Shift overlying points into clusters

cluster.overplot checks for overlying points in the x and y coordinates passed. Those points that are overlying are moved to form a small cluster of up to nine points. For large numbers of overlying points, see count.overplot or sizeplot. If you are unsure of the number of overplots in your data, run count.overplot first to see if there are any potential clusters larger than nine.

Usage

cluster.overplot(x,y,away=NULL,tol=NULL,...)

Arguments

x,y

Numeric data vectors or the first two columns of a matrix or data frame. Typically the x/y coordinates of points to be plotted.

away

How far to move overlying points in user units. Defaults to the width of a lower case "o" in the x direction and 5/8 of the height of a lower case "o" in the y direction. Must have both values.

tol

The largest distance between points that will be considered to be overlying. Defaults to 1/2 of the width of a lower case "o" in the x direction and 1/2 of the height of a lower case "o" in the y direction.

...

additional arguments returned as they are passed.

Value

A list with two components. For unique x-y pairs the elements will be the same as in the original. For overlying points up to eight additional points will be generated that will create a cluster of points instead of one.

Author

Jim Lemon - thanks to Markus Elze for the test for a current graphics device

See also

count.overplot,sizeplot

Examples

 xy.mat<-cbind(sample(1:10,200,TRUE),sample(1:10,200,TRUE))
 clusteredpoints<-
  cluster.overplot(xy.mat,col=rep(c("red","green"),each=100),
  away=rep(0.2,2))
#> Error in strwidth("o"): plot.new has not been called yet
 plot(clusteredpoints,col=clusteredpoints$col,
  main="Cluster overplot test")
#> Error in eval(expr, envir, enclos): object 'clusteredpoints' not found