I write code in both daily. I don't think there is a burning need to know both, but there are definitely tasks each is good at. The RStudio IDE is really quite wonderful for interactive stuff. The pipe operator (allowing left to right evaluation of function chains) makes for extremely literate code. Most of the basic statistical functions are substantially better than their Python equivalents. Many of the ways in which you use Python for data science stuff are just poor imitations of R (in particular, pandas is a take on R's data frames that is imo not as productive). In some conditions R can be faster [in others, slower]. The R package ecosystem for more off the beaten path statistical stuff is better than the Python package ecosystem (if you're doing more intense ML and CNNs, the opposite is true). If you're doing dashboards, Shiny is great. But in each case, you can basically work in either language and you'll be fine.

I think it makes more sense to think of R as replacing Stata, SPSS, SAS, and to a lesser extent Matlab, rather than replacing Python.

Julia is also fun.