Merge trellis objects, using same or different scales

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Description

Combine the panels of multiple trellis objects into one.

Usage

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## S3 method for class 'trellis'
c(..., x.same = NA, y.same = NA,
    layout = NULL, merge.legends = FALSE, recursive = FALSE)

xyplot.list(x, data = NULL, ..., FUN = xyplot,
    y.same = TRUE, x.same = NA, layout = NULL,
    merge.legends = 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, set the x scale relation to "same" and recalculate panel limits using data from all panels. Otherwise, the x scales in each panel will be as they were in the original objects (so in general not the same), the default behaviour.

y.same

as above, for y scales. Note that xyplot.list defaults to same y scales. Set to NA to leave them alone.

layout

value for layout of the new plot; see xyplot.

merge.legends

to keep keys or legends from all plots, not just the first. If multiple legends share the same "space", they are packed together horizontally or vertically.

recursive

for consistency with the generic method, ignored.

x

a list of objects to plot individually, and then be combined into one final plot.

FUN, data

a lattice plot function, to be called on each element of the list x, along with data and ...

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, 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.

Value

a new trellis object.

Author(s)

Felix Andrews felix@nfrac.org

See Also

marginal.plot was the original motivating application, print.trellis, update.trellis, trellis.object

Examples

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

## Merging levelplot with xyplot
levObj <- levelplot(prop.table(WorldPhones, 1) * 100)
xyObj <- xyplot(Phones ~ Year, data.frame(Phones = rowSums(WorldPhones),
      Year = row.names(WorldPhones)), type="b", ylim = c(0, 150000))
## NOTE: prepanel.levelplot (from first object) is used for entire plot.
cObj <- c(levObj, xyObj, layout = 1:2)
update(cObj, scales = list(y = list(rot = 0)),
       ylab = c("proportional distribution", "number of phones"))

## Combine two xyplots.
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)

## Force same scales (re-calculate panel limits from merged data):
c(Sepals = sepals, Petals = petals, x.same = TRUE, y.same = TRUE)

## Or - create xyplots from a list of formulas
xyplot.list(list(Sepals = Sepal.Length ~ Sepal.Width,
                 Petals = Petal.Length ~ Petal.Width),
             data = iris, groups = Species, x.same = TRUE,
             xlab = "Width", ylab = "Height")

## Create histograms from a list of objects, and merge them.
xyplot.list(iris, FUN = histogram)

## Create cumulative distribution plots from a list of objects
xyplot.list(iris[1:4], FUN = qqmath, groups = iris$Species,
            auto.key = TRUE)

## Display a table as both frequencies and proportions:
data(postdoc)
## remove last row (containing totals)
postdoc <- postdoc[1:(nrow(postdoc)-1),]
pdprops <- barchart(prop.table(postdoc, margin = 1),
                    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), layout = c(4, 1))
## suppress scales on the first 3 panels
update(qua, scales = list(at = list(NULL, NULL, NULL, NA),
                          y = list(draw = FALSE)))

## Demonstrate merging of legends and par.settings.
## Note that par.settings can conflict, thus need col.line=...
mypoints <- 
    xyplot(1:10 ~ 1:10, groups = factor(rep(1:2, each = 5)),
        par.settings = simpleTheme(pch = 16), auto.key = TRUE)
mylines <-
    xyplot(1:10 ~ 1:10, groups = factor(rep(1:5, each = 2)),
        type = "l", par.settings = simpleTheme(col.line = 1:5),
        auto.key = list(lines = TRUE, points = FALSE, columns = 5))
c(mypoints, mylines)

## Visualise statistical and spatial distributions
## (advanced!)
library(maps)
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
statemap <- map("state", plot = FALSE, fill = TRUE)
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, cut = 0, 
    scales = list(relation = "free"), ylim = c(0, NA),
    cex = 0.5, ref = TRUE) +
  layer(panel.axis("top", half = FALSE, text.cex = 0.7))
## 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))

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