Happy May! All this month, we will be focusing on the Quantified Self movement. We will be looking at the different ways you can keep data about yourself and the things you can learn about yourself through analyzing that data. For more background on the movement itself, check out this TED talk from Gary Wolf:
You never know what kinds of interesting things you’ll learn about yourself by analyzing your personal data. I’ve been tracking my listening music habits via Last.fm for several years now. Last.fm generates a lot of their own data visualizations for you to look at to analyze your data. If you have ever been to my Twitter profile, you probably have seen this one:
One day I showed this to a coworker and he pointed out the pattern of listening to sadder, slower music every couple of weeks. He asked me what that was about. I couldn’t answer him right away. When I got home that night, I stared at it a little bit more. “No…this couldn’t correspond with…” I thought to myself. I pulled out my phone and opened up the app I use to track my menstrual cycle. Wouldn’t you know, those jumps in listening to sadder music happened to be whenever I was on my period. The peak days were actually usually the start of my period, meaning that the days leading up to the first day of my cycle; I started listening to lower energy music. That means a change in my musical taste could possibly be used as a predictor for when my period will start.
I was entertained by this whole idea, so I decided to mash up some of the data in Tableau and see if I could make any more insights. I used the dates collected in my period tracker app to make a dimension that separates my listening data to days I was on my period and when I wasn’t. I found some interesting results. I noticed that normally only 2 of my top 10 artists have female vocalists, while during my period 4 of the artists do. There’s also a huge jump in the number of plays for Elliott Smith, which is my go-to brooding music. Check out my dashboard below and click the play buttons next to the track to hear my favorite songs on Spotify. See if you can hear a difference between the kind of music I listen to normally and what I listen to when Aunt Flo is in town.
Do you have an interesting data story about yourself? This month we are running our 2nd Iron Viz feeder contest and the theme is, you guessed it, Quantified Self! We will have the submissions page up soon and are currently expecting the contest to run through May 26. We want you to use quantified self data to create a dashboard about you. Create a dashboard about your diet, exercise, sleep patterns, music listening habits, social media stats, or whatever you want to learn about yourself. If you aren't already tracking data about yourself here are a couple of tools you can use to start building your dataset:
- Music tracking: Track what you listen to on your computer and mobile using Last.fm. You can have all your last.fm scrobbles automatically write to a Google spreadsheet using this IFTTT recipe.
- Sleep tracking: You can track when you went to sleep, woke up, and the quality of your sleep using apps like Sleep Cycle on iOS and SleepBot on Android.
- Anything tracking: Use Nicholas Feltron's new iOS app Reporter to track almost anything you want. The app prompts you at random times throughout your day with survey questions that you can customize yourself. It also automatically takes geolocations and ambient noise levels using sensors in your phone.
That's just a small sampling of tools that I personally use for self-tracking. An abundance of tools and ideas for quantified self are available through this guide by quantifiedself.com.
Good luck in Iron Viz! I'm excited to see everything we can learn about ourselves this month!