如何R中閃亮

Question

library(shiny) 
library(ggplot2) 

ui <- shinyUI(fluidPage(
    titlePanel("Central Limit Theorem Simulation"), 
    sidebarLayout(
     sidebarPanel(
      numericInput("sample_size", "Size of each random sample\n(max: 100)", 
         value = 30, min = 1, max = 100, step = 1), 
      sliderInput("simulation", "THe number of simulation", 
         value = 100, min = 100, max = 1000, step = 1), 
      selectInput("sample_dist", "Population Distribution where each sample is from", 
         choices = c("Binomial","Poisson", "Normal", "Uniform")), 
      numericInput("bins", "Number of bins in the histogram\n(max: 50)", 
         value = 20, min = 1, max = 50, step = 1), 
      submitButton(text = "Submit") 
     ), 

     mainPanel(
      tabsetPanel(type = "pills", 
         tabPanel("mean of random sample mean", br(), 
           textOutput(outputId = "output_mean")), 
         tabPanel("variance of random sample mean", br(), 
           textOutput(outputId = "output_var")), 
         tabPanel("summary table", br(), 
           tableOutput(outputId = "output_table")), 
         tabPanel("sample matrix", br(), 
           verbatimTextOutput(outputId = "output_sample")), 
         tabPanel("histogram of random normal sample", br(), 
           plotOutput(outputId = "output_hist")) 
      ) 
     ) 
) 


)) 


server <- shinyServer(function(input, output) { 

    # Return the random sample 
    rsample <- reactive({ 
     if (input$sample_dist == "Binomial") { 
      rsample <- rbinom(input$sample_size * input$simulation, 1, 0.5) 
     } else if (input$sample_dist == "Poisson") { 
      rsample <- rpois(input$sample_size * input$simulation, 1) 
     } else if (input$sample_dist == "Normal") { 
      rsample <- rnorm(input$sample_size * input$simulation) 
     } else { 
      rsample <- runif(input$sample_size * input$simulation) 
     } 
     rsample 
    }) 


     # Return the random sample matrix 
    rsamplematrix <- reactive({ 
     matrix(rsample(), nrow = input$simulation) 
    }) 

    # output mean of sample mean 
    output$output_mean <- renderText({ 
     sample_mean <- rowMeans(rsamplematrix()) 
     mean(sample_mean) 
    }) 

    # output variance of sample mean 
    output$output_var <- renderText({ 
     sample_mean <- rowMeans(rsamplematrix()) 
     var(sample_mean) 
    }) 

    # output summary table of sample mean 
    output$output_table <- renderTable({ 
     sample_mean <- rowMeans(rsamplematrix()) 
     data.frame(mean(sample_mean), var(sample_mean)) 
    }) 

    # output the first 5 rows and 5 columns of the sample matrix 
    output$output_sample <- renderPrint({ 
     k = rsamplematrix() 
     k[1:5, 1:5] 
    }) 

    # output histogram of sample mean 
    output$output_hist <- renderPlot({ 
     sample_mean <- rowMeans(rsamplematrix()) 
     ggplot(data.frame(sample_mean), aes(x = sample_mean, y = ..density..)) + 
      geom_histogram(bins = input$bins, fill = "steelblue", col = "white") 
    }) 

}) 


shinyApp(ui = ui, server = server) 

添加交互式的TextInput面板上面的代碼運行良好。現在假設我想爲每個分佈添加分佈參數的textinput（例如，p是二項分佈的參數，mu和sigma是正態分佈的參數）。當我爲樣本分配做出選擇時，這些參數輸入彈出。

我該怎麼辦？我應該使用哪個功能？

Answer 1

如果你想保持隱藏，除非選擇了分佈則參數輸入，我想你想conditionalPanel。

例如：當輸出$情節尚未呈現

conditionalPanel("!(output.plot)", textInput("size", "Size")) 

這個尺寸輸入面板只會顯示。

在你的情況下，我認爲你可以用相同的方式使用輸入。

conditionalPanel('input.sample_dist) != "Poisson"', textInput("parameter2", "Parameter 2")) 

僅當沒有選擇泊松時，這將顯示參數2。

您需要移除您的submitButton，以確保在您選擇新的分配時更新參數字段。如果要保持相同的功能，請將其替換爲通用的actionButton。

我還建議在sample_dist更新時，使用numericInput字段併爲服務器功能中的每個案例設置適當的標籤min，max和step updateNumericInput。