diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
index b553fdec3014c..786f8245b42dc 100644
--- a/pkg/DESCRIPTION
+++ b/pkg/DESCRIPTION
@@ -20,6 +20,7 @@ Collate:
     'RDD.R'
     'pairRDD.R'
     'column.R'
+    'group.R'
     'DataFrame.R'
     'SQLContext.R'
     'broadcast.R'
diff --git a/pkg/NAMESPACE b/pkg/NAMESPACE
index 2471c6a526a16..087d077fd6d51 100644
--- a/pkg/NAMESPACE
+++ b/pkg/NAMESPACE
@@ -90,6 +90,7 @@ exportMethods("columns",
               "dtypes",
               "explain",
               "filter",
+              "groupBy",
               "head",
               "isLocal",
               "limit",
@@ -134,6 +135,9 @@ exportMethods("asc",
               "countDistinct",
               "sumDistinct")
 
+exportClasses("GroupedData")
+exportMethods("agg")
+
 export("cacheTable",
        "clearCache",
        "createExternalTable",
diff --git a/pkg/R/DataFrame.R b/pkg/R/DataFrame.R
index b729d734cb2fd..f90bf69806101 100644
--- a/pkg/R/DataFrame.R
+++ b/pkg/R/DataFrame.R
@@ -1,6 +1,6 @@
 # DataFrame.R - DataFrame class and methods implemented in S4 OO classes
 
-#' @include jobj.R SQLTypes.R RDD.R pairRDD.R column.R
+#' @include jobj.R SQLTypes.R RDD.R pairRDD.R column.R group.R
 NULL
 
 setOldClass("jobj")
@@ -663,6 +663,33 @@ setMethod("toRDD",
             })
           })
 
+#' GroupBy
+#'
+#' Groups the DataFrame using the specified columns, so we can run aggregation on them.
+#'
+setGeneric("groupBy", function(x, ...) { standardGeneric("groupBy") })
+
+setMethod("groupBy",
+           signature(x = "DataFrame"),
+           function(x, ...) {
+             cols <- list(...)
+             if (length(cols) >= 1 && class(cols[[1]]) == "character") {
+               sgd <- callJMethod(x@sdf, "groupBy", cols[[1]], listToSeq(cols[-1]))
+             } else {
+               jcol <- lapply(cols, function(c) { c@jc })
+               sgd <- callJMethod(x@sdf, "groupBy", listToSeq(jcol))
+             }
+             groupedData(sgd)
+           })
+
+
+setMethod("agg",
+          signature(x = "DataFrame"),
+          function(x, ...) {
+            agg(groupBy(x), ...)
+          })
+
+
 ############################## RDD Map Functions ##################################
 # All of the following functions mirror the existing RDD map functions,           #
 # but allow for use with DataFrames by first converting to an RRDD before calling #
diff --git a/pkg/R/column.R b/pkg/R/column.R
index ba3343f799919..799d31072c785 100644
--- a/pkg/R/column.R
+++ b/pkg/R/column.R
@@ -73,7 +73,7 @@ createMethods <- function() {
     createOperator(op)
   }
 
-  setGeneric("avg", function(x) { standardGeneric("avg") })
+  setGeneric("avg", function(x, ...) { standardGeneric("avg") })
   setGeneric("last", function(x) { standardGeneric("last") })
   setGeneric("lower", function(x) { standardGeneric("lower") })
   setGeneric("upper", function(x) { standardGeneric("upper") })
diff --git a/pkg/R/group.R b/pkg/R/group.R
new file mode 100644
index 0000000000000..bf17efffc4b20
--- /dev/null
+++ b/pkg/R/group.R
@@ -0,0 +1,81 @@
+############################## GroupedData ########################################
+
+setClass("GroupedData",
+         slots = list(env = "environment",
+                      sgd = "jobj"))
+
+setMethod("initialize", "GroupedData", function(.Object, sgd) {
+  .Object@env <- new.env()
+  .Object@sgd <- sgd
+  .Object
+})
+
+groupedData <- function(sgd) {
+  new("GroupedData", sgd)
+}
+
+setMethod("count",
+          signature(x = "GroupedData"),
+          function(x) {
+            dataFrame(callJMethod(x@sgd, "count"))
+          })
+
+#' Agg
+#'
+#' Aggregates on the entire DataFrame without groups.
+#'
+#' df2 <- agg(df, <column> = <aggFunction>)
+#' df2 <- agg(df, newColName = aggFunction(column))
+#' @examples
+#' \dontrun{
+#'  df2 <- agg(df, age = "sum")  # new column name will be created as 'SUM(age#0)'
+#'  df2 <- agg(df, ageSum = sum(df$age)) # Creates a new column named ageSum
+#' }
+setGeneric("agg", function (x, ...) { standardGeneric("agg") })
+
+setMethod("agg",
+          signature(x = "GroupedData"),
+          function(x, ...) {
+            cols = list(...)
+            stopifnot(length(cols) > 0)
+            if (is.character(cols[[1]])) {
+              cols <- varargsToEnv(...)
+              sdf <- callJMethod(x@sgd, "agg", cols)
+            } else if (class(cols[[1]]) == "Column") {
+              ns <- names(cols)
+              if (!is.null(ns)) {
+                for (n in ns) {
+                  if (n != "") {
+                    cols[[n]] = alias(cols[[n]], n)
+                  }
+                }
+              }
+              jcols <- lapply(cols, function(c) { c@jc })
+              sdf <- callJMethod(x@sgd, "agg", jcols[[1]], listToSeq(jcols[-1]))
+            } else {
+              stop("agg can only support Column or character")
+            }
+            dataFrame(sdf)
+          })
+
+#' sum/mean/avg/min/max
+
+methods <- c("sum", "mean", "avg", "min", "max")
+
+createMethod <- function(name) {
+  setMethod(name,
+            signature(x = "GroupedData"),
+            function(x, ...) {
+              sdf <- callJMethod(x@sgd, name, toSeq(...))
+              dataFrame(sdf)
+            })
+}
+
+createMethods <- function() {
+  for (name in methods) {
+    createMethod(name)
+  }
+}
+
+createMethods()
+
diff --git a/pkg/R/utils.R b/pkg/R/utils.R
index eadb8f3fe278f..73775650fb9d0 100644
--- a/pkg/R/utils.R
+++ b/pkg/R/utils.R
@@ -370,4 +370,3 @@ toSeq <- function(...) {
 listToSeq <- function(l) {
   callJStatic("edu.berkeley.cs.amplab.sparkr.SQLUtils", "toSeq", l)
 }
-
diff --git a/pkg/inst/tests/test_sparkSQL.R b/pkg/inst/tests/test_sparkSQL.R
index 4fabec337558e..f907309a45926 100644
--- a/pkg/inst/tests/test_sparkSQL.R
+++ b/pkg/inst/tests/test_sparkSQL.R
@@ -329,6 +329,31 @@ test_that("column functions", {
   c4 <- approxCountDistinct(c) + countDistinct(c) + cast(c, "string")
 })
 
+test_that("group by", {
+  df <- jsonFile(sqlCtx, jsonPath)
+  df1 <- agg(df, name = "max", age = "sum")
+  expect_true(1 == count(df1))
+  df1 <- agg(df, age2 = max(df$age))
+  expect_true(1 == count(df1))
+  expect_true(columns(df1) == c("age2"))
+
+  gd <- groupBy(df, "name")
+  expect_true(inherits(gd, "GroupedData"))
+  df2 <- count(gd)
+  expect_true(inherits(df2, "DataFrame"))
+  expect_true(3 == count(df2))
+
+  df3 <- agg(gd, age = "sum")
+  expect_true(inherits(df3, "DataFrame"))
+  expect_true(3 == count(df3))
+
+  df4 <- sum(gd, "age")
+  expect_true(inherits(df4, "DataFrame"))
+  expect_true(3 == count(df4))
+  expect_true(3 == count(mean(gd, "age")))
+  expect_true(3 == count(max(gd, "age")))
+})
+
 test_that("sortDF() and orderBy() on a DataFrame", {
   df <- jsonFile(sqlCtx, jsonPath)
   sorted <- sortDF(df, df$age)
diff --git a/pkg/src/src/main/scala/edu/berkeley/cs/amplab/sparkr/SparkRBackendHandler.scala b/pkg/src/src/main/scala/edu/berkeley/cs/amplab/sparkr/SparkRBackendHandler.scala
index 4d1d2c0aba934..c292c59d63f29 100644
--- a/pkg/src/src/main/scala/edu/berkeley/cs/amplab/sparkr/SparkRBackendHandler.scala
+++ b/pkg/src/src/main/scala/edu/berkeley/cs/amplab/sparkr/SparkRBackendHandler.scala
@@ -1,11 +1,8 @@
 package edu.berkeley.cs.amplab.sparkr
 
-import scala.collection.mutable.HashMap
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream, DataInputStream, DataOutputStream}
 
-import java.io.ByteArrayInputStream
-import java.io.ByteArrayOutputStream
-import java.io.DataInputStream
-import java.io.DataOutputStream
+import scala.collection.mutable.HashMap
 
 import io.netty.channel.ChannelHandler.Sharable
 import io.netty.channel.ChannelHandlerContext
@@ -19,7 +16,8 @@ import edu.berkeley.cs.amplab.sparkr.SerDe._
  * this across connections ?
  */
 @Sharable
-class SparkRBackendHandler(server: SparkRBackend) extends SimpleChannelInboundHandler[Array[Byte]] {
+class SparkRBackendHandler(server: SparkRBackend)
+  extends SimpleChannelInboundHandler[Array[Byte]] {
 
   override def channelRead0(ctx: ChannelHandlerContext, msg: Array[Byte]) {
     val bis = new ByteArrayInputStream(msg)
@@ -100,11 +98,18 @@ class SparkRBackendHandler(server: SparkRBackend) extends SimpleChannelInboundHa
       val methods = cls.get.getMethods
       val selectedMethods = methods.filter(m => m.getName == methodName)
       if (selectedMethods.length > 0) {
-        val selectedMethod = selectedMethods.filter { x => 
+        val methods = selectedMethods.filter { x =>
           matchMethod(numArgs, args, x.getParameterTypes)
-        }.head
-
-        val ret = selectedMethod.invoke(obj, args:_*)
+        }
+        if (methods.isEmpty) {
+          System.err.println(s"cannot find matching method ${cls.get}.$methodName. "
+            + s"Candidates are:")
+          selectedMethods.foreach { method =>
+            System.err.println(s"$methodName(${method.getParameterTypes.mkString(",")})")
+          }
+          throw new Exception(s"No matched method found for $cls.$methodName")
+        }
+        val ret = methods.head.invoke(obj, args:_*)
 
         // Write status bit
         writeInt(dos, 0)
@@ -160,6 +165,7 @@ class SparkRBackendHandler(server: SparkRBackend) extends SimpleChannelInboundHa
         }
       }
       if (!parameterWrapperType.isInstance(args(i))) {
+        System.err.println(s"arg $i not match: expected type $parameterWrapperType, but got ${args(i).getClass()}")
         return false
       }
     }