step_integer {recipes} | R Documentation

## Convert values to predefined integers

### Description

`step_integer` creates a a specification of a recipe step that will convert new data into a set of integers based on the original data values.

### Usage

    step_integer(recipe, ..., role = "predictor", trained = FALSE,
      strict = FALSE, zero_based = FALSE, key = NULL, skip = FALSE,
      id = rand_id("integer"))

    ## S3 method for class 'step_integer'
    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 will be used to create the integer variables. See `selections()` for more details. For the `tidy` method, these are not currently used.
`role` | For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new 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.
`strict` | A logical for whether the values should be returned as integers (as opposed to double).
`zero_based` | A logical for whether the integers should start at zero and new values be appended as the largest integer.
`key` | A list that contains the information needed to create integer variables for each variable contained in `terms`. This is `NULL` until the step is trained 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
`id` | A character string that is unique to this step to identify it.
`x` | A `step_integer` object.

### Details

`step_integer` will determine the unique values of each variable from the training set (excluding missing values), order them, and then assign integers to each value. When baked, each data point is translated to its corresponding integer or a value of zero for yet unseen data (although see the `zero_based` argument above). Missing values propagate.

Factor inputs are ordered by their levels. All others are ordered by `sort`.

Despite the name, the new values are returned as numeric unless `strict = TRUE`, which will coerce the results to integers.

### 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 selectors or variables selected) and `value` is a list column with the conversion key.

### See Also

`step_factor2string()`, `step_string2factor()`, `step_regex()`, `step_count()`, `step_ordinalscore()`, `step_unorder()`, `step_other()` `step_novel()`, `step_dummy()`

### Examples

    data(okc)

    okc$location <- factor(okc$location)

    okc_tr <- okc[1:100, ]
    okc_tr$age[1] <- NA

    okc_te <- okc[101:105, ]
    okc_te$age[1] <- NA
    okc_te$diet[1] <- "fast food"
    okc_te$diet[2] <- NA

    rec <- recipe(Class ~ ., data = okc_tr) %>%
      step_integer(all_predictors()) %>%
      prep(training = okc_tr, retain = TRUE)

    bake(rec, okc_te, all_predictors())
    tidy(rec, number = 1)

[Package recipes version 0.1.5 Index]