diff --git a/demo/guide-R/basic_walkthrough.R b/demo/guide-R/basic_walkthrough.R
index bc10ebc41..0c7e677ab 100644
--- a/demo/guide-R/basic_walkthrough.R
+++ b/demo/guide-R/basic_walkthrough.R
@@ -1,14 +1,39 @@
 require(xgboost)
-
+require(methods)
 data(iris)
+# we use iris data as example dataset
+# iris is a dataset with 3 types of iris
+# we will show how to use xgboost to do binary classification here
+# so the class label will be whether the flower is of type setosa
 iris[,5] <- as.numeric(iris[,5]=='setosa')
 iris <- as.matrix(iris)
 set.seed(20)
+# random split train and test set
 test_ind <- sample(1:nrow(iris),50)
 train_ind <- setdiff(1:nrow(iris),test_ind)
+trainX = iris[train_ind,1:4]
+trainY = iris[train_ind,5]
+testX = iris[train_ind,1:4]
+testY = iris[test_ind,5]
+#-------------------------------------
+# this is the basic usage of xgboost
+# you can put matrix in data field
+bst <- xgboost(data = trainX, label = trainY, max_depth = 1, eta = 1, nround = 2,
+               objective = "binary:logistic")
+# alternatively, you can put sparse matrix, this is helpful when your data is sparse
+# for example, when you use one-hot encoding for feature vectors
+sparseX <- as(trainX, "sparseMatrix")
+bst <- xgboost(data = sparseX, label = trainY, max_depth = 1, eta = 1, nround = 2,
+               objective = "binary:logistic")
+
+# you can also specify data as file path to a LibSVM format input
+# since we do not have libsvm format file for iris, next line is only for illustration
+# bst <- xgboost(data = 'iris.svm', max_depth = 2, eta = 1, nround = 2, objective = "binary:logistic")
+
 dtrain <- xgb.DMatrix(iris[train_ind,1:4], label=iris[train_ind,5])
 dtest <- xgb.DMatrix(iris[test_ind,1:4], label=iris[test_ind,5])
 
+
 param <- list(max_depth=2,eta=1,silent=1,objective='binary:logistic')
 watchlist <- list(eval = dtest, train = dtrain)
 num_round <- 2
@@ -24,18 +49,4 @@ bst2 <- xgb.load('xgb.model')
 preds2 <- predict(bst2,dtest)
 stopifnot(sum((preds-preds2)^2)==0)
 
-
-cat('start running example of build DMatrix from numpy array\n')
-x <- iris[,1:4]
-y <- iris[,5]
-class(x)
-dtrain <- xgb.DMatrix(x, label = y)
-bst <- xgb.train(param, dtrain, num_round, watchlist)
-
-cat('start running example of build DMatrix from scipy.sparse CSR Matrix\n')
-x <- as(x,'dgCMatrix')
-class(x)
-dtrain <- xgb.DMatrix(x, label = y)
-bst <- xgb.train(param, dtrain, num_round, watchlist)
-
-
+############################ Test xgb.DMatrix with local file, sparse matrix and dense matrix in R.