xyplot

Common Bivariate Trellis Plots

These are the most commonly used high level Trellis functions to plot pairs of variables. By far the most common is xyplot, designed mainly for two continuous variates (though factors can be supplied as well, in which case they will simply be coerced to numeric), which produces Conditional Scatter plots. The others are useful when one of the variates is a factor or a shingle. Most of these arguments are also applicable to other high level functions in the lattice package, but are only documented here.

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,
       subscripts = !is.null(groups),
       subset = TRUE)

## S3 method for class 'formula':
dotplot(x,
        data,
        panel = lattice.getOption("panel.dotplot"),
        ...)
## S3 method for class 'formula':
barchart(x,
         data,
         panel = lattice.getOption("panel.barchart"),
         box.ratio = 2,
         ...)
## S3 method for class 'formula':
stripplot(x,
          data,
          panel = lattice.getOption("panel.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,
       subscripts = !is.null(groups),
       subset = TRUE)

Details

All the functions documented here are generic, with the formula method usually doing the actual work. The structure of the plot that is produced is mostly controlled by the formula. For each unique combination of the levels of the conditioning variables g1, g2, ..., a separate panel is produced using the points (x,y) for the subset of the data (also called packet) defined by that combination. The display can be though of as a 3-dimensional array of panels, consisting of one 2-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 panel.

The coordinate system used by lattice by default is like a graph, with the origin at the bottom left, with axes increasing to left and up. 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. One might wish to set a global preference for a 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)). In fact, 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. 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).

Note

Most of the arguments documented here are also applicable for the other high level functions in the lattice package. These are not described in any detail elsewhere unless relevant, and this should be considered the canonical documentation for such arguments.

Any arguments passed to these functions and not recognized by them will be passed to the panel function. Most predefined panel functions have arguments that customize its output. These arguments are described only in the help pages for these panel functions, but can usually be supplied as arguments to the high level plot.

References

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

See Also

Lattice for an overview of the package, as well as barchart.table, Lattice, print.trellis, shingle, banking, reshape, panel.xyplot, panel.bwplot, panel.barchart, panel.dotplot, panel.stripplot, panel.superpose, panel.loess, panel.linejoin, strip.default, simpleKey trellis.par.set

Aliases

Examples

## wait for user input before each new page (like 'par(ask = TRUE)')
old.prompt <- grid::grid.prompt(TRUE)

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)
## (obtained from Bill Cleveland's Homepage :
## http://cm.bell-labs.com/cm/ms/departments/sia/wsc/, also
## available at statlib)

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")



## with and without banking

plot <- xyplot(sunspot.year ~ 1700:1988, xlab = "", type = "l",
               scales = list(x = list(alternating = 2)),
               main = "Yearly Sunspots")
print(plot, position = c(0, .3, 1, .9), more = TRUE)
print(update(plot, aspect = "xy", main = "", xlab = "Year"),
      position = c(0, 0, 1, .3))




## Multiple variables in formula for grouped displays

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, label = groups[subscripts], cex=1,
             fontfamily = "HersheySans"))

barchart(yield ~ variety | site, data = barley,
         groups = year, layout = c(1,6),
         ylab = "Barley Yield (bushels/acre)",
         scales = list(x = list(abbreviate = TRUE,
                       minlength = 5)))
barchart(yield ~ variety | site, data = barley,
         groups = year, layout = c(1,6), stack = TRUE, 
         auto.key = list(points = FALSE, rectangles = TRUE, 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)

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 = 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"))

grid::grid.prompt(old.prompt)