- working interactively (i.e. your code isn't part of a larger application)
- working with relatively small datasets that fit into memory
- don't need any deep learning libraries
then both R and Python can do a great job and choosing one over the other is simply a matter of preference. I might even lean slightly towards R because its data frames are a bit easier to use than pandas and RStudio's REPL is the best.
But if you need to deploy your code somewhere, or high performance, or the latest deep learning libraries, then Python absolutely crushes R. And it's not even close.
while with high-performance you mean documented FFI and friends? this should be possible with R as well.
It also seems that the actual open-source ML-community (vs. Google: we want you to use our software to ensure you can't ever own your stuff) supports R just fine: https://mxnet.apache.org/api/r
- working interactively (i.e. your code isn't part of a larger application)
- working with relatively small datasets that fit into memory
- don't need any deep learning libraries
then both R and Python can do a great job and choosing one over the other is simply a matter of preference. I might even lean slightly towards R because its data frames are a bit easier to use than pandas and RStudio's REPL is the best.
But if you need to deploy your code somewhere, or high performance, or the latest deep learning libraries, then Python absolutely crushes R. And it's not even close.