R Shiny Make slider value dynamic - r

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

As #Edik noted the best way to do this would be to use an update.. type function. It looks like updateSliderInput doesnt allow control of the range so you can try using renderUI on the server side:
library(shiny)
runApp(list(
ui = bootstrapPage(
numericInput('n', 'Maximum of slider', 100),
uiOutput("slider"),
textOutput("test")
),
server = function(input, output) {
output$slider <- renderUI({
sliderInput("myslider", "Slider text", 1,
max(input$n, isolate(input$myslider)), 21)
})
output$test <- renderText({input$myslider})
}
))

Answer 2

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')
)
)
))

Answer 3

I think you're looking for the updateSliderInput function that allows you to programmatically update a shiny input:
http://shiny.rstudio.com/reference/shiny/latest/updateSliderInput.html. There are similar functions for other inputs as well.
observe({
x.dataset.selection = input$selection
if (x.dataset.selection == "raw") {
x.num.rows = nrow(obatch)
} else {
x.num.rows = nrow(eset.spike)
}
# Edit: Turns out updateSliderInput can't do this,
# but using a numericInput with
# updateNumericInput should do the trick.
updateSliderInput(session, "probes",
label = paste("Slider label", x.dataset.selection),
value = c(1,x.num.rows))
})

Answer 4

Another alternative can be applying a renderUI approach like it is described in one of the shiny gallery examples:
http://shiny.rstudio.com/gallery/dynamic-ui.html