The repurrrsive package provides recursive lists that are handy when
teaching or exampling functions such as purrr::map() and the
rectangling
functions in the
tidyr package. The datasets are stored as R list, JSON, and XML to
provide the full non-rectangular data experience. Enjoy!
This package also includes the main data frame from the gapminder package in 3 different forms: simple data frame (no list-columns), data frame nested by country, and split into a named list of data frames.
You can install repurrrsive from CRAN like so:
install.packages("repurrrsive")or from GitHub with:
# install.packages("pak")
pak::pak("jennybc/repurrrsive")repurrrsive contains several datasets that are recursive lists, both in the form of R objects and as JSON and/or XML files.
For example, got_chars is a list with information on the 30
point-of-view characters from the first five books in the Song of Ice
and Fire series by George R. R. Martin. Here’s how to use
purrr::map_chr() to extract the character’s names:
library(repurrrsive)
library(purrr)
map_chr(got_chars, "name")
#> [1] "Theon Greyjoy" "Tyrion Lannister" "Victarion Greyjoy"
#> [4] "Will" "Areo Hotah" "Chett"
#> [7] "Cressen" "Arianne Martell" "Daenerys Targaryen"
#> [10] "Davos Seaworth" "Arya Stark" "Arys Oakheart"
#> [13] "Asha Greyjoy" "Barristan Selmy" "Varamyr"
#> [16] "Brandon Stark" "Brienne of Tarth" "Catelyn Stark"
#> [19] "Cersei Lannister" "Eddard Stark" "Jaime Lannister"
#> [22] "Jon Connington" "Jon Snow" "Aeron Greyjoy"
#> [25] "Kevan Lannister" "Melisandre" "Merrett Frey"
#> [28] "Quentyn Martell" "Samwell Tarly" "Sansa Stark"Each set of recursive lists has its own article that gives a sense of what sort of manipulations can be demonstrated with the dataset(s):
- Game of Thrones characters
- Data on entities in the Star Wars universe
- GitHub user and repo data
- Sharla Gelfand’s music collection
- Color palettes from Wes Anderson movies
Learn more at https://jennybc.github.io/repurrrsive/articles/.
The Gapminder data, from the gapminder package, is also here in various forms to allow practice of different styles of grouped computation.
For example, the gap_nested dataset has one row per country, with a
nested data column containing longitudinal data for life expectancy,
population, and GDP per capita.
gap_nested
#> # A tibble: 142 × 3
#> country continent data
#> <fct> <fct> <list>
#> 1 Afghanistan Asia <tibble [12 × 4]>
#> 2 Albania Europe <tibble [12 × 4]>
#> 3 Algeria Africa <tibble [12 × 4]>
#> 4 Angola Africa <tibble [12 × 4]>
#> 5 Argentina Americas <tibble [12 × 4]>
#> 6 Australia Oceania <tibble [12 × 4]>
#> 7 Austria Europe <tibble [12 × 4]>
#> 8 Bahrain Asia <tibble [12 × 4]>
#> 9 Bangladesh Asia <tibble [12 × 4]>
#> 10 Belgium Europe <tibble [12 × 4]>
#> # … with 132 more rows
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