The main reason to use R is the existing set of libraries and analyses pipelines some may already have in place. Redeveloping these in Python can be a hassle be

The main reason to use R is the existing set of libraries and analyses pipelines some may already have in place. Redeveloping these in Python can be a hassle because there may not even be libraries that recreated certain functionality that one needs to recreate on-top of translating their pipeline. With that said, most common analyses needs are now handled in popular Python libraries. Sometimes though, the knowledge encapsulated in an R package is not trivial to understand and reimplement. The concepts methods use may involve math you're unfamiliar with (and use as black boxes), so then you have to look at the package code and try translating R->Python, then attempt to refactor to something sane (and hope you don't skip any underlying logic). That or learn the theory of what was implemented so you can now implement it in Python (which may not be feasible with tight deadlines).