如何使用闪亮更新多个观察事件之间的数据

Question

如何在另一个observeEvent中重用observeEvent处理的数据？

#ui.r
library(shiny)
library(ggplot2)
library(shinyWidgets)
library(DT)

dataset <- diamonds
shinyUI(fluidPage(
  
  sidebarLayout(
    sidebarPanel(
      
      actionButton("read", "read_diamonds"),
      
      pickerInput('filter1',
                  'clarity_Filter1',
                  choices=c(""),
                  multiple  =  TRUE,
                  options = list(actionsBox=TRUE,liveSearch=TRUE),
                  ),
      
      sliderInput('filter2','table_Filter2', min=0, max=100, value=c(0, 100))
      
      ),
    mainPanel(
      dataTableOutput("display"),
    ),

    )
  )
  )

#server.r
library(shiny)
library(DT)

function(input, output, session) {
  
  #Initial setting
  observeEvent(input$file, {
    
    diamond_df <- diamonds

    #filter1 update
    clarity_choise <- as.vector(unique(diamond_df$clarity))
    updatePickerInput(
      session = session,
      inputId = "clarity_Filter1",
      choices = clarity_choise,
      selected = clarity_choise,
      options = list(
        actionsBox  =  TRUE,
        liveSearch = TRUE
      ),
    )
    
    #filter2 update
    max_value <- max(as.numeric((diamond_df$table)))
    min_value <- min(as.numeric((diamond_df$table)))
    updateSliderInput(
      session = session,
      inputId = "nega_char_slier",
      max = max_value ,
      min = min_value ,
      value = c(min,max)
    )
        
    #display
    output$display = renderDataTable(diamond_df,options= list(lengthMenu = c(5, 10, 20), pageLength = 5))
    
    
  })
  
  #What to do if filter1,2 is updated
  observeEvent(c(input$filter1,
                 input$filter2),{
    
    #data update
    diamond_df <- diamond_df %>% filter(clarity %in% input$filter1)
    diamond_df <- diamond_df %>% filter(input$filter2[1]<table & input$filter2[2]>table)
    
    #display
    output$display = renderDataTable(diamond_df,options= list(lengthMenu = c(5, 10, 20), pageLength = 5))
    
    })
  }

你想用示例代码做什么

1. 按“阅读”以阅读钻石并更新净度和表。显示数据表。

2. 对1中读入的数据更新filter1和filter2，数据也随之更新，显示数据表。

但是因为1的范围和2的范围不同，所以没有数据，出现错误。

警告：过滤器错误：找不到对象'diamond_df'

遇到这种情况我该怎么办？

Answer 1

也许您可以使用如下所示的 reactiveValues 对象。

library(shiny)
library(ggplot2)
library(shinyWidgets)
library(DT)

dataset <- diamonds
ui <- shinyUI(fluidPage(
  
  sidebarLayout(
    sidebarPanel(
      
      actionButton("read", "read_diamonds"),
      
      pickerInput('filter1',
                  'clarity_Filter1',
                  choices=c(""),
                  multiple  =  TRUE,
                  options = list(actionsBox=TRUE,liveSearch=TRUE),
      ),
      
      sliderInput('filter2','table_Filter2', min=0, max=100, value=c(10, 100))
      
    ),
    mainPanel(
      DTOutput("display")
    )
    
  )
)
)

#server.r

server <- function(input, output, session) {
  
  diamond <- reactiveValues( df=NULL )
  
  ###Initial setting
  observe({diamond$df <- diamonds})
  
  observeEvent(input$read, {
  
    
    df1 <- diamonds
    
    #filter1 update
    clarity_choise <- as.vector(unique(df1$clarity))
    
    updatePickerInput(
      session = session,
      inputId = "filter1",
      choices = clarity_choise,
      selected = clarity_choise,
      options = list(
        actionsBox  =  TRUE,
        liveSearch = TRUE
      ),
    )

    #filter2 update
    max_value <- max(as.numeric(df1$table))
    min_value <- min(as.numeric(df1$table))
    updateSliderInput(
      session = session,
      inputId = "filter2",
      max = max_value ,
      min = min_value ,
      value = c(min_value,max_value)
    )
    
  })
  
  #display
  output$display = renderDT(datatable(diamond$df),options= list(lengthMenu = c(5, 10, 20), pageLength = 5))
  
  #What to do if filter1,2 is updated
  observeEvent(c(input$filter1,
                 input$filter2),{
                   
                   #data update
                   if (is.null(input$filter1)) {
                     diamond$df <- diamonds %>% 
                            dplyr::filter( input$filter2[1]<table & input$filter2[2]>table)
                   }else{
                     diamond$df <- diamonds %>% dplyr::filter(clarity %in% input$filter1) %>% 
                       dplyr::filter( input$filter2[1]<table & input$filter2[2]>table)
                   } 
                   
                 }, ignoreInit = TRUE)
  
}

shinyApp(ui = ui, server = server)