diff --git a/R-package/vignettes/xgboostPresentation.Rmd b/R-package/vignettes/xgboostPresentation.Rmd
index 77a250a2c..29b8e40f1 100644
--- a/R-package/vignettes/xgboostPresentation.Rmd
+++ b/R-package/vignettes/xgboostPresentation.Rmd
@@ -83,23 +83,24 @@ Xgboost offer a way to group them in a `xgb.DMatrix`. You can even add other met
 
 ```{r trainingDense, message=F, warning=F}
 dtrain <- xgb.DMatrix(data = train$data, label = train$label)
-bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic")
+bstDMatrix <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic")
 ```
 
-# Verbose = 0,1,2
+Below is a demonstration of the effect of verbose parameter.
+
+```{r trainingVerbose, message=T, warning=F}
 print ('train xgboost with verbose 0, no message')
 bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2,
                objective = "binary:logistic", verbose = 0)
+
 print ('train xgboost with verbose 1, print evaluation metric')
 bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2,
                objective = "binary:logistic", verbose = 1)
+
 print ('train xgboost with verbose 2, also print information about tree')
 bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2,
                objective = "binary:logistic", verbose = 2)
-
-# you can also specify data as file path to a LibSVM format input
-# since we do not have this file with us, the following line is just for illustration
-# bst <- xgboost(data = 'agaricus.train.svm', max.depth = 2, eta = 1, nround = 2,objective = "binary:logistic")
+```
 
 #--------------------basic prediction using xgboost--------------
 # you can do prediction using the following line