Loading Source R Files in a Reactive Context with Shiny

Shiny is an excellent framework for building interactive web applications. One of the key features of Shiny is its ability to create reactive UI components that respond to user input. In this article, we will explore how to load source R files in a reactive context using Shiny.

Introduction

The question arises when you want to load different source R files based on user selection from a dropdown menu or radio buttons. This is particularly useful when working with multiple models in different folders. In this response, we will delve into the solution and provide an example code that demonstrates how to achieve this.

Creating the Models/Folders

To start, you need to create the models folder in your Shiny app directory and populate it with two subfolders A and B, each containing an inputs.R file. The following R code snippet creates these folders and files:

library(shiny)

appDir <- 'c:/path/to/temp/app'
dir.create(appDir)
dir.create(file.path(appDir, "models"))
for (i in 1:2) {
    dir.create((folder = file.path(appDir, "models/", LETTERS[i])))
    code <- bquote({
        dat <- data.frame(x=rnorm(100), y=rnorm(100, mean=.(i)*x))
        mod <- lm(y ~ x, data=dat)
    })
    writeLines(deparse(code), file.path(folder, 'input.R'))
}

This code creates two subfolders A and B under the models folder, each containing an inputs.R file. The contents of these files are generated using a list-based expression that defines a linear regression model.

Creating the Shiny App

Next, we create a new R file called app.R in the same directory as our app. This file will contain the Shiny application code:

library(shiny)

ui <- fluidPage(
    selectInput("model_folder", "Select folder", c("A", "B")),
    uiOutput('info'),
    tableOutput('summ')
)

server <- function(input, output) {
    # Create a reactive expression that captures variables from the sourced input.R files
    inpts <- reactive({
        name <- file.path("models", input$model_folder, "input.R")
        source(name, local = TRUE)
        mget(ls())
    })

    # Define the UI component that displays information about the selected model
    output$info <- renderUI({
        inp <- inpts()
        list(
            helpText(sprintf("Now looking at variables from %s", inp$name)),
            radioButtons('vars', 'Variables', choices = names(inp), inline = TRUE)
        )
    })

    # Define the UI component that displays a summary of the selected model
    output$summ <- renderTable({
        inp <- inpts()
        if (input$vars == 'mod') summary(inp$mod)
    })
}

# Run the Shiny app using the runApp function
runApp(appDir = normalizePath(appDir))

This code defines a Shiny application with two UI components: one that displays information about the selected model and another that displays a summary of the model. The inpts() reactive expression captures variables from the sourced input.R files, which are then used to populate these UI components.

Running the Shiny App

To run the Shiny app, you can use the following R code:

library(shiny)
runApp(appDir = normalizePath(appDir))

This code runs the Shiny app using the appDir variable that points to the directory where our app is located.

Conclusion

In this article, we explored how to load source R files in a reactive context using Shiny. We provided an example code that demonstrates how to achieve this by creating separate folders for each model and sourcing their respective input.R files within the Shiny application. By leveraging the power of reactive expressions and UI components, we were able to create a user-friendly interface that allows users to select a folder and view information about the corresponding model.

Additional Considerations

There are several additional considerations when working with source R files in a Shiny context:

• Model Updates: When a user selects a new model folder, you need to update the reactive expression to capture variables from the new input.R file. This can be achieved using the reactiveValues function or by re-creating the entire reactive expression.
• Code Organization: As your Shiny app grows in complexity, it’s essential to keep your code organized and maintainable. Consider using a modular approach to separate concerns and make your code more readable.
• Error Handling: Always ensure that your code includes robust error handling mechanisms to prevent crashes or unexpected behavior when working with source R files.

By understanding these considerations and following the example code provided in this article, you can create effective Shiny applications that seamlessly integrate with source R files.

Last modified on 2023-06-17