"The first stage of data aggregation can be accomplished with Python. Then the data is fed into R, which applies the well-tested, optimized statistical analysis routines built into the language. It’s as if R is a library for Python. Or maybe Python is a preprocessing library for R."
I really like this approach, actually. Taking advantage of the strengths of both languages.
We often take this approach at my company. The heavy lifting of feature extraction from raw data (wearables in our case) are done by python/numpy models. The population level stuff is then often handled in R by data scientists with more of a maths/stats background than an engineering one.
I really like this approach, actually. Taking advantage of the strengths of both languages.