Set cores for CRAN checks consistently

epiforecasts · Nov 29, 2023 · 169bad9 · 169bad9
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commit 169bad9
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Showing 17 changed files with 56 additions and 20 deletions.
diff --git a/R/avail_forecasts.R b/R/avail_forecasts.R
@@ -27,7 +27,9 @@
 #' @export
 #' @keywords check-forecasts
 #' @examples
-#' \dontshow{data.table::setDTthreads(2) # only needed to avoid issues on CRAN}
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' avail_forecasts(example_quantile,
 #'   collapse = c("quantile"),

diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R
@@ -52,7 +52,9 @@
 #' @author Johannes Bracher, \email{johannes.bracher@@kit.edu}
 #' @keywords scoring
 #' @examples
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' scores <- score(example_quantile)
 #' pairwise <- pairwise_comparison(scores, by = "target_type")

diff --git a/R/pit.R b/R/pit.R
@@ -62,7 +62,9 @@
 #' @seealso [pit()]
 #' @importFrom stats runif
 #' @examples
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' ## continuous predictions
 #' true_values <- rnorm(20, mean = 1:20)

diff --git a/R/plot.R b/R/plot.R
@@ -24,7 +24,9 @@
 #' @examples
 #' library(ggplot2)
 #' library(magrittr) # pipe operator
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' scores <- score(example_quantile) %>%
 #'   summarise_scores(by = c("model", "target_type")) %>%
@@ -582,7 +584,9 @@ make_na <- make_NA
 #' @importFrom data.table dcast
 #' @export
 #' @examples
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #' scores <- score(example_quantile)
 #' scores <- summarise_scores(scores, by = c("model", "range"))
 #' plot_interval_coverage(scores)
@@ -835,7 +839,9 @@ plot_pairwise_comparison <- function(comparison_result,
 #' @importFrom stats density
 #' @return vector with the scoring values
 #' @examples
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' # PIT histogram in vector based format
 #' true_values <- rnorm(30, mean = 1:30)

diff --git a/R/score.R b/R/score.R
@@ -75,7 +75,9 @@
 #'
 #' @examples
 #' library(magrittr) # pipe operator
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #'
 #' check_forecasts(example_quantile)
 #' score(example_quantile) %>%

diff --git a/R/summarise_scores.R b/R/summarise_scores.R
@@ -42,7 +42,9 @@
 #' provided to `fun`. For more information see the documentation of the
 #' respective function.
 #' @examples
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #' library(magrittr) # pipe operator
 #'
 #' scores <- score(example_continuous)
@@ -279,7 +281,9 @@ check_summary_params <- function(scores,
 #' summary is present according to the value specified in `by`.
 #' @examples
 #' library(magrittr) # pipe operator
-#' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
+#' \dontshow{
+#'   data.table::setDTthreads(2) # restricts number of cores used on CRAN
+#' }
 #' score(example_quantile) %>%
 #'   add_coverage(by = c("model", "target_type")) %>%
 #'   summarise_scores(by = c("model", "target_type")) %>%

diff --git a/man/add_coverage.Rd b/man/add_coverage.Rd
diff --git a/man/avail_forecasts.Rd b/man/avail_forecasts.Rd
diff --git a/man/pairwise_comparison.Rd b/man/pairwise_comparison.Rd
diff --git a/man/pit_sample.Rd b/man/pit_sample.Rd
diff --git a/man/plot_interval_coverage.Rd b/man/plot_interval_coverage.Rd
diff --git a/man/plot_pit.Rd b/man/plot_pit.Rd
diff --git a/man/plot_score_table.Rd b/man/plot_score_table.Rd
diff --git a/man/score.Rd b/man/score.Rd
diff --git a/man/summarise_scores.Rd b/man/summarise_scores.Rd
diff --git a/tests/testthat/setup.R b/tests/testthat/setup.R
@@ -1,7 +1,7 @@
 # load common required test packages
 library(ggplot2, quietly = TRUE)
 suppressMessages(library(magrittr))
-data.table::setDTthreads(2) # only needed to avoid issues on CRAN
+data.table::setDTthreads(2) # restricts number of cores used on CRAN
 
 # compute quantile scores
 scores <- suppressMessages(score(example_quantile))
diff --git a/vignettes/scoringutils.Rmd b/vignettes/scoringutils.Rmd
@@ -19,7 +19,7 @@ library(magrittr)
 library(data.table)
 library(ggplot2)
 library(knitr)
-data.table::setDTthreads(2) # only needed to avoid issues on CRAN
+data.table::setDTthreads(2) # restricts number of cores used on CRAN
 ```
 
 The `scoringutils` package provides a collection of metrics and proper scoring rules that make it simple to score probabilistic forecasts against the true observed values. You can find more information in the paper [Evaluating Forecasts with scoringutils in R](https://arxiv.org/abs/2205.07090) as well as the [Metrics-Vignette](https://epiforecasts.io/scoringutils/articles/metric-details.html) and the [Scoring forecasts directly Vignette](https://epiforecasts.io/scoringutils/articles/scoring-forecasts-directly.html).