New spotifyr R Package Release

I have been a very long-time user of Charlie Thomson’s spotifyr R package, which is probably the most used open-source music analytics software in the world. It provides programmatic access to the Spotify Web API, which contains access to the former Echo Nest quantitative musicology engine.

It is an essential part of the Digital Music Observatory’s streaming analysis and our Listen Local apps designed for our trustworthy AI experiments and independent artist services. I am extremely proud to announce that after a very thorough modernization of the package’s exception handling, documentation, and code dependencies that I did in the last week, the package has passed again the peer-review standards and it is back on CRAN.

The package is an excellent starting to point for R newbies to try their hands on musicology analysis with a few keystrokes. And of course, it is an essential part of the research infrastructure of musicology worldwide in far more advanced applications.

Getting Started

You should start with the README and get your Spotify Web API access tokens to get started.

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()

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()

That’s about right…

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()

What could I say? I travelled Australia listening only to Angus & Julia Stone, the Buildings Breeding have been with me since I discovered them on my first iPod, in one of the first podcasts, the Indiefeed. I created my Kickstarter account back in 2010 to support Haley Bonar’s third album. And the year before I was very much into Japanese Breakfast and Balthazar.

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()

What’s the most joyful Joy Division song?

Let’s take a look at the 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()

Now if only there was some way to plot joy…

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

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

Our Work with spotifyR

• Trustworthy AI: Check Where the Machine Learning Algorithm is Learning From
• Recommendation Systems: What can Go Wrong with the Algorithm?
• Feasibility Study On Promoting Slovak Music In Slovakia & Abroad
• Forgetify: Popular Music That Nobody Listens To

A key mission of our Digital Music Observatory, which is our modern, subjective approach on how the future European Music Observatory should look like, is to not only to provide high-quality data on the music economy, the diversity of music, and the audience of music, but also on metadata. The quality and availability, interoperability of metadata (information about how the data should be used) is key to build trustworthy AI systems. We rely on spotifyr to fulfil this mission.

Join our open collaboration Music Data Observatory team as a data curator, developer or business developer.