berryFunctions (version 1.21.2)

classify: Classification into groups

Description

classify continuous values into categories with different methods: - linearly or logarithmically spaced equal intervals, - intervals based on quantiles (equally filled bins), - intervals based on distance from the mean in normal distributions, - user specified class borders (e.g. for legal or critical limits).

Usage

classify(
  x,
  method = "linear",
  breaks = NULL,
  Range = range(x, finite = TRUE),
  col = NULL,
  sdlab = 1,
  logbase = 1,
  quiet = FALSE,
  ...
)

Arguments

x

Vector with numeric values

method

Character string (partial matching is performed). Classification method (type of binning) to compute the class breakpoints. See section Details. DEFAULT: "linear"

breaks

Specification for method, see Details. DEFAULT: NULL (different defaults for each method)

Range

Ends of intervals. DEFAULT: range(x, finite=TRUE)

col

Function that will return a color palette, e.g. seqPal. If given, a vector of colors is returned instead of the regular list. DEFAULT: NULL (ignored)

sdlab

Type of label and breakpoints if method=standarddeviation. 1 means -0.5 sd, 0.5 sd, 2 means -1 sd, mean, 1 sd, 3 means actual numbers for type 1, 4 means numbers for type 2. DEFAULT: 1

logbase

base for logSpaced. Used only if not 1 and method="log". DEFAULT: 1

quiet

Suppress warnings, eg for values outside Range? DEFAULT: FALSE

…

Further arguments passed to the function col.

Value

if col=NULL, a list with class numbers (index) and other elements for colPoints. If col is a palette function, a vector of colors.

Details

Binning methods are explained very nicely in the link in the section References. nbins indicates the number of classes (and thus, colors).

method	| explanation	| meaning of breaks	| default
----------	| -----------	| -----------	| -------
linear	| nbins equally spaced classes	| nbins	| 100
log	| nbins logarithmically spaced	| nbins	| 100
quantile	| classes have equal number of values	| the quantiles (or number of them)	| 0:4/4
sd	| normal distributions	| number of sd in one direction from the mean	| 3
custom	| user-given breakpoints	| breakpoint values (including ends of Range)	| none

The default is set to equalinterval which makes sense for my original intent of plotting lake depth (bathymetry measured at irregularly distributed points) on a linear color scale. This is the workhorse for colPoints.

References

See this page on the effect of classification (binning) methods: http://uxblog.idvsolutions.com/2011/10/telling-truth.html

See Also

colPoints

Examples

# NOT RUN {
classify( c(1:10, 20), "lin", breaks=12)
classify( c(1:10, 20), "q", breaks=0:10/10)
classify( c(1:10, 20), "s", sdlab=2 )
classify( c(1:10, 20), "s", sdlab=1, breaks=2 )
classify( c(1:10, 20), "c", breaks=c(5,27) )
classify( c(1:10, 20), "log")

cols <- classify( c(1:10, 20), col=seqPal) ; cols
plot(c(1:10, 20), col=cols, pch=16, cex=2)

set.seed(42); rz <- rnorm(30, mean=350, sd=120)
plot(1)
classleg <- function(method="linear", breaks=100, sdlab=1, logbase=1, ...)
           do.call(colPointsLegend, owa(
           classify(rz, method=method, breaks=breaks, sdlab=sdlab, logbase=logbase),
           list(z=rz, title="", ...))   )
classleg(br=3, met="s", col=divPal(5),mar=c(0,3,1,0),hor=FALSE,x1=0.1,x2=0.25)
classleg(br=3, met="s", col=divPal(6),mar=c(0,3,1,0),hor=FALSE,x1=0.25,x2=0.4, sdlab=2)
classleg(y1=0.85, y2=1)
classleg(br=20, met="log", y1=0.70, y2=0.85)
classleg(br=20, met="log", y1=0.55, y2=0.70, logbase=1.15)
classleg(br=20, met="log", y1=0.45, y2=0.60, logbase=0.90)
classleg(br= 5, met="q", y1=0.30, y2=0.45)# quantiles: each color is equally often used
classleg(met="q", y1=0.15, y2=0.30, breaks=0:15/15, at=pretty2(rz), labels=pretty2(rz) )

# }