You bring up a good point in favor of R: Hadley Wickham and the rest of the RStudio people.
Packages like {ggplot2,rvest,dplyr,devtools, etc.} are basically creating a sub-language for R.
I use both at the moment, but I echo the OP's ideas that R's target audience is statisticians, where Python's target audience is broader and includes statisticians and computer scientists. And Python's syntax is nicer to work with. That's why it's become the primary glue language.
That said, the overhead for learning Python as your first data science language is a bit problematic for me, as you basically have to learn Python followed by Python's data science tools (pandas, matplotlib, etc). whereas with R, you're learning the language and the data science tools at the same time, even if they're a bit idiosyncratic.
Comments
You bring up a good point in favor of R: Hadley Wickham and the rest of the RStudio people.
Packages like {ggplot2,rvest,dplyr,devtools, etc.} are basically creating a sub-language for R.
I use both at the moment, but I echo the OP's ideas that R's target audience is statisticians, where Python's target audience is broader and includes statisticians and computer scientists. And Python's syntax is nicer to work with. That's why it's become the primary glue language.
That said, the overhead for learning Python as your first data science language is a bit problematic for me, as you basically have to learn Python followed by Python's data science tools (pandas, matplotlib, etc). whereas with R, you're learning the language and the data science tools at the same time, even if they're a bit idiosyncratic.