I recently had to graph some data based on an interaction of factors and I found it more difficult than I felt something this common should be in R. I suspect I'm missing something. Let's say I have a vector of 30 numbers along with a pair of factors.
n <- runif(30, min=0, max=10)
a <- gl(2, 1, 30)
b <- gl(6, 2, 30)
And I want the mean for each combination of factors.
y <- tapply(n, a:b, mean)
Now I want to use a lattice xyplot to plot these means where I have a panel for each of the two values of a. The means are the y values and the b factors are the x values. The stock xyplot formula would be something like
xyplot( y ~ b | a, data=mydf)
where mydf is a data frame with columns y, b, and ,a that were computed from tapply above. But my problem is how to disentangle the interacting factors. This is what I did.
factorSplit <- strsplit(names(y), ":")
a1 <- sapply(factorSplit, function(x) {x[1]})
b1 <- sapply(factorSplit, function(x) {x[2]})
mydf <- data.frame(y, b1, a1)
Now mydf has
> mydf
y b1 a1
1:1 3.856797 1 1
1:2 3.487181 2 1
1:3 8.411425 3 1
1:4 3.757709 4 1
1:5 4.982970 5 1
1:6 6.480346 6 1
2:1 2.778864 1 2
2:2 4.390511 2 2
2:3 7.119926 3 2
2:4 4.707945 4 2
2:5 5.546894 5 2
2:6 8.984631 6 2
and I can plot with
xyplot(y ~ b1 | a1, mydf, layout=c(1,2))
But I feel this business with strsplit of names(y) and then sapply is overkill. It seems there should be a more direct method to recover a factor interaction created with tapply.