feat(patients_in_service): calculate mean patients in service

Author: amyheather

R/get_run_results.R

@@ -31,6 +31,7 @@ get_run_results <- function(results, run_number) {
     # Calculate metrics of interest
     metrics <- list(
       calc_arrivals(results[["arrivals"]]),
+      calc_mean_patients_in_service(results[["patients_in_service"]]),
       calc_mean_queue(results[["arrivals"]]),
       calc_mean_wait(results[["arrivals"]], results[["resources"]]),
       calc_mean_serve_length(results[["arrivals"]], results[["resources"]]),
@@ -64,6 +65,38 @@ calc_arrivals <- function(arrivals, groups = NULL) {
 }
 
 
+#' Calculate the time-weighted mean number of patients in the service.
+#'
+#' @param patient_count Dataframe with patient counts over time.
+#' @param groups Optional list of columns to group by for the calculation.
+#'
+#' @return Tibble with column containing mean number of patients in the service.
+#' @export
+
+calc_mean_patients_in_service <- function(patient_count, groups = NULL) {
+  # If provided, group the dataset
+  if (!is.null(groups)) {
+    patient_count <- group_by(patient_count, across(all_of(groups)))
+  }
+  # Calculate the time-weighted number of patients in the service
+  patient_count %>%
+    # Sort by time
+    arrange(time) %>%
+    # Calculate time between this row and the next
+    mutate(interval_duration = (lead(.data[["time"]]) - .data[["time"]])) %>%
+    # Multiply each patient count by its own unique duration. The total of
+    # those is then divided by the total duration of all intervals.
+    # Hence, we are calculated a time-weighted average patient count.
+    summarise(
+      mean_patients_in_service = (
+        sum(.data[["count"]] * .data[["interval_duration"]], na.rm = TRUE) /
+          sum(.data[["interval_duration"]], na.rm = TRUE)
+      )
+    ) %>%
+    ungroup()
+}
+
+
 #' Calculate the time-weighted mean queue length.
 #'
 #' @param arrivals Dataframe with times for each patient with each resource.

R/model.R

@@ -113,7 +113,7 @@ model <- function(run_number, param, set_seed = TRUE) {
       result[["arrivals"]], time = .data[["start_time"]], change = 1L
     )
     arrivals_end <- result[["arrivals"]] %>%
-      drop_na(.data[["end_time"]]) %>%
+      drop_na(all_of("end_time")) %>%
       transmute(time = .data[["end_time"]], change = -1L)
     events <- bind_rows(arrivals_start, arrivals_end)
 
@@ -123,7 +123,7 @@ model <- function(run_number, param, set_seed = TRUE) {
       arrange(.data[["time"]], desc(.data[["change"]])) %>%
       # Use cumulative sum to find number of patients in system at each time
       mutate(count = cumsum(.data[["change"]])) %>%
-      dplyr::select(.data[["time"]], .data[["count"]])
+      dplyr::select(c("time", "count"))
 
     # Replace replication with appropriate run number (as these functions
     # assume, if not supplied with list of envs, that there was one replication)

outputs/base_run_results.csv

@@ -1,101 +1,101 @@
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