Based on googling around this topic, it seems that using RPy2 is the most common way to interface with R from Python. However all the discussions on this seem to centre around working in a desktop (single user) environment.
The one discussion I could find that deals with the issue of working with R "at scale" is this one - https://github.com/Sleepingwell/DjangoRpyDemo/blob/master/README.md#django-configuration - which indicates problems with this approach; and suggests it might be able to be overcome via creating distinct processes dedicated to run a WSGI application (although this article does not give any steps on how to do this, or whether it would work in practice). Another approach seems to be to use RPy2, with Twisted to enable multiple sessions: https://docs.google.com/presentation/d/11LJxej6jnbYKzJftpDudYFfVKjaB0BhOzrBSKaxJ2ME/edit#slide=id.p . Yet another approach might be to use Rserve (http://www.rforge.net/Rserve/) and PyRserve (http://pythonhosted.org/pyRserve/manual.html), but the latter seems to currently be in beta. Question is: does anyone have any practical experience actually using Django with R in a production environment (i.e dozens or hundreds of users doing high volume number crunching)? Thanks Derek PS Yes, we do need R and not one of the Python-based alternatives, as R offers many routines simply not available in those as yet (also, the client needs to re-use, and create new, R scripts themselves) -- You received this message because you are subscribed to the Google Groups "Django users" group. To unsubscribe from this group and stop receiving emails from it, send an email to django-users+unsubscr...@googlegroups.com. To post to this group, send email to django-users@googlegroups.com. Visit this group at http://groups.google.com/group/django-users?hl=en. For more options, visit https://groups.google.com/groups/opt_out.
