Garmin Connect has a number of plots built in, but to take a deeper dive into all your fitness data, you need to export a CSV and fire up R. This post is a quick guide to some possibilities for running data.  There’s a few things that I wanted to look at. For example, how does my speed change through the year? How does that compare to previous years? If I see some trends, is that the same for short runs and long runs?

I’ve been following the tweets from an account called Albums You Must Hear @Albums2Hear. Each tweet is an album recommended by the account owner. I’m a sucker for lists of Albums That I Must Hear Before I Die since I’m always interested in new (or not so new) music recommendations.

I’ve previously crunched times for local Half and Full Marathons here on quantixed . Last weekend was the Kenilworth Half Marathon (2018) over a new course. I thought I’d have a look at the distributions of times and paces of the runners. The times are available here. If the Time and Category for finishers are saved as a csv, the script below works to generate the following plots.

There have been several posts on this site about publication lag times. You can read them here. Lag times are the delays in the dissemination of scientific data introduced by the process of publishing the paper in a journal. Nowadays, your paper can be online in a few hours using a preprint server. However, this work is not peer reviewed. Journals organise a formal peer review and provide some sort of certification of the work.

As a geek, the added bonus of exercise is the fun that you can have with the data you’ve generated. A recent conversation on Twitter about the accuracy of wrist-based HRMs got me thinking… how does a wrist-based HRM compare with a traditional chest-strap HRM? Conventional wisdom says that the chest-strap is more accurate, but my own experience of chest-strap HRMs is that they are a bit unreliable. Time to put it to the test.

My activity on twitter revolves around four accounts. I try to segregate what happens on each account, and there’s inevitably some overlap. But what about overlap in followers? What lucky people are following all four? How many only see the individual accounts? It’s quite easy to look at this in R. So there are 36 lucky people (or bots!) following all four accounts.