R/bin2factor.R

In recipes: Preprocessing and Feature Engineering Steps for Modeling

#' Create a Factors from A Dummy Variable
#'
#' `step_bin2factor()` creates a *specification* of a recipe step that will
#' create a two-level factor from a single dummy variable.
#'
#' @inheritParams step_center
#' @inheritParams step_pca
#' @param levels A length 2 character string that indicates the
#'  factor levels for the 1's (in the first position) and the zeros
#'  (second)
#' @param ref_first Logical. Should the first level, which replaces
#' 1's, be the factor reference level?
#' @template step-return
#' @details This operation may be useful for situations where a
#'  binary piece of information may need to be represented as
#'  categorical instead of numeric. For example, naive Bayes models
#'  would do better to have factor predictors so that the binomial
#'  distribution is modeled instead of a Gaussian probability
#'  density of numeric binary data. Note that the numeric data is
#'  only verified to be numeric (and does not count levels).
#'
#'  # Tidying
#'
#'  When you [`tidy()`][tidy.recipe()] this step, a tibble with column
#'  `terms` (the columns that will be affected) is returned.
#'
#' @template case-weights-not-supported
#'
#' @family dummy variable and encoding steps
#' @export
#' @examplesIf rlang::is_installed("modeldata")
#' data(covers, package = "modeldata")
#'
#' rec <- recipe(~description, covers) %>%
#'   step_regex(description, pattern = "(rock|stony)", result = "rocks") %>%
#'   step_regex(description, pattern = "(rock|stony)", result = "more_rocks") %>%
#'   step_bin2factor(rocks)
#'
#' tidy(rec, number = 3)
#'
#' rec <- prep(rec, training = covers)
#' results <- bake(rec, new_data = covers)
#'
#' table(results$rocks, results$more_rocks)
#'
#' tidy(rec, number = 3)
step_bin2factor <-
  function(recipe,
           ...,
           role = NA,
           trained = FALSE,
           levels = c("yes", "no"),
           ref_first = TRUE,
           columns = NULL,
           skip = FALSE,
           id = rand_id("bin2factor")) {
    if (length(levels) != 2 | !is.character(levels)) {
      rlang::abort("`levels` should be a two element character string")
    }
    add_step(
      recipe,
      step_bin2factor_new(
        terms = enquos(...),
        role = role,
        trained = trained,
        levels = levels,
        ref_first = ref_first,
        columns = columns,
        skip = skip,
        id = id
      )
    )
  }

step_bin2factor_new <-
  function(terms, role, trained, levels, ref_first, columns, skip, id) {
    step(
      subclass = "bin2factor",
      terms = terms,
      role = role,
      trained = trained,
      levels = levels,
      ref_first = ref_first,
      columns = columns,
      skip = skip,
      id = id
    )
  }

#' @export
prep.step_bin2factor <- function(x, training, info = NULL, ...) {
  col_names <- recipes_eval_select(x$terms, training, info)
  check_type(training[, col_names], types = c("double", "integer", "logical"))

  step_bin2factor_new(
    terms = x$terms,
    role = x$role,
    trained = TRUE,
    levels = x$levels,
    ref_first = x$ref_first,
    columns = col_names,
    skip = x$skip,
    id = x$id
  )
}

#' @export
bake.step_bin2factor <- function(object, new_data, ...) {
  col_names <- names(object$columns)
  check_new_data(col_names, object, new_data)

  levs <- if (object$ref_first) object$levels else rev(object$levels)

  for (col_name in col_names) {
    tmp <- ifelse(
      new_data[[col_name]] == 1,
      object$levels[1],
      object$levels[2]
    )
    tmp <- factor(tmp, levels = levs)

    new_data[[col_name]] <- tmp
  }

  new_data
}

print.step_bin2factor <-
  function(x, width = max(20, options()$width - 30), ...) {
    title <- "Dummy variable to factor conversion for "
    print_step(x$columns, x$terms, x$trained, title, width)
    invisible(x)
  }


#' @rdname tidy.recipe
#' @export
tidy.step_bin2factor <- function(x, ...) {
  res <- simple_terms(x, ...)
  res$id <- x$id
  res
}