Rename again

NAMESPACE

@@ -1,11 +1,11 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(autoplot,seasonal_onset)
 S3method(autoplot,tsd)
-S3method(plot,seasonal_onset)
+S3method(autoplot,tsd_onset)
 S3method(plot,tsd)
-S3method(predict,seasonal_onset)
-S3method(summary,seasonal_onset)
+S3method(plot,tsd_onset)
+S3method(predict,tsd_onset)
+S3method(summary,tsd_onset)
 export("%>%")
 export(aedseo)
 export(autoplot)
@@ -14,6 +14,7 @@ export(fit_growth_rate)
 export(fit_quantiles)
 export(seasonal_burden_levels)
 export(seasonal_onset)
+export(to_time_series)
 export(tsd)
 importFrom(ggplot2,autoplot)
 importFrom(grDevices,devAskNewPage)

NEWS.md

@@ -4,10 +4,14 @@
 
 * `aedseo()` is now deprecated. Please use `seasonal_onset()` instead. A warning is shown when using `aedseo()` (#41).
 
+* `tsd()` is now deprecated. Please use `to_time_series()` instead. A warning is shown when using `tsd()` (#41).
+
 ## Features
 
 * Added `seasonal_onset()` as a replacement for the deprecated `aedseo()` function (#41).
 
+* Added `to_time_series()` as a replacement for the deprecated `tsd()` 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

@@ -2,10 +2,10 @@
 #'
 #' @description
 #'
-#'  This function generates a complete 'ggplot' object suitable for visualizing time series data in an `tsd` object.
+#' 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 `tsd` or `seasonal_onset_tsd` object
+#' @param object An `tsd` or `tsd_onset` 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
@@ -19,8 +19,8 @@
 #' @aliases autoplot
 #'
 #' @examples
-#' # Create an example tsd object
-#' tsd_object <- tsd(
+#' # Create an example `tsd` object
+#' time_series <- tsd(
 #'   observation = c(100, 120, 150, 180, 220, 270),
 #'   time = as.Date(c(
 #'     "2023-01-01",
@@ -33,19 +33,19 @@
 #'   time_interval = "day"
 #' )
 #'
-#' # Create a ggplot visualization for the tsd object
-#' autoplot(tsd_object)
+#' # Create a ggplot visualization for the `tsd` object
+#' autoplot(time_series)
 #'
-#' # Create an tsd object
-#' tsd_object <- seasonal_onset(
-#'   tsd = tsd_object,
+#' # Create an `tsd_onset` object
+#' time_series_with_onset <- seasonal_onset(
+#'   tsd = time_series,
 #'   k = 3,
 #'   level = 0.95,
 #'   family = "quasipoisson"
 #' )
 #'
 #' # Create a ggplot visualization of growth rates with confidence intervals
-#' autoplot(tsd_object, linewidth = 1, alpha = 0.2)
+#' autoplot(time_series_with_onset, linewidth = 1, alpha = 0.2)
 #' @importFrom ggplot2 autoplot
 #' @rdname autoplot
 #' @export
@@ -68,9 +68,9 @@ autoplot.tsd <- function(object, ...) {
 }
 #' @importFrom grDevices devAskNewPage
 #' @rdname autoplot
-#' @method autoplot seasonal_onset
+#' @method autoplot tsd_onset
 #' @export
-autoplot.seasonal_onset <- function(
+autoplot.tsd_onset <- function(
     object,
     linewidth = 0.7,
     size = 2,

R/plot.R

@@ -5,7 +5,7 @@
 #' 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 `tsd` or `seasonal_onset` object
+#' @param x An `tsd` or `tsd_onset` object
 #' @param ... Additional arguments passed to specific methods.
 #'
 #' @return A 'ggplot' object for visualizing the time series data.
@@ -15,8 +15,8 @@
 #' @seealso [autoplot()]
 #'
 #' @examples
-#' # Create an example tsd object
-#' tsd_object <- tsd(
+#' # Create an example `tsd` object
+#' time_series <- to_time_series(
 #'   observation = c(100, 120, 150, 180, 220, 270),
 #'   time = as.Date(c(
 #'     "2023-01-01",
@@ -29,12 +29,12 @@
 #'   time_interval = "day"
 #' )
 #'
-#' # Create a ggplot visualization for the tsd object
-#' plot(tsd_object)
+#' # Create a ggplot visualization for the `tsd` object
+#' plot(time_series)
 #'
-#' # Create an seasonal_onset object
+#' # Create an `tsd_onset` object
 #' object <- seasonal_onset(
-#'   tsd = tsd_object,
+#'   tsd = time_series,
 #'   k = 3,
 #'   level = 0.95,
 #'   family = "quasipoisson"
@@ -50,8 +50,8 @@ plot.tsd <- function(x, ...) {
   print(autoplot(x, ...))
 }
 #' @rdname plot
-#' @method plot seasonal_onset
+#' @method plot tsd_onset
 #' @export
-plot.seasonal_onset <- function(x, ...) {
+plot.tsd_onset <- function(x, ...) {
   print(autoplot(object = x, ...))
 }

R/predict.R

@@ -6,11 +6,11 @@
 #' 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 `seasonal_onset()` function.
+#' @param object A `tsd_onset` 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 `seasonal_onset` containing the predicted growth rates, including time,
+#' @return  A tibble-like object called `tsd_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.
 #'
@@ -20,7 +20,7 @@
 #'
 #' @examples
 #' # Analyze the data using the aedseo package
-#' tsd_data <- tsd(
+#' time_series <- to_time_series(
 #'   observation = c(100, 120, 150, 180, 220, 270),
 #'   time = as.Date(c(
 #'     "2023-01-01",
@@ -33,16 +33,16 @@
 #'   time_interval = "day"
 #' )
 #'
-#' tsd_results <- seasonal_onset(
-#'   tsd = tsd_data,
+#' time_series_with_onset <- seasonal_onset(
+#'   tsd = time_series,
 #'   k = 3,
 #'   level = 0.95,
 #'   family = "poisson"
 #' )
 #'
 #' # Predict growth rates for the next 5 time steps
-#' predict(object = tsd_results, n_step = 5)
-predict.seasonal_onset <- function(object, n_step = 3, ...) {
+#' predict(object = time_series_with_onset, n_step = 5)
+predict.tsd_onset <- function(object, n_step = 3, ...) {
   # Calculate the prediction
   res <- dplyr::last(object) %>%
     dplyr::reframe(

R/seasonal_burden_levels.R

@@ -5,7 +5,7 @@
 #' This function calculates the burden levels of time series of observations that are stratified by season.
 #' It uses the previous seasons to calculate the levels of the newest season.
 #'
-#' @param tsd A `aedseo_tsd` object containing time series data with 'time' and 'observation'.
+#' @param tsd A `tsd` object containing time series data with 'time' and 'observation'.
 #' @param season_weeks A numeric vector of length 2, `c(start, end)`, with the start and end weeks of the seasons to
 #' stratify the observations by. Must span the new year; e.g.: `season_weeks = c(21, 20)`.
 #' NOTE: The data must include data for a complete previous season to make predictions for the newest season.

R/seasonal_onset.R

@@ -161,7 +161,7 @@ seasonal_onset <- function(
   # Turn the results into an `seasonal_onset` class
   ans <- tibble::new_tibble(
     x = res,
-    class = "seasonal_onset",
+    class = "tsd_onset",
     k = k,
     level = level,
     disease_threshold = disease_threshold,
@@ -177,9 +177,12 @@ seasonal_onset <- function(
 #' Deprecated aedseo function
 #' @description
 #' `r lifecycle::badge("deprecated")`
+#' This function has been renamed to better reflect its purpose.
+#' Please use `seasonal_onset()` instead.
 #' @param ... Arguments passed to `seasonal_onset()`
+#' @keywords internal
 #' @export
 aedseo <- function(...) {
-  warning("`aedseo()` is deprecated. Please use `seasonal_onset()` instead.")
+  lifecycle::deprecate_warn("0.1.2", "aedseo()", "seasonal_onset()")
   seasonal_onset(...)
 }

R/summary.R

@@ -1,20 +1,20 @@
-#' Summary method for seasonal_onset objects
+#' Summary method for `tsd_onset` objects
 #'
 #' @description
 #'
 #' Summarize the results of an seasonal onset analysis, including the latest growth rate estimate,
 #' the confidence interval, and information about growth warnings.
 #'
-#' @param object An object of class 'seasonal_onset' containing the results of a seasonal_onset analysis.
+#' @param object An object of class 'tsd_onset' containing the results of a seasonal_onset analysis.
 #' @param ... Additional arguments (not used).
 #'
 #' @return This function is used for its side effect, which is printing a summary message to the console.
 #'
 #' @export
 #'
 #' @examples
-#' # Create a tibble object from sample data
-#' tsd_data <- tsd(
+#' # Create a `tsd_onset` object from sample data
+#' tsd_data <- to_time_series(
 #'   observation = c(100, 120, 150, 180, 220, 270),
 #'   time = as.Date(c(
 #'     "2023-01-01",
@@ -27,14 +27,14 @@
 #'   time_interval = "day"
 #' )
 #'
-#' # Calculate seasonal_onset with a 3-day window and a Poisson family model
+#' # Calculate seasonal_onset results with a 3-day window and a Poisson family model
 #' seasonal_onset(
 #'   tsd = tsd_data,
 #'   k = 3,
 #'   level = 0.95,
 #'   family = "poisson"
 #' )
-summary.seasonal_onset <- function(object, ...) {
+summary.tsd_onset <- function(object, ...) {
   # Extract the last observation
   last_observation <- dplyr::last(object)
 

R/tsd.R → R/to_time_series.R

@@ -1,4 +1,4 @@
-#' Create a S3 `tsd` (time-series data) object from observed data and corresponding dates.
+#' Create a tibble-like `tsd` (time-series data) object from observed data and corresponding dates.
 #'
 #' @description
 #'
@@ -18,7 +18,7 @@
 #'
 #' @examples
 #' # Create a `tsd` object from daily data
-#' daily_tsd <- tsd(
+#' daily_tsd <- to_time_series(
 #'   observation = c(10, 15, 20, 18),
 #'   time = as.Date(
 #'     c("2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04")
@@ -27,24 +27,24 @@
 #' )
 #'
 #' # Create a `tsd` object from weekly data
-#' weekly_tsd <- tsd(
+#' weekly_tsd <- to_time_series(
 #'   observation = c(100, 120, 130),
 #'   time = as.Date(
 #'     c("2023-01-01", "2023-01-08", "2023-01-15")
 #'   ),
 #'   time_interval = "week"
 #' )
 #'
-#' # Create a `aedseo` object from monthly data
-#' monthly_tsd <- tsd(
+#' # Create a `tsd` object from monthly data
+#' monthly_tsd <- to_time_series(
 #'   observation = c(500, 520, 540),
 #'   time = as.Date(
 #'     c("2023-01-01", "2023-02-01", "2023-03-01")
 #'   ),
 #'   time_interval = "month"
 #' )
 #'
-tsd <- function(observation, time, time_interval = c("day", "week", "month")) {
+to_time_series <- function(observation, time, time_interval = c("day", "week", "month")) {
   # Check input arguments
   coll <- checkmate::makeAssertCollection()
   checkmate::assert_date(time, add = coll)
@@ -69,3 +69,16 @@ tsd <- function(observation, time, time_interval = c("day", "week", "month")) {
 
   return(tsd)
 }
+
+#' Deprecated tsd function
+#' @description
+#' `r lifecycle::badge("deprecated")`
+#' This function has been renamed to better reflect its purpose.
+#' Please use `to_time_series()` instead.
+#' @param ... Arguments passed to `to_time_series()`
+#' @keywords internal
+#' @export
+tsd <- function(...) {
+  lifecycle::deprecate_warn("0.1.2", "tsd()", "to_time_series()")
+  to_time_series(...)
+}