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Examples: sketchit()

Source: vignettes/examples-sketchit 2.Rmd
examples-sketchit 2.Rmd

Introduction

sketchit() is designed to give users a more freeform drawing experience. Unlike drawit(), it does not require a one-to-one match between the drawing and the underlying data. Users can draw multiple lines, and the number of lines can be controlled within the function.

In this vignette, we demonstrate sketchit() in a variety of settings and highlight several options for customizing the user experience.

How to Use sketchit()

  • First, create a ggplot2 object using any combination of the following geoms:
    • geom_point
    • geom_smooth
    • geom_line
  • Then, pass that plot into sketchit(), either with a pipeline or by saving it as an object first
  • The output will display a graph with drawing controls. Click and drag inside the plot area to draw a line. Release your mouse to finish that line, then click again to begin a new one
    • The Reset button clears all drawings
    • The Undo button removes the most recently drawn line
    • The Done button locks the drawing so it can no longer be edited
    • The color buttons allow users to switch between drawing colors

Examples

sketchit() does not have the same data-matching requirements as drawit(), so it works well with a wide range of datasets.

Let’s start with a high-density dataset such as palmerpenguins.

(ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point()) |> 
  sketchit()

Now compare that to a lower-density dataset such as mtcars.

(ggplot(data = mtcars, aes(x = wt, y = mpg)) +
  geom_point()) |> 
  sketchit()

One advantage of sketchit() is that the drawing experience remains largely the same regardless of how dense the original data are. In contrast to drawit(), the user is sketching freely rather than drawing a value for each x-position. This also means that the output does not necessarily contain a user-drawn y-value for every x-value in the data.

Like drawit(), sketchit() is not a perfect replica of ggplot2. However, it does preserve much of the styling information from the ggplot object passed into it.

(ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point(color = "forestgreen") +
   labs(title = "Sketchit Penguins Example",
         subtitle = "Hope You Enjoy!",
         x = "Bill Length (mm)",
         y = "Bill Depth (mm)")
    ) |> 
  sketchit()

Aesthetic Customization

One of the strengths of sketchit() is that it allows for a variety of visual customizations that can change the overall drawing experience.

Color Options

By default, sketchit() includes a palette of “steelblue”, “orange”, “green”, and “red”. You can replace this palette by supplying your own values to the palette argument.

Note that the first color in the palette will be used as the initial drawing color.

(ggplot(data = mtcars, aes(x = wt, y = mpg)) +
    geom_line(size = 2) +
    labs(title = "Sketchit Cars Example",
         subtitle = "Hope You Enjoy!",
         x = "Weight",
         y = "Miles Per Gallon")
    ) |>
  sketchit(palette = c("red", 
                       "orange", 
                       "yellow", 
                       "green", 
                       "blue", 
                       "purple", 
                       "pink", 
                       "black"))
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

You can also change the initial drawing color with the starting_color argument. If that color is not already included in the palette, it will be added automatically.

(ggplot(data = mtcars, aes(x = wt, y = mpg)) +
    geom_smooth(method = "loess") +
    labs(title = "Sketchit Cars Example",
         subtitle = "Hope You Enjoy!",
         x = "Weight",
         y = "Miles Per Gallon")
    ) |>
  sketchit(starting_color = "violet")
#> `geom_smooth()` using formula = 'y ~ x'

If you prefer users to draw with only one color, you can turn off the color controls entirely with color_options = FALSE. This works especially well in combination with starting_color.

(ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point(color = "forestgreen") +
   labs(title = "Sketchit Penguins Example",
         subtitle = "Hope You Enjoy!",
         x = "Bill Length (mm)",
         y = "Bill Depth (mm)")
    ) |> 
  sketchit(starting_color = "violet", color_options = FALSE)

Multiple Geoms

Unlike drawit(), sketchit() is not limited to two geoms. Any combination of the supported geoms can be included in the same plot. However, all layers are displayed immediately. There is currently no way to delay the rendering of individual layers.

(ggplot(mtcars, aes(wt, mpg)) +
  geom_point(colour = "darkgreen") +
   geom_smooth(method = "lm", color = "blue") +
   geom_line(color = "orange")+
   ggtitle("This is a Title") +
  labs(title = "Sketchit Cars Example",
         subtitle = "Hope You Enjoy!",
         x = "Weight",
         y = "Miles Per Gallon")) |> 
  sketchit()

Stroke Width

It is also possible to increase the width of the lines the user draws with the stroke_width argument.

Changing the stroke width can noticeably affect the drawing experience. Thicker lines can make sketches feel bolder and easier to see, while thinner lines may feel more precise.

(ggplot(mtcars, aes(wt, mpg)) +
  geom_point(colour = "darkgreen") +
   geom_smooth(method = "lm", color = "blue") +
   ggtitle("This is a Title") +
  labs(title = "Sketchit Cars Example",
         subtitle = "Hope You Enjoy!",
         x = "Weight",
         y = "Miles Per Gallon")) |> 
  sketchit(stroke_width = 5)
#> `geom_smooth()` using formula = 'y ~ x'

Layout Controls

In some cases, the drawing controls may overlap with the plotted data. You can reposition the control buttons with the button_position argument.

This argument accepts a numeric vector of length 2 with values between 0 and 1. The default is c(1, 1), which places the buttons in the top-right corner of the graph. By contrast,c(0, 0) places them in the bottom-left corner.

For example, c(0.5, 0.5) places the controls in the center of the plot.

(ggplot(mtcars, aes(wt, mpg)) +
  geom_point(colour = "darkgreen") +
   ggtitle("This is a Title") +
  labs(title = "Sketchit Cars Example",
         subtitle = "Hope You Enjoy!",
         x = "Weight",
         y = "Miles Per Gallon")) |> 
  sketchit(button_position = c(0.5, 0.5))

Controlling the Number of Lines

When using sketchit() for graphical testing or structured input tasks, it may be useful to limit the number of lines a user can draw. In other cases, you may want to require a minimum number of lines before the user is allowed to click Done.

These behaviors can be controlled with the max_lines and min_lines arguments.

(ggplot(mtcars, aes(wt, mpg)) +
  geom_point(colour = "darkgreen") +
   ggtitle("This is a Title") +
  labs(title = "Sketchit Cars Example",
         subtitle = "Hope You Enjoy!",
         x = "Weight",
         y = "Miles Per Gallon")) |> 
  sketchit(max_lines = 3, min_lines = 2)

In this example, the user must draw at least two lines before clicking Done, and cannot draw more than three lines total.

This can be particularly useful in Shiny applications or testing settings where the number of submitted sketches matters.

Shiny Integration

The sketchit() function integrates directly with Shiny, enabling interactive drawing inputs to be captured and reused within reactive workflows. When used inside a Shiny application, sketchit() behaves differently than in static contexts. Instead of returning only an r2d3 widget, it returns a list, which contains:

  • youdrawit_plot: the interactive drawing widget
  • points: a reactive tibble containing the user’s drawn data

The points object is a reactive() expression that resolves to a tibble with:

  • x: user-drawn x-values
  • y: user-drawn y-values
  • color: the color of each user-drawn line
  • order: The chronological order in which lines were created (not accounting for undoes)
  • line_id: An identifier distinguishing separate lines

To enable communication between the browser and the Shiny server, you must supply the shiny_message_loc argument when using sketchit() in Shiny. This argument defines the input name used to send drawn data from the browser back to Shiny. The drawing interaction itself is handled in JavaScript, while Shiny runs in R on the server, so shiny_message_loc acts as the bridge between the two.

Example:

The following example demonstrates a minimal Shiny app that captures user-drawn points and returns them as a tibble

library(shiny)

# Define the User Interface (ui)
ui <- fluidPage(
  # Placeholder for the interactive drawit widget
  uiOutput("widget_ui")
)

# Define the Server Logic
server <- function(input, output, session) {
  # Create a ggplot object as the base plot for drawing
  p <- ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm)) +
    geom_point(size = 2)
  
  # This is the "bridge" between JavaScript (browser) and Shiny (R server)
  shiny_message_loc <- "scatter_points"
  
  # Initialize sketchit
   res <- sketchit(
    p, # the ggplot object
    shiny_message_loc = shiny_message_loc
    )
  # res contains:
    # - youdrawit_plot: the interactive plot
    # - points(): a reactive object containing the user-drawn points as a tibble
   
  # Render the widget in the UI placeholder
  output$widget_ui <- renderUI({
    res$youdrawit_plot
  })
  
  # observeEvent() watches res$points(), the reactive object
  # Once the user completes their drawing, res$points() is populated
  observeEvent(res$points(), {
    # Because points is reactive, call res$points() inside reactive code
    stopApp(res$points())  # stops the app and outputs the tibble of points
  })
}


# runApp() launches the app and returns the user-drawn points once complete
points <- runApp(shinyApp(ui, server))

After the user finishes drawing (by clicking Done), res$points() returns a tibble containing the recorded points from the user’s drawing. Each row corresponds to one captured point, along with information about which line it belongs to, the order in which it was drawn (including undoes), and the selected color. It might look something like this:

# A tibble: 4 x 5
      x     y color   order line_id
  <dbl> <dbl> <chr>   <int>   <int>
1  10.0  15.2 steelblue   0       1
2  10.5  15.4 steelblue   0       1
3  11.0  15.8 red         4       2
4  11.5  16.1 red         4       2

The exact number of rows will depend on the user’s drawing. The resulting x and y values will be on a similar scale to the original dataset, allowing for easy merges and comparisons.