renderCachedPlot: Plot output with cached images

Description

Renders a reactive plot, with plot images cached to disk. As of Shiny 1.6.0, this is a shortcut for using bindCache() with renderPlot().

Usage

renderCachedPlot(
  expr,
  cacheKeyExpr,
  sizePolicy = sizeGrowthRatio(width = 400, height = 400, growthRate = 1.2),
  res = 72,
  cache = "app",
  ...,
  alt = "Plot object",
  outputArgs = list(),
  width = NULL,
  height = NULL
)

Arguments

expr: An expression that generates a plot.
cacheKeyExpr: An expression that returns a cache key. This key should be a unique identifier for a plot: the assumption is that if the cache key is the same, then the plot will be the same.
sizePolicy: A function that takes two arguments, width and height, and returns a list with width and height. The purpose is to round the actual pixel dimensions from the browser to some other dimensions, so that this will not generate and cache images of every possible pixel dimension. See sizeGrowthRatio() for more information on the default sizing policy.
res: The resolution of the PNG, in pixels per inch.
cache: The scope of the cache, or a cache object. This can be "app" (the default), "session", or a cache object like a cachem::cache_disk(). See the Cache Scoping section for more information.
...: Arguments to be passed through to plotPNG(). These can be used to set the width, height, background color, etc.
alt: Alternate text for the HTML <img> tag if it cannot be displayed or viewed (i.e., the user uses a screen reader). In addition to a character string, the value may be a reactive expression (or a function referencing reactive values) that returns a character string. If the value is NA (the default), then ggplot2::get_alt_text() is used to extract alt text from ggplot objects; for other plots, NA results in alt text of "Plot object". NULL or "" is not recommended because those should be limited to decorative images.
outputArgs: A list of arguments to be passed through to the implicit call to plotOutput() when renderPlot is used in an interactive R Markdown document.
width, height: not used. They are specified via the argument sizePolicy.

Interactive plots

renderCachedPlot can be used to create interactive plots. See plotOutput() for more information and examples.

Details

expr is an expression that generates a plot, similar to that in renderPlot. Unlike with renderPlot, this expression does not take reactive dependencies. It is re-executed only when the cache key changes.

cacheKeyExpr is an expression which, when evaluated, returns an object which will be serialized and hashed using the rlang::hash() function to generate a string that will be used as a cache key. This key is used to identify the contents of the plot: if the cache key is the same as a previous time, it assumes that the plot is the same and can be retrieved from the cache.

This cacheKeyExpr is reactive, and so it will be re-evaluated when any upstream reactives are invalidated. This will also trigger re-execution of the plotting expression, expr.

The key should consist of "normal" R objects, like vectors and lists. Lists should in turn contain other normal R objects. If the key contains environments, external pointers, or reference objects --- or even if it has such objects attached as attributes --- then it is possible that it will change unpredictably even when you do not expect it to. Additionally, because the entire key is serialized and hashed, if it contains a very large object --- a large data set, for example --- there may be a noticeable performance penalty.

If you face these issues with the cache key, you can work around them by extracting out the important parts of the objects, and/or by converting them to normal R objects before returning them. Your expression could even serialize and hash that information in an efficient way and return a string, which will in turn be hashed (very quickly) by the rlang::hash() function.

Internally, the result from cacheKeyExpr is combined with the name of the output (if you assign it to output$plot1, it will be combined with "plot1") to form the actual key that is used. As a result, even if there are multiple plots that have the same cacheKeyExpr, they will not have cache key collisions.

Examples

Run this code

## Only run examples in interactive R sessions
if (interactive()) {

# A basic example that uses the default app-scoped memory cache.
# The cache will be shared among all simultaneous users of the application.
shinyApp(
  fluidPage(
    sidebarLayout(
      sidebarPanel(
        sliderInput("n", "Number of points", 4, 32, value = 8, step = 4)
      ),
      mainPanel(plotOutput("plot"))
    )
  ),
  function(input, output, session) {
    output$plot <- renderCachedPlot({
        Sys.sleep(2)  # Add an artificial delay
        seqn <- seq_len(input$n)
        plot(mtcars$wt[seqn], mtcars$mpg[seqn],
             xlim = range(mtcars$wt), ylim = range(mtcars$mpg))
      },
      cacheKeyExpr = { list(input$n) }
    )
  }
)



# An example uses a data object shared across sessions. mydata() is part of
# the cache key, so when its value changes, plots that were previously
# stored in the cache will no longer be used (unless mydata() changes back
# to its previous value).
mydata <- reactiveVal(data.frame(x = rnorm(400), y = rnorm(400)))

ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      sliderInput("n", "Number of points", 50, 400, 100, step = 50),
      actionButton("newdata", "New data")
    ),
    mainPanel(
      plotOutput("plot")
    )
  )
)

server <- function(input, output, session) {
  observeEvent(input$newdata, {
    mydata(data.frame(x = rnorm(400), y = rnorm(400)))
  })

  output$plot <- renderCachedPlot(
    {
      Sys.sleep(2)
      d <- mydata()
      seqn <- seq_len(input$n)
      plot(d$x[seqn], d$y[seqn], xlim = range(d$x), ylim = range(d$y))
    },
    cacheKeyExpr = { list(input$n, mydata()) },
  )
}

shinyApp(ui, server)


# A basic application with two plots, where each plot in each session has
# a separate cache.
shinyApp(
  fluidPage(
    sidebarLayout(
      sidebarPanel(
        sliderInput("n", "Number of points", 4, 32, value = 8, step = 4)
      ),
      mainPanel(
        plotOutput("plot1"),
        plotOutput("plot2")
      )
    )
  ),
  function(input, output, session) {
    output$plot1 <- renderCachedPlot({
        Sys.sleep(2)  # Add an artificial delay
        seqn <- seq_len(input$n)
        plot(mtcars$wt[seqn], mtcars$mpg[seqn],
             xlim = range(mtcars$wt), ylim = range(mtcars$mpg))
      },
      cacheKeyExpr = { list(input$n) },
      cache = cachem::cache_mem()
    )
    output$plot2 <- renderCachedPlot({
        Sys.sleep(2)  # Add an artificial delay
        seqn <- seq_len(input$n)
        plot(mtcars$wt[seqn], mtcars$mpg[seqn],
             xlim = range(mtcars$wt), ylim = range(mtcars$mpg))
      },
      cacheKeyExpr = { list(input$n) },
      cache = cachem::cache_mem()
    )
  }
)

}

if (FALSE) {
# At the top of app.R, this set the application-scoped cache to be a memory
# cache that is 20 MB in size, and where cached objects expire after one
# hour.
shinyOptions(cache = cachem::cache_mem(max_size = 20e6, max_age = 3600))

# At the top of app.R, this set the application-scoped cache to be a disk
# cache that can be shared among multiple concurrent R processes, and is
# deleted when the system reboots.
shinyOptions(cache = cachem::cache_disk(file.path(dirname(tempdir()), "myapp-cache")))

# At the top of app.R, this set the application-scoped cache to be a disk
# cache that can be shared among multiple concurrent R processes, and
# persists on disk across reboots.
shinyOptions(cache = cachem::cache_disk("./myapp-cache"))

# At the top of the server function, this set the session-scoped cache to be
# a memory cache that is 5 MB in size.
server <- function(input, output, session) {
  shinyOptions(cache = cachem::cache_mem(max_size = 5e6))

  output$plot <- renderCachedPlot(
    ...,
    cache = "session"
  )
}

}