Merge trellis objects, using same or different scales
Combine the panels of multiple trellis objects into one.
## 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)... |
two or more trellis objects. If these are named arguments, the names will be used in the corresponding panel strips. |
x.same |
if |
y.same |
as above, for y scales.
Note that |
layout |
value for |
merge.legends |
to keep keys or legends from all plots, not just
the first. If multiple legends share the same |
recursive |
for consistency with the generic method, ignored. |
x |
a |
FUN, data |
a lattice plot function, to be called on each element of
the list |
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.
a new trellis object.
Felix Andrews felix@nfrac.org
marginal.plot was the original motivating application,
print.trellis,
update.trellis,
trellis.object
## 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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.