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).

Until now, there were no widely available pairs plots that acommodated both numerical and categorical fields. 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.