Applied the styler package to all functions

R/aedseo.R

@@ -39,18 +39,18 @@
 #'     "2023-01-03",
 #'     "2023-01-04",
 #'     "2023-01-05",
-#'     "2023-01-06")
-#'     ),
-#'     time_interval = "day"
-#'   )
+#'     "2023-01-06"
+#'   )),
+#'   time_interval = "day"
+#' )
 #'
 #' # Calculate AEDSEO with a 3-day window and a Poisson family model
 #' aedseo_results <- aedseo(
 #'   tsd = tsd_data,
 #'   k = 3,
 #'   level = 0.95,
 #'   family = "poisson"
-#'   )
+#' )
 #'
 #' # Print the AEDSEO results
 #' print(aedseo_results)
@@ -61,8 +61,8 @@ aedseo <- function(
     level = 0.95,
     family = c(
       "poisson",
-      "quasipoisson")) {
-
+      "quasipoisson"
+    )) {
   # Throw an error if any of the inputs are not supported
   family <- rlang::arg_match(family)
 
@@ -72,8 +72,7 @@ aedseo <- function(
   # Allocate space for growth rate estimates
   res <- tibble::tibble()
 
-  for (i in k:n){
-
+  for (i in k:n) {
     # Index observations for this iteration
     obs_iter <- tsd[(i - k + 1):i, ]
 
@@ -82,7 +81,7 @@ aedseo <- function(
       observations = obs_iter$observed,
       level = level,
       family = family
-      )
+    )
 
     # See if the growth rate is significantly higher than zero
     growth_warning <- growth_rates$estimate[2] > 0
@@ -96,16 +95,14 @@ aedseo <- function(
       tibble::tibble(
         reference_time = tsd$time[i],
         growth_rate = growth_rates$estimate[1],
-        lower_growth_rate =  growth_rates$estimate[2],
-        upper_growth_rate =  growth_rates$estimate[3],
+        lower_growth_rate = growth_rates$estimate[2],
+        upper_growth_rate = growth_rates$estimate[3],
         growth_warning = growth_warning,
         SoC = SoC,
         converged = growth_rates$fit$converged
-        )
       )
-
+    )
   }
 
   return(res)
-
 }

R/tsd.R

@@ -19,34 +19,35 @@
 #' daily_tsd <- tsd(
 #'   observed = c(10, 15, 20, 18),
 #'   time = as.Date(
-#'     c("2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04")),
+#'     c("2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04")
+#'   ),
 #'   time_interval = "day"
-#'   )
+#' )
 #'
 #' # Create a tsibble object from weekly data
 #' weekly_tsd <- tsd(
 #'   observed = c(100, 120, 130),
 #'   time = as.Date(
-#'     c("2023-01-01", "2023-01-08", "2023-01-15")),
-#'     time_interval = "week"
-#'   )
+#'     c("2023-01-01", "2023-01-08", "2023-01-15")
+#'   ),
+#'   time_interval = "week"
+#' )
 #'
 #' # Create a tsibble object from monthly data
 #' monthly_tsd <- tsd(
 #'   observed = c(500, 520, 540),
 #'   time = as.Date(
-#'     c("2023-01-01", "2023-02-01", "2023-03-01")),
-#'     time_interval = "month"
-#'   )
+#'     c("2023-01-01", "2023-02-01", "2023-03-01")
+#'   ),
+#'   time_interval = "month"
+#' )
 #'
-tsd <- function(observed, time, time_interval = c("day", "week", "month")){
-
+tsd <- function(observed, time, time_interval = c("day", "week", "month")) {
   # Throw an error if any of the inputs are not supported
   time_interval <- rlang::arg_match(time_interval)
 
   # Select the correct 'time_interval' and create the 'tsd' return object
-  ans <- switch(
-    time_interval,
+  ans <- switch(time_interval,
     day = tibble(time = time, observed = observed) %>%
       tsibble::build_tsibble(
         index = time,

tests/testthat/test-aedseo.R

@@ -1,5 +1,4 @@
 test_that("The growth rate models converge", {
-
   from_date <- as.Date("2021-01-01")
   to_date <- as.Date("2021-01-31")
 
@@ -13,28 +12,29 @@ test_that("The growth rate models converge", {
   tsd_data_poisson <- tsd(
     observed = rpois(n = n, lambda = 1:n),
     time = time,
-    time_interval = "day")
+    time_interval = "day"
+  )
   tsd_data_nbinom <- tsd(
     observed = rnbinom(n = n, mu = 1:n, size = 5),
     time = time,
-    time_interval = "day")
+    time_interval = "day"
+  )
 
   # Calculate AEDSEO with a 3-day window
   aedseo_poisson <- aedseo(
     tsd = tsd_data_poisson,
     k = 3,
     level = 0.95,
     family = "poisson"
-    )
+  )
   aedseo_quasipoisson <- aedseo(
     tsd = tsd_data_nbinom,
     k = 3,
     level = 0.95,
     family = "quasipoisson"
-    )
+  )
 
   # Check if they all converge
   expect_true(object = all(aedseo_poisson$converged))
   expect_true(object = all(aedseo_quasipoisson$converged))
-
 })

tests/testthat/test-fit_growth_rate.R

@@ -1,5 +1,4 @@
 test_that("The growth rate models converge", {
-
   # Number of random data points to generate
   n <- 1e3
 
@@ -15,15 +14,14 @@ test_that("The growth rate models converge", {
     observations = data_poisson,
     level = 0.95,
     family = "poisson"
-    )
+  )
   fit_quasipoisson <- fit_growth_rate(
     observations = data_nbinom,
     level = 0.95,
     family = "quasipoisson"
-    )
+  )
 
   # Check if they all converge
   expect_true(object = fit_poisson$fit$converged)
   expect_true(object = fit_quasipoisson$fit$converged)
-
 })