Distilling my Jawbone Data

This article was originally posted in carolbento.com

As a true data geek, I have this (in)sane tendency of logging everything in my life, from the money spent on the supermarket to the type of products that bought on a daily basis.

For almost a year now, I have been using the Jawbone Up. I like the fact that it has a discrete design and is exceptional at delivering the basics of what we look for in a activity tracker these days: activity and sleep tracking.

Jawbone's mobile application gives pretty good insights on the daily activity, provides interesting tips and challenges to improve our activity, and they also have a very well built weekly newsletter, which compares the week's activity with the previous one.

Even so, I had get my hands on my Jawbone's raw data. Taking a look at the CSV file that is available in my Jawbone account, here are some of the insights from my activity during 2014.

It's interesting to realize how our daily activity can dramatically vary. Specially when our jobs force us to be sitting down in front of a desk all the time.

From the plot above, it seems that these months were quite active. However, this is still too noisy.

Looking at the monthly averages for walking distances, it's clear that December was, by far, the least active month. Well, it was December, winter, Christmas, with all the sweets and family get togethers, there's not much to do besides staying inside, wrapped in a blanket watching movies.

Nevertheless, an average walking distance above 5km is not bad at all for a software developer!

This is just the first glimpse! Next I'll looking into my sleep data. Yikes!

For the fellow data geeks, the scripts for parsing the data and creating the plots are on Github!

Thanks for reading.

I write about Data Science, Data Visualization, books and learning more effectively 📚🌱💡🚀

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