I also think Python is a bit better. (Though, unlike you, my programming skills are directly tied to my livelihood, so it benefits me if one language can cover as much ground as possible. Being locked into a specific domain just narrows the number of jobs I can take on.) You're not wrong, but it makes me pretty sad that all my homepage submissions are marked as 'showdead' and no one ever sees them. Maybe my submissions would look like rubbish by your standards. But looking at it that way, there's also the gap between what people expect and what the site's filters decide.

You're right. I think I overstated it. Since English isn't my native language, I might have used some stronger words than intended. Thank you for pointing that out

I partially agree, but I also differ on some points. The part I agree with is that probabilistic programming is difficult and that advanced programmers tend to enjoy it. Where I differ is on the claim that programming is deterministic. At the script level, programming is deterministic and sequential, but once it crosses a certain threshold, it becomes absolutely probabilistic. That's because latency, locks, and asynchronous communication start to intervene. If programming were Non deterministic , C's undefined behavior wouldn't exist; everyone would have prevented it.

R these days mostly uses the tidyverse, which feels like a variant of DOP (Data-Oriented Programming). It's a kind of data flow, so it's different from typical OOP. I also occasionally work with statisticians (being a freelancer, ETL work is more common than you'd think), and I know what you mean by Stan and PyMC. I know they're powerful tools for Bayesian statistics and multilevel modeling. I know the basic syntax and examples, but I wouldn't say I know them well. My level is mainly focused on the scientists who hire me, and those tools still don't come up often in my country.

That said, I think we differ on the bigger picture because academic code isn't everything. Academic code is typically algorithm‑centric, like LeetCode problems, but most production work revolves around code hygiene and responsibility (algorithms are usually already established ones). Anyway, that's not the main point. What you said is mostly correct, but my focus was on something else: even people who studied at that level can be surprisingly clumsy at expressing themselves through programming. Regardless, thanks for your input, and I agree that AI is good at programming. But using a programming language generally means understanding its tradeoffs, and R is tricky in that regard since it feels like a mix of OOP and DOP variants

Picking up on some dunning kruger effect here.

Programming isn’t even a field in the same way as prob&stats. Computer science does in fact have non-deterministic sub fields such as information theory.

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).

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.

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.

I mean I was just making an R programmer joke ;-)

They are the coding equivalent of orchestral viola jokes. By which I mean fundamentally grounded in truth.

It's certainly quite pleasant to work with...but I would rather use sql for etl, the backend be whatever it needs to be...

The real world data transformations can get gnarly very quickly and sql is the perfect common debiminator compared to dplyr which is still niche...

How do you feel about polars?

The proof is in the pudding. Every single grad student of mine that was brought up on the tidyverse produces gigantic R markdown files with 20 imports to accomplish something that would be shorter and much much easier to understand (and review!) with a base package or with one of a small number of packages (box, data.table) designed by people who understand programming.

Not to mention the ridiculous styling/formatting of most tidyverse users, which Wickham and others seem to promote. One of the reasons R has lost ground to other languages recently is that most R code these days is ugly

Python is just such a good Swiss army knife and it's never a waste to learn: you can do data science and you can do almost anything else. It's the BASIC of the 21st century.