How can a data ellipse be superimposed on a ggplot2 scatterplot?

Maybe this could help you:

#bootstrap
set.seed(101)
n <- 1000
x <- rnorm(n, mean=2)
y <- 1.5 + 0.4*x + rnorm(n)
df <- data.frame(x=x, y=y, group="A")
x <- rnorm(n, mean=2)
y <- 1.5*x + 0.4 + rnorm(n)
df <- rbind(df, data.frame(x=x, y=y, group="B"))

#calculating ellipses
library(ellipse)
df_ell <- data.frame()
for(g in levels(df$group)){
df_ell <- rbind(df_ell, cbind(as.data.frame(with(df[df$group==g,], ellipse(cor(x, y), 
                                         scale=c(sd(x),sd(y)), 
                                         centre=c(mean(x),mean(y))))),group=g))
}
#drawing
library(ggplot2)
p <- ggplot(data=df, aes(x=x, y=y,colour=group)) + geom_point(size=1.5, alpha=.6) +
  geom_path(data=df_ell, aes(x=x, y=y,colour=group), size=1, linetype=2)

Output looks like this:

Here is more complex example.

Keelan Evanini, Ingrid Rosenfelder and Josef Fruehwald ([email protected]) have created a ggplot2 stat implementation of a 95% confidence interval ellipses (and an easier way to plot ellipses in ggplot2):

GitHub stat-ellipse.R

their site

You can use it as:

library(ggplot2)
library(devtools)
library(digest)
source_url("https://raw.github.com/low-decarie/FAAV/master/r/stat-ellipse.R")    
qplot(data=df, x=x, y=y, colour=colour)+stat_ellipse()

To create the data

set.seed(101)
n <- 1000
x <- rnorm(n, mean=2)
y <- 1.5 + 0.4*x + rnorm(n)
colour <- sample(c("first", "second"), size=n, replace=T)
df <- data.frame(x=x, y=y, colour=colour)