Is R viable for production (deployed) code I have read a number of articles that talk about companies such as Google, Facebook, and many others using R for research. The other scenario I have read about is companies using R to prototype an analytics solution and then re-implement it in another language.
I am trying to find literature on companies using R for actual production analytics code. A use case might be a recommender system that user interacts with via a web page that gets a response from an R script executed on a remote server. The fact that I am having trouble finding such reports makes me wonder if it is unadvisable. If so why?
 A: Yes it is.  Look for example at this page for the wonderful headless RServe R server instance (by R Core member Simon Urbanek) which lists these deployments:
Some projects using Rserve:
   The Dataverse Network Project   Phenyx                     "J" interface
   Nexus BPM                       Taverna                    Bio7
   INTAMAP                         Screensaver HTS LIMS       CRISP
   WWARN

with links at the page referenced above.
"Industry" tends to be more cagey about details than academia, so you will be hardpressed to find "official" statements.  But vendors like Revolution Analytics or Oracle ship R and their salesmen may have stories for you....
A: Typically not as R is an interpreted language, which on average is many times slower than equivalent compiled code.  While converting your program to C, Fortran, or Java takes a significant investment, the code can literally run 10-100X faster than an equivalent R version.  Additionally, R has very limited tools to manipulate large datasets, especially ones that require clusters to process or require special hardware.  Moreover, most commercial implementations need to provide user interfaces and fit into existing frameworks, which are typically written in other languages.  While you could interface with existing R code from most compiled languages (there are wrappers out there), you would find that your core numerical routines would still not be any faster than the original R code.  At the end of the day, R (like Matlab) is used mostly for prototyping and/or trying out ideas using existing implementations.
A: I believe (but this is based on anecdote) that R tends to be used more as a prototyping language by the companies you name above. R excels in the task of developing and testing multiple models quickly and effectively. However, it is not a good fit for personalisation tasks as these often need to take place as a user interacts with a particular website and I believe (again, this is mostly anecdote) that such models tend to be re-written in a compiled language (Java, C, C++).
That being said, good question and I would love to be proved wrong on this. 
