As per tidyr definition unnesting (or 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:tidyr core functions for unnesting are unnest_longer(), unnest_wider(), hoist(). This guide follows the steps from tidyr vignette and translates them into unnest’s language.
With tidyr you have to unnest lists in several steps by using one of the three core functions. With unnest you do all at once in one step. unnest doesn’t produce intermediate list columns.
We’ll use the repurrrsive package as the source of our nested lists:
With tidyr you start by putting a list into a data.frame column. With unnest this is not necessary.
gh_repos is a nested list with maximal depth of 4 “user”>“repo”>“owner”>“[xyz]”.
str(gh_repos[[1]][[1]][["owner"]])Let’s say that we want a data.frame with 3 columns, “name”, “homepage” and “watchers_count”, from level 3 of repo characteristics and one,“login”, from level 4 of owner characteristics. This is how it’s done with tidyr:
repos <- tibble(repo = gh_repos)
repos <- unnest_longer(repos, repo)
hoist(repos, repo,
login = c("owner", "login"),
name = "name",
homepage = "homepage",
watchers = "watchers_count") %>%
select(-repo)
#> # A tibble: 176 × 4
#> login name homepage watchers
#> <chr> <chr> <chr> <int>
#> 1 gaborcsardi after <NA> 5
#> 2 gaborcsardi argufy <NA> 19
#> 3 gaborcsardi ask <NA> 5
#> 4 gaborcsardi baseimports <NA> 0
#> 5 gaborcsardi citest <NA> 0
#> 6 gaborcsardi clisymbols "" 18
#> 7 gaborcsardi cmaker <NA> 0
#> 8 gaborcsardi cmark <NA> 0
#> 9 gaborcsardi conditions <NA> 0
#> 10 gaborcsardi crayon <NA> 52
#> # … with 166 more rowsWith unnest:
spec <- s(stack = TRUE,
s(stack = TRUE,
s("name"),
s("homepage"),
s("watchers_count", as = "watchers"),
s("owner",
s("login"))))
unnest(gh_repos, spec)
#> # A tibble: 176 × 4
#> homepage name owner.login watchers
#> <chr> <chr> <chr> <int>
#> 1 <NA> after gaborcsardi 5
#> 2 <NA> argufy gaborcsardi 19
#> 3 <NA> ask gaborcsardi 5
#> 4 <NA> baseimports gaborcsardi 0
#> 5 <NA> citest gaborcsardi 0
#> 6 "" clisymbols gaborcsardi 18
#> 7 <NA> cmaker gaborcsardi 0
#> 8 <NA> cmark gaborcsardi 0
#> 9 <NA> conditions gaborcsardi 0
#> 10 <NA> crayon gaborcsardi 52
#> # … with 166 more rowsunnest selectors (s()) apply to corresponding levels of the hierarchy and describe which elements should be selected and how. The stack = TRUE says that the result of the extraction should be stacked row-wise (aka rbinded). stack = FALSE, means spread it across multiple columns (aka cbinded). The as argument provides the name of the output. By default it’s the entire path name to the selected leaf.
Now assume that you want the 3 components of “repos” and all components of the owner at once:
tibble(repo = gh_repos) %>%
unnest_longer(repo) %>%
hoist(repo,
name = "name",
homepage = "homepage",
watchers = "watchers_count") %>%
hoist(repo, owner = "owner") %>%
unnest_wider(owner)With unnest
spec <- s(stack = TRUE,
s(stack = TRUE,
s("name"),
s("homepage"),
s("watchers_count", as = "watchers"),
s("owner")))
unnest(gh_repos, spec) %>% tibble()
#> # A tibble: 176 × 20
#> homepage name owner.avatar_url owner.events_url owner.followers_…
#> <chr> <chr> <chr> <chr> <chr>
#> 1 <NA> after https://avatars.g… https://api.github… https://api.gith…
#> 2 <NA> argufy https://avatars.g… https://api.github… https://api.gith…
#> 3 <NA> ask https://avatars.g… https://api.github… https://api.gith…
#> 4 <NA> baseimports https://avatars.g… https://api.github… https://api.gith…
#> 5 <NA> citest https://avatars.g… https://api.github… https://api.gith…
#> 6 "" clisymbols https://avatars.g… https://api.github… https://api.gith…
#> 7 <NA> cmaker https://avatars.g… https://api.github… https://api.gith…
#> 8 <NA> cmark https://avatars.g… https://api.github… https://api.gith…
#> 9 <NA> conditions https://avatars.g… https://api.github… https://api.gith…
#> 10 <NA> crayon https://avatars.g… https://api.github… https://api.gith…
#> # … with 166 more rows, and 15 more variables: owner.following_url <chr>,
#> # owner.gists_url <chr>, owner.gravatar_id <chr>, owner.html_url <chr>,
#> # owner.id <int>, owner.login <chr>, owner.organizations_url <chr>,
#> # owner.received_events_url <chr>, owner.repos_url <chr>,
#> # owner.site_admin <lgl>, owner.starred_url <chr>,
#> # owner.subscriptions_url <chr>, owner.type <chr>, owner.url <chr>,
#> # watchers <int>Note that unnest produces namespaced column names, while [tidyr’[s is not. This is a good thing as you don’t have to worry about conflicting names. tidyr provides a “fix” for duplicated names in the form of names_repair argument to its functions.
What do you do with non-singleton leafs? Those are normally stacked, spread or melted depending on the analysis. For example the Game of Thrones dataset contains non-singleton leafs “titles”, “aliases”, “books” etc.
str(got_chars[[1]])Let’s have a look at some common scenarios.
Assume that we want a row for every book and TV series that the character appears in. That is, we want a long table with all combinations (aka cross product) of books and TV series.
tibble(char = got_chars) %>%
unnest_wider(char) %>%
select(name, books, tvSeries) %>%
unnest_longer(books) %>%
unnest_longer(tvSeries)
#> # A tibble: 236 × 3
#> name books tvSeries
#> <chr> <chr> <chr>
#> 1 Theon Greyjoy A Game of Thrones Season 1
#> 2 Theon Greyjoy A Game of Thrones Season 2
#> 3 Theon Greyjoy A Game of Thrones Season 3
#> 4 Theon Greyjoy A Game of Thrones Season 4
#> 5 Theon Greyjoy A Game of Thrones Season 5
#> 6 Theon Greyjoy A Game of Thrones Season 6
#> 7 Theon Greyjoy A Storm of Swords Season 1
#> 8 Theon Greyjoy A Storm of Swords Season 2
#> 9 Theon Greyjoy A Storm of Swords Season 3
#> 10 Theon Greyjoy A Storm of Swords Season 4
#> # … with 226 more rows
unnest(got_chars,
s(stack = T,
s("name"),
s("books,tvSeries/", stack = T)))
#> # A tibble: 236 × 3
#> books name tvSeries
#> <chr> <chr> <chr>
#> 1 A Game of Thrones Theon Greyjoy Season 1
#> 2 A Storm of Swords Theon Greyjoy Season 2
#> 3 A Feast for Crows Theon Greyjoy Season 3
#> 4 A Game of Thrones Theon Greyjoy Season 4
#> 5 A Storm of Swords Theon Greyjoy Season 5
#> 6 A Feast for Crows Theon Greyjoy Season 6
#> 7 A Game of Thrones Theon Greyjoy Season 1
#> 8 A Storm of Swords Theon Greyjoy Season 2
#> 9 A Feast for Crows Theon Greyjoy Season 3
#> 10 A Game of Thrones Theon Greyjoy Season 4
#> # … with 226 more rowsImplementation aside, [tidyr’[s intermediary steps are generally costly for two reasons. First, because intermediary data.frames are created during the processing. Second, because intermediary objects might contain columns that are not needed in the subsequent processing. In the above examples unnest_wider() produced man more columns than we need. A better approach would be to replace it with a bit more verbose hoist call.
In contrast unnest doesn’t produce intermediary data structures. In fact, unnest follows a 0-intermediary-copy semantics. The input vectors are directly copied into the output, no matter how complex the nesting is.
Cross-product is commonly useful when only one non-singleton variable is extracted. For example, let’s match title to name:
tibble(char = got_chars) %>%
hoist(char, name = "name", title = "titles") %>%
select(-char) %>%
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"
#> 7 Victarion Greyjoy "Master of the Iron Victory"
#> 8 Will ""
#> 9 Areo Hotah "Captain of the Guard at Sunspear"
#> 10 Chett ""
#> # … with 50 more rows
unnest(got_chars,
s(stack = T,
s("name"),
s("titles/", stack = T)))
#> # A tibble: 60 × 2
#> name titles
#> <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"
#> 7 Victarion Greyjoy "Master of the Iron Victory"
#> 8 Will ""
#> 9 Areo Hotah "Captain of the Guard at Sunspear"
#> 10 Chett ""
#> # … with 50 more rowsA common scenario is to stack the non-scalar leafs and replicate id labels in a separate “key” column. This is called “melting” (reshape2) or “long pivoting” (tidyr).
tibble(char = got_chars) %>%
unnest_wider(char) %>%
select(name, books, tvSeries) %>%
pivot_longer(c(books, tvSeries), names_to = "media", values_to = "value") %>%
unnest_longer(value)
unnest(got_chars,
s(stack = T,
s("name"),
s("books,tvSeries", stack = "media", as = "value",
s(stack = T))))One might want to stack id vars (media) but spread the measures (books, tvSeries) horizontally such that each row would contain all measurement for each media.
This strategy is commonly used in machine learning scenarios when large sparse tables are plugged into black-box ML algorithms. This is the default behavior in unnest.
Currently groups argument works only with the top level of the unnest specification.↩︎