Five ways to visualize your pairwise comparisons

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In data analysis it is often nice to look at all pairwise combinations of continuous variables in scatterplots. Up until recently, I have used the function splom in the package lattice, but ggplot2 has superior aesthetics, I think anyway.

Here a few ways to accomplish the task:

# load packages

require(lattice)
require(ggplot2)

1) Using base graphics, function “pairs”

pairs(iris[1:4], pch = 21)

 

 

 

 

 

 

 

 

 

 

 

2) Using lattice package, function “splom”

splom(~iris[1:4])

 

 

 

 

 

 

 

 

 

 

 

3) Using package ggplot2, function “plotmatrix”

plotmatrix(iris[1:4])

 

 

 

 

 

 

 

 

 

 

 

4) a function called ggcorplot by Mike Lawrence at Dalhousie University

-get ggcorplot function at this link

ggcorplot(
  data = iris[1:4],
  var_text_size = 5,
  cor_text_limits = c(5,10))

 

 

 

 

 

 

 

 

 

 

 

5) panel.cor function using pairs, similar to ggcorplot, but using base graphics. Not sure who wrote this function, but here is where I found it.

panel.cor <- function(x, y, digits=2, prefix="", cex.cor) 
{
    usr <- par("usr"); on.exit(par(usr)) 
    par(usr = c(0, 1, 0, 1)) 
    r <- abs(cor(x, y)) 
    txt <- format(c(r, 0.123456789), digits=digits)[1] 
    txt <- paste(prefix, txt, sep="") 
    if(missing(cex.cor)) cex <- 0.8/strwidth(txt) 
 
    test <- cor.test(x,y) 
    # borrowed from printCoefmat
    Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
                  cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1),
                  symbols = c("***", "**", "*", ".", " ")) 
 
    text(0.5, 0.5, txt, cex = cex * r) 
    text(.8, .8, Signif, cex=cex, col=2) 
}

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

 

 

 

 

 

 

 

 

 

 

 

A comparison of run times...

> system.time(pairs(iris[1:4]))
   user  system elapsed 
  0.138   0.008   0.156 
> system.time(splom(~iris[1:4]))
   user  system elapsed 
  0.003   0.000   0.003 
> system.time(plotmatrix(iris[1:4]))
   user  system elapsed 
  0.052   0.000   0.052 
> system.time(ggcorplot(
+ data = iris[1:4],
  var_text_size = 5,
  cor_text_limits = c(5,10)))
 
   user  system elapsed 
  0.130   0.001   0.131 
> system.time(pairs(iris[1:4], lower.panel=panel.smooth, upper.panel=panel.cor))
   user  system elapsed 
  0.170   0.011   0.200

...shows that splom is the fastest method, with the method using the panel.cor function pulling up the rear.

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