I did a phd in statistics. Everyone used R. I didn't know R (I was not a stats undergrad), and it seemed magical: everyone was using it to solve everything. So, I invested time learning it.
I was pretty disappointed. It really seemed like the result of a small community only knowing a single scripting language. You can do pretty much anything with pretty much any language. Why would you want to though? This isn't a case of best tool - it's just the only script tool for that community (or was at the time - I think it's changing, mercifully).
If you already know R and can accomplish a task with a R and you don't know python, I can't see a reason for you to not just use R to solve your problem.
If you already know python, then check out pandas and numpy/scipy. When I was in grad school, these tools didn't exist, and as a result, I would have told you then that it made more sense to use the packages already in R than code the specialized routines you needed in another language. Even so, R is just awful at manipulating data; I'd usually manipulate the data into the form I wanted outside R, then use read.table to read it in and pass it through the least amount of R code I needed to get the analysis done. I was hardly alone: in fact, many of my fellow grad students just wrote everything in C++ for their dissertation, using R just as a way to easily bang out graphs when needed.
Now that these python-based tools and libraries exist, however, I see no reason for a python programmer to not turn to them first, regardless of what you may hear about R.
If you do not know either R or python, please just learn python with pandas; this is the future. There is nothing inherent to the R language that makes it superior - it just has a lot of packages already written for it. However, that advantage decreases every day as more people contribute to pandas and numpy. I love stats - but the ideas behind statistical analysis aren't "owned" by a programming language. Python didn't really exist when S was created (the precursor to R). S+ and then R had real advantages over other script-based languages for a long time. It's just no longer the case.
Python can realistically be used for 20 other things, unlike R, and the reality of analysis is usually that more than 50% of the work is getting the data into a usable form. R just fails at this. As a result, I used a lot of awk and sed; but python will get things done too. I only turned to awk and sed because R was so terrible at manipulating real-world raw data. R does a fine job at analysis once you have things in table form, but it doesn't do a better job at it than python if the routine exists in both languages (and, unless you're doing something pretty obscure at this point, it likely does).
I really don't see a trade-off on this one. Unless you already know R for some reason, I believe the answer to your question is python, full stop.