1) ggplot does exactly what it is supposed to do: create data visualizations. It made no promises for interactivity or display, and in fact, it was originally designed for creating publication quality charts, which it continues to do well.
1.5) ggvis is a D3 API wrapper on ggplot and allows for interactive graphics. Do you want to pay your data scientists for creating production ready graphics or let them focus on what they're best at?
2) R has been growing - outside of neural networks (which R needs to catch up on), R gets almost every pre-processing and modeling algorithm first, and distributes it for free. Furthermore, it has better sampling options, metric options, augmentation options, and model ensabling tools (stack or meta-model) VS any other language or framework - it is the gold standard.
3) I don't think there's any "magic" in R. It's just a language with a learning curve and lack of opinions.
4) Last point: R is really not built for the web (it's older than Python!) - its built for data science. There's no reason you need to run your modeling stack in the same language as your application server. R is perfectly capable of writing to databases or sending API responces in JSON or PMML.
/endrant
Not trying to start a flame-war - but this type of difference in opinion is important to see when thinking about hiring data scientists or deploying models.