# 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:

- `unnest_longer()` takes each element of a list-column and makes a new row.
- `unnest_wider()` takes each element of a list-column and makes a new column.
- `unnest_auto()` guesses whether you want `unnest_longer()` or `unnest_wider()`.
- `hoist()` is similar to `unnest_wider()` but only plucks out selected components, and can reach down multiple levels.

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 x 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.…
    #> # … with 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>,
    #> #   public_repos <int>, public_gists <int>, followers <int>, following <int>,
    #> #   created_at <chr>, updated_at <chr>, bio <chr>, hireable <lgl>

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 x 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 x 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 x 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 x 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 x 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.…
    #> # … with 170 more rows, and 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>

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)`; no element has names
    #> Using `unnest_wider(repo)`; elements have 68 names in common
    #> # A tibble: 176 x 67
    #>       id name  full_name owner private html_url description fork  url
    #>    <int> <chr> <chr>     <lis> <lgl>   <chr>    <chr>       <lgl> <chr>
    #> 1 6.12e7 after gaborcsa… <nam… FALSE   https:/… Run Code i… FALSE http…
    #> 2 4.05e7 argu… gaborcsa… <nam… FALSE   https:/… Declarativ… FALSE http…
    #> 3 3.64e7 ask   gaborcsa… <nam… FALSE   https:/… Friendly C… FALSE http…
    #> 4 3.49e7 base… gaborcsa… <nam… FALSE   https:/… Do we get … FALSE http…
    #> 5 6.16e7 cite… gaborcsa… <nam… FALSE   https:/… Test R pac… TRUE  http…
    #> 6 3.39e7 clis… gaborcsa… <nam… FALSE   https:/… Unicode sy… FALSE http…
    #> # … with 170 more rows, and 58 more variables: forks_url <chr>, 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>,
    #> #   git_commits_url <chr>, comments_url <chr>, issue_comment_url <chr>,
    #> #   contents_url <chr>, compare_url <chr>, merges_url <chr>, archive_url <chr>,
    #> #   downloads_url <chr>, issues_url <chr>, pulls_url <chr>,
    #> #   milestones_url <chr>, notifications_url <chr>, labels_url <chr>,
    #> #   releases_url <chr>, deployments_url <chr>, created_at <chr>,
    #> #   updated_at <chr>, pushed_at <chr>, git_url <chr>, ssh_url <chr>,
    #> #   clone_url <chr>, svn_url <chr>, size <int>, stargazers_count <int>,
    #> #   watchers_count <int>, language <chr>, has_issues <lgl>,
    #> #   has_downloads <lgl>, has_wiki <lgl>, has_pages <lgl>, forks_count <int>,
    #> #   open_issues_count <int>, forks <int>, open_issues <int>, watchers <int>,
    #> #   default_branch <chr>, homepage <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 x 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 x 18
    #>   url      id name  gender culture born  died  alive titles aliases father
    #>   <chr> <int> <chr> <chr>  <chr>   <chr> <chr> <lgl> <list> <list>  <chr>
    #> 1 http…  1022 Theo… Male   "Ironb… "In … ""    TRUE  <chr … <chr [… ""
    #> 2 http…  1052 Tyri… Male   ""      "In … ""    TRUE  <chr … <chr [… ""
    #> 3 http…  1074 Vict… Male   "Ironb… "In … ""    TRUE  <chr … <chr [… ""
    #> 4 http…  1109 Will  Male   ""      ""    "In … FALSE <chr … <chr [… ""
    #> 5 http…  1166 Areo… Male   "Norvo… "In … ""    TRUE  <chr … <chr [… ""
    #> 6 http…  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 x 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 x 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 x 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)`; no element has names
    #> # A tibble: 60 x 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 x 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 x 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 x 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 [5]> 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 x 7
    #>   city    address_componen… formatted_address geometry  place_id    types status
    #>   <chr>   <list>            <chr>             <list>    <chr>       <lis> <chr>
    #> 1 Houston <list [4]>        Houston, TX, USA  <named l… ChIJAYWNSL… <lis… OK
    #> 2 LA      <list [4]>        Los Angeles, CA,… <named l… ChIJE9on3F… <lis… OK
    #> 3 New Yo… <list [3]>        New York, NY, USA <named l… ChIJOwg_06… <lis… OK
    #> 4 Chicago <list [4]>        Chicago, IL, USA  <named l… ChIJ7cv00D… <lis… OK
    #> 5 Spring… <list [5]>        Springfield, MO,… <named l… ChIJP5jIRf… <lis… OK

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 x 10
    #>   city  address_compone… formatted_addre… bounds location location_type viewport
    #>   <chr> <list>           <chr>            <list> <list>   <chr>         <list>
    #> 1 Hous… <list [4]>       Houston, TX, USA <name… <named … APPROXIMATE   <named …
    #> 2 LA    <list [4]>       Los Angeles, CA… <name… <named … APPROXIMATE   <named …
    #> 3 New … <list [3]>       New York, NY, U… <name… <named … APPROXIMATE   <named …
    #> 4 Chic… <list [4]>       Chicago, IL, USA <name… <named … APPROXIMATE   <named …
    #> 5 Spri… <list [5]>       Springfield, MO… <name… <named … APPROXIMATE   <named …
    #> # … with 3 more variables: place_id <chr>, types <list>, status <chr>

And then location:

    loc %>%
      unnest_wider(json) %>%
      unnest_longer(results) %>%
      unnest_wider(results) %>%
      unnest_wider(geometry) %>%
      unnest_wider(location)
    #> # A tibble: 5 x 11
    #>   city  address_compone… formatted_addre… bounds   lat    lng location_type
    #>   <chr> <list>           <chr>            <list> <dbl>  <dbl> <chr>
    #> 1 Hous… <list [4]>       Houston, TX, USA <name…  29.8  -95.4 APPROXIMATE
    #> 2 LA    <list [4]>       Los Angeles, CA… <name…  34.1 -118.  APPROXIMATE
    #> 3 New … <list [3]>       New York, NY, U… <name…  40.7  -74.0 APPROXIMATE
    #> 4 Chic… <list [4]>       Chicago, IL, USA <name…  41.9  -87.6 APPROXIMATE
    #> 5 Spri… <list [5]>       Springfield, MO… <name…  37.2  -93.3 APPROXIMATE
    #> # … with 4 more variables: viewport <list>, place_id <chr>, types <list>,
    #> #   status <chr>

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)`; no element has names
    #> Using `unnest_wider(results)`; elements have 5 names in common
    #> Using `unnest_wider(geometry)`; elements have 4 names in common
    #> Using `unnest_wider(location)`; elements have 2 names in common
    #> # A tibble: 5 x 11
    #>   city  address_compone… formatted_addre… bounds   lat    lng location_type
    #>   <chr> <list>           <chr>            <list> <dbl>  <dbl> <chr>
    #> 1 Hous… <list [4]>       Houston, TX, USA <name…  29.8  -95.4 APPROXIMATE
    #> 2 LA    <list [4]>       Los Angeles, CA… <name…  34.1 -118.  APPROXIMATE
    #> 3 New … <list [3]>       New York, NY, U… <name…  40.7  -74.0 APPROXIMATE
    #> 4 Chic… <list [4]>       Chicago, IL, USA <name…  41.9  -87.6 APPROXIMATE
    #> 5 Spri… <list [5]>       Springfield, MO… <name…  37.2  -93.3 APPROXIMATE
    #> # … with 4 more variables: viewport <list>, place_id <chr>, types <list>,
    #> #   status <chr>

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 x 11
    #>   city  address_compone… formatted_addre… bounds   lat    lng location_type
    #>   <chr> <list>           <chr>            <list> <dbl>  <dbl> <chr>
    #> 1 Hous… <list [4]>       Houston, TX, USA <name…  29.8  -95.4 APPROXIMATE
    #> 2 LA    <list [4]>       Los Angeles, CA… <name…  34.1 -118.  APPROXIMATE
    #> 3 New … <list [3]>       New York, NY, U… <name…  40.7  -74.0 APPROXIMATE
    #> 4 Chic… <list [4]>       Chicago, IL, USA <name…  41.9  -87.6 APPROXIMATE
    #> 5 Spri… <list [5]>       Springfield, MO… <name…  37.2  -93.3 APPROXIMATE
    #> # … with 4 more variables: viewport <list>, place_id <chr>, types <list>,
    #> #   status <chr>

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 x 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  37.2  -93.3 <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 x 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: Names must be unique.
    #> [31mx[39m These names are duplicated:
    #>   * "id" at locations 6 and 14.
    #> [34mℹ[39m 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...6
    #> * id -> id...14
    #> # A tibble: 155 x 15
    #>   instance_id date_added          labels  year artists id...6 thumb title
    #>         <int> <dttm>              <list> <int> <list>   <int> <chr> <chr>
    #> 1   354823933 2019-02-16 17:48:59 <list…  2015 <list … 7.50e6 http… Demo
    #> 2   354092601 2019-02-13 14:13:11 <list…  2013 <list … 4.49e6 http… Obse…
    #> 3   354091476 2019-02-13 14:07:23 <list…  2017 <list … 9.83e6 http… I
    #> 4   351244906 2019-02-02 11:39:58 <list…  2017 <list … 9.77e6 http… Oído…
    #> 5   351244801 2019-02-02 11:39:37 <list…  2015 <list … 7.24e6 http… A Ca…
    #> 6   351052065 2019-02-01 20:40:53 <list…  2019 <list … 1.31e7 http… Tash…
    #> # … with 149 more rows, and 7 more variables: formats <list>,
    #> #   cover_image <chr>, resource_url <chr>, master_id <int>, master_url <chr>,
    #> #   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 x 14
    #>   instance_id date_added          labels  year artists     id thumb title
    #>         <int> <dttm>              <list> <int> <list>   <int> <chr> <chr>
    #> 1   354823933 2019-02-16 17:48:59 <list…  2015 <list … 7.50e6 http… Demo
    #> 2   354092601 2019-02-13 14:13:11 <list…  2013 <list … 4.49e6 http… Obse…
    #> 3   354091476 2019-02-13 14:07:23 <list…  2017 <list … 9.83e6 http… I
    #> 4   351244906 2019-02-02 11:39:58 <list…  2017 <list … 9.77e6 http… Oído…
    #> 5   351244801 2019-02-02 11:39:37 <list…  2015 <list … 7.24e6 http… A Ca…
    #> 6   351052065 2019-02-01 20:40:53 <list…  2019 <list … 1.31e7 http… Tash…
    #> # … with 149 more rows, and 6 more variables: formats <list>,
    #> #   cover_image <chr>, resource_url <chr>, master_id <int>, master_url <chr>,
    #> #   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 x 9
    #>   instance_id date_added          title  year label artist basic_informati…
    #>         <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 Obse…  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…  2017 La V… Rata … <named list [9]>
    #> 5   351244801 2019-02-02 11:39:37 A Ca…  2015 Kato… Ivy (… <named list [9]>
    #> 6   351052065 2019-02-01 20:40:53 Tash…  2019 High… Tashme <named list [9]>
    #> # … with 149 more rows, and 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 x 8
    #>    disc_id join  name          anv   tracks role  resource_url                id
    #>      <int> <chr> <chr>         <chr> <chr>  <chr> <chr>                    <int>
    #> 1  7496378 ""    Mollot        ""    ""     ""    https://api.discogs.co… 4.62e6
    #> 2  4490852 ""    Una Bèstia I… ""    ""     ""    https://api.discogs.co… 3.19e6
    #> 3  9827276 ""    S.H.I.T. (3)  ""    ""     ""    https://api.discogs.co… 2.77e6
    #> 4  9769203 ""    Rata Negra    ""    ""     ""    https://api.discogs.co… 4.28e6
    #> 5  7237138 ""    Ivy (18)      ""    ""     ""    https://api.discogs.co… 3.60e6
    #> 6 13117042 ""    Tashme        ""    ""     ""    https://api.discogs.co… 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: 280 x 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 274 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.↩︎