| tileplot {latticeExtra} | R Documentation |
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.
tileplot(x, data = NULL, aspect = "iso",
prepanel = "prepanel.default.xyplot",
panel = "panel.voronoi", ...)
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. |
See panel.voronoi for further options and details.
Felix Andrews
tileplot
hplot
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$Income <- state.x77[,"Income"]
tileplot(Income ~ x * y, tmp, border = "black",
panel = function(x, y, ...) {
panel.voronoi(x, y, ..., points = FALSE)
panel.text(x, y, state.abb, cex = 0.6)
})
R version 2.9.0 (2009-04-17)
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ISBN 3-900051-07-0
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> library(latticeExtra)
> png(filename="images/tileplot_%03d.png" ,width=480, height=480)
> ### Name: tileplot
> ### Title: Plot a spatial mosaic from irregular 2D points
> ### Aliases: tileplot
> ### Keywords: hplot
>
> ### ** Examples
>
> xyz <- data.frame(x = rnorm(100), y = rnorm(100), z = rnorm(100))
> tileplot(z ~ x * y, xyz)
deldir 0.0-8
>
> ## tripack is faster but non-free
> ## Not run:
> ##D tileplot(z ~ x * y, xyz, use.tripack = TRUE)
> ## End(Not run)
>
> ## showing rectangular window boundary
> tileplot(z ~ x * y, xyz, xlim = c(-2, 4), ylim = c(-2, 4))
>
> ## insert some missing values
> xyz$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)
+ })
>
>
>
> dev.off()
null device
1
>
