recipes: discretize – R documentation

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discretize

Discretize Numeric Variables

Description

discretize converts a numeric vector into a factor with bins having approximately the same number of data points (based on a training set).

Usage

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, ...)

Arguments

`x`	A numeric vector
`...`	Options to pass to `stats::quantile()` that should not include `x` or `probs`.
`cuts`	An integer defining how many cuts to make of the data.
`labels`	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).
`prefix`	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.
`keep_na`	A logical for whether a factor level should be created to identify missing values in `x`.
`infs`	A logical indicating whether the smallest and largest cut point should be infinite.
`min_unique`	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.
`object`	An object of class `discretize`.
`new_data`	A new numeric object to be binned.

Details

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.

Value

discretize returns an object of class discretize and predict.discretize returns a factor vector.

Examples

library(modeldata)
data(biomass)

biomass_tr <- biomass[biomass$dataset == "Training",]
biomass_te <- biomass[biomass$dataset == "Testing",]

median(biomass_tr$carbon)
discretize(biomass_tr$carbon, cuts = 2)
discretize(biomass_tr$carbon, cuts = 2, infs = FALSE)
discretize(biomass_tr$carbon, cuts = 2, infs = FALSE, keep_na = FALSE)
discretize(biomass_tr$carbon, cuts = 2, prefix = "maybe a bad idea to bin")

carbon_binned <- discretize(biomass_tr$carbon)
table(predict(carbon_binned, biomass_tr$carbon))

carbon_no_infs <- discretize(biomass_tr$carbon, infs = FALSE)
predict(carbon_no_infs, c(50, 100))

rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
              data = biomass_tr)
rec <- rec %>% step_discretize(carbon, hydrogen)
rec <- prep(rec, biomass_tr)
binned_te <- bake(rec, biomass_te)
table(binned_te$carbon)

recipes

Preprocessing Tools to Create Design Matrices

v0.1.16

MIT + file LICENSE

Authors

Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph]

Initial release