In 2018 at the R\/Pharma conference<\/a> I first heard of the concept of using quotations. With quotations to make your shiny app code reproducible. This means you can play around in shiny and afterward get the code to generate the exact same outputs as R code. This feature is needed in Pharma. Why is that the case? The pharmaceutical industry needs to report data and analysis to regulatory authorities. I talked about this in several articles<\/a> already. How great would it be to provide a shiny-app to the regulatory authorities? Great. How great would it be to provide a shiny app that enables them to reproduce every single plot or table? Even better.<\/p>\n\n\n\n Adrian Waddell<\/a> and Doug Kelkhoff<\/a> are both my colleges of mine that proposed solutions for this task. Doug built the scriptgloss <\/a>package which reconstructs static code from shiny apps. Adrian presented a modular shiny-based exploratory framework at R\/Pharma 2018<\/a>. The framework provides dynamic encodings, variable-based filtering, and R-code generation. In this context, I started working out some concepts during my current development project. How to make the code inside a shiny app reproducible? In parallel Doug, Joe Cheng<\/a> and Carson Sievert<\/a> worked on a fascinating tool called shinymeta<\/a>, released on July 11 at the userR conference.<\/p>\n\n\n\n