[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>

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)
 }
+}