Quantified Self: Me and My Itunes Library

This blog post is a story of time: too little time and too much time. This week we were working with quantified self data. I enjoy listening to music and have spent a lot of time adding new music to my library and creating playlists. For this project I decided to analyze the data from my iTunes library to get a sense of what genres of music I listen to the most and which artists.

Originally I wanted to answer the question “how diverse is the music I listen to?” I thought of diversity geographically and wanted to map out the artists in my library by birthplace. I was interested in where my music was coming from and how music and culture can cross international borders.. I quickly found that although it was a cool project (that I will probably work on anyway) given the time constraints for this week I would have to focus on something else. Although I did not create the mapping visualization my tiny initial inquiry proved to be fruitful in answering that question. A dance/electronic song in Japanese that I enjoy was made by a young woman in Brooklyn, and a few Afrobeats artists I like are London born and bred.

Switching gears I began thinking about my taste over time. Based on the data I’ve had itunes since 2010. I’m sure my taste in music has changed over that last 9 years. The wealth of information threw me a bit off course since I could see so many possibilities and had so little time. Itunes has a wealth of information on my listening habits. It tracks everything from the date a song was added to the number of times it’s been skipped, the bit rate of each song and much more. Then began my time issues.

Itunes has information down to the minutes about when you added music, modified it, last played it, and Tableau returned every instance a song was played down to the minutes and I had trouble figuring out how to shift that to hours. I was interested in the time of day that I added music to my iTunes. I know I’m a night owl and wanted to see if there was a correlation between the time and number of songs added to Itunes.  

In the end I worked on visualizations using the genre and number of plays. I felt it could help with the answer to what I listen to often. That was when I realized a big hole in my data. Many of the songs in my library had null values for genre. I think this is a commentary on me as a user. I often look for music by song title or artist so those fields are pretty up to date. Genre however seems to be the one I was least worried about. Possibly because the song title and artist would dictate to me what the genre is.




The second visualization




Viz 2



Viz 3



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