R: Simple Value Assignments for Novel Factor Levels

step_novel {recipes} | R Documentation

## Simple Value Assignments for Novel Factor Levels

### Description

`step_novel` creates a specification of a recipe step that will assign a previously unseen factor level to a new value.

### Usage

    step_novel(recipe, ..., role = NA, trained = FALSE, new_level = "new",
      objects = NULL, skip = FALSE)

    ## S3 method for class 'step_novel'
    tidy(x, ...)

### Arguments

`recipe` | A recipe object. The step will be added to the sequence of operations for this recipe.
`...` | One or more selector functions to choose which variables that will be affected by the step. These variables should be character or factor types. See `selections()` for more details. For the `tidy` method, these are not currently used.
`role` | Not used by this step since no new variables are created.
`trained` | A logical to indicate if the quantities for preprocessing have been estimated.
`new_level` | A single character value that will be assigned to new factor levels.
`objects` | A list of objects that contain the information on factor levels that will be determined by `prep.recipe()`.
`skip` | A logical. Should the step be skipped when the recipe is baked by `bake.recipe()`? While all operations are baked when `prep.recipe()` is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using `skip = TRUE` as it may affect the computations for subsequent operations
`x` | A `step_novel` object.

### Details

The selected variables are adjusted to have a new level (given by `new_level`) that is placed in the last position. During preparation there will be no data points associated with this new level since all of the data have been seen.

Note that if the original columns are character, they will be converted to factors by this step.

Missing values will remain missing.

If `new_level` is already in the data given to `prep`, an error is thrown.

### Value

An updated version of `recipe` with the new step added to the sequence of existing steps (if any). For the `tidy` method, a tibble with columns `terms` (the columns that will be affected) and `value` (the factor levels that is used for the new value)

### See Also

`step_factor2string()`, `step_string2factor()`, `dummy_names()`, `step_regex()`, `step_count()`, `step_ordinalscore()`, `step_unorder()`, `step_other()`

### Examples

    data(okc)

    okc_tr <- okc[1:30000,]
    okc_te <- okc[30001:30006,]
    okc_te$diet[3] <- "cannibalism"
    okc_te$diet[4] <- "vampirism"

    rec <- recipe(~ diet + location, data = okc_tr)

    rec <- rec %>%
      step_novel(diet, location)
    rec <- prep(rec, training = okc_tr)

    processed <- bake(rec, okc_te)
    tibble(old = okc_te$diet, new = processed$diet)

    tidy(rec, number = 1)

[Package recipes version 0.1.2 Index]