delen/vistext huggingface dataset:
Chart description: The energy imports frequently rate changes drastically but dropped to 0.25 in 2013 but the increased over double in 2018 to 0.60.

Chart data: Year Energy imports dependency rate Dec 31, 2007 0.3355 Dec 31, 2008 0.3406 Dec 31, 2009 0.2828 Dec 31, 2010 0.2936 Dec 31, 2011 0.3064 Dec 31, 2012 0.2374 Dec 31, 2013 0.3095 Dec 31, 2014 0.4847 Dec 31, 2015 0.4595 Dec 31, 2016 0.5186 Dec 31, 2017 0.5968

Vega-Lite specification:
{"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/25491.tsv"}, "mark": "line", "encoding": {"color": {"value": "#666666"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Energy imports dependency rate"}, "field": "Energy imports dependency rate"}}, "title": ["Dependency rate on energy imports in the", "Netherlands from 2008 to 2018"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Listen to the radio is the highest share of respondents at 0.7. Watch music videos (eg Youtube) and buy CDs online (eg Amazon) are the second highest at just under 0.4 share of respondents. The lowest responses are pay for a music subscription and I pay to use a music locker, both with well under a 0.1 share of respondents.

Chart data: Share of respondents Response 0.7 Listen to the radio 0.38 Watch music videos e.g. YouTube, Vimeo 0.35 Buy CDs online (e.g. Amazon) or in the high street (e.g. HMV) 0.32 Watch music TV shows e.g. XFactor, Later With Jools Holland 0.22 Watch music TV channels e.g. VH1, Clubland TV 0.17 Listen to streaming music for free e.g. Spotity Free, Soundcloud 0.16 Go to see bands, singers, or DJs play live 0.14 Pay to download single tracks e.g. iTunes Store 0.14 Pay to download albums e.g. iTunes Store 0.08 Download music for free from file sharing networks e.g. Pirate Bay, uTorrents 0.08 I use a music cloud locker service for free e.g. Amazon Cloud Player, Apple iCloud, Google Play Music 0.08 None of the above 0.06 I trade music with my friends (swap music between phones, hard drives, online with Dropbox etc. 0.04 Pay for a music subscription e.g. Spotify Premium, Deezer 0.03 I pay to use a music cloud locker service e.g. Amazon Cloud Player, Apple iTunes Match, Google Play Music

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/25497.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ab5787"}, "x": {"type": "quantitative", "axis": {"labelAngle": 90, "title": "Share of respondents"}, "field": "Share of respondents"}, "y": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Response"}}, "title": ["Thinking about how you discover , consume", "and interact with music which of the following", "do you do at least once a month ?"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The greatest number of respondents listen to music on the radio. It is clear that most people do not want to pay for subscriptions. An equal share of respondents either buy online cds from Amazon or watch music videos on Youtube.

Chart data: Share of respondents Response 0.7 Listen to the radio 0.38 Watch music videos e.g. YouTube, Vimeo 0.35 Buy CDs online (e.g. Amazon) or in the high street (e.g. HMV) 0.32 Watch music TV shows e.g. XFactor, Later With Jools Holland 0.22 Watch music TV channels e.g. VH1, Clubland TV 0.17 Listen to streaming music for free e.g. Spotity Free, Soundcloud 0.16 Go to see bands, singers, or DJs play live 0.14 Pay to download single tracks e.g. iTunes Store 0.14 Pay to download albums e.g. iTunes Store 0.08 Download music for free from file sharing networks e.g. Pirate Bay, uTorrents 0.08 I use a music cloud locker service for free e.g. Amazon Cloud Player, Apple iCloud, Google Play Music 0.08 None of the above 0.06 I trade music with my friends (swap music between phones, hard drives, online with Dropbox etc. 0.04 Pay for a music subscription e.g. Spotify Premium, Deezer 0.03 I pay to use a music cloud locker service e.g. Amazon Cloud Player, Apple iTunes Match, Google Play Music

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/25497.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ab5787"}, "x": {"type": "quantitative", "axis": {"labelAngle": 90, "title": "Share of respondents"}, "field": "Share of respondents"}, "y": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Response"}}, "title": ["Thinking about how you discover , consume", "and interact with music which of the following", "do you do at least once a month ?"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The chart shows that the cloud service OnLive(US, UK. and Belgium) is the service that had the the maximum games available out of all the select services available in 2013 with 300 games. The results in the chart show that over half with 7 of the 12 services having less than 50 games available which appears to be the common amount of games the services provide. Apart from OnLive being the service providing the most games overall, the companies based in france are the second overall highest supplier of games with 2 of the 3 services having under 200 games between them.

Chart data: Number of games available Service name (country) 300 OnLive (U.S., U.K. and Belgium) 165 SFR (France) 160 Orange (France) 105 G-Cluster (Japan) 100 Cloud Union (China) 40 Bouygues Telecom (France) 40 Portugal Telecom (Portugal) 40 One2free (China/Hong Kong) 35 CJ Hellovision (South Korea) 20 GameNow (United States) 15 NTT Docomo (Japan) 10 NTT Hkari TV (Japan)

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/25499.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "quantitative", "axis": {"labelAngle": -90, "title": "Number of games available"}, "field": "Number of games available"}, "y": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Service name (country)"}}, "title": ["Number of games offered by select cloud", "gaming services worldwide in 2013"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The service with the largest number of games available is OnLive (US, UK and Belgium), with 300 games. This is almost twice as many as the next largest services, SFR (France) with around 165 games and Orange (France) with around 160. The services with the smallest number of games are NTT Hkari TV (Japan) with around 10 and NTT Docomo (Japan) with around 20.

Chart data: Number of games available Service name (country) 300 OnLive (U.S., U.K. and Belgium) 165 SFR (France) 160 Orange (France) 105 G-Cluster (Japan) 100 Cloud Union (China) 40 Bouygues Telecom (France) 40 Portugal Telecom (Portugal) 40 One2free (China/Hong Kong) 35 CJ Hellovision (South Korea) 20 GameNow (United States) 15 NTT Docomo (Japan) 10 NTT Hkari TV (Japan)

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/25499.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#666666"}, "x": {"type": "quantitative", "axis": {"labelAngle": -90, "title": "Number of games available"}, "field": "Number of games available"}, "y": {"type": "nominal", "axis": {"labelAngle": -45}, "bin": false, "field": "Service name (country)"}}, "title": ["Number of games offered by select cloud", "gaming services worldwide in 2013"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Roughly 70% of flight from UK airlines arrive on time. BMI Regional is the most reliable airline in terms of arriving on time. Monarch is the worst airline in terms of arriving on time, with only 60%.

Chart data: Response Share of flights on time BMI Regional 0.92 BA Cityflyer 0.904 Flybe.com 0.848 EasyJet 0.845 Ryanair 0.842 Aer Lingus 0.836 Virgin Atlantic 0.828 Thomson Airways 0.797 Thomas Cook Airlines 0.776 Jet2.com 0.748 British Airways 0.737 Monarch (scheduled flights) 0.725 Monarch (charter flights) 0.609

Vega-Lite specification:
{"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/25508.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#52d2ca"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of flights on time"}, "field": "Share of flights on time"}}, "title": ["Share of flights arriving on time for major", "United Kingdom (UK) airlines in 2013"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: BA city flyer had the largest share of on time flights. Monarch charter flights had the smallest share of on time flights.

Chart data: Response Share of flights on time BMI Regional 0.92 BA Cityflyer 0.904 Flybe.com 0.848 EasyJet 0.845 Ryanair 0.842 Aer Lingus 0.836 Virgin Atlantic 0.828 Thomson Airways 0.797 Thomas Cook Airlines 0.776 Jet2.com 0.748 British Airways 0.737 Monarch (scheduled flights) 0.725 Monarch (charter flights) 0.609

Vega-Lite specification:
{"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/25508.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#52d2ca"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"title": "Share of flights on time"}, "field": "Share of flights on time"}}, "title": ["Share of flights arriving on time for major", "United Kingdom (UK) airlines in 2013"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The graphic show us that from 2008 until 2017 we can assist a decrease 8n the number of enterprises in the manufacture of electrical equipment industry in romania . The highest number observed was in 2008 with 800 enterprises in operation. From that year it's visible a decrease in the number of enterprises 5hat become basically stable from 2011 until 2017.

Chart data: Number of enterprises Year 625 2017 612 2016 615 2015 612 2014 612 2013 633 2012 618 2011 664 2010 719 2009* 802 2008

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/25522.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d09393"}, "x": {"type": "quantitative", "axis": {"title": "Number of enterprises"}, "field": "Number of enterprises"}, "y": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}}, "title": ["Number of enterprises in the manufacture", "of electrical equipment industry in Romania", "from 2008 to 2017"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Apart from the year 2009, when there was a decrease in turnover, this company have steadily and sucessfully grown The year 2018 was very sucessful and well exceeded a turnover of 20.000million Euros.

Chart data: Year Annual turnover in million euros 2018* 23955.8 2017 22158.9 2016 19793.5 2015 19090.8 2014 18176 2013 16987.8 2012 16102.6 2011 15985.7 2010 13786.8 2009 11044.6 2008 13504.3

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/25533.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#51b2e5"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Annual turnover in million euros"}, "field": "Annual turnover in million euros"}}, "title": ["Annual turnover of the manufacture of rubber", "and plastic products in Poland from 2008", "to 2018 (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: This bar chart shows that visiting the local villages and historical sites and museums are taken up by tourists.

Chart data: Response Percentage of respondents See historical sites/monuments 0.74 Try local cuisine 0.68 See museums and other cultural institutions 0.61 Visit villages or towns outside of the major cities 0.6 Go shopping 0.47 Relax on a beach 0.37 Go on an escorted tour (a multi-day coach tour for example) 0.37 Agrotourism (vineyards, cheese, olive oil, etc.) 0.32 Take a multi-day cruise 0.27 Attend concerts, festivals, or theatre 0.23 Visit friends and family 0.21 Other 0.06

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/25539.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "nominal", "axis": {"labelAngle": 90}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Percentage of respondents"}, "field": "Percentage of respondents"}}, "title": ["Which of the following would you be likely", "to do on a European vacation ?"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The most popular thing to do is see historical sites/monuments.

Chart data: Response Percentage of respondents See historical sites/monuments 0.74 Try local cuisine 0.68 See museums and other cultural institutions 0.61 Visit villages or towns outside of the major cities 0.6 Go shopping 0.47 Relax on a beach 0.37 Go on an escorted tour (a multi-day coach tour for example) 0.37 Agrotourism (vineyards, cheese, olive oil, etc.) 0.32 Take a multi-day cruise 0.27 Attend concerts, festivals, or theatre 0.23 Visit friends and family 0.21 Other 0.06

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/25539.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#fdbf11"}, "x": {"type": "nominal", "axis": {"labelAngle": 90}, "bin": false, "field": "Response"}, "y": {"type": "quantitative", "axis": {"labelAngle": -30, "title": "Percentage of respondents"}, "field": "Percentage of respondents"}}, "title": ["Which of the following would you be likely", "to do on a European vacation ?"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: A large proportion of facilities provide multiple tailored programs.

Chart data: Percentage of facilities Specifically tailored program or group 0.53 Clients with co-occuring mental and substance abuse disorders 0.49 Adult women 0.47 Adult men 0.43 Clients who have experienced trauma 0.36 Criminal justice clients (other than DUI/DWI) 0.31 Young adults 0.28 Persons who have experienced intimate partner violence, domestic violence 0.28 Persons who have experienced sexual abuse 0.24 Adolescents 0.24 DUI or DWI clients 0.24 Pregnant or postpartum women 0.23 Seniors or older adults 0.23 Persons with HIV or AIDS 0.22 Lesbian, gay, bisexual, transgender clients 0.2 Veterans 0.14 Members of military families 0.12 Active duty military

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/25546.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#f4cae4"}, "x": {"type": "quantitative", "axis": {"title": "Percentage of facilities"}, "field": "Percentage of facilities"}, "y": {"type": "nominal", "axis": {"labelAngle": -30}, "bin": false, "field": "Specifically tailored program or group"}}, "title": ["Percentage of substance abuse treatment", "facilities in the U.S. with specifically", "tailored programs as of 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Madagascar's import of goods from the U.S. has gone up and down since 2009. Between 2009 and 2016, it varied between about $2.5 and $3.25 billion. After that, imports increased sharply. They imported more than ever in 2018, approximately $4.1 billion in goods, and imports have gone down only slightly since then, to approximately $4 billion.

Chart data: Year Imports in billion U.S. dollars Dec 31, 2008 3.2 Dec 31, 2009 2.58 Dec 31, 2010 2.91 Dec 31, 2011 3.09 Dec 31, 2012 3.26 Dec 31, 2013 3.23 Dec 31, 2014 2.79 Dec 31, 2015 2.97 Dec 31, 2016 3.67 Dec 31, 2017 4.06 Dec 31, 2018 3.93

Vega-Lite specification:
{"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/25549.tsv"}, "mark": "line", "encoding": {"color": {"value": "#3e5c69"}, "x": {"type": "temporal", "axis": {"labelAngle": -60}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Imports in billion U.S. dollars"}, "field": "Imports in billion U\\.S\\. dollars"}}, "title": ["Madagascar : Import of goods from 2009 to", "2019 (in billion U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The largest import volume of oranges was in 2013. The smallest import volume of oranges was in 2013.

Chart data: Year Import volume in thousand tons 2019* 454.99 2018 462.82 2017 462.23 2016 489.73 2015 486.81 2014 419.72 2013 574.58 2012 513.67 2011 488.2 2010 534.05 2009 544.36

Vega-Lite specification:
{"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/25557.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#56cc60"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Import volume in thousand tons"}, "field": "Import volume in thousand tons"}}, "title": ["Import volume of oranges to Germany from", "2009 to 2019 (in 1,000 tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Germany imported the most oranges in 2013. There is a general decrease of imported oranges in Germany over time. Germany imported the least oranges in 2014.

Chart data: Year Import volume in thousand tons 2019* 454.99 2018 462.82 2017 462.23 2016 489.73 2015 486.81 2014 419.72 2013 574.58 2012 513.67 2011 488.2 2010 534.05 2009 544.36

Vega-Lite specification:
{"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/25557.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#56cc60"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Import volume in thousand tons"}, "field": "Import volume in thousand tons"}}, "title": ["Import volume of oranges to Germany from", "2009 to 2019 (in 1,000 tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The bar chart shows that the import volume of oranges has remained relatively stable, with some increases and decreases, over the time period from 2009 to 2019. The volume is lower towards the end of the period than it was at the start of the period.

Chart data: Year Import volume in thousand tons 2019* 454.99 2018 462.82 2017 462.23 2016 489.73 2015 486.81 2014 419.72 2013 574.58 2012 513.67 2011 488.2 2010 534.05 2009 544.36

Vega-Lite specification:
{"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/25557.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#56cc60"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Import volume in thousand tons"}, "field": "Import volume in thousand tons"}}, "title": ["Import volume of oranges to Germany from", "2009 to 2019 (in 1,000 tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The import volume of oranges dropped from 2009 to 2011 with import volumes then increasing in the subsequent 2 years. 2014 saw the import volume drop by 100 thousand tonnes and then subsequently the import volume has stayed very similar.

Chart data: Year Import volume in thousand tons 2019* 454.99 2018 462.82 2017 462.23 2016 489.73 2015 486.81 2014 419.72 2013 574.58 2012 513.67 2011 488.2 2010 534.05 2009 544.36

Vega-Lite specification:
{"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/25557.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#56cc60"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Import volume in thousand tons"}, "field": "Import volume in thousand tons"}}, "title": ["Import volume of oranges to Germany from", "2009 to 2019 (in 1,000 tons)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The overall trend is down, however there was a slight spike in 2017. The figure started at around 1.75 in 2015 and by 2025 is projected to be just under 1.5.

Chart data: Year National debt in relation to GDP 2025* 1.4219 2024* 1.4985 2023* 1.5773 2022* 1.6566 2021* 1.7347 2020* 1.8579 2019* 1.8935 2018 1.8561 2017 2.0254 2016 1.6751 2015 1.8071

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/25558.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c4c4c4"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "National debt in relation to GDP"}, "field": "National debt in relation to GDP"}}, "title": ["Eritrea : National debt in relation to gross", "domestic product (GDP) from 2015 to 2025"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: National debt decreases between2019 and 2025. National debt was higher in 2017 than 2018.

Chart data: Year National debt in relation to GDP 2025* 1.4219 2024* 1.4985 2023* 1.5773 2022* 1.6566 2021* 1.7347 2020* 1.8579 2019* 1.8935 2018 1.8561 2017 2.0254 2016 1.6751 2015 1.8071

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/25558.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#c4c4c4"}, "x": {"type": "nominal", "axis": {"labelAngle": 30}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "National debt in relation to GDP"}, "field": "National debt in relation to GDP"}}, "title": ["Eritrea : National debt in relation to gross", "domestic product (GDP) from 2015 to 2025"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The number of road fatalities in the Netherlands declined  very slightly between 2009 and 2019 from a little over 700 to a little under 700. Between these dates the number fell to a little under 600 before increasing.

Chart data: Year Number of casualties Dec 31, 2008 720 Dec 31, 2009 640 Dec 31, 2010 661 Dec 31, 2011 650 Dec 31, 2012 570 Dec 31, 2013 570 Dec 31, 2014 621 Dec 31, 2015 629 Dec 31, 2016 613 Dec 31, 2017 678 Dec 31, 2018 661

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/25559.tsv"}, "mark": "line", "encoding": {"color": {"value": "#333333"}, "x": {"type": "temporal", "axis": {"labelAngle": -45}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Number of casualties"}, "field": "Number of casualties"}}, "title": ["Number of road fatalities in the Netherlands", "from 2009 to 2019"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Turnover fell very slightly between 2008 and 201o, but has steadily increased since then to 2018, the last year for which figures are available.

Chart data: Annual turnover in million euros Year 4213.7 2018* 4143.4 2017 4039.7 2016 3898.3 2015 3850.1 2014 3700.9 2013 3539.8 2012 3539.1 2011 3308.7 2010 3327 2009 3360 2008

Vega-Lite specification:
{"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/25562.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#386cb0"}, "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 from retail sale of food , beverages", "and tobacco in specialized stores in Austria", "from 2008 to 2018 (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The turnover from retail sale of food, beverages and tobacco in specialized stores in Austria from 2008 to 2018 has risen steadily from approximately 3,300 million euros in 2008 to 4,100 million euros in 2018.

Chart data: Annual turnover in million euros Year 4213.7 2018* 4143.4 2017 4039.7 2016 3898.3 2015 3850.1 2014 3700.9 2013 3539.8 2012 3539.1 2011 3308.7 2010 3327 2009 3360 2008

Vega-Lite specification:
{"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/25562.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#386cb0"}, "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 from retail sale of food , beverages", "and tobacco in specialized stores in Austria", "from 2008 to 2018 (in million euros)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: All categories have expenditure against them. Shelter was the highest expense closely followed by income tax, transportation and food.

Chart data: Average expenditure in Canadian dollars household expenditure  12692 Shelter 12155 Transportation 11708 Income taxes 7634 Food expenditures 4757 Household operations 4621 Personal insurance payments and pension contributions 3302 Recreation 2715 Clothing and accessories 2640 Health care 1949 Household furnishings and equipment 1893 Gifts of money, support payments and charitable contributions 1324 Miscellaneous expenditures 1237 Tobacco products and alcoholic beverages 995 Personal care 841 Education 210 Games of chance 159 Reading materials and other printed matter

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/25568.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#333333"}, "x": {"type": "quantitative", "axis": {"title": "Average expenditure in Canadian dollars"}, "field": "Average expenditure in Canadian dollars"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "household expenditure "}}, "title": ["Average annual household expenditures in", "New Brunswick , Canada in 2017 (in Canadian", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The average household spent more money on shelter, transportation and income taxes followed by expenditure on food. The lowest amount of money spent was on reading materials and other print.

Chart data: Average expenditure in Canadian dollars household expenditure  12692 Shelter 12155 Transportation 11708 Income taxes 7634 Food expenditures 4757 Household operations 4621 Personal insurance payments and pension contributions 3302 Recreation 2715 Clothing and accessories 2640 Health care 1949 Household furnishings and equipment 1893 Gifts of money, support payments and charitable contributions 1324 Miscellaneous expenditures 1237 Tobacco products and alcoholic beverages 995 Personal care 841 Education 210 Games of chance 159 Reading materials and other printed matter

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/25568.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#333333"}, "x": {"type": "quantitative", "axis": {"title": "Average expenditure in Canadian dollars"}, "field": "Average expenditure in Canadian dollars"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "household expenditure "}}, "title": ["Average annual household expenditures in", "New Brunswick , Canada in 2017 (in Canadian", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The largest expenditure is shelter followed by transportation. The smallest expenditure is reading material.

Chart data: Average expenditure in Canadian dollars household expenditure  12692 Shelter 12155 Transportation 11708 Income taxes 7634 Food expenditures 4757 Household operations 4621 Personal insurance payments and pension contributions 3302 Recreation 2715 Clothing and accessories 2640 Health care 1949 Household furnishings and equipment 1893 Gifts of money, support payments and charitable contributions 1324 Miscellaneous expenditures 1237 Tobacco products and alcoholic beverages 995 Personal care 841 Education 210 Games of chance 159 Reading materials and other printed matter

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/25568.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#333333"}, "x": {"type": "quantitative", "axis": {"title": "Average expenditure in Canadian dollars"}, "field": "Average expenditure in Canadian dollars"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "household expenditure "}}, "title": ["Average annual household expenditures in", "New Brunswick , Canada in 2017 (in Canadian", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}