R 闪亮：使用不同的变量创建反应式过滤器。

我有一个数据框架，它将社会人口统计数据与多个网站的意识测量结合起来。每个网站都有一个单独的栏目，说明该人是否知道该网站是/否。此外，每个受访者都应根据其提供的可变权重人数进行加权

我想创建一个闪亮的应用程序，为知道某个选定网站的人显示情节。该网站应通过selectInput按钮进行选择

我发现了几篇关于stackoverflow的文章，其中介绍了使用dplyr+Shining的数据集过滤器。但它们都改变了变量值，而不是变量本身

我试着使用下面的代码，但是没有成功，请参见下面的代码示例

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示例数据帧：

我想以互动的方式做什么：

我试过的

---

有没有一种方法可以改变过滤器变量而不是值？我还可以选择其他解决方案。

我认为您需要添加第二个UI，它取决于第一个UI上选择的变量。在这里，我使用renderUI在服务器中创建了它。然后，我使用所选列将数据子集为与所选变量相等的数据。希望这有帮助

library(shiny)
library(ggplot2)
library(dplyr)

ui <- fluidPage(
  selectInput(inputId = "WebsiteName", label = "Choose a Website", choices = names(df) [c(3:7)]),
  htmlOutput("variableUI"),
  plotOutput("Gender")
)


server <- function(input, output) {

  output$variableUI <- renderUI({
    selectInput(inputId = "variable", label = "Choices", choices = df[,input$WebsiteName])
  })

  dfInput <- reactive({
   ##subsetting is a bit tricky here to id the column on which to subset        
    df[ df[ , input$WebsiteName ] == input$variable, ]
  })

  output$Gender <- renderPlot({
    df1 <- dfInput()
    ggplot(df1)+
      aes(x=gender, y=popWeight/sum(popWeight))+
      stat_summary(fun.y = sum, geom = "bar")+
      scale_y_continuous("Population (%)", labels = scales::percent)
  })
}


shinyApp(ui = ui, server = server)

您可以整理数据集，以更有用的方式对其进行转换，并为自己省去一些麻烦：

整洁的数据集 服务器 更新了反应式表达式

server <- function(input, output) {

  dfInput <- reactive({
    df %>% filter(web == input$websiteName & value == "Yes")
  })

  output$Gender <- renderPlot({
    df1 <- dfInput()
    ggplot(df1) +
      aes(x = gender, y = popWeight / sum(popWeight)) +
      stat_summary(fun.y = sum, geom = "bar") +
      scale_y_continuous("Population (%)", labels = scales::percent)
  })
}

library(shiny)
library(ggplot2)
library(dplyr)

ui <- fluidPage(
  selectInput(inputId = "WebsiteName", label = "Choose a Website", choices = names(df) [c(3:7)]),
  plotOutput("Gender")
)


server <- function(input, output) {

  dfInput <- reactive({
    df %>% filter (input$WebsiteName == "Yes")
  })

  output$Gender <- renderPlot({
    df1 <- dfInput()
    ggplot(df1)+
      aes(x=gender, y=popWeight/sum(popWeight))+
      stat_summary(fun.y = sum, geom = "bar")+
      scale_y_continuous("Population (%)", labels = scales::percent)
  })
}


shinyApp(ui = ui, server = server)

library(shiny)
library(ggplot2)
library(dplyr)

ui <- fluidPage(
  selectInput(inputId = "WebsiteName", label = "Choose a Website", choices = names(df) [c(3:7)]),
  htmlOutput("variableUI"),
  plotOutput("Gender")
)


server <- function(input, output) {

  output$variableUI <- renderUI({
    selectInput(inputId = "variable", label = "Choices", choices = df[,input$WebsiteName])
  })

  dfInput <- reactive({
   ##subsetting is a bit tricky here to id the column on which to subset        
    df[ df[ , input$WebsiteName ] == input$variable, ]
  })

  output$Gender <- renderPlot({
    df1 <- dfInput()
    ggplot(df1)+
      aes(x=gender, y=popWeight/sum(popWeight))+
      stat_summary(fun.y = sum, geom = "bar")+
      scale_y_continuous("Population (%)", labels = scales::percent)
  })
}


shinyApp(ui = ui, server = server)

df<- df %>% 
    gather(web, value, -age, -gender, -popWeight)

ui <- fluidPage(
  selectInput(inputId = "websiteName", 
              label = "Choose a Website", 
              choices = unique(df$web)),
  plotOutput("Gender")
)

server <- function(input, output) {

  dfInput <- reactive({
    df %>% filter(web == input$websiteName & value == "Yes")
  })

  output$Gender <- renderPlot({
    df1 <- dfInput()
    ggplot(df1) +
      aes(x = gender, y = popWeight / sum(popWeight)) +
      stat_summary(fun.y = sum, geom = "bar") +
      scale_y_continuous("Population (%)", labels = scales::percent)
  })
}