# Welcome to taylor

taylor is an R package for accessing and exploring data related to Taylor Swift’s discography. It provides built in data sets containing information on the audio characteristics and lyrics of Taylor’s songs. Additionally, taylor offers some helper functions for creating Taylor Swift-themed data visualizations.

This document introduces you to taylor’s functionality, and shows you how to use them to learn about Taylor Swift’s music.

    library(taylor)

## 1 I trace the evidence

The main data set is `taylor_all_songs`. This data set contains audio features from Spotify and lyrics from Genius for each of Taylor Swift’s songs.

    taylor_all_songs
    #> # A tibble: 364 × 29
    #>    album_name   ep    album_release track_number track_name     artist featuring
    #>    <chr>        <lgl> <date>               <int> <chr>          <chr>  <chr>
    #>  1 Taylor Swift FALSE 2006-10-24               1 Tim McGraw     Taylo… <NA>
    #>  2 Taylor Swift FALSE 2006-10-24               2 Picture To Bu… Taylo… <NA>
    #>  3 Taylor Swift FALSE 2006-10-24               3 Teardrops On … Taylo… <NA>
    #>  4 Taylor Swift FALSE 2006-10-24               4 A Place In Th… Taylo… <NA>
    #>  5 Taylor Swift FALSE 2006-10-24               5 Cold As You    Taylo… <NA>
    #>  6 Taylor Swift FALSE 2006-10-24               6 The Outside    Taylo… <NA>
    #>  7 Taylor Swift FALSE 2006-10-24               7 Tied Together… Taylo… <NA>
    #>  8 Taylor Swift FALSE 2006-10-24               8 Stay Beautiful Taylo… <NA>
    #>  9 Taylor Swift FALSE 2006-10-24               9 Should've Sai… Taylo… <NA>
    #> 10 Taylor Swift FALSE 2006-10-24              10 Mary's Song (… Taylo… <NA>
    #> # ℹ 354 more rows
    #> # ℹ 22 more variables: bonus_track <lgl>, promotional_release <date>,
    #> #   single_release <date>, track_release <date>, danceability <dbl>,
    #> #   energy <dbl>, key <int>, loudness <dbl>, mode <int>, speechiness <dbl>,
    #> #   acousticness <dbl>, instrumentalness <dbl>, liveness <dbl>, valence <dbl>,
    #> #   tempo <dbl>, time_signature <int>, duration_ms <int>, explicit <lgl>,
    #> #   key_name <chr>, mode_name <chr>, key_mode <chr>, lyrics <list>

The audio features include the danceability, energy, and valence of each track, which are described in the documentation for the Spotify API. The data set also includes meta data for each track such as the key, tempo, time signature, and duration. Finally, the lyrics for each track are included in a nested list column. The lyrics can be accessed by using `tidyr::unnest()`, or by using `purrr::map()` to apply a function to each set of lyrics. For a detailed description of accessing lyrics, see `vignette("lyrics")`.

A related data set is `taylor_album_songs`. This data set contains all of the same information as `taylor_all_songs`, but is filtered to only include tracks that are on official studio albums. This means that standalone singles (e.g., “Only The Young”) and features (e.g., Big Red Machine’s “Renegade”) are not included. We also exclude albums Taylor doesn’t own, but for which a Taylor’s Version has been released. For example, 1989 is excluded in favor of 1989 (Taylor’s Version), but Taylor Swift (debut) is included because a Taylor’s Version of that album has not been released.

taylor also include a small data set called `taylor_albums`. This data set includes the release date for each album, as well as critic and user ratings from Metacritic.

    taylor_albums
    #> # A tibble: 17 × 5
    #>    album_name                    ep    album_release metacritic_score user_score
    #>    <chr>                         <lgl> <date>                   <int>      <dbl>
    #>  1 Taylor Swift                  FALSE 2006-10-24                  67        8.4
    #>  2 The Taylor Swift Holiday Col… TRUE  2007-10-14                  NA       NA
    #>  3 Beautiful Eyes                TRUE  2008-07-15                  NA       NA
    #>  4 Fearless                      FALSE 2008-11-11                  73        8.4
    #>  5 Speak Now                     FALSE 2010-10-25                  77        8.6
    #>  6 Red                           FALSE 2012-10-22                  77        8.6
    #>  7 1989                          FALSE 2014-10-27                  76        8.3
    #>  8 reputation                    FALSE 2017-11-10                  71        8.3
    #>  9 Lover                         FALSE 2019-08-23                  79        8.4
    #> 10 folklore                      FALSE 2020-07-24                  88        9
    #> 11 evermore                      FALSE 2020-12-11                  85        8.9
    #> 12 Fearless (Taylor's Version)   FALSE 2021-04-09                  82        8.9
    #> 13 Red (Taylor's Version)        FALSE 2021-11-12                  91        8.9
    #> 14 Midnights                     FALSE 2022-10-21                  85        8.3
    #> 15 Speak Now (Taylor's Version)  FALSE 2023-07-07                  81        9.2
    #> 16 1989 (Taylor's Version)       FALSE 2023-10-27                  90       NA
    #> 17 THE TORTURED POETS DEPARTMENT FALSE 2024-04-19                  76       NA

Finally, there is a data set dedicated to The Eras Tour, specifically the surprise songs that Taylor plays at each show. The data set, `eras_tour_surprise`, contains the date and location of each show, the color dress Taylor wore during the acoustic set, and the song that was performed on each instrument (piano and guitar). The data set also includes information on any additional songs that were performed as mashups and guests that Taylor brought out for a performance.

    eras_tour_surprise
    #> # A tibble: 298 × 9
    #>    leg                date       city  night dress instrument song  mashup guest
    #>    <chr>              <date>     <chr> <int> <chr> <chr>      <chr> <chr>  <chr>
    #>  1 North America (Le… 2023-03-17 Glen…     1 red   guitar     mirr… <NA>   <NA>
    #>  2 North America (Le… 2023-03-17 Glen…     1 red   piano      Tim … <NA>   <NA>
    #>  3 North America (Le… 2023-03-18 Glen…     2 green guitar     this… <NA>   <NA>
    #>  4 North America (Le… 2023-03-18 Glen…     2 green piano      Stat… <NA>   <NA>
    #>  5 North America (Le… 2023-03-24 Las …     1 red   guitar     Our … <NA>   <NA>
    #>  6 North America (Le… 2023-03-24 Las …     1 red   piano      Snow… <NA>   <NA>
    #>  7 North America (Le… 2023-03-25 Las …     2 green guitar     cowb… <NA>   Marc…
    #>  8 North America (Le… 2023-03-25 Las …     2 green piano      Whit… <NA>   <NA>
    #>  9 North America (Le… 2023-03-31 Arli…     1 green guitar     Sad … <NA>   <NA>
    #> 10 North America (Le… 2023-03-31 Arli…     1 green piano      Ours… <NA>   <NA>
    #> # ℹ 288 more rows

## 2 Just another picture to burn

Often as we explore data, we want to create data visualizations. Naturally, if we’re exploring data for Taylor Swift, we need Taylor Swift-themed visualizations. taylor includes several color palettes and helper functions for `{ggplot2}` to facilitate these visualizations.

First, there are color palettes inspired by each album stored in `album_palettes`. For example, we can look at a color palette based on the cover art for the Lover album.

    album_palettes$lover
    #> <color_palette[5]>
    #>     #76BAE0
    #>     #8C4F66
    #>     #B8396B
    #>     #EBBED3
    #>     #FFF5CC

There is also a color palette that contains one color for each album, which is useful when comparing albums to each other. For a complete description of color palette functionality in taylor, see `vignette("palettes")`.

    album_compare
    #> <color_palette[15]>
    #>     taylor_swift
    #>     fearless
    #>     fearless_tv
    #>     speak_now
    #>     speak_now_tv
    #>     red
    #>     red_tv
    #>     1989
    #>     1989_tv
    #>     reputation
    #>     lover
    #>     folklore
    #>     evermore
    #>     midnights
    #>     tortured_poets

taylor also includes several functions for using the built-in palettes for color and fill scales with ggplot2. As an example, `scale_color_albums()` to map the `album_compare` palette to geometries that have color mapped to the album name. In the following plot, we display the cumulative number of surprise songs played from each album and use `scale_color_albums()` to highlight each album within its respective facet.

Plot code

    library(dplyr)
    library(tidyr)
    library(ggplot2)

    leg_labels <- unique(eras_tour_surprise$leg)
    leg_labels <- gsub("South America", "South\nAmerica", leg_labels)

    surprise_song_count <- eras_tour_surprise %>%
      nest(dat = -c(leg, date, city, night)) %>%
      arrange(date) %>%
      mutate(leg = factor(leg, levels = unique(eras_tour_surprise$leg),
                          labels = leg_labels)) %>%
      mutate(show_number = seq_len(n()), .after = night) %>%
      unnest(dat) %>%
      left_join(distinct(taylor_album_songs, track_name, album_name),
                join_by(song == track_name),
                relationship = "many-to-one") %>%
      count(leg, date, city, night, show_number, album_name) %>%
      complete(nesting(leg, date, city, night, show_number), album_name) %>%
      mutate(n = replace_na(n, 0)) %>%
      arrange(album_name, date, night) %>%
      mutate(surprise_count = cumsum(n), .by = album_name) %>%
      left_join(select(taylor_albums, album_name, album_release),
                by = "album_name") %>%
      mutate(surprise_count = case_when(
        album_name == "THE TORTURED POETS DEPARTMENT" &
          date < album_release ~ NA_integer_,
        .default = surprise_count
      )) %>%
      add_row(leg = factor("Europe"), album_name = "THE TORTURED POETS DEPARTMENT",
              show_number = 83.5, surprise_count = 0L) %>%
      mutate(album_name = replace_na(album_name, "Other"),
             album_group = album_name,
             album_name = factor(album_name, c(album_levels, "Other"),
                                 labels = c(gsub("POETS DEPARTMENT",
                                                 "POETS\nDEPARTMENT",
                                                 album_levels), "Other")))

    ggplot(surprise_song_count) +
      facet_wrap(~ album_name, ncol = 3) +
      geom_line(data = ~select(.x, -album_name),
                aes(x = show_number, y = surprise_count, group = album_group),
                color = "grey80", na.rm = TRUE) +
      geom_line(aes(x = show_number, y = surprise_count, color = album_group),
                show.legend = FALSE, linewidth = 2, na.rm = TRUE) +
      scale_color_albums(na.value = "grey80") +
      scale_x_continuous(breaks = c(1, seq(20, 500, 20))) +
      labs(x = "Show", y = "Songs Played") +
      theme_minimal() +
      theme(strip.text.x = element_text(hjust = 0, size = 10),
            axis.title = element_text(size = 9))

Or we can take a closer look at 1989 (Taylor’s Version). In this figure we can see that from early June to August, Taylor took a long break between playing songs from this album. The break ended when Taylor resumed playing songs leading up to the announcement of 1989 (Taylor’s Version) at in Los Angeles at the end of the first U.S. leg of the tour. For more details on ggplot2 scales provided by taylor, see `vignette("plotting")`.

Plot code

    library(patchwork)

    missing_firsts <- tibble(date = as.Date(c("2023-11-01",
                                              "2024-02-01",
                                              "2024-05-01",
                                              "2024-10-01")))
    day_ones <- surprise_song_count %>%
      slice_min(date, by = c(leg, album_name)) %>%
      select(leg, date, album_name) %>%
      mutate(date = date - 1)

    surprise_dat <- surprise_song_count %>%
      bind_rows(missing_firsts) %>%
      arrange(date) %>%
      fill(leg, .direction = "up") %>%
      bind_rows(day_ones) %>%
      arrange(album_name, date) %>%
      group_by(album_name) %>%
      fill(surprise_count, .direction = "down")

    tour1 <- surprise_dat %>%
      filter(leg %in% c("North America (Leg 1)", "South\nAmerica")) %>%
      ggplot() +
      facet_grid(cols = vars(leg), scales = "free_x", space = "free_x") +
      geom_line(aes(x = date, y = surprise_count, group = album_name),
                color = "grey80", na.rm = TRUE) +
      geom_line(data = ~filter(.x, album_name == "1989 (Taylor's Version)"),
                aes(x = date, y = surprise_count, color = album_name),
                show.legend = FALSE, linewidth = 2, na.rm = TRUE) +
      scale_color_albums() +
      scale_x_date(breaks = "month", date_labels = "%b\n%Y", expand = c(.02, .02)) +
      expand_limits(y = c(0, 37)) +
      labs(x = NULL, y = "Songs Played") +
      theme_minimal() +
      theme(strip.text.x = element_text(hjust = 0, size = 10),
            axis.title = element_text(size = 9))

    tour2 <- surprise_dat %>%
      filter(!leg %in% c("North America (Leg 1)", "South\nAmerica")) %>%
      ggplot() +
      facet_grid(cols = vars(leg), scales = "free_x", space = "free_x") +
      geom_line(aes(x = date, y = surprise_count, group = album_name),
                color = "grey80", na.rm = TRUE) +
      geom_line(data = ~filter(.x, album_name == "1989 (Taylor's Version)"),
                aes(x = date, y = surprise_count, color = album_name),
                show.legend = FALSE, linewidth = 2, na.rm = TRUE) +
      scale_color_albums() +
      scale_x_date(breaks = "month", date_labels = "%b\n%Y", expand = c(.02, .02)) +
      expand_limits(y = c(0, 37)) +
      labs(x = NULL, y = "Songs Played") +
      theme_minimal() +
      theme(strip.text.x = element_text(hjust = 0, size = 10),
            axis.title = element_text(size = 9))

    tour1 / tour2 + plot_layout(axes = "collect")

## 3 I could show you incredible things

There are many ways we can explore the data, but, honestly, baby, who’s counting? On the package website, you can find a collection of analyses that use data from taylor that I have found in the wild. If you use the data and find it useful, please reach out—I love to see the package used!