R/augment-hai-scale-zero-one.R

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

#' Augment Function Scale Zero One
#'
#' @family Augment Function
#' @family Scale
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @description
#' Takes a numeric vector and will return a vector that has been scaled from `[0,1]`
#'
#' @details
#' Takes a numeric vector and will return a vector that has been scaled from `[0,1]`
#' The input vector must be numeric. The computation is fairly straightforward.
#' This may be helpful when trying to compare the distributions of data where a
#' distribution like beta from the `fitdistrplus` package which requires data to be
#' between 0 and 1
#'
#' \deqn{y[h] = (x - min(x))/(max(x) - min(x))}
#'
#' This function is intended to be used on its own in order to add columns to a
#' tibble.
#'
#' @param .data The data being passed that will be augmented by the function.
#' @param .value This is passed [rlang::enquo()] to capture the vectors you want
#' to augment.
#' @param .names This is set to 'auto' by default but can be a user supplied
#' character string.
#'
#' @examples
#' df <- data.frame(x = rnorm(100, 2, 1))
#' hai_scale_zero_one_augment(df, x)
#'
#' @return
#' An augmented tibble
#'
#' @export
#

hai_scale_zero_one_augment <- function(.data, .value, .names = "auto") {
  column_expr <- rlang::enquo(.value)

  if (rlang::quo_is_missing(column_expr)) {
    stop(call. = FALSE, "The .value argument must be supplied.")
  }

  col_nms <- names(tidyselect::eval_select(rlang::enquo(.value), .data))

  make_call <- function(col) {
    rlang::call2(
      "hai_scale_zero_one_vec",
      .x = rlang::sym(col),
      .ns = "healthyR.ai"
    )
  }

  grid <- expand.grid(
    col = col_nms,
    stringsAsFactors = FALSE
  )

  calls <- purrr::pmap(.l = list(grid$col), make_call)

  if (any(.names == "auto")) {
    newname <- paste0("hai_scale_zero_one_", grid$col)
  } else {
    newname <- as.list(.names)
  }

  calls <- purrr::set_names(calls, newname)

  ret <- tibble::as_tibble(dplyr::mutate(.data, !!!calls))

  return(ret)
}