As with children’s toys and clothes, books aimed at children tend to be targeted in a gender-stereotyped way. This is a bit depressing.

Another post using R and looking at Twitter data. As I was typing out a tweet, I had the feeling that my vocabulary is a bit limited. Papers I tweet about are either “great”, “awesome” or “interesting”. I wondered what my most frequently tweeted words are. Like the last post you can (probably) do what I’ll describe online somewhere, but why would you want to do that when you can DIY in R? First, I requested my tweets from Twitter.

I wondered how many of the people that I follow on Twitter do not follow me back.

Caution: this post is for nerds only. I watched this numberphile video last night and was fascinated by the point pattern that was created in it. I thought I would quickly program my own version to recreate it and then look at patterns made by more points. I didn’t realise until afterwards that there is actually a web version of the program used in the video here. It is a bit limited though so my code was still worthwhile.

BBC 6Music recently went back in time to 1994. This made me wonder what albums released that year were my favourites. As previously described on this blog, I have this information readily available. So I quickly crunched the numbers. I focused on full-length albums and, using play density (sum of all plays divided by number of album tracks) as a metric, I plotted out the Top 20.

I use a Garmin 800 GPS device to log my cycling activity. including my commutes. Since I have now built up nearly 4 years of cycling the same route, I had a good dataset to look at how accurate the device is. I wrote some code to import all of the rides tagged with commute in rubiTrack 4 Pro (technical details are below). These tracks needed categorising so that they could be compared.

Towards the end of 2015, I started distance running. I thought it’d be fun to look at the frequency of my runs over the course of 2016. Most of my runs were recorded with a GPS watch. I log my cycling data using Rubitrack, so I just added my running data to this. This software is great but to do any serious number crunching, other software is needed.

Top Trumps is a card game for children. The mind can wander when playing such games with kids… typically, I start thinking: what is the best strategy for this game? But also, as the game drags on: what is the quickest way to lose? Since Top Trumps is based on numerical values with simple outcomes, it seemed straightforward to analyse the cards and to simulate different scenarios to look at these questions.

2016 was the 400 year anniversary of William Shakespeare’s death. Stratford-upon-Avon Rotary Club held the Shakespeare Marathon on the same weekend. Runners had an option of half or full marathon. There were apparently 3.5 K runners. Only 700 of whom were doing the full marathon. The chip results were uploaded last night and can be found here. Similar to my post on the Coventry Half Marathon, I thought I’d quickly analyse the data.

Well, the 2015/2016 season was one to forget for Crewe Alexandra. Relegation to League Two (English football’s 4th tier) was confirmed on 9th April with a 3-0 defeat to local rivals Port Vale. Painful. Maybe Repeat Failure is a bit strong. Under Dario Gradi, the Railwaymen eventually broke into League One/Championship (the 2nd Tier) where they punched above their weight for 8 seasons.