Not to dispute because I have no idea so I'll assume you're correct. But how many metrics did you find and how were they obtained? And how would you know they are representative of all R users?
R has a pretty particular use case though, Python use for statistical programming/data analysis would be an apples to apples comparison. People doing a coding 101 course in Python don't really count against the R user base.
"Does what it intends to do reasonably well" is going to be widely subjective, depending on whether the user's use-case is statistical/life-sciences vs more general purpose coding and relying on many packages; prototyping/experimentation vs production code; whether the user uses base-R, or tidyverse/data.table, etc.
The issue is that even if you peel the hype (which is a fact), python is still far larger.
If you check e.g. the journal of open source software (which does not have much ML/AI bias), most of the papers are python, with an occasional R and julia submission.