R: Convert Numeric to Factor

cut {base}

R Documentation

Convert Numeric to Factor

Description

cut divides the range of x into intervals and codes the values in x according to which interval they fall. The leftmost interval corresponds to level one, the next leftmost to level two and so on.

Usage

cut(x, ...)

## Default S3 method:
cut(x, breaks, labels = NULL,
    include.lowest = FALSE, right = TRUE, dig.lab = 3, ...)

Arguments

`x`	a numeric vector which is to be converted to a factor by cutting.
`breaks`	either a vector of cut points or number giving the number of intervals which `x` is to be cut into.
`labels`	labels for the levels of the resulting category. By default, labels are constructed using `"(a,b]"` interval notation. If `labels = FALSE`, simple integer codes are returned instead of a factor.
`include.lowest`	logical, indicating if an ‘x[i]’ equal to the lowest (or highest, for `right = FALSE`) ‘breaks’ value should be included.
`right`	logical, indicating if the intervals should be closed on the right (and open on the left) or vice versa.
`dig.lab`	integer which is used when labels are not given. It determines the number of digits used in formatting the break numbers.
`...`	further arguments passed to or from other methods.

Details

If a labels parameter is specified, its values are used to name the factor levels. If none is specified, the factor level labels are constructed as "(b1, b2]", "(b2, b3]" etc. for right = TRUE and as "[b1, b2)", ... if right = FALSE. In this case, dig.lab indicates the minimum number of digits should be used in formatting the numbers b1, b2, .... A larger value (up to 12) will be used if needed to distinguish between any pair of endpoints: if this fails labels such as "Range3" will be used.

Value

A factor is returned, unless labels = FALSE which results in the mere integer level codes.

Note

Instead of table(cut(x, br)), hist(x, br, plot = FALSE) is more efficient and less memory hungry. Instead of cut(*, labels = FALSE), findInterval() is more efficient.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Examples

Z <- rnorm(10000)
table(cut(Z, br = -6:6))
sum(table(cut(Z, br = -6:6, labels=FALSE)))
sum(   hist  (Z, br = -6:6, plot=FALSE)$counts)

cut(rep(1,5),4)#-- dummy
tx0 <- c(9, 4, 6, 5, 3, 10, 5, 3, 5)
x <- rep(0:8, tx0)
stopifnot(table(x) == tx0)

table( cut(x, b = 8))
table( cut(x, br = 3*(-2:5)))
table( cut(x, br = 3*(-2:5), right = FALSE))

##--- some values OUTSIDE the breaks :
table(cx  <- cut(x, br = 2*(0:4)))
table(cxl <- cut(x, br = 2*(0:4), right = FALSE))
which(is.na(cx));  x[is.na(cx)]  #-- the first 9  values  0
which(is.na(cxl)); x[is.na(cxl)] #-- the last  5  values  8

## Label construction:
y <- rnorm(100)
table(cut(y, breaks = pi/3*(-3:3)))
table(cut(y, breaks = pi/3*(-3:3), dig.lab=4))

table(cut(y, breaks =  1*(-3:3), dig.lab=4))
# extra digits don't "harm" here
table(cut(y, breaks =  1*(-3:3), right = FALSE))
#- the same, since no exact INT!

## sometimes the default dig.lab is not enough to be avoid confusion:
aaa <- c(1,2,3,4,5,2,3,4,5,6,7)
cut(aaa, 3)
cut(aaa, 3, dig.lab=4)

[Package base version 2.2.1 Index]