Shinyscholar – a template for creating reproducible Shiny applications in R

Shinyscholar A template for creating reproducible Shiny applications in R Simon Smart and Tim Lucas, School of Medical Sciences, University of Leicester 9th September 2025 1 Shinyscholar helps create reproducible analytical apps in R • Creates an empty application with a regular structure • Analyses can be reproduced outside the app 2 Shinyscholar helps create reproducible analytical apps in R • Creates an empty application with a regular structure • Analyses can be reproduced outside the app • Make it easier to follow so�ware development best practices • Developers can concentrate on creating functionality 3 R has cutting-edge statistical methods but barriers can restrict uptake • Know how to use R 4 R has cutting-edge statistical methods but barriers can restrict uptake • Know how to use R • Able to comprehend documentation 5 R has cutting-edge statistical methods but barriers can restrict uptake • Know how to use R • Able to comprehend documentation • Access to specific data 6 R has cutting-edge statistical methods but barriers can restrict uptake • Know how to use R • Able to comprehend documentation • Access to specific data • Format data in specific ways 7 Shiny apps enable anybody to access the power of R ui <- fluidPage( numericInput("number", "Enter a number", value = 5), textOutput("answer") ) server <- function(input, output) { output$answer <- renderText(input$number *10) } shinyApp(ui, server) 8 Shiny apps are becoming increasingly popular in academia • The low barrier to entry makes Shiny popular • Substantially increased apps as a method of dissemination 9 Reproducibility relies on each module calling one function Create the function: Use it in the module and store the input: transform_multiply <- function(number) number *10 common$result <- transform_multiply(input$number) common$meta$transform_multiply$number <- input$number 16 Reproducibility relies on each module calling one function Create the function: Use it in the module and store the input: In the Rmarkdown: transform_multiply <- function(number) number *10 common$result <- transform_multiply(input$number) common$meta$transform_multiply$number <- input$number result <- transform_multiply({{transform_multiply_number}}) 17 Reproducibility relies on each module calling one function Create the function: Use it in the module and store the input: In the Rmarkdown: metadata() takes care of a lot of the boring code transform_multiply <- function(number) number *10 common$result <- transform_multiply(input$number) common$meta$transform_multiply$number <- input$number result <- transform_multiply({{transform_multiply_number}}) 18 Disagapp for disaggregation regression 19 MetaInsight for network metaanalysis 20 Learn more • install.packages("shinyscholar") • Example app at https://simonsmart.shinyapps.io/ shinyscholar/ install.packages("shinyscholar", dependencies = TRUE) • README guide to development at https://simonsmart88.github.io/shinyscholar/ • Workshop for Ukraine 21 Acknowledgments • Wellcome for funding • Wallace developers especially Rob Anderson • Tim Lucas 22