layout {base}

R Documentation

Specifying Complex Plot Arrangements

Description

layout divides the device up into as many rows and columns as there are in matrix mat, with the column-widths and the row-heights specified in the respective arguments.

Usage

layout(mat,
       widths = rep(1, dim(mat)[2]),
       heights= rep(1, dim(mat)[1]),
       respect= FALSE)

layout.show(n = 1)
lcm(x)

Arguments

mat	a matrix object specifying the location of the next N figures on the output device. Each value in the matrix must be 0 or a positive integer. If N is the largest positive integer in the matrix, then the integers \{1,\dots,N-1\} must also appear at least once in the matrix.
widths	a vector of values for the widths of columns on the device. Relative widths are specified with numeric values. Absolute widths (in centimetres) are specified with the lcm() function (see examples).
heights	a vector of values for the heights of rows on the device. Relative and absolute heights can be specified, see widths above.
respect	either a logical value or a matrix object. If the latter, then it must have the same dimensions as mat and each value in the matrix must be either 0 or 1.
n	number of figures to plot.
x	a dimension to be intepreted as a number of centimetres.

Details

Figure i is allocated a region composed from a subset of these rows and columns, based on the rows and columns in which i occurs in mat.

The respect argument controls whether a unit column-width is the same physical measurement on the device as a unit row-height.

layout.show(n) plots (part of) the current layout, namely the outlines of the next n figures.

lcm is a trivial function, to be used as the interface for specifying absolute dimensions for the widths and heights arguments of layout().

Value

layout returns the number of figures, N, see above.

Author(s)

Paul R. Murrell

References

Murrell, P. R. (1999) Layouts: A mechanism for arranging plots on a page. Journal of Computational and Graphical Statistics, 8, 121-134. Chapter 5 of Paul Murrell's Ph.D. thesis.

See Also

par with arguments mfrow, mfcol, or mfg.

Examples

def.par <- par(no.readonly = TRUE)# save default, for resetting...

## divide the device into two rows and two columns
## allocate figure 1 all of row 1
## allocate figure 2 the intersection of column 2 and row 2
layout(matrix(c(1,1,0,2), 2, 2, byrow = TRUE))
## show the regions that have been allocated to each plot
layout.show(2)

## divide device into two rows and two columns
## allocate figure 1 and figure 2 as above
## respect relations between widths and heights
nf <- layout(matrix(c(1,1,0,2), 2, 2, byrow=TRUE), respect=TRUE)
layout.show(nf)

## create single figure which is 5cm square
nf <- layout(matrix(1), widths=lcm(5), heights=lcm(5))
layout.show(nf)


##-- Create a scatterplot with marginal histograms -----

x <- pmin(3, pmax(-3, rnorm(50)))
y <- pmin(3, pmax(-3, rnorm(50)))
xhist <- hist(x, breaks=seq(-3,3,0.5), plot=FALSE)
yhist <- hist(y, breaks=seq(-3,3,0.5), plot=FALSE)
top <- max(c(xhist$counts, yhist$counts))
xrange <- c(-3,3)
yrange <- c(-3,3)
nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE)
layout.show(nf)

par(mar=c(3,3,1,1))
plot(x, y, xlim=xrange, ylim=yrange, xlab="", ylab="")
par(mar=c(0,3,1,1))
barplot(xhist$counts, axes=FALSE, ylim=c(0, top), space=0)
par(mar=c(3,0,1,1))
barplot(yhist$counts, axes=FALSE, xlim=c(0, top), space=0, horiz=TRUE)

par(def.par)#- reset to default

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