Merge pull request #20 from ssi-dk/telkamp7/issue18

Telkamp7/issue18

DESCRIPTION

@@ -6,7 +6,14 @@ Authors@R: c(
            comment = c(ORCID = "0009-0001-5126-0190")),
     person("Statens Serum Institut, SSI", role = c("cph", "fnd"))
     )
-Description: A powerful tool for automating the early detection of seasonal epidemic onsets in time series data. It offers the ability to estimate growth rates for consecutive time intervals and calculate the Sum of Cases (SoC) within those intervals. It is particularly useful for epidemiologists, public health professionals, and researchers seeking to identify and respond to seasonal epidemics in a timely fashion.
+Description: A powerful tool for automating the early detection of seasonal epidemic
+             onsets in time series data. It offers the ability to estimate growth rates
+             for consecutive time intervals and calculate the sum of cases (SoC) within 
+             those intervals. It is particularly useful for epidemiologists, public 
+             health professionals, and researchers seeking to identify and respond to 
+             seasonal epidemics in a timely fashion.
+             For reference on growth rate estimation, see Walling and Lipstich (2007) 
+             <doi:10.1098/rspb.2006.3754> and Obadia et al. (2012) <doi:10.1186/1472-6947-12-147>.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)

R/aedseo.R

@@ -5,10 +5,10 @@
 #'
 #' 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 (sum_of_cases).
+#' consecutive time intervals and calculates the sum of cases (sum_of_cases).
 #'
-#' @param tsd A `aedseo_tsd` object containing time series data with 'time,'
-#' 'observed,' and 'periodInYear.'
+#' @param tsd A `aedseo_tsd` object containing time series data with 'time' and
+#' 'observed.'
 #' @param k An integer specifying the window size for modeling growth rates.
 #' @param level The confidence level for parameter estimates, a numeric value
 #' between 0 and 1.
@@ -27,7 +27,7 @@
 #'   interval.
 #'   - 'growth_warning': Logical. Is the growth rate significantly higher than
 #'   zero?
-#'   - 'sum_of_cases': The Sum of Cases within the time window.
+#'   - 'sum_of_cases': The sum of cases within the time window.
 #'   - 'sum_of_cases_warning': Logical. Does the Sum of Cases exceed the
 #'   disease threshold?
 #'   - 'seasonal_onset_alarm': Logical. Is there a seasonal onset alarm?

R/autoplot.R

@@ -1,9 +1,9 @@
-#' Create a complete ggplot appropriate to a particular data type
+#' Create a complete 'ggplot' appropriate to a particular data type
 #'
 #' @description
 #' `r lifecycle::badge("stable")`
 #'
-#'  This function generates a complete ggplot object suitable for
+#'  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.
 #'
@@ -13,11 +13,12 @@
 #' ribbon
 #' @param ... Additional arguments (not used).
 #'
-#' @return A ggplot object for visualizing the time series data.
+#' @return A 'ggplot' object for visualizing the time series data.
 #'
 #' @aliases autoplot
 #'
 #' @examples
+#' \dontrun{
 #' # Create an example aedseo_tsd object
 #' aedseo_tsd_object <- tsd(
 #'   observed = c(100, 120, 150, 180, 220, 270),
@@ -33,7 +34,7 @@
 #' )
 #'
 #' # Create a ggplot visualization for the aedseo_tsd object
-#' # autoplot(aedseo_tsd_object)
+#' autoplot(aedseo_tsd_object)
 #'
 #' # Create an aedseo object
 #' aedseo_object <- aedseo(
@@ -44,7 +45,8 @@
 #' )
 #'
 #' # Create a ggplot visualization of growth rates with confidence intervals
-#' # autoplot(aedseo_object, linewidth = 1, alpha = 0.2)
+#' autoplot(aedseo_object, linewidth = 1, alpha = 0.2)
+#' }
 #' @importFrom ggplot2 autoplot
 #' @rdname autoplot
 #' @method autoplot aedseo_tsd

R/plot.R

@@ -1,16 +1,16 @@
-#' Create a complete ggplot appropriate to a particular data type
+#' Create a complete 'ggplot' appropriate to a particular data type
 #'
 #' @description
 #' `r lifecycle::badge("stable")`
 #'
-#'  This function generates a complete ggplot object suitable for
+#'  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.
 #'
 #' @param x An `aedseo_tsd` or `aedseo` object
 #' @param ... Additional arguments passed to specific methods.
 #'
-#' @return A ggplot object for visualizing the time series data.
+#' @return A 'ggplot' object for visualizing the time series data.
 #'
 #' @aliases plot
 #'

R/predict.R

@@ -4,7 +4,7 @@
 #' `r lifecycle::badge("stable")`
 #'
 #' 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
+#' 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.
 #'
@@ -50,7 +50,6 @@
 #' # Print the prediction
 #' print(prediction)
 #'
-# TODO: #13 Turn the results into a `aedseo_predict` class. @telkmp7
 predict.aedseo <- function(object, n_step = 3, ...) {
   # Calculate the prediction
   res <- dplyr::last(object) %>%

R/summary.R

@@ -30,7 +30,7 @@
 #'   time_interval = "day"
 #' )
 #'
-#' # Calculate AEDSEO with a 3-day window and a Poisson family model
+#' # Calculate aedseo with a 3-day window and a Poisson family model
 #' aedseo_results <- aedseo(
 #'   tsd = tsd_data,
 #'   k = 3,

cran-comments.md

@@ -1,7 +1,31 @@
-## R CMD check results
+## Test Environments
 
-0 errors | 0 warnings | 1 note
+* GitHub Actions
+  - macOS-latest (R release)
+  - windows-latest (R release)
+  - ubuntu-latest (R devel, R release, R oldrel-1)
+* Local Windows 10, R 4.2.0
 
-* This is a new release.
+## R CMD Check Results
 
-'SoC' is an abbreviation of sum of cases. This is explained in detail in both the README.md, but also in the relevant functions.
+The package has been tested on multiple platforms and no errors or warnings were identified during the tests.
+
+## Reviewer Feedback
+
+Thank you for the feedback, and we've addressed the issues mentioned:
+
+1. **References**: We've added references to the methods used in our package in the DESCRIPTION file in the required format:
+
+2. **Commented Code in Examples**: We've removed commented code lines in examples and have made sure that the examples can be executed without issues.
+
+## Abbreviations
+
+* 'SoC' is an abbreviation of sum of cases. This is explained in detail in both the README.md, DESCRIPTION file, and in the relevant functions.
+
+## Resubmission
+
+We've addressed the feedback provided, and the package is now ready for resubmission. We appreciate your review and are looking forward to having our package included on CRAN.
+
+Best regards,
+
+Kasper Schou Telkamp