step_mutate {recipes} | R Documentation

## Add new variables using `mutate`

### Description

`step_mutate` creates a specification of a recipe step that will add variables using `dplyr::mutate()`.

### Usage

    step_mutate(recipe, ..., role = "predictor", trained = FALSE,
      inputs = NULL, skip = FALSE, id = rand_id("mutate"))

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

### Arguments

`recipe` | A recipe object. The step will be added to the sequence of operations for this recipe.
`...` | Name-value pairs of expressions. See `dplyr::mutate()`. If the argument is not named, the expression is converted to a column name.
`role` | For model terms created by this step, what analysis role should they be assigned? By default, the function assumes that the new dimension columns created by the original variables will be used as predictors in a model.
`trained` | A logical to indicate if the quantities for preprocessing have been estimated.
`inputs` | Quosure(s) of `...`.
`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
`id` | A character string that is unique to this step to identify it.
`x` | A `step_mutate` object

### Details

When an object in the user's global environment is referenced in the expression defining the new variable(s), it is a good idea to use quasiquotation (e.g. `!!`) to embed the value of the object in the expression (to be portable between sessions). See the examples.

### 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 `values` which contains the `mutate` expressions as character strings (and are not reparsable).

### Examples

    rec <-
      recipe( ~ ., data = iris) %>%
      step_mutate(
        dbl_width = Sepal.Width * 2,
        half_length = Sepal.Length / 2
      )

    prepped <- prep(rec, training = iris %>% slice(1:75), retain = TRUE)

    library(dplyr)

    dplyr_train <-
      iris %>%
      as_tibble() %>%
      slice(1:75) %>%
      mutate(
        dbl_width = Sepal.Width * 2,
        half_length = Sepal.Length / 2
      )

    rec_train <- juice(prepped)
    all.equal(dplyr_train, rec_train)

    dplyr_test <-
      iris %>%
      as_tibble() %>%
      slice(76:150) %>%
      mutate(
        dbl_width = Sepal.Width * 2,
        half_length = Sepal.Length / 2
      )
    rec_test <- bake(prepped, iris %>% slice(76:150))
    all.equal(dplyr_test, rec_test)

    # Embedding objects:
    const <- 1.414

    qq_rec <-
      recipe( ~ ., data = iris) %>%
      step_mutate(
        bad_approach = Sepal.Width * const,
        best_approach = Sepal.Width * !!const
      ) %>%
      prep(training = iris, retain = TRUE)

    juice(qq_rec, contains("appro")) %>% slice(1:4)

    # The difference:
    tidy(qq_rec, number = 1)

[Package recipes version 0.1.5 Index]