R and Rstudio (an IDE for R) are inextricably tied. Using R is facilitated greatly by using this language-specific IDE making the usage much easier.
The addition of libraries makes the use of matrices easy, just like other languages.
I don't believe that one can make a proclamation, as you have done, regarding a language that has does have many advantages based almost primarily some Fibonacci calculations.
Yes, this is a review of just the base language, not any additional tooling. As for particular points:
It would be more accurate for me to run these in a Jupyter environment than with VSCode and terminal like I did, I didn't do this as it would make formatting it for the post more difficult.
The working theory for this series is that language that struggles a lot given very simple tasks will likely struggle even worse given more realistic tasks. This isn't really provable, and it might not work for some highly specialized languages (like let's say Verilog or WebGL Shader Language), but I think it's very reasonable for data science.
According to yet another survey data preparation, which is basically a very large number of such simple tasks, is the most time consuming part of the job, so I think it's fair to see how well the language is doing on such tasks.
And in the end, I rate languages relative to others in similar domain. So I'm not saying it's impossible to do a decent job with R, just that other languages (Julia and Python in this case) seem to be a lot better at the same thing.