Code Editor for app.R

library(shiny)
library(dplyr)

source("simulation_logic.R")

ui <- fluidPage(
    titlePanel("Data Simulation Tool"),
    
    sidebarLayout(
        sidebarPanel(
            numericInput("sample_size", "Sample Size:", value = 100, min = 1),
            numericInput("n_covariates", "Number of Covariates:", value = 2, min = 1),
            uiOutput("covariate_inputs"),
            textInput("formula", "Outcome Formula (use x1, x2, etc.):", 
                     value = "1 / (1 + exp(-(0.5 + 2*x1 - 1.5*x2)))"),
            downloadButton("download_data", "Download Simulated Data")
        ),
        mainPanel(
            h4("Preview of Simulated Data"),
            tableOutput("data_preview")
        )
    )
)

server <- function(input, output, session) {
    
    output$covariate_inputs <- renderUI({
        n_covs <- input$n_covariates
        
        lapply(1:n_covs, function(i) {
            div(style = "margin-bottom: 15px;",
                h4(paste("Covariate", i, "(x", i, ")")),
                selectInput(
                    paste0("dist_", i),
                    "Distribution:",
                    choices = c("Normal" = "normal",
                              "Uniform" = "uniform",
                              "Binary" = "binary")
                ),
                uiOutput(paste0("dist_params_", i))
            )
        })
    })
    
    observe({
        n_covs <- input$n_covariates
        for(i in 1:n_covs) {
            local({
                local_i <- i
                output[[paste0("dist_params_", local_i)]] <- renderUI({
                    dist <- input[[paste0("dist_", local_i)]]
                    
                    switch(dist,
                           "normal" = list(
                               numericInput(paste0("mean_", local_i), "Mean:", 0),
                               numericInput(paste0("sd_", local_i), "SD:", 1)
                           ),
                           "uniform" = list(
                               numericInput(paste0("min_", local_i), "Min:", 0),
                               numericInput(paste0("max_", local_i), "Max:", 1)
                           ),
                           "binary" = list(
                               numericInput(paste0("prob_", local_i), "Probability of 1:", 0.5)
                           )
                    )
                })
            })
        }
    })
    
    simulated_data <- reactive({
        req(input$sample_size, input$n_covariates)
        
        covariates <- list()
        for(i in 1:input$n_covariates) {
            dist <- input[[paste0("dist_", i)]]
            
            covariates[[i]] <- switch(dist,
                                    "normal" = rnorm(input$sample_size, 
                                                   input[[paste0("mean_", i)]], 
                                                   input[[paste0("sd_", i)]]),
                                    "uniform" = runif(input$sample_size, 
                                                    input[[paste0("min_", i)]], 
                                                    input[[paste0("max_", i)]]),
                                    "binary" = rbinom(input$sample_size, 1, 
                                                    input[[paste0("prob_", i)]])
            )
        }
        
        data <- as.data.frame(do.call(cbind, covariates))
        names(data) <- paste0("x", 1:input$n_covariates)
        
        # Generate outcome
        formula_text <- input$formula
        data$y <- generate_outcome(data, formula_text)
        
        data
    })
    
    output$data_preview <- renderTable({
        head(simulated_data(), 10)
    })
    
    output$download_data <- downloadHandler(
        filename = function() {
            paste0("simulated_data_", format(Sys.time(), "%Y%m%d_%H%M%S"), ".csv")
        },
        content = function(file) {
            write.csv(simulated_data(), file, row.names = FALSE)
        }
    )
}

shinyApp(ui = ui, server = server)