Picking up the pieces Recently, I’ve animated a specific running goal for one year with data from the Strava API. I thought it would also be nice to compare different years within the same animation - in a way you could say that you’re racing yourself over several years. This only needs some minor modifications to the code I posted before. Data collection stays basically the same, so I’m just re-posting this here.

I find myself counting characters on my screen more often than I feel comfortable doing it. Isn’t it weird to count things by hand when you have the power of R right in front of you? Well, I decided to but a little helper function into my .Rprofile file (i.e. the function is defined with every startup of R and available throughout the session) that cares care of that (and is less error-prone at the same time).

Full disclosure: I did not even have any running goals for 2020. But as it turned out: It has been a good year for running - at least one thing this year was good at. In this post, I want to run you through my R script which creates an animated graph that looks like I had the goal of running 500 kilometres this year (spoiler: I didn’t).

Previously, I’ve plotted a ridgeline based on a variable’s density through time. It might look nice but it’s quite obvious that time can be visualized in a more fitting way - by time itself, in an animated plot that is. So, let’s fire up the {gganimate} package again. My goal is to show a moving kernel density curve as it moves through time, based on a moving window of 30 days sliding from the past to the present.

I really like ridgeline plots but only recently I have learned how to do them myself. Of course, the most famous ridgeline plot ever is the one you find on the cover of Joy Division’s album “Unknown Pleasures”. I wonder how many ridgeline plots done with the {ggridges} package try to replicate the look of this famous (and great!) album. And - of course - I will try, too.