latticeExtra (version 0.5-1)

c.trellis: Merge trellis objects

Description

Combine the panels of multiple trellis objects into one.

Usage

## S3 method for class 'trellis':
c(..., x.same = FALSE, y.same = FALSE, recursive = FALSE)

Arguments

...

two or more trellis objects. If these are named arguments, the names will be used in the corresponding panel strips.

x.same

if TRUE, fix the x scales to those in the first object, as long as that has scales$relation = "same". Otherwise, the x scales in each panel will be as they were in the original objects (so in general not

y.same

as above, for y scales.

recursive

for consistency with the generic method, ignored.

Value

• a new trellis object.

Details

This mechanism attempts to merge the panels from multiple trellis objects into one. The same effect could generally be achieved by either a custom panel function (where the display depends on packet.number()), or using print.trellis to display multiple trellis objects. However, in some cases it is more convenient to use c(). Furthermore, it can be useful to maintain the display as a standard lattice display, rather than a composite using print.trellis, to simplify further interaction. Many properties of the display, such as titles, legends, axis settings and aspect ratio will be taken from the first object only. Note that combining panels from different types of plots does not really fit the trellis model. Some features of the plot may not work as expected. In particular, some work may be needed to show or hide scales on selected panels. An example is given below. Any trellis object with more than one conditioning variable will be "flattened" to one dimension, eliminating the multi-variate conditioning structure.

See Also

marginals was the original motivating application, print.trellis, update.trellis, trellis.object

Examples

## combine different types of plots
c(wireframe(volcano), contourplot(volcano))

## a table as both frequencies and proportions
## (together contains the same information as mosaic plot)
## remove last row (containing totals)

data(postdoc)
postdoc <- postdoc[1:(nrow(postdoc)-1),]
pdprops <- barchart(prop.table(postdoc, margin=1), xlab="Proportion",
                   auto.key=list(adj=1))
pdmargin <- barchart(margin.table(postdoc, 1))
pdboth <- c(pdprops, pdmargin)
update(pdboth, xlab=c("Proportion", "Freq"))

## conditioned 'quakes' plot combined with histogram
qua <- xyplot(lat ~ long | equal.count(depth, 3), quakes,
    aspect="iso", pch=".", cex=2, xlab=NULL, ylab=NULL)
qua <- c(qua, depth=histogram(quakes$depth))
## suppress scales on the first 3 panels
update(qua, scales=list(at=c(rep(list(NULL), 3), NA),
                 y=list(draw=FALSE)))

## visualise statistical and spatial distributions
vars <- as.data.frame(state.x77)
StateName <- tolower(state.name)
form <- StateName ~ Population + Income + Illiteracy +
   `Life Exp` + Murder + `HS Grad` + Frost + sqrt(Area)
## construct independent maps of each variable
library(maps)
statemap <- map("state", plot = FALSE, fill = TRUE)
statemap$names <- gsub(":.*", "", statemap$names)
colkey <- draw.colorkey(list(col=heat.colors(100),
    at=0:100, labels=list(labels=c("min","max"), at=c(0,100))))
panel.mapplot.each <- function(x, breaks, ...)
   panel.mapplot(x=x, breaks=quantile(x), ...)
vmaps <- mapplot(form, vars, map=statemap, colramp=heat.colors,
  panel=panel.mapplot.each, colorkey=FALSE,
  legend=list(right=list(fun=colkey)), xlab=NULL)
## construct independent densityplots of each variable
vdens <- densityplot(form[-2], vars, outer=TRUE,
   prepanel=function(...)
      list(xlim=c(0, max(prepanel.default.densityplot(...)$xlim))),
   scales=list(relation="free", x=list(axs="i")), cex=0.5, ref=TRUE)
## combine panels from both plots
combo <- c(vmaps, vdens)
## rearrange in pairs
n <- length(vars)
npairs <- rep(1:n, each=2) + c(0,n)
update(combo[npairs], scales=list(draw=FALSE),
       layout=c(4,4), between = list(x = c(0, 0.5), y = 0.5))

## this example can be done with lattice anyway, as something like:
## xyplot(Petal.Length + Sepal.Length ~ Petal.Width + Sepal.Width,
##        iris, groups=Species, outer=TRUE, scales="free")[c(1,4)]
## but here is another way to do it:
sepals <- xyplot(Sepal.Length ~ Sepal.Width, iris, groups=Species,
  xlab="Width", ylab="Height")
petals <- xyplot(Petal.Length ~ Petal.Width, iris, groups=Species)
c(Sepals=sepals, Petals=petals)
## if 'same' scales are requested, these are taken from first object
c(Sepals=sepals, Petals=petals, x.same=TRUE, y.same=TRUE)