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An analysis of music listening on a typical workday

Music is a form of language

I consume music for more than 60% of my waking hours on a daily basis. I put on a playlist as soon as I am awake in the morning. I am listening to music when I am cooking eggs for breakfast, when I am commuting to and from work, when I am deeply engrossed at work and when I am trying to substitute running for a gym workout. I spend more time choosing and queueing the songs that I want to listen to in the shower than actually showering. It is safe to say that my music streaming game is stronger than my binge watching game.

Approach and Data Collection

For the purpose of this analysis, I have divided a workday into the following parts. Assuming that the music played at each of these parts has different attributes.

  1. Workout
  2. Work
  3. Evening
  4. Cooking
  5. Dinner
  6. Night
  7. Sleep
  • 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: 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.
  • Instrumentalness: predicts whether a track contains no vocals. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content.
  • Speechiness: detects the presence of spoken words in a track. 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.
  • Valence: 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).

Radar charts are wonderful tools!

The idea here is to see (i) how the musical attributes compare with each other at any time of the day and (ii) how they change during the course of the day. The simplest way to show that usually is a time series chart but with so many attributes it would have been incomprehensible. The next logical option is to plot a grid of time series charts for different attributes but then it would not allow me to juxtapose different attributes with each other.

How does Joe’s day look?

Morning: After (hopefully) a good night’s sleep Joe wakes up listening to happy acoustic (0.43) songs. His energy (0.52) is not the highest yet but he is still dancing (0.59) around while freshening up and getting ready for a heavy workout.