Considering another criteria, I think that in some cases using Python may be much superior to R for Big Data. I know the wide-spread use of R in data science educational materials and the good data analysis libraries available for it, but sometimes it just depend on the team.
In my experience, for people already familiar with programming, using Python provides much more flexibility and productivity boost compared to a language like R, which is not as well-designed and powerful compared to Python in terms of a programming language. As an evidence, in a data mining course in my university, the best final project was written in Python, although the others has access to R's rich data analysis library. That is, sometimes the overall productivity (considering learning materials, documentation, etc.) for Python may be better than R even in the lack of special-purpose data analysis libraries for Python. Also, there are some good articles explaining the fast pace of Python in data science: Python Displacing R and Rich Scientific Data Structures in Python that may soon fill the gap of available libraries for R.
Another important reason for not using R is when working with real world Big Data problems, contrary to academical only problems, there is much need for other tools and techniques, like data parsing, cleaning, visualization, web scrapping, and a lot of others that are much easier using a general purpose programming language. This may be why the default language used in many Hadoop courses (including the Udacity's online course) is Python.
Recently DARPA has also invested $3 million to help fund Python's data processing and visualization capabilities for big data jobs, which is clearly a sign of Python's future in Big Data. (details)