(Author here) - I don't have any specific examples, but I was learning R and Python at the same time. I found Python to be very practical and easy to learn. When learning R, I kept getting tripped up. Maybe it isn't that R was harder, but that I had a head start on Python. And as for documentation, R was certainly very complete, but once again, I found it harder. I think because R is written by, and probably for, professional statisticians and mathematicians, it needs to have a different level of rigor than the Python documentation. Anyway, sorry for the lack of specificity.
R does have some weirdness (it took me ages to understand the index and slice notations), but it is very expressive.
I'm not a mathematician, and I've been programming Python for 15 years, but I'd always pick R for its stated problem domain given a choice.
I highly recommend "The Art Of R Programming" for learning R as a programming language. The statistical side of things are then easier to layer on top of that.
