[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>

.ci/test_r_package.sh

@@ -173,6 +173,7 @@ check_succeeded="yes"
 (
     R CMD check ${PKG_TARBALL} \
         --as-cran \
+        --run-dontrun \
     || check_succeeded="no"
 ) &
 

.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"

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) {
 

R-package/R/lgb.Dataset.R

@@ -725,6 +725,7 @@ Dataset <- R6::R6Class(
 #' @return constructed dataset
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -732,7 +733,7 @@ Dataset <- R6::R6Class(
 #' 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) {
 
@@ -826,6 +829,7 @@ 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)
@@ -833,7 +837,7 @@ lgb.Dataset.construct <- function(dataset) {
 #' 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) {
@@ -932,6 +937,7 @@ 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)
@@ -939,7 +945,7 @@ dimnames.lgb.Dataset <- function(x) {
 #' 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")
@@ -1071,6 +1079,7 @@ 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)
@@ -1078,7 +1087,7 @@ setinfo.lgb.Dataset <- function(dataset, name, info, ...) {
 #' 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) {
@@ -1102,6 +1111,7 @@ 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)
@@ -1109,7 +1119,7 @@ lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
 #' 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) {
 

R-package/R/lgb.convert.R

@@ -12,6 +12,7 @@
 #'         for input in \code{lgb.Dataset}.
 #'
 #' @examples
+#' \dontrun{
 #' data(iris)
 #'
 #' str(iris)
@@ -19,11 +20,10 @@
 #' # 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 ...
@@ -31,7 +31,6 @@
 #' # $ 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) {
 

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) {

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()

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) {

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,

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

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,

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))
 #' }

R-package/R/lgb.train.R

@@ -29,6 +29,7 @@
 #' @return a trained booster model \code{lgb.Booster}.
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -46,6 +47,7 @@
 #'   , learning_rate = 1.0
 #'   , early_stopping_rounds = 3L
 #' )
+#' }
 #' @export
 lgb.train <- function(params = list(),
                       data,

R-package/R/lgb.unloader.R

@@ -14,6 +14,7 @@
 #' @return NULL invisibly.
 #'
 #' @examples
+#' \dontrun{
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -31,7 +32,6 @@
 #'   , learning_rate = 1.0
 #' )
 #'
-#' \dontrun{
 #' lgb.unloader(restore = FALSE, wipe = FALSE, envir = .GlobalEnv)
 #' rm(model, dtrain, dtest) # Not needed if wipe = TRUE
 #' gc() # Not needed if wipe = TRUE
@@ -39,7 +39,6 @@
 #' library(lightgbm)
 #' # Do whatever you want again with LightGBM without object clashing
 #' }
-#'
 #' @export
 lgb.unloader <- function(restore = TRUE, wipe = FALSE, envir = .GlobalEnv) {
 

R-package/R/readRDS.lgb.Booster.R

@@ -7,7 +7,7 @@
 #' @return \code{lgb.Booster}.
 #'
 #' @examples
-#' \donttest{
+#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train

R-package/R/saveRDS.lgb.Booster.R

@@ -18,7 +18,7 @@
 #' @return NULL invisibly.
 #'
 #' @examples
-#' \donttest{
+#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train

R-package/man/dim.Rd

@@ -22,6 +22,7 @@ Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also
 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)
@@ -29,5 +30,5 @@ 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)))
-
+}
 }

R-package/man/dimnames.lgb.Dataset.Rd

@@ -24,6 +24,7 @@ Generic \code{dimnames} methods are used by \code{colnames}.
 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)
@@ -32,5 +33,5 @@ dimnames(dtrain)
 colnames(dtrain)
 colnames(dtrain) <- make.names(seq_len(ncol(train$data)))
 print(dtrain, verbose = TRUE)
-
+}
 }

R-package/man/getinfo.Rd

@@ -33,6 +33,7 @@ The \code{name} field can be one of the following:
 }
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -43,5 +44,5 @@ lightgbm::setinfo(dtrain, "label", 1 - labels)
 
 labels2 <- lightgbm::getinfo(dtrain, "label")
 stopifnot(all(labels2 == 1 - labels))
-
+}
 }

R-package/man/lgb.Dataset.Rd

@@ -40,6 +40,7 @@ Construct \code{lgb.Dataset} object from dense matrix, sparse matrix
              or local file (that was created previously by saving an \code{lgb.Dataset}).
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -47,5 +48,5 @@ data_file <- tempfile(fileext = ".data")
 lgb.Dataset.save(dtrain, data_file)
 dtrain <- lgb.Dataset(data_file)
 lgb.Dataset.construct(dtrain)
-
+}
 }

R-package/man/lgb.Dataset.construct.Rd

@@ -13,9 +13,10 @@ lgb.Dataset.construct(dataset)
 Construct Dataset explicitly
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
 lgb.Dataset.construct(dtrain)
-
+}
 }

R-package/man/lgb.Dataset.create.valid.Rd

@@ -22,11 +22,12 @@ constructed dataset
 Construct validation data according to training data
 }
 \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)
-
+}
 }

R-package/man/lgb.Dataset.save.Rd

@@ -19,8 +19,10 @@ Please note that \code{init_score} is not saved in binary file.
              If you need it, please set it again after loading 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"))
 }
+}

R-package/man/lgb.Dataset.set.categorical.Rd

@@ -21,6 +21,7 @@ Set the categorical features of an \code{lgb.Dataset} object. Use this function
              to tell LightGBM which features should be treated as categorical.
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -28,5 +29,5 @@ data_file <- tempfile(fileext = ".data")
 lgb.Dataset.save(dtrain, data_file)
 dtrain <- lgb.Dataset(data_file)
 lgb.Dataset.set.categorical(dtrain, 1L:2L)
-
+}
 }

R-package/man/lgb.Dataset.set.reference.Rd

@@ -18,6 +18,7 @@ passed dataset
 If you want to use validation data, you should set reference to training data
 }
 \examples{
+\dontrun{
 data(agaricus.train, package ="lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -25,5 +26,5 @@ data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset(test$data, test = train$label)
 lgb.Dataset.set.reference(dtest, dtrain)
-
+}
 }

R-package/man/lgb.convert.Rd

@@ -23,6 +23,7 @@ Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
              NOTE: In previous releases of LightGBM, this function was called \code{lgb.prepare}.
 }
 \examples{
+\dontrun{
 data(iris)
 
 str(iris)
@@ -30,11 +31,10 @@ 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 ...
@@ -42,5 +42,4 @@ str(lightgbm::lgb.convert(data = iris))
 # $ 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 ...
 }
-
 }

R-package/man/lgb.convert_with_rules.Rd

@@ -25,6 +25,7 @@ Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
              NOTE: In previous releases of LightGBM, this function was called \code{lgb.prepare_rules2}.
 }
 \examples{
+\dontrun{
 data(iris)
 
 str(iris)
@@ -60,5 +61,5 @@ personal_rules <- list(
 )
 newest_iris <- lgb.convert_with_rules(data = iris, rules = personal_rules)
 str(newest_iris$data) # SUCCESS!
-
+}
 }

R-package/man/lgb.cv.Rd

@@ -100,6 +100,7 @@ a trained model \code{lgb.CVBooster}.
 Cross validation logic used by LightGBM
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -113,3 +114,4 @@ model <- lgb.cv(
   , learning_rate = 1.0
 )
 }
+}

R-package/man/lgb.dump.Rd

@@ -18,7 +18,7 @@ json format of model
 Dump LightGBM model to json
 }
 \examples{
-\donttest{
+\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train

R-package/man/lgb.get.eval.result.Rd

@@ -32,6 +32,7 @@ Given a \code{lgb.Booster}, return evaluation results for a
              particular metric on a particular dataset.
 }
 \examples{
+\dontrun{
 # train a regression model
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -59,3 +60,4 @@ print(names(model$record_evals[["test"]]))
 # Get L2 values for "test" dataset
 lgb.get.eval.result(model, "test", "l2")
 }
+}

R-package/man/lgb.importance.Rd

@@ -24,6 +24,7 @@ For a tree model, a \code{data.table} with the following columns:
 Creates a \code{data.table} of feature importances in a model.
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -43,5 +44,5 @@ model <- lgb.train(
 
 tree_imp1 <- lgb.importance(model, percentage = TRUE)
 tree_imp2 <- lgb.importance(model, percentage = FALSE)
-
+}
 }

R-package/man/lgb.interprete.Rd

@@ -29,6 +29,7 @@ For regression, binary classification and lambdarank model, a \code{list} of \co
 Computes feature contribution components of rawscore prediction.
 }
 \examples{
+\dontrun{
 Logit <- function(x) log(x / (1.0 - x))
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -51,5 +52,5 @@ model <- lgb.train(
 )
 
 tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)
-
+}
 }

R-package/man/lgb.load.Rd

@@ -19,7 +19,7 @@ Load LightGBM takes in either a file path or model string.
               If both are provided, Load will default to loading from file
 }
 \examples{
-\donttest{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/lgb.model.dt.tree.Rd

@@ -39,7 +39,7 @@ The columns of the \code{data.table} are:
 Parse a LightGBM model json dump into a \code{data.table} structure.
 }
 \examples{
-
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -55,5 +55,5 @@ params <- list(
 model <- lgb.train(params, dtrain, 10L)
 
 tree_dt <- lgb.model.dt.tree(model)
-
+}
 }

R-package/man/lgb.plot.importance.Rd

@@ -37,6 +37,7 @@ The graph represents each feature as a horizontal bar of length proportional to
 Features are shown ranked in a decreasing importance order.
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -57,3 +58,4 @@ model <- lgb.train(
 tree_imp <- lgb.importance(model, percentage = TRUE)
 lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain")
 }
+}

R-package/man/lgb.plot.interpretation.Rd

@@ -34,7 +34,7 @@ The graph represents each feature as a horizontal bar of length proportional to
 contribution of a feature. Features are shown ranked in a decreasing contribution order.
 }
 \examples{
-\donttest{
+\dontrun{
 Logit <- function(x) {
   log(x / (1.0 - x))
 }

R-package/man/lgb.save.Rd

@@ -20,7 +20,7 @@ lgb.Booster
 Save LightGBM model
 }
 \examples{
-\donttest{
+\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train

R-package/man/lgb.train.Rd

@@ -83,6 +83,7 @@ a trained booster model \code{lgb.Booster}.
 Logic to train with LightGBM
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -101,3 +102,4 @@ model <- lgb.train(
   , early_stopping_rounds = 3L
 )
 }
+}

R-package/man/lgb.unloader.Rd

@@ -26,6 +26,7 @@ Attempts to unload LightGBM packages so you can remove objects cleanly without
              apparent reason and you do not want to restart R to fix the lost object.
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -43,7 +44,6 @@ model <- lgb.train(
   , learning_rate = 1.0
 )
 
-\dontrun{
 lgb.unloader(restore = FALSE, wipe = FALSE, envir = .GlobalEnv)
 rm(model, dtrain, dtest) # Not needed if wipe = TRUE
 gc() # Not needed if wipe = TRUE
@@ -51,5 +51,4 @@ gc() # Not needed if wipe = TRUE
 library(lightgbm)
 # Do whatever you want again with LightGBM without object clashing
 }
-
 }

R-package/man/predict.lgb.Booster.Rd

@@ -52,6 +52,7 @@ For regression or binary classification, it returns a vector of length \code{nro
 Predicted values based on class \code{lgb.Booster}
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -70,3 +71,4 @@ model <- lgb.train(
 )
 preds <- predict(model, test$data)
 }
+}

R-package/man/readRDS.lgb.Booster.Rd

@@ -18,7 +18,7 @@ readRDS.lgb.Booster(file = "", refhook = NULL)
 Attempts to load a model stored in a \code{.rds} file, using \code{\link[base]{readRDS}}
 }
 \examples{
-\donttest{
+\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train

R-package/man/saveRDS.lgb.Booster.Rd

@@ -42,7 +42,7 @@ Attempts to save a model using RDS. Has an additional parameter (\code{raw})
              which decides whether to save the raw model or not.
 }
 \examples{
-\donttest{
+\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train

R-package/man/setinfo.Rd

@@ -38,6 +38,7 @@ The \code{name} field can be one of the following:
 }
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -48,5 +49,5 @@ lightgbm::setinfo(dtrain, "label", 1 - labels)
 
 labels2 <- lightgbm::getinfo(dtrain, "label")
 stopifnot(all.equal(labels2, 1 - labels))
-
+}
 }

R-package/man/slice.Rd

@@ -24,6 +24,7 @@ Get a new \code{lgb.Dataset} containing the specified rows of
              original \code{lgb.Dataset} object
 }
 \examples{
+\dontrun{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -31,5 +32,5 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 dsub <- lightgbm::slice(dtrain, seq_len(42L))
 lgb.Dataset.construct(dsub)
 labels <- lightgbm::getinfo(dsub, "label")
-
+}
 }

docs/conf.py

@@ -247,7 +247,7 @@ def generate_r_docs(app):
             , install = FALSE \
             , devel = FALSE \
             , examples = TRUE \
-            , run_dont_run = FALSE \
+            , run_dont_run = TRUE \
             , seed = 42L \
             , preview = FALSE \
             , new_process = TRUE \