R: compute correlation by group

Question

In R, I have a data frame comprising a class label C (a factor) and two measurements, M1 and M2. How do I compute the correlation between M1 and M2 within each class?

Ideally, I'd get back a data frame with one row for each class and two columns: the class label C and the correlation.

Answer 1

The package plyr is the way to go.

Here is a simple solution:

xx <- data.frame(group = rep(1:4, 100), a = rnorm(400) , b = rnorm(400) )
head(xx)

require(plyr)
func <- function(xx)
{
return(data.frame(COR = cor(xx$a, xx$b)))
}

ddply(xx, .(group), func)

The output will be:

  group         COR
1     1  0.05152923
2     2 -0.15066838
3     3 -0.04717481
4     4  0.07899114

Answer 2

If you are inclined to use functions in the base package, you can use the by function, then reassemble the data:

xx <- data.frame(group = rep(1:4, 100), a = rnorm(400) , b = rnorm(400) )
head(xx)

# This returns a "by" object
result <- by(xx[,2:3], xx$group, function(x) {cor(x$a, x$b)})

# You get pretty close to what you want if you coerce it into a data frame via a matrix
result.dataframe <- as.data.frame(as.matrix(result))

# Add the group column from the row names
result.dataframe$C <- rownames(result)

Answer 3

Another example using base packages and Tal's example data:

DataCov <- do.call( rbind, lapply( split(xx, xx$group),
             function(x) data.frame(group=x$group[1], mCov=cov(x$a, x$b)) ) )