This is missing the most important difference - deployability. R was built as a language to use interactively and does things like raise warnings for things that should be errors, requires an external package (packrat) for reproducible package management, and in general is foreign to most developers running operations. Python has good error handling, scripting and logging out of the box and managable package management, and is familiar to most developers and operations. Python has much better libraries for building general purpose tools (but fewer libraries for complex statistics).

I disagree with the "learning curve"; if you've learned other programming languages Python has a pretty simple and familiar core, and Pandas (while the API is an inconsistent mess) is well documented. Base R is quirky compared with modern programming languages, and the API is pretty inconsistent.

I also strongly disagree with the Tidyverse bashing. I'd say it has the shortest learning curve (especially for someone familiar with SQL), and is one of the main reasons I still use R today outside of deep learning - I find it much more friendly to work with than any alternative.