discretize  R Documentation 
This function implements several basic unsupervised methods to convert a continuous variable into a categorical variable (factor) using different binning strategies. For convenience, a whole data.frame can be discretized (i.e., all numeric columns are discretized).
discretize( x, method = "frequency", breaks = 3, labels = NULL, include.lowest = TRUE, right = FALSE, dig.lab = 3, ordered_result = FALSE, infinity = FALSE, onlycuts = FALSE, categories = NULL, ... ) discretizeDF(df, methods = NULL, default = NULL)
x 
a numeric vector (continuous variable). 
method 
discretization method. Available are: 
breaks, categories 
either number of categories or a vector with boundaries for
discretization (all values outside the boundaries will be set to NA).

labels 
character vector; labels for the levels of the resulting
category. By default, labels are constructed using "(a,b]" interval
notation. If 
include.lowest 
logical; should the first interval be closed to the left? 
right 
logical; should the intervals be closed on the right (and open on the left) or vice versa? 
dig.lab 
integer; number of digits used to create labels. 
ordered_result 
logical; return a ordered factor? 
infinity 
logical; should the first/last break boundary changed to +/Inf? 
onlycuts 
logical; return only computed interval boundaries? 
... 
for method "cluster" further arguments are passed on to

df 
data.frame; each numeric column in the data.frame is discretized. 
methods 
named list of lists or a data.frame; the named list contains
list of discretization parameters (see parameters of 
default 
named list; parameters for 
Discretize calculates breaks between intervals using various methods and
then uses base::cut()
to convert the numeric values into intervals
represented as a factor.
Discretization may fail for several reasons. Some reasons are
A variable contains only a single value. In this case, the variable should be dropped or directly converted into a factor with a single level (see factor).
Some calculated breaks are not unique. This can happen for method frequency with very skewed data (e.g., a large portion of the values is 0). In this case, nonunique breaks are dropped with a warning. It would be probably better to look at the histogram of the data and decide on breaks for the method fixed.
discretize
only implements unsupervised discretization. See
arulesCBA::discretizeDF.supervised()
in package arulesCBA
for supervised discretization.
discretizeDF()
applies discretization to each numeric column.
Individual discretization parameters can be specified in the form:
methods = list(column_name1 = list(method = ,...), column_name2 = list(...))
.
If no discretization method is specified for a column, then the
discretization in default is applied (NULL
invokes the default method
in discretize()
). The special method "none"
can be specified
to suppress discretization for a column.
discretize()
returns a factor representing the
categorized continuous variable with
attribute "discretized:breaks"
indicating the used breaks or and
"discretized:method"
giving the used method. If onlycuts = TRUE
is used, a vector with the calculated interval boundaries is returned.
discretizeDF()
returns a discretized data.frame.
Michael Hahsler
base::cut()
,
arulesCBA::discretizeDF.supervised()
.
Other preprocessing:
hierarchy
,
itemCoding
,
merge()
,
sample()
data(iris) x < iris[,1] ### look at the distribution before discretizing hist(x, breaks = 20, main = "Data") def.par < par(no.readonly = TRUE) # save default layout(mat = rbind(1:2,3:4)) ### convert continuous variables into categories (there are 3 types of flowers) ### the default method is equal frequency table(discretize(x, breaks = 3)) hist(x, breaks = 20, main = "Equal Frequency") abline(v = discretize(x, breaks = 3, onlycuts = TRUE), col = "red") # Note: the frequencies are not exactly equal because of ties in the data ### equal interval width table(discretize(x, method = "interval", breaks = 3)) hist(x, breaks = 20, main = "Equal Interval length") abline(v = discretize(x, method = "interval", breaks = 3, onlycuts = TRUE), col = "red") ### kmeans clustering table(discretize(x, method = "cluster", breaks = 3)) hist(x, breaks = 20, main = "KMeans") abline(v = discretize(x, method = "cluster", breaks = 3, onlycuts = TRUE), col = "red") ### userspecified (with labels) table(discretize(x, method = "fixed", breaks = c(Inf, 6, Inf), labels = c("small", "large"))) hist(x, breaks = 20, main = "Fixed") abline(v = discretize(x, method = "fixed", breaks = c(Inf, 6, Inf), onlycuts = TRUE), col = "red") par(def.par) # reset to default ### prepare the iris data set for association rule mining ### use default discretization irisDisc < discretizeDF(iris) head(irisDisc) ### discretize all numeric columns differently irisDisc < discretizeDF(iris, default = list(method = "interval", breaks = 2, labels = c("small", "large"))) head(irisDisc) ### specify discretization for the petal columns and don't discretize the others irisDisc < discretizeDF(iris, methods = list( Petal.Length = list(method = "frequency", breaks = 3, labels = c("short", "medium", "long")), Petal.Width = list(method = "frequency", breaks = 2, labels = c("narrow", "wide")) ), default = list(method = "none") ) head(irisDisc) ### discretize new data using the same discretization scheme as the ### data.frame supplied in methods. Note: NAs may occure if a new ### value falls outside the range of values observed in the ### originally discretized table (use argument infinity = TRUE in ### discretize to prevent this case.) discretizeDF(iris[sample(1:nrow(iris), 5),], methods = irisDisc)
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