}}, "title": ["Selected global media and sporting events", "with the most Facebook interactions as of", "May 2017 (in million interactions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Number of Facebook interactions in millions Year 3000 2014 FIFA World Cup (Jun 12-Jul13) 1500 2016 Rio Olympic Games (Aug 5-21, 2016) 700 2015 Cricket World Cup (Jan 1-Mar 29) 674 2014 Presidential Election in Brazil 534 2015 Carnival in Brazil (Feb13-Feb 17) 459 2014 FIFA World Cup (first week of tournament, June 12-18) 350 Indian Premier League 10 (IPL 10) Apr-May 2017 312 2015 Indian Premier League (IPL) Season 8 (Apr 1-May 8) 309 2015 Copa AmΓ©rica (Jun 1-Jul 4, 2015) 265 Super Bowl XLIX (Feb 1, 2015) 185 Super Bowl XLVIII (Feb 2, 2014) 173 NBA Finals (June 4-16, 2015) 150 2016 World Series (Oct 25 - Nov 2) 120 2014 Sochi Winter Olympics 115 Mayweather vs. Pacquiao (May 2, 2015) 88 2014 FIFA World Cup Final - Argentina vs. Germany (Jul 13) 76 2015 NCAA Tournament (Mar 15-Apr 6) 76 2015 UEFA Champions League Final (Jun 6, 2015) 67 88th Academy Awards (Feb 24, 2016) 66 2014 FIFA World Cup Semi-final - Brazil vs. Germany (Jul 8)
a bit of a scatter of patterns. Events that happen in brazil/rio tend to have 500 to 1,500 million interactions on facebook. most of the events had less than 500 million interactions on facebook. The event with the most interactions on facebook was the 2014 fifa world cup.
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/12271.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d95f02"}, "x": {"type": "quantitative", "axis": {"labelAngle": 90, "title": "Number of Facebook interactions in millions"}, "field": "Number of Facebook interactions in millions"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}}, "title": ["Selected global media and sporting events", "with the most Facebook interactions as of", "May 2017 (in million interactions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Number of Facebook interactions in millions Year 3000 2014 FIFA World Cup (Jun 12-Jul13) 1500 2016 Rio Olympic Games (Aug 5-21, 2016) 700 2015 Cricket World Cup (Jan 1-Mar 29) 674 2014 Presidential Election in Brazil 534 2015 Carnival in Brazil (Feb13-Feb 17) 459 2014 FIFA World Cup (first week of tournament, June 12-18) 350 Indian Premier League 10 (IPL 10) Apr-May 2017 312 2015 Indian Premier League (IPL) Season 8 (Apr 1-May 8) 309 2015 Copa AmΓ©rica (Jun 1-Jul 4, 2015) 265 Super Bowl XLIX (Feb 1, 2015) 185 Super Bowl XLVIII (Feb 2, 2014) 173 NBA Finals (June 4-16, 2015) 150 2016 World Series (Oct 25 - Nov 2) 120 2014 Sochi Winter Olympics 115 Mayweather vs. Pacquiao (May 2, 2015) 88 2014 FIFA World Cup Final - Argentina vs. Germany (Jul 13) 76 2015 NCAA Tournament (Mar 15-Apr 6) 76 2015 UEFA Champions League Final (Jun 6, 2015) 67 88th Academy Awards (Feb 24, 2016) 66 2014 FIFA World Cup Semi-final - Brazil vs. Germany (Jul 8)
Overall Fifa world cup had the most viewers and apart from the olympics, most of the other sports had about the same as each other.
{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "data/12275.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#000000"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "port"}, "y": {"type": "quantitative", "axis": {"labelAngle": 0, "title": "Volume in million metric tons"}, "field": "Volume in million metric tons"}}, "title": ["Volume of total cargo handled across India", "in financial year 2019 , by major port (in", "million metric tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
port Volume in million metric tons Deendayal 115.4 Paradip 109.3 J L Nehru 70.71 Visakhapatnam 65.3 Mumbai 60.63 Chennai 53.01 Haldia Dock Complex 45.21 New Mangalore 42.51 Kamarajar 34.5 Chidambaranar 34.34 Cochin 32.02 Kolkata Dock System 18.55 Mormugao 17.68
Deendayal handed by far the most cargo, approximately 118 million tonnes, 2nd was paradise with roughly 114 million tonnes. Mormugao was the port handling the least with just about 19 million tonnes.
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/12277.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#7b4173"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "business activity"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "EODB ranking from 1 to 190*"}, "field": "EODB ranking from 1 to 190*"}}, "title": ["Ease of doing business (EODB) ranking of", "Russia in 2019 , by business activity"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
business activity EODB ranking from 1 to 190* Trading across borders 99 Protecting minority investors 72 Paying taxes 58 Resolving insolvency 57 Starting a business 40 DB rank 28 Dealing with construction permits 26 Getting credit 25 Enforcing contracts 21 Registering property 12 Getting electricity 7
Trading across borders has the highest EODB ranking.
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/12277.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#7b4173"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "business activity"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "EODB ranking from 1 to 190*"}, "field": "EODB ranking from 1 to 190*"}}, "title": ["Ease of doing business (EODB) ranking of", "Russia in 2019 , by business activity"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
business activity EODB ranking from 1 to 190* Trading across borders 99 Protecting minority investors 72 Paying taxes 58 Resolving insolvency 57 Starting a business 40 DB rank 28 Dealing with construction permits 26 Getting credit 25 Enforcing contracts 21 Registering property 12 Getting electricity 7
Trading across borders is ranked the easiest business activity.
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/12281.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#8c6d31"}, "x": {"type": "quantitative", "axis": {"title": "Average total attendance"}, "field": "Average total attendance"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}}, "title": ["Average total regular season home attendance", "in the NHL from 2008/09 to 2019/20"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Average total attendance Year 540120 2019/20 715705 2018/19 715299 2017/18 717526 2016/17 719490 2015/16 717606 2014/15 721083 2013/14 425295 2012/13* 715672 2011/12 697601 2010/11 696902 2009/10 716518 2008/09
in 2012/2013 and 2019/2020 the home attendance was the lowest, the other years are comparable and its about 700 000.
{"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": "data/12286.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d2d2d2"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of heating degree-days"}, "field": "Number of heating degree-days"}}, "title": ["Number of heating degree-days in the United", "States from 1950 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Number of heating degree-days Dec 31, 1949 5367 Dec 31, 1959 5404 Dec 31, 1969 5218 Dec 31, 1979 5080 Dec 31, 1989 4180 Dec 31, 1999 4494 Dec 31, 2004 4348 Dec 31, 2005 4040 Dec 31, 2006 4268 Dec 31, 2007 4494 Dec 31, 2008 4481 Dec 31, 2009 4463 Dec 31, 2010 4312 Dec 31, 2011 3769 Dec 31, 2012 4465 Dec 31, 2013 4550 Dec 31, 2014 4087 Dec 31, 2015 3878 Dec 31, 2016 3828 Dec 31, 2017 4279 Dec 31, 2018 4303
The number of days has Decreased over the years. There has been some sharp changes in the number of days within each decade.
{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "data/12294.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ff9e27"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Prison population per 100,000 population"}, "field": "Prison population per 100,000 population"}}, "title": ["Prison population rates in Malaysia from", "2006 to 2017 (per 100,000 population)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Prison population per 100,000 population Dec 31, 2005 162.14 Dec 31, 2006 188.93 Dec 31, 2007 145.48 Dec 31, 2009 136.55 Dec 31, 2011 125.5 Dec 31, 2012 131.77 Dec 31, 2013 158.75 Dec 31, 2014 166.35 Dec 31, 2015 165.64 Dec 31, 2016 175.22
The prison population rate in Malaysia per 100,000 hit its peak in 2007 at roughly 175.The rate declines for a few years till 2012. From 2012, the rate starts to rise again.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/1229.tsv"}, "mark": "area", "encoding": {"color": {"value": "#7fc97f"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of games"}, "field": "Number of games"}}, "title": ["Number of games released on Steam worldwide", "from 2004 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Number of games Dec 31, 2003 7 Dec 31, 2004 6 Dec 31, 2005 71 Dec 31, 2006 112 Dec 31, 2007 183 Dec 31, 2008 356 Dec 31, 2009 276 Dec 31, 2010 283 Dec 31, 2011 379 Dec 31, 2012 565 Dec 31, 2013 1771 Dec 31, 2014 2964 Dec 31, 2015 4207 Dec 31, 2016 7049 Dec 31, 2017 9050 Dec 31, 2018 8290
The number of games released on Steam increased at a steady rate from 2004 - 2013. The number has increased exponentially from 2013 to 2018, and then decreased slightly in 2019. The number of games on Steam doubled between 2014 and 2016, and then doubled again between 2016 and 2018.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/1229.tsv"}, "mark": "area", "encoding": {"color": {"value": "#7fc97f"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of games"}, "field": "Number of games"}}, "title": ["Number of games released on Steam worldwide", "from 2004 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Number of games Dec 31, 2003 7 Dec 31, 2004 6 Dec 31, 2005 71 Dec 31, 2006 112 Dec 31, 2007 183 Dec 31, 2008 356 Dec 31, 2009 276 Dec 31, 2010 283 Dec 31, 2011 379 Dec 31, 2012 565 Dec 31, 2013 1771 Dec 31, 2014 2964 Dec 31, 2015 4207 Dec 31, 2016 7049 Dec 31, 2017 9050 Dec 31, 2018 8290
2004 to 2006 has the least number of games released within a time period. 2018 had the record highest number of games released in that year.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/1229.tsv"}, "mark": "area", "encoding": {"color": {"value": "#7fc97f"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of games"}, "field": "Number of games"}}, "title": ["Number of games released on Steam worldwide", "from 2004 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Number of games Dec 31, 2003 7 Dec 31, 2004 6 Dec 31, 2005 71 Dec 31, 2006 112 Dec 31, 2007 183 Dec 31, 2008 356 Dec 31, 2009 276 Dec 31, 2010 283 Dec 31, 2011 379 Dec 31, 2012 565 Dec 31, 2013 1771 Dec 31, 2014 2964 Dec 31, 2015 4207 Dec 31, 2016 7049 Dec 31, 2017 9050 Dec 31, 2018 8290
The number of games released on Steam worldwide increases between the years 2004-2018. In 2018 it has the highest number of games released but then it begins to decrease after 2018.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12305.tsv"}, "mark": "area", "encoding": {"color": {"value": "#829eb1"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Foreign direct investment in billion GBP"}, "field": "Foreign direct investment in billion GBP"}}, "title": ["Global foreign direct investment (FDI) position", "of the United Kingdom (UK) from 2004 to 2018", "(in billion GBP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Foreign direct investment in billion GBP Dec 31, 2003 645.74 Dec 31, 2004 696.11 Dec 31, 2005 741.16 Dec 31, 2006 921.83 Dec 31, 2007 1119.25 Dec 31, 2008 1014.4 Dec 31, 2009 1077.14 Dec 31, 2010 1118.03 Dec 31, 2011 1073.46 Dec 31, 2012 1090.64 Dec 31, 2013 1078.69 Dec 31, 2014 1083.96 Dec 31, 2015 1274.6 Dec 31, 2016 1369.14 Dec 31, 2017 1408.72
The chart shows an increase in investment trend between 2004 and 2018. In 2004, investment stood at circa Β£600 billion, rising steadily to 2008, when investment stood at approximately Β£1,100 billion. 2009 saw a reduction to 1,000 billion, with a steady state from 2010 to 2015, holding at just over 1,000 billion. Between 2015 and 2018, investment increased to 1,400 billion.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12305.tsv"}, "mark": "area", "encoding": {"color": {"value": "#829eb1"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Foreign direct investment in billion GBP"}, "field": "Foreign direct investment in billion GBP"}}, "title": ["Global foreign direct investment (FDI) position", "of the United Kingdom (UK) from 2004 to 2018", "(in billion GBP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Foreign direct investment in billion GBP Dec 31, 2003 645.74 Dec 31, 2004 696.11 Dec 31, 2005 741.16 Dec 31, 2006 921.83 Dec 31, 2007 1119.25 Dec 31, 2008 1014.4 Dec 31, 2009 1077.14 Dec 31, 2010 1118.03 Dec 31, 2011 1073.46 Dec 31, 2012 1090.64 Dec 31, 2013 1078.69 Dec 31, 2014 1083.96 Dec 31, 2015 1274.6 Dec 31, 2016 1369.14 Dec 31, 2017 1408.72
The Global Foreign Investment in the UK has grown from 600 to 1400 billion GBP over the period 2014 to 2018.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12305.tsv"}, "mark": "area", "encoding": {"color": {"value": "#829eb1"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Foreign direct investment in billion GBP"}, "field": "Foreign direct investment in billion GBP"}}, "title": ["Global foreign direct investment (FDI) position", "of the United Kingdom (UK) from 2004 to 2018", "(in billion GBP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Foreign direct investment in billion GBP Dec 31, 2003 645.74 Dec 31, 2004 696.11 Dec 31, 2005 741.16 Dec 31, 2006 921.83 Dec 31, 2007 1119.25 Dec 31, 2008 1014.4 Dec 31, 2009 1077.14 Dec 31, 2010 1118.03 Dec 31, 2011 1073.46 Dec 31, 2012 1090.64 Dec 31, 2013 1078.69 Dec 31, 2014 1083.96 Dec 31, 2015 1274.6 Dec 31, 2016 1369.14 Dec 31, 2017 1408.72
There was a big jump in 2016/2017. Other then one peak in 2011 investments stayed the same from 2010-2015.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12315.tsv"}, "mark": "line", "encoding": {"color": {"value": "#829eb1"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Production in thousand units"}, "field": "Production in thousand units"}}, "title": ["Number of motor vehicles produced by Fiat", "worldwide from 2006 to 2016 (in 1,000s units)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Production in thousand units Dec 31, 2005 2319.64 Dec 31, 2006 2679.45 Dec 31, 2007 2524.33 Dec 31, 2008 2460.22 Dec 31, 2009 2410.02 Dec 31, 2010 2399.83 Dec 31, 2011 2127.3 Dec 31, 2012 4681.7 Dec 31, 2013 4865.76 Dec 31, 2014 4865.23 Dec 31, 2015 4681.46
Numbers of vehicles produces by Fiat increased between 2006 and 2007, but then steadily decreased from 2007 to 2012. After 2012, Fiat production skyrocketed, which more than doubled, reaching almost 500,000 units. In the following years, Fiat production remained roughly at that level.
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/12317.tsv"}, "mark": "line", "encoding": {"color": {"value": "#1b9e77"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Net income in million U.S. dollars"}, "field": "Net income in million U\\.S\\. dollars"}}, "title": ["Net income of Duke Energy from FY 2008 to", "FY 2019 (in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Net income in million U.S. dollars Dec 31, 2007 1358 Dec 31, 2008 1085 Dec 31, 2009 1323 Dec 31, 2010 1714 Dec 31, 2011 1782 Dec 31, 2012 2676 Dec 31, 2013 1889 Dec 31, 2014 2831 Dec 31, 2015 2170 Dec 31, 2016 3064 Dec 31, 2017 2644 Dec 31, 2018 3571
From 2008 to 2019, the Net income of Duke Energy has increased from just over 1000 millions USD to approximately 3500 millions USD. From 2012, the increase in net income from Duke Energy is marked by year-on-year increase followed by year-on-year decrease.
{"config": {"background": "#fff", "area": {"fill": "#3e5c69"}, "line": {"stroke": "#3e5c69"}, "rect": {"fill": "#3e5c69"}, "bar": {"fill": "#3e5c69"}, "point": {"color": "#3e5c69"}, "axis": {"domainWidth": 0.5, "grid": true, "labelPadding": 2, "tickSize": 5, "tickWidth": 0.5, "titleFontWeight": "normal"}, "axisBand": {"grid": false}, "axisX": {"gridWidth": 0.2}, "axisY": {"gridDash": [3], "gridWidth": 0.4}, "legend": {"labelFontSize": 11, "padding": 1, "symbolType": "square"}, "range": {"category": ["#3e5c69", "#6793a6", "#182429", "#0570b0", "#3690c0", "#74a9cf", "#a6bddb", "#e2ddf2"]}}, "data": {"url": "data/12323.tsv"}, "mark": "line", "encoding": {"color": {"value": "#182429"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Life expectancy at birth in years"}, "field": "Life expectancy at birth in years"}}, "title": ["Indonesia : Life expectancy at birth from", "2008 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Life expectancy at birth in years Dec 31, 2007 68.49 Dec 31, 2008 68.85 Dec 31, 2009 69.21 Dec 31, 2010 69.54 Dec 31, 2011 69.87 Dec 31, 2012 70.18 Dec 31, 2013 70.48 Dec 31, 2014 70.77 Dec 31, 2015 71.04 Dec 31, 2016 71.28 Dec 31, 2017 71.51
Between 2008 and 2018 the change in life expectancy in Indonesia has been small. In 2008 life expectancy was around 68 years. Life expectancy has risen at steady but slow rate since then reaching 72 in 2018.
{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "data/12326.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ff9e27"}, "x": {"type": "quantitative", "axis": {"title": "Inhabitants in millions"}, "field": "Inhabitants in millions"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Peru : Total population from 2015 to 2025", "(in million inhabitants)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Inhabitants in millions Year 35.2 2025* 34.85 2024* 34.51 2023* 34.17 2022* 33.83 2021* 33.49 2020* 33.16 2019* 32.16 2018 31.83 2017 31.49 2016 31.15 2015
The population of Peru has risen steadily from around 31 million in 2009 to around 32.5 million in 2019. It is expected to continue rising year-on-year and reach 25 million in 2025.
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/12343.tsv"}, "mark": "area", "encoding": {"color": {"value": "#AB47BC"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Revenue per employee in 1,000 U.S. dollars"}, "field": "Revenue per employee in 1,000 U\\.S\\. dollars"}}, "title": ["Revenue per employee of the United States", "chemical industry from 2002 to 2019 (in 1,000", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Revenue per employee in 1,000 U.S. dollars Dec 31, 2001 426 Dec 31, 2002 465 Dec 31, 2003 523 Dec 31, 2004 606 Dec 31, 2005 668 Dec 31, 2006 743 Dec 31, 2007 810 Dec 31, 2008 706 Dec 31, 2009 806 Dec 31, 2010 830 Dec 31, 2011 940 Dec 31, 2012 950 Dec 31, 2013 948 Dec 31, 2014 824 Dec 31, 2015 810 Dec 31, 2016 840 Dec 31, 2017 787 Dec 31, 2018 795
from 2005 it was increasing till 2010 where it decreased for a bit then it started increasing again but up and down.
{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "data/12346.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d67195"}, "x": {"type": "quantitative", "axis": {"title": "Sales volume in thousand tonnes"}, "field": "Sales volume in thousand tonnes"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Annual volume of cranberries and blueberries", "sold in the United Kingdom (UK) from 2008", "to 2018 (in 1,000 tonnes)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Sales volume in thousand tonnes Year 24.2 2018* 21.2 2017* 18.8 2016* 16.8 2015 * 15.1 2014 * 13.8 2013 12.6 2012 11.5 2011 10.2 2010 8.4 2009 6.6 2008
Over the period 2008 to 2013 overall sales of both Cranberries and Blueberries increased. Projected overall sales of both fruits shows a steady increase from 2008 to 2018.
{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "data/12346.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d67195"}, "x": {"type": "quantitative", "axis": {"title": "Sales volume in thousand tonnes"}, "field": "Sales volume in thousand tonnes"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Annual volume of cranberries and blueberries", "sold in the United Kingdom (UK) from 2008", "to 2018 (in 1,000 tonnes)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Sales volume in thousand tonnes Year 24.2 2018* 21.2 2017* 18.8 2016* 16.8 2015 * 15.1 2014 * 13.8 2013 12.6 2012 11.5 2011 10.2 2010 8.4 2009 6.6 2008
Sales for annual volume in cranberries and blueberries in the UK from 2008 to 2018 are increasing year on year, peaking at 2018, the last year recorded, nearly reaching 25 tonnes.
{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "data/12352.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Fertility rate"}, "field": "Fertility rate"}}, "title": ["Total fertility rate in Canada from 1860", "to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Fertility rate Dec 31, 1859 5.72 Dec 31, 1864 5.54 Dec 31, 1869 5.07 Dec 31, 1874 4.75 Dec 31, 1879 4.63 Dec 31, 1884 4.49 Dec 31, 1889 4.31 Dec 31, 1894 4.17 Dec 31, 1899 4.08 Dec 31, 1904 4.03 Dec 31, 1909 4.04 Dec 31, 1914 3.95 Dec 31, 1919 3.68 Dec 31, 1924 3.3 Dec 31, 1929 3.29 Dec 31, 1934 2.94 Dec 31, 1939 2.69 Dec 31, 1944 2.96 Dec 31, 1949 3.45 Dec 31, 1954 3.65 Dec 31, 1959 3.88 Dec 31, 1964 3.68 Dec 31, 1969 2.61 Dec 31, 1974 1.98 Dec 31, 1979 1.73 Dec 31, 1984 1.63 Dec 31, 1989 1.62 Dec 31, 1994 1.69 Dec 31, 1999 1.56 Dec 31, 2004 1.52 Dec 31, 2009 1.64 Dec 31, 2014 1.6 Dec 31, 2019 1.53
1860 had the highest fertility rate. 2000 had the lowest fertility rate. Fertility is generally trending downwards between 1860 and 2000. From 1940 to 1960 there was an increase in fertility rate. Fertility rate currently sits at around 1.5.
{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "data/12352.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Fertility rate"}, "field": "Fertility rate"}}, "title": ["Total fertility rate in Canada from 1860", "to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Fertility rate Dec 31, 1859 5.72 Dec 31, 1864 5.54 Dec 31, 1869 5.07 Dec 31, 1874 4.75 Dec 31, 1879 4.63 Dec 31, 1884 4.49 Dec 31, 1889 4.31 Dec 31, 1894 4.17 Dec 31, 1899 4.08 Dec 31, 1904 4.03 Dec 31, 1909 4.04 Dec 31, 1914 3.95 Dec 31, 1919 3.68 Dec 31, 1924 3.3 Dec 31, 1929 3.29 Dec 31, 1934 2.94 Dec 31, 1939 2.69 Dec 31, 1944 2.96 Dec 31, 1949 3.45 Dec 31, 1954 3.65 Dec 31, 1959 3.88 Dec 31, 1964 3.68 Dec 31, 1969 2.61 Dec 31, 1974 1.98 Dec 31, 1979 1.73 Dec 31, 1984 1.63 Dec 31, 1989 1.62 Dec 31, 1994 1.69 Dec 31, 1999 1.56 Dec 31, 2004 1.52 Dec 31, 2009 1.64 Dec 31, 2014 1.6 Dec 31, 2019 1.53
Fertility rates has decreased in Canada since 1860. Rates increased again in the 1950s but not reaching the heights of previous years. Rates gradually declined after the peak in 1950 and stabilised around 1990.
{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "data/12352.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Fertility rate"}, "field": "Fertility rate"}}, "title": ["Total fertility rate in Canada from 1860", "to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Fertility rate Dec 31, 1859 5.72 Dec 31, 1864 5.54 Dec 31, 1869 5.07 Dec 31, 1874 4.75 Dec 31, 1879 4.63 Dec 31, 1884 4.49 Dec 31, 1889 4.31 Dec 31, 1894 4.17 Dec 31, 1899 4.08 Dec 31, 1904 4.03 Dec 31, 1909 4.04 Dec 31, 1914 3.95 Dec 31, 1919 3.68 Dec 31, 1924 3.3 Dec 31, 1929 3.29 Dec 31, 1934 2.94 Dec 31, 1939 2.69 Dec 31, 1944 2.96 Dec 31, 1949 3.45 Dec 31, 1954 3.65 Dec 31, 1959 3.88 Dec 31, 1964 3.68 Dec 31, 1969 2.61 Dec 31, 1974 1.98 Dec 31, 1979 1.73 Dec 31, 1984 1.63 Dec 31, 1989 1.62 Dec 31, 1994 1.69 Dec 31, 1999 1.56 Dec 31, 2004 1.52 Dec 31, 2009 1.64 Dec 31, 2014 1.6 Dec 31, 2019 1.53
Fertility has dramatically declined since 1860. It peaked for a brief time in 1960/70 with the sharpest decrease happening between roughly 1970-1980.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12360.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec8431"}, "x": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Circulation in thousands"}, "field": "Circulation in thousands"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "leading newspaper"}}, "title": ["Leading regional and local newspaper publishers", "in the United Kingdom (UK) as of January", "2014 , by weekly circulation (in 1,000)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Circulation in thousands leading newspaper 8533.44 Trinity Mirror plc 5434.47 Newsquest Media Group 4697.2 Local World 4362.91 Johnston Press plc 3871.53 Associated Newspapers Ltd 3454.45 Evening Standard Ltd 1987.18 The Midland News Association Ltd 1463.94 ARCHANT 1357.61 D.C. Thomson & Co Ltd 981.07 Tindle Newspapers Ltd 645.61 City AM 446.34 Independent News & Media 398.94 Romanes Media Group 398.19 NWN Media Ltd 305.3 Bullivant Media Ltd 297.91 CN Group Ltd 245.5 Irish News Ltd 244.91 KM Group 189.14 Guiton Group 163.14 Champion Newspapers
Trinity mirror plc is the leading publisher with over 8000 in weekly circulation. All other publishers listed fall less than 6000 in weekly circulation.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12360.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec8431"}, "x": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Circulation in thousands"}, "field": "Circulation in thousands"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "leading newspaper"}}, "title": ["Leading regional and local newspaper publishers", "in the United Kingdom (UK) as of January", "2014 , by weekly circulation (in 1,000)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Circulation in thousands leading newspaper 8533.44 Trinity Mirror plc 5434.47 Newsquest Media Group 4697.2 Local World 4362.91 Johnston Press plc 3871.53 Associated Newspapers Ltd 3454.45 Evening Standard Ltd 1987.18 The Midland News Association Ltd 1463.94 ARCHANT 1357.61 D.C. Thomson & Co Ltd 981.07 Tindle Newspapers Ltd 645.61 City AM 446.34 Independent News & Media 398.94 Romanes Media Group 398.19 NWN Media Ltd 305.3 Bullivant Media Ltd 297.91 CN Group Ltd 245.5 Irish News Ltd 244.91 KM Group 189.14 Guiton Group 163.14 Champion Newspapers
Looking at the chart the highest circulation in 2014 was for 7,500,000 byTrinity Mirror Plc and champion newspapers circulated the least at approx 2,000.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12360.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec8431"}, "x": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Circulation in thousands"}, "field": "Circulation in thousands"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "leading newspaper"}}, "title": ["Leading regional and local newspaper publishers", "in the United Kingdom (UK) as of January", "2014 , by weekly circulation (in 1,000)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Circulation in thousands leading newspaper 8533.44 Trinity Mirror plc 5434.47 Newsquest Media Group 4697.2 Local World 4362.91 Johnston Press plc 3871.53 Associated Newspapers Ltd 3454.45 Evening Standard Ltd 1987.18 The Midland News Association Ltd 1463.94 ARCHANT 1357.61 D.C. Thomson & Co Ltd 981.07 Tindle Newspapers Ltd 645.61 City AM 446.34 Independent News & Media 398.94 Romanes Media Group 398.19 NWN Media Ltd 305.3 Bullivant Media Ltd 297.91 CN Group Ltd 245.5 Irish News Ltd 244.91 KM Group 189.14 Guiton Group 163.14 Champion Newspapers
Trinity mirror is the post popular publisher in the UK with over 2000 more copies in circulation than its closest competitor newsquest media group.
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/12362.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ab5787"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Online bingo related keywords"}, "y": {"type": "quantitative", "axis": {"title": "Searches in thousands"}, "field": "Searches in thousands"}}, "title": ["Online bingo related keywords ranked by", "search volume in the United Kingdom (UK)", "in March 2016 (in 1,000 searches)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Online bingo related keywords Searches in thousands bingo 110 bingo games 27.1 bingo sites 27.1 free bingo 14.8 online bingo 12.1 free bingo games 9.9 no deposit bingo 9.9 free bingo no deposit 9.9 new bingo sites 5.4 bingo online 3.6 other keywords 33.96
The search for the word bingo is the highest search while the words bingo games and bingo sites, are the next highest, but about 4/5s lower in terms of searches.
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/12366.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d190b6"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of deaths"}, "field": "Number of deaths"}}, "title": ["Number of drug-related deaths due to barbiturate", "use in England and Wales from 1993 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Number of deaths Dec 31, 1992 42 Dec 31, 1993 45 Dec 31, 1994 40 Dec 31, 1995 41 Dec 31, 1996 21 Dec 31, 1997 27 Dec 31, 1998 31 Dec 31, 1999 21 Dec 31, 2000 26 Dec 31, 2001 22 Dec 31, 2002 20 Dec 31, 2003 16 Dec 31, 2004 14 Dec 31, 2005 17 Dec 31, 2006 8 Dec 31, 2007 15 Dec 31, 2008 13 Dec 31, 2009 19 Dec 31, 2010 37 Dec 31, 2011 32 Dec 31, 2012 32 Dec 31, 2013 50 Dec 31, 2014 41 Dec 31, 2015 44 Dec 31, 2016 39 Dec 31, 2017 28 Dec 31, 2018 39
The amount of deaths due to barbiturate was very high in 1995 and decreased from that peak until 2005. From 2005 the number of deaths has been increasing before peaking in 2013. The deaths have slightly came down compared to 2013 numbers.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12378.tsv"}, "mark": "line", "encoding": {"color": {"value": "#7fc97f"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Production value in thousand U.S. dollars"}, "field": "Production value in thousand U\\.S\\. dollars"}}, "title": ["U.S. sweet potato production value from", "2000 to 2019 (in 1,000 U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Production value in thousand U.S. dollars Dec 31, 1999 210351 Dec 31, 2000 222658 Dec 31, 2001 214650 Dec 31, 2002 305448 Dec 31, 2003 281559 Dec 31, 2004 284103 Dec 31, 2005 298388 Dec 31, 2006 330060 Dec 31, 2007 390572 Dec 31, 2008 423677 Dec 31, 2009 472218 Dec 31, 2010 505938 Dec 31, 2011 461861 Dec 31, 2012 597217 Dec 31, 2013 706916 Dec 31, 2014 675583 Dec 31, 2015 651514 Dec 31, 2016 657021 Dec 31, 2017 634228 Dec 31, 2018 588125
Sweet potato production value has increased from 2000 to 2014 but not in a continuous way. There have been periods of declining value between 2000 and 2005, 2011 and 2012 and then again in 2014. The highest value was in 2014.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12378.tsv"}, "mark": "line", "encoding": {"color": {"value": "#7fc97f"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Production value in thousand U.S. dollars"}, "field": "Production value in thousand U\\.S\\. dollars"}}, "title": ["U.S. sweet potato production value from", "2000 to 2019 (in 1,000 U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Production value in thousand U.S. dollars Dec 31, 1999 210351 Dec 31, 2000 222658 Dec 31, 2001 214650 Dec 31, 2002 305448 Dec 31, 2003 281559 Dec 31, 2004 284103 Dec 31, 2005 298388 Dec 31, 2006 330060 Dec 31, 2007 390572 Dec 31, 2008 423677 Dec 31, 2009 472218 Dec 31, 2010 505938 Dec 31, 2011 461861 Dec 31, 2012 597217 Dec 31, 2013 706916 Dec 31, 2014 675583 Dec 31, 2015 651514 Dec 31, 2016 657021 Dec 31, 2017 634228 Dec 31, 2018 588125
The production of the sweet potato value in the U.S. has grown quite drastically over the period of nearly 5 years where it peaked in 2015 but appears to be on a decline towards 2019.
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/12402.tsv"}, "mark": "line", "encoding": {"color": {"value": "#DB4437"}, "x": {"type": "temporal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Youth unemployment rate"}, "field": "Youth unemployment rate"}}, "title": ["Myanmar : Youth unemployment rate from 1999", "to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Youth unemployment rate Dec 31, 1998 0.0143 Dec 31, 1999 0.0141 Dec 31, 2000 0.0142 Dec 31, 2001 0.0146 Dec 31, 2002 0.0147 Dec 31, 2003 0.0146 Dec 31, 2004 0.0144 Dec 31, 2005 0.0139 Dec 31, 2006 0.0136 Dec 31, 2007 0.0137 Dec 31, 2008 0.0153 Dec 31, 2009 0.0158 Dec 31, 2010 0.0159 Dec 31, 2011 0.016 Dec 31, 2012 0.0161 Dec 31, 2013 0.0158 Dec 31, 2014 0.0157 Dec 31, 2015 0.0259 Dec 31, 2016 0.0392 Dec 31, 2017 0.0381 Dec 31, 2018 0.04 Dec 31, 2019 0.0434
Youth unemployment rates were stable from 1999 until about 2008. There was then a slight rise up until the year 2015. In 2015 there was a sharp increase of about 250%, a slight fall in 2016, and a further sharp increase between 2017 and 2020.
{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "data/12406.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#f2cf5b"}, "x": {"type": "nominal", "axis": {"labelAngle": -90}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Export value in millioin U.S. dollars"}, "field": "Export value in millioin U\\.S\\. dollars"}}, "title": ["Exports value of fresh or chilled mushrooms", "worldwide in 2019 , by leading country (in", "million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Country Export value in millioin U.S. dollars Poland 401.55 Canada 228.49 Ireland 113.59 Belgium 36.91 United States 25.61 Lithuania 20.85 Germany 15.44 Hungary 15.14 United Kingdom 10.93 Mexico 9.64
The country with the lowest export value of fresh or chilled mushrooms was Mexico. Conversely by a significant margin the highest value was enjoyed by Poland.
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/12423.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d365ba"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "State"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Direct economic output (in billion U.S. dollars)"}, "field": "Direct economic output (in billion U\\.S\\. dollars)"}}, "title": ["Direct economic output of the golf industry", "by state in the U.S. in 2009 (in billion", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
State Direct economic output (in billion U.S. dollars) Florida 7.5 California 6.9 Texas 3.4 New York 2.9 North Carolina 2.6 Ohio 2.4 Georgia 2.4 Michigan 2.2 Illinois 2.1 Arizona 2.1 New Jersey 1.8 Virginia 1.6 Hawaii 1.4 South Carolina 1.4 Massachusetts 1.4 Wisconsin 1.2 Washington 1.2 Oregon 1.2 Minnesota 1.2 Pennsylvania 1.1 Indiana 0.91 Connecticut 0.64 Colorado 0.56 New Mexico 0.48 Louisiana 0.45 Iowa 0.43 Kentucky 0.37
the two states with the highest economic output have almost twice as much as the 3rd place state.
{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "data/12428.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ec008b"}, "x": {"type": "quantitative", "axis": {"title": "Share of respondents"}, "field": "Share of respondents"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Who is you favorite Star Wars character", "?"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Share of respondents Response 0.13 Yoda 0.13 Princess Leia 0.12 Han Solo 0.11 Luke Skywalker 0.11 R2-D2 0.1 Chewbacca 0.07 Obi Wan Kenobi 0.06 Darth Vader 0.02 Rey 0.01 Kylo Ren 0.01 Finn
Princess Leia and Yoda appear to tie with the highest votes (0.14) for favourite character. Finn and Kylo ren appear to tie with the lowest votes (0.01) for favourite character.
{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "data/12433.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#b96db8"}, "x": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Price in GBP per kilogram"}, "field": "Price in GBP per kilogram"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "Month"}}, "title": ["Average wholesale price of home grown gooseberries", "in the United Kingdom (UK) between June 2014", "and September 2019 (in GBP per kilogram)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Price in GBP per kilogram Month 4.96 Sep '19 4.26 Aug '19 3.7 Jul '19 4.21 Jun '19 2.94 Jul '18 4.08 Jun '18 1.68 Aug '17 2.62 Jul '17 3.07 Jun '17 3 Aug '16 2.94 Jul '16 2.57 Jun '16 3.97 Aug '15 2.71 Jul '15 3.33 Jun '15 1.1 Sep '14 1.5 Aug '14 2.59 Jul '14 3.79 Jun '14
That September is the best month for collecting gooseberries.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12435.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ab7fb4"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": " Chicago Bulls players"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Assists"}, "field": "Assists"}}, "title": ["Chicago Bulls all-time assists leaders from", "1966 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chicago Bulls players Assists Michael Jordan 5012 Scottie Pippen 4494 Kirk Hinrich 3811 Norm Van Lier 3676 Derrick Rose 2516 Reggie Theus 2472 John Paxson 2394 Bob Weiss 2008 Tom Boerwinkle 2007 Toni Kukoc 1840
The greatest all time assist leader was michael Jordan. The chart shows the top 3 were all in the middle 1966 and 2020. With the worst being on either end of this time scale.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12435.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ab7fb4"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": " Chicago Bulls players"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Assists"}, "field": "Assists"}}, "title": ["Chicago Bulls all-time assists leaders from", "1966 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chicago Bulls players Assists Michael Jordan 5012 Scottie Pippen 4494 Kirk Hinrich 3811 Norm Van Lier 3676 Derrick Rose 2516 Reggie Theus 2472 John Paxson 2394 Bob Weiss 2008 Tom Boerwinkle 2007 Toni Kukoc 1840
Michael Jordan had the most assists at 5,000. Toni Kikoc had the least assists at just under 2,000. 6 out of 10 had less than 3,000 assists.
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/12440.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#5C6BC0"}, "x": {"type": "quantitative", "axis": {"title": "Share of netizens ever bought product"}, "field": "Share of netizens ever bought product"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}}, "title": ["Most popular online shopping categories", "of netizens in Indonesia as of August 2013"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Share of netizens ever bought product Response 0.671 Apparel 0.202 Shoes 0.2 Bags 0.076 Watches 0.051 Airline tickets 0.051 Handphones 0.03 Car accessories 0.028 Handphone accessories 0.023 Cosmetics 0.018 Books
Apparel was the most purchased category in August 2013. Bags and Shoes were joint second but were less by more than 0.4. Books were the least popular purchase.
{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "data/12442.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#B0B0B0"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Dividend per share in euros"}, "field": "Dividend per share in euros"}}, "title": ["Dividend of Allianz Group from 2009 to 2019", "(in euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Dividend per share in euros 2019* 9.6 2018 9 2017 8 2016 7.6 2015 7.3 2014 6.85 2013 5.3 2012 4.5 2011 4.5 2010 4.5 2009 4.1
The dividend of the Allianz Group from 2009 to 2019 has increased steadily year on year.
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/12447.tsv"}, "mark": "line", "encoding": {"color": {"value": "#d6616b"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Income in Canadian dollars"}, "field": "Income in Canadian dollars"}}, "title": ["Median total family income in Nova Scotia", "from 2000 to 2018 (in Canadian dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Income in Canadian dollars Dec 31, 1999 44500 Dec 31, 2000 46900 Dec 31, 2001 48600 Dec 31, 2002 50000 Dec 31, 2003 51500 Dec 31, 2004 54000 Dec 31, 2005 56400 Dec 31, 2006 59200 Dec 31, 2007 61980 Dec 31, 2008 62550 Dec 31, 2009 64100 Dec 31, 2010 66030 Dec 31, 2011 67910 Dec 31, 2012 70020 Dec 31, 2013 72270 Dec 31, 2014 73900 Dec 31, 2015 74590 Dec 31, 2016 76710 Dec 31, 2017 78920
That income almost doubled over the 18 year period.
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/12450.tsv"}, "mark": "area", "encoding": {"color": {"value": "#66a61e"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Deposit interest rate"}, "field": "Deposit interest rate"}}, "title": ["Deposit interest rates in Vietnam from 2009", "to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Deposit interest rate Dec 31, 2008 0.0791 Dec 31, 2009 0.1119 Dec 31, 2010 0.1399 Dec 31, 2011 0.105 Dec 31, 2012 0.0714 Dec 31, 2013 0.0576 Dec 31, 2014 0.0475 Dec 31, 2015 0.0504 Dec 31, 2016 0.0481 Dec 31, 2017 0.0468
The x axis goes up in intervals of 2 yearsThe graph is in an upwards trend up until 2011, in which in then goes into a downwards trendFrom 2015, the rates have remained relatively stable2013 rates were half of 2011s.
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/12454.tsv"}, "mark": "line", "encoding": {"color": {"value": "#666666"}, "x": {"type": "temporal", "axis": {"labelAngle": -30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Real GDP in billion U.S. dollars"}, "field": "Real GDP in billion U\\.S\\. dollars"}}, "title": ["Real Gross Domestic Product (GDP) of the", "federal state of West Virginia from 2000", "to 2019 (in billion U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Real GDP in billion U.S. dollars Dec 31, 1999 62.05 Dec 31, 2000 62.02 Dec 31, 2001 62.81 Dec 31, 2002 62.84 Dec 31, 2003 63.72 Dec 31, 2004 65.55 Dec 31, 2005 66.44 Dec 31, 2006 66.52 Dec 31, 2007 67.89 Dec 31, 2008 67.56 Dec 31, 2009 68.44 Dec 31, 2010 69.66 Dec 31, 2011 69.4 Dec 31, 2012 70.16 Dec 31, 2013 69.87 Dec 31, 2014 69.76 Dec 31, 2015 68.9 Dec 31, 2016 69.9 Dec 31, 2017 71.48 Dec 31, 2018 72.16
Year by year the GDP of West Virginia increased, increasing by nearly 10 billion dollars.
{"config": {"background": "#fff", "area": {"fill": "#3e5c69"}, "line": {"stroke": "#3e5c69"}, "rect": {"fill": "#3e5c69"}, "bar": {"fill": "#3e5c69"}, "point": {"color": "#3e5c69"}, "axis": {"domainWidth": 0.5, "grid": true, "labelPadding": 2, "tickSize": 5, "tickWidth": 0.5, "titleFontWeight": "normal"}, "axisBand": {"grid": false}, "axisX": {"gridWidth": 0.2}, "axisY": {"gridDash": [3], "gridWidth": 0.4}, "legend": {"labelFontSize": 11, "padding": 1, "symbolType": "square"}, "range": {"category": ["#3e5c69", "#6793a6", "#182429", "#0570b0", "#3690c0", "#74a9cf", "#a6bddb", "#e2ddf2"]}}, "data": {"url": "data/12459.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#a6bddb"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "primary silver mines"}, "y": {"type": "quantitative", "axis": {"title": "Production in million ounces"}, "field": "Production in million ounces"}}, "title": ["Leading primary silver mines worldwide in", "2018 (in million ounces)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
primary silver mines Production in million ounces Saucito (Mexico) 19.9 Dukat (Russia)** 16.5 Uchucchacua (Peru) 15.4 Fresnillo Mine (Mexico) 15.1 San JuliΓ‘n (Mexico) 14.6 Cannington (Australia)* 13.4 San JosΓ© (Mexico) 8 Greens Creek (U.S.) 8 Imiter (Morocco) 7.8 La Colorada (Mexico) 7.5
The mine that produces the largest amount of silver is Saucito (Mexico). The mine that produces the smallest amount is La Colorada (Mexico).
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/1245.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#7fc97f"}, "x": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of imports"}, "field": "Share of imports"}}, "title": ["Breakdown of China 's crude oil imports", "in 2014 , by source country"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Response Share of imports Saudi Arabia 0.16 Angola 0.13 Russia 0.11 Oman 0.1 Iraq 0.09 Iran 0.09 Venezuela 0.04 United Arab Emirates 0.04 Kuwait 0.03 Colombia 0.03 Kazakhstan 0.02 Congo 0.02 Brazil 0.02 South Sudan 0.02 Others 0.09
Most of China's crude oil imports go to South Arabia. Least of imports go to Brazil, Kongo, Kazakshtan and South Sudan. The top 3 countries for crude oil import from China are South Arabia, Angola and Russia. There are no European countries (unless they are included in the category 'Others').
{"config": {"background": "#fff", "area": {"fill": "#3e5c69"}, "line": {"stroke": "#3e5c69"}, "rect": {"fill": "#3e5c69"}, "bar": {"fill": "#3e5c69"}, "point": {"color": "#3e5c69"}, "axis": {"domainWidth": 0.5, "grid": true, "labelPadding": 2, "tickSize": 5, "tickWidth": 0.5, "titleFontWeight": "normal"}, "axisBand": {"grid": false}, "axisX": {"gridWidth": 0.2}, "axisY": {"gridDash": [3], "gridWidth": 0.4}, "legend": {"labelFontSize": 11, "padding": 1, "symbolType": "square"}, "range": {"category": ["#3e5c69", "#6793a6", "#182429", "#0570b0", "#3690c0", "#74a9cf", "#a6bddb", "#e2ddf2"]}}, "data": {"url": "data/12463.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#0570b0"}, "x": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Share of respondents"}, "field": "Share of respondents"}, "y": {"type": "nominal", "axis": {"labelAngle": 0}, "bin": false, "field": "Response"}}, "title": ["Reasons for participating in cultural activities", "in United States as of April 2014"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Share of respondents Response 0.93 Entertainment and enjoyment 0.83 Time with friends/family 0.79 Expand my perspective 0.77 Interest in subject 0.76 Learn about other cultures 0.73 Introduction to new things 0.67 Support my community 0.66 Escape stress 0.52 Celebrate my heritage 0.23 Be "in the know"
The largest number of respondents gave entertainment and enjoyment as their reason for participating. The smallest number of respondents gave "be in the know" as their reason for participating. The results are shown as a bar graph in royal blue against a plain white background. More respondents gave the answer learn about other cultures than celebrate my heritage as an answer. The second most given answer was to spend time with friends/family.
{"config": {"background": "#fff", "area": {"fill": "#3e5c69"}, "line": {"stroke": "#3e5c69"}, "rect": {"fill": "#3e5c69"}, "bar": {"fill": "#3e5c69"}, "point": {"color": "#3e5c69"}, "axis": {"domainWidth": 0.5, "grid": true, "labelPadding": 2, "tickSize": 5, "tickWidth": 0.5, "titleFontWeight": "normal"}, "axisBand": {"grid": false}, "axisX": {"gridWidth": 0.2}, "axisY": {"gridDash": [3], "gridWidth": 0.4}, "legend": {"labelFontSize": 11, "padding": 1, "symbolType": "square"}, "range": {"category": ["#3e5c69", "#6793a6", "#182429", "#0570b0", "#3690c0", "#74a9cf", "#a6bddb", "#e2ddf2"]}}, "data": {"url": "data/12463.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#0570b0"}, "x": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Share of respondents"}, "field": "Share of respondents"}, "y": {"type": "nominal", "axis": {"labelAngle": 0}, "bin": false, "field": "Response"}}, "title": ["Reasons for participating in cultural activities", "in United States as of April 2014"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Share of respondents Response 0.93 Entertainment and enjoyment 0.83 Time with friends/family 0.79 Expand my perspective 0.77 Interest in subject 0.76 Learn about other cultures 0.73 Introduction to new things 0.67 Support my community 0.66 Escape stress 0.52 Celebrate my heritage 0.23 Be "in the know"
Many Americans take part in activities for a variety of reasons, with spending time with friends and family and for entertainment being popular.
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/12466.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#9E9D24"}, "x": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "property crime ttypes"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Rate of crime per 100,000 residents"}, "field": "Rate of crime per 100,000 residents"}}, "title": ["Property crime rate in Canada in 2019 ,", "by type"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
property crime ttypes Rate of crime per 100,000 residents Total theft under $5,000 (non-motor vehicle) 1502.36 Total mischief 757.08 Total breaking and entering 429.09 Fraud 378.14 Total theft of motor vehicle 231.62 Total possession of stolen property 65.48 Total theft over $5,000 (non-motor vehicle) 56.82 Identity fraud 52.31 Arson 21.79 Identity theft 12.46 Total trafficking in stolen property 3.03 Altering, removing or destroying Vehicle Identification Number (VIN) 0.31
There is no real pattern, however some crimes are far more prevalent including fraud, theft under $5000, breaking and entering and mischief.
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/12466.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#9E9D24"}, "x": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "property crime ttypes"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Rate of crime per 100,000 residents"}, "field": "Rate of crime per 100,000 residents"}}, "title": ["Property crime rate in Canada in 2019 ,", "by type"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
property crime ttypes Rate of crime per 100,000 residents Total theft under $5,000 (non-motor vehicle) 1502.36 Total mischief 757.08 Total breaking and entering 429.09 Fraud 378.14 Total theft of motor vehicle 231.62 Total possession of stolen property 65.48 Total theft over $5,000 (non-motor vehicle) 56.82 Identity fraud 52.31 Arson 21.79 Identity theft 12.46 Total trafficking in stolen property 3.03 Altering, removing or destroying Vehicle Identification Number (VIN) 0.31
The most common crime per 100,000 residents was total theft under $5000. The least common crimes were arson and identity theft. Total mischief was the second most common crime. The majority of crimes reported had rates of less than 500 per 100,000 residents.
{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/12467.tsv"}, "mark": "line", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Unemployment rate"}, "field": "Unemployment rate"}}, "title": ["Kyrgyz Republic : Unemployment rate from", "1999 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Unemployment rate Dec 31, 1998 0.084 Dec 31, 1999 0.0754 Dec 31, 2000 0.0784 Dec 31, 2001 0.1255 Dec 31, 2002 0.0992 Dec 31, 2003 0.0853 Dec 31, 2004 0.0811 Dec 31, 2005 0.0827 Dec 31, 2006 0.081 Dec 31, 2007 0.0822 Dec 31, 2008 0.0841 Dec 31, 2009 0.0864 Dec 31, 2010 0.0853 Dec 31, 2011 0.0843 Dec 31, 2012 0.0833 Dec 31, 2013 0.0805 Dec 31, 2014 0.0756 Dec 31, 2015 0.0721 Dec 31, 2016 0.0689 Dec 31, 2017 0.0596 Dec 31, 2018 0.0633 Dec 31, 2019 0.0664
Unemployment rate was its highest between 2000 & 2005 reaching over 0.10 but since then has been on a decline of unemployment rates of 0.07. this was until 2015 when the unemployment rate started to rise again.
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/12486.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ce6dbd"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Passenger cars produced (in millions)"}, "field": "Passenger cars produced (in millions)"}}, "title": ["Number of passenger cars produced by Renault", "worldwide from 1999 to 2014 (in million units)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Passenger cars produced (in millions) Dec 31, 1998 1.98 Dec 31, 1999 2.1 Dec 31, 2000 2.07 Dec 31, 2001 2.05 Dec 31, 2002 2.11 Dec 31, 2003 2.16 Dec 31, 2004 2.2 Dec 31, 2005 2.14 Dec 31, 2006 2.28 Dec 31, 2007 2.05 Dec 31, 2008 2.04 Dec 31, 2009 2.4 Dec 31, 2010 2.44 Dec 31, 2011 2.3 Dec 31, 2012 2.35 Dec 31, 2013 2.4
Worldwide between 1999 to 2014 Renault had pretty consistent car sales with a slight dip circa 2009.
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/12486.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ce6dbd"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Passenger cars produced (in millions)"}, "field": "Passenger cars produced (in millions)"}}, "title": ["Number of passenger cars produced by Renault", "worldwide from 1999 to 2014 (in million units)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Passenger cars produced (in millions) Dec 31, 1998 1.98 Dec 31, 1999 2.1 Dec 31, 2000 2.07 Dec 31, 2001 2.05 Dec 31, 2002 2.11 Dec 31, 2003 2.16 Dec 31, 2004 2.2 Dec 31, 2005 2.14 Dec 31, 2006 2.28 Dec 31, 2007 2.05 Dec 31, 2008 2.04 Dec 31, 2009 2.4 Dec 31, 2010 2.44 Dec 31, 2011 2.3 Dec 31, 2012 2.35 Dec 31, 2013 2.4
The line is generally steady. There is a small sharp dip in 2008 that rises past its previous height in 2010 before again becoming steady.
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/12486.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ce6dbd"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Passenger cars produced (in millions)"}, "field": "Passenger cars produced (in millions)"}}, "title": ["Number of passenger cars produced by Renault", "worldwide from 1999 to 2014 (in million units)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Passenger cars produced (in millions) Dec 31, 1998 1.98 Dec 31, 1999 2.1 Dec 31, 2000 2.07 Dec 31, 2001 2.05 Dec 31, 2002 2.11 Dec 31, 2003 2.16 Dec 31, 2004 2.2 Dec 31, 2005 2.14 Dec 31, 2006 2.28 Dec 31, 2007 2.05 Dec 31, 2008 2.04 Dec 31, 2009 2.4 Dec 31, 2010 2.44 Dec 31, 2011 2.3 Dec 31, 2012 2.35 Dec 31, 2013 2.4
2011 Shows a peak of nearly 2.5 million cars produced on the area chart, 2008 shows the lowest cars produced between 1999 and 2014 of around 2 million cars.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12499.tsv"}, "mark": "line", "encoding": {"color": {"value": "#c89d29"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of urban population in total population"}, "field": "Share of urban population in total population"}}, "title": ["Jordan : Urbanization from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Response Share of urban population in total population Dec 31, 2008 0.8493 Dec 31, 2009 0.8609 Dec 31, 2010 0.8717 Dec 31, 2011 0.8819 Dec 31, 2012 0.8913 Dec 31, 2013 0.9 Dec 31, 2014 0.9026 Dec 31, 2015 0.9051 Dec 31, 2016 0.9075 Dec 31, 2017 0.9098 Dec 31, 2018 0.912
THE POPULATION HAS INCREASED SLIGHTLY BUT THE INCREASE HAS SLOWED SINCE 2014.
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/12502.tsv"}, "mark": "area", "encoding": {"color": {"value": "#ad494a"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Unemployment rate"}, "field": "Unemployment rate"}}, "title": ["Belize : Unemployment rate from 1999 to", "2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Unemployment rate Dec 31, 1998 0.1284 Dec 31, 1999 0.1056 Dec 31, 2000 0.0907 Dec 31, 2001 0.1004 Dec 31, 2002 0.105 Dec 31, 2003 0.108 Dec 31, 2004 0.1091 Dec 31, 2005 0.0939 Dec 31, 2006 0.0851 Dec 31, 2007 0.0818 Dec 31, 2008 0.0846 Dec 31, 2009 0.0848 Dec 31, 2010 0.0843 Dec 31, 2011 0.0839 Dec 31, 2012 0.0835 Dec 31, 2013 0.0824 Dec 31, 2014 0.0758 Dec 31, 2015 0.07 Dec 31, 2016 0.066 Dec 31, 2017 0.0651 Dec 31, 2018 0.0641 Dec 31, 2019 0.0636
The unemployment rate has dropped over the 21 year period. The unemployment rate had a slight increase over 2005 but dropped after that.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12510.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#beaed4"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Average ticket price in U.S. dollars"}, "field": "Average ticket price in U\\.S\\. dollars"}}, "title": ["Average ticket price Buffalo Sabres (NHL)", "games from 2005/06 to 2014/15 (in U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Average ticket price in U.S. dollars 14/15 49.72 13/14 47.94 12/13 46.15 11/12 38.25 10/11 36.43 09/10 36.43 08/09 36.43 07/08 36.43 06/07 32.56 05/06 30.07
The average ticket price rose as time went on. Between 07/08 and 10/11, the ticket prices remained steady.
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/12520.tsv"}, "mark": "line", "encoding": {"color": {"value": "#F06292"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of civil unions"}, "field": "Number of civil unions"}}, "title": ["Total number of civil partnerships (PACS)", "contracted in France from 1999 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Number of civil unions Dec 31, 1998 6151 Dec 31, 1999 22271 Dec 31, 2000 19629 Dec 31, 2001 25305 Dec 31, 2002 31570 Dec 31, 2003 40080 Dec 31, 2004 60462 Dec 31, 2005 77347 Dec 31, 2006 101978 Dec 31, 2007 145960 Dec 31, 2008 174629 Dec 31, 2009 205550 Dec 31, 2010 152213 Dec 31, 2011 160690 Dec 31, 2012 168692 Dec 31, 2013 173731 Dec 31, 2014 188947 Dec 31, 2015 191558 Dec 31, 2016 195633 Dec 31, 2017 208871
Civil partnerships in France had a steady rise from 2000 until 2010 where is dips around 50,000 less and then starts to steadily increase again.
{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/12532.tsv"}, "mark": "area", "encoding": {"color": {"value": "#D9B300"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Percentage of population"}, "field": "Percentage of population"}}, "title": ["Poverty rate in Oklahoma from 2000 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Response Percentage of population Dec 31, 1999 0.138 Dec 31, 2000 0.155 Dec 31, 2001 0.15 Dec 31, 2002 0.161 Dec 31, 2003 0.153 Dec 31, 2004 0.165 Dec 31, 2005 0.17 Dec 31, 2006 0.159 Dec 31, 2007 0.159 Dec 31, 2008 0.162 Dec 31, 2009 0.169 Dec 31, 2010 0.172 Dec 31, 2011 0.172 Dec 31, 2012 0.168 Dec 31, 2013 0.166 Dec 31, 2014 0.161 Dec 31, 2015 0.163 Dec 31, 2016 0.158 Dec 31, 2017 0.156 Dec 31, 2018 0.152
The percentage of the population in poverty in Oklahoma stays roughly the same between 2000 and 2019. There was a decrease in the poverty rate in Oklahoma between 2010 and 2019.
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/12536.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#9E9D24"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Number of hate crimes"}, "field": "Number of hate crimes"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "crime types"}}, "title": ["Number of hate crimes recorded by the police", "in Italy in 2019 , by type"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Number of hate crimes crime types 310 Incitement to violence 241 Physical assault 152 Desecration of graves 117 Threats/ threatening behavior 87 Theft/ robbery 75 Damage to property 58 Disturbance of the peace 42 Attacks against places of workship 21 Vandalism 1 Homicide 1 Arson 14 Unspecified
In 2019 in Italy over 300 cases of incitement to violence were recorded by police. Arson and homocide as a hate crime had the lowest recordings in the chart. 150 hate crimes incidences of desecration of graves were recorded in 2019. Following incitement to violence, physical violence was the next most recorded form of hate crime by police in Italy in 2019.
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/12536.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#9E9D24"}, "x": {"type": "quantitative", "axis": {"labelAngle": 60, "title": "Number of hate crimes"}, "field": "Number of hate crimes"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "crime types"}}, "title": ["Number of hate crimes recorded by the police", "in Italy in 2019 , by type"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Number of hate crimes crime types 310 Incitement to violence 241 Physical assault 152 Desecration of graves 117 Threats/ threatening behavior 87 Theft/ robbery 75 Damage to property 58 Disturbance of the peace 42 Attacks against places of workship 21 Vandalism 1 Homicide 1 Arson 14 Unspecified
Incitement to violence and physical threat are by far the most common forms of hate crime, followed by desecration of graves (at about half the rate of incitement to violence) and threat, theft and damage to property (about a third of incitement to violence). Homicide and arson are negligible.
{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/12538.tsv"}, "mark": "line", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Deaths per 1,000 live births"}, "field": "Deaths per 1,000 live births"}}, "title": ["Australia : Infant mortality rate from 2009", "to 2019 (in deaths per 1,000 live births)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Deaths per 1,000 live births Dec 31, 2008 4.2 Dec 31, 2009 4 Dec 31, 2010 3.8 Dec 31, 2011 3.6 Dec 31, 2012 3.5 Dec 31, 2013 3.3 Dec 31, 2014 3.3 Dec 31, 2015 3.2 Dec 31, 2016 3.1 Dec 31, 2017 3.1 Dec 31, 2018 3.1
Infant mortality rate has shown improvement in Australia over the last decade.
{"config": {"view": {"stroke": "transparent"}, "background": "transparent", "font": "Segoe UI", "header": {"titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleFontSize": 16, "titleColor": "#252423", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C"}, "axis": {"ticks": false, "grid": false, "domain": false, "labelColor": "#605E5C", "labelFontSize": 12, "titleFont": "wf_standard-font, helvetica, arial, sans-serif", "titleColor": "#252423", "titleFontSize": 16, "titleFontWeight": "normal"}, "axisQuantitative": {"tickCount": 3, "grid": true, "gridColor": "#C8C6C4", "gridDash": [1, 5], "labelFlush": false}, "axisBand": {"tickExtra": true}, "axisX": {"labelPadding": 5}, "axisY": {"labelPadding": 10}, "bar": {"fill": "#118DFF"}, "line": {"stroke": "#118DFF", "strokeWidth": 3, "strokeCap": "round", "strokeJoin": "round"}, "text": {"font": "Segoe UI", "fontSize": 12, "fill": "#605E5C"}, "area": {"fill": "#118DFF", "line": true, "opacity": 0.6}, "rect": {"fill": "#118DFF"}, "point": {"fill": "#118DFF", "filled": true, "size": 75}, "legend": {"titleFont": "Segoe UI", "titleFontWeight": "bold", "titleColor": "#605E5C", "labelFont": "Segoe UI", "labelFontSize": 13.333333333333332, "labelColor": "#605E5C", "symbolType": "circle", "symbolSize": 75}, "range": {"category": ["#118DFF", "#12239E", "#E66C37", "#6B007B", "#E044A7", "#744EC2", "#D9B300", "#D64550"], "diverging": ["#DEEFFF", "#118DFF"], "heatmap": ["#DEEFFF", "#118DFF"], "ordinal": ["#DEEFFF", "#c7e4ff", "#b0d9ff", "#9aceff", "#83c3ff", "#6cb9ff", "#55aeff", "#3fa3ff", "#2898ff", "#118DFF"]}}, "data": {"url": "data/12538.tsv"}, "mark": "line", "encoding": {"color": {"value": "#744EC2"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Deaths per 1,000 live births"}, "field": "Deaths per 1,000 live births"}}, "title": ["Australia : Infant mortality rate from 2009", "to 2019 (in deaths per 1,000 live births)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Deaths per 1,000 live births Dec 31, 2008 4.2 Dec 31, 2009 4 Dec 31, 2010 3.8 Dec 31, 2011 3.6 Dec 31, 2012 3.5 Dec 31, 2013 3.3 Dec 31, 2014 3.3 Dec 31, 2015 3.2 Dec 31, 2016 3.1 Dec 31, 2017 3.1 Dec 31, 2018 3.1
infant mortality ahs decreased in australia with its lowest year being in 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": "data/12544.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#154866"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Type of retailers which consumers will likely", "visit to shop for holiday gifts in the United", "States as of September 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Response Share of respondents Internet/online retailers* 0.62 Mass merchants 0.5 Traditional department stores 0.26 Supermarkets or grocery stores 0.26 Off-price stores 0.24 Warehouse membership clubs 0.23 Bookstores 0.23 Electronics/office supply/computer stores 0.22 Specialty beauty stores 0.21 Home improvements stores 0.21 Pet stores 0.17 Furniture or home furnishings stores 0.16 Dollar stores 0.16 Specialty arts/crafts retailer 0.16 Outlet stores/centers 0.16 Specialty clothing stores 0.16
Over half of respondents said they will visit internet/online retailers making it the most popular. The least popular were durinoture outlet and specialty clothing stores.
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/12544.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#154866"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Type of retailers which consumers will likely", "visit to shop for holiday gifts in the United", "States as of September 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Response Share of respondents Internet/online retailers* 0.62 Mass merchants 0.5 Traditional department stores 0.26 Supermarkets or grocery stores 0.26 Off-price stores 0.24 Warehouse membership clubs 0.23 Bookstores 0.23 Electronics/office supply/computer stores 0.22 Specialty beauty stores 0.21 Home improvements stores 0.21 Pet stores 0.17 Furniture or home furnishings stores 0.16 Dollar stores 0.16 Specialty arts/crafts retailer 0.16 Outlet stores/centers 0.16 Specialty clothing stores 0.16
The most popular way to purchase holiday gifts in September 2020 was to buy from online retailers with over a 0.6 percentage share. The second most popular way to purchase holiday gifts was to shop at mass merchants with a 0.5 percentage share. The least popular way to purchase holiday gifts was from dollar stores, home furniture/ furnishings stores, outlet stores/centres, specialty arts/crafts centre and specialty clothing stores all with a percentage share of 0.15.
{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "data/12552.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1696d2"}, "x": {"type": "quantitative", "axis": {"title": "IPTV subscribers in millions"}, "field": "IPTV subscribers in millions"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "Quarter"}}, "title": ["Number of IPTV subscribers in China from", "1st quarter 2012 to 4th quarter 2014 (in", "millions)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
IPTV subscribers in millions Quarter 33.6 Q4 '14 35 Q3 '14 34.8 Q2 '14 32.7 Q1' 14 30.7 Q4 '13 26.77 Q2 '13 21.08 Q1 '13 19.93 Q4 '12 18.79 Q3 '12 17.65 Q2 '12 16.51 Q1 '12
There is a fluctuations every 2 quarters, there is no apparent significant trend.
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/12553.tsv"}, "mark": "area", "encoding": {"color": {"value": "#66a61e"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Turnover in million GBP"}, "field": "Turnover in million GBP"}}, "title": ["Turnover from retail sale of furniture and", "furnishings in the United Kingdom (UK) from", "2008 to 2018 (in million GBP)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Turnover in million GBP Dec 31, 2007 13839 Dec 31, 2008 13226 Dec 31, 2009 12313 Dec 31, 2010 13475 Dec 31, 2011 12395 Dec 31, 2012 13606 Dec 31, 2013 14492 Dec 31, 2014 16773 Dec 31, 2015 17002 Dec 31, 2016 17593 Dec 31, 2017 16211
After 2017, a steady drop in sales began. The lowest amount of sales was recorded in 2010 at about 12 million GPB. Between 2011 and 2012, there was a short fall in the amount of sales after which the recorded sales rose up steadily until 2017.
{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "data/12555.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#fcbfd2"}, "x": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "telecommunication companies"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of employees"}, "field": "Number of employees"}}, "title": ["Leading 10 biggest telecommunication companies", "in Denmark as of May 2020 , by number of", "employees"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
telecommunication companies Number of employees TDC A/S 1000 Telenor AS 1000 Kemp & Lauritzen A/S 1000 GlobalConnect A/S 350 TDC Telco ApS 350 Eltel Networks AS 350 Dansk Kabel TV AS 350 Stofa AS 350 Fibia PS 350 HI3G Denmark ApS 350
three of the companies have 1000 employees, whereas the others have under 400.
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/12557.tsv"}, "mark": "line", "encoding": {"color": {"value": "#ab5787"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Unemployment rate"}, "field": "Unemployment rate"}}, "title": ["Central African Republic : Unemployment", "rate from 1999 to 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Unemployment rate Dec 31, 1998 0.0397 Dec 31, 1999 0.0395 Dec 31, 2000 0.0395 Dec 31, 2001 0.04 Dec 31, 2002 0.0401 Dec 31, 2003 0.0396 Dec 31, 2004 0.039 Dec 31, 2005 0.0377 Dec 31, 2006 0.0366 Dec 31, 2007 0.0363 Dec 31, 2008 0.0387 Dec 31, 2009 0.0393 Dec 31, 2010 0.0393 Dec 31, 2011 0.0394 Dec 31, 2012 0.0396 Dec 31, 2013 0.039 Dec 31, 2014 0.0386 Dec 31, 2015 0.0382 Dec 31, 2016 0.0374 Dec 31, 2017 0.0366 Dec 31, 2018 0.0368 Dec 31, 2019 0.037
The line chart shows that the unemployment rate in Central African Republic fluctuated only a little between 2000 and 2015. The unemployment rate has decreased a little since 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": "data/12565.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#DB4437"}, "x": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Share of respondents"}, "field": "Share of respondents"}, "y": {"type": "nominal", "axis": {"labelAngle": 45}, "bin": false, "field": "Response"}}, "title": ["Social media sites and tools used by U.S.", "charity and non-profit organizations as of", "spring 2014"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Share of respondents Response 97 YouTube 92 Facebook 86 Twitter 72 Pinterest 57 Blog 57 LinkedIn 52 Google+ 50 Instagram 41 Foursquare 9 Vine 9 Snapchat 3 Tumblr
There are range of social media platforms being used. The most used are twitter Facebook and YouTube.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12568.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ffff99"}, "x": {"type": "quantitative", "axis": {"title": "Volume in thousand tonnes"}, "field": "Volume in thousand tonnes"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}}, "title": ["Forecast volume of beef and veal consumed", "in the European Union (EU 28) from 2015 to", "2028 (in 1,000 tonnes)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Volume in thousand tonnes Year 6518 2028* 6529 2027* 6540 2026* 6571 2025* 6613 2024* 6622 2023* 6644 2022* 6674 2021* 6731 2020* 6808 2019* 6826 2018** 6678 2017 6743 2016 6602 2015
Meat consumption increases from 2015 to its highest rate of consumption in the EU in 2018/2019 and then steadily decreases until 2022 and is then forecast to continue to decrease until 2028.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12568.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ffff99"}, "x": {"type": "quantitative", "axis": {"title": "Volume in thousand tonnes"}, "field": "Volume in thousand tonnes"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}}, "title": ["Forecast volume of beef and veal consumed", "in the European Union (EU 28) from 2015 to", "2028 (in 1,000 tonnes)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Volume in thousand tonnes Year 6518 2028* 6529 2027* 6540 2026* 6571 2025* 6613 2024* 6622 2023* 6644 2022* 6674 2021* 6731 2020* 6808 2019* 6826 2018** 6678 2017 6743 2016 6602 2015
The forecast is a reduction in beef and veal consumption from 2022 to 2028. There are no reasons given.
{"config": {"background": "#000", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#7fc97f", "#beaed4", "#fdc086", "#ffff99", "#386cb0", "#f0027f", "#bf5b17", "#666666"]}}, "data": {"url": "data/12568.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ffff99"}, "x": {"type": "quantitative", "axis": {"title": "Volume in thousand tonnes"}, "field": "Volume in thousand tonnes"}, "y": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}}, "title": ["Forecast volume of beef and veal consumed", "in the European Union (EU 28) from 2015 to", "2028 (in 1,000 tonnes)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Volume in thousand tonnes Year 6518 2028* 6529 2027* 6540 2026* 6571 2025* 6613 2024* 6622 2023* 6644 2022* 6674 2021* 6731 2020* 6808 2019* 6826 2018** 6678 2017 6743 2016 6602 2015
Beef and veal consumption is predicted to be strong but decline very slightly between now and 2028.
{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "data/12569.tsv"}, "mark": "line", "encoding": {"color": {"value": "#8b8b8b"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Per capita real GDP in chained 2012 U.S. dollars"}, "field": "Per capita real GDP in chained 2012 U\\.S\\. dollars"}}, "title": ["Per capita real Gross Domestic Product of", "Michigan from 2000 to 2019 (in chained 2012", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Per capita real GDP in chained 2012 U.S. dollars Dec 31, 1999 44037 Dec 31, 2000 42400 Dec 31, 2001 43457 Dec 31, 2002 44198 Dec 31, 2003 44175 Dec 31, 2004 44845 Dec 31, 2005 44171 Dec 31, 2006 44109 Dec 31, 2007 41893 Dec 31, 2008 38387 Dec 31, 2009 40591 Dec 31, 2010 41636 Dec 31, 2011 42321 Dec 31, 2012 42804 Dec 31, 2013 43456 Dec 31, 2014 44552 Dec 31, 2015 45452 Dec 31, 2016 46037 Dec 31, 2017 47128 Dec 31, 2018 47448
it remained fairly steady apart from a sharp drop around 2007/2009. it is now rising at a slow but steady rate.
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/12578.tsv"}, "mark": "line", "encoding": {"color": {"value": "#e7298a"}, "x": {"type": "temporal", "axis": {"labelAngle": 60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Total assets in millions of U.S. dollars"}, "field": "Total assets in millions of U\\.S\\. dollars"}}, "title": ["Pfizer 's total assets from 2006 to 2019", "(in million U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Total assets in millions of U.S. dollars Dec 31, 2005 115546 Dec 31, 2006 115268 Dec 31, 2007 111148 Dec 31, 2008 212949 Dec 31, 2009 195014 Dec 31, 2010 184629 Dec 31, 2011 182974 Dec 31, 2012 170415 Dec 31, 2013 167566 Dec 31, 2014 167381 Dec 31, 2015 171615 Dec 31, 2016 171797 Dec 31, 2017 159422 Dec 31, 2018 167489
Pfizer’s assets doubled between 2008 and 2009. Pfizer’s assets have remained between 150,000 and 200,000 US dollars since 2010.
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/1257.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#AB47BC"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of awards"}, "field": "Number of awards"}}, "title": ["Film studio ranking by number of `` Best", "Picture '' Academy Awards as of 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Number of awards Columbia Pictures 12 Paramount 11 20th Century Studios 9 Metro-Goldwyn-Mayer 9 Universal 9 Warner Bros 9 Searchlight Pictures 4 Miramax 4 DreamWorks 4 Orion 4 The Weinstein Company 2 Selznick International Pictures 2 RKO Pictures 1 Samuel Goldwyn Productions 1 A24 1 J. Arthur Rank-Two Cities Films 1
Columbia Pictures has the most rewards which are 12. A24, RKO Pictures, J Arthur and Samuel Goldwyn Pictures got the least reward which is 1. 21st century studios, Metro goldenwyn, universal and warned bros have great performance won 9 rewards whereas the rest of them have average awards of 4.
{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "data/12584.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#9fe4f8"}, "x": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Inhabitants in millions"}, "field": "Inhabitants in millions"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Finland : Total population from 2015 to", "2025 (in million inhabitants)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Inhabitants in millions Year 5.54 2025* 5.54 2024* 5.54 2023* 5.54 2022* 5.53 2021* 5.53 2020* 5.52 2019 5.51 2018 5.5 2017 5.49 2016 5.47 2015
The bar chart shows that the total population is the same for each year.
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/12586.tsv"}, "mark": "line", "encoding": {"color": {"value": "#adc839"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Sales volume in thousand kilograms"}, "field": "Sales volume in thousand kilograms"}}, "title": ["Sales volume of sugar confectionery manufactured", "in the United Kingdom (UK) from 2008 to 2019", "(in 1,000 kilograms)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Year Sales volume in thousand kilograms Dec 31, 2007 10807 Dec 31, 2008 13643 Dec 31, 2009 12589 Dec 31, 2010 14379 Dec 31, 2011 8039 Dec 31, 2012 9538 Dec 31, 2013 4703 Dec 31, 2014 6196 Dec 31, 2015 3805 Dec 31, 2016 8129 Dec 31, 2017 8648 Dec 31, 2018 9097
The sales volume of sugar in the UK began decreasing in 2011. The highest sugar volume was 14000 and the lowest was 4000 in 2016.
{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "data/12592.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#30a2da"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Main problems faced by companies due to", "the coronavirus (COVID-19) outbreak in Chile", "in 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Response Share of respondents Sales decrease 0.58 Work/business activities halt 0.33 Risk of insolvency 0.3 Absence of workers 0.12 Access to credit / debt payment delays 0.11 Delay in investment plans 0.09 Staff reduction 0.08 Customer communication 0.08 Workers' salary reduction 0.03 Did not answer 0.11
The highest percentage of problems seems to have been a decrease in sales, while the least effected would be the workers salarys decreasing.
{"config": {"area": {"fill": "#30a2da"}, "axis": {"domainColor": "#cbcbcb", "grid": true, "gridColor": "#cbcbcb", "gridWidth": 1, "labelColor": "#999", "labelFontSize": 10, "titleColor": "#333", "tickColor": "#cbcbcb", "tickSize": 10, "titleFontSize": 14, "titlePadding": 10, "labelPadding": 4}, "axisBand": {"grid": false}, "background": "#f0f0f0", "view": {"fill": "#f0f0f0"}, "legend": {"labelColor": "#333", "labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square", "titleColor": "#333", "titleFontSize": 14, "titlePadding": 10}, "line": {"stroke": "#30a2da", "strokeWidth": 2}, "rect": {"fill": "#30a2da"}, "bar": {"binSpacing": 2, "fill": "#30a2da", "stroke": null}, "range": {"category": ["#30a2da", "#fc4f30", "#e5ae38", "#6d904f", "#8b8b8b", "#b96db8", "#ff9e27", "#56cc60", "#52d2ca", "#52689e", "#545454", "#9fe4f8"], "diverging": ["#cc0020", "#e77866", "#f6e7e1", "#d6e8ed", "#91bfd9", "#1d78b5"], "heatmap": ["#d6e8ed", "#cee0e5", "#91bfd9", "#549cc6", "#1d78b5"]}, "point": {"filled": true, "shape": "circle"}, "title": {"anchor": "start", "fontSize": 24, "fontWeight": 600, "offset": 20}}, "data": {"url": "data/12592.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#30a2da"}, "x": {"type": "nominal", "axis": {"labelAngle": -60}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of respondents"}, "field": "Share of respondents"}}, "title": ["Main problems faced by companies due to", "the coronavirus (COVID-19) outbreak in Chile", "in 2020"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Response Share of respondents Sales decrease 0.58 Work/business activities halt 0.33 Risk of insolvency 0.3 Absence of workers 0.12 Access to credit / debt payment delays 0.11 Delay in investment plans 0.09 Staff reduction 0.08 Customer communication 0.08 Workers' salary reduction 0.03 Did not answer 0.11
During the 2020 Covid pandemic, Chile saw a Sales Decrease of more than 0.55, which according to the statics, was one of the largest effected areas. Whereas, Worker's Salary Reduction was one of the least affected areas overall.