Question

我开发了一个简单的闪亮应用，在分布均为my_x和标准偏差my_mean的分数my_sd作为输入。作为输出，应用程序返回带有标准标准分布的莱迪思图，其中z-score为my_x。请在GitHub上找到该应用的代码。

现在，我想为应用添加第二个功能：

通过检查checkboxInput，我会计算输入的pnorm，并对图表的相对区域进行着色。

我为图形编写了代码（此处是预期结果的示例），但我无法弄清楚如何使它在Shiny中工作。特别是，我无法想象如何通过使用第一个绘制图形的函数使复选框正常工作来激活该功能。

library(lattice)
e4a <- seq(60, 170, length = 10000)
e4b <- dnorm(e4a, 110, 15)
#z-score is calculated with the inputs listed above:

z_score <- (my_x - my_mean)/my_sd

plot_e4d <- xyplot(e4b ~ e4a,
               type = "l",
               main = "Plot 4",
               scales = list(x = list(at = seq(60, 170, 10)), rot = 45),
               panel = function(x,y, ...){
                   panel.xyplot(x,y, ...)
                   panel.abline(v = c(z_score, 110), lty = 2)

                   xx <- c(60, x[x>=60 & x<=z_score], z_score) 
                   yy <- c(0, y[x>=60 & x<=z_score], 0) 
                   panel.polygon(xx,yy, ..., col='red')
               })
print(plot_e4d)

Answer 1

我找到了一个有效的解决方案。我很确定它不是最有效的，但它确实有效。它由调用绘图的服务器函数中的if / else语句组成。我要感谢@ zx8754的灵感。

以下是ui.r文件：

library(shiny)

shinyUI(pageWithSidebar(
headerPanel("Standard Normal"),
sidebarPanel(
    numericInput('mean', 'Your mean', 0),
    numericInput('sd', 'Your standard deviation', 0),
    numericInput('x', 'Your score', 0),
    checkboxInput('p1', label = 'Probability of getting a score smaller than x or z', value = FALSE)
),
mainPanel(
    h3('Standard Normal'),
    plotOutput('sdNorm'),
    h4('Your z-score is:'),
    verbatimTextOutput('z'),
    h4('Your lower tail probability is:'),
    verbatimTextOutput('p1')    
    ))

）

server.R文件：

library(lattice)

shinyServer(
function(input, output){
    output$sdNorm <- renderPlot({
        dt1 <- seq(-3, 3, length = 1000)
        dt2 <- dnorm(dt1, 0, 1)
        my_mean <- input$mean
        my_sd <- input$sd
        my_x <- input$x
        z <- (my_x - my_mean)/my_sd
        if(input$p1){

            xyplot(dt2 ~ dt1,
                   type = "l",
                   main = "Lower tail probability",
                   panel = function(x,y, ...){
                       panel.xyplot(x,y, ...)
                       panel.abline(v = c(z, 0), lty = 2)
                       xx <- c(-3, x[x>=-3 & x<=z], z) 
                       yy <- c(0, y[x>=-3 & x<=z], 0) 
                       panel.polygon(xx,yy, ..., col='red')
                   })

        }else{
            xyplot(dt2 ~ dt1,
                   type = "l",
                   main = "Standard Normal Distribution",
                   panel = function(x, ...){
                       panel.xyplot(x, ...)
                       panel.abline(v = c(z, 0), lty = 2)
                   })
        }

        })
    output$z = renderPrint({
        my_mean <- input$mean
        my_sd <- input$sd
        my_x <- input$x
        z <- (my_x - my_mean)/my_sd
        z
    })
    output$p1 <- renderPrint({
        if(input$p1){
            my_mean <- input$mean
            my_sd <- input$sd
            my_x <- input$x
            p1 <- 1- pnorm(my_x, my_mean, my_sd)
            p1
        } else {
            p1 <- NULL
        }

    })

}

）

Answer 2

这应该有效：

library(shiny)
library(lattice)

shinyApp(
  ui = {
    pageWithSidebar(
      headerPanel("Standard Normal"),
      sidebarPanel(
        numericInput('mean', 'Your mean', 80),
        numericInput('sd', 'Your standard deviation', 2),
        numericInput('x', 'Your score', 250),
        checkboxInput("zScoreArea", label = "Area under z-score", value = TRUE)
      ),
      mainPanel(
        h3('Standard Normal'),
        plotOutput('sdNorm'),
        h4('Your z-score is:'),
        verbatimTextOutput('z_score')
      ))
  },
  server = {
    function(input, output){

      #data
      dt1 <- seq(60, 170, length = 10000)
      dt2 <- dnorm(dt1, 110, 15)

      #xyplot panel= function()
      myfunc <- reactive({
        if(input$zScoreArea){
          function(x,y, ...){
            panel.xyplot(x,y, ...)
            panel.abline( v = c(z_score(), 110), lty = 2)

            xx <- c(60, x[x >= 60 & x <= z_score()], z_score())
            yy <- c(0,  y[x >= 60 & x <= z_score()], 0)
            panel.polygon(xx,yy, ..., col='red')
          }
        }else{
          function(x, ...){
            panel.xyplot(x, ...)
            panel.abline(v = c(z_score(), 110), lty = 2)}

        }
      })

      #reactive z_score for plotting
      z_score <- reactive({
        my_mean <- input$mean
        my_sd <- input$sd
        my_x <- input$x

        #return z score
        (my_x - my_mean)/my_sd
      })

      output$sdNorm <- renderPlot({
        xyplot(dt2 ~ dt1,
               type = "l",
               main = "Plot 4",
               scales = list(x = list(at = seq(60, 170, 10)), rot = 45),
               panel = myfunc()
        )
      })

      output$z_score = renderPrint({ z_score() })
    }
  }
)

在Shiny

2 个答案: