diff --git a/R-package/vignettes/discoverYourData.Rmd b/R-package/vignettes/discoverYourData.Rmd
index 2ee4ed90d..9a3d4f033 100644
--- a/R-package/vignettes/discoverYourData.Rmd
+++ b/R-package/vignettes/discoverYourData.Rmd
@@ -5,7 +5,7 @@ output:
     css: vignette.css
     number_sections: yes
     toc: yes
-author: Tianqi Chen, Tong He, Michaël Benesty
+author: Tianqi Chen, Tong He, Michaël Benesty, Yuan Tang
 vignette: >
   %\VignetteIndexEntry{Discover your data}
   %\VignetteEngine{knitr::rmarkdown}
@@ -18,9 +18,9 @@ Understand your dataset with XGBoost
 Introduction
 ------------
 
-The purpose of this Vignette is to show you how to use **Xgboost** to discover and understand your own dataset better.
+The purpose of this vignette is to show you how to use **Xgboost** to discover and understand your own dataset better.
 
-This Vignette is not about predicting anything (see [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)). We will explain how to use **Xgboost** to highlight the *link* between the *features* of your data and the *outcome*.
+This vignette is not about predicting anything (see [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)). We will explain how to use **Xgboost** to highlight the *link* between the *features* of your data and the *outcome*.
 
 Pacakge loading:
 
@@ -36,7 +36,7 @@ if (!require('vcd')) install.packages('vcd')
 Preparation of the dataset
 --------------------------
 
-### Numeric VS categorical variables
+### Numeric v.s. categorical variables
 
 
 **Xgboost** manages only `numeric` vectors.
@@ -68,7 +68,7 @@ df <- data.table(Arthritis, keep.rownames = F)
 
 > `data.table` is 100% compliant with **R** `data.frame` but its syntax is more consistent and its performance for large dataset is [best in class](http://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly) (`dplyr` from **R** and `Pandas` from **Python** [included](https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping)). Some parts of **Xgboost** **R** package use `data.table`.
 
-The first thing we want to do is to have a look to the first lines of the `data.table`:
+The first thing we want to do is to have a look to the first few lines of the `data.table`:
 
 ```{r}
 head(df)
@@ -103,9 +103,9 @@ Therefore, 20 is not closer to 30 than 60. To make it short, the distance betwee
 head(df[,AgeDiscret := as.factor(round(Age/10,0))])
 ```
 
-##### Random split in two groups
+##### Random split into two groups
 
-Following is an even stronger simplification of the real age with an arbitrary split at 30 years old. I choose this value **based on nothing**. We will see later if simplifying the information based on arbitrary values is a good strategy (you may already have an idea of how well it will work...).
+Following is an even stronger simplification of the real age with an arbitrary split at 30 years old. We choose this value **based on nothing**. We will see later if simplifying the information based on arbitrary values is a good strategy (you may already have an idea of how well it will work...).
 
 ```{r}
 head(df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))])
@@ -139,7 +139,7 @@ levels(df[,Treatment])
 Next step, we will transform the categorical data to dummy variables.
 This is the [one-hot encoding](http://en.wikipedia.org/wiki/One-hot) step.
 
-The purpose is to transform each value of each *categorical* feature in a *binary* feature `{0, 1}`.
+The purpose is to transform each value of each *categorical* feature into a *binary* feature `{0, 1}`.
 
 For example, the column `Treatment` will be replaced by two columns, `Placebo`, and `Treated`. Each of them will be *binary*. Therefore, an observation which has the value `Placebo` in column `Treatment` before the transformation will have after the transformation the value `1` in the new column `Placebo` and the value `0` in the new column `Treated`. The column `Treatment` will disappear during the one-hot encoding.
 
@@ -317,8 +317,6 @@ In boosting, when a specific link between feature and outcome have been learned
 
 If you want to try Random Forests™ algorithm, you can tweak Xgboost parameters!
 
-**Warning**: this is still an experimental parameter.
-
 For instance, to compute a model with 1000 trees, with a 0.5 factor on sampling rows and columns:
 
 ```{r, warning=FALSE, message=FALSE}