#' Prep Data for C5.0 - Recipe
#'
#' @family Preprocessor
#' @family C5.0
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @details This function will automatically prep your data.frame/tibble for
#' use in the C5.0 algorithm. The C5.0 algorithm is a lazy learning classification
#' 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 C5.0 algorithm.
#'
#' @seealso \url{https://www.rulequest.com/see5-unix.html}
#'
#' @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_c50_data_prepper(.data = Titanic, .recipe_formula = Survived ~ .)
#' rec_obj <- hai_c50_data_prepper(Titanic, Survived ~ .)
#' get_juiced_data(rec_obj)
#'
#' @return
#' A recipe object
#'
#' @export
#'
hai_c50_data_prepper <- function(.data, .recipe_formula){
# Recipe ---
rec_obj <- recipes::recipe(.recipe_formula, data = .data) %>%
recipes::step_string2factor(tidyselect::vars_select_helpers$where(is.character))
# Return ----
return(rec_obj)
}
Examples:
> hai_c50_data_prepper(.data = Titanic, .recipe_formula = Survived ~ .)
Recipe
Inputs:
role #variables
outcome 1
predictor 4
Operations:
Factor variables from tidyselect::vars_select_helpers$where(is.character)
> rec_obj <- hai_c50_data_prepper(Titanic, Survived ~ .)
> get_juiced_data(rec_obj)
# A tibble: 32 x 5
Class Sex Age n Survived
<fct> <fct> <fct> <dbl> <fct>
1 1st Male Child 0 No
2 2nd Male Child 0 No
3 3rd Male Child 35 No
4 Crew Male Child 0 No
5 1st Female Child 0 No
6 2nd Female Child 0 No
7 3rd Female Child 17 No
8 Crew Female Child 0 No
9 1st Male Adult 118 No
10 2nd Male Adult 154 No
# ... with 22 more rows
Function:
Examples: