#' Prep Data for glmnet - Recipe
#'
#' @family Preprocessor
#' @family knn
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @details This function will automatically prep your data.frame/tibble for
#' use in the glmnet algorithm. It expects data to be presented in a certain fashion.
#'
#' This function will output a recipe specification.
#'
#' @description Automatically prep a data.frame/tibble for use in the glmnet algorithm.
#'
#' @param .data The data that you are passing to the function. Can be any type
#' of data that is accepted by the `data` parameter of the `recipes::reciep()`
#' function.
#' @param .recipe_formula The formula that is going to be passed. For example
#' if you are using the `iris` data then the formula would most likely be something
#' like `Species ~ .`
#'
#' @examples
#' hai_glmnet_data_prepper(.data = Titanic, .recipe_formula = Survived ~ .)
#' rec_obj <- hai_glmnet_data_prepper(Survived ~ ., Titanic)
#' get_juiced_data(rec_obj)
#'
#' @return
#' A recipe object
#'
#' @export
#'
hai_glmnet_data_prepper <- function(.data, .recipe_formula){
# Recipe ---
rec_obj <- recipes::recipe(.recipe_formula, data = .data) %>%
## For modeling, it is preferred to encode qualitative data as factors
## (instead of character).
recipes::step_string2factor(tidyselect::vars_select_helpers$where(is.character)) %>%
recipes::step_novel(recipes::all_nominal_predictors()) %>%
## This model requires the predictors to be numeric. The most common
## method to convert qualitative predictors to numeric is to create
## binary indicator variables (aka dummy variables) from these
## predictors.
recipes::step_dummy(recipes::all_nominal_predictors()) %>%
## Regularization methods sum up functions of the model slope
## coefficients. Because of this, the predictor variables should be on
## the same scale. Before centering and scaling the numeric predictors,
## any predictors with a single unique value are filtered out.
recipes::step_zv(recipes::all_predictors()) %>%
recipes::step_normalize(recipes::all_numeric_predictors())
# Return ----
return(rec_obj)
}
Example:
> hai_glmnet_data_prepper(.data = Titanic, .recipe_formula = Survived ~ .)
Recipe
Inputs:
role #variables
outcome 1
predictor 4
Operations:
Factor variables from tidyselect::vars_select_helpers$where(is.character)
Novel factor level assignment for recipes::all_nominal_predictors()
Dummy variables from recipes::all_nominal_predictors()
Zero variance filter on recipes::all_predictors()
Centering and scaling for recipes::all_numeric_predictors()
> rec_obj <- hai_glmnet_data_prepper(Survived ~ ., Titanic)
> get_juiced_data(rec_obj)
# A tibble: 32 x 7
n Survived Class_X2nd Class_X3rd Class_Crew Sex_Male Age_Child
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -0.506 No -0.568 -0.568 -0.568 0.984 0.984
2 -0.506 No 1.70 -0.568 -0.568 0.984 0.984
3 -0.248 No -0.568 1.70 -0.568 0.984 0.984
4 -0.506 No -0.568 -0.568 1.70 0.984 0.984
5 -0.506 No -0.568 -0.568 -0.568 -0.984 0.984
6 -0.506 No 1.70 -0.568 -0.568 -0.984 0.984
7 -0.381 No -0.568 1.70 -0.568 -0.984 0.984
8 -0.506 No -0.568 -0.568 1.70 -0.984 0.984
9 0.362 No -0.568 -0.568 -0.568 0.984 -0.984
10 0.627 No 1.70 -0.568 -0.568 0.984 -0.984
# ... with 22 more rows
Function:
Example: