One of my favorite tools for exploratory analysis is pairs()
, however in the case of a limited number of discrete values, it falls flat as the dots all align perfectly. Consider the following:
y <- t(rmultinom(n=1000,size=4,prob=rep(.25,4)))
pairs(y)
It doesn't really give a good sense of correlation. Is there an alternative plot style that would?
If you change y to a data.frame you can add some 'jitter' and with the col option you can set the transparency level (the 4th number in rgb):
y <- data.frame(y)
pairs(sapply(y,jitter), col = rgb(0,0,0,.2))
Or you could use ggplot2's plotmatrix:
library(ggplot2)
plotmatrix(y) + geom_jitter(alpha = .2)
Edit: Since plotmatrix in ggplot2 is deprecated use ggpairs (GGally package mentioned in @hadley's comment above)
library(GGally)
ggpairs(y, lower = list(params = c(alpha = .2, position = "jitter")))
Here is an example using corrplot
:
M <- cor(y)
corrplot.mixed(M)
You can find more examples in the intro
http://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html
Here are a couple of options using ggplot2:
library(ggplot2)
## re-arrange data (copied from plotmatrix function)
prep.plot <- function(data) {
grid <- expand.grid(x = 1:ncol(data), y = 1:ncol(data))
grid <- subset(grid, x != y)
all <- do.call("rbind", lapply(1:nrow(grid), function(i) {
xcol <- grid[i, "x"]
ycol <- grid[i, "y"]
data.frame(xvar = names(data)[ycol], yvar = names(data)[xcol],
x = data[, xcol], y = data[, ycol], data)
}))
all$xvar <- factor(all$xvar, levels = names(data))
all$yvar <- factor(all$yvar, levels = names(data))
return(all)
}
dat <- prep.plot(data.frame(y))
## plot with transparent jittered points
ggplot(dat, aes(x = x, y=y)) +
geom_jitter(alpha=.125) +
facet_grid(xvar ~ yvar) +
theme_bw()
## plot with color representing density
ggplot(dat, aes(x = factor(x), y=factor(y))) +
geom_bin2d() +
facet_grid(xvar ~ yvar) +
theme_bw()