The extent of my experience with R was a couple of somewhat introductory statistics courses, so I’m not the best person to answer probably.
But I like dealing with numpy/scipy much, much more than R. Python as a language is I think much better designed, and numpy is a really nice tool for interacting with multidimensional arrays. When I write Python code, or read Python code written by anyone competent, I find program intent very easy to design/follow. Most of the R code I’ve seen “in the wild” is kind of a mess, because it is written by non-programmers many of whom have little experience or concept of code style. Additionally, as soon as a program has to do anything other than statistical analysis (examples: text munging, internet scraping, network communication, dealing with file formats, user interaction, etc.) Python is miles ahead.
The big advantages of R that I saw: (1) it has become the tool of choice in the academic statistics community, meaning that there is quite a lot of existing code for doing various sophisticated things, some of which you might have to implement yourself in Python, (2) it has some really nice graphing tools, (3) there seemed to be a few examples where a particular few lines of R code were more compact and clearer than the equivalent Python (can’t think of anything off-hand though).
But I like dealing with numpy/scipy much, much more than R. Python as a language is I think much better designed, and numpy is a really nice tool for interacting with multidimensional arrays. When I write Python code, or read Python code written by anyone competent, I find program intent very easy to design/follow. Most of the R code I’ve seen “in the wild” is kind of a mess, because it is written by non-programmers many of whom have little experience or concept of code style. Additionally, as soon as a program has to do anything other than statistical analysis (examples: text munging, internet scraping, network communication, dealing with file formats, user interaction, etc.) Python is miles ahead.
The big advantages of R that I saw: (1) it has become the tool of choice in the academic statistics community, meaning that there is quite a lot of existing code for doing various sophisticated things, some of which you might have to implement yourself in Python, (2) it has some really nice graphing tools, (3) there seemed to be a few examples where a particular few lines of R code were more compact and clearer than the equivalent Python (can’t think of anything off-hand though).