Ever wonder how many cups of coffee you drink each month, or how many times you’ll log into Facebook this year? Perhaps you’d like to not only track your caloric intake, but to know what times of day it peaks and troughs. Data visualization house FlowingData just released an app for that, and all you have to do is Tweet your entire life.
Your.flowingdata collects information from you via direct messages on Twitter. For instance, take the coffee example; when you put away your first cup of joe in the morning, you tweet “d yfd drank coffee” from your account (“d yfd” sends a direct tweet to your.flowingdata so everyone on Twitter doesn’t have to share in your caffeine addiction). Now, do that every time you have a cup of coffee, or a bottle of water, or sneak a snack at the vending machine.
From a single tweet, you learn nothing about yourself you didn’t know before. But say you drink three to five cups over the course of a given day, and you do that every day for a month. Your.flowingdata maps that information any way you like (how many cups per day, when you tend to drink the most, how your coffee intake compares with your bottled water intake, etc.). Then you can look back at the last six months and map those cups of coffee in various ways, noting not only how much coffee you’ve had over that span, but the maximum cups you’ve had in a single day, or how many cups you’ll average this year. The more you tweet, the more data you compile about your life over time (how many hours you exercised in the last three months perhaps, or the number of hours spent on Law & Order reruns–or a comparison of the two).
Your.flowingdata also lets you chart this data in several helpful ways. For instance, you can look at a calendar view and see, via different shades of blue, which days a certain term–let’s stick with “coffee”–comes up most heavily. Knowing that you drink twice as much coffee on Wednesdays than all other days tells you something you may not have known about yourself (perhaps Tuesday night poker is taking a toll on you?). Or you can cloud your data, where most-used terms are represented larger than lesser-used terms. If “Facebook” comes up more often than “TPS Report,” maybe it’s time to get back to work.
The downside to your.flowingdata is that you have to remain vigilant in your tweeting. The data becomes less relevant if you don’t tweet all your coffees, all your calories, every exercise session, and each Facebook login. Of course, now that you have graphical data visually charting all your other eccentricities, adding one more habit to the pile may not seem like such a leap.