ian-curtis / musicanalysis

An Analysis of Spotify Songs and Their Data Across the Decades

Note that the code in the repo may not work as certain data is not public due to Spotify Developer Terms of Service.

The original data set was downloaded from the Spotify Million Playlist Challenge. The dataset was then expanded through the Spotify Web API, using Python. This expansion is still in progress; for instance, there are few tracks from 2021.

There is some odd data; for instance, sometimes year has a value of 0000, a tempo of 0, or a loudness of -60 (silent). However, these are not necessarily incorrect as some tracks are so short, a tempo cannot be determined and some tracks are actually tracks of silence. As such, we have not removed all zeros nor "corrected" any values.

To explore the code, clone this repository and use the make_feather.R script to create your own feather file (the file is too big to store here). For faster loading times, we recommend using the spotify_testing.feather to load instead (there is a commented out section app.R for this purpose). Everything is set up for proper use with the feather files; all you need to do is run the script and everything should be all set. Similarly, the Shiny app published online only uses a subset of the full data due to file size concerns. At a point in the future, we would like to connect to a database but as of right now this has not been integrated.

Description of the variables is as follows. Most are taken directly from the Spotify API Documentation on Getting Tracks and Getting Track Features).

Variable Name	Type	Possible Values	Explanation
album_uri	chr	NA	Uniform Resource Indicator, ID reference to a Spotify album page
artist_uri	chr	NA	Uniform Resource Indicator, ID reference to a Spotify artist page
artist_name	chr	NA	Name of the artist or artists on a track
album_name	chr	NA	Name of the album a track is found on
release_date	date	1885-2021	Release date of the track (ymd or y)
disc_number	int	1 - inf	Disc number the track is found on
popularity	int	0 - 100	100 is the most popular. The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are. Generally speaking, songs that are being played a lot now will have a higher popularity than songs that were played a lot in the past. Duplicate tracks (e.g. the same track from a single and an album) are rated independently.
track_number	int	1 - inf	Track number on album
duration	int	0 - inf	The duration of a track in milliseconds
explicit	logi	FALSE/TRUE	Whether or not the track has explicit lyrics (true = yes it does; false = no it does not OR unknown).
track_name	chr	NA	Name of the track
track_uri	chr	NA	Uniform Resource Indicator, ID reference to a Spotify track
danceability	num	0 - 1	How suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
energy	num	0 - 1	Represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
key	int	0 - 11	The key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1.
loudness	int	-60 - inf	The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Will rarely go above 0.
mode	binary	0, 1	Indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.
speechiness	num	0 - 1	Detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
acousticness	num	0 - 1	A confidence measure of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
liveness	num	0 - 1	Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
valence	num	0 - 1	A measure describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).
tempo	num	0 - 250	The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
time_signature	int	0 - 7	An estimated time signature. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure). The time signature ranges from 3 to 7 indicating time signatures of "3/4", to "7/4"
instrumentalness	int	0 - 1	Predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly "vocal". The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.