To try out this recipe, simply type it in the command prompt. You can also choose to save the recipe as a script so that you can use it again later on.

We will use the iris flowers dataset that we first used in the pairs plot recipe in Chapter 1, R Graphics:

panel.cor <- function(x, y, ...)
{
    par(usr = c(0, 1, 0, 1))
    txt <- as.character(format(cor(x, y), digits=2))
    text(0.5, 0.5, txt,  cex = 6* abs(cor(x, y)))
}

pairs(iris[1:4], upper.panel=panel.cor)

We have basically used the pairs() function to create the graph, but in addition to the dataset, we also set the upper.panel argument to panel.cor, which is a function that we define beforehand. The upper.panel argument refers to the squares in the top-right half of the preceding graph, the diagonal moving from the top-left corner to the bottom-right corner. Correspondingly, there is also a lower.panel argument for the bottom-left half of the graph.

The panel.cor value is defined as a function using the following notation:

newfunction<-function(arg1, arg2, ...) 
{
#function code here
}

The panel.cor function does a few different things. First, it sets the individual panel block axes limits to c(0,1,0,1) using the par() command. Then, it calculates the correlation coefficient value between a pair of variables up to two decimal values and formats it as a text string so that it can then be passed to the text() function, which places it in the center of each block. Also note that the size of the labels is set using the cex argument to a multiple of the absolute value of the correlation coefficient. Thus, the size of the value label also indicates how important the correlation is.

Panel functions are, in fact, one of the most powerful features of the lattice package. To learn more about them and the package, please refer to the excellent book Lattice: Multivariate Data Visualization with R by Deepayan Sarkar (Springer), who is also the author of the package. The book's website is located at http://lmdvr.r-forge.r-project.org/figures/figures.html.