Compare with Tidyr's Rectangling

Introduction

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:

library(tidyr)
#> Warning: replacing previous import 'lifecycle::last_warnings' by
#> 'rlang::last_warnings' when loading 'pillar'
#> Warning: replacing previous import 'lifecycle::last_warnings' by
#> 'rlang::last_warnings' when loading 'tibble'
library(dplyr)
library(repurrrsive)
library(unnest)
options(unnest.return.type = "tibble")

GitHub repos

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 rows

With 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 rows

unnest 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.

Game of Thrones characters

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.

Stacking

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 rows

Implementation 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 rows

Id-value long tables (aka long pivoting, or melting)

A 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))))

Id-value wide tables

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.

# There seem not to be an easy way to achieve this with tidyr

unnest(got_chars,
       s(stack = T,
         s("name"),
         s("books,tvSeries", stack = "media", as = "value")))

Wide Tables (aka spreading)

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 tidyr errors on double widening due to name conflicts.
# tibble(char = got_chars) %>%
#   unnest_wider(char) %>%
#   select(name, books, tvSeries) %>%
#   unnest_wider(books) %>%
#   unnest_wider(tvSeries)

unnest(got_chars, s(stack = T, s("name, books, tvSeries")))

Sharla Gelfand's discography

Finally, the most complex transformation from [tidyr'[s vignette can be achieved with unnest in a single step.

Typical entry of disog collection looks like this

str(discog[[3]])
#> List of 5
#>  $ instance_id      : int 354091476
#>  $ date_added       : chr "2019-02-13T14:07:23-08:00"
#>  $ basic_information:List of 11
#>   ..$ labels      :List of 1
#>   .. ..$ :List of 6
#>   .. .. ..$ name            : chr "La Vida Es Un Mus"
#>   .. .. ..$ entity_type     : chr "1"
#>   .. .. ..$ catno           : chr "MUS118"
#>   .. .. ..$ resource_url    : chr "https://api.discogs.com/labels/38322"
#>   .. .. ..$ id              : int 38322
#>   .. .. ..$ entity_type_name: chr "Label"
#>   ..$ year        : int 2017
#>   ..$ master_url  : chr "https://api.discogs.com/masters/1109943"
#>   ..$ artists     :List of 1
#>   .. ..$ :List of 7
#>   .. .. ..$ join        : chr ""
#>   .. .. ..$ name        : chr "S.H.I.T. (3)"
#>   .. .. ..$ anv         : chr ""
#>   .. .. ..$ tracks      : chr ""
#>   .. .. ..$ role        : chr ""
#>   .. .. ..$ resource_url: chr "https://api.discogs.com/artists/2769828"
#>   .. .. ..$ id          : int 2769828
#>   ..$ id          : int 9827276
#>   ..$ thumb       : chr "https://img.discogs.com/x6GUri3hXAcfzF2wz5jQloomOoY=/fit-in/150x150/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
#>   ..$ title       : chr "I"
#>   ..$ formats     :List of 1
#>   .. ..$ :List of 3
#>   .. .. ..$ descriptions:List of 3
#>   .. .. .. ..$ : chr "7\""
#>   .. .. .. ..$ : chr "45 RPM"
#>   .. .. .. ..$ : chr "EP"
#>   .. .. ..$ name        : chr "Vinyl"
#>   .. .. ..$ qty         : chr "1"
#>   ..$ cover_image : chr "https://img.discogs.com/7aJPlo2phtFL-T2Kt6MTBc0uftY=/fit-in/600x600/filters:strip_icc():format(jpeg):mode_rgb()"| __truncated__
#>   ..$ resource_url: chr "https://api.discogs.com/releases/9827276"
#>   ..$ master_id   : int 1109943
#>  $ id               : int 9827276
#>  $ rating           : int 0

We want to extract artists metadata and formats into separate tables.

tibble(disc = discog) %>%
  unnest_wider(disc) %>%
  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
#>  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
#> # … with 157 more rows
tibble(disc = discog) %>%
  unnest_wider(disc) %>%
  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    
#>  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    
#> # … with 271 more rows

With unnest you can achieve this in two separate passes through the list, or in a single pass with a grouped children specification. The single pass extraction returns a list of data.frames, but scans the data only once.

Separate unnest calls:

unnest(discog,
       s(stack = T,
         s("id", as = "disc_id"),
         s("basic_information/artists", as = "artist",
           s(stack = T))))

unnest(discog,
       s(stack = T,
         s("id", as = "disc_id"),
         s("basic_information/formats", as = "format",
           s(stack = T,
             s(exclude = "descriptions"),
             s("descriptions/", stack = T)))))

Single unnest pass:

unnest(discog,
       s(stack = T,
         groups =
           list(artists =
                  list(s("id", as = "disc_id"),
                       s("basic_information/artists", as = "artist",
                         s(stack = T))),
                formats =
                  list(s("id", as = "disc_id"),
                       s("basic_information/formats", as = "format",
                         s(stack = T,
                           s(exclude = "descriptions"),
                           s("descriptions/", stack = T)))))))

The unnest specs inside groups is the same as in the separate-calls case. The groups argument is just like children argument with the difference that the output of the extraction is not cross-joined, but simply returned as list.1

The benefit is grouped extraction is twofold. First, it's faster because the list is traversed only once. Second, the de-duplication works across groups. That is, when dedupe = TRUE (not shown in the above examples), the fields extracted by the preceding specs are not extracted by the specs that follow.

  1. Currently groups argument works only with the top level of the unnest specification.↩