spotifyr is a wrapper for pulling track audio features and other information from Spotify’s Web API in bulk. By automatically batching API requests, it allows you to enter an artist’s name and retrieve their entire discography in seconds, along with Spotify’s audio features and track/album popularity metrics. You can also pull song and playlist information for a given Spotify User (including yourself!).
Development version (recommended)
devtools::install_github('charlie86/spotifyr')The development version now includes functions from the geniusR
package from Josiah Parry.
CRAN version 1.0.0 (Note: this is somewhat outdated, as it takes extra time to submit and pass CRAN checks)
install.packages('spotifyr')First, set up a Dev account with Spotify to access their Web API
here.
This will give you your Client ID and Client Secret. Once you have
those, you can pull your access token into R with
get_spotify_access_token().
The easiest way to authenticate is to set your credentials to the System
Environment variables SPOTIFY_CLIENT_ID and SPOTIFY_CLIENT_SECRET.
The default arguments to get_spotify_access_token() (and all other
functions in this package) will refer to those. Alternatively, you can
set them manually and make sure to explicitly refer to your access token
in each subsequent function call.
Sys.setenv(SPOTIFY_CLIENT_ID = 'xxxxxxxxxxxxxxxxxxxxx')
Sys.setenv(SPOTIFY_CLIENT_SECRET = 'xxxxxxxxxxxxxxxxxxxxx')
access_token <- get_spotify_access_token()library(spotifyr)
beatles <- get_artist_audio_features('the beatles')library(tidyverse)
library(knitr)
beatles %>%
count(key_mode, sort = TRUE) %>%
head(5) %>%
kable()| key_mode | n |
|---|---|
| D major | 77 |
| G major | 76 |
| A major | 67 |
| E major | 67 |
| C major | 52 |
get_my_recently_played(limit = 5) %>%
select(track_name, artist_name, album_name, played_at_utc) %>%
kable()| track_name | artist_name | album_name | played_at_utc |
|---|---|---|---|
| How Do I Know | Here We Go Magic | A Different Ship | 2018-12-07 15:50:25 |
| Pale Blue Eyes | The Velvet Underground | The Velvet Underground (45th Anniversary / Deluxe Edition) | 2018-12-07 15:45:54 |
| And She Was | Talking Heads | Little Creatures | 2018-12-07 15:40:14 |
| Homewards | Mndsgn | Yawn Zen | 2018-12-07 15:36:34 |
| Mr. Tambourine Man | Bob Dylan | Bringing It All Back Home | 2018-12-07 15:35:52 |
get_my_top_artists(time_range = 'long_term', limit = 5) %>%
select(artist_name, artist_genres) %>%
rowwise %>%
mutate(artist_genres = paste(artist_genres, collapse = ', ')) %>%
ungroup %>%
kable()| artist_name | artist_genres |
|---|---|
| Radiohead | alternative rock, art rock, melancholia, modern rock, permanent wave, rock |
| Onra | alternative hip hop, chillhop, ninja, trip hop, wonky |
| Flying Lotus | alternative hip hop, chillwave, electronic, escape room, glitch, glitch hop, hip hop, indie r&b, indietronica, intelligent dance music, trip hop, wonky |
| Teebs | abstract beats, bass music, chillwave, indietronica, wonky |
| Aphex Twin | acid techno, ambient, drill and bass, electronic, fourth world, intelligent dance music, microhouse, trip hop |
get_my_top_tracks(time_range = 'short_term', limit = 5) %>%
select(track_name, artist_name, album_name) %>%
kable()| track_name | artist_name | album_name |
|---|---|---|
| Shattered Dreams | Earl Sweatshirt | Some Rap Songs |
| Nowhere2go | Earl Sweatshirt | Some Rap Songs |
| Peanut | Earl Sweatshirt | Some Rap Songs |
| The Mint (feat. Navy Blue) | Earl Sweatshirt | Some Rap Songs |
| Red Water | Earl Sweatshirt | Some Rap Songs |
My favorite audio feature has to be “valence,” a measure of musical positivity.
joy <- get_artist_audio_features('joy division')joy %>%
arrange(-valence) %>%
select(track_name, valence) %>%
head(5) %>%
kable()| track_name | valence |
|---|---|
| These Days - Live | 0.949 |
| Passover - 2007 Remastered Version | 0.941 |
| Colony - 2007 Remastered Version | 0.808 |
| Atrocity Exhibition - 2007 Remastered Version | 0.787 |
| Incubation - Live | 0.785 |
Now if only there was some way to plot joy…
library(ggjoy)
#> Loading required package: ggridges
#>
#> Attaching package: 'ggridges'
#> The following object is masked from 'package:ggplot2':
#>
#> scale_discrete_manual
#> The ggjoy package has been deprecated. Please switch over to the
#> ggridges package, which provides the same functionality. Porting
#> guidelines can be found here:
#> https://github.com/clauswilke/ggjoy/blob/master/README.md
ggplot(joy, aes(x = valence, y = album_name)) +
geom_joy() +
theme_joy() +
ggtitle("Joyplot of Joy Division's joy distributions", subtitle = "Based on valence pulled from Spotify's Web API with spotifyr")
#> Picking joint bandwidth of 0.0979
get_artist_audio_features(), get_artist_albums(),
get_album_tracks(), get_playlist_tracks(), and
get_user_playlists() can run in parallel using the furrr package. To
enable this feature, set parallelize = TRUE. You can also adjust the
evaluation strategy by setting future_plan, which accepts a string
matching one of the strategies implemented in future::plan() (defaults
to "multiprocess").
This app, powered by spotifyr, allows you to visualize the energy and valence (musical positivity) of all of Spotify’s artists and playlists.
The coolest thing about making this package has definitely been seeing all the awesome stuff other people have done with it. Here are a few examples:
Sentiment analysis of musical taste: a cross-European comparison - Paul Elvers
Blue Christmas: A data-driven search for the most depressing Christmas song - Caitlin Hudon
KendRick LamaR - David K. Laing
Vilken är Kents mest deprimerande låt? (What is Kent’s most depressing song?)
- Filip Wästberg
Black Mirror Arcade Fire - TheSociety
- Rui Barros, Rádio Renascença
Hierarchical clustering of David Bowie records
- Alyssa Goldberg
tayloR - Simran Vatsa
Long Distance Calling: Data Science meets Post-Rock…
- Sebastian Kuhn