Of the many companies I've done data science with I can only think of a few, rare exceptions where R wasn't used as much as if not more than Python.
If you're mostly dealing with Neural Nets you won't see much R, but for anything really statistical in nature R is a much better tool than Python. For anything that ends up in a report R is much better than Python (a lot of very valuable data science work ends up being a report to non-technical people).
> breaks down on data manipulation
This is very outdated. The tidyverse eco-system has bumped R back into being first in class for data manipulation now. This becomes less true as you get further and further from having your data in a matrix/df (I can't imagine doing Spark queries in R), but if you already have a basic data frame, manipulation from there is very easy.
Even for things that end up in production, whether you're in R or Python, whatever your first pass is should always be a prototype and will have to be reworked before you get close to moving it to production.
If you're mostly dealing with Neural Nets you won't see much R, but for anything really statistical in nature R is a much better tool than Python. For anything that ends up in a report R is much better than Python (a lot of very valuable data science work ends up being a report to non-technical people).
> breaks down on data manipulation
This is very outdated. The tidyverse eco-system has bumped R back into being first in class for data manipulation now. This becomes less true as you get further and further from having your data in a matrix/df (I can't imagine doing Spark queries in R), but if you already have a basic data frame, manipulation from there is very easy.
Even for things that end up in production, whether you're in R or Python, whatever your first pass is should always be a prototype and will have to be reworked before you get close to moving it to production.