Merge pull request #65 from pythonhealthdatascience/dev

Dev

Author: amyheather

DESCRIPTION

@@ -29,7 +29,10 @@ Imports:
     ggplot2,
     tibble,
     gridExtra,
-    R6
+    R6,
+    fitdistrplus,
+    lubridate,
+    plotly
 Suggests:
     testthat (>= 3.0.0),
     patrick,

R/choose_replications.R

@@ -223,7 +223,7 @@ ReplicationsAlgorithm <- R6Class("ReplicationsAlgorithm", list( # nolint: object
 
   #' @field desired_precision The target half width precision for the algorithm
   #' (i.e. percentage deviation of the confidence interval from the mean,
-  #' expressed as a proportion, e.g. 0.05 = 5\%).
+  #' expressed as a proportion, e.g. 0.1 = 10\%). Choice is fairly arbitrary.
   desired_precision = NA,
 
   #' @field initial_replications Number of initial replications to perform.
@@ -257,15 +257,17 @@ ReplicationsAlgorithm <- R6Class("ReplicationsAlgorithm", list( # nolint: object
   #' @param initial_replications Number of initial replications to perform.
   #' @param look_ahead Minimum additional replications to look ahead.
   #' @param replication_budget Maximum allowed replications.
+  #' @param verbose Boolean, whether to print messages about parameters.
   initialize = function(
     param,
     metrics = c("mean_waiting_time_nurse",
                 "mean_serve_time_nurse",
                 "utilisation_nurse"),
-    desired_precision = 0.05,
+    desired_precision = 0.1,
     initial_replications = 3L,
     look_ahead = 5L,
-    replication_budget = 1000L
+    replication_budget = 1000L,
+    verbose = TRUE
   ) {
     self$param <- param
     self$metrics <- metrics
@@ -277,6 +279,12 @@ ReplicationsAlgorithm <- R6Class("ReplicationsAlgorithm", list( # nolint: object
     # Initially set reps to the number of initial replications
     self$reps <- initial_replications
 
+    # Print the parameters
+    if (isTRUE(verbose)) {
+      print("Model parameters:")  # nolint: print_linter
+      print(self$param)
+    }
+
     # Check validity of provided parameters
     self$valid_inputs()
   },
@@ -495,16 +503,20 @@ ReplicationsAlgorithm <- R6Class("ReplicationsAlgorithm", list( # nolint: object
 #' @param replications Number of times to run the model.
 #' @param desired_precision Desired mean deviation from confidence interval.
 #' @param metric Name of performance metric to assess.
+#' @param verbose Boolean, whether to print messages about parameters.
 #'
 #' @importFrom utils tail
 #'
 #' @return Dataframe with results from each replication.
 #' @export
 
 confidence_interval_method <- function(replications, desired_precision,
-                                       metric) {
+                                       metric, verbose = TRUE) {
   # Run model for specified number of replications
   param <- parameters(number_of_runs = replications)
+  if (isTRUE(verbose)) {
+    print(param)
+  }
   results <- runner(param, use_future_seeding = FALSE)[["run_results"]]
 
   # Initialise list to store the results

R/scenarios.R

@@ -4,16 +4,21 @@
 #' different values to run in scenarios
 #' @param base_list List of parameters to use as base for scenarios, which can
 #' be partial (as will input to parameters() function).
+#' @param verbose Boolean, whether to print messages about scenarios as run.
 #'
 #' @return Tibble with results from each replication for each scenario.
 #' @export
 
-run_scenarios <- function(scenarios, base_list) {
+run_scenarios <- function(scenarios, base_list, verbose = TRUE) {
   # Generate all permutations of the scenarios
   all_scenarios <- expand.grid(scenarios)
 
   # Preview the number of scenarios
-  message(sprintf("There are %d scenarios. Running:", nrow(all_scenarios)))
+  if (isTRUE(verbose)) {
+    message(sprintf("There are %d scenarios.", nrow(all_scenarios)))
+    message("Base parameters:")
+    print(base_list)
+  }
 
   results <- list()
 
@@ -27,7 +32,11 @@ run_scenarios <- function(scenarios, base_list) {
     formatted_scenario <- toString(
       paste0(names(scenario_to_run), " = ", scenario_to_run)
     )
-    message("Scenario: ", formatted_scenario)
+
+    # Print the scenario currently running
+    if (isTRUE(verbose)) {
+      message("Scenario: ", formatted_scenario)
+    }
 
     # Create parameter list with scenario-specific values
     s_args <- c(scenario_to_run, list(scenario_name = index))

README.md

@@ -8,7 +8,7 @@ As part of the project STARS (Sharing Tools and Artefacts for Reproducible Simul
 | - | - | - |
 | **Set-up** |
 | Share code with an open licence (⭐) | ✅ | `LICENSE` and `LICENSE.md` |
-| Link publication to a specific version of the code | N/A | No publication. |
+| Link publication to a specific version of the code | N/A | The mock paper images directly from the latest GitHub, so is not stated as being linked to a specific version of the code, like a typical publication. |
 | List dependencies and versions | ✅ | `renv.lock` |
 | **Running the model** |
 | Provide code for all scenarios and sensitivity analyses (⭐) | ✅ | Within `analysis.Rmd` |

docs/heather_2025.md

@@ -15,8 +15,8 @@ RAP fundamentals offering resilience against future change.
 | Data produced by code in an open-source language (e.g., Python, R, SQL). | ✅ | R |
 | Code is version controlled (see [Git basics](https://nhsdigital.github.io/rap-community-of-practice/training_resources/git/introduction-to-git/) and [using Git collaboratively](https://nhsdigital.github.io/rap-community-of-practice/training_resources/git/using-git-collaboratively/) guides). | ✅ | [GitHub](https://github.com/pythonhealthdatascience/rap_template_r_des) |
 | Repository includes a README.md file (or equivalent) that clearly details steps a user must follow to reproduce the code (use [NHS Open Source Policy section on Readmes](https://github.com/nhsx/open-source-policy/blob/main/open-source-policy.md#b-readmes) as a guide). | ✅ | - |
-| Code has been [peer reviewed](https://nhsdigital.github.io/rap-community-of-practice/implementing_RAP/workflow/code-review/). | ❌ | TODO |
-| Code is [published in the open](https://nhsdigital.github.io/rap-community-of-practice/implementing_RAP/publishing_code/how-to-publish-your-code-in-the-open/) and linked to & from accompanying publication (if relevant). | ✅ & N/A | Shared openly. No publication. |
+| Code has been [peer reviewed](https://nhsdigital.github.io/rap-community-of-practice/implementing_RAP/workflow/code-review/). | ✅ | Peer reviewed by Tom Monks |
+| Code is [published in the open](https://nhsdigital.github.io/rap-community-of-practice/implementing_RAP/publishing_code/how-to-publish-your-code-in-the-open/) and linked to & from accompanying publication (if relevant). | ✅ & N/A | Shared openly. No publication, but mock publication included in repository. |
 
 ## 🥈 Silver