Some years back before the prevalence of notebooks, Rmarkdown was great. More recently I have worked as a data engineer alongside some brilliant datascientists, and my understanding is there is simply some models / algorithms that are not yet implemented in common python libraries - with the draw back that on sufficiently sized data sets the R machine required to be absolutely massive as most packages did not support distributed processing.

In the end we supported an R environment and left its usage up to DS team including any ongoing reports or models and inferences they needed to perform, and encouraged a move to python for any code base that would get thrown back over the fence to us for official support.