delen/vistext huggingface dataset:
Chart description: There was a linear increase in age structure from 2010 to 2015. From 2015 the linear increase levelled off with another increase from 2016.

Chart data: Year 0-14 years Dec 31, 2008 0.1501 Dec 31, 2009 0.1513 Dec 31, 2010 0.1534 Dec 31, 2011 0.1553 Dec 31, 2012 0.1571 Dec 31, 2013 0.1591 Dec 31, 2014 0.1613 Dec 31, 2015 0.1613 Dec 31, 2016 0.1622 Dec 31, 2017 0.1636 Dec 31, 2018 0.1646

Vega-Lite specification:
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Chart description: The amount of 15-64 year olds has fallen year after year from 2009 to 2019 however at all times the range has stayed within 6 percent.

Chart data: Year 15-64 years Dec 31, 2008 0.6761 Dec 31, 2009 0.6734 Dec 31, 2010 0.6692 Dec 31, 2011 0.6649 Dec 31, 2012 0.6603 Dec 31, 2013 0.6555 Dec 31, 2014 0.6505 Dec 31, 2015 0.6481 Dec 31, 2016 0.6446 Dec 31, 2017 0.6402 Dec 31, 2018 0.6354

Vega-Lite specification:
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Chart description: The proportion of Estonia's population aged between 15 and 64 years old has declined slightly from 2009 to 2019.

Chart data: Year 15-64 years Dec 31, 2008 0.6761 Dec 31, 2009 0.6734 Dec 31, 2010 0.6692 Dec 31, 2011 0.6649 Dec 31, 2012 0.6603 Dec 31, 2013 0.6555 Dec 31, 2014 0.6505 Dec 31, 2015 0.6481 Dec 31, 2016 0.6446 Dec 31, 2017 0.6402 Dec 31, 2018 0.6354

Vega-Lite specification:
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Chart description: As time progresses the trend of age increases. The age structure is highest in 2018.

Chart data: Year 65 years and older Dec 31, 2008 0.1738 Dec 31, 2009 0.1752 Dec 31, 2010 0.1774 Dec 31, 2011 0.1798 Dec 31, 2012 0.1826 Dec 31, 2013 0.1854 Dec 31, 2014 0.1882 Dec 31, 2015 0.1906 Dec 31, 2016 0.1931 Dec 31, 2017 0.1963 Dec 31, 2018 0.1999

Vega-Lite specification:
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Chart description: The highest level of fundraising and expenditure on US campaign elections was in the 2008 elections. The level of expenditure trebled in 2008 in comparison to the 2000 elections. In the period between 1976 and 2016, there is a general upwards trend in the level of expenditure.

Chart data: Year Total contributions to presidential candidates Dec 31, 1975 171 Dec 31, 1979 161.9 Dec 31, 1983 202 Dec 31, 1987 324.4 Dec 31, 1991 331.1 Dec 31, 1995 425.7 Dec 31, 1999 528.9 Dec 31, 2003 890.19 Dec 31, 2007 1741.97 Dec 31, 2011 1372.9 Dec 31, 2015 1465.03

Vega-Lite specification:
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Chart description: Starting with 2000, there has been a sharp increase in fundraising and spending, which peaked in about 2008.

Chart data: Year Total spending by presidential candidates Dec 31, 1975 66.9 Dec 31, 1979 92.3 Dec 31, 1983 103.6 Dec 31, 1987 210.7 Dec 31, 1991 192.2 Dec 31, 1995 239.9 Dec 31, 1999 343.1 Dec 31, 2003 847.64 Dec 31, 2007 1672.59 Dec 31, 2011 1369.32 Dec 31, 2015 1449.71

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": "multiColumn/data/422.tsv"}, "mark": "line", "encoding": {"color": {"value": "#a6bddb"}, "x": {"type": "temporal", "axis": {"labelAngle": 90}, "bin": false, "field": "Year"}, "y": {"type": "quantitative", "axis": {"title": "Total spending by presidential candidates"}, "field": "Total spending by presidential candidates"}}, "title": ["Fundraising and spending in U.S. presidential", "elections from 1976 to 2016 (in million U.S.", "dollars)"], "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"}
Chart description: From the graph it can be seen that the line chart is showing Fundraising and spending in U.S. presidential elections shows a gradual increases in the amount during the year 1976-2008. Even if it got reduced during the year of 2008-2011, it started to increase it afterwards.

Chart data: Year Total spending by presidential candidates Dec 31, 1975 66.9 Dec 31, 1979 92.3 Dec 31, 1983 103.6 Dec 31, 1987 210.7 Dec 31, 1991 192.2 Dec 31, 1995 239.9 Dec 31, 1999 343.1 Dec 31, 2003 847.64 Dec 31, 2007 1672.59 Dec 31, 2011 1369.32 Dec 31, 2015 1449.71

Vega-Lite specification:
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Chart description: The chart shows Boston having the longest waiting times, With San Diego having the shortest. Boston waiting times are approximately 4 times that of san diegothe average waiting time across all states shown is app. 22.7 days.

Chart data: Response Dermatology Atlanta 14 Boston 72 Dallas 17 Denver 37 Detroit 22 Houston 21 Los Angeles 14 Miami 16 Minneapolois 56 New York 24 Philadelphia 49 Portland 27 San Diego 14 Seattle 32 Washington, D.C. 17

Vega-Lite specification:
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Chart description: Overall, after reviewing wait times for dermatology appointments in the U.S, Boston seen the longest wait times, whilst Atlanta, Los Angeles & San Diego came out as the frontrunners in seeing patients the quickest.

Chart data: Response Dermatology Atlanta 14 Boston 72 Dallas 17 Denver 37 Detroit 22 Houston 21 Los Angeles 14 Miami 16 Minneapolois 56 New York 24 Philadelphia 49 Portland 27 San Diego 14 Seattle 32 Washington, D.C. 17

Vega-Lite specification:
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Chart description: Boston and Portland have higher than average wait times for OB/GYN appointments at 45 and 35 days respectively. Los Angeles has the smallest wait time at 8 days.

Chart data: OB/GYN Response 15 Atlanta 46 Boston 10 Dallas 22 Denver 16 Detroit 14 Houston 8 Los Angeles 13 Miami 10 Minneapolois 10 New York 22 Philadelphia 35 Portland 14 San Diego 10 Seattle 15 Washington, D.C.

Vega-Lite specification:
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Chart description: In 2009, the age structure was at its lowest with 0.02. In 2019, the age structure was at its highest with 0.03. The age structure rose consistently between 2009 and 2019. The age structure rose steadily but very slowly between 2009 and 2014. The age structure continued to rise steadily but much more steeply between 2014 and 2019.

Chart data: Year 65 years and older Dec 31, 2008 0.0203 Dec 31, 2009 0.02 Dec 31, 2010 0.02 Dec 31, 2011 0.0201 Dec 31, 2012 0.0203 Dec 31, 2013 0.0206 Dec 31, 2014 0.0212 Dec 31, 2015 0.0224 Dec 31, 2016 0.0238 Dec 31, 2017 0.0255 Dec 31, 2018 0.0276

Vega-Lite specification:
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Chart description: The digital music industry grew in Spain between 2014 and 2017, but it is predicted to decrease again.

Chart data: Year Mobile music 2023* 2 2022* 3 2021* 3 2020* 4 2019* 4 2018 4 2017 4 2016 3 2015 2 2014 1

Vega-Lite specification:
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Chart description: Digital music revenue increased significantly between 2016 - 17 and remained stable. However predictions are that this will drop.

Chart data: Year Mobile music 2023* 2 2022* 3 2021* 3 2020* 4 2019* 4 2018 4 2017 4 2016 3 2015 2 2014 1

Vega-Lite specification:
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Chart description: Between 2012 and 2015 the number of fans has increased quite significantly. Since mid 2015 the number of fans has levelled out at 4 million and has not changed up to 2020. In 2015 the number of fans rose to over 4 million but quickly dropped back to 4 million soon after.

Chart data: Month Facebook fans Jul 31, 2012 2.83 Jan 31, 2013 2.92 Jul 31, 2013 2.96 Jan 31, 2014 3.33 Jul 31, 2014 3.91 Jan 31, 2015 4.2 Jul 31, 2015 4 Jan 31, 2016 4.06 Jul 31, 2016 4.07 Jan 31, 2017 4.08 Jul 31, 2017 4.08 Jan 31, 2018 4.08 Jul 31, 2018 4.08 Jan 31, 2019 4.05 Jul 31, 2019 4.02 Jan 31, 2020 4.03 Jul 31, 2020 4

Vega-Lite specification:
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Chart description: There was a steady rise in fans until 2016.  The number of Facebook fans seems to have remained the same after 2016.

Chart data: Month Facebook fans Jul 31, 2012 2.83 Jan 31, 2013 2.92 Jul 31, 2013 2.96 Jan 31, 2014 3.33 Jul 31, 2014 3.91 Jan 31, 2015 4.2 Jul 31, 2015 4 Jan 31, 2016 4.06 Jul 31, 2016 4.07 Jan 31, 2017 4.08 Jul 31, 2017 4.08 Jan 31, 2018 4.08 Jul 31, 2018 4.08 Jan 31, 2019 4.05 Jul 31, 2019 4.02 Jan 31, 2020 4.03 Jul 31, 2020 4

Vega-Lite specification:
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Chart description: On average, the average quarterly revenue per user has increased year on year. Pinterest receives more revenue in Q4 compared to the other quarters on average.

Chart data: Quarter International Q3 '20 0.21 Q2 '20 0.14 Q1 '20 0.13 Q4 '19 0.21 Q3 '19 0.13 Q2 '19 0.11 Q1 '19 0.08 Q4 '18 0.09 Q3 '18 0.06 Q2 '18 0.05 Q1 '18 0.05 Q4 '17 0.07 Q3 '17 0.05 Q2 '17 0.04 Q1 '17 0.03 Q4 '16 0.04 Q3 '16 0.03 Q2 '16 0.01 Q1 '16 0

Vega-Lite specification:
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Chart description: Luxembourg has the least population size per bank employee at 20. Slovakia has the greatest population size per bank employee at 290.

Chart data: 2014 Country 290 Slovakia 271 Estonia 239 Greece 248 Finland 230 Spain 213 Latvia 193 Portugal 197 Belgium 193 Slovenia 203 Italy 179 Netherlands 160 Ireland 165 Average for euro area 162 France 125 Germany 115 Austria 97 Malta 78 Cyprus 21 Luxembourg

Vega-Lite specification:
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Chart description: There was a gradual rise of income in the period studied.

Chart data: Year Higher education - Bachelor Dec 31, 2006 3188 Dec 31, 2007 3229 Dec 31, 2008 3253 Dec 31, 2009 3359 Dec 31, 2010 3469 Dec 31, 2011 3559 Dec 31, 2012 3580 Dec 31, 2013 3601 Dec 31, 2014 3618 Dec 31, 2015 3667 Dec 31, 2016 3733

Vega-Lite specification:
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Chart description: Netflix had the largest named subscriptions but there was a huge subsciptions to sevices not named on the graphs.

Chart data: Response United Kingdom Netflix 0.18 Amazon Prime Video 0.08 YouTube 0.09 Disney+ 0.15 Spotify 0.06 Amazon Music 0.06 Apple Music 0.04 Youtube Music 0.04 iTunes 0.03 Tinder 0.03 Match 0.01 Badoo 0.02 Sky GO 0.03 Now TV 0.05 Parship 0.01 None of the above 0.64

Vega-Lite specification:
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Chart description: The trend is completly erratic and completely incomparable due to circumstances, however, April 2020 to April 2021 shows complete decimation, a decline of 600s (in thousands) of international visitors, similar to July the next month. Overall a downward trend.

Chart data: Month International tourists Feb 28, 2019 567 Mar 31, 2019 356 Apr 30, 2019 465 May 31, 2019 608 Jun 30, 2019 794 Jul 31, 2019 786 Aug 31, 2019 516 Feb 29, 2020 265 Mar 31, 2020 17 Apr 30, 2020 20 May 31, 2020 51 Jun 30, 2020 141 Jul 31, 2020 145 Aug 31, 2020 87

Vega-Lite specification:
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Chart description: There was a steep increase in Numbers from under 4 in April 2019 to 800 in 2019 October. The highest number of overnight stay international tourists was 800 in late summer 2019. Following this it had a steep decline to around 500 then a slower slope to over 200 until April 2020. This is when tourists stopped staying over and the number dropped to near zero. A steady climb from April to July 2020.

Chart data: Month International tourists Feb 28, 2019 567 Mar 31, 2019 356 Apr 30, 2019 465 May 31, 2019 608 Jun 30, 2019 794 Jul 31, 2019 786 Aug 31, 2019 516 Feb 29, 2020 265 Mar 31, 2020 17 Apr 30, 2020 20 May 31, 2020 51 Jun 30, 2020 141 Jul 31, 2020 145 Aug 31, 2020 87

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Chart description: Preceeding April there was a lot less stays in 2020 than in 2019 but April seemed to bounce back to 2019 levels.

Chart data: Month Domestic tourists Feb 28, 2019 1297 Mar 31, 2019 1222 Apr 30, 2019 1110 May 31, 2019 1679 Jun 30, 2019 2473 Jul 31, 2019 1624 Aug 31, 2019 1308 Feb 29, 2020 730 Mar 31, 2020 170 Apr 30, 2020 280 May 31, 2020 1074 Jun 30, 2020 2429 Jul 31, 2020 1514 Aug 31, 2020 1147

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Chart description: The most common age group is between 18-24, with the 65 and over age group being the least common. After the 18-24 age group the arrests per age group drop every time the age group gets older.

Chart data: Type of crime Property crime (total) Under 18 87710 18-24 159426 25-29 125086 30-34 116944 35-39 96414 40-44 64968 45-49 48508 50-54 39155 55-59 28068 60-64 13774 65 and older 8583

Vega-Lite specification:
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Chart description: The bar chart shows that the majority of crimes are seen in the younger ages. Highest rates are in the 18-24 category at 120,000. The lowest is in the over 65s.

Chart data: Larceny-theft Type of crime 62200 Under 18 121483 18-24 94072 25-29 88432 30-34 73764 35-39 50915 40-44 38842 45-49 31941 50-54 23360 55-59 11785 60-64 7493 65 and older

Vega-Lite specification:
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Chart description: The chart suggests that the this crime is heavily related to age. The younger the category, the greater numbers of crimes committed within that group.

Chart data: Larceny-theft Type of crime 62200 Under 18 121483 18-24 94072 25-29 88432 30-34 73764 35-39 50915 40-44 38842 45-49 31941 50-54 23360 55-59 11785 60-64 7493 65 and older

Vega-Lite specification:
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Chart description: The greatest number of arrests for motor vehicle theft in the united states peaks in the age group 18-24 then steadily declines after that period.

Chart data: Motor vehicle theft Type of crime 9742 Under 18 12121 18-24 10017 25-29 8986 30-34 6845 35-39 4092 40-44 2648 45-49 1758 50-54 994 55-59 361 60-64 174 65 and older

Vega-Lite specification:
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Chart description: 18-24 year olds are the most prolific offenders of motor vehicle theft with almost 10000 arrests in 2019.

Chart data: Motor vehicle theft Type of crime 9742 Under 18 12121 18-24 10017 25-29 8986 30-34 6845 35-39 4092 40-44 2648 45-49 1758 50-54 994 55-59 361 60-64 174 65 and older

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