# Learning ggplot2: 2D plot with histograms for each dimension

September 3, 2009
By

(This article was first published on bioCS, and kindly contributed to R-bloggers)

I have two 2D distributions and want to show on a 2D plot how they are related, but I also want to show the histograms (actually, density plots in this case) for each dimension. Thanks to ggplot2 and a Learning R post, I have sort of managed to do what I want to have:

There are still two problems: The overlapping labels for the bottom-right density axis, and a tiny bit of misalignment between the left side of the graphs on the left. I think that the dot in the labels for the density pushes the plot a tiny bit to the right compared with the 2D plot. Any ideas?

Here’s the code (strongly based on the afore-linked post on Learning R):

`p <- qplot(data = mtcars, mpg, hp, geom = "point", colour = cyl)p1 <- p + opts(legend.position = "none")p2 <- ggplot(mtcars, aes(x=mpg, group=cyl, colour=cyl))p2 <- p2 + stat_density(fill = NA, position="dodge")p2 <- p2 + opts(legend.position = "none", axis.title.x=theme_blank(),       axis.text.x=theme_blank())p3 <- ggplot(mtcars, aes(x=hp, group=cyl, colour=cyl))p3 <- p3 + stat_density(fill = NA, position="dodge") + coord_flip()p3 <- p3 + opts(legend.position = "none", axis.title.y=theme_blank(),       axis.text.y=theme_blank())legend <- p + opts(keep= "legend_box")## Plot Layout SetupLayout <- grid.layout( nrow = 2, ncol = 2,   widths = unit (c(2,1), c("null", "null")),   heights = unit (c(1,2), c("null", "null")) )vplayout <- function (...) { grid.newpage() pushViewport(viewport(layout= Layout))}subplot <- function(x, y) viewport(layout.pos.row=x, layout.pos.col=y)# Plottingvplayout()print(p1, vp=subplot(2,1))print(p2, vp=subplot(1,1))print(p3, vp=subplot(2,2))print(legend, vp=subplot(1,2))`

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...