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:

A very large number of data rectangling problems can be solved by combining these functions with a splash of dplyr (largely eliminating prior approaches that combined mutate() with multiple purrr::map()s).

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 avata…¹ grava…² url   html_…³ follo…⁴ follo…⁵ gists…⁶ starr…⁷
#>   <chr>      <int> <chr>   <chr>   <chr> <chr>   <chr>   <chr>   <chr>   <chr>  
#> 1 gaborcsa… 6.60e5 https:… ""      http… https:… https:… https:… https:… https:…
#> 2 jennybc   5.99e5 https:… ""      http… https:… https:… https:… https:… https:…
#> 3 jtleek    1.57e6 https:… ""      http… https:… https:… https:… https:… https:…
#> 4 juliasil… 1.25e7 https:… ""      http… https:… https:… https:… https:… https:…
#> 5 leeper    3.51e6 https:… ""      http… https:… https:… https:… https:… https:…
#> 6 masalmon  8.36e6 https:… ""      http… https:… https:… https:… https:… https:…
#> # … with 20 more variables: 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>, and abbreviated variable names ¹​avatar_url, ²​gravatar_id,
#> #   ³​html_url, ⁴​followers_url, ⁵​following_url, ⁶​gists_url, ⁷​starred_url

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 user 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]>
#> # … with 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]>
#> # … with 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 avata…¹ grava…² url   html_…³ follo…⁴ follo…⁵ gists…⁶ starr…⁷
#>   <chr>      <int> <chr>   <chr>   <chr> <chr>   <chr>   <chr>   <chr>   <chr>  
#> 1 gaborcsa… 660288 https:… ""      http… https:… https:… https:… https:… https:…
#> 2 gaborcsa… 660288 https:… ""      http… https:… https:… https:… https:… https:…
#> 3 gaborcsa… 660288 https:… ""      http… https:… https:… https:… https:… https:…
#> 4 gaborcsa… 660288 https:… ""      http… https:… https:… https:… https:… https:…
#> 5 gaborcsa… 660288 https:… ""      http… https:… https:… https:… https:… https:…
#> 6 gaborcsa… 660288 https:… ""      http… https:… https:… https:… https:… https:…
#> # … with 170 more rows, 8 more variables: subscriptions_url <chr>,
#> #   organizations_url <chr>, repos_url <chr>, events_url <chr>,
#> #   received_events_url <chr>, type <chr>, site_admin <lgl>, repo <list>, and
#> #   abbreviated variable names ¹​avatar_url, ²​gravatar_id, ³​html_url,
#> #   ⁴​followers_url, ⁵​following_url, ⁶​gists_url, ⁷​starred_url

Instead of looking at the list and carefully thinking about whether it needs to become rows or columns, you can use unnest_auto(). It uses a handful of heuristics to figure out whether unnest_longer() or unnest_wider() is appropriate, and tells you about its reasoning.

tibble(repo = gh_repos) %>% 
  unnest_auto(repo) %>% 
  unnest_auto(repo)
#> Using `unnest_longer(repo, indices_include = FALSE)`; no element has names
#> Using `unnest_wider(repo)`; elements have 68 names in common
#> # A tibble: 176 × 68
#>        id name  full_…¹ owner        private html_…² descr…³ fork  url   forks…⁴
#>     <int> <chr> <chr>   <list>       <lgl>   <chr>   <chr>   <lgl> <chr> <chr>  
#> 1  6.12e7 after gaborc… <named list> FALSE   https:… Run Co… FALSE http… https:…
#> 2  4.05e7 argu… gaborc… <named list> FALSE   https:… Declar… FALSE http… https:…
#> 3  3.64e7 ask   gaborc… <named list> FALSE   https:… Friend… FALSE http… https:…
#> 4  3.49e7 base… gaborc… <named list> FALSE   https:… Do we … FALSE http… https:…
#> 5  6.16e7 cite… gaborc… <named list> FALSE   https:… Test R… TRUE  http… https:…
#> 6  3.39e7 clis… gaborc… <named list> FALSE   https:… Unicod… FALSE http… https:…
#> # … with 170 more rows, 58 more variables: keys_url <chr>,
#> #   collaborators_url <chr>, teams_url <chr>, hooks_url <chr>,
#> #   issue_events_url <chr>, events_url <chr>, assignees_url <chr>,
#> #   branches_url <chr>, tags_url <chr>, blobs_url <chr>, git_tags_url <chr>,
#> #   git_refs_url <chr>, trees_url <chr>, statuses_url <chr>,
#> #   languages_url <chr>, stargazers_url <chr>, contributors_url <chr>,
#> #   subscribers_url <chr>, subscription_url <chr>, commits_url <chr>, …

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]>
#> # … with 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>   ""    
#> # … with 24 more rows, and 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 [3]> <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]>
#> # … with 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: 180 × 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         
#> # … with 174 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: 60 × 2
#>   name              title                                               
#>   <chr>             <chr>                                               
#> 1 Theon Greyjoy     Prince of Winterfell                                
#> 2 Theon Greyjoy     Captain of Sea Bitch                                
#> 3 Theon Greyjoy     Lord of the Iron Islands (by law of the green lands)
#> 4 Tyrion Lannister  Acting Hand of the King (former)                    
#> 5 Tyrion Lannister  Master of Coin (former)                             
#> 6 Victarion Greyjoy Lord Captain of the Iron Fleet                      
#> # … with 54 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.)

Again, we could rewrite using unnest_auto(). This is convenient for exploration, but I wouldn’t rely on it in the long term - unnest_auto() has the undesirable property that it will always succeed. That means if your data structure changes, unnest_auto() will continue to work, but might give very different output that causes cryptic failures from downstream functions.

tibble(char = got_chars) %>% 
  unnest_auto(char) %>% 
  select(name, title = titles) %>% 
  unnest_auto(title)
#> Using `unnest_wider(char)`; elements have 18 names in common
#> Using `unnest_longer(title, indices_include = FALSE)`; no element has names
#> # A tibble: 60 × 2
#>   name              title                                               
#>   <chr>             <chr>                                               
#> 1 Theon Greyjoy     Prince of Winterfell                                
#> 2 Theon Greyjoy     Captain of Sea Bitch                                
#> 3 Theon Greyjoy     Lord of the Iron Islands (by law of the green lands)
#> 4 Tyrion Lannister  Acting Hand of the King (former)                    
#> 5 Tyrion Lannister  Master of Coin (former)                             
#> 6 Victarion Greyjoy Lord Captain of the Iron Fleet                      
#> # … with 54 more rows

Geocoding with google

Next we’ll tackle a more complex form of data that comes from Google’s geocoding service. It’s against the terms of service to cache this data, so I first write a very simple wrapper around the API. This relies on having an Google maps API key stored in an environment; if that’s not available these code chunks won’t be run.

has_key <- !identical(Sys.getenv("GOOGLE_MAPS_API_KEY"), "")
if (!has_key) {
  message("No Google Maps API key found; code chunks will not be run")
}

# https://developers.google.com/maps/documentation/geocoding
geocode <- function(address, api_key = Sys.getenv("GOOGLE_MAPS_API_KEY")) {
  url <- "https://maps.googleapis.com/maps/api/geocode/json"
  url <- paste0(url, "?address=", URLencode(address), "&key=", api_key)

  jsonlite::read_json(url)
}

The list that this function returns is quite complex:

houston <- geocode("Houston TX")
str(houston)
#> List of 2
#>  $ results:List of 1
#>   ..$ :List of 5
#>   .. ..$ address_components:List of 4
#>   .. .. ..$ :List of 3
#>   .. .. .. ..$ long_name : chr "Houston"
#>   .. .. .. ..$ short_name: chr "Houston"
#>   .. .. .. ..$ types     :List of 2
#>   .. .. .. .. ..$ : chr "locality"
#>   .. .. .. .. ..$ : chr "political"
#>   .. .. ..$ :List of 3
#>   .. .. .. ..$ long_name : chr "Harris County"
#>   .. .. .. ..$ short_name: chr "Harris County"
#>   .. .. .. ..$ types     :List of 2
#>   .. .. .. .. ..$ : chr "administrative_area_level_2"
#>   .. .. .. .. ..$ : chr "political"
#>   .. .. ..$ :List of 3
#>   .. .. .. ..$ long_name : chr "Texas"
#>   .. .. .. ..$ short_name: chr "TX"
#>   .. .. .. ..$ types     :List of 2
#>   .. .. .. .. ..$ : chr "administrative_area_level_1"
#>   .. .. .. .. ..$ : chr "political"
#>   .. .. ..$ :List of 3
#>   .. .. .. ..$ long_name : chr "United States"
#>   .. .. .. ..$ short_name: chr "US"
#>   .. .. .. ..$ types     :List of 2
#>   .. .. .. .. ..$ : chr "country"
#>   .. .. .. .. ..$ : chr "political"
#>   .. ..$ formatted_address : chr "Houston, TX, USA"
#>   .. ..$ geometry          :List of 4
#>   .. .. ..$ bounds       :List of 2
#>   .. .. .. ..$ northeast:List of 2
#>   .. .. .. .. ..$ lat: num 30.1
#>   .. .. .. .. ..$ lng: num -95
#>   .. .. .. ..$ southwest:List of 2
#>   .. .. .. .. ..$ lat: num 29.5
#>   .. .. .. .. ..$ lng: num -95.8
#>   .. .. ..$ location     :List of 2
#>   .. .. .. ..$ lat: num 29.8
#>   .. .. .. ..$ lng: num -95.4
#>   .. .. ..$ location_type: chr "APPROXIMATE"
#>   .. .. ..$ viewport     :List of 2
#>   .. .. .. ..$ northeast:List of 2
#>   .. .. .. .. ..$ lat: num 30.1
#>   .. .. .. .. ..$ lng: num -95
#>   .. .. .. ..$ southwest:List of 2
#>   .. .. .. .. ..$ lat: num 29.5
#>   .. .. .. .. ..$ lng: num -95.8
#>   .. ..$ place_id          : chr "ChIJAYWNSLS4QIYROwVl894CDco"
#>   .. ..$ types             :List of 2
#>   .. .. ..$ : chr "locality"
#>   .. .. ..$ : chr "political"
#>  $ status : chr "OK"

Fortunately, we can attack the problem step by step with tidyr functions. To make the problem a bit harder (!) and more realistic, I’ll start by geocoding a few cities:

city <- c("Houston", "LA", "New York", "Chicago", "Springfield")
city_geo <- purrr::map(city, geocode)

I’ll put these results in a tibble, next to the original city name:

loc <- tibble(city = city, json = city_geo)
loc
#> # A tibble: 5 × 2
#>   city        json            
#>   <chr>       <list>          
#> 1 Houston     <named list [2]>
#> 2 LA          <named list [2]>
#> 3 New York    <named list [2]>
#> 4 Chicago     <named list [2]>
#> 5 Springfield <named list [2]>

The first level contains components status and result, which we can reveal with unnest_wider():

loc %>%
  unnest_wider(json)
#> # A tibble: 5 × 3
#>   city        results    status
#>   <chr>       <list>     <chr> 
#> 1 Houston     <list [1]> OK    
#> 2 LA          <list [1]> OK    
#> 3 New York    <list [1]> OK    
#> 4 Chicago     <list [1]> OK    
#> 5 Springfield <list [1]> 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 Springfield has two. We can pull these out into separate rows with unnest_longer():

loc %>%
  unnest_wider(json) %>% 
  unnest_longer(results)
#> # A tibble: 5 × 3
#>   city        results          status
#>   <chr>       <list>           <chr> 
#> 1 Houston     <named list [5]> OK    
#> 2 LA          <named list [5]> OK    
#> 3 New York    <named list [5]> OK    
#> 4 Chicago     <named list [5]> OK    
#> 5 Springfield <named list [7]> OK

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

loc %>%
  unnest_wider(json) %>% 
  unnest_longer(results) %>% 
  unnest_wider(results)
#> # A tibble: 5 × 9
#>   city   addre…¹ forma…² geometry     place…³ types  parti…⁴ plus_code    status
#>   <chr>  <list>  <chr>   <list>       <chr>   <list> <lgl>   <list>       <chr> 
#> 1 Houst… <list>  Housto… <named list> ChIJAY… <list> NA      <NULL>       OK    
#> 2 LA     <list>  Los An… <named list> ChIJE9… <list> NA      <NULL>       OK    
#> 3 New Y… <list>  New Yo… <named list> ChIJOw… <list> NA      <NULL>       OK    
#> 4 Chica… <list>  Chicag… <named list> ChIJ7c… <list> NA      <NULL>       OK    
#> 5 Sprin… <list>  Spring… <named list> ChIJ-x… <list> TRUE    <named list> OK    
#> # … with abbreviated variable names ¹​address_components, ²​formatted_address,
#> #   ³​place_id, ⁴​partial_match

We can find the lat and lon coordinates by unnesting geometry:

loc %>%
  unnest_wider(json) %>% 
  unnest_longer(results) %>% 
  unnest_wider(results) %>% 
  unnest_wider(geometry)
#> # A tibble: 5 × 12
#>   city    addre…¹ forma…² bounds       location     locat…³ viewport     place…⁴
#>   <chr>   <list>  <chr>   <list>       <list>       <chr>   <list>       <chr>  
#> 1 Houston <list>  Housto… <named list> <named list> APPROX… <named list> ChIJAY…
#> 2 LA      <list>  Los An… <named list> <named list> APPROX… <named list> ChIJE9…
#> 3 New Yo… <list>  New Yo… <named list> <named list> APPROX… <named list> ChIJOw…
#> 4 Chicago <list>  Chicag… <named list> <named list> APPROX… <named list> ChIJ7c…
#> 5 Spring… <list>  Spring… <NULL>       <named list> ROOFTOP <named list> ChIJ-x…
#> # … with 4 more variables: types <list>, partial_match <lgl>, plus_code <list>,
#> #   status <chr>, and abbreviated variable names ¹​address_components,
#> #   ²​formatted_address, ³​location_type, ⁴​place_id

And then location:

loc %>%
  unnest_wider(json) %>%
  unnest_longer(results) %>%
  unnest_wider(results) %>%
  unnest_wider(geometry) %>%
  unnest_wider(location)
#> # A tibble: 5 × 13
#>   city    addre…¹ forma…² bounds         lat    lng locat…³ viewport     place…⁴
#>   <chr>   <list>  <chr>   <list>       <dbl>  <dbl> <chr>   <list>       <chr>  
#> 1 Houston <list>  Housto… <named list>  29.8  -95.4 APPROX… <named list> ChIJAY…
#> 2 LA      <list>  Los An… <named list>  34.1 -118.  APPROX… <named list> ChIJE9…
#> 3 New Yo… <list>  New Yo… <named list>  40.7  -74.0 APPROX… <named list> ChIJOw…
#> 4 Chicago <list>  Chicag… <named list>  41.9  -87.6 APPROX… <named list> ChIJ7c…
#> 5 Spring… <list>  Spring… <NULL>        42.1  -72.6 ROOFTOP <named list> ChIJ-x…
#> # … with 4 more variables: types <list>, partial_match <lgl>, plus_code <list>,
#> #   status <chr>, and abbreviated variable names ¹​address_components,
#> #   ²​formatted_address, ³​location_type, ⁴​place_id

Again, unnest_auto() makes this simpler with the small risk of failing in unexpected ways if the input structure changes:

loc %>%
  unnest_auto(json) %>%
  unnest_auto(results) %>%
  unnest_auto(results) %>%
  unnest_auto(geometry) %>%
  unnest_auto(location)
#> Using `unnest_wider(json)`; elements have 2 names in common
#> Using `unnest_longer(results, indices_include = FALSE)`; no element has names
#> Using `unnest_wider(results)`; elements have 5 names in common
#> Using `unnest_wider(geometry)`; elements have 3 names in common
#> Using `unnest_wider(location)`; elements have 2 names in common
#> # A tibble: 5 × 13
#>   city    addre…¹ forma…² bounds         lat    lng locat…³ viewport     place…⁴
#>   <chr>   <list>  <chr>   <list>       <dbl>  <dbl> <chr>   <list>       <chr>  
#> 1 Houston <list>  Housto… <named list>  29.8  -95.4 APPROX… <named list> ChIJAY…
#> 2 LA      <list>  Los An… <named list>  34.1 -118.  APPROX… <named list> ChIJE9…
#> 3 New Yo… <list>  New Yo… <named list>  40.7  -74.0 APPROX… <named list> ChIJOw…
#> 4 Chicago <list>  Chicag… <named list>  41.9  -87.6 APPROX… <named list> ChIJ7c…
#> 5 Spring… <list>  Spring… <NULL>        42.1  -72.6 ROOFTOP <named list> ChIJ-x…
#> # … with 4 more variables: types <list>, partial_match <lgl>, plus_code <list>,
#> #   status <chr>, and abbreviated variable names ¹​address_components,
#> #   ²​formatted_address, ³​location_type, ⁴​place_id

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

loc %>%
  unnest_wider(json) %>%
  hoist(results, first_result = 1) %>%
  unnest_wider(first_result) %>%
  unnest_wider(geometry) %>%
  unnest_wider(location)
#> # A tibble: 5 × 13
#>   city    addre…¹ forma…² bounds         lat    lng locat…³ viewport     place…⁴
#>   <chr>   <list>  <chr>   <list>       <dbl>  <dbl> <chr>   <list>       <chr>  
#> 1 Houston <list>  Housto… <named list>  29.8  -95.4 APPROX… <named list> ChIJAY…
#> 2 LA      <list>  Los An… <named list>  34.1 -118.  APPROX… <named list> ChIJE9…
#> 3 New Yo… <list>  New Yo… <named list>  40.7  -74.0 APPROX… <named list> ChIJOw…
#> 4 Chicago <list>  Chicag… <named list>  41.9  -87.6 APPROX… <named list> ChIJ7c…
#> 5 Spring… <list>  Spring… <NULL>        42.1  -72.6 ROOFTOP <named list> ChIJ-x…
#> # … with 4 more variables: types <list>, partial_match <lgl>, plus_code <list>,
#> #   status <chr>, and abbreviated variable names ¹​address_components,
#> #   ²​formatted_address, ³​location_type, ⁴​place_id

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

loc %>%
  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 LA           34.1 -118.  <named list [2]>
#> 3 New York     40.7  -74.0 <named list [2]>
#> 4 Chicago      41.9  -87.6 <named list [2]>
#> 5 Springfield  42.1  -72.6 <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
#> # … with 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 `unpack()`:
#> ! Names must be unique.
#> ✖ These names are duplicated:
#>   * "id" at locations 7 and 14.
#> ℹ Use argument `names_repair` to specify 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…¹ date_added          labels  year maste…² artists id...7 thumb title
#>        <int> <dttm>              <list> <int> <chr>   <list>   <int> <chr> <chr>
#> 1  354823933 2019-02-16 17:48:59 <list>  2015 <NA>    <list>  7.50e6 http… Demo 
#> 2  354092601 2019-02-13 14:13:11 <list>  2013 https:… <list>  4.49e6 http… Obse…
#> 3  354091476 2019-02-13 14:07:23 <list>  2017 https:… <list>  9.83e6 http… I    
#> 4  351244906 2019-02-02 11:39:58 <list>  2017 https:… <list>  9.77e6 http… Oído…
#> 5  351244801 2019-02-02 11:39:37 <list>  2015 https:… <list>  7.24e6 http… A Ca…
#> 6  351052065 2019-02-01 20:40:53 <list>  2019 https:… <list>  1.31e7 http… Tash…
#> # … with 149 more rows, 6 more variables: formats <list>, cover_image <chr>,
#> #   resource_url <chr>, master_id <int>, id...14 <int>, rating <int>, and
#> #   abbreviated variable names ¹​instance_id, ²​master_url

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…¹ date_added          labels  year maste…² artists     id thumb title
#>        <int> <dttm>              <list> <int> <chr>   <list>   <int> <chr> <chr>
#> 1  354823933 2019-02-16 17:48:59 <list>  2015 <NA>    <list>  7.50e6 http… Demo 
#> 2  354092601 2019-02-13 14:13:11 <list>  2013 https:… <list>  4.49e6 http… Obse…
#> 3  354091476 2019-02-13 14:07:23 <list>  2017 https:… <list>  9.83e6 http… I    
#> 4  351244906 2019-02-02 11:39:58 <list>  2017 https:… <list>  9.77e6 http… Oído…
#> 5  351244801 2019-02-02 11:39:37 <list>  2015 https:… <list>  7.24e6 http… A Ca…
#> 6  351052065 2019-02-01 20:40:53 <list>  2019 https:… <list>  1.31e7 http… Tash…
#> # … with 149 more rows, 5 more variables: formats <list>, cover_image <chr>,
#> #   resource_url <chr>, master_id <int>, rating <int>, and abbreviated variable
#> #   names ¹​instance_id, ²​master_url

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_info…¹     id
#>         <int> <dttm>              <chr>   <int> <chr> <chr>  <list>        <int>
#> 1   354823933 2019-02-16 17:48:59 Demo     2015 Tobi… Mollot <named list> 7.50e6
#> 2   354092601 2019-02-13 14:13:11 Observ…  2013 La V… Una B… <named list> 4.49e6
#> 3   354091476 2019-02-13 14:07:23 I        2017 La V… S.H.I… <named list> 9.83e6
#> 4   351244906 2019-02-02 11:39:58 Oído A…  2017 La V… Rata … <named list> 9.77e6
#> 5   351244801 2019-02-02 11:39:37 A Cat'…  2015 Kato… Ivy (… <named list> 7.24e6
#> 6   351052065 2019-02-01 20:40:53 Tashme   2019 High… Tashme <named list> 1.31e7
#> # … with 149 more rows, 1 more variable: rating <int>, and abbreviated variable
#> #   name ¹​basic_information

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
#> # … with 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: 281 × 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    
#> # … with 275 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.↩︎