In tidyverse/tidyr: Tidy Messy Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

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 to unnest_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.

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]])

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)

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"
)

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

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

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

repos %>% hoist(repo,
  login = c("owner", "login"),
  name = "name",
  homepage = "homepage",
  watchers = "watchers_count"
)

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)

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

chars2 <- chars %>% unnest_wider(char)
chars2

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)

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)

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

chars2 %>%
  select(name, title = titles) %>%
  unnest_longer(title)

(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

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

repurrrsive::gmaps_cities %>%
  unnest_wider(json)

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)

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

repurrrsive::gmaps_cities %>%
  unnest_wider(json) %>%
  unnest_longer(results) %>%
  unnest_wider(results)

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)

And then location:

repurrrsive::gmaps_cities %>%
  unnest_wider(json) %>%
  unnest_longer(results) %>%
  unnest_wider(results) %>%
  unnest_wider(geometry) %>%
  unnest_wider(location)

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)

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")
  )

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-time[^readr].

[^readr]: 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.

discs <- tibble(disc = discog) %>%
  unnest_wider(disc) %>%
  mutate(date_added = as.POSIXct(strptime(date_added, "%Y-%m-%dT%H:%M:%S")))
discs

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)

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")

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)

Alternatively, we could use hoist():

discs %>%
  hoist(basic_information,
    title = "title",
    year = "year",
    label = list("labels", 1, "name"),
    artist = list("artists", 1, "name")
  )

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)

discs %>%
  hoist(basic_information, format = "formats") %>%
  select(disc_id = id, format) %>%
  unnest_longer(format) %>%
  unnest_wider(format) %>%
  unnest_longer(descriptions)