rfm-table-customer.R

#' RFM table (customer data)
#'
#' Recency, frequency, monetary and RFM score.
#'
#' @param data A \code{data.frame} or \code{tibble}.
#' @param customer_id Unique id of the customer.
#' @param n_transactions Number of transactions/orders.
#' @param recency Days since last visit or date of last visit.
#' @param total_revenue Total revenue from the customer.
#' @param analysis_date Date of analysis.
#' @param recency_bins Number of bins for recency or custom threshold.
#' @param frequency_bins Number of bins for frequency or custom threshold.
#' @param monetary_bins Number of bins for monetary or custom threshold.
#' @param ... Other arguments.
#'
#' @return \code{rfm_table_order} returns a list with the following:
#'
#' \item{rfm}{RFM table.}
#' \item{analysis_date}{Date of analysis.}
#' \item{frequency_bins}{Number of bins used for frequency score.}
#' \item{recency_bins}{Number of bins used for recency score.}
#' \item{monetary_bins}{Number of bins used for monetary score.}
#' \item{threshold}{thresholds used for generating RFM scores.}
#'
#' @examples
#' analysis_date <- as.Date('2007-01-01')
#'
#' # data includes days since last visit
#' rfm_table_customer(rfm_data_customer, customer_id, number_of_orders,
#' recency_days, revenue, analysis_date)
#'
#' # data includes last visit date
#' rfm_table_customer(rfm_data_customer, customer_id, number_of_orders,
#' most_recent_visit, revenue, analysis_date)
#'
#' # access rfm table
#' result <- rfm_table_customer(rfm_data_customer, customer_id, number_of_orders,
#' recency_days, revenue, analysis_date)
#' result$rfm
#'
#' # using custom threshold
#' rfm_table_customer(rfm_data_customer, customer_id, number_of_orders,
#' recency_days, revenue, analysis_date, recency_bins = c(115, 181, 297, 482),
#' frequency_bins = c(4, 5, 6, 8), monetary_bins = c(256, 382, 506, 666))
#'
#' @importFrom dplyr pull
#'
#' @export
#'
rfm_table_customer <- function(data = NULL, customer_id = NULL,
                               n_transactions = NULL, recency = NULL,
                               total_revenue = NULL, analysis_date = NULL,
                               recency_bins = 5, frequency_bins = 5,
                               monetary_bins = 5, ...) UseMethod("rfm_table_customer")

#' @export
#'
rfm_table_customer.default <- function(data = NULL, customer_id = NULL,
                                       n_transactions = NULL, recency = NULL,
                                       total_revenue = NULL, analysis_date = NULL,
                                       recency_bins = 5, frequency_bins = 5,
                                       monetary_bins = 5, ...) {

  col_names <- c("customer_id", "recency_days", "transaction_count", "amount")

  is_days <-
    data %>%
    pull({{ recency }}) %>%
    is.numeric()

  if (is_days) {
    result <-
      data %>%
      select({{ customer_id }}, {{ recency }}, {{ n_transactions }},
             {{ total_revenue }}) %>%
      set_names(col_names)

    other_cols <- 
      data %>%
      select(!c({{ recency }}, {{ n_transactions }}, {{ total_revenue }}))
  } else {
    result <-
      data %>%
      mutate(
        recency_days = as.numeric(analysis_date - {{ recency }},
                                  units = "days")) %>%
      select({{ customer_id }}, recency_days, {{ n_transactions }},
             {{ total_revenue }}) %>%
      set_names(col_names)

    other_cols <- 
      data %>%
      select(!c(recency_days, {{ n_transactions }}, {{ total_revenue }}))
  }

  out <- rfm_prep_bins(result, recency_bins, frequency_bins, monetary_bins,
                       analysis_date, other_cols)
  class(out) <- c("rfm_table_customer", "tibble", "data.frame")
  return(out)

}

#' @export
#'
print.rfm_table_customer <- function(x, ...) {
  print(x$rfm)
}