> it’s clear now that Python has “won” in this space and R is a tough sell to a wider team

R has always been a language for academics, and it continues to be popular in that domain, with no compelling reason to switch. It has seen usage in the private sector, but that has never been the driving force behind R's development or ecosystem, and I doubt it ever will be. For academics, even if a particular function is only available for Python, it's easy enough to call it from R and do everything else in R.

My experience is that you can "sell" R when the statistical or modelling technique is not (or not well) implemented in python. Which still includes a lot of potentially useful statistical techniques! R should/could lean into being the tool that python programmers reach for when they need something a little less mainstream, if they made it easier for non-R programmers to do so.

Oh, I agree on that, and I know that most of the time I'll be asked why something isn't in Python so I mainly reserve it for when I am sure nobody will ask "why R?" :-)

There is a benefit nowadays that I can rely on Python>=3.6 to be available by default anywhere I am deploying whereas R has to be installed in some way, so like Bash it's part of a toolbox I can rely on being available with at least a constrained set of features.