I prefer R because of the wide variety of statistical methods available in base R and CRAN. ggplot2 is nice, but having it in the same system as generalised additive models, robust regression, ARIMA and state-space time series modelling, GARCH packages, and a huge range of other statistical methods is very convenient.

My experience is that if there's a new statistical method developed, it will be often be implemented in R first. (This may be more true for statistical methods than machine learning or other fields).

I don't think there's quite such a wide range in the Python ecosystem yet (although it is rapidly growing, and is definitely a better choice for certain analytics situations).

None of this is to say that you necessarily lose anything other than convenience by using other languages eg Python for modelling.

My 2c is that R has a wealth of libraries and utilities (for statistics, machine learning, finance, etc.) that are not as easily available for other languages. Things like scikit-learn for Python close some of the gap, but looking at packages on http://cran.r-project.org/, it seems that Python might have some catching up to do.

I make no representation as to the quality of the libs available for R, as I don't have a whole lot of experience with it yet.

Quality is all over the map in R, but given the choice between an R library and a Python library for the same recently developed stats method, I would think that the R library has been more widely used, had more attention from experts, and is probably more robust.

It's more about the libraries. It's hard to find a statistical method that doesn't have an R implementation. That said, the quality of these libraries can be very low.