* Great environment with RStudio
* Awesome plotting and visualization with ggplot
* Very welcoming community with a very diverse background ranging from social science to quantitative finance
Basically for everything except deep learning R is a great alternative to python. If you rely on advanced statistics I think R is the way to go.
My experience has been the exact opposite. R is great as an interactive statistical environment. But if you need to build data-intensive applications in it - it's still powerful but somewhat slow and also a bit of a mess: many exceptions, special cases, strange design choices and multiple ways to do the same thing. It can get the job done, but there is an unpleasant learning curve to get used to all the weirdness.
Having said that tinyverse basically calls methods written in C and has a beautiful syntax. As Rcpp let’s you write whatever needs to go quickly in c++ then easily expose it in R.
Being able to do these things thoroughly and quickly, has (in my experience) been the most important technical skill for getting data science right, regardless of the language production models end up getting deployed in.