bin {OneR} | R Documentation |
Binning function
Description
Discretizes all numerical data in a data frame into categorical bins of equal length or content or based on automatically determined clusters.
Usage
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. |
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.
Value
A data frame or vector.
Author(s)
Holger von Jouanne-Diedrich
References
See Also
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)