From a pharma perspective, I think there are two scenarios to think about:
(1) Exploratory analysis, ad hoc analysis, simulation studies, etc.
(2) Production statistical reporting of clinical trial data
In the case of #1, the use of R is not at all uncommon. Most folks in Biotech are well aware of the advantages of R and its benefits in these scenarios.
In the case of #2, I think you are not taking the business view of SAS in the pharma industry. Large pharma companies have huge macro pipelines and templates built around SDTM/ADaM/TFLs that took an enormous amount of human capital to develop and are easily deployable using SAS for all historical, ongoing, and near-future studies. So while this could theoretically be achieved using R, there is also absolutely no benefit to doing so while simultaneously introducing a lot of expense and complications to redo that entire pipeline. Standard analyses in pharma are wwwaaaayyyyyy within the bounds of SAS' technical capabilities.
Also, you have to also keep in mind that an NDA being submitted today includes data from a phase I study conducted 10 years ago. To aid in the evaluation of your submission package, it goes an awful long way to keep a large degree of consistency in the SDTM/ADaM/TFL production between your various studies. so why would you do the analysis of a Ph3 study in R all the sudden after the first several clinical trials were all done in SAS? Right, you wouldn't.
Ok, so then what about smaller biotechs? Well, they are outsourcing the work to CROs (they don't have the resources in house) which all have the exact same pipeline set up as the large pharmas. CROs would have to charge wwwaaayyyyyyyy more to redo all of these pipelines using R. Thus the end result would be way more expensive to cash-strapped small biotechs with little to no upside. So also not gonna happen any time soon.
We can argue about whether #2 is a "good" thing until we are blue in the face. But at least in 2022 this is why SAS remains dominant in clinical trial reporting.
Could this change 10 or 20 years in the future? Perhaps. But seeing the lack of penetration of R in the industry in the \~10 years I have been in it, I am a bit skeptical that it will happen any time soon.