Learning R is dangerous. It entices us in by presenting an incredibly powerful tool to solve our particular problem; for free! And as we learn how to do that, we uncover more things that make our solution even better. But then we start to look around our organisation or institution and see how it could make everyone's lives better too. And that's the dangerous part; R's got us hooked and we can't give up the belief that everyone else should be using this, right now. Even though R is free, open source software, there are often barriers to introducing it organisation-wide. This could be because of such things as IT or quality policies, the need for management buy-in or because of perceptions in learning the language. This presentation will first discuss the aspects required to understand these barriers to entry, and the different types of resolution for these. It will then use three projects to show how, by understanding the requirements of the organisation, and developing situation-specific roll-out strategies, these barriers to entry can be overcome. The first example is a large organisation who wanted to quickly (within 6 weeks) show management how Shiny could improve information dissemination. As server policies made a proof of concept difficult to run internally, this project used a cloud hosted environment for R, Shiny and a source database. The second example is around two SME's who required access to a validated version of R, which was provided via the Amazon and Azure marketplaces. The key aspect of these projects is the value to IT departments of being able to distribute a pre-configured machine around the organisation.
