Another post about my time on Twitter. I will post the code in a separate post so that the R-bloggers don’t syndicate this one, which is about music . In my time on Twitter I occasionally posted about what I was listening to. I did this with a #NowPlaying hashtag. I wanted to preserve these tweets – and they can all be found below.

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’s plenty of guides to getting going on Mastodon, aimed at people leaving Twitter. I just wanted to post a couple of technical points about making the switch that might be of interest to people who maintain webpages with Twitter content (feeds, embeds). Mastodon status updates (feed/timeline) Twitter provided a widget that meant that an account’s timeline could be embedded on a website.

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.

Here is a summary of the info I gleaned from asking for recommendations for the best human cell line. These were my criteria: For context, we currently use a number of human cell lines in the lab: HeLa, RPE1, HCT116, SKOV3; as well as many others in the past: HEK293, DLD-1, U2OS. I consider HeLa to be the almost perfect cell line.

I recently did a webinar for ASCB called “From Microscope To Figure“. For posterity, I am re-posting the webinar here with some additional info.

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.