2023 has been a great year in running for me. Previous running round-ups are here (2022, 2021). My two main goals for 2023 were to run 3000 km and also to run 50 HM-or-more distance runs. I managed both with a couple of weeks left. I also bagged new PBs for 5K, 10K and half marathon as well as a handful of segments on Strava. I won no races but I did win two little running competitions at work.

We have an analysis routine for proteomics data written for IgorPro. One output is a volcano plot . These plots show the fold change in one sample compared to another and plot that against a p-value to estimate how reproducible any changes observed are. This post is not about that software, but on the topic of how we can recreate this plot in R . What steps need to be considered?

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

I read about aesthetic uses of Latin Squares on John Cook’s site a few months ago. Since I maintain a resource to use colour tables in Igor Pro, I thought it would be fun to use Latin Squares to display colour tables for easy visualisation. Briefly, I wrote some code to generate a 9 x 9 latin square and assigned the values 1 – 9 to a colour table wave. The results were nice.

When preparing images for publication, it is good practice to check how accessible they are for colour blind people. Using a simple bit of code, it is possible to check an image – or a whole figure – in ImageJ for accessibility. For example, Figure 1 from our recent paper. Originally looked like this: Using the script we can see how it appears to people with different types of colour blindness.

A colleague said to me “Everyone is running on fumes with Easter being so late this year.” Really? Is it late? I admit to being completely perplexed by this religious date and its movement around the calendar. I always feel like I am the last to know when it is, let alone whether it is early or late. Let’s have a look at what constitutes an early or a late Easter. The data are available here. This is a quick post with no code example (sorry).