How to use selectizeGroupUI along with dplyr in R Shiny-CodePudding

I used an example from the official documentation of selectizeGroup-module I just replaced the data with my own. I use the same data transformations in renderplotly and renderDataTable as indicated in the example. I would like to change this so that the code doesn't repeat itself, I tried to use a recative function but it doesn't work.

My code below:

if (interactive()) {
  
  library(shiny)
  library(shinyWidgets)
  library(plotly)
  library(magrittr)
  library(dplyr)
  library(DT)
  library(lubridate)
  library(tidyr)
  
  DATE = rep(seq(as.Date('2018/01/01'), as.Date('2018/03/01'), by = "day"), each = 4, 3)
  CATEGORY = rep(c('aa', 'bb'), each = 360)
  SOURCE = rep(c("A", "B", "C", "D"), 180)
  REVENUE = as.numeric(sample(c(1000:2000), 720, replace = T)) 
  PLAN = 1500
  MONTH = months(DATE)
  df <- data.frame(DATE, MONTH, CATEGORY, SOURCE, REVENUE, PLAN)
  
  ui <- fluidPage(
    fluidRow(
      column(
        width = 10, offset = 1,
        tags$h3("Filter data with selectize group"),
        panel(
          selectizeGroupUI(
            id = "my-filters",
            params = list(
              MONTH = list(inputId = "MONTH", title = "MONTH:"),
              CATEGORY = list(inputId = "CATEGORY", title = "CATEGORY:"),
              SOURCE = list(inputId = "SOURCE", title = "SOURCE:")
            )
          ), status = "primary"
        ),
        DT::dataTableOutput(outputId = "table"),
        plotlyOutput("plot")
        
      )
    )
  )
  
  server <- function(input, output, session) {
    
    res_mod <- callModule(
      module = selectizeGroupServer,
      id = "my-filters",
      data = df,
      vars = c("MONTH", "CATEGORY", "SOURCE")
    )
    
    output$table <- DT::renderDataTable({
      req(res_mod())
      
      #I'd like to change this part to a shorter unique  solution
      mod_data <- res_mod()
      mod_data%>% 
        group_by(DATE, MONTH) %>%
        summarise(Revenue = sum(REVENUE),
                  Plan = sum(PLAN)) %>%
        ungroup() %>%
        mutate(Revenue_cum = cumsum(Revenue),
               Plan_cum = cumsum(Plan))
      ######################################################
      
      
    })
    
    output$plot <- renderPlotly({
      mod_data <- res_mod()
      
      #I'd like to change this part to a shorter unique solution
      mod_data%>% 
        group_by(DATE, MONTH) %>%
        summarise(Revenue = sum(REVENUE),
                  Plan = sum(PLAN)) %>%
        ungroup() %>%
        mutate(Revenue_cum = cumsum(Revenue),
               Plan_cum = cumsum(Plan))%>%
      ######################################################
        plot_ly(
          x = ~ DATE,
          y = ~ as.numeric(Revenue_cum),
          type = 'scatter',
          mode = 'lines markers'
        ) %>%
        add_trace(y = ~ as.numeric(Plan_cum),
                  mode = 'lines markers')
      
    })
    
  }
  
  shinyApp(ui, server)
  
}

CodePudding user response：

As you already tried or guessed you can move the whole data wrangling part into a reactive which could then be reused in both renderDataTable and renderPlotly:

``` r
library(shiny)
library(shinyWidgets)
library(plotly)
library(dplyr)
library(DT)

server <- function(input, output, session) {
  
  res_mod <- callModule(
    module = selectizeGroupServer,
    id = "my-filters",
    data = df,
    vars = c("MONTH", "CATEGORY", "SOURCE")
  )
  
  dat <- reactive({
    req(res_mod())
    
    res_mod() %>% 
      group_by(DATE, MONTH) %>%
      summarise(Revenue = sum(REVENUE),
                Plan = sum(PLAN)) %>%
      ungroup() %>%
      mutate(Revenue_cum = cumsum(Revenue),
             Plan_cum = cumsum(Plan))
  })
  
  output$table <- DT::renderDataTable({
    dat()  
  })
  
  output$plot <- renderPlotly({
    
    plot_ly(
      dat(),
      x = ~ DATE,
      y = ~ as.numeric(Revenue_cum),
      type = 'scatter',
      mode = 'lines markers'
    ) %>%
      add_trace(y = ~ as.numeric(Plan_cum),
                mode = 'lines markers')
    
  })
  
}

shinyApp(ui, server)
#> 
#> Listening on http://127.0.0.1:3563
#> `summarise()` has grouped output by 'DATE'. You can override using the
#> `.groups` argument.