Question

先谢谢大家。我试图缩小范围，但我不知所措，所以我在缩小范围时遇到了麻烦。我的主要困惑是反应性与其他类型的反应性（？）结构。

我要做的是列出R基本数据包。我在选择框中有这个。这样可行。选择后，R将显示该数据集的字段，以便用户可以接受Y和X值。（我意识到有些套装没有格式化。）下面列出的代码给出了＆＃34;不正确的维数和＃34;在主面板中。错误似乎在调用名称的某处。我似乎并不了解如何在他们创作之后将反应元素纳入ui。关于这个的任何提示？

同样，数据集列表也有效。数据表被正确拉出。但这些名字并不存在，因此可以选择它们来构建情节。

library(shiny)
set.seed(10101)

dataPkgs <- data(package='datasets')[[3]]
pkgNames <- dataPkgs[,3]

ui <- fluidPage(
  headerPanel('R Data k-means clustering'),
  sidebarPanel(

    selectInput('choosedata', 'Choose a Data Set',pkgNames,selected="mtcars"),

    helpText("Listing should make available all basic datasets available in base R install.
             Remember many of the data sets will be ill-fit for this type of analysis."),

    selectInput('ycol', 'Select Independent Y', textOutput('selDFNames')),
    selectInput('xcol', 'Select Dependent X', textOutput('selDFNames')),

    numericInput('clusters', '1-7 clusters', 3,
                 min = 1, max = 7)
  ),
  mainPanel(
    plotOutput('plot1')
  )
)


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

  selDF <- renderTable({
    get(input$choosedata)
  })

  output$selDFNames <- renderText({
    names(selDF())
  })

  selDFset <- reactive({
    selDF()[, c(input$xcol, input$ycol)]
  })

  clusters <- reactive({
    kmeans(selDFset(), input$clusters)
  })

  output$plot1 <- renderPlot({

    plot(selDFset(),
         col = clusters()$cluster,
         pch = 20, cex = 3)
    points(clusters()$centers, pch = 4, cex = 4, lwd = 4)
  })

}

shinyApp(ui = ui, server = server)

Answer 1

使用observe和updateSelectInput更新您的选择输入。这是服务器功能，它运行良好。祝你好运。

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

  selDF <- reactive({
    get(input$choosedata, envir = as.environment('package:datasets'))
  })

  observe({
    updateSelectInput(session, "ycol", choices = names(selDF()))
  })

  observe({
    updateSelectInput(session, "xcol", choices = names(selDF()))
  })

  selDFset <- reactive({
    selDF()[, c(input$xcol, input$ycol)]
  })

  clusters <- reactive({
    kmeans(selDFset(), input$clusters)
  })

  output$plot1 <- renderPlot({

  plot(selDFset(),
     col = clusters()$cluster,
     pch = 20, cex = 3)
 points(clusters()$centers, pch = 4, cex = 4, lwd = 4)
  })
}

shinyApp(ui = ui, server = server)

Answer 2

我会用我的答案添加更多的测试用例和捕获量，但如果你得到你知道的数据集而不仅仅是要求所有这些数据集，那么它是最好的：

library(shiny)
set.seed(10101)

dataPkgs <- data(package='datasets')[[3]]
pkgNames <- dataPkgs[,3]

longley[,c("GNP.deflator","GNP.deflator")]

ui <- fluidPage(
  headerPanel('R Data k-means clustering'),
  sidebarPanel(

    selectInput('choosedata', 'Choose a Data Set',pkgNames,selected="mtcars"),

    helpText("Listing should make available all basic datasets available in base R install.
             Remember many of the data sets will be ill-fit for this type of analysis."),

    selectInput('ycol', 'Select Independent Y', choices = NULL),
    selectInput('xcol', 'Select Dependent X', choices = NULL),

    numericInput('clusters', '1-7 clusters', 3,min = 1, max = 7)
    ),
  mainPanel(
    plotOutput('plot1')
  )
)

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

  selDF <- reactive({
    data <- tryCatch(get(input$choosedata), error=function(e) NULL)
    if(is.null(data)){
      return()
    }
    data
  })

  observeEvent(selDF(),{
    updateSelectInput(session,"xcol",choices=c(names(selDF())))
    updateSelectInput(session,"ycol",choices=c(names(selDF())))
  })

  selDFset <- eventReactive(c(input$xcol, input$ycol),{
    req(input$xcol)
    req(input$ycol)
    selDF()[,c(input$xcol, input$ycol)]
  })

  clusters <- reactive({
    kmeans(selDFset(), input$clusters)
  })

  output$plot1 <- renderPlot({
    req(selDFset())
    plot(selDFset(),col = clusters()$cluster,pch = 20, cex = 3)
    points(clusters()$centers, pch = 4, cex = 4, lwd = 4)
  })
}

shinyApp(ui = ui, server = server)

R Programming Shiny Reactive基础知识 - 数据类型

2 个答案: