bin: Binning function

In OneR: One Rule Machine Learning Classification Algorithm with Enhancements

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

https://github.com/vonjd/OneR

See Also

OneR, optbin

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)

Example output

'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: Factor w/ 5 levels "(4.3,5.02]","(5.02,5.74]",..: 2 1 1 1 1 2 1 1 1 1 ...
 $ Sepal.Width : Factor w/ 5 levels "(2,2.48]","(2.48,2.96]",..: 4 3 3 3 4 4 3 3 2 3 ...
 $ Petal.Length: Factor w/ 5 levels "(0.994,2.18]",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Petal.Width : Factor w/ 5 levels "(0.0976,0.58]",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: Factor w/ 3 levels "(4.3,5.5]","(5.5,6.7]",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Sepal.Width : Factor w/ 3 levels "(2,2.8]","(2.8,3.6]",..: 2 2 2 2 2 3 2 2 2 2 ...
 $ Petal.Length: Factor w/ 3 levels "(0.994,2.97]",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Petal.Width : Factor w/ 3 levels "(0.0976,0.9]",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: Factor w/ 3 levels "small","medium",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Sepal.Width : Factor w/ 3 levels "small","medium",..: 2 2 2 2 2 3 2 2 2 2 ...
 $ Petal.Length: Factor w/ 3 levels "small","medium",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Petal.Width : Factor w/ 3 levels "small","medium",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

(-3.01,-0.735]  (-0.735,1.54]    (1.54,3.82] 
           212            623             65 

(-3.01,-0.423] (-0.423,0.444]   (0.444,3.82] 
           300            300            300 
 [1] (0.991,5.5] (0.991,5.5] (0.991,5.5] (0.991,5.5] (0.991,5.5] (5.5,10]   
 [7] (5.5,10]    (5.5,10]    (5.5,10]    (5.5,10]    NA         
Levels: (0.991,5.5] (5.5,10] NA
 [1] (0.991,5.5] (0.991,5.5] (0.991,5.5] (0.991,5.5] (0.991,5.5] (5.5,10]   
 [7] (5.5,10]    (5.5,10]    (5.5,10]    (5.5,10]   
Levels: (0.991,5.5] (5.5,10]
Warning message:
In bin(c(1:10, NA), nbins = 2) :
  1 instance(s) removed due to missing values

OneR documentation built on May 2, 2019, 9:33 a.m.