One of the things that always reassures me about LLMs is that as well as being trained on languages with reasonably well-designed grammars, they will also have seen lots of examples of good practice in their training set.

Two things that make me wonder if they can possibly turn out good quality R.

Perhaps a true test of AGI will be when you ask it to write an application in R and it refuses for fear of what people might think.

> People I've worked with that used R and manged data / did analysis didn't really seem too concerned with long term maintenance.

Unless you’re the poor schmuck who is given the task of running the code written by the previous analyst, who has probably already left the company. Often it’s easier to just throw something together from scratch and then look for a new job, perpetuating the problem.

At my job I switch between writing analysis code for research projects and writing code for apps. The difference in mindset is so dramatic. In the same way that good software has consistent names and interfaces that are ~useless when you just need the code to run once, research code has its own requirements that are ~useless in software. It's honestly a big challenge to switch back and forth. So I think it just reflects the main skillset of the people who use it (caring is not enough).

Bingo. The typical data scientist has a masters or PhD in a non-CS quantitative field, and has had exactly zero CS or software eng classes. It’s a shame, because once you get over some of the idiosyncrasies, R is a really powerful and flexible functional language.