Merge pull request #518 from tidymodels/no-suggests

Enable checks without suggested packages

.github/workflows/R-CMD-check-no-suggests.yaml

@@ -0,0 +1,59 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+#
+# NOTE: This workflow only directly installs "hard" dependencies, i.e. Depends,
+# Imports, and LinkingTo dependencies. Notably, Suggests dependencies are never
+# installed, with the exception of testthat, knitr, and rmarkdown. The cache is
+# never used to avoid accidentally restoring a cache containing a suggested
+# dependency.
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+
+name: R-CMD-check-no-suggests.yaml
+
+permissions: read-all
+
+jobs:
+  check-no-suggests:
+    runs-on: ${{ matrix.config.os }}
+
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - {os: ubuntu-latest,   r: 'release'}
+
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          r-version: ${{ matrix.config.r }}
+          http-user-agent: ${{ matrix.config.http-user-agent }}
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          dependencies: '"hard"'
+          cache: false
+          extra-packages: |
+            any::rcmdcheck
+            any::testthat
+            any::knitr
+            any::rmarkdown
+          needs: check
+
+      - uses: r-lib/actions/check-r-package@v2
+        with:
+          upload-snapshots: true
+          build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")'

R/conf_mat.R

@@ -39,7 +39,7 @@
 #' [summary.conf_mat()] for computing a large number of metrics from one
 #' confusion matrix.
 #'
-#' @examples
+#' @examplesIf rlang::is_installed(c("tidyr", "ggplot2"))
 #' library(dplyr)
 #' data("hpc_cv")
 #'
@@ -342,7 +342,7 @@ flatten <- function(xtab, call = caller_env()) {
 #'
 #' [conf_mat()]
 #'
-#' @examples
+#' @examplesIf rlang::is_installed(c("tidyr"))
 #' data("two_class_example")
 #'
 #' cmat <- conf_mat(two_class_example, truth = "truth", estimate = "predicted")

R/prob-gain_capture.R

@@ -36,7 +36,7 @@
 #'
 #' @template examples-binary-prob
 #' @template examples-multiclass-prob
-#' @examples
+#' @examplesIf rlang::is_installed(c("ggplot2"))
 #' # ---------------------------------------------------------------------------
 #' # Visualize gain_capture()
 #'

R/prob-gain_curve.R

@@ -72,7 +72,7 @@
 #' @author Max Kuhn
 #'
 #' @template examples-binary-prob
-#' @examples
+#' @examplesIf rlang::is_installed(c("ggplot2"))
 #' # ---------------------------------------------------------------------------
 #' # `autoplot()`
 #'

R/prob-lift_curve.R

@@ -61,7 +61,7 @@
 #' @author Max Kuhn
 #'
 #' @template examples-binary-prob
-#' @examples
+#' @examplesIf rlang::is_installed(c("ggplot2"))
 #' # ---------------------------------------------------------------------------
 #' # `autoplot()`
 #'

R/prob-pr_curve.R

@@ -27,7 +27,7 @@
 #'
 #' @author Max Kuhn
 #' @template examples-binary-prob
-#' @examples
+#' @examplesIf rlang::is_installed(c("ggplot2"))
 #' # ---------------------------------------------------------------------------
 #' # `autoplot()`
 #'

R/prob-roc_curve.R

@@ -29,7 +29,7 @@
 #'
 #' @author Max Kuhn
 #' @template examples-binary-prob
-#' @examples
+#' @examplesIf rlang::is_installed(c("ggplot2"))
 #' # ---------------------------------------------------------------------------
 #' # `autoplot()`
 #'

R/surv-brier_survival.R

@@ -19,13 +19,13 @@
 #'
 #' @param ... The column identifier for the survival probabilities this
 #' should be a list column of data.frames corresponding to the output given when
-#' predicting with [censored](https://censored.tidymodels.org/) model. This 
-#' should be an unquoted column name although this argument is passed by 
-#' expression and supports [quasiquotation][rlang::quasiquotation] (you can 
+#' predicting with [censored](https://censored.tidymodels.org/) model. This
+#' should be an unquoted column name although this argument is passed by
+#' expression and supports [quasiquotation][rlang::quasiquotation] (you can
 #' unquote column names). For `_vec()` functions, the dots are not used.
-#' 
-#' @param estimate A list column of data.frames corresponding to the output 
-#' given when predicting with [censored](https://censored.tidymodels.org/) 
+#'
+#' @param estimate A list column of data.frames corresponding to the output
+#' given when predicting with [censored](https://censored.tidymodels.org/)
 #' model. See the details for more information regarding format.
 #'
 #' @details
@@ -59,7 +59,9 @@
 #' comparison of prognostic classification schemes for survival data,”
 #' _Statistics in Medicine_, vol. 18, no. 17-18, pp. 2529–2545, 1999.
 #'
-#' @examples
+#' @examplesIf rlang::is_installed(c("tidyr"))
+#' # example code
+#'
 #' library(dplyr)
 #'
 #' lung_surv %>%

R/surv-brier_survival_integrated.R

@@ -43,7 +43,7 @@
 #' and comparison of prognostic classification schemes for survival data,”
 #' Statistics in Medicine, vol. 18, no. 17-18, pp. 2529–2545, 1999.
 #'
-#' @examples
+#' @examplesIf rlang::is_installed(c("tidyr"))
 #' library(dplyr)
 #'
 #' lung_surv %>%

R/surv-roc_auc_survival.R

@@ -47,7 +47,7 @@
 #' and comparison of prognostic classification schemes for survival data.
 #' _Statist. Med._, 18: 2529-2545.
 #'
-#' @examples
+#' @examplesIf rlang::is_installed(c("tidyr"))
 #' library(dplyr)
 #'
 #' lung_surv %>%

R/surv-roc_curve_survival.R

@@ -49,7 +49,7 @@
 #' and comparison of prognostic classification schemes for survival data.
 #' _Statist. Med._, 18: 2529-2545.
 #'
-#' @examples
+#' @examplesIf rlang::is_installed(c("ggplot2"))
 #' result <- roc_curve_survival(
 #'   lung_surv,
 #'   truth = surv_obj,
@@ -180,25 +180,25 @@ roc_curve_survival_impl <- function(truth,
 
 roc_curve_survival_impl_one <- function(event_time, delta, data, case_weights) {
   res <- dplyr::tibble(.threshold = sort(unique(c(-Inf, data$.pred_survival, Inf)), decreasing = TRUE))
-  
+
   obs_time_le_time <- event_time <= data$.eval_time
   obs_time_gt_time <- event_time > data$.eval_time
   n <- nrow(data)
-  
+
   sensitivity_denom <- sum(obs_time_le_time * delta * data$.weight_censored * case_weights, na.rm = TRUE)
   specificity_denom <- sum(obs_time_gt_time * data$.weight_censored * case_weights, na.rm = TRUE)
-  
+
   data_df <- data.frame(
     le_time = obs_time_le_time,
     ge_time = obs_time_gt_time,
     delta = delta,
     weight_censored = data$.weight_censored,
     case_weights = case_weights
   )
-  
+
   data_split <- vec_split(data_df, data$.pred_survival)
   data_split <- data_split$val[order(data_split$key)]
-  
+
   specificity <- vapply(
     data_split,
     function(x) sum(x$ge_time * x$weight_censored * x$case_weights, na.rm = TRUE),
@@ -217,14 +217,14 @@ roc_curve_survival_impl_one <- function(event_time, delta, data, case_weights) {
     function(x) sum(x$le_time * x$delta * x$weight_censored * x$case_weights, na.rm = TRUE),
     FUN.VALUE = numeric(1)
   )
-  
+
   sensitivity <- cumsum(sensitivity)
   sensitivity <- sensitivity / sensitivity_denom
   sensitivity <- dplyr::if_else(sensitivity > 1, 1, sensitivity)
   sensitivity <- dplyr::if_else(sensitivity < 0, 0, sensitivity)
   sensitivity <- c(0, sensitivity, 1)
   res$sensitivity <- sensitivity
-  
+
   res
 }