I first learned about R at a python user group meeting. It was when Pandas was new, and we were having a bunch of talks about it. Wes McKinney even came to give one before going to Pycon.
Anyway, the general consensus at the time was that R was much nicer once you had your data, and if all you had to do was transform it. But that everything else was better in Python.
One of our group members did an experimental project, where you could open R inside of python and share memory. So you could theoretically do your API calls and screen scraping and whatnot in Python, then transform your data in R, then take the output and use it to do something else in Python. It was pretty cool, but I think it was just a POC and never really went anywhere.
I tried learning R after that, but didn't get very far with it.
I use rpy2 for that. The scientists wrote the calc engine in R (via my Python transscription of the Excel original :) and I wrote the dashboard and data pipeline in Python. The data pipeline batch process embeds the R interpreter to avoid spawning processes all the time.
The library ‘ryp’[1] now exists that does the same, the data lives in an arrow format so you can write R inside Python without re-writing to disk
[1] https://github.com/briscverse/ryp