style(lint): linting R code

Author: amyheather

.lintr

@@ -1,3 +1,3 @@
-linters: all_linters(packages = "lintr")
+linters: all_linters(packages = "lintr", undesirable_function_linter = NULL)
 encoding: "UTF-8"
 exclusions: list("renv")

pages/inputs/input_data.qmd

@@ -247,7 +247,7 @@ The way you might set these up depends on whether you are allowed to share the r
 ## 🧪 Test yourself
 
 ```{r, echo = FALSE}
-library(webexercises) # nolint: undesirable_function_linter
+library(webexercises) # nolint: library_call_linter
 ```
 
 :::{.callout-note}
@@ -257,7 +257,10 @@ library(webexercises) # nolint: undesirable_function_linter
 ```{r, results="asis", echo = FALSE}
 cat(longmcq(c(
   "Do not share or describe anything.",
-  answer = "Describe the dataset, and if possible also share a data dictionary and a synthetic dataset."
+  answer = paste0(
+    "Describe the dataset, and if possible also share a data dictionary and a",
+    " synthetic dataset."
+  )
 )))
 ```
 
@@ -283,7 +286,10 @@ cat(longmcq(c(
 ```{r, results="asis", echo = FALSE}
 cat(longmcq(c(
   "Duplicate all simulation code across both repositories.",
-  answer = "Develop the simulation code as a package so it can be shared across repositories without code duplication."
+  answer = paste(
+    "Develop the simulation code as a package so it can be shared across ",
+    "repositories without code duplication."
+  )
 )))
 ```
 

pages/inputs/parameters_script.qmd

@@ -34,7 +34,7 @@ This page helps you meet reproducibility criteria from:
 :::{.r-content}
 ```{r}
 # Load required packages
-library(R6) # nolint: undesirable_function_linter
+library(R6)
 ```
 :::
 
@@ -1238,7 +1238,7 @@ Not applicable as the stroke example stores [parameters in a file](parameters_fi
 ## 🧪 Test yourself
 
 ```{r, echo = FALSE}
-library(webexercises) # nolint: undesirable_function_linter
+library(webexercises) # nolint: library_call_linter
 ```
 
 :::{.callout-note}
@@ -1261,7 +1261,10 @@ cat(longmcq(c(
 
 ```{r, results="asis", echo = FALSE}
 cat(longmcq(c(
-  answer = "It ensures that each scenario only uses the parameters intended for it, and avoids accidental reuse between runs.",
+  answer = paste0(
+    "It ensures that each scenario only uses the parameters intended for it, ",
+    "and avoids accidental reuse between runs."
+  ),
   "It always makes your code shorter.",
   "It allows you to skip documenting your parameters."
 )))

pages/inputs/parameters_validation.qmd

@@ -829,7 +829,7 @@ The prevention of new fields is thanks to the default `lock_objects = TRUE` sett
 #' @field transfer_prob Numeric. Transfer probability (0-1).
 
 ParamClass <- R6Class( # nolint: object_name_linter
-  lock_objects = FALSE, 
+  lock_objects = FALSE,
   public = list(
     transfer_prob = NULL,
 
@@ -851,7 +851,7 @@ params <- ParamClass$new()
 try({
   params$transfer_probs <- 0.4
 })
-params 
+params
 ```
 
 #### Setting parameters within a list
@@ -867,15 +867,15 @@ You may choose to store parameters in a list instead, to make it easier to acces
 
 ParamClass <- R6Class( # nolint: object_name_linter
   public = list(
-    parameters = NULL, 
+    parameters = NULL,
 
     #' @description
     #' Initialises the R6 object.
 
     initialize = function(transfer_prob = 0.3) {
-      self$parameters <- list( 
+      self$parameters <- list(
         transfer_prob = transfer_prob
-      ) 
+      )
     }
   )
 )
@@ -889,7 +889,7 @@ params <- ParamClass$new()
 try({
   params$parameters$transfer_probs <- 0.4
 })
-params$parameters 
+params$parameters
 ```
 
 <br>
@@ -912,20 +912,20 @@ ParamClass <- R6Class( # nolint: object_name_linter
       self$transfer_prob <- transfer_prob
     },
 
-    #' @description 
-    #' Returns parameters as a named list. 
-
-    get_params = function() { 
-      # Get all non-function fields 
-      all_names <- ls(self) 
-      is_not_function <- vapply( 
-        all_names, 
-        function(x) !is.function(self[[x]]), 
-        FUN.VALUE = logical(1L) 
-      ) 
-      param_names <- all_names[is_not_function] 
-      mget(param_names, envir = self) 
-    } 
+    #' @description
+    #' Returns parameters as a named list.
+
+    get_params = function() {
+      # Get all non-function fields
+      all_names <- ls(self)
+      is_not_function <- vapply(
+        all_names,
+        function(x) !is.function(self[[x]]),
+        FUN.VALUE = logical(1L)
+      )
+      param_names <- all_names[is_not_function]
+      mget(param_names, envir = self)
+    }
   )
 )
 ```
@@ -939,8 +939,8 @@ try({
   params$transfer_probs <- 0.4
 })
 
-# Get all parameters 
-params$get_params() 
+# Get all parameters
+params$get_params()
 ```
 
 ::::
@@ -1055,7 +1055,7 @@ model <- function(param) {
 
   # Simulation code...
 
-  print("The simulation has run!")
+  cat("The simulation has run!")
 }
 
 
@@ -1180,26 +1180,26 @@ ParamClass <- R6Class( # nolint: object_name_linter
       self$transfer_prob <- transfer_prob
     },
 
-    #' @description 
-    #' Check that transfer_prob is between 0 and 1. 
-    #' @return No return value; throws an error if invalid. 
-
-    validate_param = function() { 
-      if (self$transfer_prob < 0L || self$transfer_prob > 1L) { 
-        stop( 
-          "transfer_prob must be between 0 and 1, but is: ", 
-          self$transfer_prob, call. = FALSE 
-        ) 
-      } 
-    } 
+    #' @description
+    #' Check that transfer_prob is between 0 and 1.
+    #' @return No return value; throws an error if invalid.
+
+    validate_param = function() {
+      if (self$transfer_prob < 0L || self$transfer_prob > 1L) {
+        stop(
+          "transfer_prob must be between 0 and 1, but is: ",
+          self$transfer_prob, call. = FALSE
+        )
+      }
+    }
   )
 )
 ```
 
 ```{r}
 # Create instance with invalid transfer_prob and run method
 param <- ParamClass$new(transfer_prob = 1.4)
-try(param$validate_param()) 
+try(param$validate_param())
 ```
 
 :::
@@ -1287,4 +1287,4 @@ If you haven't already, now's the time to try out **parameter validation** in pr
 
 * Repeat this task, but with an **invalid value** for a parameter (e.g. outside the expected range).
 
-<br><br>
+<br><br>

pages/intro/foss.qmd

@@ -79,7 +79,7 @@ Both have extensive **documentation**, **active user communities**, and **proven
 ## 🧪 Test yourself
 
 ```{r, echo = FALSE}
-library(webexercises) # nolint: undesirable_function_linter
+library(webexercises)
 ```
 
 :::{.callout-note}

pages/intro/rap.qmd

@@ -86,7 +86,7 @@ This book does both! As detailed on the [guidelines](guidelines.qmd) page:
 ## 🧪 Test yourself
 
 ```{r, echo = FALSE}
-library(webexercises) # nolint: undesirable_function_linter
+library(webexercises)
 ```
 
 :::{.callout-note}

pages/model/distributions.qmd

@@ -353,7 +353,7 @@ On [the next page](patients.qmd), we'll learn how to add sampling to your model
 ## 🧪 Test yourself
 
 ```{r, echo = FALSE}
-library(webexercises) # nolint: undesirable_function_linter
+library(webexercises) # nolint: library_call_linter
 ```
 
 :::{.callout-note}
@@ -363,7 +363,10 @@ library(webexercises) # nolint: undesirable_function_linter
 ```{r, results="asis", echo = FALSE}
 cat(longmcq(c(
   "A source of truly unpredictable numbers from physical processes",
-  answer = "An algorithm that produces numbers that appear random but are determined by a starting seed",
+  answer = paste0(
+    "An algorithm that produces numbers that appear random but are determined",
+    " by a starting seed"
+  ),
   "A hardware device that measures system noise to generate randomness"
 )))
 ```
@@ -392,7 +395,9 @@ cat(longmcq(c(
 cat(longmcq(c(
   "To make the simulation run faster",
   "To avoid requiring a seed",
-  answer = "To prevent one process's sampling from shifting another process's results"
+  answer = (
+    "To prevent one process's sampling from shifting another process's results"
+  )
 )))
 ```
 
@@ -441,7 +446,9 @@ cat(longmcq(c(
 ```{r, results="asis", echo = FALSE}
 cat(longmcq(c(
   "It cannot set seeds",
-  answer = "It relies on a single global stream, making independent streams difficult",
+  answer = (
+    "It relies on a single global stream, making independent streams difficult"
+  ),
   "It doesn't support exponential sampling"
 )))
 ```

pages/model/patients.qmd

@@ -190,7 +190,7 @@ class Model:
         ss = np.random.SeedSequence(self.run_number)
         seeds = ss.spawn(1)
 
-        # Create an exponential distribution for patient inter-arrival times
+        # Initialise distributions
         self.arrival_dist = Exponential(mean=self.param.interarrival_time,
                                         random_seed=seeds[0])
 
@@ -263,7 +263,7 @@ A random seed sequence is created based on the `run_number`.
 <br>
 
 ```{.python}
-# Create an exponential distribution for patient inter-arrival times
+# Initialise distributions
 self.arrival_dist = Exponential(mean=self.param.interarrival_time,
                                 random_seed=seeds[0])
 ```
@@ -376,7 +376,7 @@ We'll then create a function for the model (explained line by line below).
 #'
 #' @param param List. Model parameters.
 #' @param run_number Int. Run number for random seed generation.
-#' 
+#'
 #' @importFrom simmer add_generator run simmer timeout trajectory
 
 model <- function(param, run_number) {
@@ -389,7 +389,7 @@ model <- function(param, run_number) {
 
   # Define the patient trajectory
   patient <- trajectory("patient_path") |>
-    timeout(1)
+    timeout(1L)
 
   env <- env |>
     # Add patient generator
@@ -474,8 +474,8 @@ The simulation is then run for a specified duration using the `run()` method. Be
 To try running the model, set up the parameters and run `model()`.
 
 ```{r}
-param = create_params()
-model(param = param, run_number = 1)
+param <- create_params()
+model(param = param, run_number = 1L)
 ```
 
 :::
@@ -845,7 +845,7 @@ create_params <- function(interarrival_time = 5L,
 #'
 #' @param param List. Model parameters.
 #' @param run_number Int. Run number for random seed generation.
-#' 
+#'
 #' @importFrom simmer add_generator run simmer timeout trajectory
 
 model <- function(param, run_number) {
@@ -864,7 +864,7 @@ model <- function(param, run_number) {
 
   # Define the patient trajectory
   patient <- trajectory("patient_path") |>
-    timeout(1)
+    timeout(1L)
 
   env <- env |>
     # Add patient generator
@@ -882,8 +882,8 @@ model <- function(param, run_number) {
 We can see that results are consistent with before.
 
 ```{r}
-param = create_params()
-model(param = param, run_number = 1)
+param <- create_params()
+model(param = param, run_number = 1L)
 ```
 
 :::

pages/output_analysis/n_reps.qmd

@@ -2,4 +2,6 @@
 title: ❌ Number of replications
 ---
 
+<!--separate seeds for each replication-->
+
 <br><br>

pages/setup/code_structure.qmd

@@ -136,7 +136,7 @@ Classes are less common in R than functions, but they are used sometimes for com
 The example below uses **R6**, since this style is closest to how Python handles classes, making it easier to draw parallels between the two languages.
 
 ```{r}
-library(R6) # nolint: undesirable_function_linter
+library(R6)
 
 Patient <- R6Class("Patient",  # nolint: object_name_linter
   public = list(