B_00_xyplot: Common Bivariate Trellis Plots

Description

This help page documents several commonly used high-level Lattice functions. xyplot produces bivariate scatterplots or time-series plots, bwplot produces box-and-whisker plots, dotplot produces Cleveland dot plots, barchart produces bar plots, and stripplot produces one-dimensional scatterplots. All these functions, along with other high-level Lattice functions, respond to a common set of arguments that control conditioning, layout, aspect ratio, legends, axis annotation, and many other details in a consistent manner. These arguments are described extensively in this help page, and should be used as the reference for other high-level functions as well.

For control and customization of the actual display in each panel, the help page of the respective default panel function will often be more informative. In particular, these help pages describe many arguments commonly used when calling the corresponding high-level function but are specific to them.

Usage

xyplot(x, data, ...)
dotplot(x, data, ...)
barchart(x, data, ...)
stripplot(x, data, ...)
bwplot(x, data, ...)
## S3 method for class 'formula':
xyplot(x,
       data,
       allow.multiple = is.null(groups) || outer,
       outer = !is.null(groups),
       auto.key = FALSE,
       aspect = "fill",
       panel = lattice.getOption("panel.xyplot"),
       prepanel = NULL,
       scales = list(),
       strip = TRUE,
       groups = NULL,
       xlab,
       xlim,
       ylab,
       ylim,
       drop.unused.levels = lattice.getOption("drop.unused.levels"),
       ...,
       lattice.options = NULL,
       default.scales,
       default.prepanel = lattice.getOption("prepanel.default.xyplot"),
       subscripts = !is.null(groups),
       subset = TRUE)
## S3 method for class 'formula':
dotplot(x,
        data,
        panel = lattice.getOption("panel.dotplot"),
        default.prepanel = lattice.getOption("prepanel.default.dotplot"),
        ...)
## S3 method for class 'formula':
barchart(x,
         data,
         panel = lattice.getOption("panel.barchart"),
         default.prepanel = lattice.getOption("prepanel.default.barchart"),
         box.ratio = 2,
         ...)
## S3 method for class 'formula':
stripplot(x,
          data,
          panel = lattice.getOption("panel.stripplot"),
          default.prepanel = lattice.getOption("prepanel.default.stripplot"),
          ...)
## S3 method for class 'formula':
bwplot(x,
       data,
       allow.multiple = is.null(groups) || outer,
       outer = FALSE,
       auto.key = FALSE,
       aspect = "fill",
       panel = lattice.getOption("panel.bwplot"),
       prepanel = NULL,
       scales = list(),
       strip = TRUE,
       groups = NULL,
       xlab,
       xlim,
       ylab,
       ylim,
       box.ratio = 1,
       horizontal = NULL,
       drop.unused.levels = lattice.getOption("drop.unused.levels"),
       ...,
       lattice.options = NULL,
       default.scales,
       default.prepanel = lattice.getOption("prepanel.default.bwplot"),
       subscripts = !is.null(groups),
       subset = TRUE)

Arguments

Value

The high-level functions documented here, as well as other high-level Lattice functions, return an object of class "trellis". The update method can be used to subsequently update components of the object, and the print method (usually called by default) will plot it on an appropriate plotting device.

Details

The high-level functions documented here, as well as other high-level Lattice functions, are generic, with the formula method usually doing the most substantial work. The structure of the plot that is produced is mostly controlled by the formula (implicitly in the case of the non-formula methods). For each unique combination of the levels of the conditioning variables g1, g2, ..., a separate packet is produced, consisting of the points (x,y) for the subset of the data defined by that combination. The display can be thought of as a three-dimensional array of panels, consisting of one two-dimensional matrix per page. The dimensions of this array are determined by the layout argument. If there are no conditioning variables, the plot produced consists of a single packet. Each packet usually corresponds to one panel, but this is not strictly necessary (see the entry for index.cond above).

The coordinate system used by lattice by default is like a graph, with the origin at the bottom left, with axes increasing to the right and top. In particular, panels are by default drawn starting from the bottom left corner, going right and then up, unless as.table = TRUE, in which case panels are drawn from the top left corner, going right and then down. It is possible to set a global preference for the table-like arrangement by changing the default to as.table=TRUE; this can be done by setting lattice.options(default.args = list(as.table = TRUE)). Default values can be set in this manner for the following arguments: as.table, aspect, between, page, main, sub, par.strip.text, layout, skip and strip. Note that these global defaults are sometimes overridden by individual functions.

The order of the panels depends on the order in which the conditioning variables are specified, with g1 varying fastest, followed by g2, and so on. Within a conditioning variable, the order depends on the order of the levels (which for factors is usually in alphabetical order). Both of these orders can be modified using the index.cond and perm.cond arguments, possibly using the update (and other related) method(s).

References

Sarkar, Deepayan (2008) Lattice: Multivariate Data Visualization with R, Springer. http://lmdvr.r-forge.r-project.org/

Examples

Run this code

require(stats)

## Tonga Trench Earthquakes

Depth <- equal.count(quakes$depth, number=8, overlap=.1)
xyplot(lat ~ long | Depth, data = quakes)
update(trellis.last.object(),
       strip = strip.custom(strip.names = TRUE, strip.levels = TRUE),
       par.strip.text = list(cex = 0.75),
       aspect = "iso")

## Examples with data from `Visualizing Data' (Cleveland, 1993) obtained
## from http://cm.bell-labs.com/cm/ms/departments/sia/wsc/

EE <- equal.count(ethanol$E, number=9, overlap=1/4)

## Constructing panel functions on the fly; prepanel
xyplot(NOx ~ C | EE, data = ethanol,
       prepanel = function(x, y) prepanel.loess(x, y, span = 1),
       xlab = "Compression Ratio", ylab = "NOx (micrograms/J)",
       panel = function(x, y) {
           panel.grid(h = -1, v = 2)
           panel.xyplot(x, y)
           panel.loess(x, y, span=1)
       },
       aspect = "xy")

## Extended formula interface 

xyplot(Sepal.Length + Sepal.Width ~ Petal.Length + Petal.Width | Species,
       data = iris, scales = "free", layout = c(2, 2),
       auto.key = list(x = .6, y = .7, corner = c(0, 0)))


## user defined panel functions

states <- data.frame(state.x77,
                     state.name = dimnames(state.x77)[[1]],
                     state.region = state.region)
xyplot(Murder ~ Population | state.region, data = states,
       groups = state.name,
       panel = function(x, y, subscripts, groups) {
           ltext(x = x, y = y, labels = groups[subscripts], cex=1,
                 fontfamily = "HersheySans")
       })

## Stacked bar chart

barchart(yield ~ variety | site, data = barley,
         groups = year, layout = c(1,6), stack = TRUE,
         auto.key = list(space = "right"),
         ylab = "Barley Yield (bushels/acre)",
         scales = list(x = list(rot = 45)))

bwplot(voice.part ~ height, data=singer, xlab="Height (inches)")

dotplot(variety ~ yield | year * site, data=barley)

## Grouped dot plot showing anomaly at Morris

dotplot(variety ~ yield | site, data = barley, groups = year,
        key = simpleKey(levels(barley$year), space = "right"),
        xlab = "Barley Yield (bushels/acre) ",
        aspect=0.5, layout = c(1,6), ylab=NULL)

stripplot(voice.part ~ jitter(height), data = singer, aspect = 1,
          jitter.data = TRUE, xlab = "Height (inches)")

## Interaction Plot

xyplot(decrease ~ treatment, OrchardSprays, groups = rowpos,
       type = "a",
       auto.key =
       list(space = "right", points = FALSE, lines = TRUE))

## longer version with no x-ticks

bwplot(decrease ~ treatment, OrchardSprays, groups = rowpos,
       panel = "panel.superpose",
       panel.groups = "panel.linejoin",
       xlab = "treatment",
       key = list(lines = Rows(trellis.par.get("superpose.line"),
                  c(1:7, 1)),
                  text = list(lab = as.character(unique(OrchardSprays$rowpos))),
                  columns = 4, title = "Row position"))

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