delen/vistext huggingface dataset:
Chart description: There is a wide variety in the number of camp sites in each state. The state with the highest number of camp sites is Bavaria and the state with the lowest number of camp sites is Breman.

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

Vega-Lite specification:
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Chart description: The more developed countries have used significantly more energy than those countries which are less industrialised. The usage by the United States far exceeds the total used by al” other countries shown.

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

Vega-Lite specification:
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Chart description: Some countries for example China and the USA use a lot more power to heat rooms than more primitive or warmer countries.

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

Vega-Lite specification:
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Chart description: Generally companies in russia use between 100  and 450 heavy commerce vehicles in 2019 - 2020.

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

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

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

Vega-Lite specification:
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Chart description: In May 2020 Moscow had the largest number of HCVs and Krasnodar Krai and the Rostov Oblast had the fewest. All regions had over 100 HCVs with four regions had over 200.

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

Vega-Lite specification:
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Chart description: Moscow has significantly more heavy commercial vehicles. The majority of regions have between 100 and 250 HMVs.

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

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

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

Vega-Lite specification:
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Chart description: The majority of the operators are below the 1,000000 mark, APM Maersk are way ahead of the rest of the operators, only 2 owe more than 1,000000.

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

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

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

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

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

Vega-Lite specification:
{"config": {"range": {"category": ["#4c78a8", "#9ecae9", "#f58518", "#ffbf79", "#54a24b", "#88d27a", "#b79a20", "#f2cf5b", "#439894", "#83bcb6", "#e45756", "#ff9d98", "#79706e", "#bab0ac", "#d67195", "#fcbfd2", "#b279a2", "#d6a5c9", "#9e765f", "#d8b5a5"]}}, "data": {"url": "multiColumn/data/6903.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#9e765f"}, "x": {"type": "quantitative", "axis": {"title": "2016/17"}, "field": "2016/17"}, "y": {"type": "nominal", "axis": {"labelAngle": 0}, "bin": false, "field": "BBC radio stations"}}, "title": ["Cost to deliver BBC radio services to individual", "users per user hour in the United Kingdom", "(UK) from 2015/16 to 2019/20 , by station", "(in pence)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The graph shows an overall steady increase in private sector health expenditure on professional care in Canada between 2010 and 2019. The graph shows that in 2010 approximately 5,200 hospitals allocated expenditure to professional care. The number of hospitals increased gradually up until 2016 when there was a total of approximately 6,600 hospitals. The graph shows a slight dip in numbers in 2017, reducing to a total of 6,400 hospitals. But numbers have been gradually increasing again since and forecasted numbers for 2019 are expected to be 6,900 hospitals.

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

Vega-Lite specification:
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Chart description: I can see from the graph that there was a spiking numbers in 2016. Than after 2026 there was a small decline before numbers started rising again.

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

Vega-Lite specification:
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Chart description: As the number of hospitals increases, the expenditure grows year on year until 2017 when it slightly drops. As the years go on, Canadian spending on professional care increases quite steadily.

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

Vega-Lite specification:
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Chart description: Expenditure has grown year on year from 2011. The expenditure from 2010-2011 was the same.

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

Vega-Lite specification:
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Chart description: There has been an increase in spending on private dental services from 2010 to 2019. This increase was fairly gradual between 2010 to 2014. We see a greater increase from 2014 to 2019.

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

Vega-Lite specification:
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Chart description: Coronavirus had the most negative impact on the retail sector of clothing and clothing accessories between March and April 2020 in the United States.

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

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

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

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

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

Vega-Lite specification:
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Chart description: electronics had the biggest growth due to coronavirus, whereas sporting goods had the worst performance.

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

Vega-Lite specification:
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Chart description: Covid has impacted the sports clothing and clothing industry the most. Covid has had little effect on health stores. Nonstore retailers have benefitted from covid.

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

Vega-Lite specification:
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Chart description: The non-store retail sector saw the biggest improvement, whereas clothing and sporting goods sectors experienced the biggest loss.

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

Vega-Lite specification:
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Chart description: The chart shows that the highest number of Spanish and International albums sold in Spain occurred around 2013. This may be due to a prevalent artist having a release during this year.

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

Vega-Lite specification:
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Chart description: Industrial distribution in North Macedonia showed a small increase in 2013 and another greater increase in 2018. There have been no times of decline.

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

Vega-Lite specification:
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Chart description: The threat to the economy has fallen since 2008. The threat to the economy was lower in 2015 than it was in 1995.

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

Vega-Lite specification:
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Chart description: There aren't any clear patterns to be observed from this visualization.

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

Vega-Lite specification:
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