discretize() converts a numeric vector into a factor with
bins having approximately the same number of data points (based
on a training set).
discretize(x, ...)
# Default S3 method
discretize(x, ...)
# S3 method for class 'numeric'
discretize(
x,
cuts = 4,
labels = NULL,
prefix = "bin",
keep_na = TRUE,
infs = TRUE,
min_unique = 10,
...
)
# S3 method for class 'discretize'
predict(object, new_data, ...)A numeric vector
Options to pass to
stats::quantile() that should not include x
or probs.
An integer defining how many cuts to make of the data.
A character vector defining the factor levels
that will be in the new factor (from smallest to largest). This
should have length cuts+1 and should not include a level
for missing (see keep_na below).
A single parameter value to be used as a prefix
for the factor levels (e.g. bin1, bin2, ...). If
the string is not a valid R name, it is coerced to one.
If prefix = NULL then the factor levels will be labelled
according to the output of cut().
A logical for whether a factor level should be
created to identify missing values in x. If keep_na is
set to TRUE then na.rm = TRUE is used when calling
stats::quantile().
A logical indicating whether the smallest and largest cut point should be infinite.
An integer defining a sample size line of
dignity for the binning. If (the number of unique
values)/(cuts+1) is less than min_unique, no
discretization takes place.
An object of class discretize.
A new numeric object to be binned.
discretize returns an object of class
discretize and predict.discretize returns a factor
vector.
discretize estimates the cut points from
x using percentiles. For example, if cuts = 3, the
function estimates the quartiles of x and uses these as
the cut points. If cuts = 2, the bins are defined as
being above or below the median of x.
The predict method can then be used to turn numeric
vectors into factor vectors.
If keep_na = TRUE, a suffix of "_missing" is used as a
factor level (see the examples below).
If infs = FALSE and a new value is greater than the
largest value of x, a missing value will result.
data(biomass, package = "modeldata")
biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]
median(biomass_tr$carbon)
#> [1] 47.1
discretize(biomass_tr$carbon, cuts = 2)
#> Bins: 3 (includes missing category)
#> Breaks: -Inf, 47.1, Inf
discretize(biomass_tr$carbon, cuts = 2, infs = FALSE)
#> Bins: 3 (includes missing category)
#> Breaks: 14.61, 47.1, 97.18
discretize(biomass_tr$carbon, cuts = 2, infs = FALSE, keep_na = FALSE)
#> Bins: 2
#> Breaks: 14.61, 47.1, 97.18
discretize(biomass_tr$carbon, cuts = 2, prefix = "maybe a bad idea to bin")
#> Warning: The prefix "maybe a bad idea to bin" is not a valid R name. It has been changed
#> to "maybe.a.bad.idea.to.bin".
#> Bins: 3 (includes missing category)
#> Breaks: -Inf, 47.1, Inf
carbon_binned <- discretize(biomass_tr$carbon)
table(predict(carbon_binned, biomass_tr$carbon))
#>
#> bin1 bin2 bin3 bin4
#> 114 115 113 114
carbon_no_infs <- discretize(biomass_tr$carbon, infs = FALSE)
predict(carbon_no_infs, c(50, 100))
#> [1] bin4 <NA>
#> Levels: bin1 bin2 bin3 bin4