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("display", 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("display", 8, width = 56, # 56 days = 8 weeks default width
        data = list(strength = 50), snap = snapGrid(),
        heading = dateHeading("%b %e"), select = TRUE)

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

The call to overlayServer() takes as its first argument the ID of the overlayPlotOutput() from the UI. Here, we initialize (up to) 8 overlays that we can use. We also set 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).

The data argument to overlayServer() is a list of additional attributes to be associated with each overlay. Here we’re specifying that each overlay will have an associated strength attribute, which we’ll use to determine how much each overlay affects the output. We also use snap = snapGrid() to specify a snapping function; the default parameters for snapGrid() ensure that each overlay’s position and width is snapped to the nearest whole number.

The next two arguments to overlayServer() relate to the overlay dropdown menus. 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 additional components to the dropdown in the call to overlayServer().

The heading argument to overlayServer() allows us to add a small heading to the top of each overlay’s dropdown menu; we are using the built-in dateHeading() function here to specify the format of the heading. In this case, "%b %e" translates to the abbreviated month name followed by the day of the month. So, for example, if the overlay extends over the x-axis range 1st January to 28th February, the heading will show "Jan 1 – Feb 28". Finally, the select = TRUE argument means that each dropdown menu will also allow us to change the type of the overlay, i.e. "Grow" or "Shrink".

After the call to overlayServer(), 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.

    # --- OVERLAY DROPDOWN MENU ---

    # Render dropdown menu when an overlay is being edited
    ov$menu <- function(ov, i) {
        list(
            sliderInput("display_strength", "Strength", min = 0, max = 100, value = ov$data$strength[i]),
            dateInput("display_cx", "Start date", value = ov$cx0[i]),
            sliderInput("display_cw", "Duration", min = 1, max = floor(ov$bound_cw), value = ov$cx1[i] - ov$cx0[i])
        )
    }

Here, we add some additional, custom components to the overlay dropdown menu by assigning a function to the variable ov$menu. We can also pass this function in as the menu argument to overlayServer(), but the two approaches are equivalent here.

For our purposes, we’ll add a sliderInput() to choose the percentage “strength” associated with the overlay. We also allow the user to manually enter the start date of each overlay ("display_cx") and the width of each overlay ("display_cw"). Here, "display_cx" and "display_cw" will automatically set the position and width of each overlay because "cx" and "cw" are interpreted specially by overshiny. "strength" doesn’t have any special interpretation so it will be applied to ov$data$strength. Note that the IDs of all these UI widgets start with "display_" because we gave our overlayPlotOutput() the ID "display" in the UI. See the documentation for overlayServer() for more details.

It’s important here to set the “starting value” for each of the three custom input widgets using values from the ov object. If we just supplied some “default” value for each of these, this value would reset each time we opened the dropdown menu.

In this example, 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 i is being edited.

    # --- 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(ov$cx0[i], origin = "1970-01-01")
                end <- as.Date(ov$cx1[i], origin = "1970-01-01")
                in_range <- date_seq >= start & date_seq <= end
                factor <- ov$data$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 the ov object returned by overlayServer() to do this.

    # --- RENDERING OF DATA ---

    # Render plot and align overlays to current axis limits
    output$display <- 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.

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