shinymods

The goal of shinymods is to package some convenient Shiny (Chang et al. 2021) modules that are often used to build an app. Some of the modules are inspired by the book Mastering Shiny (Wickham 2020).

You can also vendor the source R code to your own project to prevent having excessive dependencies.

Credits

This R (R Core Team 2022) package is intended to help you construct Shiny (Chang et al. 2021) applications. Some of the modules are derived from examples in the book Mastering Shiny: Build Interactive Apps, Reports, and Dashboards Powered by R by Wickham (2020).

Installation

You can install the development version of shinymods from GitHub with:

# install.packages("devtools")
devtools::install_github("MartinSchobben/shinymods")

Example

This is a basic example of a shiny app that requires filtering based on the variables in a dataframe, where the controllers are conditional to the value ranges of that particular variable.

library(shiny)
library(shinymods)
## basic example code

ui <- fluidPage(
        sidebarLayout(
          sidebarPanel(
            dataset_ui("test", filter = is.data.frame),
            filter_ui("test")
          ),
          mainPanel(
            tableOutput("table")
          )
        )
      )


server <- function(input, output, session) {
  dt <- dataset_server("test")
  ft <- filter_server(dt, "test", shinyjs = FALSE)
  output$table <- renderTable({req(ft()); ft()})
}

shinyApp(ui, server)

References

Chang, Winston, Joe Cheng, JJ Allaire, Carson Sievert, Barret Schloerke, Yihui Xie, Jeff Allen, Jonathan McPherson, Alan Dipert, and Barbara Borges. 2021. Shiny: Web Application Framework for r. https://shiny.rstudio.com/.

R Core Team. 2022. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Wickham, Hadley. 2020. Mastering Shiny: Build Interactive Apps, Reports, and Dashboards Powered by r. O’Reilly Media, Inc. https://https://mastering-shiny.org//.