style(lint): linting all + re-running rmd

amyheather · amyheather · commit a0ea710ab7f8 · 2025-03-17T16:55:44.000Z
diff --git a/R/choose_replications.R b/R/choose_replications.R
@@ -459,12 +459,6 @@ confidence_interval_method <- function(replications, desired_precision,
   param <- parameters(number_of_runs = replications)
   results <- runner(param, use_future_seeding = FALSE)[["run_results"]]
 
-  # If mean of metric is less than 1, multiply by 100
-  # if (mean(results[[metric]]) < 1L) {
-  #   results[[paste0("adj_", metric)]] <- results[[metric]] * 100L
-  #   metric <- paste0("adj_", metric)
-  # }
-
   # Initialise list to store the results
   cumulative_list <- list()
 
@@ -476,7 +470,7 @@ confidence_interval_method <- function(replications, desired_precision,
     subset_data <- results[[metric]][1L:i]
 
     # Get latest data point
-    last_data_point <- tail(subset_data, n=1)
+    last_data_point <- tail(subset_data, n = 1L)
 
     # Calculate mean
     mean_value <- mean(subset_data)
@@ -530,28 +524,31 @@ confidence_interval_method <- function(replications, desired_precision,
     warning("Running ", replications, " replications did not reach ",
             "desired precision (", desired_precision, ").", call. = FALSE)
   }
-  return(cumulative)
+  cumulative
 }
 
 
-#' Generate a plot of metric and confidence intervals with increasing simulation replications
+#' Generate a plot of metric and confidence intervals with increasing
+#' simulation replications
 #'
-#' @param conf_ints A dataframe containing confidence interval statistics, including cumulative mean, upper/lower bounds, and deviations. As returned by ReplicationTabuliser summary_table() method.
+#' @param conf_ints A dataframe containing confidence interval statistics,
+#' including cumulative mean, upper/lower bounds, and deviations. As returned
+#' by ReplicationTabuliser summary_table() method.
 #' @param yaxis_title Label for y axis.
 #' @param file_path Path and filename to save the plot to.
-#' @param min_rep The number of replications required to meet the desired precision.
+#' @param min_rep The number of replications required to meet the desired
+#' precision.
 
-plot_replication_ci = function(
-    conf_ints, yaxis_title, file_path = NULL, min_rep = NULL
-)
-{
+plot_replication_ci <- function(
+  conf_ints, yaxis_title, file_path = NULL, min_rep = NULL
+) {
   # Plot the cumulative mean and confidence interval
   p <- ggplot2::ggplot(conf_ints,
                        ggplot2::aes(x = .data[["replications"]],
                                     y = .data[["cumulative_mean"]])) +
     ggplot2::geom_line() +
     ggplot2::geom_ribbon(
-      ggplot2::aes(ymin = lower_ci, ymax = upper_ci),
+      ggplot2::aes(ymin = .data[["lower_ci"]], ymax = .data[["upper_ci"]]),
       alpha = 0.2
     )
 
diff --git a/outputs/cores1.png b/outputs/cores1.png
diff --git a/outputs/cores2.png b/outputs/cores2.png
diff --git a/rmarkdown/analysis.Rmd b/rmarkdown/analysis.Rmd
@@ -74,8 +74,8 @@ run_results %>%
   group_by(key) %>%
   reframe(mean = mean(value, na.rm = TRUE),
           std_dev = stats::sd(value, na.rm = TRUE),
-          ci_lower = stats::t.test(value)[["conf.int"]][[1]],
-          ci_upper = stats::t.test(value)[["conf.int"]][[2]])
+          ci_lower = stats::t.test(value)[["conf.int"]][[1L]],
+          ci_upper = stats::t.test(value)[["conf.int"]][[2L]])
 ```
 
 ## View spread of results across replication
diff --git a/rmarkdown/analysis.md b/rmarkdown/analysis.md
@@ -1,7 +1,7 @@
 Analysis
 ================
 Amy Heather
-2025-03-12
+2025-03-17
 
 - [Set up](#set-up)
 - [Default run](#default-run)
@@ -105,8 +105,8 @@ run_results %>%
   group_by(key) %>%
   reframe(mean = mean(value, na.rm = TRUE),
           std_dev = stats::sd(value, na.rm = TRUE),
-          ci_lower = stats::t.test(value)[["conf.int"]][[1]],
-          ci_upper = stats::t.test(value)[["conf.int"]][[2]])
+          ci_lower = stats::t.test(value)[["conf.int"]][[1L]],
+          ci_upper = stats::t.test(value)[["conf.int"]][[2L]])
 ```
 
     ## # A tibble: 5 × 5
@@ -391,7 +391,7 @@ print(table_latex)
 ```
 
     ## % latex table generated in R 4.4.1 by xtable 1.8-4 package
-    ## % Wed Mar 12 16:11:33 2025
+    ## % Mon Mar 17 15:49:56 2025
     ## \begin{table}[ht]
     ## \centering
     ## \begin{tabular}{rrllll}
@@ -647,7 +647,7 @@ print(sensitivity_table_latex)
 ```
 
     ## % latex table generated in R 4.4.1 by xtable 1.8-4 package
-    ## % Wed Mar 12 16:12:14 2025
+    ## % Mon Mar 17 15:50:37 2025
     ## \begin{table}[ht]
     ## \centering
     ## \begin{tabular}{rrl}
@@ -694,18 +694,18 @@ tail(nan_experiment[["arrivals"]])
 ```
 
     ##             name start_time end_time activity_time resource replication
-    ## 16136 patient131   64.07388       NA            NA    nurse         100
-    ## 16137  patient63   32.62716       NA            NA    nurse         100
+    ## 16136  patient78   38.80769       NA            NA    nurse         100
+    ## 16137  patient74   37.63360       NA            NA    nurse         100
     ## 16138  patient76   38.71731       NA            NA    nurse         100
-    ## 16139  patient99   49.99761       NA            NA    nurse         100
-    ## 16140  patient69   35.83208       NA            NA    nurse         100
+    ## 16139 patient134   64.35663       NA            NA    nurse         100
+    ## 16140  patient73   36.35539       NA            NA    nurse         100
     ## 16141  patient77   38.74436       NA            NA    nurse         100
     ##       serve_start serve_length wait_time wait_time_unseen
-    ## 16136          NA           NA        NA         15.92612
-    ## 16137          NA           NA        NA         47.37284
+    ## 16136          NA           NA        NA         41.19231
+    ## 16137          NA           NA        NA         42.36640
     ## 16138          NA           NA        NA         41.28269
-    ## 16139          NA           NA        NA         30.00239
-    ## 16140          NA           NA        NA         44.16792
+    ## 16139          NA           NA        NA         15.64337
+    ## 16140          NA           NA        NA         43.64461
     ## 16141          NA           NA        NA         41.25564
 
 ``` r
@@ -764,4 +764,4 @@ seconds <- as.integer(runtime %% 60L)
 cat(sprintf("Notebook run time: %dm %ds", minutes, seconds))
 ```
 
-    ## Notebook run time: 2m 5s
+    ## Notebook run time: 2m 19s
diff --git a/rmarkdown/choosing_cores.md b/rmarkdown/choosing_cores.md
@@ -1,7 +1,7 @@
 Choosing cores
 ================
 Amy Heather
-2025-03-11
+2025-03-17
 
 - [Set up](#set-up)
 - [Run time with varying number of CPU
@@ -32,18 +32,17 @@ devtools::install()
 ```
 
     ## 
-    ## ── R CMD build ──────────────────────────────────────────────────────────────────────────────────
+    ## ── R CMD build ───────────────────────────────────────────────────────────────────────────────
     ##      checking for file ‘/home/amy/Documents/stars/rap_template_r_des/DESCRIPTION’ ...  ✔  checking for file ‘/home/amy/Documents/stars/rap_template_r_des/DESCRIPTION’
     ##   ─  preparing ‘simulation’:
     ##    checking DESCRIPTION meta-information ...  ✔  checking DESCRIPTION meta-information
     ##   ─  checking for LF line-endings in source and make files and shell scripts
     ##   ─  checking for empty or unneeded directories
-    ##    Removed empty directory ‘simulation/tests/testthat/_snaps’
     ##    Omitted ‘LazyData’ from DESCRIPTION
     ##   ─  building ‘simulation_0.1.0.tar.gz’
     ##      
     ## Running /opt/R/4.4.1/lib/R/bin/R CMD INSTALL \
-    ##   /tmp/RtmptoyQW1/simulation_0.1.0.tar.gz --install-tests 
+    ##   /tmp/RtmpJhKlRH/simulation_0.1.0.tar.gz --install-tests 
     ## * installing to library ‘/home/amy/.cache/R/renv/library/rap_template_r_des-cd7d6844/linux-ubuntu-noble/R-4.4/x86_64-pc-linux-gnu’
     ## * installing *source* package ‘simulation’ ...
     ## ** using staged installation
@@ -217,4 +216,4 @@ seconds <- as.integer(runtime %% 60L)
 cat(sprintf("Notebook run time: %dm %ds", minutes, seconds))
 ```
 
-    ## Notebook run time: 2m 13s
+    ## Notebook run time: 2m 22s
diff --git a/rmarkdown/choosing_replications.Rmd b/rmarkdown/choosing_replications.Rmd
@@ -175,7 +175,8 @@ plot_replication_ci(
   conf_ints = filter(alg$summary_table, metric == "mean_serve_time_nurse"),
   yaxis_title = "Mean time with nurse",
   file_path = path,
-  min_rep = alg$nreps[["mean_serve_time_nurse"]])
+  min_rep = alg$nreps[["mean_serve_time_nurse"]]
+)
 include_graphics(path)
 ```
 
diff --git a/rmarkdown/choosing_replications.md b/rmarkdown/choosing_replications.md
@@ -384,7 +384,8 @@ plot_replication_ci(
   conf_ints = filter(alg$summary_table, metric == "mean_serve_time_nurse"),
   yaxis_title = "Mean time with nurse",
   file_path = path,
-  min_rep = alg$nreps[["mean_serve_time_nurse"]])
+  min_rep = alg$nreps[["mean_serve_time_nurse"]]
+)
 include_graphics(path)
 ```
 
@@ -416,4 +417,4 @@ seconds <- as.integer(runtime %% 60L)
 cat(sprintf("Notebook run time: %dm %ds", minutes, seconds))
 ```
 
-    ## Notebook run time: 3m 1s
+    ## Notebook run time: 1m 37s
diff --git a/rmarkdown/choosing_warmup.md b/rmarkdown/choosing_warmup.md
@@ -1,7 +1,7 @@
 Choosing warm-up length
 ================
 Amy Heather
-2025-03-12
+2025-03-17
 
 - [Set-up](#set-up)
 - [Determining appropriate warm-up
diff --git a/rmarkdown/generate_exp_results.Rmd b/rmarkdown/generate_exp_results.Rmd
@@ -61,7 +61,7 @@ results <- runner(param)
 
 # Arrivals
 head(results[["arrivals"]])
-write.csv(results[["arrivals"]] %>% arrange(replication, start_time),
+write.csv(arrange(results[["arrivals"]], replication, start_time),
           file.path(testdata_dir, "base_arrivals.csv"),
           row.names = FALSE)
 
@@ -147,7 +147,7 @@ param <- parameters(
 
 # Run the confidence_interval_method()
 rep_results <- confidence_interval_method(
-  replications = 15,
+  replications = 15L,
   desired_precision = 0.05,
   metric = "mean_serve_time_nurse"
 )
diff --git a/rmarkdown/generate_exp_results.md b/rmarkdown/generate_exp_results.md
@@ -93,7 +93,7 @@ head(results[["arrivals"]])
     ## 6     7.809445         0               NA
 
 ``` r
-write.csv(results[["arrivals"]] %>% arrange(replication, start_time),
+write.csv(arrange(results[["arrivals"]], replication, start_time),
           file.path(testdata_dir, "base_arrivals.csv"),
           row.names = FALSE)
 
@@ -248,7 +248,7 @@ param <- parameters(
 
 # Run the confidence_interval_method()
 rep_results <- confidence_interval_method(
-  replications = 15,
+  replications = 15L,
   desired_precision = 0.05,
   metric = "mean_serve_time_nurse"
 )
@@ -295,4 +295,4 @@ seconds <- as.integer(runtime %% 60L)
 cat(sprintf("Notebook run time: %dm %ds", minutes, seconds))
 ```
 
-    ## Notebook run time: 0m 4s
+    ## Notebook run time: 0m 2s
diff --git a/rmarkdown/logs.md b/rmarkdown/logs.md
@@ -1,7 +1,7 @@
 Logs
 ================
 Amy Heather
-2025-03-11
+2025-03-17
 
 - [Set up](#set-up)
 - [Simulation run with logs printed to the
diff --git a/rmarkdown/using_set_attributes.md b/rmarkdown/using_set_attributes.md
@@ -1,7 +1,7 @@
 Using `set_attributes()`
 ================
 Amy Heather
-2025-03-12
+2025-03-17
 
 - [Original model](#original-model)
 - [Model with record of allocation to a nurse
diff --git a/tests/testthat/test-backtest-replications.R b/tests/testthat/test-backtest-replications.R
@@ -12,7 +12,7 @@ test_that("results from confidence_interval_method are consistent", {
 
   # Run the confidence_interval_method()
   rep_results <- suppressWarnings(confidence_interval_method(
-    replications = 15,
+    replications = 15L,
     desired_precision = 0.05,
     metric = "mean_serve_time_nurse"
   ))
@@ -21,7 +21,7 @@ test_that("results from confidence_interval_method are consistent", {
   exp_results <- read.csv(test_path("testdata", "choose_rep_results.csv"))
 
   # Compare to those generated
-  expect_equal(rep_results, exp_results)
+  expect_equal(rep_results, exp_results) # nolint: expect_identical_linter
 })
 
 
@@ -38,17 +38,18 @@ test_that("results from ReplicationsAlgorithm are consistent", {
   # Run the confidence_interval_method()
   alg <- ReplicationsAlgorithm$new(
     param = param,
-    metrics = c("mean_serve_time_nurse"),
+    metrics = "mean_serve_time_nurse",
     desired_precision = 0.05,
-    initial_replications = 15,
-    look_ahead = 0,
-    replication_budget = 15)
+    initial_replications = 15L,
+    look_ahead = 0L,
+    replication_budget = 15L
+  )
   suppressWarnings(alg$select())
   rep_results <- alg$summary_table
 
   # Import the expected results
   exp_results <- read.csv(test_path("testdata", "choose_rep_results.csv"))
 
   # Compare to those generated
-  expect_equal(rep_results, exp_results)
+  expect_equal(rep_results, exp_results) # nolint: expect_identical_linter
 })
diff --git a/tests/testthat/test-functionaltest-replications.R b/tests/testthat/test-functionaltest-replications.R
@@ -42,48 +42,48 @@ test_that(
       metrics = c(metric),
       desired_precision = desired_precision,
       initial_replications = reps,
-      look_ahead = 0,
+      look_ahead = 0L,
       replication_budget = reps
     )
     suppressWarnings(alg$select())
 
     # Compare dataframes
-    expect_equal(man_df, alg$summary_table)
+    expect_equal(man_df, alg$summary_table) # nolint: expect_identical_linter
   }
 )
 
 
 test_that("ReplicationsAlgorithm initial_replications consistent to without", {
 
   #' Helper function to run the algorithm (ignoring unsolved warnings)
-  helper_alg <- function(initial_replications){
+  helper_alg <- function(initial_replications) {
     alg <- ReplicationsAlgorithm$new(
       param = parameters(),
-      metrics = c("mean_serve_time_nurse"),
+      metrics = "mean_serve_time_nurse",
       desired_precision = 0.05,
       initial_replications = initial_replications,
-      look_ahead = 10,
-      replication_budget = 10
+      look_ahead = 10L,
+      replication_budget = 10L
     )
     suppressWarnings(alg$select())
-    head(alg$summary_table, 10)
+    head(alg$summary_table, 10L)
   }
 
   # Run with three initial replications
-  alg_3 <- helper_alg(initial_replications = 3)
+  alg_3 <- helper_alg(initial_replications = 3L)
 
   # Run with ten initial replications (matches budget)
-  alg_10 <- helper_alg(initial_replications = 10)
+  alg_10 <- helper_alg(initial_replications = 10L)
 
-  expect_equal(alg_3, alg_10)
+  expect_equal(alg_3, alg_10) # nolint: expect_identical_linter
 })
 
 
 test_that("running algorithm with < 3 replications has no solution", {
   alg <- ReplicationsAlgorithm$new(param = parameters(),
-                                   initial_replications=0,
-                                   replication_budget=2,
-                                   look_ahead=0)
+                                   initial_replications = 0L,
+                                   replication_budget = 2L,
+                                   look_ahead = 0L)
   # Check that it runs with a warning
   expect_warning(alg$select())
   # Check that there is no solution
diff --git a/tests/testthat/test-functionaltest.R b/tests/testthat/test-functionaltest.R
