A lot of people... in fact a huge portion of statisticians, epidemiologists, econometrics, use it as their primary language.
I do genetic epidemiology (which is considerably more compute intensive than regular epidemiology), and R is still the most common language, with the most libraries and packages being used for it, compared to python for example.
I think maybe you should consider being less forthcoming with your opinions on topics which you are not well informed on.
I worked in data science for a few start ups, and even though I know Python (it's my LeetCode language of choice), R just dominates when it comes to accessing academic methods and computational analysis. If you are going to push the boundaries of what you can and can't analysis for statistical effects and leverage academic learnings, it's R.
Before I knew command line, I tried to install python and spent the next 3 days resolving an installation issue with 'wheel'.
By contrast, from first downloading R to running my first R script took about 1 hour (the most difficult part was opening the 'script' pane in RStudio IDE, which doesn't open by default on new installations, for some reason).
There's huge demand out there for statistical software that's accessible to people whose primary pursuit is not programming/cs, but genetics, bioinformatics, economics, ecology and other disciplines that necessitate tooling much more powerful than excel, but with barriers to entry not much greater than excel. R is a fairly amazing fit for those folks.
R and CRAN really get package management right. Even as a very infrequent R user, there are no surprises, it "just works". Compare that to my daily Python usage where I am continually flummoxed by dependency issues.
Everyone in my branch of Toxicology? Tons of people in biological sciences. Just because you have bias against the tool and don't run in the same circles doesn't mean that R isn't used and love by a subset of devs.
Respectfully, I'm going to ask, "what what?". I can't swing a cat without hitting dplyr. It's probably industry dependent though - I could see a dataset that's 99% text having absolutely no reason to even look at R at all.
R sucks as a language but it excels at that specific application, just because of its tremendous ecosystem (putting even python to shame in some niche areas).
R is fine, it's no more absurd than other non-typed languages like javascript. Most languages are very good at one or two things, then not so good or appropriate for other tasks. For R, that's statistics, modeling, and exploratory analysis, which it absolutely crushes at due to ecosystem effects.
Well… I also consider Javascript to be a horrible language. Python is horrible as well, but better than R. IMO python and javascript are in the same ballpark.
Not all non-typed languages are bad. Clojure, for example, is one if the most elegant languages I’ve worked with (despite my dislike of the JVM).
I find it depends on what you want. There is no canonical GAM (gen. addative model) library in python but there are a few options - which are not easy to use. The statsmodels GAM implementation appears to be broken. R, of course, has a stupid easy to use GAM library that is pretty fast.
On the other hand, R has too many obscure options for what I can find in scipy or sklearn. So I find it easier to just jump into sklearn, use the very nice unified interface "pipelines" to churn through a whole bunch of different estimators without having to do any munging on my data.
So I think it just depends on your field. But R seems to stick more with academia.
I do genetic epidemiology (which is considerably more compute intensive than regular epidemiology), and R is still the most common language, with the most libraries and packages being used for it, compared to python for example.
I think maybe you should consider being less forthcoming with your opinions on topics which you are not well informed on.