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