XGBoost is a multi-language library designed and optimized for boosting trees algorithms. The underlying algorithm of xgboost is an extension of the classic gradient boosting machine algorithm. By employing multi-threads and imposing regularization, xgboost is able to utilize more computational power and get more accurate prediction compared to the traditional version. Moreover, a friendly user interface and comprehensive documentation are provided for user convenience. The package has been downloaded for more than 4,000 times on average from CRAN per-month, and the number is growing rapidly. It has now been widely applied in both industrial business and academic researches. The R package has won the 2016 John M. Chambers Statistical Software Award. From the very beginning of the work, our goal is to make a package which brings convenience and joy to the users. In this talk, I will briefly introduce the usage of xgboost, as well as several highlights that we think users would love to know.