I like learning. It’s one of the reasons I stayed in tertiary education; I’m essentially a professional learner. One of my great joys of the last few years has been learning programming. But after being so busy with teaching since July 2016, I feel like it’s time to spend some time deliberately practicing and learning programming again.
There is something deeply satisfying about solving a problem or completing a task programatically.
When I started running, the idea of doing so every day seemed… mad… dangerous. Now I can see its virtues, one of which is fitness (but I think fun is pretty important, too). Since late-November I have been running every day (as of 18/02/18 I’ve run for 82 consecutive days). My last post was an intro and half-way(ish) update on my N=1 experiment on the effect of running every day for a month upon my ‘relative running economy’.
My last post was inspired by my desire to pick up my running game, which had waned this year thanks largely to a heavy work schedule. Looking at my relative economy inspired me to do a little experiment. In the recreational running world, running every day is known as a ‘streak’. The online magazine, Runner’s World has a yearly Thanksgiving-to-New-Year’s-Day streak, and there are regularly stories published about runners going on decades-long streaks, and the r/running subreddit often has tales of people on multi-year streaks.
Lots of people record their movement when they go out for run/walk/swim/cycle, and subsequently upload those data to sites like Strava or Endomondo. While such sites often provide useful and interesting assessments of one’s records and ‘fitness’, there is usually little possibility for besopke analyses. By using APIs and R, it is possible to examine one’s data and do some cool analyses and visualisations.
I’ve been running on and off for about 3 years now.
An incomplete walkthtrough of my blogdown setup.