Interactive overlays in Shiny

overshiny provides draggable and resizable rectangular elements that overlay plots in Shiny apps. This may be useful in applications where users need to define regions on the plot for further input or processing.

Let’s take a look at a simple user interface that includes two overlayToken()s, which are small labels that can be dragged onto the plot to create new overlays, and an overlayPlotOutput(), which is a plot where the overlays will appear:

library(shiny)
library(ggplot2)
library(overshiny)

# --- User interface ---
ui <- fluidPage(
    titlePanel("Overlay demo"),

    sidebarLayout(
        sidebarPanel(
            # Control whether overlays are displayed and whether they alter the plot
            checkboxInput("show_overlays", "Show overlays", value = TRUE),
            checkboxInput("enable_logic", "Enable overlay logic", value = TRUE),
            tags$hr(),

            # Select date range for the plot
            dateRangeInput("date_range", "Date range", start = "2025-01-01", end = "2025-12-31"),
            tags$hr(),

            # Overlay controls: tokens that can be dragged onto the plot
            h5("Drag tokens below onto the plot:"),
            overlayToken("grow", "Grow"),
            overlayToken("shrink", "Shrink")
        ),

        mainPanel(
            # Main plot with support for overlays
            overlayPlotOutput("plot", width = "100%", height = 300)
        )
    )
)

This sets up a sidebar layout, with controls on the left (including the overlay tokens) and a display area on the right, which includes the plot the overlays will be used with.

Now let’s put together our server function. We start by setting up the overlays:

# --- App logic ---
server <- function(input, output, session)
{
    # --- OVERLAY SETUP ---

    # Initialise 8 draggable/resizable overlays
    ov <- overlayServer("plot", 8, width = 56) # 56 days = 8 weeks default width

    # Reactive values to store custom per-overlay settings
    opt <- reactiveValues(
        type = rep("Grow", 8),        # type of overlay action
        strength = rep(50, 8)         # strength as a percentage
    )

    # Toggle overlay visibility based on checkbox
    observe({
        ov$show <- isTRUE(input$show_overlays)
    })

The call to overlayServer() initializes (up to) 8 overlays that we can use. It also sets the default width of new overlays to 56, which is in plot coordinates. We’ll be plotting a time series, so this means 56 days (8 weeks).

Then we create opt, a reactiveValues() object that we will use to store additional properties of the overlays. Here we’re going to keep track of a “type” for each overlay (“Grow” or “Shrink”) as well as a “strength” for each overlay (in percent).

Finally, we start with some of the reactive logic of the overlays. We have a checkbox in our UI to control whether the overlays are shown or not, and the call to observe() makes the overlays show or hide based on the value of this checkbox.

Continuing on:

    # --- OVERLAY DROPDOWN MENU ---

    # Render dropdown menu when an overlay is being edited
    output$plot_menu <- renderUI({
        i <- req(ov$editing)  # Current overlay being edited
        tagList(
            textOutput("dates"),
            selectInput("type", NULL, choices = c("Grow", "Shrink"), selected = ov$label[i]),
            sliderInput("strength", "Strength", min = 0, max = 100, value = opt$strength[i])
        )
    })

Each overlay automatically has a dropdown menu for adjusting settings for the overlay. By default, this only includes a “remove” button that can be used to remove the overlay. But we can add extra elements to these menus by using renderUI().

Since we created an overlayPlotOutput() with the output ID "plot", overshiny has also created a UI output slot named "plot_menu" which is used to add extra elements to each overlay’s dropdown menu. For our purposes, we’ll include a textOutput() element which shows the date range for the overlay, a selectInput() to choose between “Grow” and “Shrink” type overlays, and a sliderInput() to choose the percentage “strength” associated with the overlay.

The line i <- req(ov$editing) just gets the index (1 to 8) of the current overlay being edited. The call to req() ensures that the rest of the code in the renderUI() call won’t be run unless there is an overlay currently being edited via its dropdown menu.

Note that above, each overlay has the same elements in its dropdown menu, but we could choose to return different contents for the dropdown menu depending on which overlay is being edited.

Now let’s fill in some extra logic around those dropdown menus.

    # Display date range for the currently edited overlay
    output$dates <- renderText({
        i <- req(ov$editing)
        fmt <- function(t) format(as.Date(round(t), origin = "1970-01-01"), "%b %d")
        paste(fmt(ov$cx0[i]), "–", fmt(ov$cx1[i]))
    })

    # Update stored strength when the slider changes
    observeEvent(input$strength, {
        i <- req(ov$editing)
        opt$strength[i] <- input$strength
    })

    # Update stored type and overlay label when dropdown changes
    observeEvent(input$type, {
        i <- req(ov$editing)
        opt$type[i] <- input$type
        ov$label[i] <- input$type
    })

Above, we specify the “dates” label that will appear on the dropdown menus, and create some observeEvent()s that will update the strength and type elements of the opt reactiveValues() object we created earlier based on what is done in the dropdown menu. We also change the label of the overlay depending on what type is chosen from the menu.

Now let’s make some data to plot based on the overlays and their properties:

    # --- DATA PROCESSING BASED ON OVERLAY POSITION ---

    # Reactive dataset: oscillating signal modified by active overlays
    data <- reactive({
        date_seq <- seq(input$date_range[1], input$date_range[2], by = "1 day")
        y <- 1 + 0.5 * sin(as.numeric(date_seq) / 58)  # oscillating signal

        # Modify signal according to active overlays if logic is enabled
        if (isTRUE(input$enable_logic)) {
            for (i in which(ov$active)) {
                start <- as.Date(round(ov$cx0[i]), origin = "1970-01-01")
                end <- as.Date(round(ov$cx1[i]), origin = "1970-01-01")
                in_range <- date_seq >= start & date_seq <= end
                factor <- opt$strength[i] / 100
                y[in_range] <- y[in_range] * if (ov$label[i] == "Grow") (1 + factor) else (1 - factor)
            }
        }

        data.frame(date = date_seq, y = y)
    })

Above, we create a reactive() data.frame. We set up a sinusoidally-varying time series, then (if the “Enable overlay logic” checkbox is checked) we either “grow” or “shrink” this time series where it overlaps with each active overlay. We’re using both the ov object returned by overlayServer() and our own opt object to do this.

Finally, we render the time series:

    # --- RENDERING OF DATA ---

    # Render plot and align overlays to current axis limits
    output$plot <- renderPlot({
        plot <- ggplot(data()) +
            geom_line(aes(x = date, y = y)) +
            ylim(0, 3) +
            labs(x = NULL, y = "Signal")

        overlayBounds(ov, plot,
            xlim = c(input$date_range),
            ylim = c(0, NA))
    })
}

This just creates a ggplot() plot of the time series, and includes a call to overlayBounds() at the end of the renderPlot() expression block to ensure the overlays are aligned properly. overlayBounds() itself returns the plot so this also returns our plot object to Shiny to be plotted.

Now all that’s left is to run the app:

# --- Run app ---
if (interactive()) {
    shinyApp(ui, server)
}