R is really not for production deployment. It lacks a lot of what made python popular, and its target users were radically different.

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

This is maybe just a reflection that R and R's libraries were being designed for interactive use by humans doing exploratory data analysis, model fitting etc, unlike other programming languages which are used to automate things or build software products that can be shipped.

It's general purpose and really there is no issue with doing production with it really outside of the mindset and the lispy nature of it. Source - was working on R in production for financial sector.

This is really a non issue now. R's problem back in the day was that it was really specialized in analysis and interactivity, but a lot of the general purpose stuff that made Python popular is now easily achievable in R and well-developed and maintained. RestRServe and Plumber are both excellent tools for REST APIs.

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.