Rectangling

Introduction

Rectangling is the art and craft of taking a deeply nested list (often sourced from wild caught JSON or XML) and taming it into a tidy data set of rows and columns. There are three functions from tidyr that are particularly useful for rectangling:

(Alternative, for complex inputs where you need to rectangle a nested list according to a specification, see the tibblify package.)

A very large number of data rectangling problems can be solved by combining jsonlite::read_json() with these functions and a splash of dplyr (largely eliminating prior approaches that combined mutate() with multiple purrr::map()s). Note that jsonlite has another important function called fromJSON(). We don’t recommend it here because it performs its own automatic simplification (simplifyVector = TRUE). This often works well, particularly in simple cases, but we think you’re better off doing the rectangling yourself so you know exactly what’s happening and can more easily handle the most complicated nested structures.

To illustrate these techniques, we’ll use the repurrrsive package, which provides a number deeply nested lists originally mostly captured from web APIs.

library(tidyr)
library(dplyr)
library(repurrrsive)

GitHub users

We’ll start with gh_users, a list which contains information about six GitHub users. To begin, we put the gh_users list into a data frame:

users <- tibble(user = gh_users)

This seems a bit counter-intuitive: why is the first step in making a list simpler to make it more complicated? But a data frame has a big advantage: it bundles together multiple vectors so that everything is tracked together in a single object.

Each user is a named list, where each element represents a column.

names(users$user[[1]])
#>  [1] "login"               "id"                  "avatar_url"         
#>  [4] "gravatar_id"         "url"                 "html_url"           
#>  [7] "followers_url"       "following_url"       "gists_url"          
#> [10] "starred_url"         "subscriptions_url"   "organizations_url"  
#> [13] "repos_url"           "events_url"          "received_events_url"
#> [16] "type"                "site_admin"          "name"               
#> [19] "company"             "blog"                "location"           
#> [22] "email"               "hireable"            "bio"                
#> [25] "public_repos"        "public_gists"        "followers"          
#> [28] "following"           "created_at"          "updated_at"

There are two ways to turn the list components into columns. unnest_wider() takes every component and makes a new column:

users %>% unnest_wider(user)
#> # A tibble: 6 × 30
#>   login     id avatar_url gravatar_id url   html_url followers_url following_url
#>   <chr>  <int> <chr>      <chr>       <chr> <chr>    <chr>         <chr>        
#> 1 gabo… 6.60e5 https://a… ""          http… https:/… https://api.… https://api.…
#> 2 jenn… 5.99e5 https://a… ""          http… https:/… https://api.… https://api.…
#> 3 jtle… 1.57e6 https://a… ""          http… https:/… https://api.… https://api.…
#> 4 juli… 1.25e7 https://a… ""          http… https:/… https://api.… https://api.…
#> 5 leep… 3.51e6 https://a… ""          http… https:/… https://api.… https://api.…
#> 6 masa… 8.36e6 https://a… ""          http… https:/… https://api.… https://api.…
#> # ℹ 22 more variables: gists_url <chr>, starred_url <chr>,
#> #   subscriptions_url <chr>, organizations_url <chr>, repos_url <chr>,
#> #   events_url <chr>, received_events_url <chr>, type <chr>, site_admin <lgl>,
#> #   name <chr>, company <chr>, blog <chr>, location <chr>, email <chr>,
#> #   hireable <lgl>, bio <chr>, public_repos <int>, public_gists <int>,
#> #   followers <int>, following <int>, created_at <chr>, updated_at <chr>

But in this case, there are many components and we don’t need most of them so we can instead use hoist(). hoist() allows us to pull out selected components using the same syntax as purrr::pluck():

users %>% hoist(user, 
  followers = "followers", 
  login = "login", 
  url = "html_url"
)
#> # A tibble: 6 × 4
#>   followers login       url                            user             
#>       <int> <chr>       <chr>                          <list>           
#> 1       303 gaborcsardi https://github.com/gaborcsardi <named list [27]>
#> 2       780 jennybc     https://github.com/jennybc     <named list [27]>
#> 3      3958 jtleek      https://github.com/jtleek      <named list [27]>
#> 4       115 juliasilge  https://github.com/juliasilge  <named list [27]>
#> 5       213 leeper      https://github.com/leeper      <named list [27]>
#> 6        34 masalmon    https://github.com/masalmon    <named list [27]>

hoist() removes the named components from the user list-column, so you can think of it as moving components out of the inner list into the top-level data frame.

GitHub repos

We start off gh_repos similarly, by putting it in a tibble:

repos <- tibble(repo = gh_repos)
repos
#> # A tibble: 6 × 1
#>   repo       
#>   <list>     
#> 1 <list [30]>
#> 2 <list [30]>
#> 3 <list [30]>
#> 4 <list [26]>
#> 5 <list [30]>
#> 6 <list [30]>

This time the elements of repos are a list of repositories that belong to that user. These are observations, so should become new rows, so we use unnest_longer() rather than unnest_wider():

repos <- repos %>% unnest_longer(repo)
repos
#> # A tibble: 176 × 1
#>   repo             
#>   <list>           
#> 1 <named list [68]>
#> 2 <named list [68]>
#> 3 <named list [68]>
#> 4 <named list [68]>
#> 5 <named list [68]>
#> 6 <named list [68]>
#> # ℹ 170 more rows

Then we can use unnest_wider() or hoist():

repos %>% hoist(repo, 
  login = c("owner", "login"), 
  name = "name",
  homepage = "homepage",
  watchers = "watchers_count"
)
#> # A tibble: 176 × 5
#>   login       name        homepage watchers repo             
#>   <chr>       <chr>       <chr>       <int> <list>           
#> 1 gaborcsardi after       <NA>            5 <named list [65]>
#> 2 gaborcsardi argufy      <NA>           19 <named list [65]>
#> 3 gaborcsardi ask         <NA>            5 <named list [65]>
#> 4 gaborcsardi baseimports <NA>            0 <named list [65]>
#> 5 gaborcsardi citest      <NA>            0 <named list [65]>
#> 6 gaborcsardi clisymbols  ""             18 <named list [65]>
#> # ℹ 170 more rows

Note the use of c("owner", "login"): this allows us to reach two levels deep inside of a list. An alternative approach would be to pull out just owner and then put each element of it in a column:

repos %>% 
  hoist(repo, owner = "owner") %>% 
  unnest_wider(owner)
#> # A tibble: 176 × 18
#>   login     id avatar_url gravatar_id url   html_url followers_url following_url
#>   <chr>  <int> <chr>      <chr>       <chr> <chr>    <chr>         <chr>        
#> 1 gabo… 660288 https://a… ""          http… https:/… https://api.… https://api.…
#> 2 gabo… 660288 https://a… ""          http… https:/… https://api.… https://api.…
#> 3 gabo… 660288 https://a… ""          http… https:/… https://api.… https://api.…
#> 4 gabo… 660288 https://a… ""          http… https:/… https://api.… https://api.…
#> 5 gabo… 660288 https://a… ""          http… https:/… https://api.… https://api.…
#> 6 gabo… 660288 https://a… ""          http… https:/… https://api.… https://api.…
#> # ℹ 170 more rows
#> # ℹ 10 more variables: gists_url <chr>, starred_url <chr>,
#> #   subscriptions_url <chr>, organizations_url <chr>, repos_url <chr>,
#> #   events_url <chr>, received_events_url <chr>, type <chr>, site_admin <lgl>,
#> #   repo <list>

Game of Thrones characters

got_chars has a similar structure to gh_users: it’s a list of named lists, where each element of the inner list describes some attribute of a GoT character. We start in the same way, first by creating a data frame and then by unnesting each component into a column:

chars <- tibble(char = got_chars)
chars
#> # A tibble: 30 × 1
#>   char             
#>   <list>           
#> 1 <named list [18]>
#> 2 <named list [18]>
#> 3 <named list [18]>
#> 4 <named list [18]>
#> 5 <named list [18]>
#> 6 <named list [18]>
#> # ℹ 24 more rows

chars2 <- chars %>% unnest_wider(char)
chars2
#> # A tibble: 30 × 18
#>   url            id name  gender culture born  died  alive titles aliases father
#>   <chr>       <int> <chr> <chr>  <chr>   <chr> <chr> <lgl> <list> <list>  <chr> 
#> 1 https://ww…  1022 Theo… Male   "Ironb… "In … ""    TRUE  <chr>  <chr>   ""    
#> 2 https://ww…  1052 Tyri… Male   ""      "In … ""    TRUE  <chr>  <chr>   ""    
#> 3 https://ww…  1074 Vict… Male   "Ironb… "In … ""    TRUE  <chr>  <chr>   ""    
#> 4 https://ww…  1109 Will  Male   ""      ""    "In … FALSE <chr>  <chr>   ""    
#> 5 https://ww…  1166 Areo… Male   "Norvo… "In … ""    TRUE  <chr>  <chr>   ""    
#> 6 https://ww…  1267 Chett Male   ""      "At … "In … FALSE <chr>  <chr>   ""    
#> # ℹ 24 more rows
#> # ℹ 7 more variables: mother <chr>, spouse <chr>, allegiances <list>,
#> #   books <list>, povBooks <list>, tvSeries <list>, playedBy <list>

This is more complex than gh_users because some component of char are themselves a list, giving us a collection of list-columns:

chars2 %>% select_if(is.list)
#> # A tibble: 30 × 7
#>   titles    aliases    allegiances books     povBooks  tvSeries  playedBy 
#>   <list>    <list>     <list>      <list>    <list>    <list>    <list>   
#> 1 <chr [2]> <chr [4]>  <chr [1]>   <chr [3]> <chr [2]> <chr [6]> <chr [1]>
#> 2 <chr [2]> <chr [11]> <chr [1]>   <chr [2]> <chr [4]> <chr [6]> <chr [1]>
#> 3 <chr [2]> <chr [1]>  <chr [1]>   <chr [3]> <chr [2]> <chr [1]> <chr [1]>
#> 4 <chr [1]> <chr [1]>  <NULL>      <chr [1]> <chr [1]> <chr [1]> <chr [1]>
#> 5 <chr [1]> <chr [1]>  <chr [1]>   <chr [3]> <chr [2]> <chr [2]> <chr [1]>
#> 6 <chr [1]> <chr [1]>  <NULL>      <chr [2]> <chr [1]> <chr [1]> <chr [1]>
#> # ℹ 24 more rows

What you do next will depend on the purposes of the analysis. Maybe you want a row for every book and TV series that the character appears in:

chars2 %>% 
  select(name, books, tvSeries) %>% 
  pivot_longer(c(books, tvSeries), names_to = "media", values_to = "value") %>% 
  unnest_longer(value)
#> # A tibble: 179 × 3
#>   name          media    value            
#>   <chr>         <chr>    <chr>            
#> 1 Theon Greyjoy books    A Game of Thrones
#> 2 Theon Greyjoy books    A Storm of Swords
#> 3 Theon Greyjoy books    A Feast for Crows
#> 4 Theon Greyjoy tvSeries Season 1         
#> 5 Theon Greyjoy tvSeries Season 2         
#> 6 Theon Greyjoy tvSeries Season 3         
#> # ℹ 173 more rows

Or maybe you want to build a table that lets you match title to name:

chars2 %>% 
  select(name, title = titles) %>% 
  unnest_longer(title)
#> # A tibble: 59 × 2
#>   name              title                                               
#>   <chr>             <chr>                                               
#> 1 Theon Greyjoy     Prince of Winterfell                                
#> 2 Theon Greyjoy     Lord of the Iron Islands (by law of the green lands)
#> 3 Tyrion Lannister  Acting Hand of the King (former)                    
#> 4 Tyrion Lannister  Master of Coin (former)                             
#> 5 Victarion Greyjoy Lord Captain of the Iron Fleet                      
#> 6 Victarion Greyjoy Master of the Iron Victory                          
#> # ℹ 53 more rows

(Note that the empty titles ("") are due to an infelicity in the input got_chars: ideally people without titles would have a title vector of length 0, not a title vector of length 1 containing an empty string.)

Geocoding with google

Next we’ll tackle a more complex form of data that comes from Google’s geocoding service, stored in the repurssive package

repurrrsive::gmaps_cities
#> # A tibble: 5 × 2
#>   city       json            
#>   <chr>      <list>          
#> 1 Houston    <named list [2]>
#> 2 Washington <named list [2]>
#> 3 New York   <named list [2]>
#> 4 Chicago    <named list [2]>
#> 5 Arlington  <named list [2]>

json is a list-column of named lists, so it makes sense to start with unnest_wider():

repurrrsive::gmaps_cities %>%
  unnest_wider(json)
#> # A tibble: 5 × 3
#>   city       results    status
#>   <chr>      <list>     <chr> 
#> 1 Houston    <list [1]> OK    
#> 2 Washington <list [2]> OK    
#> 3 New York   <list [1]> OK    
#> 4 Chicago    <list [1]> OK    
#> 5 Arlington  <list [2]> OK

Notice that results is a list of lists. Most of the cities have 1 element (representing a unique match from the geocoding API), but Washington and Arlington have two. We can pull these out into separate rows with unnest_longer():

repurrrsive::gmaps_cities %>%
  unnest_wider(json) %>% 
  unnest_longer(results)
#> # A tibble: 7 × 3
#>   city       results          status
#>   <chr>      <list>           <chr> 
#> 1 Houston    <named list [5]> OK    
#> 2 Washington <named list [5]> OK    
#> 3 Washington <named list [5]> OK    
#> 4 New York   <named list [5]> OK    
#> 5 Chicago    <named list [5]> OK    
#> 6 Arlington  <named list [5]> OK    
#> # ℹ 1 more row

Now these all have the same components, as revealed by unnest_wider():

repurrrsive::gmaps_cities %>%
  unnest_wider(json) %>% 
  unnest_longer(results) %>% 
  unnest_wider(results)
#> # A tibble: 7 × 7
#>   city  address_components formatted_address geometry     place_id types  status
#>   <chr> <list>             <chr>             <list>       <chr>    <list> <chr> 
#> 1 Hous… <list [4]>         Houston, TX, USA  <named list> ChIJAYW… <list> OK    
#> 2 Wash… <list [2]>         Washington, USA   <named list> ChIJ-bD… <list> OK    
#> 3 Wash… <list [4]>         Washington, DC, … <named list> ChIJW-T… <list> OK    
#> 4 New … <list [3]>         New York, NY, USA <named list> ChIJOwg… <list> OK    
#> 5 Chic… <list [4]>         Chicago, IL, USA  <named list> ChIJ7cv… <list> OK    
#> 6 Arli… <list [4]>         Arlington, TX, U… <named list> ChIJ05g… <list> OK    
#> # ℹ 1 more row

We can find the latitude and longitude by unnesting geometry:

repurrrsive::gmaps_cities %>%
  unnest_wider(json) %>% 
  unnest_longer(results) %>% 
  unnest_wider(results) %>% 
  unnest_wider(geometry)
#> # A tibble: 7 × 10
#>   city       address_components formatted_address   bounds       location    
#>   <chr>      <list>             <chr>               <list>       <list>      
#> 1 Houston    <list [4]>         Houston, TX, USA    <named list> <named list>
#> 2 Washington <list [2]>         Washington, USA     <named list> <named list>
#> 3 Washington <list [4]>         Washington, DC, USA <named list> <named list>
#> 4 New York   <list [3]>         New York, NY, USA   <named list> <named list>
#> 5 Chicago    <list [4]>         Chicago, IL, USA    <named list> <named list>
#> 6 Arlington  <list [4]>         Arlington, TX, USA  <named list> <named list>
#> # ℹ 1 more row
#> # ℹ 5 more variables: location_type <chr>, viewport <list>, place_id <chr>,
#> #   types <list>, status <chr>

And then location:

repurrrsive::gmaps_cities %>%
  unnest_wider(json) %>%
  unnest_longer(results) %>%
  unnest_wider(results) %>%
  unnest_wider(geometry) %>%
  unnest_wider(location)
#> # A tibble: 7 × 11
#>   city       address_components formatted_address   bounds         lat    lng
#>   <chr>      <list>             <chr>               <list>       <dbl>  <dbl>
#> 1 Houston    <list [4]>         Houston, TX, USA    <named list>  29.8  -95.4
#> 2 Washington <list [2]>         Washington, USA     <named list>  47.8 -121. 
#> 3 Washington <list [4]>         Washington, DC, USA <named list>  38.9  -77.0
#> 4 New York   <list [3]>         New York, NY, USA   <named list>  40.7  -74.0
#> 5 Chicago    <list [4]>         Chicago, IL, USA    <named list>  41.9  -87.6
#> 6 Arlington  <list [4]>         Arlington, TX, USA  <named list>  32.7  -97.1
#> # ℹ 1 more row
#> # ℹ 5 more variables: location_type <chr>, viewport <list>, place_id <chr>,
#> #   types <list>, status <chr>

We could also just look at the first address for each city:

repurrrsive::gmaps_cities %>%
  unnest_wider(json) %>%
  hoist(results, first_result = 1) %>%
  unnest_wider(first_result) %>%
  unnest_wider(geometry) %>%
  unnest_wider(location)
#> # A tibble: 5 × 12
#>   city       address_components formatted_address  bounds             lat    lng
#>   <chr>      <list>             <chr>              <list>           <dbl>  <dbl>
#> 1 Houston    <list [4]>         Houston, TX, USA   <named list [2]>  29.8  -95.4
#> 2 Washington <list [2]>         Washington, USA    <named list [2]>  47.8 -121. 
#> 3 New York   <list [3]>         New York, NY, USA  <named list [2]>  40.7  -74.0
#> 4 Chicago    <list [4]>         Chicago, IL, USA   <named list [2]>  41.9  -87.6
#> 5 Arlington  <list [4]>         Arlington, TX, USA <named list [2]>  32.7  -97.1
#> # ℹ 6 more variables: location_type <chr>, viewport <list>, place_id <chr>,
#> #   types <list>, results <list>, status <chr>

Or use hoist() to dive deeply to get directly to lat and lng:

repurrrsive::gmaps_cities %>%
  hoist(json,
    lat = list("results", 1, "geometry", "location", "lat"),
    lng = list("results", 1, "geometry", "location", "lng")
  )
#> # A tibble: 5 × 4
#>   city         lat    lng json            
#>   <chr>      <dbl>  <dbl> <list>          
#> 1 Houston     29.8  -95.4 <named list [2]>
#> 2 Washington  47.8 -121.  <named list [2]>
#> 3 New York    40.7  -74.0 <named list [2]>
#> 4 Chicago     41.9  -87.6 <named list [2]>
#> 5 Arlington   32.7  -97.1 <named list [2]>

Sharla Gelfand’s discography

We’ll finish off with the most complex list, from Sharla Gelfand’s discography. We’ll start the usual way: putting the list into a single column data frame, and then widening so each component is a column. I also parse the date_added column into a real date-time1.

discs <- tibble(disc = discog) %>% 
  unnest_wider(disc) %>% 
  mutate(date_added = as.POSIXct(strptime(date_added, "%Y-%m-%dT%H:%M:%S"))) 
discs
#> # A tibble: 155 × 5
#>   instance_id date_added          basic_information       id rating
#>         <int> <dttm>              <list>               <int>  <int>
#> 1   354823933 2019-02-16 17:48:59 <named list [11]>  7496378      0
#> 2   354092601 2019-02-13 14:13:11 <named list [11]>  4490852      0
#> 3   354091476 2019-02-13 14:07:23 <named list [11]>  9827276      0
#> 4   351244906 2019-02-02 11:39:58 <named list [11]>  9769203      0
#> 5   351244801 2019-02-02 11:39:37 <named list [11]>  7237138      0
#> 6   351052065 2019-02-01 20:40:53 <named list [11]> 13117042      0
#> # ℹ 149 more rows

At this level, we see information about when each disc was added to Sharla’s discography, not any information about the disc itself. To do that we need to widen the basic_information column:

discs %>% unnest_wider(basic_information)
#> Error in `unnest_wider()`:
#> ! Can't duplicate names between the affected columns and the original
#>   data.
#> ✖ These names are duplicated:
#>   ℹ `id`, from `basic_information`.
#> ℹ Use `names_sep` to disambiguate using the column name.
#> ℹ Or use `names_repair` to specify a repair strategy.

Unfortunately that fails because there’s an id column inside basic_information. We can quickly see what’s going on by setting names_repair = "unique":

discs %>% unnest_wider(basic_information, names_repair = "unique")
#> New names:
#> • `id` -> `id...7`
#> • `id` -> `id...14`
#> # A tibble: 155 × 15
#>   instance_id date_added          labels  year master_url   artists id...7 thumb
#>         <int> <dttm>              <list> <int> <chr>        <list>   <int> <chr>
#> 1   354823933 2019-02-16 17:48:59 <list>  2015 <NA>         <list>  7.50e6 http…
#> 2   354092601 2019-02-13 14:13:11 <list>  2013 https://api… <list>  4.49e6 http…
#> 3   354091476 2019-02-13 14:07:23 <list>  2017 https://api… <list>  9.83e6 http…
#> 4   351244906 2019-02-02 11:39:58 <list>  2017 https://api… <list>  9.77e6 http…
#> 5   351244801 2019-02-02 11:39:37 <list>  2015 https://api… <list>  7.24e6 http…
#> 6   351052065 2019-02-01 20:40:53 <list>  2019 https://api… <list>  1.31e7 http…
#> # ℹ 149 more rows
#> # ℹ 7 more variables: title <chr>, formats <list>, cover_image <chr>,
#> #   resource_url <chr>, master_id <int>, id...14 <int>, rating <int>

The problem is that basic_information repeats the id column that’s also stored at the top-level, so we can just drop that:

discs %>% 
  select(!id) %>% 
  unnest_wider(basic_information)
#> # A tibble: 155 × 14
#>   instance_id date_added          labels  year master_url   artists     id thumb
#>         <int> <dttm>              <list> <int> <chr>        <list>   <int> <chr>
#> 1   354823933 2019-02-16 17:48:59 <list>  2015 <NA>         <list>  7.50e6 http…
#> 2   354092601 2019-02-13 14:13:11 <list>  2013 https://api… <list>  4.49e6 http…
#> 3   354091476 2019-02-13 14:07:23 <list>  2017 https://api… <list>  9.83e6 http…
#> 4   351244906 2019-02-02 11:39:58 <list>  2017 https://api… <list>  9.77e6 http…
#> 5   351244801 2019-02-02 11:39:37 <list>  2015 https://api… <list>  7.24e6 http…
#> 6   351052065 2019-02-01 20:40:53 <list>  2019 https://api… <list>  1.31e7 http…
#> # ℹ 149 more rows
#> # ℹ 6 more variables: title <chr>, formats <list>, cover_image <chr>,
#> #   resource_url <chr>, master_id <int>, rating <int>

Alternatively, we could use hoist():

discs %>% 
  hoist(basic_information,
    title = "title",
    year = "year",
    label = list("labels", 1, "name"),
    artist = list("artists", 1, "name")
  )
#> # A tibble: 155 × 9
#>   instance_id date_added          title      year label artist basic_information
#>         <int> <dttm>              <chr>     <int> <chr> <chr>  <list>           
#> 1   354823933 2019-02-16 17:48:59 Demo       2015 Tobi… Mollot <named list [9]> 
#> 2   354092601 2019-02-13 14:13:11 Observan…  2013 La V… Una B… <named list [9]> 
#> 3   354091476 2019-02-13 14:07:23 I          2017 La V… S.H.I… <named list [9]> 
#> 4   351244906 2019-02-02 11:39:58 Oído Abs…  2017 La V… Rata … <named list [9]> 
#> 5   351244801 2019-02-02 11:39:37 A Cat's …  2015 Kato… Ivy (… <named list [9]> 
#> 6   351052065 2019-02-01 20:40:53 Tashme     2019 High… Tashme <named list [9]> 
#> # ℹ 149 more rows
#> # ℹ 2 more variables: id <int>, rating <int>

Here I quickly extract the name of the first label and artist by indexing deeply into the nested list.

A more systematic approach would be to create separate tables for artist and label:

discs %>% 
  hoist(basic_information, artist = "artists") %>% 
  select(disc_id = id, artist) %>% 
  unnest_longer(artist) %>% 
  unnest_wider(artist)
#> # A tibble: 167 × 8
#>    disc_id join  name                     anv   tracks role  resource_url     id
#>      <int> <chr> <chr>                    <chr> <chr>  <chr> <chr>         <int>
#> 1  7496378 ""    Mollot                   ""    ""     ""    https://api… 4.62e6
#> 2  4490852 ""    Una Bèstia Incontrolable ""    ""     ""    https://api… 3.19e6
#> 3  9827276 ""    S.H.I.T. (3)             ""    ""     ""    https://api… 2.77e6
#> 4  9769203 ""    Rata Negra               ""    ""     ""    https://api… 4.28e6
#> 5  7237138 ""    Ivy (18)                 ""    ""     ""    https://api… 3.60e6
#> 6 13117042 ""    Tashme                   ""    ""     ""    https://api… 5.21e6
#> # ℹ 161 more rows

discs %>% 
  hoist(basic_information, format = "formats") %>% 
  select(disc_id = id, format) %>% 
  unnest_longer(format) %>% 
  unnest_wider(format) %>% 
  unnest_longer(descriptions)
#> # A tibble: 258 × 5
#>   disc_id descriptions text  name     qty  
#>     <int> <chr>        <chr> <chr>    <chr>
#> 1 7496378 "Numbered"   Black Cassette 1    
#> 2 4490852 "LP"         <NA>  Vinyl    1    
#> 3 9827276 "7\""        <NA>  Vinyl    1    
#> 4 9827276 "45 RPM"     <NA>  Vinyl    1    
#> 5 9827276 "EP"         <NA>  Vinyl    1    
#> 6 9769203 "LP"         <NA>  Vinyl    1    
#> # ℹ 252 more rows

Then you could join these back on to the original dataset as needed.

  1. I’d normally use readr::parse_datetime() or lubridate::ymd_hms(), but I can’t here because it’s a vignette and I don’t want to add a dependency to tidyr just to simplify one example.↩︎