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?

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’m not a big movie person. Nonetheless I have a media library with quite a few films in and I wondered how many “films to see before you die”-type movies I had in the collection, and how many were missing. I used R to find the answers. I’ve described previously how to get a plain text dump of a Plex database using WebTools-NG. I did that for the Movies library of my Plex Media Server.

I like to set up a standardised directory structure for RStudio projects. The idea came from here. In brief, the structure is: Data/ Output/Data/ Output/Plots/ Script/ My typical workflow is therefore to: select File > New Project in RStudio make a new directory and RProj file then use this R script or these shell commands to setup the directories. So far, so good. However, this process is a bit tedious.

I have long admired albums2hear, a Twitter bot that posts albums. You can read a bit more about it here. There was no mastodon equivalent and so I decided to build one. You can follow the bot – currently called Albums Albums Albums (or AlbumsX3) – here. Idea behind the bot The idea is to periodically post an album.

I have a long-running Raspberry Pi camera project to capture images of the view from a window (more details here). A recent post on mastodon, which showed a keogram, encouraged me to take my PiCam images and turn them into art. The finished product This is the finished wall art, printed on canvas. Ready to hang on the wall.

It’s a New Year and so it is time to post the papers I have selected for a module that I teach on (MD9A8, the module formerly known as MD997). Previous selections are grouped here or here. The list serves as a snapshot of interesting papers published in the previous 12 months or so. I hope it is useful to others who are looking for lists of papers to read, for student selections or for anything else.

There are a few one-liners that I use in the shell to do some really nifty stuff. I struggle to quickly find and reuse these and asked for a solution on Mastodon.

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.

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.