smoothScatter with ggplot2

The motivation for this plot is the function: graphics::smoothScatter; Basically a plot of a two dimensional density estimator. In the following I want to reproduce the features with ggplot2.

smoothScatter

To have some data I draw some random numbers from a two dimensional normal distribution:

library(ggplot2)
library(MASS)
set.seed(2)
dat <- data.frame(
  mvrnorm(n=1000, mu=c(0, 0), Sigma=matrix(rep(c(1, 0.2), 2), nrow = 2, ncol = 2)))
names(dat) <- c("x", "y")

smoothScatter is basically a scatter plot with a two dimensional density estimation. This is nice especially in the case of a lot of observations and for outlier detection.

par(mfrow=c(1,2))
plot(dat$x, dat$y)
smoothScatter(dat$x, dat$y)

smoothScatter in ggplot2

OK, very pretty, lets reproduce this feature in ggplot2. First thing is to add the necessary layers, which I already mentioned is a two dimensional density estimation, combined with the geom called ‘tile’. Also I use the fill aesthetic to add colour and a different palette:

ggplot(data = dat, aes(x, y)) +
  stat_density2d(aes(fill = ..density..^0.25), geom = "tile", contour = FALSE, n = 200) +
  scale_fill_continuous(low = "white", high = "dodgerblue4")

I add one additional layer; a simple scatter plot. To make the points transparent I choose alpha to be 1/10 which is a relative quantity with respect to the number of observations.

last_plot() +
  geom_point(alpha = 0.1, shape = 20)

A similar approach is also discussed on StackOverflow. Actually that version is closer to smoothScatter.

Changing the theme

The last step is to tweak the theme-elements. Not that the following adds to any form of information but it looks nice. Starting from a standard theme, theme_classic, which is close to where I want to get, I get rid of all labels, axis and the legend.

last_plot() +
  theme_classic() +
  theme(
    legend.position = "none", 
    axis.line = element_blank(), 
    axis.ticks = element_blank(), 
    axis.text = element_blank(), 
    text = element_blank(), 
    plot.margin = unit(c(-1, -1, -1, -1), "cm")
  )

The last thing is to save the plot in the correct format for display:

ggsave(
  "../images/plotNo1.jpg", 
  plot = last_plot(), 
  width = 54.19, 
  height = 13.55, 
  dpi = 300, 
  units = "cm"
)

And that’s it, a nice picture which used to be a statistical graph.