Using stat_binhex() with ggpairs()

Question

I would like to use the stat_binhex() statistic from ggplot2 with the ggpairs() function (GGally R package). For example, I would like to use stat_binhex() in this plot instead of geom_point(). Is that possible?

Thanks for your help!

Answer 1

set.seed(1)
library(GGally)
library(hexbin)
df <- as.data.frame(matrix(rnorm(20*3), ncol=3))
p <- ggpairs(df, lower="blank")
seq <- 1:ncol(df)
for (x in seq)
  for (y in seq) 
    if (y>x) 
      p <- putPlot(p, ggplot(df, aes_string(x=names(df)[x],y=names(df)[y])) + stat_binhex(bins=4), y,x)
p

Answer 2

Cannot believe that this is still not part of the GGally's ggpair()'s customisation.

Building on lukeA's answer, let us convert that to a function:

Setup requirements and define the function:

require(ggplot2)[enter image description here][1]
require(GGally)
require(hexbin)


ggpairs_hex <- function(df, hexbins = 10) {
  # REF: https://stackoverflow.com/questions/20872133/using-stat-binhex-with-ggpairs
  p <- ggpairs(df, lower="blank")
  seq <- 1:ncol(df)
  for (x in seq)
    for (y in seq) 
      if (y>x) 
        p <- putPlot(p, ggplot(df, aes_string(x=names(df)[x],y=names(df)[y])) + stat_binhex(bins = hexbins), y,x)
  
  return(p)
}

Generate some data:

require(MASS)

# data generation from:
# https://predictivehacks.com/how-to-generate-correlated-data-in-r/
set.seed(312)
# create the variance covariance matrix
sigma<-rbind(c(1,-0.8,-0.7), c(-0.8,1, 0.9), c(-0.7,0.9,1))
# create the mean vector
mu<-c(10, 5, 2) 
# generate the multivariate normal distribution
df<-as.data.frame(MASS::mvrnorm(n=10000, mu=mu, Sigma=sigma))

Test the function:

ggpairs_hex(df, hexbins = 5)
ggpairs_hex(df, hexbins = 10)
ggpairs_hex(df, hexbins = 20)