Reticulate vs Python
I actually tested this recently on a random forest fitting using two approaches:


Using Jupyter notebooks to fit my model via python with data that had been tidied in the same notebook also using python.


vs:


Using reticulate in a rstudio notebook to fit the same model in python but using a converted dataset I had tidied in r in the same notebook.


I made the following observations:


Native python is faster, in my benchmark it fitted the model ~1 min faster (9 vs 10 mins) on my 16GB Ram laptop.
Additionally you lose some time converting objects between the two languages, especially if we talk about bigger datasets.


Conclusion

In most cases doing r stuff first, saving objects to CSV and then opening jupyter or python command lines to finish a job is not necessary thanks to reticulate. 

If you do a repeated or time-critical job try to use python natively but otherwise reticulate is fine especially, if like me you save a lot of time doing prep work in r.