diff --git a/R-package/R/xgb.importance.R b/R-package/R/xgb.importance.R
index d3a5910b4..78fcaf3ac 100644
--- a/R-package/R/xgb.importance.R
+++ b/R-package/R/xgb.importance.R
@@ -48,9 +48,8 @@
 #' # Both dataset are list with two items, a sparse matrix and labels 
 #' # (labels = outcome column which will be learned). 
 #' # Each column of the sparse Matrix is a feature in one hot encoding format.
-#' train <- agaricus.train
 #' 
-#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2, 
 #'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' 
 #' # train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
diff --git a/R-package/R/xgb.plot.deepness.R b/R-package/R/xgb.plot.deepness.R
index bebb7605a..b6c05f727 100644
--- a/R-package/R/xgb.plot.deepness.R
+++ b/R-package/R/xgb.plot.deepness.R
@@ -93,7 +93,7 @@ get.paths.to.leaf <- function(dt.tree) {
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #'
-#' bst <- xgboost(data = agaricus.train$data, max.depth = 15,
+#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 15,
 #'                  eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
 #'                  min_child_weight = 50)
 #'
diff --git a/R-package/R/xgb.plot.tree.R b/R-package/R/xgb.plot.tree.R
index ea7fabef7..59822ec83 100644
--- a/R-package/R/xgb.plot.tree.R
+++ b/R-package/R/xgb.plot.tree.R
@@ -33,9 +33,8 @@
 #' #Both dataset are list with two items, a sparse matrix and labels 
 #' #(labels = outcome column which will be learned). 
 #' #Each column of the sparse Matrix is a feature in one hot encoding format.
-#' train <- agaricus.train
 #' 
-#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2, 
 #'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' 
 #' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.
diff --git a/R-package/man/xgb.importance.Rd b/R-package/man/xgb.importance.Rd
index 1f8498deb..14604312e 100644
--- a/R-package/man/xgb.importance.Rd
+++ b/R-package/man/xgb.importance.Rd
@@ -54,9 +54,8 @@ data(agaricus.train, package='xgboost')
 # Both dataset are list with two items, a sparse matrix and labels 
 # (labels = outcome column which will be learned). 
 # Each column of the sparse Matrix is a feature in one hot encoding format.
-train <- agaricus.train
 
-bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2, 
                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 
 # train$data@Dimnames[[2]] represents the column names of the sparse matrix.
diff --git a/R-package/man/xgb.plot.deepness.Rd b/R-package/man/xgb.plot.deepness.Rd
index d011a4dc5..6488514dd 100644
--- a/R-package/man/xgb.plot.deepness.Rd
+++ b/R-package/man/xgb.plot.deepness.Rd
@@ -35,7 +35,7 @@ This function is inspired by this blog post \url{http://aysent.github.io/2015/11
 \examples{
 data(agaricus.train, package='xgboost')
 
-bst <- xgboost(data = agaricus.train$data, max.depth = 15,
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 15,
                  eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
                  min_child_weight = 50)
 
diff --git a/R-package/man/xgb.plot.tree.Rd b/R-package/man/xgb.plot.tree.Rd
index 16e80f9ee..164b013c1 100644
--- a/R-package/man/xgb.plot.tree.Rd
+++ b/R-package/man/xgb.plot.tree.Rd
@@ -43,9 +43,8 @@ data(agaricus.train, package='xgboost')
 #Both dataset are list with two items, a sparse matrix and labels 
 #(labels = outcome column which will be learned). 
 #Each column of the sparse Matrix is a feature in one hot encoding format.
-train <- agaricus.train
 
-bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2, 
                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 
 #agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.