rstudio::conf(2022)

This summer the RStudio Connect team will announce a feature which has been over two years in the making: “Remote” off-host content execution with launcher in Kubernetes.

We have been quietly beta testing the Launcher feature with select partners and customers for several months while we prepare for the public announcement.

This talk will highlight why someone might want to use this new execution mode with Connect, show just how seamless it is to get everything configured in a fresh environment on EKS, and finally set some critical context for what publishers and administrators should expect by addressing the anticipated FAQs.

Talk materials are available at https://kelly.quarto.pub/rstudioconf-talk-2022/.

Benjy Braun, Chief Architect for 202 Group, shows why he and the 202 Group team decided to use RStudio Connect to build customer facing applications and secure websites while focusing on data science and not having to worry about hiring for—or learning—a bunch of web tools like JavaScript and php. With well organized git repos and Rmarkdown, you can build websites for customers with embedded shiny apps and dynamic visualizations and publish and deploy them using RStudio Connect. Using this method, you won’t confine your analysis to static documents like PowerPoint and pdfs and your customers will always see the latest and most up do date information. The tools are out there to do this and it’s easy to start and iterate with more features overtime.

I will share how we published an R Shiny application to AWS, the decisions we made, and what we learned in the process.

One challenge we faced was figuring out how we could develop collaboratively. We needed to define our development workflow, including version control, dependency management, and quality assurance.

Then, we needed to define the deployment method. R Studio is great for development, but it may hide many of the aspects that break the application. We used CI/CD workflows as much as possible to make sure our code was robust before pushing the changes to production.

Lastly, our infrastructure team designed a framework that is replicable, so we are ready to deploy new R Shiny applications quickly and focus on data analysis.

R is often maligned as a poor fit for production deployment systems. At dv01 we deploy Plumber API that serves machine learning models to Tape Cracker, a client facing web application. With R in production we use the same codebase throughout the data science pipeline, saving time and resources while reducing the chance for bugs in the hand-off. To accomplish this we use industry standard continuous integration and deployment tools to deploy our API to compute clusters in the cloud. Our pipeline progresses stepwise through staging, release and production environments. Automated integration testing at each step enables a robust and reliable deployment. In this talk R users will learn strategies to deploy R code in production environments.