stat_density2d.R

 
# Simulate randomly-distributed data
nObs <- 5000
myData <- data.frame(X = rnorm(nObs), Y = rnorm(nObs))
nClusters <- 7  # Cluster it
kMeans <- kmeans(myData, centers = nClusters)
myData$Cluster <- as.factor(kMeans$cluster)
 
# Plot points colored by cluster
p1 <- ggplot(myData,
              aes(x = X, y = Y, colour = Cluster))
p1 <- p1 + geom_point()
print(p1)
 
# Illustration of stat_density2d, encoding density into alpha
p2 <- ggplot(myData,
              aes(x = X, y = Y))
p2 <- p2 + stat_density2d(aes(fill = Cluster, colour = Cluster,
                                alpha = ..level..),
                            geom = "polygon")
p2 <- p2 + scale_alpha(range = c(0, 1/2), guide = "none")  # Narrow alpha range
p2 <- p2 + coord_equal()
print(p2)
 
# Illustration of stat_density2d, encoding density into linewidth
p3 <- ggplot(myData,
              aes(x = X, y = Y))
p3 <- p3 + stat_density2d(aes(colour = Cluster,
                                size = ..level..),
                            fill = "transparent",
                            geom = "polygon")
p3 <- p3 + scale_size(range = c(0, 2), guide = "none")  # Narrow size/linewidth range
p3 <- p3 + coord_equal()
print(p3)
 
# Combination of both
p4 <- ggplot(myData,
              aes(x = X, y = Y))
p4 <- p4 + stat_density2d(aes(fill = Cluster, colour = Cluster,
                                alpha = ..level..,
                                size = ..level..),
                            geom = "polygon")
p4 <- p4 + scale_alpha(range = c(0, 1/2), guide = "none")  # Narrow alpha range
p4 <- p4 + scale_size(range = c(0, 3/2), guide = "none")  # Narrow size/linewidth range
p4 <- p4 + coord_equal()
print(p4)

ggsave("stat_density2d.jpeg", p4, width=5.5, height=5.5, dpi=600)