How to download graphs which are dynamic in R Shiny?

Shiny Modules [*] would be a neat possibility here.

Note. I did not fully understand what you tried with your dynamic checkboxGroup, so I replaced it by a static one. Also I was not quite clear what you want to plot in particular. This is however anyways not crucial to the problem at hand, which can be described as follows

Download a dynamic amount of figures in one file.

So here we go, explanation below.

Setup

library(shiny)
library(dplyr)
library(gridExtra)

d <- data.frame(
   year         = c(1995, 1995, 1995, 1996, 1996, 1996, 1997, 1997, 1997),
   Product_Name = c("Table", "Chair", "Bed", "Table", "Chair", "Bed", "Table",
                    "Chair", "Bed"),
   Product_desc = rep(LETTERS[24:26], each = 3),
   Cost         = c(1, 2, 3, 4, 2, 3, 4, 5, 6)
)

Shiny Modules

plot_ui <- function(id) {
   ns <- NS(id)
   plotOutput(ns("graph"))
}

plot_server <- function(input, output, session, my_data, graph_type) {
   
   get_graph <- reactive({
      base_plot <- ggplot(my_data,
                          aes(Product_Name, Cost)) +
         theme(strip.placement = "outside") +
         theme_bw()
      if (graph_type() == "b") {
         res <- base_plot +
            geom_col(aes(fill = Product_desc),
                     position = position_dodge(preserve = "single")) +
            facet_wrap(~year)
      } else if (graph_type() == "p") {
         res <- base_plot +
            geom_point()
      }
      res
   })
   
   output$graph <- renderPlot({
      get_graph()
   })
   
   list(graph = get_graph)
}

Main App

ui <- fluidPage(
   titlePanel("Modules to the Rescue!"),
   sidebarLayout(
      sidebarPanel(
         radioButtons(
            "type",
            "Graph Type",
            c(Bars = "b", Points = "p")
         ),
         checkboxGroupInput("selector",
                            "Year",
                            choices = unique(d$year)),
         downloadButton("download", "Download Graphs")
         ),
      mainPanel(div(id = "container", div("test content")))
   )
)

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

   ## store active plot handlers
   all_plots <- reactiveVal()
   
   ## counter to ensure unique ids for the module uis
   cnt <- reactiveVal(0)
   
   ## when we change selector draw plots anew
   observe({
      ## remove all existing plots
      removeUI("#container *", immediate = TRUE, multiple = TRUE)
      ## for each selection create a new plot
      ## SIDE EFFECT: create the UI
      handlers <- lapply(input$selector, function(x) {
         cnt(isolate(cnt()) + 1)
         my_dat <- d %>%
            dplyr::filter(year == x)
         new_id <- paste("plot", isolate(cnt()))
         insertUI("#container", ui = plot_ui(new_id))
         callModule(plot_server, new_id, 
                    my_data = my_dat, 
                    graph_type = reactive(input$type))
      })
      all_plots(handlers)
   })
   
   output$download <- downloadHandler(
      filename = function() {
         paste0("plots-", Sys.Date(), ".png")
      }, content = function(file) {
         my_plots <- all_plots()
         ggsave(file,
                plot = marrangeGrob(lapply(my_plots, function(handle) handle$graph()),
                                    ncol = 1, nrow = length(my_plots)))
      }
   )
}

shinyApp(ui, server)

Explanation

(The linked document describes in depth what modules are doing so I focus on I used them, rather on how they work in general.)

1. We create a module whihc does the plotting for us.
2. The module creates a reactive which produces the plot.
3. This reactive is used twice: once in the renderPlot function to render the plot, and once as a return parameter of the module.
4. In the main app, we keep track about all created modules (all_plots), through which we can communicate with the model and in particular to retrieve the plot.
5. To draw the plots, we listen to the checkboxGroup and whenever there is a change we dynamically remove all plots, and add them afresh and update all_plots through which we can in the last step retrieve the plots for the downloadHandler.
6. In the downloadHandler we loop through all plots and use gridExtra::marrange to put all of the ggplots into one file via ggsave.

[*] Note that I still use the old callModule syntax as I have noi yet upgraded shiny.

I had to adjust your data as product_desc was not clearly available for each year. I defined it as Product_desc = c("X", "Y", "Z", "X", "Y", "Z", "X", "Y", "Z") Then a reactive dataframe was defined. Next you need to create an object to save. Lastly, you need to place download buttons. Download handler will let you download. You can enhance it further by changing how facets are displayed.

The following code generates the required output:

ui <- shinyUI(fluidPage(
  useShinydashboard(),
  tabPanel(
    "Plot",
    sidebarLayout(
      sidebarPanel(
        uiOutput('checkbox'),
        #width = 2,
        position = "bottom"),
      mainPanel(#uiOutput("graph"),
                plotOutput("mygraph"),
                #DT::dataTableOutput("testtable"),
                uiOutput("saveplotsbtn")
                )

    )
  )
))

server <- function(input, output, session) {
  session_store <- reactiveValues() 
  output$checkbox <- renderUI({
    checkboxGroupInput("year", "year", choices = (unique(d$year)))
  })

  output$graph <- renderUI({
    # create tabPanel with datatable in it
    req(input$year)
    tabPanel("Plots",
             fluidRow(lapply(as.list(paste0("plot", seq_along(input$year))), plotOutput)))

  })

  observe(lapply(length(input$year), function(i) {
    #because expressions are evaluated at app init
    #print("I am in Render")
    output[[paste0("plot", i)]] <- renderPlot({
      #print ("bbb")
      if (length(input$year) > 0) {
        d %>%
          ggplot(aes(Product_Name, Cost)) +
          geom_col(aes(fill = Product_desc),
                   position = position_dodge(preserve = "single")) +
          facet_wrap( ~ input$year[i],
                      scales = "free_x",
                      strip.position = "bottom") +
          theme(strip.placement = "outside") +
          theme_bw()
      }
    })

  }))

  output$saveplotsbtn <-  renderUI({
    tagList(
      div(style="display: block; height: 20px; width: 5px;",HTML("<br>")),
      div(style="display: inline; padding: 50px; color: #ad1d28; font-size: 28px ; width: 190px;",HTML("Save Graph as <br>")),
      div(style="display: block; padding: 5px 350px 15px 50px ;",
          downloadBttn("savePDF",
                       HTML(" PDF"),
                       style = "fill",
                       color = "danger",
                       size = "lg",
                       block = TRUE,
                       no_outline = TRUE
          ) ),
      div(style="display: block; width: 5px;",HTML("<br>")),
      div(style="display: block; padding: 5px 350px 15px 50px;",
          downloadBttn("savePNG",
                       label= " PNG",
                       style = "fill",
                       color = "warning",
                       size = "lg",
                       block = TRUE,
                       no_outline = TRUE
          ) )
    )
  })

  mydf <- eventReactive(input$year ,{
    req(input$year)
    data <- d[d$year %in% input$year,]
    data
  })

  output$testtable <- DT::renderDataTable(
    mydf(),
    class = "display nowrap compact", 
    options = list(  # options
      scrollX = TRUE # allow user to scroll wide tables horizontally
    )
  )

  output$mygraph <- renderPlot({
    if(is.null(mydf())){
      myplot <- NULL
    }
    else{
      myplot <- ggplot(data=mydf(), aes(Product_Name, Cost, fill = Product_desc)) +
        geom_bar(#aes(fill = factor(Product_desc)),
                 stat = "identity" , # position = "dodge",
                 position = position_dodge(preserve = "single")) +
        facet_wrap( ~ year,
                    scales = "free_x",
                    strip.position = "bottom") +
        theme(strip.placement = "outside") +
        theme_bw()
    }
    session_store$plt <- myplot
    session_store$plt
  })

  output$savePNG <- downloadHandler(
    filename = function(){
      paste0('myplot', Sys.Date(), '.png', sep='')
    },

    content = function(file) {
      ggsave(file, plot = session_store$plt, width = 6, height = 5, dpi = 100, units = "in",
             device="png", path=input$file$datapath)
    }
  )

  output$savePDF <- downloadHandler(
    filename = function(){
      paste0('myplot', Sys.Date(), '.pdf', sep='')
    },

    content = function(file) {
      ggsave(file, plot = session_store$plt, width = 6, height = 5, dpi = 100, units = "in",
             device="pdf", path=input$file$datapath)
    }
  )

}

shinyApp(ui, server)

You get the following output: