R studio shiny code#
Deployment is any push of code to an environment (staging, production).Particularly, I wanted to explore the idea of maintaining Shiny applications in production in a way that decouples deployments from releases.
In the end, I dropped some placeholders ("thought exercises") into the course materials and promised myself I’d get back to it. We wanted to address the growing popularity of DevOps as a topic of interest to R Admins in the data science community, but at the time our DevOps story was mostly limited to programmatic deployment. I started thinking about feature toggles for shiny application release management while preparing for the Shiny in Production workshop at RStudio Conf last January. This post describes a few experimental projects (mad-science hacks) that push the boundaries of what the current RStudio Connect feature-set includes with the hope that it may inspire the development of future functionality. RStudio Connect is actually an incredibly flexible platform that can enable all sorts of creative solutions. With that being said, I also believe that a certain amount of exploratory hacking is healthy for product development. I would highly encourage you to explore those "basic builds" while you’re learning the ins and outs of Connect.
I write how-to articles that cover basic solutions for RStudio Connect based workflows. At RStudio, we recommend staying (or at least starting) within the boundaries of a set of best practices when it comes to the use of our professional products.
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