R/sv_get_picking_speed.R

In anthonypileggi/skuvaultr: Access the Skuvault API

#' Get picking speed (overall; user-specific)
#' @param start_date first day of data (Date/scalar)
#' @param end_date last day of data (Date/scalar)
#' @importFrom magrittr "%>%"
#' @export
sv_get_picking_speed <- function(start_date = Sys.Date(),
                                 end_date = Sys.Date(),
                                 show_plot = TRUE) {

  x <- sv_get_transactions(start_date = start_date, end_date = end_date) %>%
    dplyr::filter(
      TransactionType == "Pick",
      Location != "WH1--DROP-SHIPS"
      ) %>%
    dplyr::arrange(TransactionDate)

  # overall
  out1 <- x %>%
    dplyr::mutate(
      diff = as.numeric(difftime(TransactionDate, dplyr::lag(TransactionDate), units = "secs")),
      diff = ifelse(diff > 60*30, NA, diff)
    ) %>%
    dplyr::summarize(
      User = "Total",
      picked = sum(Quantity),
      avg = mean(diff, na.rm = TRUE),
      recent = mean(tail(diff, 20), na.rm = TRUE)
    )

  # users
  out2 <- x %>%
    dplyr::group_by(User) %>%
    dplyr::mutate(
      diff = as.numeric(difftime(TransactionDate, dplyr::lag(TransactionDate), units = "secs")),
      diff = ifelse(diff > 60*30, NA, diff)
    ) %>%
    dplyr::summarize(
      picked = sum(Quantity),
      avg = mean(diff, na.rm = TRUE),
      recent = mean(tail(diff, 20), na.rm = TRUE)
    )

  out <- dplyr::bind_rows(out2, out1)

  if (show_plot) {
    require(ggplot2)
    g1 <- x %>%
      dplyr::group_by(User) %>%
      dplyr::mutate(count = cumsum(Quantity)) %>%
      ggplot(aes(x = TransactionDate, y = count, group = User, color = User)) +
      geom_point() +
      labs(x = NULL, y = "Total Picked", color = NULL) +
      theme_bw()
    print(g1)
  }

  return(out)
}