spotifyr

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Overview

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!).

Installation

CRAN version 2.1.1 (recommended)

install.packages('spotifyr')

Development version

devtools::install_github('charlie86/spotifyr')

Authentication

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()

Authorization Code Flow

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.

Usage

What Was the Beatles’ Favorite Key?

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
C major 104
D major 98
G major 82
A major 76
E major 62

Get your most recently played tracks

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(
      all_of(c("track.name", "artist.name", "track.album.name", "played_at"))
      ) %>% 
    kable()
track.name artist.name track.album.name played_at
A Case of You Tristen A Case of You 2021-06-14 09:54:44
Paper Cup Real Estate Paper Cup 2021-06-10 20:20:11
Wrong with You Tristen Wrong with You 2021-06-10 20:17:24
Animal - Edit LUMP Animal 2021-06-10 20:13:21
Streets Of Your Town DOPE LEMON Streets Of Your Town 2021-06-10 18:23:00

Find Your All Time Favorite Artists

get_my_top_artists_or_tracks(type = 'artists', 
                             time_range = 'long_term', 
                             limit = 5) %>% 
    select(.data$name, .data$genres) %>% 
    rowwise %>% 
    mutate(genres = paste(.data$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

Find your favorite tracks at the moment

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
Hot & Heavy Lucy Dacus Hot & Heavy
Sea Urchin Mystic Braves Sea Urchin
Human Freedom Fry Human
Hot Motion Temples Hot Motion
Animal - Edit LUMP Animal

What’s the most joyful Joy Division song?

My favorite audio feature has to be “valence,” a measure of musical positivity.

joy <- get_artist_audio_features('joy division')
joy %>% 
    arrange(-valence) %>% 
    select(.data$track_name, .data$valence) %>% 
    head(5) %>% 
    kable()
track_name valence
Passover - 2020 Digital Master 0.946
Passover - 2007 Remaster 0.941
Colony - 2020 Digital Master 0.829
Colony - 2007 Remaster 0.808
Atrocity Exhibition - 2020 Digital Master 0.790

Now if only there was some way to plot joy…

Joyplot of the emotional rollercoasters that are Joy Division’s albums

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")

Sentify: A Shiny app

This app, powered by spotifyr, allows you to visualize the energy and valence (musical positivity) of all of Spotify’s artists and playlists.

Dope Stuff Other People Have Done with spotifyr

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

Blue Christmas: A data-driven search for the most depressing Christmas song, Caitlin Hudon

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

Code of Conduct

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.