R: Up-Sample a Data Set Based on a Factor Variable

step_upsample {recipes} | R Documentation

## Up-Sample a Data Set Based on a Factor Variable

### Description

`step_upsample` creates a specification of a recipe step that will replicate rows of a data set to make the occurrence of levels in a specific factor level equal.

### Usage

    step_upsample(recipe, ..., ratio = 1, role = NA, trained = FALSE,
      column = NULL, target = NA, skip = TRUE)

    ## S3 method for class 'step_upsample'
    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 variable is used to sample the data. See `selections()` for more details. The selection should result in single factor variable. For the `tidy` method, these are not currently used.
`ratio` | A numeric value for the ratio of the majority-to-minority frequencies. The default value (1) means that all other levels are sampled up to have the same frequency as the most occurring level. A value of 0.5 would mean that the minority levels will have (at most) (approximately) half as many rows than the majority level.
`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.
`column` | A character string of the variable name that will be populated (eventually) by the `...` selectors.
`target` | An integer that will be used to subsample. This should not be set by the user and will be populated by `prep`.
`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_upsample` object.

### Details

Up-sampling is intended to be performed on the training set alone. For this reason, the default is `skip = TRUE`. It is advisable to use `prep(recipe, retain = TRUE)` when preparing the recipe; in this way `juice()` can be used to obtain the up-sampled version of the data.

If there are missing values in the factor variable that is used to define the sampling, missing data are selected at random in the same way that the other factor levels are sampled. Missing values are not used to determine the amount of data in the majority level (see example below).

For any data with factor levels occurring with the same frequency as the majority level, all data will be retained.

All columns in the data are sampled and returned by `juice()` and `bake()`.

### 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` which is the variable used to sample.

### Examples

    data(okc)

    orig <- table(okc$diet, useNA = "always")

    sort(orig, decreasing = TRUE)

    up_rec <- recipe( ~ ., data = okc) %>%
      # Bring the minority levels up to about 200 each
      # 200/16562 is approx 0.0121
      step_upsample(diet, ratio = 0.0121) %>%
      prep(training = okc, retain = TRUE)

    training <- table(juice(up_rec)$diet, useNA = "always")

    # Since `skip` defaults to TRUE, baking the step has no effect
    baked_okc <- bake(up_rec, newdata = okc)
    baked <- table(baked_okc$diet, useNA = "always")

    # Note that if the original data contained more rows than the
    # target n (= ratio * majority_n), the data are left alone:
    data.frame(
      level = names(orig),
      orig_freq = as.vector(orig),
      train_freq = as.vector(training),
      baked_freq = as.vector(baked)
    )

[Package recipes version 0.1.2 Index]