Convert a Continuous Variable into a Categorical Variable
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, ...) discretizeDF(df, methods = NULL, default = NULL)
x |
a numeric vector (continuous variable). |
method |
discretization method. Available are: |
breaks, categories |
|
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
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 caclulated 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, non-unique 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
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.
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
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")
### k-means clustering
table(discretize(x, method = "cluster", breaks = 3))
hist(x, breaks = 20, main = "K-Means")
abline(v = discretize(x, method = "cluster", breaks = 3,
onlycuts = TRUE), col = "red")
### user-specified (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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.