This is the first post at quantixed about Raspberry Pi computing. Pi Zero is a minimalist Raspberry Pi that can be coupled to a camera. With this little rig, you can make time-lapse footage amongst other things. I’ve set up a couple of these now. One was to make a time-lapse movie of some plants growing through a plastic maze. The results were pretty good and I thought I’d upload the video and a brief how-to guide.

The Green Leek 10.5 km run is a mixed terrain race now in its third year. Today’s was a wet and muddy edition. The chip times were posted this afternoon and using my previous code, I took a look at the results.

I’d seen the small multiple artwork of running and cycling routes from Marcus Volz’s R package Strava all over the web. Ads for “posters of your GPS tracks” pop up on Reddit and I’d notice a few #Rstats people put up their posters on Twitter. I’ve had the package bookmarked for a while and this week I finally got round to generating a small multiple poster of some of my cycling routes.

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.

Some great scientific data gets posted on Twitter. Sometimes I want to take a closer look and this post describes a strategy to do so. Edit: I received a request to take down the 3D volume images derived from the example dataset I used in this post. I’ve edited the post below so that is now a general guide. Grab the video It can be a bit difficult to the grab video from Twitter. The best way I’ve found is using youtube-dl.

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.

I read about Antonio Sánchez Chinchón’s clever approach to use the Travelling Salesperson algorithm to generate some math-art in R. The follow up was even nicer in my opinion, Pencil Scribbles. The subject was Boris Karloff as the monster in Frankenstein. I was interested in running the code (available here and here), so I thought I’d run it on a famous scientist.

A few days ago, I read an article about Damien Hirst’s new spot paintings.

I recently got a new GPS running watch, a Garmin Fēnix 5. As well as tracking runs, cycling and swimming, it does “activity tracking” – number of steps taken in a day, sleep, and so on. The step goals are set to move automatically and I wondered how it worked. With a quick number crunch, the algorithm revealed itself. Read on if you are interested how it works. The watch started out with a step target of 7500 steps in one day.