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Spotify Top 100 Yearly Playlist Analysis

Wesley, Why?

Every year Spotify comes out with our top 100 songs. Since I've been using Spotify from some time now, I have a couple playlists built up. I always wondered how my music taste has changed throughout the years. Wielding Spotify's API and being able to accesss different Audio Features I thought it might be worth checking out.

Spotify's Audio Features:

From The Spotify API Documentation we get a description of the different features.

  • acousticness - A confidence measure from 0.0 to 1.0 of whether the track is acoustic.
  • danceability - 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.
  • duration_ms - The duration of the track in milliseconds.
  • energy - Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity.
  • instrulmentalness - Predicts whether a track contains no vocals.
  • liveness - Detects the presence of an audience in the recording.
  • loudness - The overall loudness of a track in decibels (dB).
  • speechiness - Speechiness detects the presence of spoken words in a track.
  • tempo - The overall estimated tempo of a track in beats per minute (BPM)
  • valence - A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track.

What I found:

I was able to get a really cool look at the different scores over the years. It's neat to see how my taste has changed.

Summary of each feature by year

Below are the distributions of each feature by year of playlist:

Picture of Yearly Playlist Summary by Audio Feature

Correlation of Variables

Thanks to the correlation package I'm also able to dive into the correlation between features:

Correlation Matrix

Picture of Correlation Matrix by Audio Feature

Gaussian Graphical Model of Audio Features

Picture of the Gaussian Graphical Model of Audio Features

Conclusion:

All in all, I thought it was a really cool experience to be able to see the features change throughout time. I look forward to see how the features change in the future 😁

Reference

Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2019). Methods and Algorithms for Correlation Analysis in R. Journal of Open Source Software, 5(51), 2306. doi:10.21105/joss.02306

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