diff --git a/R-package/demo/00Index b/R-package/demo/00Index
index 43df3ae61..0112eb9e1 100644
--- a/R-package/demo/00Index
+++ b/R-package/demo/00Index
@@ -6,5 +6,5 @@ generalized_linear_model        Generalized Linear Model
 cross_validation                Cross validation
 create_sparse_matrix            Create Sparse Matrix
 predict_leaf_indices            Predicting the corresponding leaves
-early_Stopping                  Early Stop in training
+early_stopping                  Early Stop in training
 poisson_regression              Poisson Regression on count data
diff --git a/R-package/demo/early_stopping.R b/R-package/demo/early_stopping.R
new file mode 100644
index 000000000..34dfebc0b
--- /dev/null
+++ b/R-package/demo/early_stopping.R
@@ -0,0 +1,39 @@
+require(xgboost)
+# load in the agaricus dataset
+data(agaricus.train, package='xgboost')
+data(agaricus.test, package='xgboost')
+dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
+dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
+# note: for customized objective function, we leave objective as default
+# note: what we are getting is margin value in prediction
+# you must know what you are doing
+param <- list(max.depth=2,eta=1,nthread = 2, silent=1)
+watchlist <- list(eval = dtest)
+num_round <- 20
+# user define objective function, given prediction, return gradient and second order gradient
+# this is loglikelihood loss
+logregobj <- function(preds, dtrain) {
+  labels <- getinfo(dtrain, "label")
+  preds <- 1/(1 + exp(-preds))
+  grad <- preds - labels
+  hess <- preds * (1 - preds)
+  return(list(grad = grad, hess = hess))
+}
+# user defined evaluation function, return a pair metric_name, result
+# NOTE: when you do customized loss function, the default prediction value is margin
+# this may make buildin evalution metric not function properly
+# for example, we are doing logistic loss, the prediction is score before logistic transformation
+# the buildin evaluation error assumes input is after logistic transformation
+# Take this in mind when you use the customization, and maybe you need write customized evaluation function
+evalerror <- function(preds, dtrain) {
+  labels <- getinfo(dtrain, "label")
+  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
+  return(list(metric = "error", value = err))
+}
+print ('start training with early Stopping setting')
+# training with customized objective, we can also do step by step training
+# simply look at xgboost.py's implementation of train
+bst <- xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror, maximize = FALSE,
+                 early.stop.round = 3)
+bst <- xgb.cv(param, dtrain, num_round, nfold=5, obj=logregobj, feval = evalerror,
+              maximize = FALSE, early.stop.round = 3)
diff --git a/R-package/demo/runall.R b/R-package/demo/runall.R
index 2ea4c446e..7311ec95e 100644
--- a/R-package/demo/runall.R
+++ b/R-package/demo/runall.R
@@ -7,5 +7,5 @@ demo(generalized_linear_model)
 demo(cross_validation)
 demo(create_sparse_matrix)
 demo(predict_leaf_indices)
-demo(early_Stopping)
+demo(early_stopping)
 demo(poisson_regression)