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cutfac acts like cut, dividing the range of x into intervals and coding the values in x according in which interval they fall. However, it gives nicer labels for the factor levels and by default chooses convenient breaks among the values based on deciles.

It is particularly useful for plots in which one wants to make a numeric variable discrete for the purpose of getting boxplots, spinograms or mosaic plots.

Usage

cutfac(x, breaks = NULL, q = 10)

Arguments

x

a numeric vector which is to be converted to a factor by cutting

breaks

either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which x is to be cut.

q

the number of quantile groups used to define breaks, if that has not been specified.

Details

By default, cut chooses breaks by equal lengths of the range of x, whereas cutfac uses quantile to choose breaks of roughly equal count.

Value

A factor corresponding to x is returned

References

Friendly, M. and Meyer, D. (2016). Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data. Boca Raton, FL: Chapman & Hall/CRC. http://ddar.datavis.ca.

Author

Achim Zeileis

See also

Examples

if (require(AER)) {
data("NMES1988", package="AER")
nmes <- NMES1988[, c(1, 6:8, 13, 15, 18)]

plot(log(visits+1) ~ cutfac(chronic), 
  data = nmes,
  ylab = "Physician office visits (log scale)", 
  xlab = "Number of chronic conditions", main = "chronic")

plot(log(visits+1) ~ cutfac(hospital, c(0:2, 8)), 
  data = nmes,
  ylab = "Physician office visits (log scale)", 
  xlab = "Number of hospital stays", main = "hospital")
}
#> Loading required package: AER
#> Loading required package: lmtest
#> Loading required package: zoo
#> 
#> Attaching package: 'zoo'
#> The following objects are masked from 'package:base':
#> 
#>     as.Date, as.Date.numeric
#> Loading required package: sandwich
#> Loading required package: survival