r - 将 renderUI 输入从一个 Shiny 模块传递到另一个

Question

我正在尝试模块化 Shiny 代码，以将 CSV 文件作为输入上传到 scatterD3 图中。额外的 UI 控件将从 renderUI 更改 x 变量和 y 变量。这只是来自 How to organize large R Shiny apps? 的 Mikael Jumppanen 答案的一个小修改，但我一直在努力，无法让这最后一点发挥作用。

对于这个数据集，我使用 mtcars 数据集 https://gallery.shinyapps.io/066-upload-file/_w_469e9927/mtcars.csv

## load libraries
library(shiny)
library(stringr)
library(scatterD3)

#source("/Users/echang/scratch/tmp/MSD_D3scatter/csvFile_Module.R")
csvFileInput <- function(id, label="CSV file") {
  ## Create namespace
  ns<-NS(id)
  tagList(
    uiOutput(ns("controls"))
  )
}

csvFileControl <- function(id){
  ns <- NS(id)
  tagList(
    column(width=3, uiOutput(ns("ColName"))),
    column(width=3, uiOutput(ns("ColEntry")))
  )
}

csvFileUI <- function(id){
  ns <- NS(id)
  tagList(
    uiOutput(ns("csvTable"))
  )
}

## server module
csvFile <- function(input, output, session, stringsAsFactors) {
  ns <- session$ns
  ## to reuse namespace, session must be first!!!

  ## User selected file
  userFile <- reactive({
    # If no file is selected, don't do anything
    validate(need(input$file, message = FALSE))
    input$file
  })

  dataframe <- reactive({
    read.csv(
      userFile()$datapath,
      header = input$header,
      sep=input$sep,
      quote = input$quote,
      stringsAsFactors = stringsAsFactors
    )
  })
  # We can run observers in here if we want to
  observe({
    msg <- sprintf("File %s was uploaded", userFile()$name)
    cat(msg, "\n")
  })

  output$controls <- renderUI({
    ## use taglist to keep everything together
    tagList(
      fileInput(ns('file'), 'Choose CSV file', 
                accept=c('txt/csv','text/comma-separated-values,text/plain','.csv')),
      checkboxInput(ns('header'), 'Has heading', TRUE),
      radioButtons(ns('sep'),'Separator', c(Comma=',',Semicolon=';',Tab='\t'), ','),
      selectInput(ns('quote'),'Quote', c(None ='','Double Quote'='"','Single Quote'="'"),'"')
    )
  })

  ## use renderUI to display table
  output$csvTable <- renderUI({
    output$table <- renderDataTable(dataframe())
    dataTableOutput(ns("table"))
  })

  ## Column Name
  output$ColName <- renderUI({
    df <- dataframe()
    if (is.null(df)) return(NULL)
    items=names(df)
    names(items)=items
    tagList(
      selectInput(ns("xvar"), "Column Names", items),
      selectInput(ns("yvar"), "Column Names", items)
    )
  })

  ## Column Entry
  output$ColEntry <- renderUI({
    df <- dataframe()
    if (is.null(input$col)) return(NULL)
    tagList(
      selectInput(ns("entry"), "Entry Names", df[,input$xvar])
    )
  })

  # Return the reactive that yields the data frame
  return(dataframe)

}## End of module


## scatterD3 module -------------------------------------------------------------

D3scatterUI <- function(id){
  ns<-NS(id)
  tagList(
    scatterD3Output(ns("scatterplot1"))
    )
}

D3scatter <- function(input,output,session,data,xvar,yvar){
  ns <- session$ns

  output$scatterplot1 <- renderScatterD3({
    #scatterD3(data = data, x=mpg, y=carb,
    scatterD3(data = data, x=xvar, y=yvar,
              labels_size= 9, point_opacity = 1,
              #col_var=cyl, symbol_var= data$Assay,
              #lab= paste(mpg, carb, sep="|") , lasso=TRUE,
              #xlab= "IFN-γ", ylab= "IL-10",
              #click_callback = "function(id, index) {
              #  alert('scatterplot ID: ' + id + ' - Point index: ' + index) 
              #  }", 
              transitions= T)
  })
}


## Shiny ######################################################################
ui <- fluidPage(
  titlePanel("Upload"),

  tabsetPanel(type="tabs",
    tabPanel("tab1",
      sidebarLayout(
        sidebarPanel(csvFileInput("basic")),
        mainPanel(csvFileUI("basic"))
        )
      ),
    tabPanel("tab2",
      tagList(
        fluidRow(csvFileControl("basic")),
        fluidRow(D3scatterUI("first"))
        )
      )
    )
)

server <- function(input, output, session) {
  ## Option 1. CSV uploaded file
  datafile <- callModule(csvFile, "basic", stringsAsFactors = FALSE) 

  ## Option 2. mtcar data loaded at start
  #datafile <- reactive({mtcars}) ## data loaded at runApp()
  #callModule(csvFile, "basic") 

  xvar <- reactive(input$xvar) 
  yvar <- reactive(input$yvar)

  callModule(D3scatter, "first", datafile(), xvar, yvar)

}

shinyApp(ui, server)

我还从 https://itsalocke.com/shiny-module-design-patterns-pass-module-input-to-other-modules/ 咨询了 Shiny 模块设计。

我观看了网络研讨会，但无法正确理解逻辑。 https://www.rstudio.com/resources/webinars/understanding-shiny-modules/任何帮助将不胜感激!!

Answer 1

好的，这确实有点困难，因为使用模块并不是很简单。你很接近...你的主要问题是没有收拾行李全部 列表中的 react 物并将它们传递到需要它们的地方。

我做了以下更改:

csvFile : 声明了额外的 react 函数 xvar和 yvar在 csvFile服务器模块功能类似于您已经为 dataframe 所做的工作.

csvFile :将所有需要的响应式(Reactive)打包成一个列表，并将其作为返回值返回，如您帖子中的设计模式链接中所述。 (谢谢斯蒂芬洛克)。

server : 在 callModule(D3scatter,... ) 中传递该列表，再次如该链接中所述。

D3scatter : 通过调用 scatterD3 进行了一些重构使用从指定数据帧中提取的向量。这是因为我无法让它将字符串用作列说明符(但肯定有某种方法)。

以下是上面更改的代码部分:

csv文件服务器模块

csvFile <- function(input, output, session, stringsAsFactors) {
  ns <- session$ns
  ## to reuse namespace, session must be first!!!

  ## User selected file
  userFile <- reactive({
    # If no file is selected, don't do anything
    validate(need(input$file, message = FALSE))
    input$file
  })

  dataframe <- reactive({
    read.csv(
      userFile()$datapath,
      header = input$header,
      sep=input$sep,
      quote = input$quote,
      stringsAsFactors = stringsAsFactors
    )
  })
  # We can run observers in here if we want to
  observe({
    msg <- sprintf("File %s was uploaded", userFile()$name)
    cat(msg, "\n")
  })

  xvar <- reactive({input[[ "xvar" ]] })
  yvar <- reactive({input[[ "yvar" ]] })

  output$controls <- renderUI({
    ## use taglist to keep everything together
    tagList(
      fileInput(ns('file'), 'Choose CSV file', 
                accept=c('txt/csv','text/comma-separated-values,text/plain','.csv')),
      checkboxInput(ns('header'), 'Has heading', TRUE),
      radioButtons(ns('sep'),'Separator', c(Comma=',',Semicolon=';',Tab='\t'), ','),
      selectInput(ns('quote'),'Quote', c(None ='','Double Quote'='"','Single Quote'="'"),'"')
    )
  })

  ## use renderUI to display table
  output$csvTable <- renderUI({
    output$table <- renderDataTable(dataframe())
    dataTableOutput(ns("table"))
  })

  ## Column Name
  output$ColName <- renderUI({
    df <- dataframe()
    if (is.null(df)) return(NULL)
    items=names(df)
    print(items)
    names(items)=items
    tagList(
      selectInput(ns("xvar"), "Column Names", items),
      selectInput(ns("yvar"), "Column Names", items)
    )
  })

  ## Column Entry
  output$ColEntry <- renderUI({
    df <- dataframe()
    if (is.null(input$col)) return(NULL)
    tagList(
      selectInput(ns("entry"), "Entry Names", df[,input$xvar])
    )
  })

  rlist <- list(dataframe=dataframe,xvar=xvar,yvar=yvar)
  # Return the reactive that yields the data frame
  return(rlist)

}## End of module

服务器

server <- function(input, output, session) {
  ## Option 1. CSV uploaded file
  rlist <- callModule(csvFile, "basic", stringsAsFactors = FALSE) 

  ## Option 2. mtcar data loaded at start
  #datafile <- reactive({mtcars}) ## data loaded at runApp()
  #callModule(csvFile, "basic") 

  callModule(D3scatter, "first", rlist)

}

D3scatter

D3scatter <- function(input,output,session,rlist){
  ns <- session$ns

  output$scatterplot1 <- renderScatterD3({
    #scatterD3(data = data, x=mpg, y=carb,
    mtdf <- rlist$dataframe()
    x <- mtdf[[rlist$xvar()]]
    y <- mtdf[[rlist$yvar()]]
    scatterD3(x=x,y=y,
              labels_size= 9, point_opacity = 1,
              #col_var=cyl, symbol_var= data$Assay,
              #lab= paste(mpg, carb, sep="|") , lasso=TRUE,
              #xlab= "IFN-γ", ylab= "IL-10",
              #click_callback = "function(id, index) {
              #  alert('scatterplot ID: ' + id + ' - Point index: ' + index) 
              #  }", 
              transitions= T)
  })
}

然后它起作用了:



这是所有正在运行的代码，以防我忘记了某处的更改，或者有人只想运行它。顺便说一句，散点图从一个情节变为另一个情节的方式非常酷……它以类似动画的效果连续变形。异常。

整个应用程序在一个文件中

## load libraries
library(shiny)
library(stringr)
library(scatterD3)

#source("/Users/echang/scratch/tmp/MSD_D3scatter/csvFile_Module.R")
csvFileInput <- function(id, label="CSV file") {
  ## Create namespace
  ns<-NS(id)
  tagList(
    uiOutput(ns("controls"))
  )
}

csvFileControl <- function(id){
  ns <- NS(id)
  tagList(
    column(width=3, uiOutput(ns("ColName"))),
    column(width=3, uiOutput(ns("ColEntry")))
  )
}

csvFileUI <- function(id){
  ns <- NS(id)
  tagList(
    uiOutput(ns("csvTable"))
  )
}

## server module
csvFile <- function(input, output, session, stringsAsFactors) {
  ns <- session$ns
  ## to reuse namespace, session must be first!!!

  ## User selected file
  userFile <- reactive({
    # If no file is selected, don't do anything
    validate(need(input$file, message = FALSE))
    input$file
  })

  dataframe <- reactive({
    read.csv(
      userFile()$datapath,
      header = input$header,
      sep=input$sep,
      quote = input$quote,
      stringsAsFactors = stringsAsFactors
    )
  })
  # We can run observers in here if we want to
  observe({
    msg <- sprintf("File %s was uploaded", userFile()$name)
    cat(msg, "\n")
  })

  xvar <- reactive({input[[ "xvar" ]] })
  yvar <- reactive({input[[ "yvar" ]] })

  output$controls <- renderUI({
    ## use taglist to keep everything together
    tagList(
      fileInput(ns('file'), 'Choose CSV file', 
                accept=c('txt/csv','text/comma-separated-values,text/plain','.csv')),
      checkboxInput(ns('header'), 'Has heading', TRUE),
      radioButtons(ns('sep'),'Separator', c(Comma=',',Semicolon=';',Tab='\t'), ','),
      selectInput(ns('quote'),'Quote', c(None ='','Double Quote'='"','Single Quote'="'"),'"')
    )
  })

  ## use renderUI to display table
  output$csvTable <- renderUI({
    output$table <- renderDataTable(dataframe())
    dataTableOutput(ns("table"))
  })

  ## Column Name
  output$ColName <- renderUI({
    df <- dataframe()
    if (is.null(df)) return(NULL)
    items=names(df)
    print(items)
    names(items)=items
    tagList(
      selectInput(ns("xvar"), "Column Names", items),
      selectInput(ns("yvar"), "Column Names", items)
    )
  })

  ## Column Entry
  output$ColEntry <- renderUI({
    df <- dataframe()
    if (is.null(input$col)) return(NULL)
    tagList(
      selectInput(ns("entry"), "Entry Names", df[,input$xvar])
    )
  })

  rlist <- list(dataframe=dataframe,xvar=xvar,yvar=yvar)
  # Return the reactive that yields the data frame
  return(rlist)

}## End of module


## scatterD3 module -------------------------------------------------------------

D3scatterUI <- function(id){
  ns<-NS(id)
  tagList(
    scatterD3Output(ns("scatterplot1"))
  )
}

D3scatter <- function(input,output,session,rlist){
  ns <- session$ns

  output$scatterplot1 <- renderScatterD3({
    #scatterD3(data = data, x=mpg, y=carb,
    mtdf <- rlist$dataframe()
    x <- mtdf[[rlist$xvar()]]
    y <- mtdf[[rlist$yvar()]]
    scatterD3(x=x,y=y,
              labels_size= 9, point_opacity = 1,
              #col_var=cyl, symbol_var= data$Assay,
              #lab= paste(mpg, carb, sep="|") , lasso=TRUE,
              #xlab= "IFN-γ", ylab= "IL-10",
              #click_callback = "function(id, index) {
              #  alert('scatterplot ID: ' + id + ' - Point index: ' + index) 
              #  }", 
              transitions= T)
  })
}


## Shiny ######################################################################
ui <- fluidPage(
  titlePanel("Upload"),

  tabsetPanel(type="tabs",
              tabPanel("tab1",
                       sidebarLayout(
                         sidebarPanel(csvFileInput("basic")),
                         mainPanel(csvFileUI("basic"))
                       )
              ),
              tabPanel("tab2",
                       tagList(
                         fluidRow(csvFileControl("basic")),
                         fluidRow(D3scatterUI("first"))
                       )
              )
  )
)

server <- function(input, output, session) {
  ## Option 1. CSV uploaded file
  rlist <- callModule(csvFile, "basic", stringsAsFactors = FALSE) 

  ## Option 2. mtcar data loaded at start
  #datafile <- reactive({mtcars}) ## data loaded at runApp()
  #callModule(csvFile, "basic") 

  callModule(D3scatter, "first", rlist)

}

shinyApp(ui, server)