delen/vistext huggingface dataset:
Chart description: Catergory 'Perfume' shows the highest price in dollars, in comparison to 'Hair styling product' which shows the lowest. Overall, make-up and beauty seem to show the highest.

Chart data: Price in U.S. dollars Category 28 Perfume 27 Eyeshadow 27 Bronzer/Blush 25 Sunscreen 22 Mascara 21 Shampoo/Conditioner 20 Skin cream 19 Lipstick 9 Nail polish 7 Shaving Prep 5.5 Deodorant 5 Hair styling product

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": "data/22426.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ff9d98"}, "x": {"type": "quantitative", "axis": {"labelAngle": -90, "title": "Price in U.S. dollars"}, "field": "Price in U\\.S\\. dollars"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Category"}}, "title": ["Representative retail prices of high-end", "mass market personal care and cosmetic products", "in China in 2015 , by category (in U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: All the products listed have a price point of over $5 and the most expensive is perfume. The least expensive is hair styling products.

Chart data: Price in U.S. dollars Category 28 Perfume 27 Eyeshadow 27 Bronzer/Blush 25 Sunscreen 22 Mascara 21 Shampoo/Conditioner 20 Skin cream 19 Lipstick 9 Nail polish 7 Shaving Prep 5.5 Deodorant 5 Hair styling product

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": "data/22426.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#ff9d98"}, "x": {"type": "quantitative", "axis": {"labelAngle": -90, "title": "Price in U.S. dollars"}, "field": "Price in U\\.S\\. dollars"}, "y": {"type": "nominal", "axis": {}, "bin": false, "field": "Category"}}, "title": ["Representative retail prices of high-end", "mass market personal care and cosmetic products", "in China in 2015 , by category (in U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: The national debt in relation to gross domestic product was at its highest in 2016 and predicted to be at its lowest in 2025.

Chart data: Year National debt in relation to GDP 2025* 0.5185 2024* 0.5506 2023* 0.5876 2022* 0.6264 2021* 0.6671 2020* 0.7364 2019 0.7307 2018 0.8308 2017 0.8584 2016 0.953 2015 0.858

Vega-Lite specification:
{"config": {"background": "#ffffff", "title": {"anchor": "start", "color": "#000000", "font": "Benton Gothic Bold, sans-serif", "fontSize": 22, "fontWeight": "normal"}, "area": {"fill": "#82c6df"}, "line": {"stroke": "#82c6df", "strokeWidth": 2}, "rect": {"fill": "#82c6df"}, "bar": {"fill": "#82c6df"}, "point": {"color": "#82c6df", "size": 30}, "axis": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "labelFontWeight": "normal", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "axisX": {"labelPadding": 4, "tickSize": 3}, "axisY": {"labelBaseline": "middle", "maxExtent": 45, "minExtent": 45, "tickSize": 2, "titleAlign": "left", "titleAngle": 0, "titleX": -45, "titleY": -11}, "legend": {"labelFont": "Benton Gothic, sans-serif", "labelFontSize": 11.5, "symbolType": "square", "titleFont": "Benton Gothic Bold, sans-serif", "titleFontSize": 13, "titleFontWeight": "normal"}, "range": {"category": ["#ec8431", "#829eb1", "#c89d29", "#3580b1", "#adc839", "#ab7fb4"], "diverging": ["#e68a4f", "#f4bb6a", "#f9e39c", "#dadfe2", "#a6b7c6", "#849eae"], "heatmap": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ordinal": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"], "ramp": ["#fbf2c7", "#f9e39c", "#f8d36e", "#f4bb6a", "#e68a4f", "#d15a40", "#ab4232"]}}, "data": {"url": "data/22427.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#3580b1"}, "x": {"type": "nominal", "axis": {"labelAngle": -90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "National debt in relation to GDP"}, "field": "National debt in relation to GDP"}}, "title": ["S\u00c3\u00a3o Tom\u00c3\u00a9 and Pr\u00c3\u00adncipe : 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: Electricity generation has increased steadily over the years until dropping in 2019, due to the drop there was a significant increase in generation in 2020.

Chart data: Electricity generation in billion kilowatt hours Year 46.38 FY 2020 37.7 FY 2019 38.35 FY 2018 37.7 FY 2017 37.4 FY 2016 36.1 FY 2015 34.2 FY 2014 32.9 FY 2013 32.3 FY 2012 26.3 FY 2011 18.6 FY 2010

Vega-Lite specification:
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Chart description: In general there is a year on year increase across the decade, with a substantial increase in the last year.

Chart data: Electricity generation in billion kilowatt hours Year 46.38 FY 2020 37.7 FY 2019 38.35 FY 2018 37.7 FY 2017 37.4 FY 2016 36.1 FY 2015 34.2 FY 2014 32.9 FY 2013 32.3 FY 2012 26.3 FY 2011 18.6 FY 2010

Vega-Lite specification:
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Chart description: The power capacity in Puducherry has increased at a steady rate over the last 13 years. There has been a growth of around 200 megawatts in this time period.

Chart data: Year Installed power capacity in megawatts FY 2018 368.61 FY 2017 366.98 FY 2016 334.63 FY 2015 329.88 FY 2014 284.13 FY 2013 281.9 FY 2012 279.66 FY 2011 264.12 FY 2010 255.79 FY 2009 256.62 FY 2008 256.62 FY 2007 260.24 FY 2006 260.24 FY 2005 245.21

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/22451.tsv"}, "mark": "bar", "encoding": {"color": {"value": "#d95f02"}, "x": {"type": "nominal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Installed power capacity in megawatts"}, "field": "Installed power capacity in megawatts"}}, "title": ["Installed power capacity across Puducherry", "in India from financial year 2005 to 2018", "(in megawatts)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: Approximately 1/3 of respondents had not used any of the sources for foreign travel advice, representing the most common answer. The NHS was the most commonly used source for travel advice, followed by news sources/outlets. Less than 5% of respondents used a source not listed.

Chart data: Share of respondents Response 0.31 NHS 0.29 News sources/outlets (e.g. BBC) 0.23 World Health Organization 0.2 Public Health England 0.15 Foreign & Commonwealth Office 0.11 Medical websites 0.11 Airlines (e.g. via their social media/website/app 0.11 Health professional(s) 0.1 Trending posts on social media 0.07 Travel guides/websites (e.g. Lonely Planet) 0.06 Travel agents 0.06 Booking websites (e.g. Booking.com, Expedia) 0.06 Hotels/resorts (e.g. via their social media/website/app) 0.05 Social media influencers 0.02 Other 0.34 None of the above

Vega-Lite specification:
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Chart description: The production value of breading shrimp in the US between 2000 and 2018 has fluctuated drastically, but has returned to a similar figure in 2000 (375,000). The amount reached a high of 550,000 in 2010, and a low 150,000 in around 2008.

Chart data: Year Production value in thousand U.S. dollars Dec 31, 1999 375453 Dec 31, 2000 539705 Dec 31, 2001 463781 Dec 31, 2002 465347 Dec 31, 2003 306456 Dec 31, 2004 277613 Dec 31, 2005 347152 Dec 31, 2006 200147 Dec 31, 2007 159416 Dec 31, 2008 251594 Dec 31, 2009 562928 Dec 31, 2010 240976 Dec 31, 2011 193837 Dec 31, 2012 311211 Dec 31, 2013 314713 Dec 31, 2014 379688 Dec 31, 2015 379862 Dec 31, 2016 344274 Dec 31, 2017 338958

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/22466.tsv"}, "mark": "area", "encoding": {"color": {"value": "#f0027f"}, "x": {"type": "temporal", "axis": {}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"labelAngle": -45, "title": "Production value in thousand U.S. dollars"}, "field": "Production value in thousand U\\.S\\. dollars"}}, "title": ["Production value of breaded shrimp in the", "United States from 2000 to 2018 (in 1,000", "U.S. dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: This is a graph that shows how much breaded shrimp was produced in the US between 2000 and 2018. There was a dip in production from 2000 to 2008 or so, after which there was a spike in 2010, and then another dip, after which production increased again.

Chart data: Year Production value in thousand U.S. dollars Dec 31, 1999 375453 Dec 31, 2000 539705 Dec 31, 2001 463781 Dec 31, 2002 465347 Dec 31, 2003 306456 Dec 31, 2004 277613 Dec 31, 2005 347152 Dec 31, 2006 200147 Dec 31, 2007 159416 Dec 31, 2008 251594 Dec 31, 2009 562928 Dec 31, 2010 240976 Dec 31, 2011 193837 Dec 31, 2012 311211 Dec 31, 2013 314713 Dec 31, 2014 379688 Dec 31, 2015 379862 Dec 31, 2016 344274 Dec 31, 2017 338958

Vega-Lite specification:
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Chart description: We can see that the operating income has been increasing over the years. There are some drawbacks (less income from previous years) but the following years outperformed. The last 3 years on the graph show a rapid and best income from all the past years.

Chart data: Operating income in million U.S. dollars Year 110 18/19 103 17/18 95 16/17 62.7 15/16 74.6 14/15 38 13/14 63.7 12/13 26.6 11/12 17.9 10/11 35.9 09/10 30.3 08/09 31.2 07/08 29.7 06/07 21.4 05/06 35.5 04/05 25.8 03/04 15.2 02/03 6.7 01/02

Vega-Lite specification:
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Chart description: Tesco far exceeds their annual value than any other brand on the bar chart.

Chart data: Brand Value in million U.S. dollars Tesco 10810 Marks and Spencers 6550 Boots 3295 Asda 1674 Next 1370 Sainsbury's 1062 Argos 805 Morrisons 451 Waitrose 429 asos 416 B&Q 405 John Lewis 314 Debenhams 303 The Body Shop 299 Primark 171

Vega-Lite specification:
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Chart description: It shows that less respondents confirmed they played games at home or elsewhere via PC, laptop or notebook over time. More people confirmed they played games at home or elsewhere in 2007 and 2013 than any other year recorded.

Chart data: Response Share of respondents Dec 31, 2006 0.17 Dec 31, 2008 0.14 Dec 31, 2009 0.11 Dec 31, 2010 0.13 Dec 31, 2011 0.15 Dec 31, 2012 0.17 Dec 31, 2013 0.16 Dec 31, 2014 0.14 Dec 31, 2015 0.11 Dec 31, 2016 0.09 Dec 31, 2017 0.12 Dec 31, 2018 0.1

Vega-Lite specification:
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Chart description: Hypolipidemics is by far the leading category on this chart.

Chart data: Response Percentage of men enrolled in TT prescription Hypolipidemics 0.37 SSRIs 0.207 Ace inhibitors 0.181 NSAIDs 0.163 Beta blockers 0.155 Anti-diabetes drugs 0.152 Other cardiac drugs 0.138 Corticosteroids 0.127 Calcium channel blockers 0.117 Diuretics 0.104 Insulin 0.039 Antiplatelets 0.034 Anti-hypertensive NOS 0.032 Anticoagulant 0.028 Vasodialators 0.02

Vega-Lite specification:
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Chart description: Ravensburger has increased its number of employees greatly since 2004. The company is increasing its number of employees over time. The period between 2004 and 2006 saw employee numbers decrease. Since 2006 employee numbers have steadily increased. The trend is for the company to continue increasing employee numbers.

Chart data: Year Number of employees in thousands Dec 31, 2003 1448 Dec 31, 2004 1333 Dec 31, 2005 1310 Dec 31, 2006 1356 Dec 31, 2007 1383 Dec 31, 2008 1405 Dec 31, 2009 1471 Dec 31, 2010 1640 Dec 31, 2011 1667 Dec 31, 2012 1719 Dec 31, 2013 1838 Dec 31, 2014 2007 Dec 31, 2015 2109 Dec 31, 2016 2133 Dec 31, 2017 2153

Vega-Lite specification:
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Chart description: The graph appears to show a general upward trend and increase in employees for the years shown on the graph.

Chart data: Year Number of employees in thousands Dec 31, 2003 1448 Dec 31, 2004 1333 Dec 31, 2005 1310 Dec 31, 2006 1356 Dec 31, 2007 1383 Dec 31, 2008 1405 Dec 31, 2009 1471 Dec 31, 2010 1640 Dec 31, 2011 1667 Dec 31, 2012 1719 Dec 31, 2013 1838 Dec 31, 2014 2007 Dec 31, 2015 2109 Dec 31, 2016 2133 Dec 31, 2017 2153

Vega-Lite specification:
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Chart description: There has been a gradual increase of number of employees at Ravensburger since 2004. There was a slight dip in 2005.

Chart data: Year Number of employees in thousands Dec 31, 2003 1448 Dec 31, 2004 1333 Dec 31, 2005 1310 Dec 31, 2006 1356 Dec 31, 2007 1383 Dec 31, 2008 1405 Dec 31, 2009 1471 Dec 31, 2010 1640 Dec 31, 2011 1667 Dec 31, 2012 1719 Dec 31, 2013 1838 Dec 31, 2014 2007 Dec 31, 2015 2109 Dec 31, 2016 2133 Dec 31, 2017 2153

Vega-Lite specification:
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Chart description: The graph shows that over 13 years the amount of Italians who belong to a trade union havent been that varied. The lowest was between 2006 and 2008 but then started to increase. 2012 and 2013 had the most trade union members.

Chart data: Response Percentage of employees Dec 31, 1999 0.348 Dec 31, 2000 0.342 Dec 31, 2001 0.338 Dec 31, 2002 0.337 Dec 31, 2003 0.341 Dec 31, 2004 0.338 Dec 31, 2005 0.336 Dec 31, 2006 0.34 Dec 31, 2007 0.339 Dec 31, 2008 0.352 Dec 31, 2009 0.36 Dec 31, 2010 0.363 Dec 31, 2011 0.369 Dec 31, 2012 0.373

Vega-Lite specification:
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Chart description: I observed the pattern of an increase in trade union members from 2000 - 2013 in Italy. The increase is from 0.35 % in 2000 to 0.4% in 2012.

Chart data: Response Percentage of employees Dec 31, 1999 0.348 Dec 31, 2000 0.342 Dec 31, 2001 0.338 Dec 31, 2002 0.337 Dec 31, 2003 0.341 Dec 31, 2004 0.338 Dec 31, 2005 0.336 Dec 31, 2006 0.34 Dec 31, 2007 0.339 Dec 31, 2008 0.352 Dec 31, 2009 0.36 Dec 31, 2010 0.363 Dec 31, 2011 0.369 Dec 31, 2012 0.373

Vega-Lite specification:
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Chart description: Trade union density generally decreased from 2000 until 2008, but then started to increase again until 2012.

Chart data: Response Percentage of employees Dec 31, 1999 0.348 Dec 31, 2000 0.342 Dec 31, 2001 0.338 Dec 31, 2002 0.337 Dec 31, 2003 0.341 Dec 31, 2004 0.338 Dec 31, 2005 0.336 Dec 31, 2006 0.34 Dec 31, 2007 0.339 Dec 31, 2008 0.352 Dec 31, 2009 0.36 Dec 31, 2010 0.363 Dec 31, 2011 0.369 Dec 31, 2012 0.373

Vega-Lite specification:
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Chart description: From 2000 to 2002 there was a steady decrease in percentage employees part of a trade Union, this then became a steady number until 2008. From 2008 to 2013 there was an increase in employees part of a trade Union.

Chart data: Response Percentage of employees Dec 31, 1999 0.348 Dec 31, 2000 0.342 Dec 31, 2001 0.338 Dec 31, 2002 0.337 Dec 31, 2003 0.341 Dec 31, 2004 0.338 Dec 31, 2005 0.336 Dec 31, 2006 0.34 Dec 31, 2007 0.339 Dec 31, 2008 0.352 Dec 31, 2009 0.36 Dec 31, 2010 0.363 Dec 31, 2011 0.369 Dec 31, 2012 0.373

Vega-Lite specification:
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Chart description: 2009 to 2011 there is a decline in sale volume. 2011 to 2014 the sales increase. In 2018 sales are at there highest since 2010.

Chart data: Year Sales volume in thousand kilograms Dec 31, 2007 28878 Dec 31, 2008 31151 Dec 31, 2009 22468 Dec 31, 2010 18402 Dec 31, 2011 20535 Dec 31, 2012 21479 Dec 31, 2013 22934 Dec 31, 2014 18782 Dec 31, 2015 26031 Dec 31, 2016 24107 Dec 31, 2017 28570

Vega-Lite specification:
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Chart description: Between 2014 and 2016 the number of Asylum applications spiked. They hit 150,000 at the peak.

Chart data: Year Number of applications Dec 31, 2008 24194 Dec 31, 2009 31819 Dec 31, 2010 29648 Dec 31, 2011 43887 Dec 31, 2012 54259 Dec 31, 2013 81301 Dec 31, 2014 162877 Dec 31, 2015 28939 Dec 31, 2016 25666 Dec 31, 2017 21502 Dec 31, 2018 21958

Vega-Lite specification:
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Chart description: Guatemala, Honduras, and Paraguay are the friendliest countries towards LGBT communities in Latin America, with scores of 14 each. The least friendly are Argentina and Uruguay, with scores below 2. More than half the country (9 out of 15) have scores of 8 and above.

Chart data: Country Friendliness score on the scale from 1 to 14* Uruguay 1 Argentina 1 Brazil 3 Colombia 4 Chile 6 Mexico 6 Ecuador 8 Bolivia 9 Peru 10 El Salvador 10 Costa Rica 12 Nicaraguas 13 Honduras 14 Guatemala 14 Paraguay 14

Vega-Lite specification:
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Chart description: 2016 to 2019 recorded the highest number of  live births.

Chart data: Year Number of live births Dec 31, 2001 719250 Dec 31, 2002 706721 Dec 31, 2003 705622 Dec 31, 2004 685795 Dec 31, 2005 672724 Dec 31, 2006 684862 Dec 31, 2007 682514 Dec 31, 2008 665126 Dec 31, 2009 677947 Dec 31, 2010 662685 Dec 31, 2011 673544 Dec 31, 2012 682069 Dec 31, 2013 714927 Dec 31, 2014 737575 Dec 31, 2015 792141 Dec 31, 2016 784901 Dec 31, 2017 787523 Dec 31, 2018 778090

Vega-Lite specification:
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Chart description: 2014 to 2019 saw the most revenue with the highest being in 2014 at 21 billion. The revenue in the quarters of 2020 if continued into 3rd and 4th i estimate will be larger than the combined 2019 showing a continued trend.

Chart data: Year Revenue in billion U.S. dollars Q2 2020 5.8 Q1 2020 4.7 2019 19.6 1H 2019 9.1 2018 20.6 2017 20.2 1H 2017 9.4 1H 2016 10 2015 20.1 2014 20.9

Vega-Lite specification:
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Chart description: Smart city has the biggest share of projects by far. All but 3 responses have at least 0.5 share in projects and most responses are 'connected' with higher shares of projects.

Chart data: Share of projects Response 0.23 Smart city 0.17 Connected industry 0.12 Connected building 0.11 Connected car 0.1 Smart energy 0.08 Other 0.06 Connected health 0.05 Smart supply chain 0.04 Smart agriculture 0.04 Smart retail

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