I disagree. R is just not a very nice language.

It has some really great statistical and data science packages that were well ahead of the competition 10-15 years ago. The web frameworks were good enough for dashboards and what most people were using R for.

But if you wanted to write fast and elegant nom-vectorized code, R is really lacking. I left it for Julia for that reason.

Agree 100% on tidyverse becoming part of the standard library. Some of the language's greatest libraries (like Hyndman's forecasting stuff) basically assume you're using tidyverse already

R was developed for and by statisticians, for better and worse. I used R a little bit 15-20 years ago, what I remember was that quite a few libraries and function interfaces seemed to be designed to be convenient for interactive use, but if you tried to use them in an automated script, e.g. some analysis you wanted to scale up and repeat 10,000 times while bootstrap sampling or hyperparameter sweeping or what have you, those same library and interface design choices involved bizarre edge cases where functions would sometimes do something completely different (perhaps changing the return type) when invoked with slightly different arguments. All these automation hostile edge causes were annoying to discover and then work around.

None of this was forced by R the language, it was purely a library design thing by the folks writing the libraries. Whereas in contrast, you simply wouldn't and didn't get such library design in mainstream general purpose programming languages (e.g. in C++, java some of this stuff wouldn't even type check) and similarly in python, even though python being dynamic was fertile ground for people to develop completely bonkers and unautomatable numeric and scientific libraries, the customs for how libraries should work were different

Completely disagree. I work at a central bank, helping people make some of the most important economic decisions in my country and plenty of analyses are done purely with R.