# `Tucan.Datasets`
[🔗](https://github.com/pnezis/tucan/blob/v0.7.0/lib/tucan/datasets.ex#L31)

Common datasets for `Tucan` demos and docs.

## Supported datasets

Currently the following datasets are supported:

#### airports

The airports of the United States and its territories with their location.
 [[Data]](https://vega.github.io/vega-datasets/data/airports.csv).

**Columns: ** `iata`, `name`, `city`, `state`, `country`, `latitude`, `longitude`

#### barley

[Yield data](https://stat.ethz.ch/R-manual/R-devel/library/lattice/html/barley.html) from a
Minnesota barley trial. Includes total yield in bushels per acre for 10 varieties at 6 sites
in each of two years.
 [[Data]](https://vega.github.io/editor/data/barley.json).

**Columns: ** `yield`, `variety`, `year`, `site`

#### cars

This was the 1983 ASA Data Exposition dataset. The dataset was collected by Ernesto Ramos and
David Donoho and dealt with automobiles. I don't remember the instructions for analysis. Data
on mpg, cylinders, displacement, etc. (8 variables) for 406 different cars. [[Source]](http://lib.stat.cmu.edu/datasets/)
 [[Data]](https://vega.github.io/editor/data/cars.json).

**Columns: ** `Name`, `Miles_per_Gallon`, `Cylinders`, `Displacement`, `Horsepower`, `Weight_in_lbs`, `Acceleration`, `Year`, `Origin`

#### corruption

Corruption Perceptions Index (CPI) and Human Development Index (HDI) for 176 countries,
from 2012 to 2015.
 [[Data]](https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/corruption.csv).

**Columns: ** `country`, `region`, `year`, `cpi`, `iso3c`, `hdi`

#### flights

Monthly airline passengers from 1949 to 1960.
 [[Data]](https://raw.githubusercontent.com/mwaskom/seaborn-data/master/flights.csv).

**Columns: ** `year`, `month`, `passengers`

#### flights_airport

The number of flights between pairs of US airports in 2008. The airports are
identified by their IATA codes, which can be joined with the `:airports` dataset.
 [[Data]](https://vega.github.io/vega-datasets/data/flights-airport.csv).

**Columns: ** `origin`, `destination`, `count`

#### gapminder

[Gapminder](https://www.gapminder.org/) health & income by country data.
 [[Data]](https://vega.github.io/vega-datasets/data/gapminder-health-income.csv).

**Columns: ** `country`, `income`, `health`, `population`, `region`

#### glue

Data from the [GLUE (The General Language Understanding Evaluation) benchmark
leaderboard](https://gluebenchmark.com/leaderboard).
 [[Data]](https://raw.githubusercontent.com/mwaskom/seaborn-data/master/glue.csv).

**Columns: ** `Model`, `Year`, `Encoder`, `Task`, `Score`

#### iris

This is one of the earliest datasets used in the literature on classification methods and widely
used in statistics and machine learning.  The data set contains 3 classes of 50 instances each,
where each class refers to a type of iris plant.
 [[Data]](https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/0e7a9b0a5d22642a06d3d5b9bcbad9890c8ee534/iris.csv).

**Columns: ** `sepal_length`, `sepal_width`, `petal_length`, `petal_width`, `species`

#### movies

Movies dataset including IMDB scores. The dataset has well known and intentionally included
errors. This dataset is used for instructional purposes, including the need to reckon with
dirty data.
 [[Data]](https://vega.github.io/editor/data/movies.json).

**Columns: ** `Creative Type`, `Director`, `Distributor`, `IMDB Rating`, `IMDB Votes`, `MPAA Rating`, `Major Genre`, `Production Budget`, `Release Date`, `Rotten Tomatoes Rating`, `Running Time min`, `Source`, `Title`, `US DVD Sales`, `US Gross`, `Worldwide Gross`

#### ohlc

The dataset contains the performance of the Chicago Board Options Exchange Volatility Index (VIX) in
the summer of 2009.
 [[Data]](https://vega.github.io/editor/data/ohlc.json).

**Columns: ** `date`, `open`, `high`, `low`, `close`, `signal`, `short`

#### penguins

The [dataset](https://github.com/allisonhorst/palmerpenguins) contains data for 344 penguins.
There are 3 different species of penguins in this dataset, collected from 3 islands in the
Palmer Archipelago, Antarctica. This is an excellent dataset for data exploration
& visualization, as an alternative to `:iris`.

Data were collected and made available by [Dr. Kristen Gorman](https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php)
and the [Palmer Station, Antarctica LTER](https://pallter.marine.rutgers.edu/), a member of
the [Long Term Ecological Research Network](https://lternet.edu/).
 [[Data]](https://raw.githubusercontent.com/vega/vega-datasets/next/data/penguins.json).

**Columns: ** `Beak Depth (mm)`, `Beak Length (mm)`, `Body Mass (g)`, `Flipper Length (mm)`, `Island`, `Sex`, `Species`

#### stocks

Daily closing prices of various stocks. [[Data]](https://vega.github.io/editor/data/stocks.csv).

**Columns: ** `symbol`, `date`, `price`

#### tips

[Tipping data](https://rdrr.io/cran/reshape2/man/tips.html) as collected by one waiter.
Information about each tip he received over a period of a few months working in one restaurant
is included.
 [[Data]](https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv).

**Columns: ** `total_bill`, `tip`, `sex`, `smoker`, `day`, `time`, `size`

#### titanic

Titanic survival data from the [legendary Kaggle competition](https://www.kaggle.com/competitions/titanic/data).
 [[Data]](https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv).

**Columns: ** `PassengerId`, `Survived`, `Pclass`, `Name`, `Sex`, `Age`, `SibSp`, `Parch`, `Ticket`, `Fare`, `Cabin`, `Embarked`

#### unemployment

US unemployment data across various industries from 2000 to 2009.
 [[Data]](https://vega.github.io/editor/data/unemployment-across-industries.json).

**Columns: ** `count`, `date`, `month`, `rate`, `series`, `year`

#### us_10m

The boundaries of the US states and counties in TopoJSON format. The states and
counties are identified by their FIPS codes.
 [[Data]](https://vega.github.io/vega-datasets/data/us-10m.json).

**TopoJSON objects: ** `states`, `counties`, `land`

#### us_county_unemployment

The unemployment rate of the US counties in 2009 as a fraction, e.g. `0.097`. The
counties are identified by their FIPS codes.
 [[Data]](https://vega.github.io/vega-datasets/data/unemployment.tsv).

**Columns: ** `id`, `rate`

#### us_state_population

The population of the US states in 2016, the share of engineers in their employed
population in 2016 and the number of hurricane landfalls since 1851. The states
are identified by their FIPS codes.
 [[Data]](https://vega.github.io/vega-datasets/data/population_engineers_hurricanes.csv).

**Columns: ** `state`, `id`, `population`, `engineers`, `hurricanes`

#### weather

Daily weather records from Seattle with metric units. [Data from NOAA](https://www.ncdc.noaa.gov/cdo-web/datatools/records).
 [[Data]](https://vega.github.io/editor/data/weather.csv).

**Columns: ** `date`, `precipitation`, `temp_max`, `temp_min`, `wind`, `weather`

#### world_110m

The boundaries of the countries of the world in TopoJSON format. The countries are
identified by their ISO 3166-1 numeric codes.
 [[Data]](https://vega.github.io/vega-datasets/data/world-110m.json).

**TopoJSON objects: ** `countries`, `land`

# `t`

```elixir
@type t() ::
  :airports
  | :barley
  | :cars
  | :corruption
  | :flights
  | :flights_airport
  | :gapminder
  | :glue
  | :iris
  | :movies
  | :ohlc
  | :penguins
  | :stocks
  | :tips
  | :titanic
  | :unemployment
  | :us_10m
  | :us_county_unemployment
  | :us_state_population
  | :weather
  | :world_110m
```

# `dataset`

```elixir
@spec dataset(atom()) :: String.t()
```

Reruns the url of the given dataset.

Raises an error if the dataset is invalid.

---

*Consult [api-reference.md](api-reference.md) for complete listing*