r语言 - 在闪亮应用程序中并行处理

Question

我有一个闪亮的应用程序，可以生成带有美国各县特定特征信息的绘图地图。该应用程序运行良好，但每次调用地图时都需要 40 秒才能创建地图。我使用带有if的反应表达式（rt2）和elseif进行预处理有没有办法进行某种并行处理或类似的东西来节省时间？

数据集示例

County_ID County_Name        EQI    air_EQI  water_EQI   land_EQI sociod_EQI
      <int>       <chr>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>
1      1001 Autauga, AL  0.0041379  0.9553846 -1.1097280 -0.7065906  0.6704357
2      1003 Baldwin, AL  0.2002343  0.7179643 -0.5659107 -1.0842990  0.5530728
3      1005 Barbour, AL -0.9506388  0.1310074 -0.9780902 -1.2814700 -1.2362940
4      1007    Bibb, AL -1.0889200  0.0652890 -0.9681726 -0.8274103 -0.6000178
5      1009  Blount, AL -0.5139221  0.4021944 -0.7186447 -0.6229339  0.2965088
6      1011 Bullock, AL -2.0828290 -0.3091858 -1.4513350 -1.2580140 -1.8239700

UIR

library(plotly)
library(shiny)
library(tidyr)
library(maps)
library(shinydashboard)
library(viridis)
library(ggplot2)
ui <- navbarPage(
  title="Computational Modelling and Investigation of Neuropsychiatric Disease (MIND)",
  tabPanel("Home Tab",
           sidebarLayout(
             sidebarPanel(
               uiOutput("selectcol11"),
               uiOutput("selectsol11")
             ),
             mainPanel(plotlyOutput("plot2")))))

服务器.r

    server <- function(input, output) {
output$selectcol11<-renderUI({
        selectInput("selectcol11","Select Measure",choices = c("EQI","air_EQI","water_EQI",  
                                                                     "land_EQI","sociod_EQI","built_EQI","good_days","bad_days" ),selected = "EQI")
      })
 output$selectsol11<- renderUI({
            sliderInput("selectsol11", "Select Resolution",
                        min = 2, max = 7, value = 5,step =1)})

            rt2 <- reactive({
                if(input$selectcol11== "EQI"){
                foo <- cbind(data.frame(do.call('rbind', strsplit(as.character(df$County_Name),',',fixed=TRUE))),df[,c(1,3:10)])
                colnames(foo)[1] <- "County"
                colnames(foo)[2] <- "State"
                foo1 <- map_data("county")
                eqi1 <- foo %>%
                  group_by(County) %>%
                  summarise(EQI = sum(EQI))
                eqi10 <- foo %>%
                  group_by(County) %>%
                  summarise(County_ID = sum(County_ID))
                eqi<- cbind(eqi1,eqi10[,2])
                eqi$County <- tolower(eqi$County) # matching string
                foo1_eqi <- merge(foo1, eqi, by.x = "subregion", by.y = "County")
                foo1_eqi
                final.plot1<-foo1_eqi[order(foo1_eqi$order), ]
                final.plot1$County_Name_ID <- with(final.plot1, paste("County:",subregion, '<br>', "County_ID:", County_ID ))
                final.plot1
                }
                else if(input$selectcol11== "air_EQI"){
                  foo <- cbind(data.frame(do.call('rbind', strsplit(as.character(df$County_Name),',',fixed=TRUE))),df[,c(1,3:10)])
                  colnames(foo)[1] <- "County"
                  colnames(foo)[2] <- "State"
                  foo1 <- map_data("county")
                  eqi1 <- foo %>%
                    group_by(County) %>%
                    summarise(air_EQI = sum(air_EQI))
                  eqi10 <- foo %>%
                    group_by(County) %>%
                    summarise(County_ID = sum(County_ID))
                  eqi<- cbind(eqi1,eqi10[,2])
                  eqi$County <- tolower(eqi$County) # matching string
                  foo1_eqi <- merge(foo1, eqi, by.x = "subregion", by.y = "County")
                  foo1_eqi
                  final.plot1<-foo1_eqi[order(foo1_eqi$order), ]
                  final.plot1$County_Name_ID <- with(final.plot1, paste("County:",subregion, '<br>', "County_ID:", County_ID ))
                  final.plot1
                }
            })
            output$plot2 <- renderPlotly ({
                p<-  ggplot(rt2(), aes(x = long, y = lat, group=group,text=County_Name_ID,fill = cut_number(rt2()[,7],input$selectsol11)))
                p<- p+  geom_polygon(colour=alpha("black", 1/2))
                p<- p+coord_equal() 
                p<- p+  viridis::scale_fill_viridis(discrete = TRUE)
                p<- p+ scale_fill_discrete("Measure Density")
                ggplotly(p, tooltip = c("County_Name_ID"))
              })
            }

Answer 1

由于我无法重现该示例，因此我无法知道您的应用程序中需要什么时间。所以我建议你在运行应用程序时做一些分析：Rstudio中有一个直接可用的工具 -

->"开始分析"，然后运行您的应用程序

-> 加载应用程序后"停止分析"。

由此，您将能够看到花费最多时间（或内存）的部分是什么。根据您展示的内容，我建议这是应该花费最多计算时间的foo1 <- map_data("county")。因此，如果是这种情况，您应该了解如何加速此功能或找到另一个功能。对于map_data()，我认为实现并行处理无关紧要。