I’ve been working on converting a couple of my dissertation chapters into standalone articles, so I’ve been revisiting and improving my older R code. As part of my dissertation work, I ran a global survey of international NGOs to see how they adjust their programs and strategies when working under authoritarian legal restrictions in dictatorship.

I’ve posted in the past about analysing race results in R (most recently here). I ran the 2023 MK Marathon and wanted to have a look at the finishing times. The days of race results being made available as a csv or xls for easy analysis seem to be behind us. Instead they tend to be served up on multiple webpages of 50 athletes’ results at a time. Oh no, 29 pages of results and now Download option…. let’s scrape the data!

I’ve taught a course on data visualization with R since 2017, and it’s become one of my more popular classes, especially since it’s all available asynchronously online with hours of Creative Commons-licensed videos and materials.

In The Two Towers , while talking with Eowyn, Aragorn casually mentions that he’s actually 87 years old. When Aragorn is off running for miles and miles and fighting orcs and trolls and Uruk-hai and doing all his other Lord of the Rings adventures, he hardly behaves like a regular human 87-year-old. How old is he really?

2022 was my best year for running to date. In 2021, my goal was to run 2021 km. For 2022, I wanted to see if I could run 2500 km and also to run 50 HM-or-more distance runs. I managed both and ended the year on a total of 2734 km. I also bagged two PBs for half marathon. Of course, if you subscribe to Strava or VeloViewer or whatever, you can get a nice data visualisation of your year in running.

Another post looking at Twitter data in R. It follows this one and this one. I wanted to look again at my tweeting frequency over the 12 years on Twitter, but this time do it in a calendar view. Something like a GitHub commit calendar would be perfect. I have used a library for this in the past.

Please consider this a “supplementary analysis” to my previous post looking at the frequency of tweets from my personal account over the last 12 years. I was curious about what times I was active on Twitter (measured by when I tweeted). Others might be interested in a solution to look at this in R. The code As in the previous post, we need to get the data into R and then make sure we have a date object to work with.

At the time of writing, I have essentially left Twitter. It was a fun ride and without going into what’s happening there now, this is a good opportunity to look at my 12 years on the platform. Early in November, I downloaded my data and locked my Twitter account. This gave me all the data I needed. Using R, a few nifty libraries and the tweets.js file that was part of the download, I could gain quite a lot of insight.

I always forget how to deal with logged values in ggplot—particularly things that use the natural log.

By 30th September 2022, I had clocked up a total of over 2000 km of running in 2022. This milestone was a good opportunity to look at how I got to this point. The code is shown below. First, we can make a histogram to look at the distance of runs. From this type of plot it’s clear that my runs this year consist of a lot of 4-5 km runs and then a chunk of 21 km plus.