Since its introduction in Spark 1.4, SparkR has received contributions from both the Spark community and the R community. In this talk, we will summarize recent community efforts on extending SparkR for scalable advanced analytics. We start with the computation of summary statistics on distributed datasets, including single-pass approximate algorithms. Then we demonstrate MLlib machine learning algorithms that have been ported to SparkR and compare them with existing solutions on R, e.g., generalized linear models, classification and clustering algorithms. We also show how to integrate existing R packages with SparkR to accelerate existing R workflows.