Very few enterprise data science teams are 100% Python (in fact none I've heard of). R is still very heavily used (and in fact all data science teams I've worked in it has been the dominant technology).
R, python and Julia are all Turing-complete languages, so of course you can drop any two and get by with just the third.
The real selection happens when you consider what's available in opensource world. What code you don't have to write? What high-quality libraries are available vs which ones you will have to write yourself?
On this topic, R has vast advantage over python in some domains, such as bioinformatics for example, while python definitely shines when it comes to deep learning (and using for loops).
You can't just claim that one shouldn't look at R because you personally know one language better the other, quite likely because in your domain it's not being used as much.
I do prefer the deep learnin, NLP and production serving story in python, but you will have to pry dplyr+ggplot from my cold dead hands for quick analysis and charting. Not to mention that pandas's API is a clusterfuck compared to R's native data frames.
Very few enterprise data science teams are 100% Python (in fact none I've heard of). R is still very heavily used (and in fact all data science teams I've worked in it has been the dominant technology).
There is a reason Microsoft purchased Revolution.