Parallel processing in shiny app

[euthymios kasvikis]

i have a shiny app that produces plotly maps with info about specific characteristics of us counties. The app works fine but it takes 40sec in order to create a map every time i call one. I use a reactive expression (rt2) with if and else if in order to do the pre-processing Is there a way to do some kind of parallel processing or something like this to save time?

Sample of dataset

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

ui.r
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")))))
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"))
              }) 
            }