dlookr (version 0.3.9)

binning: Binning the Numeric Data

Description

The binning() converts a numeric variable to a categorization variable.

Usage

binning(x, nbins, type = c("quantile", "equal", "pretty", "kmeans",
  "bclust"), ordered = TRUE, labels = NULL)

Arguments

x

numeric vector for binning.

nbins

number of classes. required. if missing, nclass.Sturges is used.

type

binning method. one of "quantile", "equal", "equal", "pretty", "kmeans", "bclust" The "quantile" style provides quantile breaks. The "equal" style divides the range of the variable into nbins parts. The "pretty" style chooses a number of breaks not necessarily equal to nbins using base::pretty function. The "kmeans" style uses stats::kmeans function to generate the breaks. The "bclust" style uses e1071::bclust function to generate the breaks using bagged clustering. "kmeans" and "bclust" type logic was implemented by classInt::classIntervals function.

ordered

whether to build an ordered factor or not.

labels

the label names to use for each of the bins.

Value

An object of bins class. Attributes of bins class is as follows.

• type : binning type, "quantile", "equal", "pretty", "kmeans", "bclust".

• breaks : the number of intervals into which x is to be cut.

• levels : levels of binned value.

• raw : raw data, x argument value.

"bins" class attributes information

Attributes of the "bins" classs that is as follows.

• class : "bins".

• levels : factor or ordered factor levels

• type : binning method

• breaks : breaks for binning

• raw : before the binned the raw data

See vignette("transformation") for an introduction to these concepts.

Details

This function is useful when used with the mutate/transmute function of the dplyr package.

See Also

binning_by, print.bins, summary.bins.

Examples

# NOT RUN {
# Generate data for the example
carseats <- ISLR::Carseats
carseats[sample(seq(NROW(carseats)), 20), "Income"] <- NA
carseats[sample(seq(NROW(carseats)), 5), "Urban"] <- NA
# Binning the carat variable. default type argument is "quantile"
bin <- binning(carseats$Income)
# Print bins class object
bin
# Summarise bins class object
summary(bin)
# Plot bins class object
plot(bin)
# Using labels argument
bin <- binning(carseats$Income, nbins = 4,
              labels = c("LQ1", "UQ1", "LQ3", "UQ3"))
bin
# Using another type argument
bin <- binning(carseats$Income, nbins = 5, type = "equal")
bin
bin <- binning(carseats$Income, nbins = 5, type = "pretty")
bin
bin <- binning(carseats$Income, nbins = 5, type = "kmeans")
bin
bin <- binning(carseats$Income, nbins = 5, type = "bclust")
bin

# -------------------------
# Using pipes & dplyr
# -------------------------
library(dplyr)

carseats %>%
 mutate(Income_bin = binning(carseats$Income)) %>%
 group_by(ShelveLoc, Income_bin) %>%
 summarise(freq = n()) %>%
 arrange(desc(freq)) %>%
 head(10)
# }