The taylor_all_songs and
taylor_album_songs data sets contain the lyrics for each of
Taylor Swift’s songs, as well as audio characteristics. In each data
set, the lyrics are stored as a list-column.
library(taylor)
library(dplyr)
track_lyrics <- taylor_album_songs %>%
select(album_name, track_name, lyrics)
track_lyrics
#> # A tibble: 240 × 3
#> album_name track_name lyrics
#> <chr> <chr> <list>
#> 1 Taylor Swift Tim McGraw <tibble [55 × 4]>
#> 2 Taylor Swift Picture To Burn <tibble [33 × 4]>
#> 3 Taylor Swift Teardrops On My Guitar <tibble [36 × 4]>
#> 4 Taylor Swift A Place In This World <tibble [27 × 4]>
#> 5 Taylor Swift Cold As You <tibble [24 × 4]>
#> 6 Taylor Swift The Outside <tibble [37 × 4]>
#> 7 Taylor Swift Tied Together With A Smile <tibble [36 × 4]>
#> 8 Taylor Swift Stay Beautiful <tibble [51 × 4]>
#> 9 Taylor Swift Should've Said No <tibble [44 × 4]>
#> 10 Taylor Swift Mary's Song (Oh My My My) <tibble [38 × 4]>
#> # ℹ 230 more rowsIn other words, both taylor_all_songs and
taylor_ablum_songs are nested data
frames. Each has one row per track, and the lyrics for each track
are are stored in another data frame nested within each row.
There are three primary ways to access data from a nested list-column. The first is to extract individual list elements. For example, if we want to see the lyrics for “Cruel Summer,” we can look up which row of the data set contains “Cruel Summer” and then access that element of the list.
track_row <- which(track_lyrics$track_name == "Cruel Summer")
track_lyrics$lyrics[[track_row]]
#> # A tibble: 62 × 4
#> line lyric element element_artist
#> <int> <chr> <chr> <chr>
#> 1 1 (Yeah, yeah, yeah, yeah) Intro Taylor Swift
#> 2 2 Fever dream high in the quiet of the night Verse 1 Taylor Swift
#> 3 3 You know that I caught it (Oh yeah, you're righ… Verse 1 Taylor Swift
#> 4 4 Bad, bad boy, shiny toy with a price Verse 1 Taylor Swift
#> 5 5 You know that I bought it (Oh yeah, you're righ… Verse 1 Taylor Swift
#> 6 6 Killing me slow, out the window Pre-Ch… Taylor Swift
#> 7 7 I'm always waiting for you to be waiting below Pre-Ch… Taylor Swift
#> 8 8 Devils roll the dice, angels roll their eyes Pre-Ch… Taylor Swift
#> 9 9 What doesn't kill me makes me want you more Pre-Ch… Taylor Swift
#> 10 10 And it's new, the shape of your body Chorus Taylor Swift
#> # ℹ 52 more rowsAs expected, this returns another data frame, with one row for each
line in the song. However, this approach only allows us to unnest one
track at a time. A more efficient method for extracting data in a nested
list-column is to use tidyr::unnest(). This approach
unnests all of the data in a list-column at once. Rather than having a
data set that is one row per song, we now have a data set that is one
row per line per song.
library(tidyr)
track_lyrics %>%
unnest(lyrics)
#> # A tibble: 12,151 × 6
#> album_name track_name line lyric element element_artist
#> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 Taylor Swift Tim McGraw 1 "He said the way my blu… Verse 1 Taylor Swift
#> 2 Taylor Swift Tim McGraw 2 "Put those Georgia star… Verse 1 Taylor Swift
#> 3 Taylor Swift Tim McGraw 3 "I said, \"That's a lie… Verse 1 Taylor Swift
#> 4 Taylor Swift Tim McGraw 4 "Just a boy in a Chevy … Verse 1 Taylor Swift
#> 5 Taylor Swift Tim McGraw 5 "That had a tendency of… Verse 1 Taylor Swift
#> 6 Taylor Swift Tim McGraw 6 "On backroads at night" Verse 1 Taylor Swift
#> 7 Taylor Swift Tim McGraw 7 "And I was right there … Verse 1 Taylor Swift
#> 8 Taylor Swift Tim McGraw 8 "And then the time we w… Verse 1 Taylor Swift
#> 9 Taylor Swift Tim McGraw 9 "But when you think Tim… Chorus Taylor Swift
#> 10 Taylor Swift Tim McGraw 10 "I hope you think my fa… Chorus Taylor Swift
#> # ℹ 12,141 more rowsIf we are interested in the lyrics for only a specific album or a
specific song, we can always use dplyr::filter() to include
only the data we are interested in.
track_lyrics %>%
filter(track_name == "Cruel Summer") %>%
unnest(lyrics)
#> # A tibble: 62 × 6
#> album_name track_name line lyric element element_artist
#> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 Lover Cruel Summer 1 (Yeah, yeah, yeah, yeah) Intro Taylor Swift
#> 2 Lover Cruel Summer 2 Fever dream high in the… Verse 1 Taylor Swift
#> 3 Lover Cruel Summer 3 You know that I caught … Verse 1 Taylor Swift
#> 4 Lover Cruel Summer 4 Bad, bad boy, shiny toy… Verse 1 Taylor Swift
#> 5 Lover Cruel Summer 5 You know that I bought … Verse 1 Taylor Swift
#> 6 Lover Cruel Summer 6 Killing me slow, out th… Pre-Ch… Taylor Swift
#> 7 Lover Cruel Summer 7 I'm always waiting for … Pre-Ch… Taylor Swift
#> 8 Lover Cruel Summer 8 Devils roll the dice, a… Pre-Ch… Taylor Swift
#> 9 Lover Cruel Summer 9 What doesn't kill me ma… Pre-Ch… Taylor Swift
#> 10 Lover Cruel Summer 10 And it's new, the shape… Chorus Taylor Swift
#> # ℹ 52 more rowsFinally, sometimes we want to perform a calculation on each element
of a list-column. In this case, we don’t necessarily need to unnest each
element. Instead, we can use a combination of
dplyr::mutate() and purrr::map() to apply a
function to each element of the list-column. For example, we if want to
know the number of lines in each song, we can apply nrow()
to each element of the list column. Because nrow() returns
and integer value, we’ll use vapply() (we could also use
purrr::map_int()).
track_lyrics %>%
filter(album_name == "Lover") %>%
mutate(lines = vapply(lyrics, nrow, integer(1)))
#> # A tibble: 18 × 4
#> album_name track_name lyrics lines
#> <chr> <chr> <list> <int>
#> 1 Lover I Forgot That You Existed <tibble [45 × 4]> 45
#> 2 Lover Cruel Summer <tibble [62 × 4]> 62
#> 3 Lover Lover <tibble [33 × 4]> 33
#> 4 Lover The Man <tibble [48 × 4]> 48
#> 5 Lover The Archer <tibble [45 × 4]> 45
#> 6 Lover I Think He Knows <tibble [65 × 4]> 65
#> 7 Lover Miss Americana & The Heartbreak Prince <tibble [62 × 4]> 62
#> 8 Lover Paper Rings <tibble [65 × 4]> 65
#> 9 Lover Cornelia Street <tibble [53 × 4]> 53
#> 10 Lover Death By A Thousand Cuts <tibble [60 × 4]> 60
#> 11 Lover London Boy <tibble [58 × 4]> 58
#> 12 Lover Soon You'll Get Better <tibble [46 × 4]> 46
#> 13 Lover False God <tibble [50 × 4]> 50
#> 14 Lover You Need To Calm Down <tibble [40 × 4]> 40
#> 15 Lover Afterglow <tibble [48 × 4]> 48
#> 16 Lover ME! <tibble [64 × 4]> 64
#> 17 Lover It's Nice To Have A Friend <tibble [28 × 4]> 28
#> 18 Lover Daylight <tibble [59 × 4]> 59The resulting data frame is still one row per song, because we have not unnested the lyrics. However, our summary statistic has been added as an additional column.