Merge pull request #47 from nhs-r-community/8-lint-action

Add linting to continuous integration

.github/workflows/lint.yaml

@@ -0,0 +1,32 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+
+name: lint
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::lintr, local::.
+          needs: lint
+
+      - name: Lint
+        run: lintr::lint_package()
+        shell: Rscript {0}
+        env:
+          LINTR_ERROR_ON_LINT: true

.lintr

@@ -0,0 +1,5 @@
+linters: linters_with_defaults(
+  object_usage_linter(NULL))
+exclude: "# nolint"
+exclude_start: "# Begin Exclude Linting"
+exclude_end: "# End Exclude Linting"

R/average_wait.R

@@ -1,23 +1,29 @@
-#' @title Average Waiting Time
-#'
-#' @description This calculates the target mean wait given the two inputs of target_wait and a numerical value for factor
-#' The average wait is actually the target mean wait and is calculated as follows: target_wait / factor
-#' If we want to have a chance between 1.8%-0.2% of making a waiting time target, then the average patient should
-#' have a waiting time between a quarter and a sixth of the target. Therefore:
-#' The mean wait should sit somewhere between target_wait/factor=6 < Average Waiting Time < target_wait/factor=4
-#'
-#' @param target_wait Numeric value of the number of weeks that has been set as the target within which the patient should be seen.
-#' @param factor  Numeric factor used in average wait calculation - to get a quarter of the target use factor=4 and one sixth of the target use factor = 6 etc. Defaults to 4.
-#'
-#' @return Numeric value of target mean waiting time to achieve a given target wait.
-#' @export
-#'
-#' @examples 
-#' # If the target wait is 52 weeks then the target mean wait with a factor of 4 would be 13
-#' # weeks and with a factor of 6 it would be 8.67 weeks.
-#' average_wait(52, 4)
-#'
-average_wait <- function(target_wait, factor = 4) {
-  target_mean_wait <- target_wait / factor
-  return(target_mean_wait)
-}
+#' @title Average Waiting Time
+#'
+#' @description This calculates the target mean wait given the two inputs of
+#'   target_wait and a numerical value for factor. The average wait is actually
+#'   the target mean wait and is calculated as follows: target_wait / factor. If
+#'   we want to have a chance between 1.8%-0.2% of making a waiting time target,
+#'   then the average patient should have a waiting time between a quarter and a
+#'   sixth of the target. Therefore: The mean wait should sit somewhere between
+#'   target_wait/factor=6 < Average Waiting Time < target_wait/factor=4.
+#'
+#' @param target_wait Numeric value of the number of weeks that has been set as
+#'   the target within which the patient should be seen.
+#' @param factor  Numeric factor used in average wait calculation - to get a
+#'   quarter of the target use factor=4 and one sixth of the target use factor =
+#'   6 etc. Defaults to 4.
+#'
+#' @return Numeric value of target mean waiting time to achieve a given target
+#'   wait.
+#'
+#' @export
+#'
+#' @examples
+#' # If the target wait is 52 weeks then the target mean wait with a factor of 4
+#' # would be 13 weeks and with a factor of 6 it would be 8.67 weeks.
+#' average_wait(52, 4)
+average_wait <- function(target_wait, factor = 4) {
+  target_mean_wait <- target_wait / factor
+  return(target_mean_wait)
+}

R/queue_load.R

@@ -1,25 +1,25 @@
-#' @title Queue Load
-#'
-#' @description
-#' Calculates the queue load. The queue load is the number of arrivals that occur for every patient leaving the queue (given that the
-#' waiting list did not empty).
-#' It could also be described as the rate of service at the queue.
-#' The queue load is calculated by dividing the demand by the capacity:
-#' queue_load = demand / capacity
-#'
-#' @param demand Numeric value of rate of demand in same units as target wait - e.g. if target wait is weeks, then demand in units of patients/week.
-#' @param capacity Numeric value of the number of patients that can be served (removals) from the waiting list each week.
-#'
-#' @return Numeric value of load which is the ratio between demand and capacity
-#' @export
-#'
-#' @examples 
-#' # If 30 patients are added to the waiting list each week (demand) and 27 removed (capacity)
-#' # this results in a queue load of 1.11 (30/27)
-#' queue_load(30,27)
-#'
-#'
-queue_load <- function(demand, capacity) {
-  load <- demand / capacity
-  return (load)
-}
+#' @title Calculate Queue Load
+#'
+#' @description Calculates the queue load. The queue load is the number of
+#'   arrivals that occur for every patient leaving the queue (given that the
+#'   waiting list did not empty). It could also be described as the rate of
+#'   service at the queue. The queue load is calculated by dividing the demand
+#'   by the capacity: queue_load = demand / capacity.
+#'
+#' @param demand Numeric value of rate of demand in same units as target wait -
+#'   e.g. if target wait is weeks, then demand in units of patients/week.
+#' @param capacity Numeric value of the number of patients that can be served
+#'   (removals) from the waiting list each week.
+#'
+#' @return Numeric value of load which is the ratio between demand and capacity.
+#'
+#' @export
+#'
+#' @examples
+#' # If 30 patients are added to the waiting list each week (demand) and 27
+#' removed (capacity) this results in a queue load of 1.11 (30/27).
+#' queue_load(30,27)
+queue_load <- function(demand, capacity) {
+  load <- demand / capacity
+  return(load)
+}

R/relief_capacity.R

@@ -1,29 +1,35 @@
-#' @title Relief Capacity
-#'
-#' @description
-#' Calculates required relief capacity to achieve target queue size in a given period of time as a function of demand, queue size, target queue size and time period.
-#'
-#' Relief Capacity is required if Queue Size > 2 * Target Queue Size.
-#'
-#' Relief Capacity = Current Demand + (Queue Size - Target Queue Size)/Time Steps
-#'
-#' @param demand Numeric value of rate of demand in same units as target wait - e.g. if target wait is weeks, then demand in units of patients/week.
-#' @param queue_size Numeric value of  current number of patients in queue.
-#' @param target_queue_size Numeric value of desired number of patients in queue.
-#' @param weeks_to_target Numeric value of desired number of time-steps to reach the target queue size by.
-#'
-#' @return A numeric value of the required rate of capacity to achieve a target queue size in a given period of time.
-#' @export
-#'
-#' @examples
-#' # If demand is 30 patients per week, the current queue size is 1200 and the
-#' # target is to achieve a queue size of 390 in 26 weeks, then
-#'
-#' # Relief Capacity = 30 + (1200 - 390)/26 = 61.15 patients per week.
-#'
-#' relief_capacity(30, 1200, 390, 26)
-#'
-relief_capacity <- function(demand, queue_size, target_queue_size, weeks_to_target) {
-  rel_cap <- demand + (queue_size  -  target_queue_size) / weeks_to_target
-  return(rel_cap)
-}
+#' @title Calculate Relief Capacity
+#'
+#' @description Calculates required relief capacity to achieve target queue size
+#'   in a given period of time as a function of demand, queue size, target queue
+#'   size and time period.
+#'
+#'   Relief Capacity is required if Queue Size > 2 * Target Queue Size.
+#'
+#'   Relief Capacity = Current Demand + (Queue Size - Target Queue Size) / Time
+#'   Steps.
+#'
+#' @param demand Numeric value of rate of demand in same units as target wait -
+#'   e.g. if target wait is weeks, then demand in units of patients/week.
+#' @param queue_size Numeric value of  current number of patients in queue.
+#' @param target_queue_size Numeric value of desired number of patients in
+#'   queue.
+#' @param weeks_to_target Numeric value of desired number of time-steps to reach
+#'   the target queue size by.
+#'
+#' @return A numeric value of the required rate of capacity to achieve a target
+#'   queue size in a given period of time.
+#'
+#' @export
+#'
+#' @examples
+#' # If demand is 30 patients per week, the current queue size is 1200 and the
+#' # target is to achieve a queue size of 390 in 26 weeks, then
+#' # Relief Capacity = 30 + (1200 - 390) / 26 = 61.15 patients per week.
+#'
+#' relief_capacity(30, 1200, 390, 26)
+relief_capacity <- function(
+    demand, queue_size, target_queue_size, weeks_to_target) {
+  rel_cap <- demand + (queue_size - target_queue_size) / weeks_to_target
+  return(rel_cap)
+}

R/target_capacity.R

@@ -1,26 +1,30 @@
-#' @title Target Capacity
-#'
-#' @description
-#' Calculates the target capacity to achieve a given target waiting time as a function of observed demand, target waiting time and a variability coefficient F.
-#' 
-#' Target Capacity = Demand + 2 * ( 1 + 4 * F ) / Target Wait
-#' F defaults to 1.
-#'
-#' @param demand Numeric value of rate of demand in same units as target wait - e.g. if target wait is weeks, then demand in units of patients/week.
-#' @param target_wait Numeric value of number of weeks that has been set as the target within which the patient should be seen.
-#' @param F Variability coefficient, F = V/C * (D/C)^2 where C is the current number of operations per week; V is the current variance in the number of operations per week; D is the observed demand. Defaults to 1.
-#'
-#' @return A numeric value of target capacity required to achieve a target waiting time.
-#' @export
-#'
-#' @examples
-#'
-#' # If the target wait is 52 weeks, demand is 30 patients per week and F = 3 then
-#' # Target capacity = 30 + 2*(1+4*3)/52 = 30.5 patients per week.
-#'
-#' target_capacity(30,52,3)
-#'
-target_capacity <- function(demand, target_wait, F = 1) {
-  target_cap <- demand +  2 * ( 1 + 4 * F ) / target_wait
-  return(target_cap)
-}
+#' @title Calculate Target Capacity
+#'
+#' @description Calculates the target capacity to achieve a given target waiting
+#' time as a function of observed demand, target waiting time and a variability
+#' coefficient F.
+#'
+#' Target Capacity = Demand + 2 * ( 1 + 4 * F ) / Target Wait F defaults to 1.
+#'
+#' @param demand Numeric value of rate of demand in same units as target wait -
+#'   e.g. if target wait is weeks, then demand in units of patients/week.
+#' @param target_wait Numeric value of number of weeks that has been set as the
+#'   target within which the patient should be seen.
+#' @param F Variability coefficient, F = V/C * (D/C)^2 where C is the current
+#'   number of operations per week; V is the current variance in the number of
+#'   operations per week; D is the observed demand. Defaults to 1.
+#'
+#' @return A numeric value of target capacity required to achieve a target
+#'   waiting time.
+#'
+#' @export
+#'
+#' @examples
+#'
+#' # If the target wait is 52 weeks, demand is 30 patients per week and F = 3
+#' # then Target capacity = 30 + 2 * (1 + 4 * 3)/52 = 30.5 patients per week.
+#' target_capacity(30,52,3)
+target_capacity <- function(demand, target_wait, F = 1) {
+  target_cap <- demand +  2 * (1 + 4 * F) / target_wait
+  return(target_cap)
+}

R/target_queue_size.R

@@ -1,31 +1,34 @@
-#' @title Target Queue Size
-#'
-#' @description
-#' Uses Little's Law to calculate the target queue size to achieve a target waiting time as a function of observed demand, target wait and a variability factor used in the target mean waiting time calculation.
-#' 
-#' Target Queue Size = Demand * Target Wait / 4.
-#'
-#' The average wait should sit somewhere between 
-#' target_wait/factor=6 < Average Waiting Time < target_wait/factor=4
-#' The factor defaults to 4.
-#' 
-#' Only applicable when Capacity > Demand.
-#'
-#' @param demand Numeric value of rate of demand in same units as target wait - e.g. if target wait is weeks, then demand in units of patients/week.
-#' @param target_wait Numeric value of number of weeks that has been set as the target within which the patient should be seen.
-#' @param factor Numeric factor used in average wait calculation - to get a quarter of the target use factor=4 and one sixth of the target use factor = 6 etc. Defaults to 4.
-#'
-#' @return Numeric target queue length.
-#' @export
-#'
-#' @examples
-#' # If demand  is 30 patients per week and the target wait is 52 weeks, then the 
-#' # Target queue size = 30 * 52/4 = 390 patients.
-#'
-#' target_queue_size(30,52,4)
-#' 
-target_queue_size <- function(demand, target_wait, factor = 4) {
-  mean_wait <- average_wait(target_wait, factor)
-  target_queue_length <- demand * mean_wait
-  return(target_queue_length)
-}
+#' @title Calculate Target Queue Size
+#'
+#' @description Uses Little's Law to calculate the target queue size to achieve
+#'   a target waiting time as a function of observed demand, target wait and a
+#'   variability factor used in the target mean waiting time calculation.
+#'
+#'   Target Queue Size = Demand * Target Wait / 4.
+#'
+#'   The average wait should sit somewhere between target_wait/factor=6 <
+#'   Average Waiting Time < target_wait/factor=4 The factor defaults to 4.
+#'
+#'   Only applicable when Capacity > Demand.
+#'
+#' @param demand Numeric value of rate of demand in same units as target wait -
+#'   e.g. if target wait is weeks, then demand in units of patients/week.
+#' @param target_wait Numeric value of number of weeks that has been set as the
+#'   target within which the patient should be seen.
+#' @param factor Numeric factor used in average wait calculation - to get a
+#'   quarter of the target use factor=4 and one sixth of the target use factor =
+#'   6 etc. Defaults to 4.
+#'
+#' @return Numeric target queue length.
+#'
+#' @export
+#'
+#' @examples
+#' # If demand is 30 patients per week and the target wait is 52 weeks, then the
+#' # Target queue size = 30 * 52 / 4 = 390 patients.
+#' target_queue_size(30, 52, 4)
+target_queue_size <- function(demand, target_wait, factor = 4) {
+  mean_wait <- average_wait(target_wait, factor)
+  target_queue_length <- demand * mean_wait
+  return(target_queue_length)
+}

R/waiting_list_pressure.R

@@ -1,22 +1,24 @@
-#' @title Calculate the waiting list pressure
-#'
-#' @description For a waiting list with target waiting time, the pressure on the waiting list is twice
-#'  the mean delay divided by the waiting list target.
-#'  The pressure of any given waiting list should be less than 1.
-#'  If the pressure is greater than 1 then the waiting list is most likely going to miss its target.
-#'  The waiting list pressure is calculated as follows:
-#'  pressure = 2 x mean_wait / target_wait
-#'
-#' @param mean_wait Numeric value of target mean waiting time to achieve a given target wait
-#' @param target_wait Numeric value of the number of weeks that has been set as the target within which the patient should be seen
-#'
-#' @return Numeric value of wait_pressure which is the waiting list pressure
-#' @export
-#'
-#' @examples
-#' waiting_list_pressure(63,52)
-#'
-waiting_list_pressure <- function(mean_wait, target_wait) {
-  wait_pressure <- 2 * mean_wait / target_wait
-  return(wait_pressure)
-}
+#' @title Calculate Waiting List Pressure
+#'
+#' @description For a waiting list with target waiting time, the pressure on the
+#'   waiting list is twice the mean delay divided by the waiting list target.
+#'   The pressure of any given waiting list should be less than 1. If the
+#'   pressure is greater than 1 then the waiting list is most likely going to
+#'   miss its target. The waiting list pressure is calculated as follows:
+#'   pressure = 2 * mean_wait / target_wait.
+#'
+#' @param mean_wait Numeric value of target mean waiting time to achieve a given
+#'   target wait.
+#' @param target_wait Numeric value of the number of weeks that has been set as
+#'   the target within which the patient should be seen.
+#'
+#' @return Numeric value of wait_pressure which is the waiting list pressure.
+#'
+#' @export
+#'
+#' @examples
+#' waiting_list_pressure(63, 52)
+waiting_list_pressure <- function(mean_wait, target_wait) {
+  wait_pressure <- 2 * mean_wait / target_wait
+  return(wait_pressure)
+}