Yeah, basically this. I assume HN has a higher number of people who work in ML jobs in fields like finance etc. If you're working in any sort of social/public health research, then most new methods seem to be implemented as R packages. I'm thinking of things like new methods for propensity score, sequential trial designs etc. Also seems to be the preferred language on the Stats Stack Exchange posts.
Any sort of statistical or econometric estimator is typically published as an R package.
So for example, I recently saw a paper with a quite complex estimator based on dynamic panels and network (or spacial) interdependence that could identify missing network ties.
For that, an R package exists.
If you want to use it in Python, you'd have to replicate a whole estimation infrastructure yourself, starting by extending the basic models in statsmodels.
That example is quite typical in my opinion.
Like I said, really like to code in Python and I don't like R all that much.
But if someone says: "Why would you use R, Python is better", then we can confidently say the person does not know what R is actually used for.
