For exploring data, plotting data, and fitting complex models like GLMs and GAMs, using R is essentially as fast as thought at this point.
So if you have tabular data, it’s a no brainer to use R.
Getting the data into table form is often better suited for python. Fitting models that leverage autograd are also better with python.
> Getting the data into table form is often better suited for python. Fitting models that leverage autograd are also better with python.
Totally agree. If it's in a dataframe, I prefer R. If I get involved pre-dataframe, I prefer Python.
The unique qualities of R that allow this are that it's so easy to use, extremely reliable for package installation (problems occur approximately never), and the tidyverse makes it incredible easy to translate ideas into code, not only in its broad, easy to understand and powerful vocabulary, but in there being little 'nesting' required; instead working left to right and top to bottom (via the magrittr pipe) - i.e. your code, for the most part, is like reading a page in a book.