2.11.0 | GitHub | 2024-12-21 16:09:47 [UTC]
Common repository for example datasets used by Vega related projects. BSD-3-Clause license applies only to package code and infrastructure. Users should verify their use of datasets complies with the license terms of the original sources. Dataset license information, where included, is a reference starting point only and is provided without any warranty of accuracy or completeness.
| name | path | title |
|---|---|---|
| BSD-3-Clause | https://opensource.org/license/bsd-3-clause | The 3-Clause BSD License |
| title | path |
|---|---|
| UW Interactive Data Lab | http://idl.cs.washington.edu |
| vega-datasets contributors | https://github.com/vega/vega-datasets/graphs/contributors |
7zip.png
Application icons from open-source software projects.
airports.csv
| name | type |
|---|---|
| iata | string |
| name | string |
| city | string |
| state | string |
| country | string |
| latitude | number |
| longitude | number |
annual-precip.json
A raster grid of global annual precipitation for the year 2016 at a resolution 1 degree of lon/lat per cell.
| title | path |
|---|---|
| Climate Forecast System Version 2 | https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/climate-forecast-system-version2-cfsv2 |
anscombe.json
Graphs in Statistical Analysis, F. J. Anscombe, The American Statistician.
| name | type |
|---|---|
| Series | string |
| X | integer |
| Y | number |
barley.json
The result of a 1930s agricultural experiment in Minnesota, this dataset contains yields for 10 different varieties of barley at six different sites.
It was first published by agronomists F.R. Immer, H.K. Hayes, and L. Powers in the 1934 paper "Statistical Determination of Barley Varietal Adaption".
R.A. Fisher's popularized its use in the field of statistics when he included it in his book "The Design of Experiments".
Since then it has been used to demonstrate new statistical techniques, including the trellis charts developed by Richard Becker, William Cleveland and others in the 1990s.
| name | type |
|---|---|
| yield | number |
| variety | string |
| year | integer |
| site | string |
| title | path |
|---|---|
| The Design of Experiments Reference | https://en.wikipedia.org/wiki/The_Design_of_Experiments |
| Trellis Charts Paper | http://ml.stat.purdue.edu/stat695t/writings/TrellisDesignControl.pdf |
birdstrikes.csv
Records of reported wildlife strikes received by the U.S. FAA
| name | type |
|---|---|
| Airport Name | string |
| Aircraft Make Model | string |
| Effect Amount of damage | string |
| Flight Date | date |
| Aircraft Airline Operator | string |
| Origin State | string |
| Phase of flight | string |
| Wildlife Size | string |
| Wildlife Species | string |
| Time of day | string |
| Cost Other | integer |
| Cost Repair | integer |
| Cost Total $ | integer |
| Speed IAS in knots | integer |
| title | path |
|---|---|
| FAA Wildlife Strike Database | http://wildlife.faa.gov |
budget.json
Historical and forecasted federal revenue/receipts produced in 2016 by the U.S. Office of Management and Budget.
| name | type |
|---|---|
| Source Category Code | integer |
| Source category name | string |
| Source subcategory | integer |
| Source subcategory name | string |
| Agency code | integer |
| Agency name | string |
| Bureau code | integer |
| Bureau name | string |
| Account code | integer |
| Account name | string |
| Treasury Agency code | integer |
| On- or off-budget | string |
| 1962 | string |
| 1963 | string |
| 1964 | string |
| 1965 | string |
| 1966 | string |
| 1967 | string |
| 1968 | string |
| 1969 | string |
| 1970 | string |
| 1971 | string |
| 1972 | string |
| 1973 | string |
| 1974 | string |
| 1975 | string |
| 1976 | string |
| TQ | string |
| 1977 | string |
| 1978 | string |
| 1979 | string |
| 1980 | string |
| 1981 | string |
| 1982 | string |
| 1983 | string |
| 1984 | string |
| 1985 | string |
| 1986 | string |
| 1987 | string |
| 1988 | string |
| 1989 | string |
| 1990 | string |
| 1991 | string |
| 1992 | string |
| 1993 | string |
| 1994 | string |
| 1995 | string |
| 1996 | string |
| 1997 | string |
| 1998 | string |
| 1999 | string |
| 2000 | string |
| 2001 | string |
| 2002 | string |
| 2003 | string |
| 2004 | string |
| 2005 | string |
| 2006 | string |
| 2007 | string |
| 2008 | string |
| 2009 | string |
| 2010 | string |
| 2011 | string |
| 2012 | string |
| 2013 | string |
| 2014 | string |
| 2015 | string |
| 2016 | string |
| 2017 | string |
| 2018 | string |
| 2019 | string |
| 2020 | string |
| title | path |
|---|---|
| Office of Management and Budget - Budget FY 2016 - Receipts | https://www.govinfo.gov/app/details/BUDGET-2016-DB/BUDGET-2016-DB-3 |
budgets.json
| name | type |
|---|---|
| budgetYear | integer |
| forecastYear | integer |
| value | number |
burtin.json
The burtin.json dataset is based on graphic designer Will Burtin's 1951 visualization of antibiotic effectiveness, originally published in Scope Magazine.
The dataset compares the performance of three antibiotics against 16 different bacteria.
Numerical values in the dataset represent the minimum inhibitory concentration (MIC) of each antibiotic, measured in units per milliliter, with lower values indicating higher antibiotic effectiveness.
The dataset was featured as an example in the Protovis project, a precursor to D3.js.
As noted in the Protovis example, "Recreating this display revealed some minor errors in the original: a missing grid line at 0.01 μg/ml, and an exaggeration of some values for penicillin".
The vega-datsets version is largely consistent with the Protovis version of the dataset, with one correction (changing 'Brucella antracis' to the correct 'Bacillus anthracis') and the addition of a new column, 'Genus', to group related bacterial species together.
The caption of the original 1951 visualization reads as follows:
The chart compares the in vitro sensitivities to neomycin of some of the common pathogens (gram+ in red and gram- in blue) with their sensitivities to penicillin, and streptomycin.
The effectiveness of the antibiotics is expressed as the highest dilution in μ/ml. which inhibits the test organism.
High dilutions are toward the periphery; consequently the length of the colored bar is proportional to the effectiveness.
It is apparent that neomycin is especially effective against Staph. albus and aureus, Streph. fecalis, A. aerogenes, S. typhosa, E. coli, Ps. aeruginosa, Br. abortus, K. pneumoniae, Pr. vulgaris, S. schottmuelleri and M. tuberculosis.
Unfortunately, some strains of proteus, pseudomonas and hemolytic streptococcus are resistant to neomycin, although the majority of these are sensitive to neomycin.
It also inhibits actinomycetes, but is inactive against viruses and fungi. Its mode of action is not understood.
| name | type |
|---|---|
| Bacteria | string |
| Penicillin | number |
| Streptomycin | number |
| Neomycin | number |
| Gram_Staining | string |
| Genus | string |
| title | path |
|---|---|
| Scope Magazine | https://graphicdesignarchives.org/projects/scope-magazine-vol-iii-5/ |
| Protovis Antibiotics Example | https://mbostock.github.io/protovis/ex/antibiotics-burtin.html |
cars.json
Collection of car specifications and performance metrics from various automobile manufacturers.
| name | type |
|---|---|
| Name | string |
| Miles_per_Gallon | integer |
| Cylinders | integer |
| Displacement | number |
| Horsepower | integer |
| Weight_in_lbs | integer |
| Acceleration | number |
| Year | date |
| Origin | string |
| title | path |
|---|---|
| StatLib Datasets Archive | http://lib.stat.cmu.edu/datasets/ |
co2-concentration.csv
Scripps CO2 program data ut modified to only include date, CO2, seasonally adjusted CO2. Only includes rows with valid data.
| name | type |
|---|---|
| Date | date |
| CO2 | number |
| adjusted CO2 | number |
| title | path |
|---|---|
| Scripps CO2 Program | https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record |
countries.json
This dataset combines key demographic indicators (life expectancy at birth and fertility rate measured as babies per woman) for various countries from 1955 to 2000 at 5-year intervals. It includes both current values and adjacent time period values (previous and next) for each indicator. Gapminder's data documentation notes that its philosophy is to fill data gaps with estimates and use current geographic boundaries for historical data. Gapminder states that it aims to "show people the big picture" rather than support detailed numeric analysis.
| name | type | description |
|---|---|---|
| _comment | string | |
| year | integer | Years from 1955 to 2000 at 5-year intervals |
| fertility | number | Fertility rate (average number of children per woman) for the given year |
| life_expect | number | Life expectancy in years for the given year |
| n_fertility | number | Fertility rate for the next 5-year interval |
| n_life_expect | number | Life expectancy for the next 5-year interval |
| country | string | Name of the country |
| title | path | version |
|---|---|---|
| Gapminder Foundation - Life Expectancy | https://docs.google.com/spreadsheets/d/1RehxZjXd7_rG8v2pJYV6aY0J3LAsgUPDQnbY4dRdiSs/edit?gid=176703676#gid=176703676 | 14 |
| Gapminder Foundation - Fertility | https://docs.google.com/spreadsheets/d/1aLtIpAWvDGGa9k2XXEz6hZugWn0wCd5nmzaRPPjbYNA/edit?gid=176703676#gid=176703676 | 14 |
| title | path |
|---|---|
| Creative Commons Attribution 4.0 International | https://www.gapminder.org/free-material/ |
crimea.json
This dataset, which informed Florence Nightingale's groundbreaking work in public health, details monthly mortality rates from British military hospitals during the Crimean War (1854-1856).
Nightingale credits Dr. William Farr for compiling the data from the 1858 Medical and Surgical History of the British Army. The dataset categorizes deaths into "zymotic" diseases (preventable infectious diseases), wounds/injuries, and other causes. Covering the period from April 1854 to March 1856, the dataset includes monthly army strength alongside mortality figures. Nightingale transformed this data into her now-famous polar area diagrams.
The annual mortality rates plotted in the chart can be calculated from the dataset using the formula
(Deaths × 1000 × 12) ÷ Army Size.
As The Lancet argued in 2020, Nightingale's innovative visualizations proved that "far more men died of disease, infection, and exposure than in battle—a fact that shocked the British nation." Her work also vividly illustrated the dramatic impact of sanitary reforms, particularly in reducing preventable deaths.
| name | type | description |
|---|---|---|
| date | date | First day of each month during the observation period, in ISO 8601 format (YYYY-MM-DD) |
| wounds | integer | Deaths from "Wounds and Injuries" which comprised: Luxatio (dislocation), Sub-Luxatio (partial dislocation), Vulnus Sclopitorum (gunshot wounds), Vulnus Incisum (incised wounds), Contusio (bruising), Fractura (fractures), Ambustio (burns) and Concussio-Cerebri (brain concussion) |
| other | integer | Deaths from All Other Causes |
| disease | integer | Deaths from Zymotic Diseases (preventable infectious diseases) |
| army_size | integer | Estimated Average Monthly Strength of the Army |
| title | path |
|---|---|
| Nightingale, Florence. A contribution to the sanitary history of the British army during the late war with Russia. London : John W. Parker and Son, 1859. Table II. Table showing the Estimated Average Monthly Strength of the Army; and the Deaths and Annual Rate of Mortality per 1,000 in each month, from April 1854, to March 1856 (inclusive), in the Hospitals of the Army in the East. | https://nrs.lib.harvard.edu/urn-3:hms.count:1177146?n=21 |
disasters.csv
Annual number of deaths from disasters.
| name | type |
|---|---|
| Entity | string |
| Year | integer |
| Deaths | integer |
| title | path |
|---|---|
| Our World in Data - Natural Catastrophes | https://ourworldindata.org/natural-catastrophes |
driving.json
| name | type |
|---|---|
| side | string |
| year | integer |
| miles | integer |
| gas | number |
| title | path |
|---|---|
| New York Times | https://archive.nytimes.com/www.nytimes.com/imagepages/2010/05/02/business/02metrics.html |
earthquakes.json
Earthquake data retrieved Feb 6, 2018
| title | path |
|---|---|
| USGS Earthquake Feed | https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_week.geojson |
ffox.png
Application icons from open-source software projects.
flare-dependencies.json
| name | type |
|---|---|
| source | integer |
| target | integer |
flare.json
| name | type |
|---|---|
| id | integer |
| name | string |
flights-10k.json
Flight delay statistics from U.S. Bureau of Transportation Statistics. Transformed using /scripts/flights.py
| name | type |
|---|---|
| date | string |
| delay | integer |
| distance | integer |
| origin | string |
| destination | string |
| title | path |
|---|---|
| U.S. Bureau of Transportation Statistics | https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr |
flights-200k.arrow
Flight delay statistics from U.S. Bureau of Transportation Statistics. Transformed using /scripts/flights.py
| name | type |
|---|---|
| delay | integer |
| distance | integer |
| time | number |
| title | path |
|---|---|
| U.S. Bureau of Transportation Statistics | https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr |
flights-200k.json
Flight delay statistics from U.S. Bureau of Transportation Statistics. Transformed using /scripts/flights.py
| name | type |
|---|---|
| delay | integer |
| distance | integer |
| time | number |
| title | path |
|---|---|
| U.S. Bureau of Transportation Statistics | https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr |
flights-20k.json
Flight delay statistics from U.S. Bureau of Transportation Statistics. Transformed using /scripts/flights.py
| name | type |
|---|---|
| date | string |
| delay | integer |
| distance | integer |
| origin | string |
| destination | string |
| title | path |
|---|---|
| U.S. Bureau of Transportation Statistics | https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr |
flights-2k.json
Flight delay statistics from U.S. Bureau of Transportation Statistics. Transformed using /scripts/flights.py
| name | type |
|---|---|
| date | string |
| delay | integer |
| distance | integer |
| origin | string |
| destination | string |
| title | path |
|---|---|
| U.S. Bureau of Transportation Statistics | https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr |
flights-3m.parquet
Flight delay statistics from U.S. Bureau of Transportation Statistics. Transformed using /scripts/flights.py
| name | type |
|---|---|
| date | datetime |
| delay | integer |
| distance | integer |
| origin | string |
| destination | string |
| title | path |
|---|---|
| U.S. Bureau of Transportation Statistics | https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr |
flights-5k.json
Flight delay statistics from U.S. Bureau of Transportation Statistics. Transformed using /scripts/flights.py
| name | type |
|---|---|
| date | string |
| delay | integer |
| distance | integer |
| origin | string |
| destination | string |
| title | path |
|---|---|
| U.S. Bureau of Transportation Statistics | https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr |
flights-airport.csv
Flight delay statistics from U.S. Bureau of Transportation Statistics. Transformed using /scripts/flights.py
| name | type |
|---|---|
| origin | string |
| destination | string |
| count | integer |
| title | path |
|---|---|
| U.S. Bureau of Transportation Statistics | https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FGJ&QO_fu146_anzr=b0-gvzr |
football.json
Football match outcomes across multiple divisions from 2013 to 2017, part of a larger dataset from OpenFootball. The subset was made such that there are records for all five chosen divisions over the time period.
| name | type |
|---|---|
| date | date |
| division | string |
| home_team | string |
| away_team | string |
| home_score | integer |
| away_score | integer |
| title | path |
|---|---|
| OpenFootball | https://github.com/openfootball/football.json |
gapminder-health-income.csv
Per-capita income, life expectancy, population and regional grouping. Dataset does not specify the reference year for the data. Gapminder historical data is subject to revisions.
Gapminder (v30, 2023) defines per-capita income as follows:
"This is real GDP per capita (gross domestic product per person adjusted for inflation) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States."
| name | type |
|---|---|
| country | string |
| income | integer |
| health | number |
| population | integer |
| region | string |
| title | path |
|---|---|
| Gapminder Foundation | https://www.gapminder.org |
| Gapminder GDP Per Capita Data | https://docs.google.com/spreadsheets/d/1i5AEui3WZNZqh7MQ4AKkJuCz4rRxGR_pw_9gtbcBOqQ/edit?gid=501532268#gid=501532268 |
| title | path |
|---|---|
| Creative Commons Attribution 4.0 International | https://www.gapminder.org/free-material/ |
gapminder.json
This dataset combines key demographic indicators (life expectancy at birth, population, and fertility rate measured as babies per woman) for various countries from 1955 to 2005 at 5-year intervals. It also includes a 'cluster' column, a categorical variable grouping countries. Gapminder's data documentation notes that its philosophy is to fill data gaps with estimates and use current geographic boundaries for historical data. Gapminder states that it aims to "show people the big picture" rather than support detailed numeric analysis.
Notes:
-
Country Selection: The set of countries in this file matches the version of this dataset originally added to this collection in 2015. The specific criteria for country selection in that version are not known. Data for Aruba are no longer available in the new version. Hong Kong has been revised to Hong Kong, China in the new version.
-
Data Precision: The precision of float values may have changed from the original version. These changes reflect the most recent source data used for each indicator.
-
Regional Groupings: The 'cluster' column represents a regional mapping of countries corresponding to the 'six_regions' schema in Gapminder's Data Geographies dataset. To preserve continuity with previous versions of this dataset, we have retained the column name 'cluster' instead of renaming it to 'six_regions'. The six regions represented are:
0: south_asia, 1: europe_central_asia, 2: sub_saharan_africa, 3: america, 4: east_asia_pacific, 5: middle_east_north_africa.
| name | type | description |
|---|---|---|
| year | integer | Years from 1955 to 2005 at 5-year intervals |
| country | string | Name of the country |
| cluster | integer | A categorical variable (values 0-5) grouping countries by region |
| pop | integer | Population of the country |
| life_expect | number | Life expectancy in years |
| fertility | number | Fertility rate (average number of children per woman |
gimp.png
Application icons from open-source software projects.
github.csv
Generated using /scripts/github.py.
| name | type |
|---|---|
| time | string |
| count | integer |
global-temp.csv
Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies (Land-Ocean Temperature Index, L-OTI), 1880-2023.
| name | type |
|---|---|
| year | integer |
| temp | number |
| title | path |
|---|---|
| NASA Goddard Institute for Space Studies | https://data.giss.nasa.gov/gistemp/ |
income.json
| name | type |
|---|---|
| name | string |
| region | string |
| id | integer |
| pct | number |
| total | integer |
| group | string |
iowa-electricity.csv
The state of Iowa has dramatically increased its production of renewable wind power in recent years. This file contains the annual net generation of electricity in the state by source in thousand megawatthours. U.S. EIA data downloaded on May 6, 2018. It is useful for illustrating stacked area charts.
| name | type |
|---|---|
| year | date |
| source | string |
| net_generation | integer |
jobs.json
U.S. census data on occupations by sex and year across decades between 1850 and 2000. The dataset was obtained from IPUMS USA, which "collects, preserves and harmonizes U.S. census microdata" from as early as 1790.
Originally created for a 2006 data visualization project called sense.us by IBM Research (Jeff Heer, Martin Wattenberg and Fernanda Viégas), described here. The dataset is also referenced in this vega example.
Data is based on a tabulation of the OCC1950 variable by sex across IPUMS USA samples. The dataset appears to be derived from Version 6.0 (2015) of IPUMS USA, according to 2024 correspondence with the IPUMS Project. IPUMS has made improvements to occupation coding since version 6, particularly for 19th-century samples, which may result in discrepancies between this dataset and current IPUMS data. Details on data revisions are available here.
IPUMS USA confirmed in 2024 correspondence that hosting this dataset on vega-datasets is permissible, stating:
We're excited to hear that this dataset made its way to this repository and is being used by students for data visualization. We allow for these types of redistributions of summary data so long as the underlying microdata records are not shared.
This dataset contains only summary statistics and does not include any underlying microdata records.
- This dataset represents summary data. The underlying microdata records are not included.
- Users attempting to replicate or extend this data should use the PERWT (person weight) variable as an expansion factor when working with IPUMS USA extracts.
- Due to coding revisions, figures for earlier years (particularly 19th century) may not match current IPUMS USA data exactly.
When using this dataset, please refer to IPUMS USA terms of use. The organization requests use of the following citation for this json file:
Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 6.0. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D010.V6.0
| name | type | description |
|---|---|---|
| job | string | The occupation title |
| sex | string | Sex (men/women) |
| year | integer | Census year |
| count | integer | Number of individuals in the occupation |
| perc | number | Percentage of the workforce in the occupation |
| title | path | version |
|---|---|---|
| IPUMS USA | https://usa.ipums.org/usa/ | 6 |
la-riots.csv
More than 60 people lost their lives amid the looting and fires that ravaged Los Angeles for five days starting on April 29, 1992. This file contains metadata about each person, including the geographic coordinates of their death. Compiled and published by the Los Angeles Times Data Desk.
| name | type |
|---|---|
| first_name | string |
| last_name | string |
| age | integer |
| gender | string |
| race | string |
| death_date | date |
| address | string |
| neighborhood | string |
| type | string |
| longitude | number |
| latitude | number |
| title | path |
|---|---|
| LA Riots Deaths, Los Angeles Times Data Desk | http://spreadsheets.latimes.com/la-riots-deaths/ |
londonBoroughs.json
Boundaries of London boroughs reprojected and simplified from London_Borough_Excluding_MHW shapefile.
Original data "contains National Statistics data © Crown copyright and database right (2015)"
and "Contains Ordnance Survey data © Crown copyright and database right [2015].
| title | path |
|---|---|
| Statistical GIS Boundary Files, London Datastore | https://data.london.gov.uk/dataset/statistical-gis-boundary-files-london |
londonCentroids.json
Calculated from londonBoroughs.json using d3.geoCentroid.
| name | type |
|---|---|
| name | string |
| cx | number |
| cy | number |
londonTubeLines.json
Selected rail lines simplified from source.
| title | path |
|---|---|
| London Tube Data | https://github.com/oobrien/vis/tree/master/tube/data |
lookup_groups.csv
| name | type |
|---|---|
| group | integer |
| person | string |
lookup_people.csv
| name | type |
|---|---|
| name | string |
| age | integer |
| height | integer |
miserables.json
monarchs.json
A chronological list of English and British monarchs from Elizabeth I through George IV. Each entry includes:
The dataset contains two intentional inaccuracies to maintain compatibility with the Wheat and Wages example visualization:
- the start date for the reign of Elizabeth I is shown as 1565, instead of 1558;
- the end date for the reign of George IV is shown as 1820, instead of 1830.
These discrepancies align the
monarchs.jsondataset with the start and end dates of thewheat.jsondataset used i the visualization. The entry "W&M" represents the joint reign of William III and Mary II. While the dataset shows their reign as 1689-1702, the official Web site of the British royal family indicates that Mary II's reign ended in 1694, though William III continued to rule until 1702. Thecommonwealthfield is used to flag the period from 1649 to 1660, which includes the Commonwealth of England, the Protectorate, and the period leading to the Restoration. While historically more accurate to call this the "interregnum," the field name ofcommonwealthfrom the original dataset is retained for backwards compatibility. The dataset was revised in Aug. 2024. James II's reign now ends in 1688 (previously 1689). Source data has been verified against the kings & queens and interregnum pages of the official website of the British royal family (retrieved in Aug. 2024). Content on the site is protected by Crown Copyright. Under the UK Government Licensing Framework, most Crown copyright information is available under the Open Government Licence v3.0.
| name | type | description |
|---|---|---|
| name | string | The ruler's name or identifier (e.g., "W&M" for William and Mary, "Cromwell" for the period of interregnum) |
| start | integer | The year their rule began |
| end | integer | The year their rule ended |
| index | integer | A zero-based sequential number assigned to each entry, representing the chronological order of rulers |
| title | path |
|---|---|
| The Royal Family - Kings & Queens | https://www.royal.uk/kings-and-queens-1066 |
| The Royal Family - Interregnum | https://www.royal.uk/interregnum-1649-1660 |
movies.json
The dataset has well known and intentionally included errors. This dataset is provided for instructional purposes, including the need to reckon with dirty data.
| name | type |
|---|---|
| Title | string |
| US Gross | integer |
| Worldwide Gross | integer |
| US DVD Sales | integer |
| Production Budget | integer |
| Release Date | string |
| MPAA Rating | string |
| Running Time min | integer |
| Distributor | string |
| Source | string |
| Major Genre | string |
| Creative Type | string |
| Director | string |
| Rotten Tomatoes Rating | integer |
| IMDB Rating | number |
| IMDB Votes | integer |
normal-2d.json
| name | type |
|---|---|
| u | number |
| v | number |
obesity.json
| name | type |
|---|---|
| id | integer |
| rate | number |
| state | string |
ohlc.json
This dataset contains the performance of the Chicago Board Options Exchange Volatility Index (VIX) in the summer of 2009.
| name | type |
|---|---|
| date | date |
| open | number |
| high | number |
| low | number |
| close | number |
| signal | string |
| ret | number |
| title | path |
|---|---|
| Yahoo Finance VIX Data | https://finance.yahoo.com/chart/%5EVIX |
penguins.json
Palmer Archipelago (Antarctica) penguin data collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of the Long Term Ecological Research Network.
| name | type |
|---|---|
| Species | string |
| Island | string |
| Beak Length (mm) | number |
| Beak Depth (mm) | number |
| Flipper Length (mm) | integer |
| Body Mass (g) | integer |
| Sex | string |
| title | path |
|---|---|
| Palmer Station Antarctica LTER | https://pal.lternet.edu/ |
| Allison Horst's Penguins Repository | https://github.com/allisonhorst/penguins |
platformer-terrain.json
Assets from the video game Celeste.
| name | type |
|---|---|
| x | integer |
| y | integer |
| lumosity | number |
| saturation | integer |
| name | string |
| id | string |
| color | string |
| key | string |
| title | path |
|---|---|
| Celeste Game | http://www.celestegame.com/ |
points.json
| name | type |
|---|---|
| x | number |
| y | number |
political-contributions.json
Summary financial information on contributions to candidates for U.S.
elections. An updated version of this datset is available from the "all candidates" files
(in pipe-delimited format) on the bulk data download page of the U.S. Federal Election
Commission, or, alternatively, via OpenFEC. Information on each of the 25 columns is
available from the FEC All Candidates File Description.
The sample dataset in political-contributions.json contains 58 records with dates from 2015.
FEC data is subject to the commission's:
Additionally, the FEC's Github repository states:
This project is in the public domain within the United States, and we waive worldwide copyright and related rights through CC0 universal public domain dedication. Read more on our license page. A few restrictions limit the way you can use FEC data. For example, you can't use contributor lists for commercial purposes or to solicit donations. Learn more on FEC.gov.
| name | type |
|---|---|
| Candidate_Identification | string |
| Candidate_Name | string |
| Incumbent_Challenger_Status | string |
| Party_Code | integer |
| Party_Affiliation | string |
| Total_Receipts | number |
| Transfers_from_Authorized_Committees | integer |
| Total_Disbursements | number |
| Transfers_to_Authorized_Committees | number |
| Beginning_Cash | number |
| Ending_Cash | number |
| Contributions_from_Candidate | number |
| Loans_from_Candidate | integer |
| Other_Loans | integer |
| Candidate_Loan_Repayments | number |
| Other_Loan_Repayments | integer |
| Debts_Owed_By | number |
| Total_Individual_Contributions | integer |
| Candidate_State | string |
| Candidate_District | integer |
| Contributions_from_Other_Political_Committees | integer |
| Contributions_from_Party_Committees | integer |
| Coverage_End_Date | string |
| Refunds_to_Individuals | integer |
| Refunds_to_Committees | integer |
| title | path |
|---|---|
| Federal Election Commission Bulk Data | https://www.fec.gov/data/browse-data/?tab=bulk-data |
| OpenFEC API | https://api.open.fec.gov/developers/ |
population.json
United States population statistics by sex and age group across decades between 1850 and 2000. The dataset was obtained from IPUMS USA, which "collects, preserves and harmonizes U.S. census microdata" from as early as 1790.
IPUMS updates and revises datasets over time, which may result in discrepancies between this dataset and current IPUMS data. Details on data revisions are available here.
When using this dataset, please refer to IPUMS USA terms of use. The organization requests the use of the following citation for this json file: Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 6.0. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D010.V6.0
| name | type | description |
|---|---|---|
| year | integer | Four-digit year of the survey |
| age | integer | Age group in 5-year intervals (0=0-4, 5=5-9, 10=10-14, ..., 90=90+) |
| sex | integer | Sex (1=men, 2=women) |
| people | integer | Number of individuals (IPUMS PERWT) |
| title | path |
|---|---|
| IPUMS USA | https://usa.ipums.org/usa/ |
population_engineers_hurricanes.csv
Per-state data on population, number of engineers, and hurricanes. Used in Vega-Lite example, Three Choropleths Representing Disjoint Data from the Same Table
| name | type |
|---|---|
| state | string |
| id | integer |
| population | integer |
| engineers | number |
| hurricanes | integer |
| title | path |
|---|---|
| Bureau of Labor Statistics | https://www.bls.gov/oes/tables.htm |
| American Community Survey | https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_07_3YR_S1901&prodType=table |
| NOAA National Climatic Data Center | https://www.ncdc.noaa.gov/cdo-web/datatools/records |
seattle-weather-hourly-normals.csv
Hourly weather normals with metric units. The 1981-2010 Climate Normals are NCDC's three-decade averages of climatological variables, including temperature and precipitation. Learn more in the documentation. We only included temperature, wind, and pressure and updated the format to be easier to parse.
| name | type |
|---|---|
| date | datetime |
| pressure | number |
| temperature | number |
| wind | number |
| title | path |
|---|---|
| NOAA National Climatic Data Center (NCDC) | https://www.ncdc.noaa.gov/cdo-web/datatools/normals |
seattle-weather.csv
Daily weather records with metric units. Transformed using /scripts/weather.py.
The categorical "weather" field is synthesized from multiple fields in the original dataset.
This data is intended for instructional purposes.
| name | type |
|---|---|
| date | date |
| precipitation | number |
| temp_max | number |
| temp_min | number |
| wind | number |
| weather | string |
| title | path |
|---|---|
| NOAA National Climatic Data Center | https://www.ncdc.noaa.gov/cdo-web/datatools/records |
sp500-2000.csv
S&P 500 index values from 2000 to 2020.
| name | type |
|---|---|
| date | date |
| open | number |
| high | number |
| low | number |
| close | number |
| adjclose | number |
| volume | integer |
| title | path |
|---|---|
| Yahoo Finance | https://finance.yahoo.com/quote/%5EDJI/history/ |
sp500.csv
| name | type |
|---|---|
| date | string |
| price | number |
stocks.csv
| name | type |
|---|---|
| symbol | string |
| date | string |
| price | number |
udistrict.json
| name | type |
|---|---|
| key | string |
| lat | number |
unemployment-across-industries.json
Industry-level unemployment statistics from the Current Population Survey (CPS), published monthly by the U.S. Bureau of Labor Statistics. Includes unemployed persons and unemployment rate across 11 private industries, as well as agricultural, government, and self-employed workers. Covers January 2000 through February 2010. Industry classification follows format of CPS Table A-31.
The dataset can be replicated using the BLS API. For more, see the scripts folder of this
repository.
The BLS Web site states:
"Users of the public API should cite the date that data were accessed or retrieved using the API. Users must clearly state that "BLS.gov cannot vouch for the data or analyses derived from these data after the data have been retrieved from BLS.gov." The BLS.gov logo may not be used by persons who are not BLS employees or on products (including web pages) that are not BLS-sponsored."
See full BLS terms of service.
| name | type | description |
|---|---|---|
| series | string | Industry name |
| year | integer | Year (2000-2010) |
| month | integer | Month (1-12) |
| count | integer | Number of unemployed persons (in thousands) |
| rate | number | Unemployment rate (percentage) |
| date | datetime | ISO 8601-formatted date string (e.g., "2000-01-01T08:00:00.000Z") |
| title | path |
|---|---|
| U.S. Census Bureau Current Population Survey | https://www.census.gov/programs-surveys/cps.html |
| BLS LAUS Data Tools | https://www.bls.gov/lau/data.htm |
| Bureau of Labor Statistics Table A-31 | https://www.bls.gov/web/empsit/cpseea31.htm |
unemployment.tsv
This dataset contains county-level unemployment rates in the United States, with data generally consistent with levels reported in 2009. The dataset is structured as tab-separated values. The unemployment rate represents the number of unemployed persons as a percentage of the labor force. According to the Bureau of Labor Statistics (BLS) glossary:
Unemployed persons (Current Population Survey) [are] persons aged 16 years and older who had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment sometime during the 4-week period ending with the reference week. Persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work to be classified as unemployed.
This dataset is derived from the Local Area Unemployment Statistics (LAUS) program, a federal-state cooperative effort overseen by the Bureau of Labor Statistics (BLS). The LAUS program produces monthly and annual employment, unemployment, and labor force data for census regions and divisions, states, counties, metropolitan areas, and many cities and towns.
For the most up-to-date LAUS data:
- Monthly and Annual Data Downloads:
- Visit the LAUS Data Tools page for monthly and annual county data.
- BLS Public Data API:
- The BLS provides an API for developers to access various datasets, including LAUS data.
- To use the API for LAUS data, refer to the LAUS Series ID Formats to construct your query.
- API documentation and examples are available on the BLS Developers page.
When using BLS public data API and datasets, users should adhere to the BLS Terms of Service.
| name | type | description |
|---|---|---|
| id | integer | The combined state and county FIPS code |
| rate | number | The unemployment rate for the county |
| title | path |
|---|---|
| BLS Developers API | https://www.bls.gov/developers/ |
| BLS Handbook of Methods | https://www.bls.gov/opub/hom/lau/home.htm |
uniform-2d.json
| name | type |
|---|---|
| u | number |
| v | number |
us-10m.json
us-employment.csv
In the mid 2000s the global economy was hit by a crippling recession. One result: Massive job losses across the United States. The downturn in employment, and the slow recovery in hiring that followed, was tracked each month by the Current Employment Statistics program at the U.S. Bureau of Labor Statistics.
This file contains the monthly employment total in a variety of job categories from January 2006 through December 2015. The numbers are seasonally adjusted and reported in thousands. The data were downloaded on Nov. 11, 2018, and reformatted for use in this library.
Totals are included for the 22 "supersectors" tracked by the BLS. The "nonfarm" total is the category typically used by economists and journalists as a stand-in for the country's employment total.
A calculated "nonfarm_change" column has been appended with the month-to-month change in that supersector's employment. It is useful for illustrating how to make bar charts that report both negative and positive values.
| name | type |
|---|---|
| month | date |
| nonfarm | integer |
| private | integer |
| goods_producing | integer |
| service_providing | integer |
| private_service_providing | integer |
| mining_and_logging | integer |
| construction | integer |
| manufacturing | integer |
| durable_goods | integer |
| nondurable_goods | integer |
| trade_transportation_utilties | integer |
| wholesale_trade | number |
| retail_trade | number |
| transportation_and_warehousing | number |
| utilities | number |
| information | integer |
| financial_activities | integer |
| professional_and_business_services | integer |
| education_and_health_services | integer |
| leisure_and_hospitality | integer |
| other_services | integer |
| government | integer |
| nonfarm_change | integer |
| title | path |
|---|---|
| U.S. Bureau of Labor Statistics Current Employment Statistics | https://www.bls.gov/ces/ |
us-state-capitals.json
| name | type |
|---|---|
| lon | number |
| lat | number |
| state | string |
| city | string |
volcano.json
Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid. Digitized from a topographic map by Ross Ihaka, adapted from R datasets. These data should not be regarded as accurate.
| title | path |
|---|---|
| R Datasets | https://stat.ethz.ch/R-manual/R-patched/library/datasets/html/volcano.html |
weather.csv
NOAA data transformed using /scripts/weather.py. Categorical "weather" field synthesized
from multiple fields in the original dataset. This data is intended for instructional purposes.
| name | type |
|---|---|
| location | string |
| date | date |
| precipitation | number |
| temp_max | number |
| temp_min | number |
| wind | number |
| weather | string |
| title | path |
|---|---|
| NOAA Climate Data Online | http://www.ncdc.noaa.gov/cdo-web/datatools/findstation |
weekly-weather.json
Instructional dataset showing actual and predicted temperature data.
Important
Named weather.json in previous versions (v1.4.0 - v2.11.0).
wheat.json
In an 1822 letter to Parliament, William Playfair, a Scottish engineer who is often credited as the founder of statistical graphics, published an elegant chart on the price of wheat. It plots 250 years of prices alongside weekly wages and the reigning monarch. He intended to demonstrate that:
"never at any former period was wheat so cheap, in proportion to mechanical labour, as it is at the present time."
| name | type |
|---|---|
| year | integer |
| wheat | number |
| wages | number |
| title | path |
|---|---|
| 1822 Playfair Chart | http://dh101.humanities.ucla.edu/wp-content/uploads/2014/08/Vis_2.jpg |
windvectors.csv
Simulated wind patterns over northwestern Europe.
| name | type |
|---|---|
| longitude | number |
| latitude | number |
| dir | integer |
| dirCat | integer |
| speed | number |
world-110m.json
zipcodes.csv
GeoNames.org
| name | type |
|---|---|
| zip_code | integer |
| latitude | number |
| longitude | number |
| city | string |
| state | string |
| county | string |
| title | path |
|---|---|
| GeoNames | https://www.geonames.org |