[R-package] move all examples to dontrun() to fix R CMD CHECK notes (#…

…3270) * Move all examples to dontrun * update docs * fix nested dontrun * remove :: in examples * run_dontrun in pkgdown Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
microsoft · Aug 6, 2020 · c454d5f · c454d5f
1 parent 6f54ec3
commit c454d5f
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diff --git a/.ci/test_r_package.sh b/.ci/test_r_package.sh
@@ -173,6 +173,7 @@ check_succeeded="yes"
 (
     R CMD check ${PKG_TARBALL} \
         --as-cran \
+        --run-dontrun \
     || check_succeeded="no"
 ) &
 

diff --git a/.ci/test_r_package_windows.ps1 b/.ci/test_r_package_windows.ps1
@@ -160,7 +160,7 @@ if ($env:COMPILER -ne "MSVC") {
   }
 
   Write-Output "Running R CMD check as CRAN"
-  Run-R-Code-Redirect-Stderr "result <- processx::run(command = 'R.exe', args = c('CMD', 'check', '--no-multiarch', '--as-cran', '$PKG_FILE_NAME'), echo = TRUE, windows_verbatim_args = FALSE)" ; $check_succeeded = $?
+  Run-R-Code-Redirect-Stderr "result <- processx::run(command = 'R.exe', args = c('CMD', 'check', '--no-multiarch', '--as-cran', '--run-dontrun', '$PKG_FILE_NAME'), echo = TRUE, windows_verbatim_args = FALSE)" ; $check_succeeded = $?
 
   Write-Output "R CMD check build logs:"
   $INSTALL_LOG_FILE_NAME = "lightgbm.Rcheck\00install.out"

diff --git a/R-package/R/lgb.Booster.R b/R-package/R/lgb.Booster.R
@@ -718,6 +718,7 @@ Booster <- R6::R6Class(
 #'         number of columns corresponding to the number of trees.
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -735,6 +736,7 @@ Booster <- R6::R6Class(
 #'   , learning_rate = 1.0
 #' )
 #' preds <- predict(model, test$data)
+#' }
 #' @export
 predict.lgb.Booster <- function(object,
                                 data,
@@ -774,7 +776,7 @@ predict.lgb.Booster <- function(object,
 #' @return lgb.Booster
 #'
 #' @examples
-#' \donttest{
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -834,7 +836,7 @@ lgb.load <- function(filename = NULL, model_str = NULL) {
 #' @return lgb.Booster
 #'
 #' @examples
-#' \donttest{
+#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -882,7 +884,7 @@ lgb.save <- function(booster, filename, num_iteration = NULL) {
 #' @return json format of model
 #'
 #' @examples
-#' \donttest{
+#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -930,6 +932,7 @@ lgb.dump <- function(booster, num_iteration = NULL) {
 #' @return vector of evaluation result
 #'
 #' @examples
+#' \dontrun{
 #' # train a regression model
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -956,6 +959,7 @@ lgb.dump <- function(booster, num_iteration = NULL) {
 #'
 #' # Get L2 values for "test" dataset
 #' lgb.get.eval.result(model, "test", "l2")
+#' }
 #' @export
 lgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_err = FALSE) {
 

diff --git a/R-package/R/lgb.Dataset.R b/R-package/R/lgb.Dataset.R
@@ -725,14 +725,15 @@ Dataset <- R6::R6Class(
 #' @return constructed dataset
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #' data_file <- tempfile(fileext = ".data")
 #' lgb.Dataset.save(dtrain, data_file)
 #' dtrain <- lgb.Dataset(data_file)
 #' lgb.Dataset.construct(dtrain)
-#'
+#' }
 #' @export
 lgb.Dataset <- function(data,
                         params = list(),
@@ -770,13 +771,14 @@ lgb.Dataset <- function(data,
 #' @return constructed dataset
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-#'
+#' }
 #' @export
 lgb.Dataset.create.valid <- function(dataset, data, info = list(), ...) {
 
@@ -796,11 +798,12 @@ lgb.Dataset.create.valid <- function(dataset, data, info = list(), ...) {
 #' @param dataset Object of class \code{lgb.Dataset}
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #' lgb.Dataset.construct(dtrain)
-#'
+#' }
 #' @export
 lgb.Dataset.construct <- function(dataset) {
 
@@ -826,14 +829,15 @@ lgb.Dataset.construct <- function(dataset) {
 #' be directly used with an \code{lgb.Dataset} object.
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #'
 #' stopifnot(nrow(dtrain) == nrow(train$data))
 #' stopifnot(ncol(dtrain) == ncol(train$data))
 #' stopifnot(all(dim(dtrain) == dim(train$data)))
-#'
+#' }
 #' @rdname dim
 #' @export
 dim.lgb.Dataset <- function(x, ...) {
@@ -860,6 +864,7 @@ dim.lgb.Dataset <- function(x, ...) {
 #' Since row names are irrelevant, it is recommended to use \code{colnames} directly.
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -868,7 +873,7 @@ dim.lgb.Dataset <- function(x, ...) {
 #' colnames(dtrain)
 #' colnames(dtrain) <- make.names(seq_len(ncol(train$data)))
 #' print(dtrain, verbose = TRUE)
-#'
+#' }
 #' @rdname dimnames.lgb.Dataset
 #' @export
 dimnames.lgb.Dataset <- function(x) {
@@ -932,14 +937,15 @@ dimnames.lgb.Dataset <- function(x) {
 #' @return constructed sub dataset
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #'
 #' dsub <- lightgbm::slice(dtrain, seq_len(42L))
 #' lgb.Dataset.construct(dsub)
 #' labels <- lightgbm::getinfo(dsub, "label")
-#'
+#' }
 #' @export
 slice <- function(dataset, ...) {
   UseMethod("slice")
@@ -978,6 +984,7 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
 #' }
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -988,7 +995,7 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
 #'
 #' labels2 <- lightgbm::getinfo(dtrain, "label")
 #' stopifnot(all(labels2 == 1 - labels))
-#'
+#' }
 #' @export
 getinfo <- function(dataset, ...) {
   UseMethod("getinfo")
@@ -1031,6 +1038,7 @@ getinfo.lgb.Dataset <- function(dataset, name, ...) {
 #' }
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -1041,7 +1049,7 @@ getinfo.lgb.Dataset <- function(dataset, name, ...) {
 #'
 #' labels2 <- lightgbm::getinfo(dtrain, "label")
 #' stopifnot(all.equal(labels2, 1 - labels))
-#'
+#' }
 #' @export
 setinfo <- function(dataset, ...) {
   UseMethod("setinfo")
@@ -1071,14 +1079,15 @@ setinfo.lgb.Dataset <- function(dataset, name, info, ...) {
 #' @return passed dataset
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #' data_file <- tempfile(fileext = ".data")
 #' lgb.Dataset.save(dtrain, data_file)
 #' dtrain <- lgb.Dataset(data_file)
 #' lgb.Dataset.set.categorical(dtrain, 1L:2L)
-#'
+#' }
 #' @rdname lgb.Dataset.set.categorical
 #' @export
 lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
@@ -1102,14 +1111,15 @@ lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
 #' @return passed dataset
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package ="lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset(test$data, test = train$label)
 #' lgb.Dataset.set.reference(dtest, dtrain)
-#'
+#' }
 #' @rdname lgb.Dataset.set.reference
 #' @export
 lgb.Dataset.set.reference <- function(dataset, reference) {
@@ -1133,10 +1143,12 @@ lgb.Dataset.set.reference <- function(dataset, reference) {
 #' @return passed dataset
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #' lgb.Dataset.save(dtrain, tempfile(fileext = ".bin"))
+#' }
 #' @export
 lgb.Dataset.save <- function(dataset, fname) {
 

diff --git a/R-package/R/lgb.convert.R b/R-package/R/lgb.convert.R
@@ -12,26 +12,25 @@
 #'         for input in \code{lgb.Dataset}.
 #'
 #' @examples
+#' \dontrun{
 #' data(iris)
 #'
 #' str(iris)
 #'
 #' # Convert all factors/chars to integer
 #' str(lgb.convert(data = iris))
 #'
-#' \dontrun{
 #' # When lightgbm package is installed, and you do not want to load it
 #' # You can still use the function!
 #' lgb.unloader()
-#' str(lightgbm::lgb.convert(data = iris))
+#' str(lgb.convert(data = iris))
 #' # 'data.frame':	150 obs. of  5 variables:
 #' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 #' # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : int  1 1 1 1 1 1 1 1 1 1 ...
 #' }
-#'
 #' @export
 lgb.convert <- function(data) {
 

diff --git a/R-package/R/lgb.convert_with_rules.R b/R-package/R/lgb.convert_with_rules.R
@@ -13,6 +13,7 @@
 #'         \code{lgb.Dataset}.
 #'
 #' @examples
+#' \dontrun{
 #' data(iris)
 #'
 #' str(iris)
@@ -48,7 +49,7 @@
 #' )
 #' newest_iris <- lgb.convert_with_rules(data = iris, rules = personal_rules)
 #' str(newest_iris$data) # SUCCESS!
-#'
+#' }
 #' @importFrom data.table set
 #' @export
 lgb.convert_with_rules <- function(data, rules = NULL) {

diff --git a/R-package/R/lgb.cv.R b/R-package/R/lgb.cv.R
@@ -56,6 +56,7 @@ CVBooster <- R6::R6Class(
 #' @return a trained model \code{lgb.CVBooster}.
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -68,6 +69,7 @@ CVBooster <- R6::R6Class(
 #'   , min_data = 1L
 #'   , learning_rate = 1.0
 #' )
+#' }
 #' @importFrom data.table data.table setorderv
 #' @export
 lgb.cv <- function(params = list()

diff --git a/R-package/R/lgb.importance.R b/R-package/R/lgb.importance.R
@@ -13,6 +13,7 @@
 #' }
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -32,7 +33,7 @@
 #'
 #' tree_imp1 <- lgb.importance(model, percentage = TRUE)
 #' tree_imp2 <- lgb.importance(model, percentage = FALSE)
-#'
+#' }
 #' @importFrom data.table := setnames setorderv
 #' @export
 lgb.importance <- function(model, percentage = TRUE) {

diff --git a/R-package/R/lgb.interprete.R b/R-package/R/lgb.interprete.R
@@ -16,6 +16,7 @@
 #'         Contribution columns to each class.
 #'
 #' @examples
+#' \dontrun{
 #' Logit <- function(x) log(x / (1.0 - x))
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -38,7 +39,7 @@
 #' )
 #'
 #' tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)
-#'
+#' }
 #' @importFrom data.table as.data.table
 #' @export
 lgb.interprete <- function(model,

diff --git a/R-package/R/lgb.model.dt.tree.R b/R-package/R/lgb.model.dt.tree.R
@@ -28,7 +28,7 @@
 #' }
 #'
 #' @examples
-#'
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -44,7 +44,7 @@
 #' model <- lgb.train(params, dtrain, 10L)
 #'
 #' tree_dt <- lgb.model.dt.tree(model)
-#'
+#' }
 #' @importFrom data.table := rbindlist
 #' @importFrom jsonlite fromJSON
 #' @export

diff --git a/R-package/R/lgb.plot.importance.R b/R-package/R/lgb.plot.importance.R
@@ -18,6 +18,7 @@
 #' and silently returns a processed data.table with \code{top_n} features sorted by defined importance.
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -37,6 +38,7 @@
 #'
 #' tree_imp <- lgb.importance(model, percentage = TRUE)
 #' lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain")
+#' }
 #' @importFrom graphics barplot par
 #' @export
 lgb.plot.importance <- function(tree_imp,

diff --git a/R-package/R/lgb.plot.interpretation.R b/R-package/R/lgb.plot.interpretation.R
@@ -15,7 +15,7 @@
 #' The \code{lgb.plot.interpretation} function creates a \code{barplot}.
 #'
 #' @examples
-#' \donttest{
+#' \dontrun{
 #' Logit <- function(x) {
 #'   log(x / (1.0 - x))
 #' }