Quick review of purrr::map()

• Map functions in R for Data Science
• Notes on map functions

We can use purrr::map() to extract elements from lists.

Name and position shortcuts

Let’s extract the name element for each Game of Thrones character. To do this, we can use map() and extract list elements based on their name:

map(got_chars[1:4], "name")

## [[1]]
## [1] "Theon Greyjoy"
## 
## [[2]]
## [1] "Tyrion Lannister"
## 
## [[3]]
## [1] "Victarion Greyjoy"
## 
## [[4]]
## [1] "Will"

A companion shortcut is to extract elements by their integer position in the list. For example, extract the 3rd element of each character’s list like so:

map(got_chars[5:8], 3)

## [[1]]
## [1] "Areo Hotah"
## 
## [[2]]
## [1] "Chett"
## 
## [[3]]
## [1] "Cressen"
## 
## [[4]]
## [1] "Arianne Martell"

To recap, here are two shortcuts for making the .f function that map() will apply:

• Provide “TEXT” to extract the element named “TEXT”
  • Equivalent to function(x) x[["TEXT"]]
• Provide i to extract the i-th element
  • Equivalent to function(x) x[[i]]

And as always, we can use map() with the pipe %>%:

got_chars %>% 
  map("name")
got_chars %>% 
  map(3)

Type-specific map

map() always returns a list, but if you know that the elements are all the same type (e.g. numeric, character, boolean) and are each of length one, you can use the map_() function appropriate for that type of vector.

map_chr(got_chars[9:12], "name")

## [1] "Daenerys Targaryen" "Davos Seaworth"     "Arya Stark"        
## [4] "Arys Oakheart"

map_chr(got_chars[13:16], 3)

## [1] "Asha Greyjoy"    "Barristan Selmy" "Varamyr"         "Brandon Stark"

Extract multiple values

What if you want to retrieve elements? What if you want to know the character’s name and gender? For a single user, we can use traditional subsetting:

# Victarion element
got_chars[[3]]

## $url
## [1] "http://www.anapioficeandfire.com/api/characters/1074"
## 
## $id
## [1] 1074
## 
## $name
## [1] "Victarion Greyjoy"
## 
## $gender
## [1] "Male"
## 
## $culture
## [1] "Ironborn"
## 
## $born
## [1] "In 268 AC or before, at Pyke"
## 
## $died
## [1] ""
## 
## $alive
## [1] TRUE
## 
## $titles
## [1] "Lord Captain of the Iron Fleet" "Master of the Iron Victory"    
## 
## $aliases
## [1] "The Iron Captain"
## 
## $father
## [1] ""
## 
## $mother
## [1] ""
## 
## $spouse
## [1] ""
## 
## $allegiances
## [1] "House Greyjoy of Pyke"
## 
## $books
## [1] "A Game of Thrones" "A Clash of Kings"  "A Storm of Swords"
## 
## $povBooks
## [1] "A Feast for Crows"    "A Dance with Dragons"
## 
## $tvSeries
## list()
## 
## $playedBy
## list()

# specific elements for Victarion
got_chars[[3]][c("name", "culture", "gender", "born")]

## $name
## [1] "Victarion Greyjoy"
## 
## $culture
## [1] "Ironborn"
## 
## $gender
## [1] "Male"
## 
## $born
## [1] "In 268 AC or before, at Pyke"

We use a single square bracket indexing and a character vector to index by name. To adapt this to the map() framework, recall:

map(.x, .f, ...)

The function .f will be [ and ... will be the character vector identifying the names of the elements to extract.

x <- map(got_chars, `[`, c("name", "culture", "gender", "born"))
str(x[16:17])

## List of 2
##  $ :List of 4
##   ..$ name   : chr "Brandon Stark"
##   ..$ culture: chr "Northmen"
##   ..$ gender : chr "Male"
##   ..$ born   : chr "In 290 AC, at Winterfell"
##  $ :List of 4
##   ..$ name   : chr "Brienne of Tarth"
##   ..$ culture: chr ""
##   ..$ gender : chr "Female"
##   ..$ born   : chr "In 280 AC"

Alternatively, we can use magrittr::extract() to do the same thing. It looks a bit more clean:

library(magrittr)

## 
## Attaching package: 'magrittr'

## The following object is masked from 'package:purrr':
## 
##     set_names

## The following object is masked from 'package:tidyr':
## 
##     extract

x <- map(got_chars, extract, c("name", "culture", "gender", "born"))
str(x[18:19])

## List of 2
##  $ :List of 4
##   ..$ name   : chr "Catelyn Stark"
##   ..$ culture: chr "Rivermen"
##   ..$ gender : chr "Female"
##   ..$ born   : chr "In 264 AC, at Riverrun"
##  $ :List of 4
##   ..$ name   : chr "Cersei Lannister"
##   ..$ culture: chr "Westerman"
##   ..$ gender : chr "Female"
##   ..$ born   : chr "In 266 AC, at Casterly Rock"

Data frame output

Notice that even by extracting multiple elements at once, we are still left with a list. But we want a simplified data frame! Remember that the output of map() is always a list. To force the output to be a data frame, use map_df():

map_df(got_chars, extract, c("name", "culture", "gender", "id", "born", "alive"))

## Warning in flatten_bindable(dots_values(...)): '.Random.seed' is not an
## integer vector but of type 'NULL', so ignored

## # A tibble: 29 x 6
##                  name  culture gender    id
##                 <chr>    <chr>  <chr> <int>
##  1      Theon Greyjoy Ironborn   Male  1022
##  2   Tyrion Lannister            Male  1052
##  3  Victarion Greyjoy Ironborn   Male  1074
##  4               Will            Male  1109
##  5         Areo Hotah Norvoshi   Male  1166
##  6              Chett            Male  1267
##  7            Cressen            Male  1295
##  8    Arianne Martell  Dornish Female   130
##  9 Daenerys Targaryen Valyrian Female  1303
## 10     Davos Seaworth Westeros   Male  1319
## # ... with 19 more rows, and 2 more variables: born <chr>, alive <lgl>

Now we have an automatically type converted data frame. It was quite simple to perform, however it is not very robust. It takes more code, but it is generally better to explicitly specify the type of each column to ensure the output is as you would expect:

got_chars %>% {
  tibble(
    name = map_chr(., "name"),
    culture = map_chr(., "culture"),
    gender = map_chr(., "gender"),       
    id = map_int(., "id"),
    born = map_chr(., "born"),
    alive = map_lgl(., "alive")
  )
}

## # A tibble: 29 x 6
##                  name  culture gender    id
##                 <chr>    <chr>  <chr> <int>
##  1      Theon Greyjoy Ironborn   Male  1022
##  2   Tyrion Lannister            Male  1052
##  3  Victarion Greyjoy Ironborn   Male  1074
##  4               Will            Male  1109
##  5         Areo Hotah Norvoshi   Male  1166
##  6              Chett            Male  1267
##  7            Cressen            Male  1295
##  8    Arianne Martell  Dornish Female   130
##  9 Daenerys Targaryen Valyrian Female  1303
## 10     Davos Seaworth Westeros   Male  1319
## # ... with 19 more rows, and 2 more variables: born <chr>, alive <lgl>

The dot . above is the placeholder for the primary input: got_chars in this case. The curly braces {} surrounding the tibble() call prevent got_chars from being passed in as the first argument of tibble().

Vector input to extraction shortcuts

Now we use the indexing shortcuts in a more complicated setting. Instead of providing a single name or position, we use a vector:

• the j-th element addresses the j-th level of the hierarchy

Here we get the full name (element 3) of the first repository listed for each user.

gh_repos %>%
  map_chr(c(1, 3))

## [1] "gaborcsardi/after"   "jennybc/2013-11_sfu" "jtleek/advdatasci"  
## [4] "juliasilge/2016-14"  "leeper/ampolcourse"  "masalmon/aqi_pdf"

Note that this does NOT give elements 1 and 3 of gh_repos. It extracts the first repo for each user and, within that, the 3rd piece of information for the repo.

Get it into a data frame

Our objective is to get a data frame with one row per repository, with variables identifying which GitHub user owns it, the repository name, etc.

(unames <- map_chr(gh_repos, c(1, 4, 1)))

## [1] "gaborcsardi" "jennybc"     "jtleek"      "juliasilge"  "leeper"     
## [6] "masalmon"

(udf <- gh_repos %>%
    set_names(unames) %>% 
    enframe("username", "gh_repos"))

## # A tibble: 6 x 2
##      username    gh_repos
##         <chr>      <list>
## 1 gaborcsardi <list [30]>
## 2     jennybc <list [30]>
## 3      jtleek <list [30]>
## 4  juliasilge <list [26]>
## 5      leeper <list [30]>
## 6    masalmon <list [30]>

udf %>% 
  mutate(n_repos = map_int(gh_repos, length))

## # A tibble: 6 x 3
##      username    gh_repos n_repos
##         <chr>      <list>   <int>
## 1 gaborcsardi <list [30]>      30
## 2     jennybc <list [30]>      30
## 3      jtleek <list [30]>      30
## 4  juliasilge <list [26]>      26
## 5      leeper <list [30]>      30
## 6    masalmon <list [30]>      30

# one_user is a list of repos for one user
one_user <- udf$gh_repos[[1]]

# one_user[[1]] is a list of info for one repo
one_repo <- one_user[[1]]
str(one_repo, max.level = 1, list.len = 5)

## List of 68
##  $ id               : int 61160198
##  $ name             : chr "after"
##  $ full_name        : chr "gaborcsardi/after"
##  $ owner            :List of 17
##  $ private          : logi FALSE
##   [list output truncated]

# a highly selective list of tibble-worthy info for one repo
one_repo[c("name", "fork", "open_issues")]

## $name
## [1] "after"
## 
## $fork
## [1] FALSE
## 
## $open_issues
## [1] 0

# make a data frame of that info for all a user's repos
map_df(one_user, `[`, c("name", "fork", "open_issues"))

## # A tibble: 30 x 3
##           name  fork open_issues
##          <chr> <lgl>       <int>
##  1       after FALSE           0
##  2      argufy FALSE           6
##  3         ask FALSE           4
##  4 baseimports FALSE           0
##  5      citest  TRUE           0
##  6  clisymbols FALSE           0
##  7      cmaker  TRUE           0
##  8       cmark  TRUE           0
##  9  conditions  TRUE           0
## 10      crayon FALSE           7
## # ... with 20 more rows

map_df(one_user, extract, c("name", "fork", "open_issues"))

## # A tibble: 30 x 3
##           name  fork open_issues
##          <chr> <lgl>       <int>
##  1       after FALSE           0
##  2      argufy FALSE           6
##  3         ask FALSE           4
##  4 baseimports FALSE           0
##  5      citest  TRUE           0
##  6  clisymbols FALSE           0
##  7      cmaker  TRUE           0
##  8       cmark  TRUE           0
##  9  conditions  TRUE           0
## 10      crayon FALSE           7
## # ... with 20 more rows

udf %>% 
  mutate(repo_info = gh_repos %>%
           map(. %>%
                 map_df(extract, c("name", "fork", "open_issues"))))

## # A tibble: 6 x 3
##      username    gh_repos         repo_info
##         <chr>      <list>            <list>
## 1 gaborcsardi <list [30]> <tibble [30 x 3]>
## 2     jennybc <list [30]> <tibble [30 x 3]>
## 3      jtleek <list [30]> <tibble [30 x 3]>
## 4  juliasilge <list [26]> <tibble [26 x 3]>
## 5      leeper <list [30]> <tibble [30 x 3]>
## 6    masalmon <list [30]> <tibble [30 x 3]>

(rdf <- udf %>% 
   mutate(
     repo_info = gh_repos %>%
       map(. %>%
             map_df(extract, c("name", "fork", "open_issues")))
   ) %>% 
   select(-gh_repos) %>% 
   tidyr::unnest())

## # A tibble: 176 x 4
##       username        name  fork open_issues
##          <chr>       <chr> <lgl>       <int>
##  1 gaborcsardi       after FALSE           0
##  2 gaborcsardi      argufy FALSE           6
##  3 gaborcsardi         ask FALSE           4
##  4 gaborcsardi baseimports FALSE           0
##  5 gaborcsardi      citest  TRUE           0
##  6 gaborcsardi  clisymbols FALSE           0
##  7 gaborcsardi      cmaker  TRUE           0
##  8 gaborcsardi       cmark  TRUE           0
##  9 gaborcsardi  conditions  TRUE           0
## 10 gaborcsardi      crayon FALSE           7
## # ... with 166 more rows