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.

There are lots of ways for runners and cyclists to analyse training data. A key question most fitness enthusiasts want to know is “how am I doing?”. “How you are doing” is referred to as form . Unsurprisingly, form can be estimated in many ways. One method is using training stress scores (acute training load and chronic training load) to assess form as training stress balance.

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.

Sometimes an update causes a chain reaction which borks a system. Here are some notes on how I fixed a SMTP mail issue on my Raspberry Pi camera project. A quick recap: the RPi camera takes pictures every ten minutes, each night the images get sent to a server and each week the transferred images get deleted.

It’s been a while since I posted a breakdown of half marathon times. The last time seems to have been 2018. I decided to give my old code a clean-up and quickly crunched the numbers from the 2022 Kenilworth Half Marathon. First, the results: Briefly, the code below reads in a csv file of race results downloaded from the provider.

This website is all about niche tech tips and this post is no exception. I run a Plex media server. This allows me to stream my music collection when I am out and about. Plex pass owners get the nifty plexamp app for listening to music, which I really like. The databasing for the movie and TV show side of Plex works great, but the music side has its peculiarities. The problem I have a large music library.

Niche tech tips are the cornerstone of this website, and here is another. How to upgrade an iTunes/Music library whilst maintaining the database. I know everyone streams music these days – hence this is likely a very niche tech tip – but I still maintain a large music database on a server. The data in my library is precious (to me) since it tracks my listening habits over 17 years.

Previously I wrote about latin squares and set a puzzle. We can demonstrate this for an n x n latin square where n = 12 In the above images, the normalised latin square only has 12 different pairs out of a possible 66. The density plot shows the pairings in the lower triangle, grey represents 0. A randomly generated latin square (where all rows and all columns feature 1 to 12 exactly once) is shown where all possible pairs are captured.