In my course on the GLM, we are discussing residual plots this week. Given that it is also Halloween this Saturday, it seems like a perfect time to code up a residual plot made of ghosts.

Ghost plotThe process I used to create this plot is as follows:

  1. Find an icon that you want to use in place of the points on your scatterplot (or dot plot).

I used a ghost icon (created by Andrea Mazzini) obtained from The Noun Project. After downloading the icon, I used Preview to create a new PNG file that had cut out the citation text in the downloaded image. I will add the citation text at a later stage in the plot itself. This new icon was 450x450 pixels.

  1. Use ggplot to create a scatterplot of a set of data, making the size of the points 0.

Here is the code that will create the data and make the plot that I used.

plotData = data.frame(
  .fitted = c(76.5, 81.3, 75.5, 79.5, 80.1, 78.5, 79.5, 77.5, 81.2, 80.4, 78.1, 79.5, 76.6, 79.4, 75.9, 86.6, 84.2, 83.1, 82.4, 78.4, 81.6, 79.6, 80.4, 82.3, 78.6, 82.1, 76.6, 82.1, 87, 82.2, 82.1, 87.2, 80.5, 84.9, 78.5, 79, 78.5, 81.5, 77.4, 76.8, 79.4, 75.5, 80.2, 80.4, 81.5, 81.5, 80.5, 79.2, 82.2, 83, 78.5, 79.2, 80.6, 78.6, 85.9, 76.5, 77.5, 84.1, 77.6, 81.2, 74.8, 83.4, 80.4, 77.6, 78.6, 83.3, 80.4, 80.5, 80.4, 83.8, 85.1, 82.2, 84.1, 80.2, 75.7, 83, 81.5, 83.1, 78.3, 76.9, 82, 82.3, 85.8, 78.5, 75.9, 80.4, 82.3, 75.7, 73.9, 80.4, 83.2, 85.2, 84.9, 80.4, 85.9, 76.8, 83.3, 80.2, 83.1, 77.6),
  .stdresid = c(0.2, -0.3, 0.5, 1.4, 0.3, -0.2, 1.2, -1.1, 0.7, -0.1, -0.3, -1.1, -1.5, -0.1, 0, -1, 1, 0.3, -0.5, 0.5, 1.8, 1.6, -0.1, -1.3, -0.2, -0.9, 1.1, -0.2, 1.5, -0.3, -1.2, -0.6, -0.4, -3, 0.5, 0.3, -0.8, 0.8, 0.5, 1.3, 1.8, 0.5, -1.6, -2, -2.1, -0.8, 0.4, -0.9, 0.4, -0.4, 0.6, 0.4, 1.4, -1.4, 1.3, 0.4, -0.8, -0.2, 0.5, 0.7, 0.5, 0.1, 0.1, -0.8, -2.1, 0, 1.9, -0.5, -0.1, -1.4, 0.6, 0.7, -0.3, 1, -0.7, 0.7, -0.2, 0.8, 1.3, -0.7, -0.4, 1.5, 2.1, 1.6, -1, 0.7, -1, 0.9, -0.3, 0.9, -0.3, -0.7, -0.9, -0.2, 1.2, -0.8, -0.9, -1.7, 0.6, -0.5)
  )

library(ggplot2)

p = ggplot(data = plotData, aes(x = .fitted, y = .stdresid)) +
    theme_bw() + 
    geom_hline(yintercept = 0) +
    geom_point(size = 0) +
    theme_bw() +
    xlab("Fitted values") +
    ylab("Standarized Residuals") +
    annotate("text", x = 76, y = -3, label = "Ghost created by Andrea Mazzini from Noun Project")
  1. Read in the icon (which is a PNG file).

Here we use the readPNG() function from the png library to bring the icon into R.

library(png)
ghost = readPNG("/Users/andrewz/Desktop/ghost.png", TRUE)
  1. **Use a for() loop to add annotation_custom() layers (one for each point) that contain the image. **

The idea is that since we have saved our plot in the object p, we can add new layers (in our case each layer will be an additional point) by recursively adding the layer and then writing this into p. The pseudo-like code for this is:

for(i in 1:nrow(plotData)){
    p = p + 
      annotation_custom(
        our_image,
        xmin = minimum_x_value_for_the_image, 
        xmax = maximum_x_value_for_the_image, 
        ymin = minimum_y_value_for_the_image, 
        ymax = maximum_y_value_for_the_image
        ) 
    }

In order for the image to be plotted, we first have to make it plot-able by making it a graphical object, or GROB.

The rasterGrob() function (found in the grid,/b> package) renders a bitmap image (raster image) into a graphical object or GROB which can then be displayed at a specified location, orientation, etc. Read more about using Raster images in R here.

The arguments xmin, xmax, ymin, and ymax give the horizontal and vertical locations (in data coordinates) of the raster image. In our residual plot, we want the center of the image to be located at the coordinates (.fitted, .stdresid). In the syntax below, we add a small bit to the maximum values and subtract a small bit from the minimum values to force the icon into a box that will plot the icons a bit smaller than their actual size. (#protip: play around with this value until you get a plot that looks good.)

library(grid)

for(i in 1:nrow(plotData)){
    p = p + annotation_custom(
      rasterGrob(ghost),
      xmin = plotData$.fitted[i]-0.2, xmax = plotData$.fitted[i]+0.2, 
      ymin = plotData$.stdresid[i]-0.2, ymax = plotData$.stdresid[i]+0.2
      ) 
    }

Finally we print the plot to our graphics device using

print(p)

And the result is eerily pleasant!