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, recursive = FALSE) xyplot.list(x, data = NULL, ..., FUN = xyplot, y.same = TRUE, x.same = NA, layout = NULL)
- two or more trellis objects. If these are named arguments, the names will be used in the corresponding panel strips.
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 sa
- as above, for y scales.
xyplot.listdefaults to same y scales. Set to
NAto leave them alone.
- value for
layoutof the new plot; see
- for consistency with the generic method, ignored.
list; the function
FUN, which defaults to
xyplot, will be called on each element of
x, and the resulting plots combined into one.
- FUN, data
- a lattice plot function, to be called on each element of
x, along with
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
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
print.trellis, to simplify
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
- a new 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))) ## 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) 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))