R Shiny Make slider value dynamic

Question

I've got a dropdown selector and a slider scale. I want to render a plot with the drop down selector being the source of data. - I've got this part working

I simply want the slider's max value to change based on which dataset is selected.

Any suggestions?

server.R

library(shiny)
shinyServer(function(input, output) {

source("profile_plot.R")
load("test.Rdata")

output$distPlot <- renderPlot({
  if(input$selection == "raw") {
    plot_data <- as.matrix(obatch[1:input$probes,1:36])
  } else if(input$selection == "normalised") {
  plot_data <- as.matrix(eset.spike[1:input$probes,1:36])
  } 

  plot_profile(plot_data, treatments = treatment, sep = TRUE)
  })
})

ui.R library(shiny)

shinyUI(fluidPage(
  titlePanel("Profile Plot"),

  sidebarLayout(
    sidebarPanel(width=3,
    selectInput("selection", "Choose a dataset:", 
                 choices=c('raw', 'normalised')),
    hr(),
    sliderInput("probes",
              "Number of probes:",
              min = 2,
              max = 3540,
              value = 10)
    ),
    mainPanel(
      plotOutput("distPlot")
    )
  )
))

Answer 1

Hopefully this post will help someone learning Shiny:

The information in the answers is useful conceptually and mechanically, but doesn't help the overall question.

So the most useful feature I found in the UI API is conditionalPanel() here

This means I could create a slider function for each dataset loaded and get the max value by loading in the data initially in global.R. For those that don't know, objects loaded into global.R can be referenced from ui.R.

global.R - Loads in a ggplo2 method and test data objects (eset.spike & obatch)

source("profile_plot.R")
load("test.Rdata")

server.R -

library(shiny)
library(shinyIncubator)
shinyServer(function(input, output) {
  values <- reactiveValues()

  datasetInput <- reactive({
    switch(input$dataset,
           "Raw Data" = obatch,
           "Normalised Data - Pre QC" = eset.spike)
  })

  sepInput <- reactive({
    switch(input$sep,
           "Yes" = TRUE,
           "No" = FALSE)
  })

  rangeInput <- reactive({
    df <- datasetInput()
    values$range  <- length(df[,1])
    if(input$unit == "Percentile") {
      values$first  <- ceiling((values$range/100) * input$percentile[1])
      values$last   <- ceiling((values$range/100) * input$percentile[2])
    } else {
      values$first  <- 1
      values$last   <- input$probes      
    }
  })

  plotInput <- reactive({
    df     <- datasetInput()
    enable <- sepInput()
    rangeInput()
    p      <- plot_profile(df[values$first:values$last,],
                           treatments=treatment, 
                           sep=enable)
  })

  output$plot <- renderPlot({
    print(plotInput())
  })

  output$downloadData <- downloadHandler(
    filename = function() { paste(input$dataset, '_Data.csv', sep='') },
    content = function(file) {
      write.csv(datasetInput(), file)
    }
  )

  output$downloadRangeData <- downloadHandler(
    filename = function() { paste(input$dataset, '_', values$first, '_', values$last, '_Range.csv', sep='') },
    content = function(file) {
      write.csv(datasetInput()[values$first:values$last,], file)
    }
  )

  output$downloadPlot <- downloadHandler(
    filename = function() { paste(input$dataset, '_ProfilePlot.png', sep='') },
    content = function(file) {
      png(file)
      print(plotInput())
      dev.off()
    }
  )

})

ui.R

library(shiny)
library(shinyIncubator)

shinyUI(pageWithSidebar(
  headerPanel('Profile Plot'),
  sidebarPanel(
    selectInput("dataset", "Choose a dataset:", 
                choices = c("Raw Data", "Normalised Data - Pre QC")),

    selectInput("sep", "Separate by Treatment?:",
                choices = c("Yes", "No")),

    selectInput("unit", "Unit:",
                choices = c("Percentile", "Absolute")),


    wellPanel( 
      conditionalPanel(
        condition = "input.unit == 'Percentile'",
        sliderInput("percentile", 
                    label = "Percentile Range:",
                    min = 1, max = 100, value = c(1, 5))
      ),

      conditionalPanel(
        condition = "input.unit == 'Absolute'",
        conditionalPanel(
          condition = "input.dataset == 'Normalised Data - Pre QC'",
          sliderInput("probes",
                      "Probes:",
                      min = 1,
                      max = length(eset.spike[,1]),
                      value = 30)
        ),

        conditionalPanel(
          condition = "input.dataset == 'Raw Data'",
          sliderInput("probes",
                      "Probes:",
                      min = 1,
                      max = length(obatch[,1]),
                      value = 30)
        )
      )
    )
  ),

  mainPanel(
    plotOutput('plot'), 
    wellPanel(
      downloadButton('downloadData', 'Download Data Set'),
      downloadButton('downloadRangeData', 'Download Current Range'),
      downloadButton('downloadPlot', 'Download Plot')
    )
  )
))