Shiny: How to export table with both selectInput and manual input-CodePudding

I want to create a table that will take values from both selectizeInput and manual input. And then export to CVS. I have the table ready but when I tried to export(click on "CVS") it won't take the changes made by manual input. Below is an example of it. How to make the csv export take both selectizeInput and manual input? I also feel my code is a bit superfluous. Is there a simplified way to create the table? Thanks!

library(shiny)
library(shinydashboard)
library(DT)

ui <- dashboardPage(
  dashboardHeader(title = "test"),
  dashboardSidebar(),
dashboardBody(shiny::selectizeInput(inputId = "apple_lbs_filter",
                                    label = "Apple lbs",
                                    choices = c(1:10)),
              shiny::selectizeInput(inputId = "cherry_lbs_filter",
                                    label = "Cherry lbs",
                                    choices = c(1:10)),
              shiny::selectizeInput(inputId = "pineapple_lbs_filter",
                                    label = "Pineapple lbs",
                                    choices = c(1:10)),
              shiny::selectizeInput(inputId = "pear_lbs_filter",
                                    label = "Pear lbs",
                                    choices = c(1:10)),
              DT::DTOutput("fruit"),
              shiny::actionButton(inputId = "update_lbs",label = "Update lbs"),
              
              shiny::downloadButton(outputId = "export",label = "CSV"))
)

server <- function(input, output, session) {

  fruit_df <- shiny::reactiveValues()
  fruit_df$df <- data.frame(fruit_name = c("apple","cherry","pineapple","pear"),
                            fruit_lbs = c(2,5,6,3))  
  
  output$fruit <- DT::renderDT({
      DT::datatable(fruit_df$df,editable = TRUE)
    })
  
  lbs_newentry <- shiny::observe({
    if(input$update_lbs > 0) {
      lbs_newline <- shiny::isolate(c(
        input$apple_lbs_filter,
        input$cherry_lbs_filter,
        input$pineapple_lbs_filter,
        input$pear_lbs_filter
      ))
      shiny::isolate(fruit_df$df <- cbind(fruit_name = c("apple",
                                               "cherry",
                                               "pineapple",
                                               "pear"),
                                       fruit_lbs = lbs_newline))
    }
  })
  
  
  
  output[["export"]] <- shiny::downloadHandler(
    filename = function(){paste("fruit",Sys.Date(),".csv")},
    content = function(file){
      write.csv(fruit_df$df,file,row.names = FALSE)
    }
  )
}

shinyApp(ui, server)

CodePudding user response：

This is only a partial answer, because I've removed the selectizeInput() to try and condense the code per your request to simplify. Editing of the table can be done directly in the table. Additionally, I added the buttons extension to directly download the table as a csv. Hopefully this is helpful.

library(shiny)
library(shinydashboard)
library(DT)

ui <- dashboardPage(
  dashboardHeader(title = "test"),
  dashboardSidebar(),
  dashboardBody(h6("Table is editable"),
                DT::DTOutput("fruit", width = "400px"))
)

server <- function(input, output, session) {
  fruit_df <- reactiveValues("df" = data.frame(fruit_name = c("apple","cherry","pineapple","pear"),
                                                     fruit_lbs = c(2,5,6,3))  )
  
  output$fruit <- DT::renderDT({
    DT::datatable(fruit_df$df, editable = T,
                  extensions = 'Buttons', #Extra functions for the table
                  options = list("dom" = 'T<"clear">lBfrtip',
                                 buttons = "csv"
                  )
    )
  })

}

shinyApp(ui, server)

CodePudding user response：

Thanks to Silentdevildoll on the "'Buttons' extension of DT" advice I was able to achieve what I wanted. There are other suggestions on this board that I didn't get to explore yet. Thank you all for your valuable input!

library(shiny)
library(shinydashboard)
library(DT)

ui <- dashboardPage(
  dashboardHeader(title = "test"),
  dashboardSidebar(),
dashboardBody(shiny::selectizeInput(inputId = "apple_lbs_filter",
                                    label = "Apple lbs",
                                    choices = c(1:10)),
              shiny::selectizeInput(inputId = "cherry_lbs_filter",
                                    label = "Cherry lbs",
                                    choices = c(1:10)),
              shiny::selectizeInput(inputId = "pineapple_lbs_filter",
                                    label = "Pineapple lbs",
                                    choices = c(1:10)),
              shiny::selectizeInput(inputId = "pear_lbs_filter",
                                    label = "Pear lbs",
                                    choices = c(1:10)),
              shiny::actionButton(inputId = "update_lbs",label = "Update lbs"),
              DT::DTOutput("fruit"))
)

server <- function(input, output, session) {

  fruit_df <- shiny::reactiveValues()
  fruit_df$df <- data.frame(fruit_name = c("apple","cherry","pineapple","pear"),
                            fruit_lbs = c(2,5,6,3))  
  
  output$fruit <- DT::renderDT({
      DT::datatable(fruit_df$df,editable = TRUE,extensions = 'Buttons',options = list(
        dom = 'frtBip',
        buttons = c('csv')
      ))
    })
  
  lbs_newentry <- shiny::observe({
    if(input$update_lbs > 0) {
      lbs_newline <- shiny::isolate(c(
        input$apple_lbs_filter,
        input$cherry_lbs_filter,
        input$pineapple_lbs_filter,
        input$pear_lbs_filter
      ))
      shiny::isolate(fruit_df$df <- cbind(fruit_name = c("apple",
                                               "cherry",
                                               "pineapple",
                                               "pear"),
                                       fruit_lbs = lbs_newline))
    }
  })
}

shinyApp(ui, server)