bin function

Binning function

Discretizes all numerical data in a data frame into categorical bins of equal length or content or based on automatically determined clusters.

bin(data, nbins = 5, labels = NULL, method = c("length", "content",
  "clusters"), na.omit = TRUE)

Arguments

• data: data frame or vector which contains the data.
• nbins: number of bins (= levels).
• labels: character vector of labels for the resulting category.
• method: character string specifying the binning method, see 'Details'; can be abbreviated.
• na.omit: logical value whether instances with missing values should be removed.

Returns

A data frame or vector.

Details

Character strings and logical strings are coerced into factors. Matrices are coerced into data frames. When called with a single vector only the respective factor (and not a data frame) is returned. Method "length" gives intervals of equal length, method "content" gives intervals of equal content (via quantiles). Method "clusters" determins "nbins" clusters via 1D kmeans with deterministic seeding of the initial cluster centres (Jenks natural breaks optimization).

When "na.omit = FALSE" an additional level "NA" is added to each factor with missing values.

Examples

data <- iris
str(data)
str(bin(data))
str(bin(data, nbins = 3))
str(bin(data, nbins = 3, labels = c("small", "medium", "large")))

## Difference between methods "length" and "content"
set.seed(1); table(bin(rnorm(900), nbins = 3))
set.seed(1); table(bin(rnorm(900), nbins = 3, method = "content"))

## Method "clusters"
intervals <- paste(levels(bin(faithful$waiting, nbins = 2, method = "cluster")), collapse = " ")
hist(faithful$waiting, main = paste("Intervals:", intervals))
abline(v = c(42.9, 67.5, 96.1), col = "blue")

## Missing values
bin(c(1:10, NA), nbins = 2, na.omit = FALSE) # adds new level "NA"
bin(c(1:10, NA), nbins = 2)                  # omits missing values by default (with warning)

References

https://github.com/vonjd/OneR

See Also

OneR, optbin

Author(s)

Holger von Jouanne-Diedrich