Python is great for plumbing, R is great for new analyses. I use Python to knit Rmarkdown documents from premade templates. Of course you can achieve something

Python is great for plumbing, R is great for new analyses. I use Python to knit Rmarkdown documents from premade templates. Of course you can achieve something similar with Jupiter notebooks. The real benefit is the perceived ability to get going with a new dataset. With respect to massaging and visualizing rectangular data, I can get much further much faster in R tidyverse than python. Just as we see python as a language that allows for rapid development with its straightforward syntax - I see R tidyverse providing a faster data analysis exploration “experience”. Tidyverse also introduced me to functional programming which I have found to be very useful for data analysis; you can very strictly separate procedures in a way I have found beneficial. (Not to say you cannot acquire this knowledge another way, however.) I remain an avid user of pandas data frames inside the python ecosystem. On more than one occasion, I have translated rapidly written tidyverse data wrangling routines into more maintainable functions in python with pandas. YMMV