The repurrrsive package provides recursive lists that are handy when teaching or exampling functions such as purrr::map(). Datasets are stored as R list, JSON, and XML to provide the full non-rectangular data experience. Enjoy!
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.
repurrrsive is used in this purrr tutorial:
https://jennybc.github.io/purrr-tutorial/
You can install repurrrsive from CRAN like so:
install.packages("repurrrsive", dependencies = TRUE)or from GitHub with:
# install.packages("devtools")
devtools::install_github("jennybc/repurrrsive")wesanderson is the simplest list, containing color palettes, from the wesanderson package. Here's a glimpse: one component per palette, each containing a character vector of hex colors. Screenshot is of RStudio's Object Explorer, i.e. from calling View(wesanderson).
library(repurrrsive)
library(purrr)
wesanderson[1:3]
#> $GrandBudapest
#> [1] "#F1BB7B" "#FD6467" "#5B1A18" "#D67236"
#>
#> $Moonrise1
#> [1] "#F3DF6C" "#CEAB07" "#D5D5D3" "#24281A"
#>
#> $Royal1
#> [1] "#899DA4" "#C93312" "#FAEFD1" "#DC863B"Use wesanderson to demonstrate mapping functions over a list.
map_chr(wesanderson, 1)
#> GrandBudapest Moonrise1 Royal1 Moonrise2 Cavalcanti
#> "#F1BB7B" "#F3DF6C" "#899DA4" "#798E87" "#D8B70A"
#> Royal2 GrandBudapest2 Moonrise3 Chevalier Zissou
#> "#9A8822" "#E6A0C4" "#85D4E3" "#446455" "#3B9AB2"
#> FantasticFox Darjeeling Rushmore BottleRocket Darjeeling2
#> "#DD8D29" "#FF0000" "#E1BD6D" "#A42820" "#ECCBAE"
map_int(wesanderson, length)
#> GrandBudapest Moonrise1 Royal1 Moonrise2 Cavalcanti
#> 4 4 4 4 5
#> Royal2 GrandBudapest2 Moonrise3 Chevalier Zissou
#> 5 4 5 4 5
#> FantasticFox Darjeeling Rushmore BottleRocket Darjeeling2
#> 5 5 5 7 5
map_chr(wesanderson[7:9], paste, collapse = ", ")
#> GrandBudapest2
#> "#E6A0C4, #C6CDF7, #D8A499, #7294D4"
#> Moonrise3
#> "#85D4E3, #F4B5BD, #9C964A, #CDC08C, #FAD77B"
#> Chevalier
#> "#446455, #FDD262, #D3DDDC, #C7B19C"The same wesanderson data is also present as JSON and XML files. Accessor functions provide the local file path.
wesanderson_json()
#> [1] "/Users/jenny/resources/R/library/repurrrsive/extdata/wesanderson.json"
wesanderson_xml()
#> [1] "/Users/jenny/resources/R/library/repurrrsive/extdata/wesanderson.xml"Practice bringing data from JSON into an R list.
library(jsonlite)
json <- fromJSON(wesanderson_json())
json$wesanderson[1:3]
#> $GrandBudapest
#> [1] "#F1BB7B" "#FD6467" "#5B1A18" "#D67236"
#>
#> $Moonrise1
#> [1] "#F3DF6C" "#CEAB07" "#D5D5D3" "#24281A"
#>
#> $Royal1
#> [1] "#899DA4" "#C93312" "#FAEFD1" "#DC863B"
identical(wesanderson, json$wesanderson)
#> [1] TRUEPractice bringing data into R from XML. You can get it into an R list with xml2::as_list(), but to get a list as nice as those above? That requires a bit more work. Such is XML life.
library(xml2)
xml <- read_xml(wesanderson_xml())
xml_child(xml)
#> {xml_node}
#> <palette name="GrandBudapest">
#> [1] <hex>#F1BB7B</hex>
#> [2] <hex>#FD6467</hex>
#> [3] <hex>#5B1A18</hex>
#> [4] <hex>#D67236</hex>
as_list(xml_child(xml))
#> $hex
#> $hex[[1]]
#> [1] "#F1BB7B"
#>
#>
#> $hex
#> $hex[[1]]
#> [1] "#FD6467"
#>
#>
#> $hex
#> $hex[[1]]
#> [1] "#5B1A18"
#>
#>
#> $hex
#> $hex[[1]]
#> [1] "#D67236"
#>
#>
#> attr(,"name")
#> [1] "GrandBudapest"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. Retrieved from An API Of Ice And Fire.
library(purrr)
(nms <- 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"
map_df(got_chars, `[`, c("name", "gender", "culture", "born"))
#> # A tibble: 30 x 4
#> name gender culture
#> <chr> <chr> <chr>
#> 1 Theon Greyjoy Male Ironborn
#> 2 Tyrion Lannister Male
#> 3 Victarion Greyjoy Male Ironborn
#> 4 Will Male
#> 5 Areo Hotah Male Norvoshi
#> 6 Chett Male
#> 7 Cressen Male
#> 8 Arianne Martell Female Dornish
#> 9 Daenerys Targaryen Female Valyrian
#> 10 Davos Seaworth Male Westeros
#> # ... with 20 more rows, and 1 more variables: born <chr>The same got_chars data is also present as JSON and XML files. Accessor functions provide the local file path.
got_chars_json()
#> [1] "/Users/jenny/resources/R/library/repurrrsive/extdata/got_chars.json"
got_chars_xml()
#> [1] "/Users/jenny/resources/R/library/repurrrsive/extdata/got_chars.xml"Practice bringing data from JSON into an R list.
library(jsonlite)
json <- fromJSON(got_chars_json(), simplifyDataFrame = FALSE)
json[[1]][c("name", "titles", "playedBy")]
#> $name
#> [1] "Theon Greyjoy"
#>
#> $titles
#> [1] "Prince of Winterfell"
#> [2] "Captain of Sea Bitch"
#> [3] "Lord of the Iron Islands (by law of the green lands)"
#>
#> $playedBy
#> [1] "Alfie Allen"
identical(got_chars, json)
#> [1] TRUEPractice bringing data into R from XML. You can get it into an R list with xml2::as_list(), but to get a list as nice as those above? That requires a bit more work. Such is XML life.
library(xml2)
xml <- read_xml(got_chars_xml())
xml_child(xml)
#> {xml_node}
#> <elem>
#> [1] <url>https://www.anapioficeandfire.com/api/characters/1022</url>
#> [2] <id>1022</id>
#> [3] <name>Theon Greyjoy</name>
#> [4] <gender>Male</gender>
#> [5] <culture>Ironborn</culture>
#> [6] <born>In 278 AC or 279 AC, at Pyke</born>
#> [7] <died/>
#> [8] <alive>TRUE</alive>
#> [9] <titles>\n <elem>Prince of Winterfell</elem>\n <elem>Captain of S ...
#> [10] <aliases>\n <elem>Prince of Fools</elem>\n <elem>Theon Turncloak< ...
#> [11] <father/>
#> [12] <mother/>
#> [13] <spouse/>
#> [14] <allegiances>House Greyjoy of Pyke</allegiances>
#> [15] <books>\n <elem>A Game of Thrones</elem>\n <elem>A Storm of Sword ...
#> [16] <povBooks>\n <elem>A Clash of Kings</elem>\n <elem>A Dance with D ...
#> [17] <tvSeries>\n <elem>Season 1</elem>\n <elem>Season 2</elem>\n <el ...
#> [18] <playedBy>Alfie Allen</playedBy>gh_users and gh_repos are lists with information for 6 GitHub users and up to 30 of each user's repositories.
GitHub users.
library(purrr)
map_chr(gh_users, "login")
#> [1] "gaborcsardi" "jennybc" "jtleek" "juliasilge" "leeper"
#> [6] "masalmon"
map_chr(gh_users, 18)
#> [1] "Gábor Csárdi" "Jennifer (Jenny) Bryan"
#> [3] "Jeff L." "Julia Silge"
#> [5] "Thomas J. Leeper" "Maëlle Salmon"
map_df(gh_users, `[`, c("login", "name", "id", "location"))
#> # A tibble: 6 x 4
#> login name id location
#> <chr> <chr> <int> <chr>
#> 1 gaborcsardi Gábor Csárdi 660288 Chippenham, UK
#> 2 jennybc Jennifer (Jenny) Bryan 599454 Vancouver, BC, Canada
#> 3 jtleek Jeff L. 1571674 Baltimore,MD
#> 4 juliasilge Julia Silge 12505835 Salt Lake City, UT
#> 5 leeper Thomas J. Leeper 3505428 London, United Kingdom
#> 6 masalmon Maëlle Salmon 8360597 Barcelona, SpainFirst ~30 repos of these users. Peek at some info from first repo for the first user. Get full name of each user's 11th repo.
str(gh_repos[[1]][[1]][c("full_name", "html_url", "description")])
#> List of 3
#> $ full_name : chr "gaborcsardi/after"
#> $ html_url : chr "https://github.com/gaborcsardi/after"
#> $ description: chr "Run Code in the Background"
map_chr(gh_repos, list(11, "full_name"))
#> [1] "gaborcsardi/debugme"
#> [2] "jennybc/access-r-source"
#> [3] "jtleek/datawomenontwitter"
#> [4] "juliasilge/juliasilge.github.io"
#> [5] "leeper/congressional-district-boundaries"
#> [6] "masalmon/geoparsing_tweets"Want to parse it yourself? Paths to local JSON and XML files.
c(gh_users_json(), gh_repos_json(), gh_users_xml(), gh_repos_xml())
#> [1] "/Users/jenny/resources/R/library/repurrrsive/extdata/gh_users.json"
#> [2] "/Users/jenny/resources/R/library/repurrrsive/extdata/gh_repos.json"
#> [3] "/Users/jenny/resources/R/library/repurrrsive/extdata/gh_users.xml"
#> [4] "/Users/jenny/resources/R/library/repurrrsive/extdata/gh_repos.xml"Redo this: Get full name of each user's 11th repo. But using only the XML.
library(xml2)
repo_xml <- read_xml(gh_repos_xml())
repo_names <- map_chr(xml_find_all(repo_xml, "//full_name"), xml_text)
elevenses <-
11 + cumsum(c(0, head(table(gsub("(.*)/.*", "\\1", repo_names)), -1)))
repo_names[elevenses]
#> [1] "gaborcsardi/debugme"
#> [2] "jennybc/access-r-source"
#> [3] "jtleek/datawomenontwitter"
#> [4] "juliasilge/juliasilge.github.io"
#> [5] "leeper/congressional-district-boundaries"
#> [6] "masalmon/geoparsing_tweets"sw_people, sw_films, sw_species, sw_planets, sw_starships and sw_vehicles are interrelated lists about entities in the Star Wars Universe retrieved from the Star Wars API using the package rwars.
library(purrr)
map_chr(sw_films, "title")
#> [1] "A New Hope" "Attack of the Clones"
#> [3] "The Phantom Menace" "Revenge of the Sith"
#> [5] "Return of the Jedi" "The Empire Strikes Back"
#> [7] "The Force Awakens"Elements that contain URLs provide a way to link the lists together. For example, the films element of each person contains URLs for the films they have appeared in. For example, Luke Skywalker has been in five films.
luke <- sw_people[[1]]
names(luke)
#> [1] "name" "height" "mass" "hair_color" "skin_color"
#> [6] "eye_color" "birth_year" "gender" "homeworld" "films"
#> [11] "species" "vehicles" "starships" "created" "edited"
#> [16] "url"
luke[["films"]]
#> [1] "http://swapi.co/api/films/6/" "http://swapi.co/api/films/3/"
#> [3] "http://swapi.co/api/films/2/" "http://swapi.co/api/films/1/"
#> [5] "http://swapi.co/api/films/7/"These URLs can be looked up in the the sw_films list to find the titles of the films.
# Create a mapping between titles and urls
film_lookup <- map_chr(sw_films, "title") %>%
set_names(map_chr(sw_films, "url"))
# The films Luke is in
film_lookup[luke[["films"]]] %>% unname()
#> [1] "Revenge of the Sith" "Return of the Jedi"
#> [3] "The Empire Strikes Back" "A New Hope"
#> [5] "The Force Awakens"Use the Gapminder data in various forms to practice different styles of grouped computation.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(purrr)
library(tibble)
## group_by() + summarize()
gap_simple %>%
group_by(country) %>%
summarize(cor = cor(lifeExp, year))
#> # A tibble: 142 x 2
#> country cor
#> <fctr> <dbl>
#> 1 Afghanistan 0.9735051
#> 2 Albania 0.9542420
#> 3 Algeria 0.9925307
#> 4 Angola 0.9422392
#> 5 Argentina 0.9977816
#> 6 Australia 0.9897716
#> 7 Austria 0.9960592
#> 8 Bahrain 0.9832293
#> 9 Bangladesh 0.9946662
#> 10 Belgium 0.9972665
#> # ... with 132 more rows
## nest() + map_*() inside mutate()
gap_nested %>%
mutate(cor = data %>% map_dbl(~ cor(.x$lifeExp, .x$year)))
#> # A tibble: 142 x 4
#> country continent data cor
#> <fctr> <fctr> <list> <dbl>
#> 1 Afghanistan Asia <tibble [12 x 4]> 0.9735051
#> 2 Albania Europe <tibble [12 x 4]> 0.9542420
#> 3 Algeria Africa <tibble [12 x 4]> 0.9925307
#> 4 Angola Africa <tibble [12 x 4]> 0.9422392
#> 5 Argentina Americas <tibble [12 x 4]> 0.9977816
#> 6 Australia Oceania <tibble [12 x 4]> 0.9897716
#> 7 Austria Europe <tibble [12 x 4]> 0.9960592
#> 8 Bahrain Asia <tibble [12 x 4]> 0.9832293
#> 9 Bangladesh Asia <tibble [12 x 4]> 0.9946662
#> 10 Belgium Europe <tibble [12 x 4]> 0.9972665
#> # ... with 132 more rows
## split + map_*()
gap_split %>%
map_dbl(~ cor(.x$lifeExp, .x$year)) %>%
head()
#> Afghanistan Albania Algeria Angola Argentina Australia
#> 0.9735051 0.9542420 0.9925307 0.9422392 0.9977816 0.9897716
## split + map_*() + tibble::enframe()
gap_split %>%
map_dbl(~ cor(.x$lifeExp, .x$year)) %>%
enframe()
#> # A tibble: 142 x 2
#> name value
#> <chr> <dbl>
#> 1 Afghanistan 0.9735051
#> 2 Albania 0.9542420
#> 3 Algeria 0.9925307
#> 4 Angola 0.9422392
#> 5 Argentina 0.9977816
#> 6 Australia 0.9897716
#> 7 Austria 0.9960592
#> 8 Bahrain 0.9832293
#> 9 Bangladesh 0.9946662
#> 10 Belgium 0.9972665
#> # ... with 132 more rows