Impute numeric data using the mean

step_impute_mean() creates a specification of a recipe step that will substitute missing values of numeric variables by the training set mean of those variables.

Usage

step_impute_mean(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  means = NULL,
  trim = 0,
  skip = FALSE,
  id = rand_id("impute_mean")
)

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 variables for this step. See selections() for more details.
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.
means: A named numeric vector of means. This is NULL until computed by prep(). Note that, if the original data are integers, the mean will be converted to an integer to maintain the same data type.
trim: The fraction (0 to 0.5) of observations to be trimmed from each end of the variables before the mean is computed. Values of trim outside that range are taken as the nearest endpoint.
skip: A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() 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.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

step_impute_mean() estimates the variable means from the data used in the training argument of prep(). bake() then applies the new values to new data sets using these averages.

As of recipes 0.1.16, this function name changed from step_meanimpute() to step_impute_mean().

Tidying

When you tidy() this step, a tibble is returned with columns terms, value , and id:

terms: character, the selectors or variables selected
value: numeric, the mean value
id: character, id of this step

Tuning Parameters

This step has 1 tuning parameters:

trim: Amount of Trimming (type: double, default: 0)

Sparse data

This step can be applied to sparse_data such that it is preserved. Nothing needs to be done for this to happen as it is done automatically.

Case weights

This step performs an unsupervised operation that can utilize case weights. As a result, case weights are only used with frequency weights. For more information, see the documentation in case_weights and the examples on tidymodels.org.

Examples

data("credit_data", package = "modeldata")

## missing data per column
vapply(credit_data, function(x) mean(is.na(x)), c(num = 0))
#>       Status    Seniority         Home         Time          Age 
#> 0.0000000000 0.0000000000 0.0013471037 0.0000000000 0.0000000000 
#>      Marital      Records          Job     Expenses       Income 
#> 0.0002245173 0.0000000000 0.0004490346 0.0000000000 0.0855410867 
#>       Assets         Debt       Amount        Price 
#> 0.0105523125 0.0040413112 0.0000000000 0.0000000000 

set.seed(342)
in_training <- sample(1:nrow(credit_data), 2000)

credit_tr <- credit_data[in_training, ]
credit_te <- credit_data[-in_training, ]
missing_examples <- c(14, 394, 565)

rec <- recipe(Price ~ ., data = credit_tr)

impute_rec <- rec |>
  step_impute_mean(Income, Assets, Debt)

imp_models <- prep(impute_rec, training = credit_tr)

imputed_te <- bake(imp_models, new_data = credit_te)

credit_te[missing_examples, ]
#>      Status Seniority  Home Time Age Marital Records     Job Expenses
#> 28     good        15 owner   36  43 married      no   fixed       75
#> 688    good         2  rent   60  32 married      no partime       87
#> 1002   good        21  rent   60  39 married      no   fixed      124
#>      Income Assets Debt Amount Price
#> 28      251   4000    0   1800  2557
#> 688     115   2000    0   1250  1517
#> 1002    191   2000    0   2000  2536
imputed_te[missing_examples, names(credit_te)]
#> # A tibble: 3 × 14
#>   Status Seniority Home   Time   Age Marital Records Job     Expenses
#>   <fct>      <int> <fct> <int> <int> <fct>   <fct>   <fct>      <int>
#> 1 good          15 owner    36    43 married no      fixed         75
#> 2 good           2 rent     60    32 married no      partime       87
#> 3 good          21 rent     60    39 married no      fixed        124
#> # ℹ 5 more variables: Income <int>, Assets <int>, Debt <int>,
#> #   Amount <int>, Price <int>

tidy(impute_rec, number = 1)
#> # A tibble: 3 × 3
#>   terms  value id               
#>   <chr>  <dbl> <chr>            
#> 1 Income    NA impute_mean_Hlm2y
#> 2 Assets    NA impute_mean_Hlm2y
#> 3 Debt      NA impute_mean_Hlm2y
tidy(imp_models, number = 1)
#> # A tibble: 3 × 3
#>   terms  value id               
#>   <chr>  <dbl> <chr>            
#> 1 Income   142 impute_mean_Hlm2y
#> 2 Assets  5378 impute_mean_Hlm2y
#> 3 Debt     364 impute_mean_Hlm2y