mh12345

Regarding the limited frontend capabilities, I had a similar opinion at one point, but with Shiny's HTML templating (https://shiny.rstudio.com/articles/templates.html) functionality one can circumvent the limited HTML that Shiny has out of the box. Besides that, there is also the possibility to communicate with R using JavaScript (https://shiny.rstudio.com/articles/communicating-with-js.htm...). These two functionalities combined allow for a frontend that is much more flexible, when compared to traditional Shiny applications. Of course, there might definitely be better solutions out there that fit your use case and Shiny's real use is primarily in sharing data analysis.

Interesting, why do you advise people to stay away from Shiny?

Indeed, the one from Plotly! I gave Dash a quick shot about a year ago, it worked quite well to generate interactive reports.

ShinyProxy is amazing. It is pretty easy to setup, but does require quite some specialized knowledge compared to the RStudio solutions.

R has a nice web development framework called Shiny. While it is not comparable to say Django or Flask, Shiny does make it incredibly easy to share data analysis. If one wants to share statistical analysis or create a data oriented dashboard, then there is definitely a reason to consider R and Shiny. Note that Python has Dash, which is comparable to Shiny, but it is less mature as far as I know.

While previously Shiny was primarily deployed through RStudio's solutions, there are now open source initiatives such as ShinyProxy, introducing Kubernetes as an option for deploying Shiny applications. The latest iterations of Shiny related libraries are facilitating automated testing and deployment. These developments allow companies to use Shiny in production, but it has to be said that the R ecosystem is not as developed as Python's from a traditional software development perspective.