what are the problem domains ? Do you think something like Python Pandas... and especially Jupyter/IPython can replace it ?
Also, mathematicians and statisticians think functionally and the general attitude in python is to do object oriented programming while R is strictly functional programming with a little bit of object programming.
However, in my experience, for the data munging required as a preliminary to the analyses, R is worse than bad. It's as if satan himself designed a language.
I find that what then happens is this: data scientists/statisticians/[your favorite word here] become reliant on programmers to clean/format the data to do the analyses.
This is all fine, but those same scientists are then put off learning python, where they could do all of their own munging, and probably 95% of the analysis they need to do, and where they could further add value by writing programs that are easier to production-alize.
Job security for those who know how to write production code, I guess.
[1] http://opiateforthemass.es/articles/james-bond-film-ratings/
I don't know anyone who considers themselves a "data scientist" of any sort that doesn't view their job as 80% or more data wrangling/munging/cleaning.
I write production ETL processes in R at my current job. AMA.
I'm always interested in learning a new tool, though.
I have largely avoided ts, zoo, etc where possible. Time series stuff seems to have a lot of specialized tooling all of which tends to be much more strict about data structure than I'm comfortable with for my flow.
BUT perl + R was a really nice combination for a while.