delen/vistext huggingface dataset:
Chart description: solar and wind have the highest number of paid employees. geothermal has the lowest number of paid employees. rest of the catergories are evenly distributed.

Chart data: Number of employees technology 248034 Solar 114774 Wind 79711 Coal 75661 Advanced natural gas 60916 Nuclear 55468 Traditional hydro 46151 Natural gas 43520 Bioenery/CHP 12722 Oil 12304 Low impact hydro 8794 Geothermal 41117 Other

Vega-Lite specification:
{"config": {"background": "#fff", "area": {"fill": "#4572a7"}, "line": {"stroke": "#4572a7", "strokeWidth": 2}, "rect": {"fill": "#4572a7"}, "bar": {"fill": "#4572a7"}, "point": {"color": "#4572a7", "strokeWidth": 1.5, "size": 50}, "axis": {"bandPosition": 0.5, "grid": true, "gridColor": "#000000", "gridOpacity": 1, "gridWidth": 0.5, "labelPadding": 10, "tickSize": 5, "tickWidth": 0.5}, "axisBand": {"grid": false, "tickExtra": true}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 50, "symbolType": "square"}, "range": {"category": ["#4572a7", "#aa4643", "#8aa453", "#71598e", "#4598ae", "#d98445", "#94aace", "#d09393", "#b9cc98", "#a99cbc"]}}, "data": {"url": "data/27611.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#71598e"}, "x": {"type": "quantitative", "axis": {"title": "Number of employees"}, "field": "Number of employees"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "technology"}}, "title": ["Number of paid employees in electric power", "generation in the U.S. in 2019 , by technology"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Solar Technology employs the highest number of people in electric power generation. The number of employees in Solar (250,000) is 2.5 times the number employed in the next highest, which is Wind (100,000).Geothermal employees the least number of people.

Chart data: Number of employees technology 248034 Solar 114774 Wind 79711 Coal 75661 Advanced natural gas 60916 Nuclear 55468 Traditional hydro 46151 Natural gas 43520 Bioenery/CHP 12722 Oil 12304 Low impact hydro 8794 Geothermal 41117 Other

Vega-Lite specification:
{"config": {"background": "#fff", "area": {"fill": "#4572a7"}, "line": {"stroke": "#4572a7", "strokeWidth": 2}, "rect": {"fill": "#4572a7"}, "bar": {"fill": "#4572a7"}, "point": {"color": "#4572a7", "strokeWidth": 1.5, "size": 50}, "axis": {"bandPosition": 0.5, "grid": true, "gridColor": "#000000", "gridOpacity": 1, "gridWidth": 0.5, "labelPadding": 10, "tickSize": 5, "tickWidth": 0.5}, "axisBand": {"grid": false, "tickExtra": true}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 50, "symbolType": "square"}, "range": {"category": ["#4572a7", "#aa4643", "#8aa453", "#71598e", "#4598ae", "#d98445", "#94aace", "#d09393", "#b9cc98", "#a99cbc"]}}, "data": {"url": "data/27611.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#71598e"}, "x": {"type": "quantitative", "axis": {"title": "Number of employees"}, "field": "Number of employees"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "technology"}}, "title": ["Number of paid employees in electric power", "generation in the U.S. in 2019 , by technology"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Lombardy has the highest amount of money laundering with 12,000. Aosta Valley has the lowest amount of of money laundering.

Chart data: Number of suspected cases region of Italy 12083 Lombardy 11038 Campania 7524 Lazio 5216 Emilia-Romagna 3610 Sicily 3490 Veneto 3474 Piedmont 3160 Tuscany 2605 Apulia 1377 Liguria 1298 Calabria 1138 Marche 939 Abruzzo 603 Friuli-Venezia Giulia 572 Umbria 442 Sardinia 378 Trentino-South Tyrol 352 Basilicata 236 Molise 80 Aosta Valley

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/27618.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c4c4c4"}, "x": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Number of suspected cases"}, "field": "Number of suspected cases"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "region of Italy"}}, "title": ["Number of reports of money laundering in", "Italy in 1st half of 2019 , by region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The chart shows that there has been an increase in the foreign population living in Italy.  There has been an increase of 1650 thousand foreigners in Italy as in 2009 the population of foreigners was 3600 thousand but in 2019 the population of foreigners was 5250 thousand. The foreign population rose steeply between 2009 and 2014. The foreign population stayed at a similar level until 2017 when is started to gradually grow again.

Chart data: Year Number of foreigners in thousands Dec 31, 2009 3648 Dec 31, 2010 3879 Dec 31, 2011 4052 Dec 31, 2012 4388 Dec 31, 2013 4922 Dec 31, 2014 5014 Dec 31, 2015 5026 Dec 31, 2016 5047 Dec 31, 2017 5144 Dec 31, 2018 5255

Vega-Lite specification:
{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "data/27630.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fff2ae"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of foreigners in thousands"}, "field": "Number of foreigners in thousands"}}, "title": ["Foreign population living in Italy from", "2010 to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The number of foreigners living in Italy increased by more than 1000 in thousands from 2010 to 2018.

Chart data: Year Number of foreigners in thousands Dec 31, 2009 3648 Dec 31, 2010 3879 Dec 31, 2011 4052 Dec 31, 2012 4388 Dec 31, 2013 4922 Dec 31, 2014 5014 Dec 31, 2015 5026 Dec 31, 2016 5047 Dec 31, 2017 5144 Dec 31, 2018 5255

Vega-Lite specification:
{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "data/27630.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fff2ae"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of foreigners in thousands"}, "field": "Number of foreigners in thousands"}}, "title": ["Foreign population living in Italy from", "2010 to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Overall, the number of foreigners living in Italy has increased over this time period. There was a fairly steep increase from 2010 - 2014. It appears that there was a period between 2015 - 2017 where this slowed considerably and almost plateaued but continued to rise more sharply from 2017 onwards.

Chart data: Year Number of foreigners in thousands Dec 31, 2009 3648 Dec 31, 2010 3879 Dec 31, 2011 4052 Dec 31, 2012 4388 Dec 31, 2013 4922 Dec 31, 2014 5014 Dec 31, 2015 5026 Dec 31, 2016 5047 Dec 31, 2017 5144 Dec 31, 2018 5255

Vega-Lite specification:
{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "data/27630.tsv"}, "mark": "line", "encoding": {"color": {"value": "#fff2ae"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Number of foreigners in thousands"}, "field": "Number of foreigners in thousands"}}, "title": ["Foreign population living in Italy from", "2010 to 2019 (in 1,000s)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Although there has been an increase in urbanisation it has been slow, moving from just under 0.8 to just over 0.8 in the 10 years that the data has been collected. There have been no peaks and troughs, just a slow steady increase.

Chart data: Response Share of urban population Dec 31, 2008 0.7752 Dec 31, 2009 0.7782 Dec 31, 2010 0.7811 Dec 31, 2011 0.7841 Dec 31, 2012 0.787 Dec 31, 2013 0.7899 Dec 31, 2014 0.7929 Dec 31, 2015 0.7958 Dec 31, 2016 0.7987 Dec 31, 2017 0.8016 Dec 31, 2018 0.8044

Vega-Lite specification:
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/2763.tsv"}, "mark": "area", "encoding": {"color": {"value": "#66a61e"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of urban population"}, "field": "Share of urban population"}}, "title": ["Mexico : Rate of urbanization from 2009", "to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The turnover increased year on year during this period from about 4,000 to 12,000 by the end of 2018. This increase was fairly linear  for the years of 2010-2012 then became more parabolic from 2012 to 2019, looking fairly parabolic.

Chart data: Year Turnover in million euros Dec 31, 2009 3737 Dec 31, 2010 4159 Dec 31, 2011 4470 Dec 31, 2012 4875 Dec 31, 2013 5570 Dec 31, 2014 6351 Dec 31, 2015 7690 Dec 31, 2016 9053 Dec 31, 2017 10514 Dec 31, 2018 11933

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/27641.tsv"}, "mark": "area", "encoding": {"color": {"value": "#d190b6"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Turnover in million euros"}, "field": "Turnover in million euros"}}, "title": ["Turnover of the whole organic food sector", ", including restaurant and other catering", "facilities , in France between 2010 and 2019", "(in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Venezuela has the highest EMBI+ at just over 30 000 and Peru has one of the lowest at just over 0.

Chart data: Country Emerging Markets Bond Index (EMBI+) Venezuela 30701.35 Ecuador 2824 Argentina 2342.29 Belize 1510.47 El Salvador 799.29 Costa Rica 700.35 Regional average* 523.94 Dominican Republic 518.94 Trinidad and Tobago 510.47 Mexico 508.65 Jamaica 449.88 Brazil 359.41 Guatemala 339.59 Colombia 275.76 Chile 202.59 Panama 201 Uruguay 198.94 Peru 172.35

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/27656.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d190b6"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"title": "Emerging Markets Bond Index (EMBI+)"}, "field": "Emerging Markets Bond Index (EMBI+)"}}, "title": ["Emerging Markets Bond Index (EMBI+) in selected", "Latin American countries in July 2020 (in", "index points)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Venezuela is by far the strongest market for the EMBI at 30,000. None of the other selected Latin American countries come close, with the next nearest being Ecuador at around 2,500. The majority of the countries in the sample register below 1,000 on the index.

Chart data: Country Emerging Markets Bond Index (EMBI+) Venezuela 30701.35 Ecuador 2824 Argentina 2342.29 Belize 1510.47 El Salvador 799.29 Costa Rica 700.35 Regional average* 523.94 Dominican Republic 518.94 Trinidad and Tobago 510.47 Mexico 508.65 Jamaica 449.88 Brazil 359.41 Guatemala 339.59 Colombia 275.76 Chile 202.59 Panama 201 Uruguay 198.94 Peru 172.35

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/27656.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d190b6"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"title": "Emerging Markets Bond Index (EMBI+)"}, "field": "Emerging Markets Bond Index (EMBI+)"}}, "title": ["Emerging Markets Bond Index (EMBI+) in selected", "Latin American countries in July 2020 (in", "index points)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There are 3 countries that have an exponentially larger number of military personnel compared to all other countries named.

Chart data: Country Number of personnel Brazil 334500 Colombia 295000 Mexico 277000 Venezuela 120000 Peru 90000 Chile 77000 Argentina 75000 Dominican Republic 55000 Cuba 50000 Ecuador 40000 Bolivia 35000 Uruguay 25000 El Salvador 25000 Guatemala 20000 Honduras 15000 Nicaragua 12000 Paraguay 10500 Panama 10000 Suriname 1850

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/2766.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "nominal", "axis": {"labelAngle": 90}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Number of personnel"}, "field": "Number of personnel"}}, "title": ["Countries with the largest number of active", "military personnel in Latin America and the", "Caribbean in 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In 2009, the response of percentage of change in volume of U.S. imports of trade goods was significantly less than any other year, dramatically dropping between -0.2 and -0.3.

Chart data: Response Percentage of change Dec 31, 1989 0.043 Dec 31, 1990 −0.015 Dec 31, 1991 0.093 Dec 31, 1992 0.099 Dec 31, 1993 0.135 Dec 31, 1994 0.121 Dec 31, 1995 0.072 Dec 31, 1996 0.092 Dec 31, 1997 0.048 Dec 31, 1998 0.127 Dec 31, 1999 0.189 Dec 31, 2000 −0.063 Dec 31, 2001 0.017 Dec 31, 2002 0.084 Dec 31, 2003 0.17 Dec 31, 2004 0.139 Dec 31, 2005 0.108 Dec 31, 2006 0.058 Dec 31, 2007 0.078 Dec 31, 2008 −0.262 Dec 31, 2009 0.227 Dec 31, 2010 0.155 Dec 31, 2011 0.029 Dec 31, 2012 −0.004 Dec 31, 2013 0.04 Dec 31, 2014 −0.047 Dec 31, 2015 −0.029 Dec 31, 2016 0.069 Dec 31, 2017 0.086 Dec 31, 2018 −0.017

Vega-Lite specification:
{"config": {"background": "#f9f9f9", "area": {"fill": "#ab5787"}, "line": {"stroke": "#ab5787"}, "rect": {"fill": "#ab5787"}, "bar": {"fill": "#ab5787"}, "point": {"fill": "#ab5787", "size": 30}, "axis": {"domainColor": "#979797", "domainWidth": 0.5, "gridWidth": 0.2, "labelColor": "#979797", "tickColor": "#979797", "tickWidth": 0.2, "titleColor": "#979797"}, "axisBand": {"grid": false}, "axisX": {"grid": true, "tickSize": 10}, "axisY": {"domain": false, "grid": true, "tickSize": 0}, "legend": {"labelFontSize": 11, "padding": 1, "symbolSize": 30, "symbolType": "square"}, "range": {"category": ["#ab5787", "#51b2e5", "#703c5c", "#168dd9", "#d190b6", "#00609f", "#d365ba", "#154866", "#666666", "#c4c4c4"]}}, "data": {"url": "data/27677.tsv"}, "mark": "area", "encoding": {"color": {"value": "#d190b6"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": 30, "title": "Percentage of change"}, "field": "Percentage of change"}}, "title": ["Percentage of change in volume of U.S. imports", "of trade goods from 1990 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Boehringer Ingelheim 's expenditure on research as a percentage of revenue was at its highest in 2015 and at its lowest in 2005. 2015 was the only year where the expenditure was over 0.2 percent.

Chart data: Response Percentage of revenue Dec 31, 2003 0.151 Dec 31, 2004 0.143 Dec 31, 2005 0.149 Dec 31, 2006 0.173 Dec 31, 2007 0.182 Dec 31, 2008 0.174 Dec 31, 2009 0.195 Dec 31, 2010 0.191 Dec 31, 2011 0.19 Dec 31, 2012 0.195 Dec 31, 2013 0.199 Dec 31, 2014 0.203 Dec 31, 2015 0.196 Dec 31, 2016 0.17 Dec 31, 2017 0.181 Dec 31, 2018 0.182

Vega-Lite specification:
{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "data/27678.tsv"}, "mark": "line", "encoding": {"color": {"value": "#cbd5e8"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Percentage of revenue"}, "field": "Percentage of revenue"}}, "title": ["Boehringer Ingelheim 's expenditure on research", "and development as a percentage of revenue", "from 2004 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Spending on research has generally increased over this 14 year period.

Chart data: Response Percentage of revenue Dec 31, 2003 0.151 Dec 31, 2004 0.143 Dec 31, 2005 0.149 Dec 31, 2006 0.173 Dec 31, 2007 0.182 Dec 31, 2008 0.174 Dec 31, 2009 0.195 Dec 31, 2010 0.191 Dec 31, 2011 0.19 Dec 31, 2012 0.195 Dec 31, 2013 0.199 Dec 31, 2014 0.203 Dec 31, 2015 0.196 Dec 31, 2016 0.17 Dec 31, 2017 0.181 Dec 31, 2018 0.182

Vega-Lite specification:
{"config": {"background": "#666", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#fbb4ae", "#b3cde3", "#ccebc5", "#decbe4", "#fed9a6", "#ffffcc", "#e5d8bd", "#fddaec", "#f2f2f2", "#b3e2cd", "#fdcdac", "#cbd5e8", "#f4cae4", "#e6f5c9", "#fff2ae", "#f1e2cc", "#cccccc"]}}, "data": {"url": "data/27678.tsv"}, "mark": "line", "encoding": {"color": {"value": "#cbd5e8"}, "x": {"type": "temporal", "axis": {"labelAngle": -90}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": -60, "title": "Percentage of revenue"}, "field": "Percentage of revenue"}}, "title": ["Boehringer Ingelheim 's expenditure on research", "and development as a percentage of revenue", "from 2004 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The most significant number of deaths was recorded in Peru in 1962 when 2000 people died. Fewer than 300 people have died during any of the other disasters.

Chart data: Number of deaths major dry disasters 2000 Landslide in Peru (January 10, 1962) 300 Landslide in the Philippines (October 21, 1985) 277 Landslide in China (March 7, 1983) 261 Rockslide in Turkey (January 1, 1992) 160 Rockfall in Colombia (July 28, 1983) 150 Landslide in Nepal (August 10, 1963) 131 Landslide in Indonesia (May 4, 1987) 100 Rockslide in Afghanistan (March 1993) 98 Rockfall in Egypt (September 6, 2008) 76 Rockfall in Canada (April 29, 1903)

Vega-Lite specification:
{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "data/27679.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1A1A1A"}, "x": {"type": "quantitative", "axis": {"title": "Number of deaths"}, "field": "Number of deaths"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "major dry disasters"}}, "title": ["Number of deaths globally due to major dry", "disasters - landslides , avalanches or rockfalls", "- between 1900 and 2016"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Landslide in Peru had the highest death count out of all dry disasters, with 2000 deaths. The disaster with the lowest death count was Rockfall in Canada, with under 500 deaths (around 100). Landslide in Peru had the highest death count of all the landslide disasters, Rockfall in Colombia had the highest death count of all rockfall disasters and Rockslide in Turkey had the highest death count of rockslide disasters. Landslide in Peru was the only disaster to have a death count over 500 people.

Chart data: Number of deaths major dry disasters 2000 Landslide in Peru (January 10, 1962) 300 Landslide in the Philippines (October 21, 1985) 277 Landslide in China (March 7, 1983) 261 Rockslide in Turkey (January 1, 1992) 160 Rockfall in Colombia (July 28, 1983) 150 Landslide in Nepal (August 10, 1963) 131 Landslide in Indonesia (May 4, 1987) 100 Rockslide in Afghanistan (March 1993) 98 Rockfall in Egypt (September 6, 2008) 76 Rockfall in Canada (April 29, 1903)

Vega-Lite specification:
{"config": {"view": {"fill": "#e5e5e5"}, "area": {"fill": "#000"}, "line": {"stroke": "#000"}, "rect": {"fill": "#000"}, "bar": {"fill": "#000"}, "point": {"color": "#000", "size": 40}, "axis": {"domain": false, "grid": true, "gridColor": "#FFFFFF", "gridOpacity": 1, "labelColor": "#7F7F7F", "labelPadding": 4, "tickColor": "#7F7F7F", "tickSize": 5.67, "titleFontSize": 16, "titleFontWeight": "normal"}, "legend": {"labelBaseline": "middle", "labelFontSize": 11, "symbolSize": 40}, "range": {"category": ["#000000", "#7F7F7F", "#1A1A1A", "#999999", "#333333", "#B0B0B0", "#4D4D4D", "#C9C9C9", "#666666", "#DCDCDC"]}}, "data": {"url": "data/27679.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#1A1A1A"}, "x": {"type": "quantitative", "axis": {"title": "Number of deaths"}, "field": "Number of deaths"}, "y": {"type": "nominal", "axis": {"labelAngle": 60}, "bin": false, "field": "major dry disasters"}}, "title": ["Number of deaths globally due to major dry", "disasters - landslides , avalanches or rockfalls", "- between 1900 and 2016"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The largest cash subsidy was to NCR and was to the value of 8000 pesos. The lowest cash subsidy was to ARM, CARAGA and Regions 12, 4B, 5, 8 and 9 which all received 5000 philippine pesos. All regions received a cash subsidy of at least 5000 philippine pesos.

Chart data: Cash subsidy in Philippine pesos Region of Philippines 8000 NCR 5500 CAR 5500 Region 1 5500 Region 2 6500 Region 3 6500 Region 4-A 5000 Region 4-B 5000 Region 5 6000 Region 6 6000 Region 7 5000 Region 8 5000 Region 9 6000 Region 10 6000 Region 11 5000 Region 12 5000 CARAGA 5000 ARMM

Vega-Lite specification:
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/2767.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#7b4173"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Cash subsidy in Philippine pesos"}, "field": "Cash subsidy in Philippine pesos"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Region of Philippines"}}, "title": ["Value of emergency cash subsidy for every", "low-income families due to the coronavirus", "COVID-19 in the Philippines in 2020 , by", "region (in Philippine pesos)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The most common subsidy was between 4000 and 6000 peso. The highest was given to the NCR area at 8000 peso compared to multi areas closer to the 5000 level.  Overall the amounts given were relatively similar, except for the NCR region which was significantly higher than all other areas.

Chart data: Cash subsidy in Philippine pesos Region of Philippines 8000 NCR 5500 CAR 5500 Region 1 5500 Region 2 6500 Region 3 6500 Region 4-A 5000 Region 4-B 5000 Region 5 6000 Region 6 6000 Region 7 5000 Region 8 5000 Region 9 6000 Region 10 6000 Region 11 5000 Region 12 5000 CARAGA 5000 ARMM

Vega-Lite specification:
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/2767.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#7b4173"}, "x": {"type": "quantitative", "axis": {"labelAngle": 45, "title": "Cash subsidy in Philippine pesos"}, "field": "Cash subsidy in Philippine pesos"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Region of Philippines"}}, "title": ["Value of emergency cash subsidy for every", "low-income families due to the coronavirus", "COVID-19 in the Philippines in 2020 , by", "region (in Philippine pesos)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Brokerage fees in English-speaking European countries are significantly lower than those in other European countries, and than those of all the other countries including the US.

Chart data: Country Brokerage fees in euros Austria 7600 Germany* 6968 Spain 6000 U.S.* 5900 Sweden 5000 France 5000 Italy* 4200 Portugal* 4000 United Kingdom* 2550 Ireland 2500

Vega-Lite specification:
{"config": {"range": {"category": ["#393b79", "#5254a3", "#6b6ecf", "#9c9ede", "#637939", "#8ca252", "#b5cf6b", "#cedb9c", "#8c6d31", "#bd9e39", "#e7ba52", "#e7cb94", "#843c39", "#ad494a", "#d6616b", "#e7969c", "#7b4173", "#a55194", "#ce6dbd", "#de9ed6"]}}, "data": {"url": "data/27682.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#cedb9c"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Country"}, "y": {"type": "quantitative", "axis": {"title": "Brokerage fees in euros"}, "field": "Brokerage fees in euros"}}, "title": ["International brokerage fees in 2011 , by", "region"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Over the years it has kept quite steady however in 2015 it is at its lowest.

Chart data: Response Share of employees Dec 31, 1994 0.556 Dec 31, 1995 0.55 Dec 31, 1996 0.556 Dec 31, 1997 0.531 Dec 31, 1998 0.537 Dec 31, 1999 0.539 Dec 31, 2000 0.532 Dec 31, 2001 0.55 Dec 31, 2002 0.55 Dec 31, 2003 0.554 Dec 31, 2004 0.555 Dec 31, 2005 0.55 Dec 31, 2006 0.554 Dec 31, 2007 0.542 Dec 31, 2008 0.531 Dec 31, 2009 0.524 Dec 31, 2010 0.516 Dec 31, 2011 0.521 Dec 31, 2012 0.518 Dec 31, 2013 0.503 Dec 31, 2014 0.518 Dec 31, 2015 0.48 Dec 31, 2016 0.481 Dec 31, 2017 0.476

Vega-Lite specification:
{"config": {"background": "#FFFFFF", "title": {"anchor": "start", "fontSize": 18, "font": "Lato"}, "axisX": {"domain": true, "domainColor": "#000000", "domainWidth": 1, "grid": false, "labelFontSize": 12, "labelFont": "Lato", "tickColor": "#000000", "tickSize": 5, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato"}, "axisY": {"domain": false, "domainWidth": 1, "grid": true, "gridColor": "#DEDDDD", "gridWidth": 1, "labelFontSize": 12, "labelFont": "Lato", "labelPadding": 8, "ticks": false, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "titleAngle": 0, "titleY": -10, "titleX": 18}, "legend": {"labelFontSize": 12, "labelFont": "Lato", "symbolSize": 100, "titleFontSize": 12, "titlePadding": 10, "titleFont": "Lato", "orient": "right", "offset": 10}, "view": {"stroke": "transparent"}, "range": {"category": ["#1696d2", "#ec008b", "#fdbf11", "#000000", "#d2d2d2", "#55b748"], "diverging": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "heatmap": ["#ca5800", "#fdbf11", "#fdd870", "#fff2cf", "#cfe8f3", "#73bfe2", "#1696d2", "#0a4c6a"], "ordinal": ["#cfe8f3", "#a2d4ec", "#73bfe2", "#46abdb", "#1696d2", "#12719e"], "ramp": ["#CFE8F3", "#A2D4EC", "#73BFE2", "#46ABDB", "#1696D2", "#12719E", "#0A4C6A", "#062635"]}, "area": {"fill": "#1696d2"}, "rect": {"fill": "#1696d2"}, "line": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 5}, "trail": {"color": "#1696d2", "stroke": "#1696d2", "strokeWidth": 0, "size": 1}, "point": {"color": "#1696d2", "size": 30}, "text": {"font": "Lato", "color": "#1696d2", "fontSize": 11, "align": "center", "fontWeight": 400, "size": 11}, "style": {"bar": {"fill": "#1696d2", "stroke": null}}}, "data": {"url": "data/27690.tsv"}, "mark": "area", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of employees"}, "field": "Share of employees"}}, "title": ["Trade union density : proportion of employees", "that were members of a trade union in the", "education sector in the United Kingdom from", "1995 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: In this chart, the information shows an upward trend of the Share of population of the over 60’s in china. However, this chart is an estimate as it goes into the future so the information isn’t reliable.

Chart data: Response Share of persons aged 60+ Dec 31, 1949 0.074 Dec 31, 1959 0.062 Dec 31, 1969 0.062 Dec 31, 1979 0.075 Dec 31, 1989 0.085 Dec 31, 1999 0.1 Dec 31, 2009 0.122 Dec 31, 2019 0.174 Dec 31, 2029 0.248 Dec 31, 2039 0.299 Dec 31, 2049 0.346 Dec 31, 2059 0.359 Dec 31, 2069 0.361 Dec 31, 2079 0.372 Dec 31, 2089 0.374 Dec 31, 2099 0.378

Vega-Lite specification:
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/2769.tsv"}, "mark": "area", "encoding": {"color": {"value": "#4285F4"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of persons aged 60+"}, "field": "Share of persons aged 60+"}}, "title": ["Share of population aged 60 and older in", "China from 1950 to 2100"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The share of persons aged 60 or more has been rising quite significantly from 1960 to 2040 then it reached a sort of plateau where it did rise still but less sharply.

Chart data: Response Share of persons aged 60+ Dec 31, 1949 0.074 Dec 31, 1959 0.062 Dec 31, 1969 0.062 Dec 31, 1979 0.075 Dec 31, 1989 0.085 Dec 31, 1999 0.1 Dec 31, 2009 0.122 Dec 31, 2019 0.174 Dec 31, 2029 0.248 Dec 31, 2039 0.299 Dec 31, 2049 0.346 Dec 31, 2059 0.359 Dec 31, 2069 0.361 Dec 31, 2079 0.372 Dec 31, 2089 0.374 Dec 31, 2099 0.378

Vega-Lite specification:
{"config": {"area": {"fill": "#3366CC"}, "rect": {"fill": "#3366CC"}, "bar": {"fill": "#3366CC"}, "point": {"stroke": "#3366CC"}, "circle": {"fill": "#3366CC"}, "background": "#fff", "padding": {"top": 10, "right": 10, "bottom": 10, "left": 10}, "style": {"guide-label": {"font": "Arial, sans-serif", "fontSize": 12}, "guide-title": {"font": "Arial, sans-serif", "fontSize": 12}, "group-title": {"font": "Arial, sans-serif", "fontSize": 12}}, "title": {"font": "Arial, sans-serif", "fontSize": 14, "fontWeight": "bold", "dy": -3, "anchor": "start"}, "axis": {"gridColor": "#ccc", "tickColor": "#ccc", "domain": false, "grid": true}, "range": {"category": ["#4285F4", "#DB4437", "#F4B400", "#0F9D58", "#AB47BC", "#00ACC1", "#FF7043", "#9E9D24", "#5C6BC0", "#F06292", "#00796B", "#C2185B"], "heatmap": ["#c6dafc", "#5e97f6", "#2a56c6"]}}, "data": {"url": "data/2769.tsv"}, "mark": "area", "encoding": {"color": {"value": "#4285F4"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of persons aged 60+"}, "field": "Share of persons aged 60+"}}, "title": ["Share of population aged 60 and older in", "China from 1950 to 2100"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The line/area chart shows that between 2004 and 2016 the sales of Bentley Mulsanne cars registered in the UK decreased slightly until 2008 and then rose sharply after that. The number of cars sold at the beginning of 2004 was fewer than 100 per year, slightly fewer than that for the period 2004 to 2008.  After 2008 the rate of sales increased rapidly and steadily from around 90 per year to just over 600 by 2015, when the rate of increased sales started to slow down.  However, in 2016 Bentley Musanne sold around 700 and the trend upwards continues, albeit slightly more slowly.

Chart data: Year Number of registered cars Dec 31, 2003 73 Dec 31, 2004 67 Dec 31, 2005 68 Dec 31, 2006 67 Dec 31, 2007 64 Dec 31, 2008 59 Dec 31, 2009 113 Dec 31, 2010 209 Dec 31, 2011 300 Dec 31, 2012 367 Dec 31, 2013 440 Dec 31, 2014 529 Dec 31, 2015 654 Dec 31, 2016 707 Dec 31, 2017 718

Vega-Lite specification:
{"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/27707.tsv"}, "mark": "area", "encoding": {"color": {"value": "#12239E"}, "x": {"type": "temporal", "axis": {"labelAngle": 45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of registered cars"}, "field": "Number of registered cars"}}, "title": ["Number of Bentley Mulsanne cars registered", "in Great Britain from 2004 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: 2017 was the best year in terms of annual turnover. 2009 was the worst year in terms of annual turnover. Overall, the trend for annual turnover has been to increase, with occasional falls in 2009 and 2013.

Chart data: Annual turnover in million euros Year 56907.8 2017** 55382.5 2016* 47436.8 2015 43099.8 2014 41491.8 2013 45898 2012 44419.6 2011 38678.4 2010 35180.2 2009 40679.9 2008

Vega-Lite specification:
{"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/27710.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#118DFF"}, "x": {"type": "quantitative", "axis": {"title": "Annual turnover in million euros"}, "field": "Annual turnover in million euros"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Turnover of the manufacture of machinery", "and equipment n.e.c . industry in the United", "Kingdom (UK) from 2008 to 2017 (in million", "euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: There is a growing trend of annual turnover increasing each year. 2009 had the lowest turn over. 2017 has the highest turnover.

Chart data: Annual turnover in million euros Year 56907.8 2017** 55382.5 2016* 47436.8 2015 43099.8 2014 41491.8 2013 45898 2012 44419.6 2011 38678.4 2010 35180.2 2009 40679.9 2008

Vega-Lite specification:
{"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/27710.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#118DFF"}, "x": {"type": "quantitative", "axis": {"title": "Annual turnover in million euros"}, "field": "Annual turnover in million euros"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}}, "title": ["Turnover of the manufacture of machinery", "and equipment n.e.c . industry in the United", "Kingdom (UK) from 2008 to 2017 (in million", "euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The visualisation shows the global demand for gold decreased from 2015 to 2019.

Chart data: Year Demand in metric tons Dec 31, 2004 440 Dec 31, 2005 472 Dec 31, 2006 478 Dec 31, 2007 465 Dec 31, 2008 414 Dec 31, 2009 460 Dec 31, 2010 427 Dec 31, 2011 379 Dec 31, 2012 354 Dec 31, 2013 346 Dec 31, 2014 332 Dec 31, 2015 323 Dec 31, 2016 333 Dec 31, 2017 335 Dec 31, 2018 327

Vega-Lite specification:
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/27712.tsv"}, "mark": "line", "encoding": {"color": {"value": "#7570b3"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Demand in metric tons"}, "field": "Demand in metric tons"}}, "title": ["Global technology gold demand from 2005", "to 2019 (in metric tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: From 2010 to 2018 there has been an increased drop in gold tonnes purchased demand. In 2007 and 2010 the maximum gold purchased was 480 tonnes and 450 tonnes, the highest purchases in the span from 2006 to 2018. The lowest gold purchase was 310 tonnes in 2016. The highest increases in gold purchases in tonnes was 2006 to  2008 and then in 2010. From 2010 there was a steady decline in gold purchase in tonnes.

Chart data: Year Demand in metric tons Dec 31, 2004 440 Dec 31, 2005 472 Dec 31, 2006 478 Dec 31, 2007 465 Dec 31, 2008 414 Dec 31, 2009 460 Dec 31, 2010 427 Dec 31, 2011 379 Dec 31, 2012 354 Dec 31, 2013 346 Dec 31, 2014 332 Dec 31, 2015 323 Dec 31, 2016 333 Dec 31, 2017 335 Dec 31, 2018 327

Vega-Lite specification:
{"config": {"background": "#333", "title": {"color": "#fff", "subtitleColor": "#fff"}, "style": {"guide-label": {"fill": "#fff"}, "guide-title": {"fill": "#fff"}}, "axis": {"domainColor": "#fff", "gridColor": "#888", "tickColor": "#fff"}, "range": {"category": ["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666"]}}, "data": {"url": "data/27712.tsv"}, "mark": "line", "encoding": {"color": {"value": "#7570b3"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Demand in metric tons"}, "field": "Demand in metric tons"}}, "title": ["Global technology gold demand from 2005", "to 2019 (in metric tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}