Not the OP, and I don't have a beef with R, but I prefer python.
A lot of it is just my background. I was a math major, but I didn't use Matlab (much) - my professors just happened to be the kind who prefer C (numerical recipes types). So my intro to math was through programming languages rather than through math environments that can be programmed. I'd say that's probably the big difference between Python and R. I'm sure there's absolutely nothing you can't do with R, but python is a general purpose programming language with great libraries for ML, R is a stats environment with general purpose programming capabilities. Having also been a programmer for 15 years since my school days, I'm more productive in the former (general programming language with great libraries).
There's one other reason. People often speak derisively of "cramming things into a random forest." In my experience, it takes a remarkable amount of intricate programming to cram things into a random forest! Even a kaggle assignment, the sort of thing where you have the question and reasonably cleaned data set available, even then you have to poke and prod and threaten and beg the data into different formats.
And in the wild? Sheesh. Python seems to be the heir to the perl crowd, the "pipe it here and twist it there and cram it" sort of thing. I'd rather be doing that with Python than R.
A lot of it is just my background. I was a math major, but I didn't use Matlab (much) - my professors just happened to be the kind who prefer C (numerical recipes types). So my intro to math was through programming languages rather than through math environments that can be programmed. I'd say that's probably the big difference between Python and R. I'm sure there's absolutely nothing you can't do with R, but python is a general purpose programming language with great libraries for ML, R is a stats environment with general purpose programming capabilities. Having also been a programmer for 15 years since my school days, I'm more productive in the former (general programming language with great libraries).
There's one other reason. People often speak derisively of "cramming things into a random forest." In my experience, it takes a remarkable amount of intricate programming to cram things into a random forest! Even a kaggle assignment, the sort of thing where you have the question and reasonably cleaned data set available, even then you have to poke and prod and threaten and beg the data into different formats.
And in the wild? Sheesh. Python seems to be the heir to the perl crowd, the "pipe it here and twist it there and cram it" sort of thing. I'd rather be doing that with Python than R.