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. -
hoist()is similar tounnest_wider()but only plucks out selected components, and can reach down multiple levels.
(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.
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
#> <chr> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 gaborcsar… 6.60e5 https://a… "" http… https:/… https://api.…
#> 2 jennybc 5.99e5 https://a… "" http… https:/… https://api.…
#> 3 jtleek 1.57e6 https://a… "" http… https:/… https://api.…
#> 4 juliasilge 1.25e7 https://a… "" http… https:/… https://api.…
#> 5 leeper 3.51e6 https://a… "" http… https:/… https://api.…
#> 6 masalmon 8.36e6 https://a… "" http… https:/… https://api.…
#> # ℹ 23 more variables: following_url <chr>, 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>, …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>
#> 2 780 jennybc https://github.com/jennybc <named list>
#> 3 3958 jtleek https://github.com/jtleek <named list>
#> 4 115 juliasilge https://github.com/juliasilge <named list>
#> 5 213 leeper https://github.com/leeper <named list>
#> 6 34 masalmon https://github.com/masalmon <named list>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]>
#> 7 <named list [68]>
#> 8 <named list [68]>
#> 9 <named list [68]>
#> 10 <named list [68]>
#> # ℹ 166 more rowsThen 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]>
#> 7 gaborcsardi cmaker NA 0 <named list [65]>
#> 8 gaborcsardi cmark NA 0 <named list [65]>
#> 9 gaborcsardi conditions NA 0 <named list [65]>
#> 10 gaborcsardi crayon NA 52 <named list [65]>
#> # ℹ 166 more rowsNote 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
#> <chr> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 gaborcsa… 660288 https://a… "" http… https:/… https://api.…
#> 2 gaborcsa… 660288 https://a… "" http… https:/… https://api.…
#> 3 gaborcsa… 660288 https://a… "" http… https:/… https://api.…
#> 4 gaborcsa… 660288 https://a… "" http… https:/… https://api.…
#> 5 gaborcsa… 660288 https://a… "" http… https:/… https://api.…
#> 6 gaborcsa… 660288 https://a… "" http… https:/… https://api.…
#> 7 gaborcsa… 660288 https://a… "" http… https:/… https://api.…
#> 8 gaborcsa… 660288 https://a… "" http… https:/… https://api.…
#> 9 gaborcsa… 660288 https://a… "" http… https:/… https://api.…
#> 10 gaborcsa… 660288 https://a… "" http… https:/… https://api.…
#> # ℹ 166 more rows
#> # ℹ 11 more variables: following_url <chr>, 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]>
#> 7 <named list [18]>
#> 8 <named list [18]>
#> 9 <named list [18]>
#> 10 <named list [18]>
#> # ℹ 20 more rows
chars2 <- chars %>% unnest_wider(char)
chars2
#> # A tibble: 30 × 18
#> url id name gender culture born died alive titles aliases
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <list> <list>
#> 1 https:/… 1022 Theo… Male "Ironb… "In … "" TRUE <chr> <chr>
#> 2 https:/… 1052 Tyri… Male "" "In … "" TRUE <chr> <chr>
#> 3 https:/… 1074 Vict… Male "Ironb… "In … "" TRUE <chr> <chr>
#> 4 https:/… 1109 Will Male "" "" "In … FALSE <chr> <chr>
#> 5 https:/… 1166 Areo… Male "Norvo… "In … "" TRUE <chr> <chr>
#> 6 https:/… 1267 Chett Male "" "At … "In … FALSE <chr> <chr>
#> 7 https:/… 1295 Cres… Male "" "In … "In … FALSE <chr> <chr>
#> 8 https:/… 130 Aria… Female "Dorni… "In … "" TRUE <chr> <chr>
#> 9 https:/… 1303 Daen… Female "Valyr… "In … "" TRUE <chr> <chr>
#> 10 https:/… 1319 Davo… Male "Weste… "In … "" TRUE <chr> <chr>
#> # ℹ 20 more rows
#> # ℹ 8 more variables: father <chr>, 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> <chr [2]> <chr [6]> <chr>
#> 2 <chr [2]> <chr [11]> <chr [1]> <chr> <chr [4]> <chr [6]> <chr>
#> 3 <chr [2]> <chr [1]> <chr [1]> <chr> <chr [2]> <chr [1]> <chr>
#> 4 <chr [1]> <chr [1]> <NULL> <chr> <chr [1]> <chr [1]> <chr>
#> 5 <chr [1]> <chr [1]> <chr [1]> <chr> <chr [2]> <chr [2]> <chr>
#> 6 <chr [1]> <chr [1]> <NULL> <chr> <chr [1]> <chr [1]> <chr>
#> 7 <chr [1]> <chr [1]> <NULL> <chr> <chr [1]> <chr [1]> <chr>
#> 8 <chr [1]> <chr [1]> <chr [1]> <chr> <chr [1]> <chr [1]> <chr>
#> 9 <chr [5]> <chr [11]> <chr [1]> <chr> <chr [4]> <chr [6]> <chr>
#> 10 <chr [4]> <chr [5]> <chr [2]> <chr> <chr [3]> <chr [5]> <chr>
#> # ℹ 20 more rowsWhat 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
#> 7 Theon Greyjoy tvSeries Season 4
#> 8 Theon Greyjoy tvSeries Season 5
#> 9 Theon Greyjoy tvSeries Season 6
#> 10 Tyrion Lannister books A Feast for Crows
#> # ℹ 169 more rowsOr 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 la…
#> 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"
#> 7 Will ""
#> 8 Areo Hotah "Captain of the Guard at Sunspear"
#> 9 Chett ""
#> 10 Cressen "Maester"
#> # ℹ 49 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 repurrrsive 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]> OKNotice 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
#> 7 Arlington <named list [5]> OKNow 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
#> <chr> <list> <chr> <list> <chr>
#> 1 Houston <list [4]> Houston, TX, USA <named list> ChIJAYW…
#> 2 Washington <list [2]> Washington, USA <named list> ChIJ-bD…
#> 3 Washington <list [4]> Washington, DC, … <named list> ChIJW-T…
#> 4 New York <list [3]> New York, NY, USA <named list> ChIJOwg…
#> 5 Chicago <list [4]> Chicago, IL, USA <named list> ChIJ7cv…
#> 6 Arlington <list [4]> Arlington, TX, U… <named list> ChIJ05g…
#> 7 Arlington <list [4]> Arlington, VA, U… <named list> ChIJD6e…
#> # ℹ 2 more variables: types <list>, status <chr>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 Houst… <list [4]> Houston, TX, USA <named list> <named list>
#> 2 Washi… <list [2]> Washington, USA <named list> <named list>
#> 3 Washi… <list [4]> Washington, DC, … <named list> <named list>
#> 4 New Y… <list [3]> New York, NY, USA <named list> <named list>
#> 5 Chica… <list [4]> Chicago, IL, USA <named list> <named list>
#> 6 Arlin… <list [4]> Arlington, TX, U… <named list> <named list>
#> 7 Arlin… <list [4]> Arlington, VA, U… <named list> <named list>
#> # 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 Houst… <list [4]> Houston, TX, USA <named list> 29.8 -95.4
#> 2 Washi… <list [2]> Washington, USA <named list> 47.8 -121.
#> 3 Washi… <list [4]> Washington, DC, … <named list> 38.9 -77.0
#> 4 New Y… <list [3]> New York, NY, USA <named list> 40.7 -74.0
#> 5 Chica… <list [4]> Chicago, IL, USA <named list> 41.9 -87.6
#> 6 Arlin… <list [4]> Arlington, TX, U… <named list> 32.7 -97.1
#> 7 Arlin… <list [4]> Arlington, VA, U… <named list> 38.9 -77.1
#> # 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 Houst… <list [4]> Houston, TX, USA <named list> 29.8 -95.4
#> 2 Washi… <list [2]> Washington, USA <named list> 47.8 -121.
#> 3 New Y… <list [3]> New York, NY, USA <named list> 40.7 -74.0
#> 4 Chica… <list [4]> Chicago, IL, USA <named list> 41.9 -87.6
#> 5 Arlin… <list [4]> Arlington, TX, U… <named list> 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
#> 7 350315345 2019-01-29 15:48:37 <named list [11]> 7113575 0
#> 8 350315103 2019-01-29 15:47:22 <named list [11]> 10540713 0
#> 9 350314507 2019-01-29 15:44:08 <named list [11]> 11260950 0
#> 10 350314047 2019-01-29 15:41:35 <named list [11]> 11726853 0
#> # ℹ 145 more rowsAt 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
#> <int> <dttm> <list> <int> <chr> <list>
#> 1 354823933 2019-02-16 17:48:59 <list [1]> 2015 NA <list>
#> 2 354092601 2019-02-13 14:13:11 <list [1]> 2013 https://ap… <list>
#> 3 354091476 2019-02-13 14:07:23 <list [1]> 2017 https://ap… <list>
#> 4 351244906 2019-02-02 11:39:58 <list [3]> 2017 https://ap… <list>
#> 5 351244801 2019-02-02 11:39:37 <list [1]> 2015 https://ap… <list>
#> 6 351052065 2019-02-01 20:40:53 <list [1]> 2019 https://ap… <list>
#> 7 350315345 2019-01-29 15:48:37 <list [1]> 2014 https://ap… <list>
#> 8 350315103 2019-01-29 15:47:22 <list [1]> 2015 https://ap… <list>
#> 9 350314507 2019-01-29 15:44:08 <list [1]> 2017 https://ap… <list>
#> 10 350314047 2019-01-29 15:41:35 <list [1]> 2017 NA <list>
#> # ℹ 145 more rows
#> # ℹ 9 more variables: id...7 <int>, thumb <chr>, 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
#> <int> <dttm> <list> <int> <chr> <list>
#> 1 354823933 2019-02-16 17:48:59 <list [1]> 2015 NA <list>
#> 2 354092601 2019-02-13 14:13:11 <list [1]> 2013 https://ap… <list>
#> 3 354091476 2019-02-13 14:07:23 <list [1]> 2017 https://ap… <list>
#> 4 351244906 2019-02-02 11:39:58 <list [3]> 2017 https://ap… <list>
#> 5 351244801 2019-02-02 11:39:37 <list [1]> 2015 https://ap… <list>
#> 6 351052065 2019-02-01 20:40:53 <list [1]> 2019 https://ap… <list>
#> 7 350315345 2019-01-29 15:48:37 <list [1]> 2014 https://ap… <list>
#> 8 350315103 2019-01-29 15:47:22 <list [1]> 2015 https://ap… <list>
#> 9 350314507 2019-01-29 15:44:08 <list [1]> 2017 https://ap… <list>
#> 10 350314047 2019-01-29 15:41:35 <list [1]> 2017 NA <list>
#> # ℹ 145 more rows
#> # ℹ 8 more variables: id <int>, thumb <chr>, 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
#> <int> <dttm> <chr> <int> <chr> <chr>
#> 1 354823933 2019-02-16 17:48:59 Demo 2015 Tobi… Mollot
#> 2 354092601 2019-02-13 14:13:11 Observant Com El… 2013 La V… Una B…
#> 3 354091476 2019-02-13 14:07:23 I 2017 La V… S.H.I…
#> 4 351244906 2019-02-02 11:39:58 Oído Absoluto 2017 La V… Rata …
#> 5 351244801 2019-02-02 11:39:37 A Cat's Cause, N… 2015 Kato… Ivy (…
#> 6 351052065 2019-02-01 20:40:53 Tashme 2019 High… Tashme
#> 7 350315345 2019-01-29 15:48:37 Demo 2014 Mind… Desgr…
#> 8 350315103 2019-01-29 15:47:22 Let The Miracles… 2015 Not … Phant…
#> 9 350314507 2019-01-29 15:44:08 Sub Space 2017 Not … Sub S…
#> 10 350314047 2019-01-29 15:41:35 Demo 2017 Pres… Small…
#> # ℹ 145 more rows
#> # ℹ 3 more variables: basic_information <list>, 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 In… "" "" "" 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
#> 7 7113575 "" Desgraciados "" "" "" https://api… 4.45e6
#> 8 10540713 "" Phantom Head "" "" "" https://api… 4.27e6
#> 9 11260950 "" Sub Space (2) "" "" "" https://api… 5.69e6
#> 10 11726853 "" Small Man (2) "" "" "" https://api… 6.37e6
#> # ℹ 157 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
#> 7 9769203 "Album" NA Vinyl 1
#> 8 7237138 "7\"" NA Vinyl 1
#> 9 7237138 "45 RPM" NA Vinyl 1
#> 10 13117042 "7\"" NA Vinyl 1
#> # ℹ 248 more rowsThen you could join these back on to the original dataset as needed.