In lattice, there is a function called splom for the display of scatter plot matrices. For large datasets, the panel.hexbinplot from the hexbin package is a better option than the default panel.
As an example, let’s use some meteorological data from MAPA-SIAR:
library(solaR)
library(hexbin)
aranjuez <- readSIAR(prov=28, est=3, start='01/01/2004', end='31/12/2010')
aranjuezDF <- subset(as.data.frame(getData(aranjuez)),
select=c('TempMedia', 'TempMax', 'TempMin',
'HumedadMedia', 'DirViento', 'EtPMon',
'Precipitacion', 'G0'))
Now we can use splom with panel.hexbinplot and panel.loess. Besides, I have included some changes to diag.panel in order to show the univariate density of each variable (adapted from here). UPDATED: I include the use of colramp and BTC to change the color of the hexagon bins.
splom(aranjuezDF,
panel=panel.hexbinplot,
colramp=BTC,
diag.panel = function(x, ...){
yrng <- current.panel.limits()$ylim
d <- density(x, na.rm=TRUE)
d$y <- with(d, yrng[1] + 0.95 * diff(yrng) * y / max(y) )
panel.lines(d)
diag.panel.splom(x, ...)
},
lower.panel = function(x, y, ...){
panel.hexbinplot(x, y, ...)
panel.loess(x, y, ..., col = 'red')
},
pscale=0, varname.cex=0.7
)

Finally, it is interesting to identify some points. This task is easy with panel.link.splom. The points are selected via mouse clicks. Clicks other than left-clicks terminate the procedure.
trellis.focus('panel', 1, 1)
idx <- panel.link.splom(pch=13, cex=0.6, col='green')
aranjuezDF[idx,]
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