R/roi.R

In modelimpact: Functions to Assess the Business Impact of Churn Prediction Models

#' Calculate Return on investment (ROI)
#'
#' Calculates ROI after sorting observations with ROI defined as (Current Value - Start Value) / Start Value
#' @param x A data frame containing predicted probabilities of a target event and the actual outcome/class.
#' @param fixed_cost Fixed cost (e.g. of a campaign)
#' @param var_cost Variable cost (e.g. discount offered)
#' @param tp_val The average value of a True Positive
#' @param prob_col The unquoted name of the column with probabilities of the event of interest.
#' @param truth_col The unquoted name of the column with the actual outcome/class. Possible values are 'Yes' and 'No'.
#'
#' @return
#' A data frame with the following columns:
#'
#' row       = row numbers \cr
#' pct       = percentiles \cr
#' cum_rev   = cumulated revenue \cr
#' cost_sum  = cumulated costs \cr
#' roi       = return on investment
#'
#' @export
#' @examples
#' roi(predictions,
#'    fixed_cost = 1000,
#'    var_cost   = 100,
#'    tp_val     = 2000,
#'    prob_col   = Yes,
#'    truth_col  = Churn)

roi <- function(x,
                fixed_cost = 0,
                var_cost   = 0,
                tp_val     = 0,
                prob_col   = NA,
                truth_col  = NA ) {

  x %>%
    dplyr::arrange(dplyr::desc({{ prob_col }})) %>%
    dplyr::mutate(row = dplyr::row_number()) %>%
    dplyr::mutate(pct = dplyr::ntile(row,100)) %>%
    dplyr::mutate(cost = var_cost) %>%
    dplyr::mutate(cost_sum = cumsum(cost) + fixed_cost) %>%
    dplyr::mutate(rev = dplyr::if_else({{ truth_col }} == "Yes",tp_val,0)) %>%
    dplyr::mutate(cum_rev = cumsum(rev)) %>%
    dplyr::mutate(roi = (cum_rev - cost_sum) /cost_sum ) %>%
    dplyr::select(row,pct,cum_rev,cost_sum,roi)

}

utils::globalVariables(c("fixed_cost", "var_cost","tp_val","prob_col","truth_col","pct","cost","cost_sum","cum_rev","preds"))