R/hai-data-trig.R

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

#' Data Preprocessor - Trigonometric Functions
#'
#' @family Data Recipes
#' @family Preprocessor
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @description
#' Takes in a recipe and will scale values using a selected recipe. To call the
#' recipe use a quoted argument like "sinh", "cosh" or "tanh".
#'
#' @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_hyperbolic()]
#' @seealso \url{https://recipes.tidymodels.org/reference/step_hyperbolic.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 .type_of_scale This is a quoted argument and can be one of the following:
#' -  "sinh"
#' -  "cosh"
#' -  "tanh"
#' @param .inverse A logical: should the inverse function be used? Default is FALSE
#'
#' @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_trig(
#'   .recipe_object = rec_obj,
#'   value,
#'   .type_of_scale = "sinh"
#' )$scale_rec_obj %>%
#'   get_juiced_data()
#'
#' @return
#' A list object
#'
#' @export
#'

hai_data_trig <- function(.recipe_object = NULL, ...,
                          .type_of_scale = "sinh", .inverse = FALSE) {

  # 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(...)
  scale_type <- as.character(.type_of_scale)
  inverse_bool <- as.logical(.inverse)

  # * Checks ----
  if (!tolower(scale_type) %in% c(
    "sinh", "cosh", "tanh"
  )
  ) {
    stop(call. = FALSE, "(.type_of_scale) is not implemented. Please choose
             from 'sinh','cosh','tanh'")
  }

  # If Statement to get the recipe desired ----
  if (scale_type == "sinh") {
    scale_obj <- recipes::step_hyperbolic(
      recipe  = rec_obj,
      func    = scale_type,
      inverse = inverse_bool,
      !!!terms
    )
  } else if (scale_type == "cosh") {
    scale_obj <- recipes::step_hyperbolic(
      recipe  = rec_obj,
      func    = scale_type,
      inverse = inverse_bool,
      !!!terms
    )
  } else if (scale_type == "tanh") {
    scale_obj <- recipes::step_hyperbolic(
      recipe  = rec_obj,
      func    = scale_type,
      inverse = inverse_bool,
      !!!terms
    )
  }

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

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