Bands have been known to declare “No Synths!” on their albums. This statement was a badge of pride indicating that the artists hadn’t used any modern trickery in their recordings. Today, the growing use of artificial intelligence (AI) and large language models (LLMs) in science has created a similar scenario. Advocates argue that these tools improve every aspect of science, including the publication process.
Hopefully I will soon break out of this funk of posting about either Mastodon or Twitter. But not yet! This post is to say that: I made a static archive of tweets for @quantixed and for @clathrin. There, you can read all my posts, which ended in 2022. How did I do it and why? I made the archive with this excellent tool written by Darius Kazemi.
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.
