I agree - R has performant and robust dataframe functions. dplyr is great for small-medium sized datasets, data.table seems to be really performant for larger sets.

And now there is dtplyr, which simply creates a data.table back end with dplyr syntax on the front end.

I think this is key: there was a massive learning effort that went into the current dplyr interface. This risked fragmenting the community, but Hadley and his collaborators navigated those waters effectively. That's super tough work and R benefits significantly from it. If I had a magic wand for Pandas, I feel giving that team the opportunity to rework interfaces without killing momentum and community would go so far.