When we want to compare the distributions of two variables in a scatterplot, sometimes it is hard to see the marginal distributions.
To observe the marginal distributions more clearly, we can add “rugs” using the rug function.
A rug is a one-dimensional density plot drawn on the axis of a plot.
Let's start with some data for two groups.
set.seed(1)
x1 <- rnorm(1000)
x2 <- rbinom(1000, 1, 0.7)
y <- x1 + 5 * x2 + 3 * (x1 * x2) + rnorm(1000, 0, 3)
We can plot the scatterplot for each group separately in red and blue.
We can then add some marginal “rugs” to each side. We could do this for all the data or separately for each group.
To do it separately for each group, we need to specify the line parameter so that the rugs don't overwrite each other.
plot(x1[x2 == 1], y[x2 == 1], col = "tomato3", xaxt = "n", yaxt = "n", xlab = "",
ylab = "", bty = "n")
points(y[x2 == 0] ~ x1[x2 == 0], col = "royalblue3")
# x-axis rugs for each group
rug(x1[x2 == 1], side = 1, line = 0, col = "tomato1", tck = 0.01)
rug(x1[x2 == 0], side = 1, line = 0.5, col = "royalblue1", tck = 0.01)
# y-axis rugs for each group
rug(y[x2 == 1], side = 2, line = 0, col = "tomato1", tck = 0.01)
rug(y[x2 == 0], side = 2, line = 0.5, col = "royalblue1", tck = 0.01)
# Note: The `tck` parameter specifies how tall the rug is. A shorter rug
# uses less ink to communicate the same information.
axis(1, line = 1)
axis(2, line = 1)
The last two lines add some axes a little farther out than they normally would be on the plots.
We might also want to add some more descriptives to the plot. For example, the marginal means for each group as a small black line of the rugs:
plot(x1[x2 == 1], y[x2 == 1], col = "tomato3", xaxt = "n", yaxt = "n", xlab = "",
ylab = "", bty = "n")
points(y[x2 == 0] ~ x1[x2 == 0], col = "royalblue3")
rug(x1[x2 == 1], side = 1, line = 0, col = "tomato1", tck = 0.01)
rug(x1[x2 == 0], side = 1, line = 0.5, col = "royalblue1", tck = 0.01)
rug(y[x2 == 1], side = 2, line = 0, col = "tomato1", tck = 0.01)
rug(y[x2 == 0], side = 2, line = 0.5, col = "royalblue1", tck = 0.01)
axis(1, line = 1)
axis(2, line = 1)
# means(on x-axis rugs)
Axis(at = mean(x1[x2 == 1]), side = 1, line = 0, labels = "", col = "black",
lwd.ticks = 3, tck = 0.01)
Axis(at = mean(x1[x2 == 0]), side = 1, line = 0.5, labels = "", col = "black",
lwd.ticks = 3, tck = 0.01)
# means(on y-axis rugs)
Axis(at = mean(y[x2 == 1]), side = 2, line = 0, labels = "", col = "black",
lwd.ticks = 3, tck = 0.01)
Axis(at = mean(y[x2 == 0]), side = 2, line = 0.5, labels = "", col = "black",
lwd.ticks = 3, tck = 0.01)
As should be clear, the means of x1 are similar in both groups, but the means of y in each group differ considerably.
By combining the scatterplot with the rug, we are able to communicate considerable information with little ink.