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使用DT和R Shiny进行持久选择

[英]Persistent Selections Using DT and R Shiny

 原文  2017-08-09 14:05:13  8   2    r/ shiny/ dt

问题描述

我有一个Shiny用例，我想允许用户通过选择列并查看某些摘要统计信息来过滤其数据。 这样做的目的是使他们能够快速深入到更细粒度的组并查看结果。 除非用户在较高级别上进行选择，否则它将很好地工作，然后重置所有过滤器和选择并需要再次选择。 我一直在使这些过滤器永久存在并且仅在某些情况下进行更新时遇到麻烦。

例如，用户想要查看瑞士和德国（第2级）的工程师的收入中位数（第1级），并按年龄显示收入（第3级）。 他们将按每个表上方的selectInput值进行排序，以选择类别，然后在表中选择值，以包含变量，例如“ Engineer”，如下图所示。

应用价值过滤

如果他们想查看“试点”如何更改结果，则国家/地区过滤条件将消失。 我希望所有这些都保留在原处，而这一直是给我合适的部分。

关于如何解决这个问题有什么想法吗？ 此示例的代码如下：

服务器： 

library(shiny)
library(DT)
library(plyr)
library(dplyr)

# Generate income data

n <- 1000
age <- sample(20:60, n, replace=TRUE)
sex <- sample(c("M", "F"), n, replace=TRUE)
country <- sample(c("US", "CA", "UK", "DE", "CH", "NL"), n, replace=TRUE)
occupation <- sample(c("Engineer", "Doctor", "Retail", "Pilot"), n, replace=TRUE)
income <- sample(20000:120000, n, replace=TRUE)

df <- data.frame(age, sex, country, income, occupation)
categories <- c("None", "age", "sex", "country", "occupation")

shinyServer(function(input, output, session) {

  output$selection_1 <- renderUI({
    selectInput("selection_1", "Level 1 Selection", selected = "None",
                choices = categories)
  })

  output$selection_2 <- renderUI({
    selectInput("selection_2", "Level 2 Selection", selected = "None",
                choices = categories)
  })

  output$selection_3 <- renderUI({
    selectInput("selection_3", "Level 3 Selection", selected = "None",
                choices = categories)
  })

  table_1 <- reactive({
    validate(
      need(input$selection_1 != "None", "Select a variable for aggregation.")
    )
    ddply(df, input$selection_1, summarize,
          Count = length(income),
          Med_Income = median(income))
    })

  output$table_1_agg <- DT::renderDataTable(
    table_1(),
    rownames = TRUE,
    selection = list(selected = "")
    )

  # Get values to match on subsequent tables
  table_1_vals <- reactive({
    table_1()[input$table_1_agg_rows_selected, 1]
  })

  # Filter table 2
  table_2 <- reactive({
    validate(
      need(input$selection_2 != "None", "Select a variable for aggregation.")
    )
    # Filter selected values from table_1
    if(length(table_1_vals())>0){
      sel_1_col <- grep(input$selection_1, names(df))
      df2 <- df[df[,sel_1_col] %in% table_1_vals(),]
    }else{
      df2 <- df
    }
    ddply(df2, input$selection_2, summarize,
          Count = length(income),
          Med_Income = median(income))
  })

  output$table_2_agg <- DT::renderDataTable(
    table_2(),
    rownames = TRUE,
    selection = list(selected = "")
  )

  # Get values to match on subsequent tables
  table_2_vals <- reactive({
    table_2()[input$table_2_agg_rows_selected, 1]
  })

  # Filter table 3
  table_3 <- reactive({
    validate(
      need(input$selection_3 != "None", "Select a variable for aggregation.")
    )
    df3 <- df
    # Filter selected values from table_1
    if(length(table_1_vals())>0){
      sel_1_col <- grep(input$selection_1, names(df))
      df3 <- df3[df3[,sel_1_col] %in% table_1_vals(),]
    }
    if(length(table_2_vals())>0){
        sel_2_col <- grep(input$selection_2, names(df))
        df3 <- df3[df3[,sel_2_col] %in% table_2_vals(),]
    }
    ddply(df3, input$selection_3, summarize,
          Count = length(income),
          Med_Income = median(income))
  })

  output$table_3_agg <- DT::renderDataTable(
    table_3(),
    rownames = TRUE,
    selection = list(selected = "")
  )
})

用户界面： 

shinyUI(fluidPage(
  fluidRow(
    column(6,
           uiOutput("selection_1"),
           DT::dataTableOutput("table_1_agg")),
    column(6,
           uiOutput("selection_2"),
           DT::dataTableOutput("table_2_agg"))
  ),
  fluidRow(
    column(6,
           br(),
           uiOutput("selection_3"),
           DT::dataTableOutput("table_3_agg"))
  )
))

谢谢！

2 个解决方案

解决方案1
 0  2017-08-09 22:39:39

一种选择是存储选定的行，并在稍后重绘表时使用。 可以使用附加的renderUI放置表的创建并使用参数selection指示要选择的行。 

library(shiny)
library(DT)
library(dplyr)
library(plyr)

# Generate income data

n <- 1000
age <- sample(20:60, n, replace=TRUE)
sex <- sample(c("M", "F"), n, replace=TRUE)
country <- sample(c("US", "CA", "UK", "DE", "CH", "NL"), n, replace=TRUE)
occupation <- sample(c("Engineer", "Doctor", "Retail", "Pilot"), n, replace=TRUE)
income <- sample(20000:120000, n, replace=TRUE)

df <- data.frame(age, sex, country, income, occupation)
categories <- c("None", "age", "sex", "country", "occupation")

ui <- shinyUI(fluidPage(
  fluidRow(
    column(6,
           uiOutput("selection_1"),
           DT::dataTableOutput("table_1_agg")),
    column(6,
           uiOutput("selection_2"),
           uiOutput("table_2_aggUI")
    )
  ),
  fluidRow(
    column(6,
           br(),
           uiOutput("selection_3"),
           uiOutput("table_3_aggUI")
    )
  )
))

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

  table2_selected <- NULL
  table3_selected <- NULL

  output$selection_1 <- renderUI({
    selectInput("selection_1", "Level 1 Selection", selected = "None",
                choices = categories)
  })

  output$selection_2 <- renderUI({
    selectInput("selection_2", "Level 2 Selection", selected = "None",
                choices = categories)
  })

  output$selection_3 <- renderUI({
    selectInput("selection_3", "Level 3 Selection", selected = "None",
                choices = categories)
  })

  table_1 <- reactive({
    validate(
      need(input$selection_1 != "None", "Select a variable for aggregation.")
    )
    ddply(df, input$selection_1, summarize,
          Count = length(income),
          Med_Income = median(income))
  })

  output$table_1_agg <- DT::renderDataTable(
    table_1(),
    rownames = TRUE,
    selection = list(selected = "")
  )

  # Get values to match on subsequent tables
  table_1_vals <- reactive({
    table_1()[input$table_1_agg_rows_selected, 1]
  })

  # Filter table 2
  table_2 <- reactive({
    validate(
      need(input$selection_2 != "None", "Select a variable for aggregation.")
    )
    # Filter selected values from table_1
    if(length(table_1_vals())>0){
      sel_1_col <- grep(input$selection_1, names(df))
      df2 <- df[df[,sel_1_col] %in% table_1_vals(),]
    }else{
      df2 <- df
    }
    ddply(df2, input$selection_2, summarize,
          Count = length(income),
          Med_Income = median(income))
  })


  output$table_2_aggUI <- renderUI({
    # to redraw UI if data on table_2() change
    table_2()
    output$table_2_agg <- DT::renderDataTable(
      isolate(table_2()),
      rownames = TRUE,
      selection = list(target = 'row', selected = table2_selected)
    )
    DT::dataTableOutput("table_2_agg")
  })

  # keep record of selected rows
  observeEvent(input$table_2_agg_rows_selected, {
    table2_selected <<- as.integer(input$table_2_agg_rows_selected)
    cat("Table 2 selected:", table2_selected, "\n")
  })

  # Get values to match on subsequent tables
  table_2_vals <- reactive({
    table_2()[input$table_2_agg_rows_selected, 1]
  })

  # Filter table 3
  table_3 <- reactive({
    validate(
      need(input$selection_3 != "None", "Select a variable for aggregation.")
    )
    df3 <- df
    # Filter selected values from table_1
    if(length(table_1_vals())>0){
      sel_1_col <- grep(input$selection_1, names(df))
      df3 <- df3[df3[,sel_1_col] %in% table_1_vals(),]
    }
    if(length(table_2_vals())>0){
      sel_2_col <- grep(input$selection_2, names(df))
      df3 <- df3[df3[,sel_2_col] %in% table_2_vals(),]
    }
    ddply(df3, input$selection_3, summarize,
          Count = length(income),
          Med_Income = median(income))
  })


  output$table_3_aggUI <- renderUI({
    # to redraw UI if data on table_3() change
    table_3()
    output$table_3_agg <- DT::renderDataTable(
      isolate(table_2()),
      rownames = TRUE,
      selection = list(target = 'row', selected = table3_selected)
    )
    DT::dataTableOutput("table_3_agg")
  })

  # keep record of selected rows
  observeEvent(input$table_3_agg_rows_selected, {
    table3_selected <<- as.integer(input$table_3_agg_rows_selected)
    cat("Table 3 selected:", table3_selected, "\n")
  })

})

shinyApp(ui = ui, server = server)

解决方案2
 0  2017-08-11 16:29:36

您可以通过添加以下功能来实现：

  1. 初始化临时反应变量。 在t0时刻，此变量将从NULL或0值开始，但是在重新绘制它们之前，它将临时捕获表的当前选定行和过滤器选项 

     prev_selections = reactiveValues(table2 = NULL, prev_rows_t2 = NULL, new_rows_t2 = NULL, filterop_t2 = 0, table3 = NULL, prev_rows_t3 = NULL, new_rows_t3 = NULL, filterop_t3 = 0)

  2. 因为您在表N中选择的行将过滤表N + 1，所以您需要在重绘下游表之前创建一个副本。 使用observeEvent捕获应用的过滤器的表和值（表2下方） 

     observeEvent(input$table_2_agg_rows_selected,{ prev_selections$table2 = table_2() prev_selections$filterop_t2 = input$selection_2 })

  3. 为每个表创建第二个observeEvent集合，以捕获重绘表之前和之后的当前选定行。 观察observeEvent此集合observeEvent在上游表中进行的行选择触发（表2下方） 

     observeEvent({input$table_1_agg_rows_selected input$selection_2}, { prev_selections$prev_rows_t2 = isolate(prev_selections$table2[input$table_2_agg_rows_selected,][1]) prev_selections$new_rows_t2 = isolate(if ( input$selection_2 == prev_selections$filterop_t2 ) {which(table_2()[,1] %in% prev_selections$prev_rows_t2[,1])} else {NULL}) })

  4. 将步骤3中的值用作DT::renderDataTable的selection = list(selected = )参数的DT::renderDataTable 。 不要忘记调用datatable从内DT::renderDataTable按这里HubertL的答案

完整代码如下： 

library(shiny)
library(DT)
library(plyr)
library(dplyr)

# Generate income data

n <- 1000
age <- sample(20:60, n, replace=TRUE)
sex <- sample(c("M", "F"), n, replace=TRUE)
country <- sample(c("US", "CA", "UK", "DE", "CH", "NL"), n, replace=TRUE)
occupation <- sample(c("Engineer", "Doctor", "Retail", "Pilot"), n, replace=TRUE)
income <- sample(20000:120000, n, replace=TRUE)

df <- data.frame(age, sex, country, income, occupation)
categories <- c("None", "age", "sex", "country", "occupation")

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

  output$selection_1 <- renderUI({
    selectInput("selection_1", "Level 1 Selection", selected = "None",
                choices = categories)
  })

  output$selection_2 <- renderUI({
    selectInput("selection_2", "Level 2 Selection", selected = "None",
                choices = categories)
  })

  output$selection_3 <- renderUI({
    selectInput("selection_3", "Level 3 Selection", selected = "None",
                choices = categories)
  })

  table_1 <- reactive({
    validate(
      need(input$selection_1 != "None", "Select a variable for aggregation.")
    )
    ddply(df, input$selection_1, summarize,
          Count = length(income),
          Med_Income = median(income))
  })

  output$table_1_agg <- DT::renderDataTable(
    table_1(),
    rownames = TRUE,
    selection = list(selected = "")
  )

  # Get values to match on subsequent tables
  table_1_vals <- reactive({
    table_1()[input$table_1_agg_rows_selected, 1]
  })

  # Filter table 2
  table_2 <- reactive({
    validate(
      need(input$selection_2 != "None", "Select a variable for aggregation.")
    )
    # Filter selected values from table_1
    if(length(table_1_vals())>0){
      sel_1_col <- grep(input$selection_1, names(df))
      df2 <- df[df[,sel_1_col] %in% table_1_vals(),]
    }else{
      df2 <- df
    }
    ddply(df2, input$selection_2, summarize,
          Count = length(income),
          Med_Income = median(income))
  })

  output$table_2_agg <- DT::renderDataTable(
    datatable(table_2(),
    rownames = TRUE,
    selection = list(target = 'row', selected = prev_selections$new_rows_t2))
  )

  # Get values to match on subsequent tables
  table_2_vals <- reactive({
    table_2()[input$table_2_agg_rows_selected, 1]
  })

  # Filter table 3
  table_3 <- reactive({
    validate(
      need(input$selection_3 != "None", "Select a variable for aggregation.")
    )
    df3 <- df
    # Filter selected values from table_1
    if(length(table_1_vals())>0){
      sel_1_col <- grep(input$selection_1, names(df))
      df3 <- df3[df3[,sel_1_col] %in% table_1_vals(),]
    }
    if(length(table_2_vals())>0){
      sel_2_col <- grep(input$selection_2, names(df))
      df3 <- df3[df3[,sel_2_col] %in% table_2_vals(),]
    }
    ddply(df3, input$selection_3, summarize,
          Count = length(income),
          Med_Income = median(income))
  })

  output$table_3_agg <- DT::renderDataTable(
    datatable(table_3(),
    rownames = TRUE,
    selection = list(target = 'row', selected = prev_selections$new_rows_t3))
  )


  ## Retain highlighted rows in temp variables and enable persistent filtering

  #initialize temp variables
  prev_selections = reactiveValues(table2 = NULL, prev_rows_t2 = NULL, new_rows_t2 = NULL, filterop_t2 = 0,
                                   table3 = NULL, prev_rows_t3 = NULL, new_rows_t3 = NULL, filterop_t3 = 0)

  #Capture current selections/highlights in Table N
  observeEvent(input$table_2_agg_rows_selected, 
               {
                 prev_selections$table2 = table_2()
                 prev_selections$filterop_t2 = input$selection_2
               })

  observeEvent(input$table_3_agg_rows_selected, 
               {
                 prev_selections$table3 = table_3()
                 prev_selections$filterop_t3 = input$selection_3
               })

  #Observe upstream events (e.g. highlights in Table N-1,...) and enable persistent selection
  #Table 2
  observeEvent({input$table_1_agg_rows_selected
    input$selection_2}, 
    {
      prev_selections$prev_rows_t2 = isolate(prev_selections$table2[input$table_2_agg_rows_selected,][1])
      prev_selections$new_rows_t2 = isolate(if ( input$selection_2 == prev_selections$filterop_t2 ) 
      {which(table_2()[,1] %in% prev_selections$prev_rows_t2[,1])} else {NULL})

    })

  #Table 3
  observeEvent({
    input$table_1_agg_rows_selected
    input$table_2_agg_rows_selected
    input$selection_3
  }, 
  {
    prev_selections$prev_rows_t3 = isolate(prev_selections$table3[input$table_3_agg_rows_selected,][1])
    prev_selections$new_rows_t3 = isolate(if ( input$selection_3 == prev_selections$filterop_t3 ) 
    {which(table_3()[,1] %in% prev_selections$prev_rows_t3[,1])} else {NULL})

  })


})


ui <- shinyUI(fluidPage(
  fluidRow(
    column(6,
           uiOutput("selection_1"),
           DT::dataTableOutput("table_1_agg")),
    column(6,
           uiOutput("selection_2"),
           DT::dataTableOutput("table_2_agg"))
  ),
  fluidRow(
    column(6,
           br(),
           uiOutput("selection_3"),
           DT::dataTableOutput("table_3_agg"))
  )
))

shinyApp(ui = ui, server = server)

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