Shining-如何使用checkboxGroupInput筛选表和箱线图中的变量

我不知道如何使用checkboxGroupInput编写服务器代码来筛选变量选择。我想过滤将在箱线图和表中显示的变量。我在UI中添加了所有变量

我的代码：（我使用multi-multi.csv数据集）

UI

shinyUI(fluidPage(
  sidebarLayout(
    sidebarPanel(
      fileInput("fileInPath", label= h4("Import danych")),
      sliderInput("kolor", h4("Wybierz kolor"),min = 0, max = 1, value = 0.5, step=0.05),
      checkboxGroupInput("kolumna", h4("Wybierz kolumny"), choices = c('L1', 'L2', 'L3', 'L4', 'L5', 'L6', 'L7', 'L8', 'L9', 'L10', 'L11', 'L12', 'L13','L14', 'L15', 'L16', 'L17', 'L18', 'L19', 'L20')),
      mainPanel(
      tabsetPanel(type = "tabs",
                  tabPanel("Table", tableOutput("daneIn")),
                  tabPanel("Plot", plotOutput("plot")),
                  tabPanel("Model", verbatimTextOutput("model"))))) ))

服务器

library(shiny)
library(ggplot2)
library(reshape)

model <- function(d){
  tmpTab <- table(as.integer(unlist((d[,-c(1:4)]))))
  ret <- as.integer(head(names(tmpTab[order(tmpTab,decreasing=T)]),10))
  ret <- as.data.frame(matrix(ret,ncol=1))
  return(ret)
}
shinyServer(function(input, output) {
  dataIn <- reactive({
    inFile <- input$fileInPath
    if (is.null(inFile)) {
      return(NULL)
    } read.table(file=inFile$datapath,sep=";",dec=",",header=T,stringsAsFactors=FALSE)
  })
  output$daneIn <- renderTable({
    ret <- rbind(
      head(dataIn(),5),
      tail(dataIn(),5)
    )
    return(ret)
  },include.rownames=FALSE)
  output$plot <- renderPlot({
    d <- dataIn()
    InColor <- rgb(as.numeric(input$kolor),0,0,1)
    d <- melt(d[,-c(2:4)],id.vars="Numer")
    wyk <- (
      ggplot(d,aes(x=variable,y=value)) 
      + geom_boxplot(color = InColor) 
      + xlab("") + ylab("Kula")
    ) 
    return(wyk)
  }) 
  output$model <- renderPrint({
    d <- dataIn()
    ret <- model(d)
    return(ret)
  }) 
  })

库（闪亮）
图书馆（GG2）
图书馆（重塑）
model您要做的是根据用户触发
复选框时应用的规则创建一个条件反应过滤器。我无法根据提供的代码推断规则，但我可以提出以下建议：

将以下代码添加到服务器.R（其中“您的规则”指的是您要应用的筛选规则）：


现在，您将能够动态呈现已过滤和未过滤的数据

我希望这能奏效。如果可行，请不要忘记投票：-）。
@alicjaZ此解决方案有帮助吗？
filteredData <- shiny::reactive({
switch(
  input$kolumna,
  'L1' = dataIn() %>% filter(Your Rule), 
  'L2' = dataIn() %>% filter(Your Rule), 
  'L3' = dataIn() %>% filter(Your Rule), 
  'L4' = dataIn() %>% filter(Your Rule), 
  'L5' = dataIn() %>% filter(Your Rule), 
  'L6' = dataIn() %>% filter(Your Rule), 
  'L7' = dataIn() %>% filter(Your Rule), 
  'L8' = dataIn() %>% filter(Your Rule), 
  'L9' = dataIn() %>% filter(Your Rule), 
  'L10' = dataIn() %>% filter(Your Rule), 
  'L11' = dataIn() %>% filter(Your Rule), 
  'L12' = dataIn() %>% filter(Your Rule), 
  'L13' = dataIn() %>% filter(Your Rule),
  'L14' = dataIn() %>% filter(Your Rule), 
  'L15' = dataIn() %>% filter(Your Rule), 
  'L16' = dataIn() %>% filter(Your Rule), 
  'L17' = dataIn() %>% filter(Your Rule), 
  'L18' = dataIn() %>% filter(Your Rule), 
  'L19' = dataIn() %>% filter(Your Rule), 
  'L20' = dataIn() %>% filter(Your Rule)
  )
})

output$model <- renderPrint({
 # You have not selected any filters, hence your original upload will be rendered
 if (is.null(input$kolumna)) {
   d <- dataIn()
   ret <- model(d)
   return(ret)
 } else { # You have filtered your data, which filteredData() will handle
   d <- filteredData()
   ret <- model(d)
   return(ret)
 }
})