As a celebration of Florence Henderson’s 79th birthday (on February 14), I have created this scatterplot to use in my regression course.

The plot depicts the relationship between time spent on mathematics homework outside of school (expressed as z-scores) and mathematics achievement scores (expressed as T-scores, M=50, SD=10) for 200 8th-graders taken from the 1988 National Education Longitudinal Study. The color–in a display of very poor data science–is just randomly applied to the observations rather than meaning anything substantial. (Bloggers Note: I think it fits with the spirit of Valentine’s Day…a gratuitous, yet meaningless, gesture intended to make the receiver feel all gooshy.)

I created the plot using the Valentine package (available here) which applies a Valentine’s Day theme to ggplot. I also applied a picture of Cupid into the background of the plot and used hearts instead of points to plot the observations. Lastly, I changed the default color and fill on the regression smoother to more aptly fit the color scheme.

Below I will explain the how-to of creating this plot.

# Reading in the NELS Data

First, I read the NELS data into R. These data and its codebook are available via my regression course website.

# Using Hearts Instead of Points

I first needed to find an image of a heart that I liked. For icons of all sorts, I generally use The Noun Project. (This particular heart can be found here.) All of the images at The Noun Project are SVG files. This makes them very useful for display in browsers. To use them in ggplot, I converted the SVG image to a PNG file using the free image manipulation program GIMP (Perhaps you can use the SVG format directly without converting it, but I have never done that, so I don’t know.)

Using GIMP, I also replaced the black color to the hexadecimal color “ad97c6” by selecting Color > Map > Color Exchange. (Double-clicking the color swatch under “To Color” allows for color entry in hexadecimal.) After this I saved the heart as “heart1.png”, and repeated the process four more times, but using the colors “e58cbc” (heart2.png), “f2935b” (heart3.png), “9fc8b6” (heart4.png), and “eddc74” (heart5.png). (Note: These colors are the same colors I chose for the Valentine’s Day theme color and fill palettes and are based on the colors of the candy hearts you get in elementary school.)

I then used the png package to read the five PNG files into R. (Note: For anyone who doesn’t want to go through the hassle of coloring the hearts and reformatting both them and Cupid, I have made those files available for download here.)

library(png)

To randomly assign each observation to one of the five hearts (h1–h5), I used the sample() function inside the paste() function to concatenate the letter “h” and a random value from 1–5. I then used the do() function from the mosaic package to “do” this 200 times. Lastly, I appended this vector to the nels data frame, in the process, coercing it to characters (to be sure it isn’t appended as a factor–which will be needed later for use in the get() function).

library(mosaic)
heart <- do(200) * paste("h", sample(1:5, size = 1), sep="")
nels\$Heart <- as.character(heart[, 1])
#  ID   Homework   Math Heart
#1  1 -0.3329931 42.432    h4
#2  2 -0.2136822 53.698    h2
#3  3 -1.0077991 49.205    h2
#4  4  0.2059000 53.698    h1
#5  5 -0.1177185 55.980    h5
#6  6  0.1413540 65.331    h3

This is all that is needed until we get to actually creating the plot.

# Adding a Cupid into the Plot’s Background

I obtained the image of Cupid by doing a Google search on “Cupid, Public Domain”. (The actual image I used is available here.) Since the image was a JPEG, I again converted the image to a PNG (this time using Preview) and changed the image size to have a width of 800 pixels. I also used the Instant Alpha tool in Preview’s toolbar to make the white background transparent. As a sidebar, this could all be done in GIMP as well.

I again used the readPNG() function to read the image file into R, but this time setting the native= argument to FALSE. This will represent the image as an array rather than rasterizing it. I chose to do this so that I could make the image more transparent before rasterizing it.

w <- matrix(rgb(cupid[ , , 1], cupid[ , , 2], cupid[ , , 3], cupid[ , , 4] * 0.2), nrow = dim(cupid)[1])

# Creating the Plot

Finally, we are ready to create the plot. In the initial calls to ggplot, I use the rasterGrob() function from the grid package to rasterize Cupid which is placed in the plot using the annotation_custom() layer. The color= and fill= arguments to the geom_smooth() layer set the beautiful magenta/pink color for the regression smoother. The theme_valentine() layer sets ggplot’s theme to use some specialized fonts (see the Valentine package on GitHub). The size of the points in the geom_point() layer is set to 0, to leave a blank canvas for us to add our hearts. This is assigned into the object p.

library(ggplot2)
library(grid)
library(Valentine)
p <- ggplot(data = nels, aes(x = Homework, y = Math)) +
geom_point(size = 0) +
annotation_custom(rasterGrob(w)) +
theme_valentine() +
geom_smooth(method = "lm", color = "#ec008c", fill = "#ec008c", lwd = 1.5) +
ggtitle("HAPPY BIRTHDAY FLORENCE HENDERSON")

The hearts (points) are now added to the plot by cycling through a for loop. Each time we are going to rasterize the heart and add it to the plot. The optional arguments in the annotation_custom() layer set the horizontal and vertical position of the hearts in the plot.

for(i in 1:nrow(nels)){
p <- p + annotation_custom(
rasterGrob(get(nels\$Heart[i])),
xmin = nels\$Homework[i] - 0.5, xmax = nels\$Homework[i] + 0.5, ymin = nels\$Math[i] - 0.5, ymax = nels\$Math[i] + 0.5
)
}