Deprecating aedseo and tsd, renaming to seasonal_onset and to_time_series

NAMESPACE

@@ -1,18 +1,19 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(autoplot,aedseo)
-S3method(autoplot,aedseo_tsd)
-S3method(plot,aedseo)
-S3method(plot,aedseo_tsd)
-S3method(predict,aedseo)
-S3method(summary,aedseo)
+S3method(autoplot,seasonal_onset)
+S3method(autoplot,tsd)
+S3method(plot,seasonal_onset)
+S3method(plot,tsd)
+S3method(predict,seasonal_onset)
+S3method(summary,seasonal_onset)
 export("%>%")
 export(aedseo)
 export(autoplot)
 export(epi_calendar)
 export(fit_growth_rate)
 export(fit_quantiles)
 export(seasonal_burden_levels)
+export(seasonal_onset)
 export(tsd)
 importFrom(ggplot2,autoplot)
 importFrom(grDevices,devAskNewPage)

NEWS.md

@@ -1,7 +1,13 @@
 # aedseo (development version)
 
+## Deprecations
+
+* `aedseo()` is now deprecated. Please use `seasonal_onset()` instead. A warning is shown when using `aedseo()` (#41).
+
 ## Features
 
+* Added `seasonal_onset()` as a replacement for the deprecated `aedseo()` function (#41).
+
 * Added the `seasonal_burden_levels()` function, which calculates burden levels based on data from previous seasons with two different methods; "peak_levels" or "intensity_levels" (#37).
 
 * Added the `fit_quantiles()` function, which optimises a user selected distribution and calculates the quantiles based on observations and weights. It is meant to be used within the `seasonal_burden_levels()` function (#35, #37).

R/autoplot.R

@@ -2,11 +2,10 @@
 #'
 #' @description
 #'
-#'  This function generates a complete 'ggplot' object suitable for
-#'  visualizing time series data in an `aedseo_tsd` object. It creates a line
-#'  plot connecting the observations and adds points at each data point.
+#'  This function generates a complete 'ggplot' object suitable for visualizing time series data in an `tsd` object.
+#' It creates a line plot connecting the observations and adds points at each data point.
 #'
-#' @param object An `aedseo_tsd` or `aedseo` object
+#' @param object An `tsd` or `seasonal_onset_tsd` object
 #' @param linewidth Numeric, the width of the line for the growth rate
 #' @param size Numeric, size of observational points.
 #' @param width Numeric, the width of the error bar employed to show the
@@ -20,8 +19,8 @@
 #' @aliases autoplot
 #'
 #' @examples
-#' # Create an example aedseo_tsd object
-#' aedseo_tsd_object <- tsd(
+#' # Create an example tsd object
+#' tsd_object <- tsd(
 #'   observation = c(100, 120, 150, 180, 220, 270),
 #'   time = as.Date(c(
 #'     "2023-01-01",
@@ -34,29 +33,29 @@
 #'   time_interval = "day"
 #' )
 #'
-#' # Create a ggplot visualization for the aedseo_tsd object
-#' autoplot(aedseo_tsd_object)
+#' # Create a ggplot visualization for the tsd object
+#' autoplot(tsd_object)
 #'
-#' # Create an aedseo object
-#' aedseo_object <- aedseo(
-#'   tsd = aedseo_tsd_object,
+#' # Create an tsd object
+#' tsd_object <- seasonal_onset(
+#'   tsd = tsd_object,
 #'   k = 3,
 #'   level = 0.95,
 #'   family = "quasipoisson"
 #' )
 #'
 #' # Create a ggplot visualization of growth rates with confidence intervals
-#' autoplot(aedseo_object, linewidth = 1, alpha = 0.2)
+#' autoplot(tsd_object, linewidth = 1, alpha = 0.2)
 #' @importFrom ggplot2 autoplot
 #' @rdname autoplot
 #' @export
 autoplot <- function(object, ...) {
   UseMethod("autoplot")
 }
 #' @rdname autoplot
-#' @method autoplot aedseo_tsd
+#' @method autoplot tsd
 #' @export
-autoplot.aedseo_tsd <- function(object, ...) {
+autoplot.tsd <- function(object, ...) {
   object %>%
     ggplot2::ggplot(
       mapping = ggplot2::aes(
@@ -69,9 +68,9 @@ autoplot.aedseo_tsd <- function(object, ...) {
 }
 #' @importFrom grDevices devAskNewPage
 #' @rdname autoplot
-#' @method autoplot aedseo
+#' @method autoplot seasonal_onset
 #' @export
-autoplot.aedseo <- function(
+autoplot.seasonal_onset <- function(
     object,
     linewidth = 0.7,
     size = 2,

R/epi_calendar.R

@@ -2,14 +2,12 @@
 #'
 #' @description
 #'
-#' This function identifies the epidemiological season, (must span new year)
-#' to which a given date belongs.
-#' The epidemiological season is defined by a start and end week, where weeks
-#' are numbered according to the ISO week date system.
+#' This function identifies the epidemiological season, (must span new year) to which a given date belongs.
+#' The epidemiological season is defined by a start and end week, where weeks are numbered according to the
+#' ISO week date system.
 #'
 #' @param date A date object representing the date to check.
-#' @param start An integer specifying the start week of the epidemiological
-#' season.
+#' @param start An integer specifying the start week of the epidemiological season.
 #' @param end An integer specifying the end week of the epidemiological season.
 #'
 #' @return A character vector indicating the season:

R/fit_growth_rate.R

@@ -2,15 +2,12 @@
 #'
 #' @description
 #'
-#' This function fits a growth rate model to time series observations and
-#' provides parameter estimates along with confidence intervals.
+#' This function fits a growth rate model to time series observations and provides parameter estimates along with
+#' confidence intervals.
 #'
-#' @param observations A numeric vector containing the time series
-#' observations.
-#' @param level The confidence level for parameter estimates, a numeric
-#' value between 0 and 1.
-#' @param family A character string specifying the family for modeling.
-#' Choose between "poisson," or "quasipoisson".
+#' @param observations A numeric vector containing the time series observations.
+#' @param level The confidence level for parameter estimates, a numeric value between 0 and 1.
+#' @param family A character string specifying the family for modeling. Choose between "poisson," or "quasipoisson".
 #'
 #' @return A list containing:
 #'   - 'fit': The fitted growth rate model.
@@ -24,15 +21,11 @@
 #' # Fit a growth rate model to a time series of counts
 #' # (e.g., population growth)
 #' data <- c(100, 120, 150, 180, 220, 270)
-#' growth_rate_model <- fit_growth_rate(
+#' fit_growth_rate(
 #'   observations = data,
 #'   level = 0.95,
 #'   family = "poisson"
 #' )
-#'
-#' # Print the estimated growth rate and confidence interval
-#' print(growth_rate_model$estimate)
-#'
 fit_growth_rate <- function(
     observations,
     level = 0.95,

R/plot.R

@@ -2,11 +2,10 @@
 #'
 #' @description
 #'
-#'  This function generates a complete 'ggplot' object suitable for
-#'  visualizing time series data in an `aedseo_tsd` object. It creates a line
-#'  plot connecting the observations and adds points at each data point.
+#' This function generates a complete 'ggplot' object suitable for visualizing time series data in an `tsd` object.
+#' It creates a line plot connecting the observations and adds points at each data point.
 #'
-#' @param x An `aedseo_tsd` or `aedseo` object
+#' @param x An `tsd` or `seasonal_onset` object
 #' @param ... Additional arguments passed to specific methods.
 #'
 #' @return A 'ggplot' object for visualizing the time series data.
@@ -16,8 +15,8 @@
 #' @seealso [autoplot()]
 #'
 #' @examples
-#' # Create an example aedseo_tsd object
-#' aedseo_tsd_object <- tsd(
+#' # Create an example tsd object
+#' tsd_object <- tsd(
 #'   observation = c(100, 120, 150, 180, 220, 270),
 #'   time = as.Date(c(
 #'     "2023-01-01",
@@ -30,29 +29,29 @@
 #'   time_interval = "day"
 #' )
 #'
-#' # Create a ggplot visualization for the aedseo_tsd object
-#' plot(aedseo_tsd_object)
+#' # Create a ggplot visualization for the tsd object
+#' plot(tsd_object)
 #'
-#' # Create an aedseo object
-#' aedseo_object <- aedseo(
-#'   tsd = aedseo_tsd_object,
+#' # Create an seasonal_onset object
+#' object <- seasonal_onset(
+#'   tsd = tsd_object,
 #'   k = 3,
 #'   level = 0.95,
 #'   family = "quasipoisson"
 #' )
 #'
 #' # Create a ggplot visualization of growth rates with confidence intervals
-#' plot(aedseo_object, linewidth = 1, alpha = 0.2)
+#' plot(object, linewidth = 1, alpha = 0.2)
 #' @importFrom graphics plot
 #' @rdname plot
-#' @method plot aedseo_tsd
+#' @method plot tsd
 #' @export
-plot.aedseo_tsd <- function(x, ...) {
+plot.tsd <- function(x, ...) {
   print(autoplot(x, ...))
 }
 #' @rdname plot
-#' @method plot aedseo
+#' @method plot seasonal_onset
 #' @export
-plot.aedseo <- function(x, ...) {
+plot.seasonal_onset <- function(x, ...) {
   print(autoplot(object = x, ...))
 }

R/predict.R

@@ -2,20 +2,17 @@
 #'
 #' @description
 #'
-#' This function is used to predict future growth rates based on a model object
-#' created using the 'aedseo' package. It takes the model object and the number
-#' of future time steps (`n_step`) for which you want to make predictions and
+#' This function is used to predict future growth rates based on a model object created using the 'aedseo' package.
+#' It takes the model object and the number of future time steps (`n_step`) for which you want to make predictions and
 #' returns a prediction tibble.
 #'
-#' @param object A model object created using the `aedseo` package, typically
-#' the result of the `aedseo()` function.
-#' @param n_step An integer specifying the number of future time steps for
-#' which you want to predict growth rates. Default is 3.
+#' @param object A model object created using the `seasonal_onset()` function.
+#' @param n_step An integer specifying the number of future time steps for which you want to predict growth rates.
 #' @param ... Additional arguments (not used).
 #'
-#' @return  A tibble S3 object called `aedseo` containing the predicted growth
-#' rates, including time, estimated growth rate, lower confidence interval,
-#' and upper confidence interval for the specified number of future time steps.
+#' @return  A tibble S3 object called `seasonal_onset` containing the predicted growth rates, including time,
+#' estimated growth rate, lower confidence interval, and upper confidence interval for the specified number of
+#' future time steps.
 #'
 #' @export
 #'
@@ -36,20 +33,16 @@
 #'   time_interval = "day"
 #' )
 #'
-#' aedseo_results <- aedseo(
+#' tsd_results <- seasonal_onset(
 #'   tsd = tsd_data,
 #'   k = 3,
 #'   level = 0.95,
 #'   family = "poisson"
 #' )
 #'
 #' # Predict growth rates for the next 5 time steps
-#' prediction <- predict(object = aedseo_results, n_step = 5)
-#'
-#' # Print the prediction
-#' print(prediction)
-#'
-predict.aedseo <- function(object, n_step = 3, ...) {
+#' predict(object = tsd_results, n_step = 5)
+predict.seasonal_onset <- function(object, n_step = 3, ...) {
   # Calculate the prediction
   res <- dplyr::last(object) %>%
     dplyr::reframe(
@@ -68,10 +61,10 @@ predict.aedseo <- function(object, n_step = 3, ...) {
   level <- attributes_object$level
   family <- attributes_object$family
 
-  # Turn the results into an `aedseo` class
+  # Turn the results into a class
   ans <- tibble::new_tibble(
     x = res,
-    class = "aedseo_predict",
+    class = "tsd_predict",
     k = k,
     level = level,
     family = family

R/seasonal_burden_levels.R

@@ -104,7 +104,7 @@ seasonal_burden_levels <- function(
   method <- rlang::arg_match(method)
   coll <- checkmate::makeAssertCollection()
   checkmate::assert_data_frame(tsd, add = coll)
-  checkmate::assert_class(tsd, "aedseo_tsd", add = coll)
+  checkmate::assert_class(tsd, "tsd", add = coll)
   checkmate::assert_names(colnames(tsd), identical.to = c("time", "observation"), add = coll)
   checkmate::assert_integerish(season_weeks, len = 2, lower = 1, upper = 53,
                                null.ok = FALSE, add = coll)