Sprtify data

load data

library(readr)
url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-21/spotify_songs.csv"
sportify <- readr::read_csv(url)
sportify

# Or read in with tidytuesdayR package (https://github.com/thebioengineer/tidytuesdayR)
# PLEASE NOTE TO USE 2020 DATA YOU NEED TO UPDATE tidytuesdayR from GitHub

# Either ISO-8601 date or year/week works!

# Install via devtools::install_github("thebioengineer/tidytuesdayR")

#tuesdata <- tidytuesdayR::tt_load('2020-01-21') 
#tuesdata <- tidytuesdayR::tt_load(2020, week = 4)


#spotify_songs <- tuesdata$spotify_songs

data source

The data comes from Spotify via the spotifyr package. Charlie Thompson, Josiah Parry, Donal Phipps, and Tom Wolff authored this package to make it easier to get either your own data or general metadata arounds songs from Spotify’s API. Make sure to check out the spotifyr package website to see how you can collect your own data! Kaylin Pavlik had a recent blogpost using the audio features to explore and classify songs. She used the spotifyr package to collect about 5000 songs from 6 main categories (EDM, Latin, Pop, R&B, Rap, & Rock). The information about the data set can be found here.

There are 12 audio features for each track, including confidence measures like acousticness, liveness, speechiness and instrumentalness, perceptual measures like energy, loudness, danceability and valence (positiveness), and descriptors like duration, tempo, key, and mode.

Phenoenom

  • Danceability: Describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity.

  • All the top 10 songs are high on Danceability (above 60%)

library(tidyverse)

sportify%>%
  select(track_album_id, track_name, track_popularity, danceability)%>%
  filter(!duplicated(track_album_id))%>%
  mutate(pop_rank= min_rank(desc(track_popularity)))%>%
  arrange(pop_rank)%>%
  top_n(-10)
## # A tibble: 10 × 5
##    track_album_id         track_name      track_popularity danceability pop_rank
##    <chr>                  <chr>                      <dbl>        <dbl>    <int>
##  1 0UywfDKYlyiu1b38DRrzYD Dance Monkey                 100        0.824        1
##  2 6HJDrXs0hpebaRFKA1sF90 ROXANNE                       99        0.621        2
##  3 7mKevNHhVnZER3BLgI8O4F Tusa                          98        0.803        3
##  4 3nR9B40hYLKLcR0Eph3Goc Memories                      98        0.764        3
##  5 2ZfHkwHuoAZrlz7RMj0PDz Blinding Lights               98        0.513        3
##  6 4g1ZRSobMefqF6nelkgibi Circles                       98        0.695        3
##  7 52u4anZbHd6UInnmHRFzba The Box                       98        0.896        3
##  8 4i3rAwPw7Ln2YrKDusaWyT everything i w…               97        0.704        8
##  9 0ix3XtPV1LwmZADsprKxcp Don't Start Now               97        0.794        8
## 10 1Czfd5tEby3DbdYNdqzrCa Falling                       97        0.784        8

visualization

#feature_names <- names(sportify)[12:23]
#sportify%>%
#  select(c('track_popularity','playlist_genre', feature_names)) #%>%
#  pivot_longer(cols = feature_names) %>%
#  ggplot(aes(x = value, 
#                 y=track_popularity) +
#  geom_point(alpha = .10)
library(tidyverse)

ggplot(data=sportify) +
  geom_point(aes(x = danceability, 
                 y = track_popularity),
             alpha = .10)+
  geom_density(aes(x= danceability, color = playlist_genre))+
  geom_smooth(aes(x = danceability, 
                 y = track_popularity,
                 color = playlist_genre))+
  scale_y_continuous(name = "track popularity") +
  scale_x_continuous(name = "danceability", labels = scales::percent) +
  ggtitle("Sportify data",
          subtitle = "danceability vs. track_popularity over genre")

From the plot, we can notice that:

data dictionary

variable class description
track_popularity double Song Popularity (0-100) where higher is better
playlist_genre character Playlist genre
danceability double Danceability describes 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 double Energy is a measure from 0.0 to 1.0 and 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 double The estimated overall key of the track. 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 double 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). Values typical range between -60 and 0 db.
mode double Mode 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 double Speechiness 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 double A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
instrumentalness double 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.
liveness double 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 double A measure from 0.0 to 1.0 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 double 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.
duration_ms double Duration of song in milliseconds