hardhat (version 1.3.0)

get_levels: Extract factor levels from a data frame

Description

get_levels() extracts the levels from any factor columns in data. It is mainly useful for extracting the original factor levels from the predictors in the training set. get_outcome_levels() is a small wrapper around get_levels() for extracting levels from a factor outcome that first calls standardize() on y.

Usage

get_levels(data)
get_outcome_levels(y)

Value

A named list with as many elements as there are factor columns in data

or y. The names are the names of the factor columns, and the values are character vectors of the levels.

If there are no factor columns, NULL is returned.

Arguments

data

A data.frame to extract levels from.

y

The outcome. This can be:

• A factor vector

• A numeric vector

• A 1D numeric array

• A numeric matrix with column names

• A 2D numeric array with column names

• A data frame with numeric or factor columns

See Also

stats::.getXlevels()

Examples

# Factor columns are returned with their levels
get_levels(iris)

# No factor columns
get_levels(mtcars)

# standardize() is first run on `y`
# which converts the input to a data frame
# with an automatically named column, `".outcome"`
get_outcome_levels(y = factor(letters[1:5]))