Merge pull request #103 from pythonhealthdatascience/dev

Dev

Author: amyheather

.Rbuildignore

@@ -13,5 +13,4 @@
 ^rmarkdown$
 ^CITATION\.cff$
 ^run_rmarkdown\.sh$
-^inputs$
 ^mock_paper\.md$

CITATION.cff

@@ -14,10 +14,15 @@ authors:
     email: [email protected]
     affiliation: University of Exeter
     orcid: 'https://orcid.org/0000-0002-6596-3479'
+  - given-names: Tom
+    family-names: Monks
+    email: [email protected]
+    affiliation: University of Exeter Medical School
+    orcid: http://orcid.org/0000-0003-2631-4481
 repository-code: >-
   https://github.com/pythonhealthdatascience/rdesrap_mms
 abstract: >-
   Reproducible analytical pipeline (RAP) for R discrete-event simulation (DES)
   implementing a simple M/M/s queueing model.
-version: 0.4.0
-date-released: '2025-06-04'
+version: 0.5.0
+date-released: '2025-11-07'

DESCRIPTION

@@ -1,12 +1,17 @@
 Package: simulation
 Type: Package
 Title: Simulation
-Version: 0.4.0
+Version: 0.5.0
 Authors@R: c(
     person(
       "Amy", "Heather",
       email = "[email protected]",
       role = c("aut", "cre")
+    ),
+    person(
+      "Tom", "Monks",
+      email = "[email protected]",
+      role = c("ctb")
     )
   )
 URL: https://github.com/pythonhealthdatascience/rdesrap_mms
@@ -19,7 +24,6 @@ RoxygenNote: 7.3.2
 Imports:
     simmer,
     dplyr,
-    purrr,
     rlang,
     tidyr,
     tidyselect,

NAMESPACE

@@ -24,37 +24,40 @@ export(valid_inputs)
 importFrom(R6,R6Class)
 importFrom(dplyr,across)
 importFrom(dplyr,arrange)
-importFrom(dplyr,bind_cols)
 importFrom(dplyr,bind_rows)
 importFrom(dplyr,desc)
 importFrom(dplyr,filter)
-importFrom(dplyr,full_join)
 importFrom(dplyr,group_by)
 importFrom(dplyr,lead)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
 importFrom(dplyr,n)
 importFrom(dplyr,n_distinct)
+importFrom(dplyr,pull)
 importFrom(dplyr,rename)
+importFrom(dplyr,row_number)
 importFrom(dplyr,select)
 importFrom(dplyr,slice)
+importFrom(dplyr,slice_head)
 importFrom(dplyr,summarise)
 importFrom(dplyr,transmute)
 importFrom(dplyr,ungroup)
+importFrom(future,availableCores)
 importFrom(future,multisession)
 importFrom(future,plan)
 importFrom(future,sequential)
 importFrom(future.apply,future_lapply)
+importFrom(ggplot2,aes)
 importFrom(ggplot2,aes_string)
 importFrom(ggplot2,annotate)
 importFrom(ggplot2,geom_line)
+importFrom(ggplot2,geom_ribbon)
 importFrom(ggplot2,geom_vline)
 importFrom(ggplot2,ggplot)
 importFrom(ggplot2,ggsave)
 importFrom(ggplot2,labs)
 importFrom(ggplot2,theme_minimal)
 importFrom(gridExtra,marrangeGrob)
-importFrom(purrr,reduce)
 importFrom(rlang,.data)
 importFrom(simmer,add_generator)
 importFrom(simmer,add_resource)
@@ -73,7 +76,9 @@ importFrom(simmer,timeout)
 importFrom(simmer,trajectory)
 importFrom(simmer,wrap)
 importFrom(stats,rexp)
+importFrom(stats,sd)
 importFrom(stats,setNames)
+importFrom(stats,t.test)
 importFrom(tibble,tibble)
 importFrom(tidyr,drop_na)
 importFrom(tidyr,pivot_wider)

NEWS.md

@@ -1,6 +1,35 @@
-# Simple M/M/s queuing model: R DES RAP (unreleased)
+# Simple M/M/s queuing model: R DES RAP v0.5.0
 
-TBC.
+This update adds new performance measures and tests, improves the GitHub actions, implements some fixes and documentation improvements, among other changes.
+
+## New features
+
+* Add choice of operating system when manually trigger R-CMD-check GitHub action.
+* Add check in model validation for non-null file path when `log_to_file = TRUE`.
+* Calculate test coverage.
+* Environment - add `covr` and `queueing` and `DT` and switched to "all" snapshot type.
+* Add `seed_offset` and ran sensitivity analysis on seed in `choosing_replications.Rmd`.
+* Add test comparing against analytical queueing theory model from `queueing` package.
+* Add mean time in system to performance measures.
+* Add `CODE_OF_CONDUCT.md`.
+
+## Bug fixes
+
+* Skip parallel processing test on Mac GitHub action.
+* Fix deviation calculation so it doesn't get stuck for NA/0.
+* Corrected choice of warm-up to run model for longer.
+* Fixed/expanded back tests.
+
+## Other changes
+
+* Replaced `rap_template_r_des` with `rdesrap_mms`.
+* Changed all magrittr's `%>%` to be base R's `>|` (as per tidyverse style guide).
+* Improved `README.md` and `CONTRIBUTING.md`.
+* Simplify package loading.
+* Reorganised tests.
+* Update parameters to match python version of model.
+* Adjusted `analysis.Rmd` bin widths to be more consistent with python version of analysis.
+* Other minor corrections and amendments (e.g., linting, docstrings, tests).
 
 # R DES RAP Template 0.4.0
 

R/choose_replications.R

@@ -514,6 +514,8 @@ ReplicationsAlgorithm <- R6Class("ReplicationsAlgorithm", list( # nolint: object
 #' @param metric Name of performance metric to assess.
 #' @param verbose Boolean, whether to print messages about parameters.
 #'
+#' @importFrom dplyr filter pull select slice_head
+#' @importFrom stats sd t.test
 #' @importFrom utils tail
 #'
 #' @return Dataframe with results from each replication.
@@ -607,6 +609,10 @@ confidence_interval_method <- function(replications, desired_precision,
 #' @param file_path Path and filename to save the plot to.
 #' @param min_rep The number of replications required to meet the desired
 #' precision.
+#'
+#' @importFrom ggplot2 aes geom_line geom_ribbon geom_vline ggplot ggsave labs
+#' @importFrom ggplot2 theme_minimal
+#' @importFrom rlang .data
 
 plot_replication_ci <- function(
   conf_ints, yaxis_title, file_path = NULL, min_rep = NULL

R/choose_warmup.R

@@ -5,18 +5,23 @@
 #' @param result Named list with `arrivals` containing output from
 #' `get_mon_arrivals()` and `resources` containing output from
 #' `get_mon_resources()` (`per_resource = TRUE` and `ongoing = TRUE`).
+#' @param simulation_end_time Time at end of simulation run.
 #' @param file_path Path to save figure to.
 #' @param warm_up Location on X axis to plot vertical red line indicating the
 #' chosen warm-up period. Defaults to NULL, which will not plot a line.
 #'
-#' @importFrom dplyr rename
-#' @importFrom ggplot2 ggplot geom_line aes_string labs theme_minimal geom_vline
-#' @importFrom ggplot2 annotate ggsave
+#' @importFrom dplyr arrange group_by mutate rename select ungroup
+#' @importFrom ggplot2 aes_string annotate geom_line geom_vline ggsave labs
+#' @importFrom ggplot2 theme_minimal ggplot
 #' @importFrom gridExtra marrangeGrob
+#' @importFrom rlang .data
+#' @importFrom tidyselect all_of
 #'
 #' @export
 
-time_series_inspection <- function(result, file_path, warm_up = NULL) {
+time_series_inspection <- function(
+  result, simulation_end_time, file_path, warm_up = NULL
+) {
 
   plot_list <- list()
 
@@ -29,24 +34,28 @@ time_series_inspection <- function(result, file_path, warm_up = NULL) {
   # Wait time of each patient at each time point
   metrics[[1L]] <- result[["arrivals"]] |>
     rename(time = .data[["serve_start"]]) |>
-    select(.data[["replication"]],
-           .data[["time"]],
-           .data[["wait_time"]])
-
-  # Service length of each patient at each time point
-  metrics[[2L]] <- result[["arrivals"]] |>
-    rename(time = .data[["serve_start"]]) |>
-    select(.data[["replication"]],
-           .data[["time"]],
-           .data[["serve_length"]])
+    dplyr::select(all_of(c("replication", "time", "wait_time")))
 
   # Utilisation at each time point
-  metrics[[3L]] <- calc_utilisation(result[["resources"]],
+  metrics[[2L]] <- calc_utilisation(result[["resources"]],
+                                    simulation_end_time = simulation_end_time,
                                     groups = c("resource", "replication"),
                                     summarise = FALSE) |>
-    select(.data[["replication"]],
-           .data[["time"]],
-           .data[["utilisation"]])
+    dplyr::select(all_of(c("replication", "time", "utilisation")))
+
+  # Queue length at each timepoint
+  metrics[[3L]] <- result[["arrivals"]] |>
+    rename(time = .data[["start_time"]]) |>
+    dplyr::select(all_of(c("replication", "time", "queue_on_arrival")))
+
+  # Time in system at each timepoint
+  metrics[[4L]] <- result[["arrivals"]] |>
+    rename(time = .data[["start_time"]]) |>
+    dplyr::select(all_of(c("replication", "time", "time_in_system")))
+
+  # Patients in system at each timepoint
+  metrics[[5L]] <- rename(result[["patients_in_service"]],
+                          patients_in_system = .data[["count"]])
 
   # Loop through all the dataframes in df_list
   for (i in seq_along(metrics)) {