diff --git a/R-package/R/utils.R b/R-package/R/utils.R
index e58601df8..732ef0d11 100644
--- a/R-package/R/utils.R
+++ b/R-package/R/utils.R
@@ -103,17 +103,21 @@ xgb.Booster.check <- function(bst, saveraw = TRUE)
 ## ----the following are low level iteratively function, not needed if
 ## you do not want to use them ---------------------------------------
 # get dmatrix from data, label
-xgb.get.DMatrix <- function(data, label = NULL, missing = NULL) {
+xgb.get.DMatrix <- function(data, label = NULL, missing = NULL, weight = NULL) {
   inClass <- class(data)
   if (inClass == "dgCMatrix" || inClass == "matrix") {
     if (is.null(label)) {
       stop("xgboost: need label when data is a matrix")
     }
+    dtrain <- xgb.DMatrix(data, label = label)
     if (is.null(missing)){
       dtrain <- xgb.DMatrix(data, label = label)
     } else {
       dtrain <- xgb.DMatrix(data, label = label, missing = missing)
     }
+    if (!is.null(weight)){
+      xgb.setinfo(dtrain, "weight", weight)
+    }
   } else {
     if (!is.null(label)) {
       warning("xgboost: label will be ignored.")
@@ -122,6 +126,9 @@ xgb.get.DMatrix <- function(data, label = NULL, missing = NULL) {
       dtrain <- xgb.DMatrix(data)
     } else if (inClass == "xgb.DMatrix") {
       dtrain <- data
+    } else if (inClass == "data.frame") {
+      stop("xgboost only support numerical matrix input, 
+           use 'data.frame' to transform the data.")
     } else {
       stop("xgboost: Invalid input of data")
     }
diff --git a/R-package/R/xgboost.R b/R-package/R/xgboost.R
index c96a7d89e..164dc1838 100644
--- a/R-package/R/xgboost.R
+++ b/R-package/R/xgboost.R
@@ -31,6 +31,7 @@
 #' @param print.every.n Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.
 #' @param missing Missing is only used when input is dense matrix, pick a float 
 #'     value that represents missing value. Sometimes a data use 0 or other extreme value to represents missing values.
+#' @param weight a vector indicating the weight for each row of the input.
 #' @param early.stop.round If \code{NULL}, the early stopping function is not triggered. 
 #'     If set to an integer \code{k}, training with a validation set will stop if the performance 
 #'     keeps getting worse consecutively for \code{k} rounds.
@@ -58,14 +59,11 @@
 #' 
 #' @export
 #' 
-xgboost <- function(data = NULL, label = NULL, missing = NULL, params = list(), nrounds, 
+xgboost <- function(data = NULL, label = NULL, missing = NULL, weight = NULL, 
+                    params = list(), nrounds, 
                     verbose = 1, print.every.n = 1L, early.stop.round = NULL,
                     maximize = NULL, save_period = 0, save_name = "xgboost.model", ...) {
-  if (is.null(missing)) {
-    dtrain <- xgb.get.DMatrix(data, label)
-  } else {
-    dtrain <- xgb.get.DMatrix(data, label, missing)
-  }
+  dtrain <- xgb.get.DMatrix(data, label, missing, weight)
     
   params <- append(params, list(...))
   
diff --git a/R-package/man/xgboost.Rd b/R-package/man/xgboost.Rd
index 5dfeeacee..a05560a19 100644
--- a/R-package/man/xgboost.Rd
+++ b/R-package/man/xgboost.Rd
@@ -4,9 +4,10 @@
 \alias{xgboost}
 \title{eXtreme Gradient Boosting (Tree) library}
 \usage{
-xgboost(data = NULL, label = NULL, missing = NULL, params = list(),
-  nrounds, verbose = 1, print.every.n = 1L, early.stop.round = NULL,
-  maximize = NULL, save_period = 0, save_name = "xgboost.model", ...)
+xgboost(data = NULL, label = NULL, missing = NULL, weight = NULL,
+  params = list(), nrounds, verbose = 1, print.every.n = 1L,
+  early.stop.round = NULL, maximize = NULL, save_period = 0,
+  save_name = "xgboost.model", ...)
 }
 \arguments{
 \item{data}{takes \code{matrix}, \code{dgCMatrix}, local data file or
@@ -18,6 +19,8 @@ if data is local data file or  \code{xgb.DMatrix}.}
 \item{missing}{Missing is only used when input is dense matrix, pick a float
 value that represents missing value. Sometimes a data use 0 or other extreme value to represents missing values.}
 
+\item{weight}{a vector indicating the weight for each row of the input.}
+
 \item{params}{the list of parameters.
 
 Commonly used ones are: