# NOT RUN {
if (!require("spData", quietly=TRUE)) {
message("spData package needed for examples")
run <- FALSE
} else {
run <- TRUE
}
if (run) {
data(jenks71, package="spData")
pal1 <- c("wheat1", "red3")
opar <- par(mfrow=c(2,3))
plot(classIntervals(jenks71$jenks71, n=5, style="fixed",
fixedBreaks=c(15.57, 25, 50, 75, 100, 155.30)), pal=pal1, main="Fixed")
plot(classIntervals(jenks71$jenks71, n=5, style="sd"), pal=pal1, main="Pretty standard deviations")
plot(classIntervals(jenks71$jenks71, n=5, style="equal"), pal=pal1, main="Equal intervals")
plot(classIntervals(jenks71$jenks71, n=5, style="quantile"), pal=pal1, main="Quantile")
set.seed(1)
plot(classIntervals(jenks71$jenks71, n=5, style="kmeans"), pal=pal1, main="K-means")
plot(classIntervals(jenks71$jenks71, n=5, style="hclust", method="complete"),
pal=pal1, main="Complete cluster")
}
if (run) {
plot(classIntervals(jenks71$jenks71, n=5, style="hclust", method="single"),
pal=pal1, main="Single cluster")
set.seed(1)
plot(classIntervals(jenks71$jenks71, n=5, style="bclust", verbose=FALSE),
pal=pal1, main="Bagged cluster")
plot(classIntervals(jenks71$jenks71, n=5, style="fisher"), pal=pal1,
main="Fisher's method")
plot(classIntervals(jenks71$jenks71, n=5, style="jenks"), pal=pal1,
main="Jenks' method")
par(opar)
}
if (run) {
print(classIntervals(jenks71$jenks71, n=5, style="fixed",
fixedBreaks=c(15.57, 25, 50, 75, 100, 155.30)))
}
if (run) {
print(classIntervals(jenks71$jenks71, n=5, style="sd"))
}
if (run) {
print(classIntervals(jenks71$jenks71, n=5, style="equal"))
}
if (run) {
print(classIntervals(jenks71$jenks71, n=5, style="quantile"))
}
if (run) {
set.seed(1)
print(classIntervals(jenks71$jenks71, n=5, style="kmeans"))
}
if (run) {
set.seed(1)
print(classIntervals(jenks71$jenks71, n=5, style="kmeans", intervalClosure="right"))
}
if (run) {
set.seed(1)
print(classIntervals(jenks71$jenks71, n=5, style="kmeans", dataPrecision=0))
}
if (run) {
set.seed(1)
print(classIntervals(jenks71$jenks71, n=5, style="kmeans"), cutlabels=FALSE)
}
if (run) {
print(classIntervals(jenks71$jenks71, n=5, style="hclust", method="complete"))
}
if (run) {
print(classIntervals(jenks71$jenks71, n=5, style="hclust", method="single"))
}
if (run) {
set.seed(1)
print(classIntervals(jenks71$jenks71, n=5, style="bclust", verbose=FALSE))
}
if (run) {
print(classIntervals(jenks71$jenks71, n=5, style="bclust",
hclust.method="complete", verbose=FALSE))
}
if (run) {
print(classIntervals(jenks71$jenks71, n=5, style="fisher"))
}
if (run) {
print(classIntervals(jenks71$jenks71, n=5, style="jenks"))
}
if (run) {
x <- c(0, 0, 0, 1, 2, 50)
print(classIntervals(x, n=3, style="fisher"))
}
if (run) {
print(classIntervals(x, n=3, style="jenks"))
}
# Argument 'unique' will collapse the label of classes containing a
# single value. This is particularly useful for 'censored' variables
# that contain for example many zeros.
if (run) {
data_censored<-c(rep(0,10), rnorm(100, mean=20,sd=1),rep(26,10))
plot(density(data_censored))
}
if (run) {
cl2<-classIntervals(data_censored, n=5, style="jenks", dataPrecision=2)
print(cl2, unique=FALSE)
}
if (run) {
print(cl2, unique=TRUE)
}
# }
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