Not a bad thing, it's just R Notebooks kind of seem like old news to me.
Addendum: I looked it up and it appears that Jupyter Notebooks actually have an R kernel (https://irkernel.github.io/).
There wasn't migration to python because there's a total lack of anything to migrate to in python. It has only textbook examples as far as statistical models go. That's around 10% coverage and pretty much 0 coverage for all the abstractions and utility packages in R. Pretty much the same with machine learning (minus neural networks where it is strong). Python took some share from matlab and data processing tools but I don't see it retaining that. IMO it will lose it to Julia for the engineering & science people and to Scala for the data processing people.
The "second best language for everything" doesn't really work too well in the "data science" world.
Most of the nice stuff from RStudio is making its way into https://github.com/jupyterlab/jupyterlab which is starting to look nice.
R Notebooks allow you to prepare something for presentation, not just for running/testing. I.e.: You can run code and then choose to present the code and the output, just the code, just the output or none of them (for instance just putting a sentence in the presentation where you say you applied some mathematical function to the data, but show the mathematica function (latex) instead of the code.
This is really important to publish and to present data and it's not possible to do in Jupyter/iPhython.
Also, it allows you to export directly to HTML/PDF. Something Jupyter doesn't.
I am really excited about using R Notebooks.