In this article I want to update and improve an analysis I did some time ago, and published on LinkedIn, which can be accessed through this link: Credit Granting can be used as a proxy for the Financial Cycle, which tends to present a “boom” before the beginning of a recession. Intro I will start with the definition of a cycle: a cycle is a low-frequency variation event.

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.

Rambling (proxy for an introduction) We are experiencing the chaos of the information age, and its technological advents with voracious evolution and expansion. And in the midst of all this we have the advent of Big Data that has reached the delights of many with Data Science, Machine Learning and Artificial Intelligence. As a result, the idea of mathematical and statistical “models” became the rage, gaining visibility.

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.