R: Create a Factors from A Dummy Variable

step_bin2factor {recipes} | R Documentation

## Create a Factors from A Dummy Variable

### Description

`step_bin2factor` creates a specification of a recipe step that will create a two-level factor from a single dummy variable.

### Usage

    step_bin2factor(recipe, ..., role = NA, trained = FALSE, levels = c("yes",
      "no"), columns = NULL, skip = FALSE)

    ## S3 method for class 'step_bin2factor'
    tidy(x, ...)

### Arguments

`recipe` | A recipe object. The step will be added to the sequence of operations for this recipe.
`...` | Selector functions that choose which variables will be converted. 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.
`levels` | A length 2 character string that indicate the factor levels for the 1's (in the first position) and the zeros (second)
`columns` | A vector with the selected variable names. This is `NULL` until computed 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_bin2factor` object.

### Details

This operation may be useful for situations where a binary piece of information may need to be represented as categorical instead of numeric. For example, naive Bayes models would do better to have factor predictors so that the binomial distribution is modeled in stead of a Gaussian probability density of numeric binary data. Note that the numeric data is only verified to be numeric (and does not count levels).

### 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).

### Examples

    data(covers)

    rec <- recipe(~ description, covers) %>%
     step_regex(description, pattern = "(rock|stony)", result = "rocks") %>%
     step_regex(description, pattern = "(rock|stony)", result = "more_rocks") %>%
     step_bin2factor(rocks)

    tidy(rec, number = 3)

    rec <- prep(rec, training = covers)
    results <- bake(rec, newdata = covers)

    table(results$rocks, results$more_rocks)

    tidy(rec, number = 3)

[Package recipes version 0.1.2 Index]