layer {latticeExtra} | R Documentation |
Add layers to a lattice plot, optionally using a new data source
Description
A mechanism to add new layers to a trellis object, optionally using a
new data source. This is an alternative to modifying the
panel function. Note the non-standard evaluation in layer()
.
Usage
layer(..., data, magicdots, exclude,
packets, rows, columns, groups,
style, force, theme, under, superpose)
layer_(...)
glayer(...)
glayer_(...)
## S3 method for class 'trellis'
object + lay
drawLayer(lay, panelArgs = trellis.panelArgs())
flattenPanel(object)
Arguments
... |
expressions as they would appear in a panel function. These can
refer to the panel function arguments (such as |
data |
optional. A named |
magicdots , exclude |
if |
packets , rows , columns , groups |
restricts the layer to draw only in specified packets (which refer
to individual panels, but are independent of their layout), or rows
or columns of the trellis layout ( |
style |
style index of the layer, used only to set lattice graphical
parameters (same effect as in grouped displays). Note that this will
use the theme settings in effect in the existing plot, which may or
may not be what is desired. It may be necessary to use |
force |
|
theme |
a style specification to be passed to |
under |
whether the layer should be drawn before the existing panel
function. This defaults to |
superpose |
if |
object |
a trellis object. |
lay |
a layer object. |
panelArgs |
list of arguments to the panel function. |
Details
The layer
mechanism is a method for augmenting a panel
function. It allows expressions to be added to the panel function
without knowing what the original panel function was. In this way it
can be useful for convenient augmentation of trellis plots.
Note that the evaluation used in layer
is non-standard, and can
be confusing at first: you typically refer to variables as if inside
the panel function (x
, y
, etc); you can usually refer to
objects which exist in the global environment (workspace), but it is
safer to pass them in by name in the data
argument to
layer
. (And this should not to be confused with the data
argument to the original xyplot
.)
A simple example is adding a reference line to each panel:
layer(panel.refline(h = 0))
. Note that the expressions are
quoted, so if you have local variables they will need to be either
accessible globally, or passed in via the data
argument. For
example:
layer(panel.refline(h = myVal)) ## if myVal is global
layer(panel.refline(h = h), data = list(h = myVal))
Another non-standard aspect is that the special argument
“...
” will, by default, only pass through those
argument not already named. For example, this will over-ride the
x
argument and pass on the remaining arguments:
layer(panel.xyplot(x = jitter(x), ...))
The first un-named argument is assumed to be "x", so that is the same as
layer(panel.xyplot(jitter(x), ...))
The layer mechanism should probably still be considered experimental.
drawLayer()
actually draws the given layer object, applying the
panel specification, style settings and so on. It should only be
called while a panel is in focus.
The flattenPanel
function will construct a human-readable
function incorporating code from all layers (and the original panel
function). Note that this does not return a usable function, as it
lacks the correct argument list and ignores any extra data sources
that layers might use. It is intended be edited manually.
Value
a layer
object is defined as a list of expression objects,
each of which may have a set of attributes. The result of "adding"
a layer to a trellis object (+.trellis
) is the updated trellis
object.
Author(s)
Felix Andrews felix@nfrac.org
See Also
update.trellis
,
as.layer
for overlaying entire plots
Examples
foo <- xyplot(ozone ~ wind, environmental)
foo
## overlay reference lines
foo <- foo + layer(panel.abline(h = 0)) +
layer(panel.lmline(x, y, lty = 2))
## underlay a flat color
foo <- foo + layer(panel.fill(grey(.95)), under = TRUE)
foo
## layers can access the panel function arguments
foo <- foo + layer({ ok <- (y>100);
panel.text(x[ok], y[ok], y[ok], pos = 1) })
foo
## over-ride arguments by name
foo <- foo +
layer(panel.xyplot(y = ave(y, x, FUN = max), type = "a", ...))
foo
## see a sketch of the complete panel function
flattenPanel(foo)
## group layers, drawn for each group in each panel
dotplot(VADeaths, type = "o") +
glayer(ltext(x[5], y[5], group.value, srt = 40))
## a quick way to print out the panel.groups arguments:
dotplot(VADeaths, type = "o") + glayer(str(list(...)))
## layers with superposed styles
xyplot(ozone ~ wind | equal.count(temperature, 2),
data = environmental) +
layer(panel.loess(x, y, span = 0.5), style = 1) +
layer(panel.loess(x, y, span = 1.0), style = 2) +
layer(panel.key(c("span = 0.5", "span = 1.0"), corner = c(1,.98),
lines = TRUE, points = FALSE), packets = 1)
## note that styles come from the settings in effect in the plot,
## which is not always what you want:
xyplot(1:10 ~ 1:10, type = "b", par.settings = simpleTheme(col = "red")) +
layer(panel.lines(x = jitter(x, 2), ...)) + ## drawn in red
layer(panel.lines(x = jitter(x, 2), ...), force = TRUE) ## reset theme
## using other variables from the original `data` object
## NOTE: need subscripts = TRUE in original call!
zoip <- xyplot(wind ~ temperature | equal.count(radiation, 2),
data = environmental, subscripts = TRUE)
zoip + layer(panel.points(..., pch = 19,
col = grey(1 - ozone[subscripts] / max(ozone))),
data = environmental)
## restrict drawing to specified panels
barchart(yield ~ variety | site, data = barley,
groups = year, layout = c(1,6), as.table = TRUE,
scales = list(x = list(rot = 45))) +
layer(ltext(tapply(y, x, max), lab = abbreviate(levels(x)),
pos = 3), rows = 1)
## example of a new data source
qua <- xyplot(lat ~ long | cut(depth, 2), quakes,
aspect = "iso", pch = ".", cex = 2)
qua
## add layer showing distance from Auckland
newdat <- with(quakes, expand.grid(
gridlat = seq(min(lat), max(lat), length = 60),
gridlon = seq(min(long), max(long), length = 60)))
newdat$dist <- with(newdat, sqrt((gridlat - -36.87)^2 +
(gridlon - 174.75)^2))
qua + layer_(panel.contourplot(x = gridlon, y = gridlat, z = dist,
contour = TRUE, subscripts = TRUE), data = newdat)