healthyR.ai (version 0.1.0)

hai_data_impute: Data Preprocessor - Imputation

Description

Takes in a recipe and will impute missing values using a selected recipe. To call the recipe use a quoted argument like "median" or "bagged".

Usage

hai_data_impute(
  .recipe_object = NULL,
  ...,
  .seed_value = 123,
  .type_of_imputation = "mean",
  .number_of_trees = 25,
  .neighbors = 5,
  .mean_trim = 0,
  .roll_statistic,
  .roll_window = 5
)

Value

A list object

Arguments

.recipe_object

The data that you want to process

...

One or more selector functions to choose variables to be imputed. When used with imp_vars, these dots indicate which variables are used to predict the missing data in each variable. See selections() for more details

.seed_value

To make results reproducible, set the seed.

.type_of_imputation

This is a quoted argument and can be one of the following:

• "bagged"

• "knn"

• "linear"

• "lower"

• "mean"

• "median"

• "mode"

• "roll"

.number_of_trees

This is used for the recipes::step_impute_bag() trees parameter. This should be an integer.

.neighbors

This should be filled in with an integer value if .type_of_imputation selected is "knn".

.mean_trim

This should be filled in with a fraction if .type_of_imputation selected is "mean".

.roll_statistic

This should be filled in with a single unquoted function that takes with it a single argument such as mean. This should be filled in if .type_of_imputation selected is "roll".

.roll_window

This should be filled in with an integer value if .type_of_imputation selected is "roll".

Author

Steven P. Sanderson II, MPH

Details

This function will get your data ready for processing with many types of ml/ai models.

This is intended to be used inside of the data processor and therefore is an internal function. This documentation exists to explain the process and help the user understand the parameters that can be set in the pre-processor function.

See Also

https://recipes.tidymodels.org/reference/index.html#section-step-functions-imputation/

step_impute_bag

recipes::step_impute_bag()

https://recipes.tidymodels.org/reference/step_impute_bag.html

step_impute_knn

recipes::step_impute_knn()

https://recipes.tidymodels.org/reference/step_impute_knn.html

step_impute_linear

recipes::step_impute_linear()

https://recipes.tidymodels.org/reference/step_impute_linear.html

step_impute_lower

recipes::step_impute_lower()

https://recipes.tidymodels.org/reference/step_impute_lower.html

step_impute_mean

recipes::step_impute_mean()

https://recipes.tidymodels.org/reference/step_impute_mean.html

step_impute_median

recipes::step_impute_median()

https://recipes.tidymodels.org/reference/step_impute_median.html

step_impute_mode

recipes::step_impute_mode()

https://recipes.tidymodels.org/reference/step_impute_mode.html

step_impute_roll

recipes::step_impute_roll()

https://recipes.tidymodels.org/reference/step_impute_roll.html

Other Data Recipes: hai_data_poly(), hai_data_scale(), hai_data_transform(), hai_data_trig(), pca_your_recipe()

Other Preprocessor: hai_c50_data_prepper(), hai_cubist_data_prepper(), hai_data_poly(), hai_data_scale(), hai_data_transform(), hai_data_trig(), hai_earth_data_prepper(), hai_glmnet_data_prepper(), hai_knn_data_prepper(), hai_ranger_data_prepper(), hai_svm_poly_data_prepper(), hai_svm_rbf_data_prepper(), hai_xgboost_data_prepper()

Examples

suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(recipes))

date_seq <- seq.Date(from = as.Date("2013-01-01"), length.out = 100, by = "month")
val_seq <- rep(c(rnorm(9), NA), times = 10)
df_tbl <- tibble(
  date_col = date_seq,
  value    = val_seq
)

rec_obj <- recipe(value ~ ., df_tbl)

hai_data_impute(
  .recipe_object = rec_obj,
  value,
  .type_of_imputation = "roll",
  .roll_statistic = median
)$impute_rec_obj %>%
  get_juiced_data()