Python has comparative advantages over R in production roles. R has comparative advantage in statistical libraries, visualization, and meta programming. Neither are exemplars for production deployment or meta programming (R is an exemplar for stats libraries however).
Yeah, you've probably never heard of it
ggplot is something that I don't think matplotlib is comparable to at all, though. I am so much faster at iterating on a visualization with R/ggplot than Python/matplotlib. Maybe it is my tooling, though. How about others who have used both? What are your experiences?
Once I was a lead on a new project and asked the intern to write some basic ETL code for data in some spreadsheets. I said she could write it in Python if she wanted, because "Python is good for ETL", right?
This intern was not dumb by any means, but she wrote code that took 5 minutes to do something that can be done in <1 second with the obvious dplyr approach.
Also, if your bank analysts pick up dplyr, they can use dbplyr to write SQL for them :)
Edit: or maybe it's not dead? I just found http://www.user2019.fr/static/pres/t246174.pdf
Second issue, in my field (bioinformatics) the script is still a pretty common unit of code. Without cached compilation being a simple flag, Julia often is slower.
In regards to Julia's compilation problem, you can use https://github.com/JuliaLang/PackageCompiler.jl to precompile an image, allowing you to avoid paying the JIT performance penalty over and over again.
Personally, I prefer R for my use case which is longitudinal analysis of experimental data.
I teach classes involving data analysis, some in Python and some in R (different topics). The amount of time the Python students spend fighting pandas---looking up errors, trying to parse the docs, trying out new arcane indexing strategies---is obscene. On the other hand, the R students progress rapidly. I'd move everything to R if I could, but Python is still better for NLP pipelines.
Python is wonderful but the cognitive load for switching in industry and academia without a clear cost benefit isn't worth it to most people I know in my shoes. I encourage new coders to learn Python but discounting R feels a bit asinine.
Hadley is still actively doing work for R which has led to a graphing packages that is substantially better than anything in Python (last I check). I have no doubt that Python will steal it and implement it eventually (as they should) but R is still doing firsts that Python hasn't (note the native implementation of Piping, they're late to the party on lambda functions obviously)
Also I used to love Python... Until I got a full time job and learned why static typing exists.
So for example, I recently saw a paper with a quite complex estimator based on dynamic panels and network (or spacial) interdependence that could identify missing network ties. For that, an R package exists.
If you want to use it in Python, you'd have to replicate a whole estimation infrastructure yourself, starting by extending the basic models in statsmodels.
That example is quite typical in my opinion.
Like I said, really like to code in Python and I don't like R all that much. But if someone says: "Why would you use R, Python is better", then we can confidently say the person does not know what R is actually used for.