Styled and linted the package

R/aedseo.R

@@ -5,7 +5,7 @@
 #'
 #' This function performs automated and early detection of seasonal epidemic
 #' onsets (aedseo) on a time series dataset. It estimates growth rates for
-#' consecutive time intervals and calculates the Sum of Cases (SoC).
+#' consecutive time intervals and calculates the Sum of Cases (sum_of_cases).
 #'
 #' @param tsd A tsibble object containing time series data with 'time,'
 #' 'observed,' and 'periodInYear.'
@@ -24,7 +24,7 @@
 #'   interval.
 #'   - 'growth_warning': Logical. Is the growth rate significantly higher than
 #'   zero?
-#'   - 'SoC': The Sum of Cases within the time window.
+#'   - 'sum_of_cases': The Sum of Cases within the time window.
 #'   - 'converged': Logical. Was the IWLS judged to have converged?
 #'
 #' @export
@@ -86,8 +86,8 @@ aedseo <- function(
     # See if the growth rate is significantly higher than zero
     growth_warning <- growth_rates$estimate[2] > 0
 
-    # Calculate Sum of Cases (SoC)
-    SoC <- base::sum(obs_iter$observed)
+    # Calculate Sum of Cases (sum_of_cases)
+    sum_of_cases <- base::sum(obs_iter$observed)
 
     # Collect the results
     res <- dplyr::bind_rows(
@@ -98,7 +98,7 @@ aedseo <- function(
         lower_growth_rate = growth_rates$estimate[2],
         upper_growth_rate = growth_rates$estimate[3],
         growth_warning = growth_warning,
-        SoC = SoC,
+        sum_of_cases = sum_of_cases,
         converged = growth_rates$fit$converged
       )
     )

R/tsd.R

@@ -1,16 +1,21 @@
-#' Create a tsibble (time-series data) object from observed data and corresponding dates.
+#' Create a tsibble (time-series data) object from observed data and
+#' corresponding dates.
 #'
 #' @description
 #' `r lifecycle::badge("experimental")`
 #'
-#' This function takes observed data and the corresponding date vector and converts it into a tsibble object,
-#' which is a time series data structure that can be used for time series analysis.
+#' This function takes observed data and the corresponding date vector and
+#' converts it into a tsibble object,
+#' which is a time series data structure that can be used for time series
+#' analysis.
 #'
 #' @param observed A numeric vector containing the observations.
 #' @param time A date vector containing the corresponding dates.
-#' @param time_interval A character vector specifying the time interval, choose between "day," "week," or "month."
+#' @param time_interval A character vector specifying the time interval.
+#' Choose between "day," "week," or "month."
 #'
-#' @return A tsibble object containing time, the observations, and the periodInYear.
+#' @return A tsibble object containing time, the observations, and the
+#' periodInYear.
 #'
 #' @export
 #'

vignettes/aedseo_introduction.Rmd

@@ -43,7 +43,15 @@ To assess the effectiveness of the `aedseo` function, we simulate some data. The
 \end{equation}
 
 ```{r}
-mu_t <- function(t, theta = 1, exp_beta = 1.001, gamma_sin = 1, gamma_cos = 1, trend = 1, j = 1, ...) {
+mu_t <- function(
+    t,
+    theta = 1,
+    exp_beta = 1.001,
+    gamma_sin = 1,
+    gamma_cos = 1,
+    trend = 1,
+    j = 1,
+    ...) {
   # Start construction of linear predictor
   linear_predictor <- theta
   # ... add a trend if the scenario request it
@@ -74,7 +82,8 @@ simulate_from_nbinom <- function(...) {
   # Match call
   mc <- as.list(match.call())[-1]
   # ... and change parameters relative to the call
-  mc <- append(mc, default_pars[!names(default_pars) %in% names(mc)])[names(default_pars)]
+  index_mc <- !names(default_pars) %in% names(mc)
+  mc <- append(mc, default_pars[index_mc])[names(default_pars)]
 
   # Set the seed
   set.seed(mc$seed)
@@ -99,20 +108,24 @@ weeks <- 52
 # ... calculate the total number of observations
 n <- years * weeks
 # ... and a vector containing the overall time passed
-timeOverall <- 1:n
+time_overall <- 1:n
 # Create arbitrary dates
 dates <- as.POSIXct(
-  x = timeOverall * 86400 * 7,
+  x = time_overall * 86400 * 7,
   origin = "2010-01-01",
   tz = "UTC",
   format = "%F"
 )
 
 # Simulate the data
-simulation <- simulate_from_nbinom(t = timeOverall, theta = log(1000), phi = 5)
+simulation <- simulate_from_nbinom(t = time_overall, theta = log(1000), phi = 5)
 
 # Collect the data in a 'tibble'
-sim_data <- tibble(Date = dates, mu_t = simulation$mu_t, y = simulation$simulation)
+sim_data <- tibble(
+  Date = dates,
+  mu_t = simulation$mu_t,
+  y = simulation$simulation
+)
 ```
 
 A total of three years of weekly data are then simulated with the following set of parameters:
@@ -149,15 +162,24 @@ Next, the time series data object is passed to the `aedseo()` function. Here, a
 
 ```{r}
 # Apply the 'aedseo' algorithm
-aedseo_results <- aedseo(tsd = tsd_data, k = 5, level = 0.95, family = "quasipoisson")
+aedseo_results <- aedseo(
+  tsd = tsd_data,
+  k = 5,
+  level = 0.95,
+  family = "quasipoisson"
+)
 ```
 
 In the figure below, the observed values from the simulations is visualized alongside the local estimate of the growth rate and its corresponding 95\% confidence interval.
 
 
 ```{r}
 # Join the observations and estimated growth rates
-full_data <- full_join(x = tsd_data, y = aedseo_results, by = join_by("time" == "reference_time"))
+full_data <- full_join(
+  x = tsd_data,
+  y = aedseo_results,
+  by = join_by("time" == "reference_time")
+)
 
 # Data to add horizontal line in growth rate
 ablines <- tibble(name = c("growth_rate", "observed"), x = c(0, NA))