Generalized Pairs Plot: It’s about time!

JW Emerson, WA Green, B Schloerke, J Crowley, D Cook, H Hofmann, H Wickham (2013) The Generalized Pairs Plot. Journal of Computational and Graphical Statistics 22(1). Here’s a free preprint version.

Until this new paper and implementation by Emerson et al., there were no widely available pairs plots that accommodated both numerical and categorical fields. ***Update 3/29/2013: ggpairs in the GGally package has been around since 2010***. A browse through the R Graph Gallery confirms this (as of 1/30/2013). See here too: a post on the Quick-R blog. I had been working on such a plot when I discovered the above article. Hence, I’m using this post to share my work, which I will probably abandon in favor of the above.

Any number of statistical graphics might be used instead of a scatterplot for numeric/numeric pairs; maybe a hexbin plot. A sieve plot or an association plot might be used as an alternative to the mosaicplot for factor/factor pairs. A beeswarm boxplot plot might be used in place of side-by-side boxplots for numeric/factor pairs.

Here was my provisional version of the generalized pairs plot, which I had called an ‘association matrix plot’:

pairsdf <- function(df, abbr = TRUE, abbr.len = 4) {
    par(mfrow = rep(length(df), 2))
    for (row in 1:length(df)) {
        xr <- df[[row]]
        if (is.character(xr) || is.logical(xr)) 
            xr <- as.factor(xr)
        if (is.factor(xr) && abbr) 
            levels(xr) <- abbreviate(levels(xr), 4)
        for (col in 1:length(df)) {
            xc <- df[[col]]
            if (is.character(xc) || is.logical(xc)) 
                xc <- as.factor(xc)
            if (inherits(xc, "factor") && abbr) 
                levels(xc) <- abbreviate(levels(xc), 4)
            cnm <- names(df)[col]
            rnm <- names(df)[row]
            if (col == row) {
                plot(c(0, 1), c(0, 1), type = "n", xaxt = "n", 
                  yaxt = "n", bty = "n", xlab = "", ylab = "", 
                  main = "")
                text(x = 0.5, y = 0.5, labels = cnm, adj = c(0.5, 
                  0.5), cex = 2)
            }
            else {
                iscf <- is.factor(xc)
                iscn <- is.numeric(xc)
                isrf <- is.factor(xr)
                isrn <- is.numeric(xr)
                if (isrf && iscf) {
                  mosaicplot(table(xc, xr), xlab = cnm, ylab = rnm, 
                    main = "", las = 2, color = TRUE, cex = 1.1)
                }
                else if (isrn && iscn) {
                  plot(xc, xr, xlab = cnm, ylab = rnm, main = "", 
                    las = 2, cex = 1.1)
                }
                else if (isrn && iscf) {
                  boxplot(xr ~ xc, xlab = cnm, ylab = rnm, main = "", 
                    las = 2, cex = 1.1)
                }
                else if (isrf && iscn) {
                  boxplot(xc ~ factor(xr, levels = rev(levels(xr))), 
                    xlab = cnm, ylab = rnm, main = "", las = 2, 
                    cex = 1.1, horizontal = TRUE)
                }
                else stop("urecognized variable type")
            }
        }
    }
}

Below are several association matrix plots generated by the above function (i.e., pairsdf) for data sets found in the MASS package. When there are many fields, I recommend using three to four square inches per plot.

It’s easy to see that the coop data set describes a simple factorial experiment.


However, the Rabbit data clearly arose from a more complicated experiment.


The fields of the farms data set are all of the factor class.