diff --git a/R-package/R/predict.xgb.Booster.R b/R-package/R/predict.xgb.Booster.R
index 0c50b2504..902260258 100644
--- a/R-package/R/predict.xgb.Booster.R
+++ b/R-package/R/predict.xgb.Booster.R
@@ -31,7 +31,7 @@ setClass("xgb.Booster",
 #' @export
 #' 
 setMethod("predict", signature = "xgb.Booster", 
-          definition = function(object, newdata, missing = NULL, 
+          definition = function(object, newdata, missing = NA, 
                                 outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE) {
   if (class(object) != "xgb.Booster"){
     stop("predict: model in prediction must be of class xgb.Booster")
@@ -39,11 +39,7 @@ setMethod("predict", signature = "xgb.Booster",
     object <- xgb.Booster.check(object, saveraw = FALSE)
   }
   if (class(newdata) != "xgb.DMatrix") {
-    if (is.null(missing)) {
-      newdata <- xgb.DMatrix(newdata)
-    } else {
-      newdata <- xgb.DMatrix(newdata, missing = missing)
-    }
+    newdata <- xgb.DMatrix(newdata, missing = missing)
   }
   if (is.null(ntreelimit)) {
     ntreelimit <- 0
diff --git a/R-package/R/utils.R b/R-package/R/utils.R
index 732ef0d11..eecc5e260 100644
--- a/R-package/R/utils.R
+++ b/R-package/R/utils.R
@@ -103,18 +103,13 @@ 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, weight = NULL) {
+xgb.get.DMatrix <- function(data, label = NULL, missing = NA, 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)
-    }
+    dtrain <- xgb.DMatrix(data, label = label, missing = missing)
     if (!is.null(weight)){
       xgb.setinfo(dtrain, "weight", weight)
     }
diff --git a/R-package/R/xgb.DMatrix.R b/R-package/R/xgb.DMatrix.R
index 8c3ea80bc..970fab394 100644
--- a/R-package/R/xgb.DMatrix.R
+++ b/R-package/R/xgb.DMatrix.R
@@ -18,7 +18,7 @@
 #' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
 #' @export
 #' 
-xgb.DMatrix <- function(data, info = list(), missing = 0, ...) {
+xgb.DMatrix <- function(data, info = list(), missing = NA, ...) {
   if (typeof(data) == "character") {
     handle <- .Call("XGDMatrixCreateFromFile_R", data, as.integer(FALSE), 
                     PACKAGE = "xgboost")
diff --git a/R-package/R/xgb.cv.R b/R-package/R/xgb.cv.R
index a5364db52..9811bba38 100644
--- a/R-package/R/xgb.cv.R
+++ b/R-package/R/xgb.cv.R
@@ -91,7 +91,7 @@
 #' print(history)
 #' @export
 #'
-xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NULL, 
+xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NA, 
                    prediction = FALSE, showsd = TRUE, metrics=list(), 
                    obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, verbose = T, print.every.n=1L,
                    early.stop.round = NULL, maximize = NULL, ...) {
@@ -107,11 +107,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
     if (nfold <= 1) {
         stop("nfold must be bigger than 1")
     }
-    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)
     dot.params = list(...)
     nms.params = names(params)
     nms.dot.params = names(dot.params)
diff --git a/R-package/R/xgboost.R b/R-package/R/xgboost.R
index 164dc1838..e11052add 100644
--- a/R-package/R/xgboost.R
+++ b/R-package/R/xgboost.R
@@ -59,7 +59,7 @@
 #' 
 #' @export
 #' 
-xgboost <- function(data = NULL, label = NULL, missing = NULL, weight = NULL, 
+xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL, 
                     params = list(), nrounds, 
                     verbose = 1, print.every.n = 1L, early.stop.round = NULL,
                     maximize = NULL, save_period = 0, save_name = "xgboost.model", ...) {