Summary

Rectangling is the process of untangling lists into tables.

Overview

This post is an abbreviated version of the tidyr vignette on rectangling, along with a few diversions. According to the vignette, “[r]ectangling 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.” If you prefer to interactively explore the vignette from the command line, you’ll need to install tidyr, dplyr and repurrrsive packages and call the vignette.

#load packages
library(tidyr)
library(dplyr)
library(repurrrsive)
#load vignette in help window
vignette("rectangle", package = "tidyr")

Or the vignette is available on its own web page. The workhorse of the effort are permutations of the unnest_* function and hoist(). Both are in the tidyr package.

The general workfow of rectangling is to populate a tibble with a list. Nested lists will be converted to list-columns whereby the various “unnest” functions can be applied like unnest_wider, unnest_longer and unnest_auto. When only small portions of a list are to be retained, hoist() may be a more efficient alternative.

Dragons – Base Case

The examples under ?tidyr::hoist() are a good place to start for rectangling. Here, we’ll begin with the “dragon” example. The data set is a two-column tibble with the first column being an atomic vector of character type. The second column is a list-column.

Insert into tibble

df <- tibble(
  character = c("Toothless", "Dory"),
  metadata = list(
    list(
      species = "dragon",
      color = "black",
      films = c(
        "How to Train Your Dragon",
        "How to Train Your Dragon 2",
        "How to Train Your Dragon: The Hidden World"
       )
    ),
    list(
      species = "blue tang",
      color = "blue",
      films = c("Finding Nemo", "Finding Dory")
    )
  )
)

Inspect

# A tibble: 2 × 2
  character metadata        
  <chr>     <list>          
1 Toothless <named list [3]>
2 Dory      <named list [3]>

unnest_wider()

The first step is to use tidyr::unnest_wider().

df %>% unnest_wider(metadata)

# A tibble: 2 × 4
  character species   color films    
  <chr>     <chr>     <chr> <list>   
1 Toothless dragon    black <chr [3]>
2 Dory      blue tang blue  <chr [2]>

hoist()

The hoist() function is helpful when you want to extract specific components.

df %>% hoist(metadata,
  "species",
  first_film = list("films", 1L),
  third_film = list("films", 3L)
)

# A tibble: 2 × 5
  character species   first_film               third_film           metadata    
  <chr>     <chr>     <chr>                    <chr>                <list>      
1 Toothless dragon    How to Train Your Dragon How to Train Your D… <named list>
2 Dory      blue tang Finding Nemo             <NA>                 <named list>

Full tibble

df %>%
  unnest_wider(metadata) %>%
  unnest_longer(films)

# A tibble: 5 × 4
  character species   color films                                     
  <chr>     <chr>     <chr> <chr>                                     
1 Toothless dragon    black How to Train Your Dragon                  
2 Toothless dragon    black How to Train Your Dragon 2                
3 Toothless dragon    black How to Train Your Dragon: The Hidden World
4 Dory      blue tang blue  Finding Nemo                              
5 Dory      blue tang blue  Finding Dory

GitHub users – unnest_wider()

Insert into tibble

users <- tibble(user = gh_users)

Inspect

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"

unnest_wider()

users |> unnest_wider(user)

# A tibble: 6 × 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>,
#   hireable <lgl>, bio <chr>, public_repos <int>, public_gists <int>,
#   followers <int>, following <int>, created_at <chr>, updated_at <chr>

hoist()

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 [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]>

GitHub repos - unnest_longer()

Insert into tibble

repos <- tibble(repo = repurrrsive::gh_repos)

Inspect

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]>

unnest_longer()

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]>
# … with 166 more rows

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]>
# … with 166 more rows

Game of Throne Characters

Insert into tibble

Inspect

# 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]>
# … with 20 more rows

unnest_wider()

# A tibble: 30 × 18
   url           id name  gender culture born  died  alive titles aliases father
   <chr>      <int> <chr> <chr>  <chr>   <chr> <chr> <lgl> <list> <list>  <chr> 
 1 https://w…  1022 Theo… Male   "Ironb… "In … ""    TRUE  <chr>  <chr>   ""    
 2 https://w…  1052 Tyri… Male   ""      "In … ""    TRUE  <chr>  <chr>   ""    
 3 https://w…  1074 Vict… Male   "Ironb… "In … ""    TRUE  <chr>  <chr>   ""    
 4 https://w…  1109 Will  Male   ""      ""    "In … FALSE <chr>  <chr>   ""    
 5 https://w…  1166 Areo… Male   "Norvo… "In … ""    TRUE  <chr>  <chr>   ""    
 6 https://w…  1267 Chett Male   ""      "At … "In … FALSE <chr>  <chr>   ""    
 7 https://w…  1295 Cres… Male   ""      "In … "In … FALSE <chr>  <chr>   ""    
 8 https://w…   130 Aria… Female "Dorni… "In … ""    TRUE  <chr>  <chr>   ""    
 9 https://w…  1303 Daen… Female "Valyr… "In … ""    TRUE  <chr>  <chr>   ""    
10 https://w…  1319 Davo… Male   "Weste… "In … ""    TRUE  <chr>  <chr>   ""    
# … with 20 more rows, and 7 more variables: mother <chr>, spouse <chr>,
#   allegiances <list>, books <list>, povBooks <list>, tvSeries <list>,
#   playedBy <list>

When the tibble was unnested, there are a collection of list-columns. To see which columns are of the class list, you can use dplyr’s select_if function.

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 [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]>
 7 <chr [1]> <chr [1]>  <NULL>      <chr [2]> <chr [1]> <chr [1]> <chr [1]>
 8 <chr [1]> <chr [1]>  <chr [1]>   <chr [4]> <chr [1]> <chr [1]> <chr [1]>
 9 <chr [5]> <chr [11]> <chr [1]>   <chr [1]> <chr [4]> <chr [6]> <chr [1]>
10 <chr [4]> <chr [5]>  <chr [2]>   <chr [1]> <chr [3]> <chr [5]> <chr [1]>
# … with 20 more rows

Build table

# A tibble: 180 × 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
# … with 170 more rows

Sharla Gelfand’s Discography

Insert into tibble

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

Inspect

Examine data.

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
# … with 145 more rows

The list-column “basic_information” needs to be unnested so the disc information can be viewed.

discs |> unnest_wider(basic_information)

Error in `unpack()`:
! Names must be unique.
x These names are duplicated:
  * "id" at locations 7 and 14.
ℹ Use argument `names_repair` to specify repair strategy.

Fix duplicate names

discs %>% unnest_wider(basic_information, names_repair = "unique")

# A tibble: 155 × 15
   instance_id date_added          labels  year master_url  artists id...7 thumb
         <int> <dttm>              <list> <int> <chr>       <list>   <int> <chr>
 1   354823933 2019-02-16 17:48:59 <list>  2015 <NA>        <list>  7.50e6 "htt…
 2   354092601 2019-02-13 14:13:11 <list>  2013 https://ap… <list>  4.49e6 "htt…
 3   354091476 2019-02-13 14:07:23 <list>  2017 https://ap… <list>  9.83e6 "htt…
 4   351244906 2019-02-02 11:39:58 <list>  2017 https://ap… <list>  9.77e6 "htt…
 5   351244801 2019-02-02 11:39:37 <list>  2015 https://ap… <list>  7.24e6 "htt…
 6   351052065 2019-02-01 20:40:53 <list>  2019 https://ap… <list>  1.31e7 "htt…
 7   350315345 2019-01-29 15:48:37 <list>  2014 https://ap… <list>  7.11e6 "htt…
 8   350315103 2019-01-29 15:47:22 <list>  2015 https://ap… <list>  1.05e7 "htt…
 9   350314507 2019-01-29 15:44:08 <list>  2017 https://ap… <list>  1.13e7 ""   
10   350314047 2019-01-29 15:41:35 <list>  2017 <NA>        <list>  1.17e7 "htt…
# … with 145 more rows, and 7 more variables: title <chr>, formats <list>,
#   cover_image <chr>, resource_url <chr>, master_id <int>, id...14 <int>,
#   rating <int>

drop id column

basic-information repeats the id column so drop it.

discs %>%
    select(!id) %>%
    unnest_wider(basic_information)

# A tibble: 155 × 14
   instance_id date_added          labels  year master_url  artists     id thumb
         <int> <dttm>              <list> <int> <chr>       <list>   <int> <chr>
 1   354823933 2019-02-16 17:48:59 <list>  2015 <NA>        <list>  7.50e6 "htt…
 2   354092601 2019-02-13 14:13:11 <list>  2013 https://ap… <list>  4.49e6 "htt…
 3   354091476 2019-02-13 14:07:23 <list>  2017 https://ap… <list>  9.83e6 "htt…
 4   351244906 2019-02-02 11:39:58 <list>  2017 https://ap… <list>  9.77e6 "htt…
 5   351244801 2019-02-02 11:39:37 <list>  2015 https://ap… <list>  7.24e6 "htt…
 6   351052065 2019-02-01 20:40:53 <list>  2019 https://ap… <list>  1.31e7 "htt…
 7   350315345 2019-01-29 15:48:37 <list>  2014 https://ap… <list>  7.11e6 "htt…
 8   350315103 2019-01-29 15:47:22 <list>  2015 https://ap… <list>  1.05e7 "htt…
 9   350314507 2019-01-29 15:44:08 <list>  2017 https://ap… <list>  1.13e7 ""   
10   350314047 2019-01-29 15:41:35 <list>  2017 <NA>        <list>  1.17e7 "htt…
# … with 145 more rows, and 6 more variables: title <chr>, formats <list>,
#   cover_image <chr>, resource_url <chr>, master_id <int>, rating <int>

`hoist()` alternative

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 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 Observan…  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 Abs…  2017 La V… Rata … <named list [9]>
 5   351244801 2019-02-02 11:39:37 A Cat's …  2015 Kato… Ivy (… <named list [9]>
 6   351052065 2019-02-01 20:40:53 Tashme     2019 High… Tashme <named list [9]>
 7   350315345 2019-01-29 15:48:37 Demo       2014 Mind… Desgr… <named list [9]>
 8   350315103 2019-01-29 15:47:22 Let The …  2015 Not … Phant… <named list [9]>
 9   350314507 2019-01-29 15:44:08 Sub Space  2017 Not … Sub S… <named list [9]>
10   350314047 2019-01-29 15:41:35 Demo       2017 Pres… Small… <named list [9]>
# … with 145 more rows, and 2 more variables: id <int>, rating <int>

Better alternative

A better alternative would be to create separate tables for artist and label:

# artist
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 Incontrolab… ""    ""     ""    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

#discs
discs %>%
    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

Conclusion

This post loosely followed the tidyr vignette “Rectangling.” That said, the section on google geocoding was omitted. The vignette demonstrated the use of unnest_wider(), unnest_longer(), and hoist(). It also showed how to solve an error when names in a list are duplicated by adding the argument names_repair = "unique".

Acknowledgements

This blog post was made possible thanks to:

Jenny Bryan, the maintainer of the repurrrsive package.

References

[1]

R Core Team, R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2022 [Online]. Available: https://www.R-project.org/

[2]

Y. Xie, C. Dervieux, and A. Presmanes Hill, Blogdown: Create blogs and websites with r markdown. 2022 [Online]. Available: https://CRAN.R-project.org/package=blogdown

[3]

H. Wickham, R. François, L. Henry, and K. Müller, Dplyr: A grammar of data manipulation. 2022 [Online]. Available: https://CRAN.R-project.org/package=dplyr

[4]

L. Henry and H. Wickham, Purrr: Functional programming tools. 2020 [Online]. Available: https://CRAN.R-project.org/package=purrr

[5]

J. Bryan, Repurrrsive: Examples of recursive lists and nested or split data frames. 2019 [Online]. Available: https://github.com/jennybc/repurrrsive

[6]

H. Wickham and M. Girlich, Tidyr: Tidy messy data. 2022 [Online]. Available: https://CRAN.R-project.org/package=tidyr

Disclaimer

The views, analysis and conclusions presented within this paper represent the author’s alone and not of any other person, organization or government entity. While I have made every reasonable effort to ensure that the information in this article was correct, it will nonetheless contain errors, inaccuracies and inconsistencies. It is a working paper subject to revision without notice as additional information becomes available. Any liability is disclaimed as to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from negligence, accident, or any other cause. The author(s) received no financial support for the research, authorship, and/or publication of this article.

Reproducibility

─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.1.3 (2022-03-10)
 os       macOS Big Sur/Monterey 10.16
 system   x86_64, darwin17.0
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/Chicago
 date     2022-04-22
 pandoc   2.14.1 @ /usr/local/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
 package     * version    date (UTC) lib source
 assertthat    0.2.1      2019-03-21 [1] CRAN (R 4.1.0)
 blogdown    * 1.9        2022-03-28 [1] CRAN (R 4.1.2)
 bookdown      0.25       2022-03-16 [1] CRAN (R 4.1.2)
 brio          1.1.3      2021-11-30 [1] CRAN (R 4.1.0)
 bslib         0.3.1.9000 2022-03-04 [1] Github (rstudio/bslib@888fbe0)
 cachem        1.0.6      2021-08-19 [1] CRAN (R 4.1.0)
 callr         3.7.0      2021-04-20 [1] CRAN (R 4.1.0)
 cli           3.2.0      2022-02-14 [1] CRAN (R 4.1.2)
 colorspace    2.0-3      2022-02-21 [1] CRAN (R 4.1.2)
 crayon        1.5.1      2022-03-26 [1] CRAN (R 4.1.0)
 DBI           1.1.2      2021-12-20 [1] CRAN (R 4.1.0)
 desc          1.4.1      2022-03-06 [1] CRAN (R 4.1.2)
 devtools    * 2.4.3      2021-11-30 [1] CRAN (R 4.1.0)
 digest        0.6.29     2021-12-01 [1] CRAN (R 4.1.0)
 dplyr       * 1.0.8      2022-02-08 [1] CRAN (R 4.1.2)
 ellipsis      0.3.2      2021-04-29 [1] CRAN (R 4.1.0)
 evaluate      0.15       2022-02-18 [1] CRAN (R 4.1.2)
 fansi         1.0.3      2022-03-24 [1] CRAN (R 4.1.2)
 fastmap       1.1.0      2021-01-25 [1] CRAN (R 4.1.0)
 fs            1.5.2      2021-12-08 [1] CRAN (R 4.1.0)
 generics      0.1.2      2022-01-31 [1] CRAN (R 4.1.2)
 ggplot2       3.3.5      2021-06-25 [1] CRAN (R 4.1.0)
 ggthemes    * 4.2.4      2021-01-20 [1] CRAN (R 4.1.0)
 glue          1.6.2      2022-02-24 [1] CRAN (R 4.1.2)
 gtable        0.3.0      2019-03-25 [1] CRAN (R 4.1.0)
 htmltools     0.5.2      2021-08-25 [1] CRAN (R 4.1.0)
 jquerylib     0.1.4      2021-04-26 [1] CRAN (R 4.1.0)
 jsonlite      1.8.0      2022-02-22 [1] CRAN (R 4.1.2)
 knitr         1.38       2022-03-25 [1] CRAN (R 4.1.0)
 lifecycle     1.0.1      2021-09-24 [1] CRAN (R 4.1.0)
 magrittr      2.0.3      2022-03-30 [1] CRAN (R 4.1.2)
 memoise       2.0.1      2021-11-26 [1] CRAN (R 4.1.0)
 munsell       0.5.0.9000 2021-10-19 [1] Github (cwickham/munsell@e539541)
 pillar        1.7.0      2022-02-01 [1] CRAN (R 4.1.2)
 pkgbuild      1.3.1      2021-12-20 [1] CRAN (R 4.1.0)
 pkgconfig     2.0.3      2019-09-22 [1] CRAN (R 4.1.0)
 pkgload       1.2.4      2021-11-30 [1] CRAN (R 4.1.0)
 prettyunits   1.1.1      2020-01-24 [1] CRAN (R 4.1.0)
 processx      3.5.3      2022-03-25 [1] CRAN (R 4.1.0)
 ps            1.6.0      2021-02-28 [1] CRAN (R 4.1.0)
 purrr       * 0.3.4      2020-04-17 [1] CRAN (R 4.1.0)
 R6            2.5.1      2021-08-19 [1] CRAN (R 4.1.0)
 remotes       2.4.2      2021-11-30 [1] CRAN (R 4.1.0)
 repurrrsive * 1.0.0      2019-07-15 [1] CRAN (R 4.1.0)
 rlang         1.0.2      2022-03-04 [1] CRAN (R 4.1.2)
 rmarkdown     2.13       2022-03-10 [1] CRAN (R 4.1.2)
 rprojroot     2.0.3      2022-04-02 [1] CRAN (R 4.1.0)
 rstudioapi    0.13       2020-11-12 [1] CRAN (R 4.1.0)
 sass          0.4.1      2022-03-23 [1] CRAN (R 4.1.2)
 scales        1.1.1      2020-05-11 [1] CRAN (R 4.1.0)
 sessioninfo   1.2.2      2021-12-06 [1] CRAN (R 4.1.0)
 stringi       1.7.6      2021-11-29 [1] CRAN (R 4.1.0)
 stringr       1.4.0      2019-02-10 [1] CRAN (R 4.1.0)
 testthat      3.1.3      2022-03-29 [1] CRAN (R 4.1.2)
 tibble        3.1.6      2021-11-07 [1] CRAN (R 4.1.0)
 tidyr       * 1.2.0      2022-02-01 [1] CRAN (R 4.1.2)
 tidyselect    1.1.2      2022-02-21 [1] CRAN (R 4.1.2)
 usethis     * 2.1.5      2021-12-09 [1] CRAN (R 4.1.0)
 utf8          1.2.2      2021-07-24 [1] CRAN (R 4.1.0)
 vctrs         0.4.0      2022-03-30 [1] CRAN (R 4.1.2)
 withr         2.5.0      2022-03-03 [1] CRAN (R 4.1.0)
 xfun          0.30       2022-03-02 [1] CRAN (R 4.1.2)
 yaml          2.3.5      2022-02-21 [1] CRAN (R 4.1.2)

 [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library

──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

Rectangling-Part-05

Summary

Table of Contents

Overview

Dragons – Base Case

Insert into tibble

Inspect

unnest_wider()

hoist()

Full tibble

GitHub users – unnest_wider()

Insert into tibble

Inspect

unnest_wider()

hoist()

GitHub repos - unnest_longer()

Insert into tibble

Inspect

unnest_longer()

hoist()

Game of Throne Characters

Insert into tibble

Inspect

unnest_wider()

Build table

Sharla Gelfand’s Discography

Insert into tibble

Inspect

Fix duplicate names

drop id column

`hoist()` alternative

Better alternative

Conclusion

Acknowledgements

References

Disclaimer

Reproducibility

Rectangling-Part-05

Summary

Table of Contents

Insert into tibble

Inspect

unnest_wider()

hoist()

Full tibble

Insert into tibble

Inspect

unnest_wider()

hoist()

Insert into tibble

Inspect

unnest_longer()

hoist()

Insert into tibble

Inspect

unnest_wider()

Build table

Insert into tibble

Inspect

Fix duplicate names

drop id column

hoist() alternative

Better alternative

`hoist()` alternative