As mentioned above, use the language that you feel most comfortable with.
That being said, I have become a generalist as time goes on.
I use R to make nice graphs, python for automation/text sifting, php for webifying my scripts so other scientists can use them. By using many tools, you can lean on individual language strengths, and not use them in places that they seem weak.
R's I/O speed can be dreadful, so beforehand I would use python to reformat/clean data, then feed it to R for pretty picturing. I have called C/C++ programs from python to benefit from their speed if some serious computation needs to be done.
In bioinformatics, the results are what matter, not the language used. To me, a stack of programs each with a strength is also good for maintainability. You can make sure the formats farther up the pipeline stay the same, so that you can replace parts as they get better.
A one size fits all is definitely no good for a field moving so fast.
Disclaimer: I'm a C guy who used to use Perl, and now uses Python.