Super Duper Itunes Analysis

Fair warning, I’m a bit of a data geek. And I’m stubbornly persistant when I come up with an idea that I’m curious about seeing through. So when I thought about the growth rate of my music collection within iTunes, I simply had to figure out a way to graph it.

Exporting the 65mb iTunes Library XML file seemed like a reasonable start, but the file was too large to actually do anything with. Excel crashed. Numbers crashed. Even Google Docs didn’t know what to do with it.

Trying to split the file didn’t work well either, as even TextMate could barely handle the file. Sure, I could have done some command line kung-fu, but it was late and I’d never dealt much with XML files before. So I turned to Google.

After a dozen failed searches, I came across a Java applet from a few years back called Super Analyzer. It charts library growth as well as a multitude of other fun things. Here’s a few examples of what it turned up from my library:

Track Count: 34,826 tracks
Play Count: 179,778 tracks played
Total Time: 3.3 months
Total Play Time: 1.4 years
Artist Count: 2,604 artists
Album Count: 3,010 albums
Genre Count: 287 genres
Most Songs Played At: 12:00 PM
Average Track Length: 4.1 minutes
Average Play Count: 5.2 plays per song
Average Album Completeness: 30% complete
Complete Albums: 25% are complete
Average Bit Rate: 220.6 kbps
Average File Size: 6.6 MB
Total Library Size: 225.0 GB
All Songs Played: 57% played at least once
Compilations: 7%
Library Age: 6.2 years
Average Growth Rate: 108.2 songs/week

Yep, I have a lot of music

;

The most common words in song titles read like a strange love poem.
I particularly like that the top words are “love your song time“. OK, I can do that.

Mostly modern music

In the middle of the day

And it’s high quality. V0 FTW

Full report with more graphs here.

Want to see your own stats? Download Super Analyzer from nosleep.org

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