通过使用 observeEvent 的内存泄漏

Question

我使用以下代码累积内存。每次我在操作按钮 1 和 2 之间切换时，使用的内存都会增加。

library(ggplot2)
library(shiny)
library(lobstr)

ui <- navbarPage("Test",fluidPage(fluidRow(
                     column(width = 1, actionButton("action_input_1", label = "1")), 
                     column(width = 1, actionButton("action_input_2", label = "2")),
                     column(width = 10, plotOutput("plot", width = 1400, height = 800)))))

server <- function(input, output) {
  # 1
  observeEvent(input$action_input_1, {
    output$plot <- renderPlot({
      plot(rnorm(100))
    })
    print(cat(paste0("mem used 1: ", capture.output(print(mem_used())),"\n")))
  })

  # 2
  observeEvent(input$action_input_2, {
    output$plot <- renderPlot({
      plot(rnorm(1000))
    })
    print(cat(paste0("mem used 2: ", capture.output(print(mem_used())),"\n")))
  })
}
shinyApp(ui, server)

由于this 帖子中的建议，我尝试不使用observeEvent。这是服务器功能：

server <- function(input, output) {
  # 1
  output$plot <- renderPlot({
    input$action_input_1
    plot(rnorm(100))
    print(cat(paste0("mem used 1: ", capture.output(print(mem_used())),"\n")))
  })

  # 2
  output$plot <- renderPlot({
    input$action_input_2
    plot(rnorm(1000))
    print(cat(paste0("mem used 2: ", capture.output(print(mem_used())),"\n")))
  })
}

这里的内存没有增加，但只有第二个操作按钮（=最后一个代码块？）在工作。是否有防止内存泄漏并使两个按钮都工作的解决方案？

Answer 1

如何使用 reactiveVal ：

reactiveData <- reactiveVal(NULL)
observeEvent(input$action_input_1, reactiveData(rnorm(100)))
observeEvent(input$action_input_2, reactiveData(rnorm(1000)))
output$plot <- renderPlot(plot(reactiveData()))

响应式值的语法略有不同：

reactiveData <- reactiveValues(rnorm = NULL, bool_val = NULL) 

observeEvent(input$action_input_1,  {# reactiveData(rnorm(100), bool_val <- TRUE)) 
   reactiveData$rnorm <- rnorm(100)
   reactiveData$bool_val <- TRUE
})

observeEvent(input$action_input_2, { #reactiveData(rnorm(1000), bool_val <- FALSE)) 
  reactiveData$rnorm <- rnorm(1000)
  reactiveData$bool_val <-  FALSE
})

output$plot <- renderPlot(plot(reactiveData$rnorm))

虽然您的变量会同时发生变化，但从技术上讲，您仍然可以使用reactiveVal

reactiveData <- reactiveVal(list(rnorm = NULL, bool_val = NULL)) 

observeEvent(input$action_input_1, reactiveData(list(rnorm = 100, bool_val = TRUE)))

observeEvent(input$action_input_2, reactiveData(list(rnorm = 1000, bool_val = FALSE)))

output$plot <- renderPlot(plot(reactiveData()$rnorm))