Not a question, but a remark about RStudio's current branding and positioning strategy.
In the process of establishing a new data science team and operations for a financial tech startup, I'm running into the usual DevOps/Data Science cultural divide, facing strong prejudices against R as a viable language/ecosystem for production models and client-facing solutions.
Our entire analytical infrastructure relies on (community edition) RStudio Server/Shiny Server/OpenCPU/plumber at the moment (including ETL for business intelligence), and these pieces have performed well so far -- I would only concede that Shiny does not come with standard drilldown/through widgets out of the box, and is not (yet?) a replacement for a BI-centric tool.
Nonetheless after a review of collaborative data science platforms (incl. Domino, Knime, Cloudera, Dataiku, DataRobot, etc.) RStudio Server/Connect did not even make the 1st cut (even though the majority of these platforms actually include RStudio Server). The only reason is that RStudio starts with "R" and that's a plain no-no ("R is for spaghetti code", "R is slow" "You can't do ML with R" "R interface to TensorFlow pales in comparison to Python" "You can't debug R code" "R doesn't play well with AirFlow" etc..).
Not helping either is the fact that RStudio suite does not make it to Gartner's quadrant of Data Science platforms.
I'm very curious if other Community members have had to face similar arguments, and have any advice to share?
Sad to say but a name change and total rebranding would probably help a great deal (-: