diff --git a/R-package/vignettes/discoverYourData.Rmd b/R-package/vignettes/discoverYourData.Rmd
index 210930dd6..e6a99bd56 100644
--- a/R-package/vignettes/discoverYourData.Rmd
+++ b/R-package/vignettes/discoverYourData.Rmd
@@ -5,7 +5,6 @@ output:
     css: vignette.css
     number_sections: yes
     toc: yes
-date: "Wednesday, January 28, 2015"
 ---
 
 Introduction
diff --git a/R-package/vignettes/xgboostPresentation.Rmd b/R-package/vignettes/xgboostPresentation.Rmd
new file mode 100644
index 000000000..9dcd12f39
--- /dev/null
+++ b/R-package/vignettes/xgboostPresentation.Rmd
@@ -0,0 +1,113 @@
+---
+title: "Xgboost presentation"
+output:
+  html_document:
+    css: vignette.css
+    number_sections: yes
+    toc: yes
+---
+
+require(xgboost)
+require(methods)
+# we load in the agaricus dataset
+# In this example, we are aiming to predict whether a mushroom can be eated
+data(agaricus.train, package='xgboost')
+data(agaricus.test, package='xgboost')
+train <- agaricus.train
+test <- agaricus.test
+# the loaded data is stored in sparseMatrix, and label is a numeric vector in {0,1}
+class(train$label)
+class(train$data)
+
+#-------------Basic Training using XGBoost-----------------
+# this is the basic usage of xgboost you can put matrix in data field
+# note: we are puting in sparse matrix here, xgboost naturally handles sparse input
+# use sparse matrix when your feature is sparse(e.g. when you using one-hot encoding vector)
+print("training xgboost with sparseMatrix")
+bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2,
+               objective = "binary:logistic")
+# alternatively, you can put in dense matrix, i.e. basic R-matrix
+print("training xgboost with Matrix")
+bst <- xgboost(data = as.matrix(train$data), label = train$label, max.depth = 2, eta = 1, nround = 2,
+               objective = "binary:logistic")
+
+# you can also put in xgb.DMatrix object, stores label, data and other meta datas needed for advanced features
+print("training xgboost with xgb.DMatrix")
+dtrain <- xgb.DMatrix(data = train$data, label = train$label)
+bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nround = 2, objective = "binary:logistic")
+
+# Verbose = 0,1,2
+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
+# you can put in Matrix, sparseMatrix, or xgb.DMatrix 
+pred <- predict(bst, test$data)
+err <- mean(as.numeric(pred > 0.5) != test$label)
+print(paste("test-error=", err))
+
+#-------------------save and load models-------------------------
+# save model to binary local file
+xgb.save(bst, "xgboost.model")
+# load binary model to R
+bst2 <- xgb.load("xgboost.model")
+pred2 <- predict(bst2, test$data)
+# pred2 should be identical to pred
+print(paste("sum(abs(pred2-pred))=", sum(abs(pred2-pred))))
+
+# save model to R's raw vector
+raw = xgb.save.raw(bst)
+# load binary model to R
+bst3 <- xgb.load(raw)
+pred3 <- predict(bst3, test$data)
+# pred2 should be identical to pred
+print(paste("sum(abs(pred3-pred))=", sum(abs(pred2-pred))))
+
+#----------------Advanced features --------------
+# to use advanced features, we need to put data in xgb.DMatrix
+dtrain <- xgb.DMatrix(data = train$data, label=train$label)
+dtest <- xgb.DMatrix(data = test$data, label=test$label)
+#---------------Using watchlist----------------
+# watchlist is a list of xgb.DMatrix, each of them tagged with name
+watchlist <- list(train=dtrain, test=dtest)
+# to train with watchlist, use xgb.train, which contains more advanced features
+# watchlist allows us to monitor the evaluation result on all data in the list 
+print ('train xgboost using xgb.train with watchlist')
+bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nround=2, watchlist=watchlist,
+                 objective = "binary:logistic")
+# we can change evaluation metrics, or use multiple evaluation metrics
+print ('train xgboost using xgb.train with watchlist, watch logloss and error')
+bst <- xgb.train(data=dtrain, max.depth=2, eta=1, nround=2, watchlist=watchlist,
+                 eval.metric = "error", eval.metric = "logloss",
+                 objective = "binary:logistic")
+
+# xgb.DMatrix can also be saved using xgb.DMatrix.save
+xgb.DMatrix.save(dtrain, "dtrain.buffer")
+# to load it in, simply call xgb.DMatrix
+dtrain2 <- xgb.DMatrix("dtrain.buffer")
+bst <- xgb.train(data=dtrain2, max.depth=2, eta=1, nround=2, watchlist=watchlist,
+                 objective = "binary:logistic")
+# information can be extracted from xgb.DMatrix using getinfo
+label = getinfo(dtest, "label")
+pred <- predict(bst, dtest)
+err <- as.numeric(sum(as.integer(pred > 0.5) != label))/length(label)
+print(paste("test-error=", err))
+
+# You can dump the tree you learned using xgb.dump into a text file
+xgb.dump(bst, "dump.raw.txt", with.stats = T)
+
+# Finally, you can check which features are the most important.
+print("Most important features (look at column Gain):")
+print(xgb.importance(feature_names = train$data@Dimnames[[2]], filename_dump = "dump.raw.txt"))