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tileplot

Plot a spatial mosaic from irregular 2D points


Description

Represents an irregular set of (x, y) points with a color covariate. Polygons are drawn enclosing the area closest to each point. This is known variously as a Voronoi mosaic, a Dirichlet tesselation, or Thiessen polygons.

Usage

tileplot(x, data = NULL, aspect = "iso",
         prepanel = "prepanel.default.xyplot",
         panel = "panel.voronoi", ...)

Arguments

x, data

formula and data as in levelplot, except that it expects irregularly spaced points rather than a regular grid.

aspect

aspect ratio: "iso" is recommended as it reproduces the distances used in the triangulation calculations.

panel, prepanel

see xyplot.

...

further arguments to the panel function, which defaults to panel.voronoi.

Details

See panel.voronoi for further options and details.

Author(s)

Felix Andrews felix@nfrac.org

See Also

Examples

xyz <- data.frame(x = rnorm(100), y = rnorm(100), z = rnorm(100))
tileplot(z ~ x * y, xyz)

## tripack is faster but non-free
## Not run: 
tileplot(z ~ x * y, xyz, use.tripack = TRUE)

## End(Not run)

## showing rectangular window boundary
tileplot(z ~ x * y, xyz, xlim = c(-2, 4), ylim = c(-2, 4))

## insert some missing values
xyz$z[1:10] <- NA
## the default na.rm = FALSE shows missing polygons
tileplot(z ~ x * y, xyz, border = "black",
  col.regions = grey.colors(100),
  pch = ifelse(is.na(xyz$z), 4, 21),
  panel = function(...) {
    panel.fill("hotpink")
    panel.voronoi(...)
  })
## use na.rm = TRUE to ignore points with missing values
update(trellis.last.object(), na.rm = TRUE)

## a quick and dirty approximation to US state boundaries
tmp <- state.center
tmp$Income <- state.x77[,"Income"]
tileplot(Income ~ x * y, tmp, border = "black",
  panel = function(x, y, ...) {
    panel.voronoi(x, y, ..., points = FALSE)
    panel.text(x, y, state.abb, cex = 0.6)
  })

latticeExtra

Extra Graphical Utilities Based on Lattice

v0.6-29
GPL (>= 2)
Authors
Deepayan Sarkar <deepayan.sarkar@r-project.org>, Felix Andrews <felix@nfrac.org>
Initial release
2019-12-18

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