R/hai-data-poly.R

In healthyR.ai: The Machine Learning and AI Modeling Companion to 'healthyR'

#' Data Preprocessor - Polynomial Function
#'
#' @family Data Recipes
#' @family Preprocessor
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @description
#' Takes in a recipe and will scale values using a selected recipe.
#'
#' @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.
#'
#' [recipes::step_poly()]
#' @seealso \url{https://recipes.tidymodels.org/reference/step_poly.html}
#'
#' @param .recipe_object The data that you want to process
#' @param ... 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
#' @param .p_degree The polynomial degree, an integer.
#'
#' @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(rnorm(10, mean = 6, sd = 2), times = 10)
#' df_tbl <- tibble(
#'   date_col = date_seq,
#'   value    = val_seq
#' )
#'
#' rec_obj <- recipe(value ~ ., df_tbl)
#'
#' hai_data_poly(
#'   .recipe_object = rec_obj,
#'   value
#' )$scale_rec_obj %>%
#'   get_juiced_data()
#'
#' @return
#' A list object
#'
#' @export
#'

hai_data_poly <- function(.recipe_object = NULL, ...,
                          .p_degree = 2) {

  # Make sure a recipe was passed
  if (is.null(.recipe_object)) {
    rlang::abort("`.recipe_object` must be passed, please add.")
  } else {
    rec_obj <- .recipe_object
  }

  # * Parameters ----
  terms <- rlang::enquos(...)
  degree <- as.double(.p_degree)

  # * Checks ----
  if (!is.double(degree)) {
    stop(call. = FALSE, "(.p_degree) must be an integer.")
  }

  # If Statement to get the recipe desired ----
  scale_obj <- recipes::step_poly(
    recipe  = rec_obj,
    degree  = degree,
    !!!terms
  )

  # * Recipe List ---
  output <- list(
    rec_base      = rec_obj,
    scale_rec_obj = scale_obj
  )

  # * Return ----
  return(output)
}