Also, ML academics tend towards R for reference implementations of novel algorithms. They are often available in R first. This cuts both ways; sometimes the Python implementation that comes later misses some subtleties of the R implementation that the original authors nailed, and other times the R implementation is a proof of concept, while a later implementation is more real-world ready. But the latest and greatest tends to be available in R long before it has made its way into e.g. SciPy.