The reason is that it's more common for a statistician to encounter a problem that needs switching to Python (either because there are Python tools for it, or b

The reason is that it's more common for a statistician to encounter a problem that needs switching to Python (either because there are Python tools for it, or because of a need to interact with non-statisticians who are using Python), than it is for a software engineer to get into statistical research. It is quite telling that a lot of people here mention packages such as ggplot2 as the advantage of R -- ggplot2 is really quite awesome, but I can see it being similarly powerful had it been done for Python (maybe with a bit more unwieldy syntax, but that stuff is really subjective). R really shines when you are doing actual data analysis -- things like the data.frame object, first-class missing values, proper attention paid to inference (something I quite often see missing from stuff written in Python or even Matlab), model syntax, estimation objects with actually useful pretty-printed summary, huge variety of statistical plots, everything designed to be convenient to use in the REPL, etc. -- and since these things are expected, you will usually find them in CRAN packages as well. Python (and Matlab) are IMO much less consistent in that regard. But if your task is primarily to implement a particular type of analysis, put it in production to be easily repeated in the future, and go to next problem, R indeed offers little benefit over Python unless you rely on a specific package (which, btw, you should only do if you understand exactly what it does, because most R packages are written by statisticians, and as a result have very little fool-proofing).