This is the crux of the problem with R and why R is increasingly blacklisted at large orgs. It attracts non-programmers which may have been okay 5 years ago but is no longer acceptable.
With the exception of some engineering powerhouses hiring pure research PhDs to write R code, the trend established over the last 2 years is that fewer and fewer employers are hiring data scientists that aren't programmers. There are too many candidates who know data science and can also do data engineering and even generalist SE tasks. Non-programmer data scientists are not competitive in the industry anymore except that small top-end research niche that doesn't exist in most orgs.
Which brings us back to the fact that R was a successful niche language that allowed non-programmers to write models, but that's simply not enough anymore. Businesses want models that can be plugged into production pipelines, models that can scale without needing a dedicated team to re-implement them, and they want staff who do engineering in addition to whatever it is they specialise in.
Virtually all data scientists graduating today are programmers, and pretty good ones. Candidates who only know R can't compete against them.
> Lack of a magrittr style infix operator, though seemingly minor, actually emerges as a real pain point once you become accustomed to using
So you'd agree that you fall into the 'I prefer the syntax' bucket then? I don't really see any arguments against Python in your comment. Funnily enough, it's trivial to implement a pipe style operator in Python and there's at least two popular libraries for that.