spotifyr is an R 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!).
CRAN version 2.1.0 (recommended)
install.packages('spotifyr')
Development version
devtools::install_github('charlie86/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()
For certain functions and applications, you’ll need to log in as a
Spotify user. To do this, your Spotify Developer application needs to
have a callback url. You can set this to whatever you want that will
work with your application, but a good default option is
http://localhost:1410/
(see image below). For more information on
authorization, visit the offical Spotify Developer
Guide.
library(spotifyr)
beatles <- get_artist_audio_features('the beatles')
library(dplyr)
library(purrr)
library(knitr)
beatles %>%
count(key_mode, sort = TRUE) %>%
head(5) %>%
kable()
key_mode | n |
---|---|
D major | 24 |
G major | 21 |
A major | 13 |
F major | 12 |
C major | 11 |
library(lubridate)
get_my_recently_played(limit = 5) %>%
mutate(
artist.name = map_chr(track.artists, function(x) x$name[1]),
played_at = as_datetime(played_at)
) %>%
select(track.name, artist.name, track.album.name, played_at) %>%
kable()
track.name | artist.name | track.album.name | played_at |
---|---|---|---|
Humbug Mountain Song | Fruit Bats | Absolute Loser | 2021-06-07 21:40:33 |
Texas Sun | Khruangbin | Texas Sun | 2021-06-07 21:37:28 |
Dylan Thomas | Better Oblivion Community Center | Better Oblivion Community Center | 2021-06-07 21:33:15 |
Hot & Heavy | Lucy Dacus | Hot & Heavy | 2021-06-07 21:29:38 |
7 Seconds | Porridge Radio | 7 Seconds | 2021-06-07 21:25:27 |
get_my_top_artists_or_tracks(type = 'artists',
time_range = 'long_term',
limit = 5) %>%
select(name, genres) %>%
rowwise %>%
mutate(genres = paste(genres, collapse = ', ')) %>%
ungroup %>%
kable()
name | genres |
---|---|
Japanese Breakfast | art pop, bubblegrunge, eugene indie, indie pop, indie rock, philly indie |
Haley Bonar | melancholia, stomp and holler |
Balthazar | belgian indie, belgian rock, dutch indie, dutch rock, ghent indie |
Buildings Breeding | indie fuzzpop |
Angus & Julia Stone | australian indie folk, indie folk, stomp and holler |
get_my_top_artists_or_tracks(type = 'tracks',
time_range = 'short_term',
limit = 5) %>%
mutate(
artist.name = map_chr(artists, function(x) x$name[1])
) %>%
select(name, artist.name, album.name) %>%
kable()
name | artist.name | album.name |
---|---|---|
Animal - Edit | LUMP | Animal |
Hot & Heavy | Lucy Dacus | Hot & Heavy |
Wrong with You | Tristen | Wrong with You |
Sea Urchin | Mystic Braves | Sea Urchin |
Hot Motion | Temples | Hot Motion |
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 |
---|---|
Passover - 2020 Digital Master | 0.946 |
Colony - 2020 Digital Master | 0.829 |
Atrocity Exhibition - 2020 Digital Master | 0.790 |
Isolation - 2020 Digital Master | 0.778 |
A Means to an End - 2020 Digital Master | 0.774 |
Now if only there was some way to plot joy…
library(ggplot2)
library(ggridges)
ggplot(
joy,
aes(x = valence, y = album_name)
) +
geom_density_ridges() +
theme_ridges() +
labs(title = "Joyplot of Joy Division's joy distributions",
subtitle = "Based on valence pulled from Spotify's Web API with spotifyr")
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:
Exploring the Spotify API with R: A tutorial for beginners, by a beginner, Mia Smith
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
Чёрное зеркало Arcade Fire (Black Mirror Arcade Fire), TheSociety
Sente-se triste quando ouve “Amar pelos dois”? Não é o único (Do you feel sad when you hear “Love for both?” You’re not alone), Rui Barros, Rádio Renascença
Using Data to Find the Angriest Death Grips Song, Evan Oppenheimer
Hierarchical clustering of David Bowie records, Alyssa Goldberg
tayloR, Simran Vatsa
Please note that the spotifyr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.