diff --git a/R-package/vignettes/discoverYourData.Rmd b/R-package/vignettes/discoverYourData.Rmd
index 2a4ad6614..4a4e1811a 100644
--- a/R-package/vignettes/discoverYourData.Rmd
+++ b/R-package/vignettes/discoverYourData.Rmd
@@ -156,7 +156,7 @@ head(sparse_matrix)
 Create the output `numeric` vector (not as a sparse `Matrix`):
 
 ```{r}
-output_vector = df[,Improved] == "Marked"
+output_vector <- df[,Improved] == "Marked"
 ```
 
 1. set `Y` vector to `0`;
diff --git a/R-package/vignettes/xgboostPresentation.Rmd b/R-package/vignettes/xgboostPresentation.Rmd
index fecc25a92..64038e02d 100644
--- a/R-package/vignettes/xgboostPresentation.Rmd
+++ b/R-package/vignettes/xgboostPresentation.Rmd
@@ -423,7 +423,7 @@ file.remove("dtrain.buffer")
 Information can be extracted from `xgb.DMatrix` using `getinfo` function. Hereafter we will extract `label` data.
 
 ```{r getinfo, message=F, warning=F}
-label = getinfo(dtest, "label")
+label <- getinfo(dtest, "label")
 pred <- predict(bst, dtest)
 err <- as.numeric(sum(as.integer(pred > 0.5) != label))/length(label)
 print(paste("test-error=", err))
diff --git a/demo/data/gen_autoclaims.R b/demo/data/gen_autoclaims.R
index 4723c1dd0..5d5e6289f 100644
--- a/demo/data/gen_autoclaims.R
+++ b/demo/data/gen_autoclaims.R
@@ -8,11 +8,11 @@ library(dummies)
 library(insuranceData)
 
 data(AutoClaims)
-data = AutoClaims
+data <- AutoClaims
 
-data$STATE = as.factor(data$STATE)
-data$CLASS = as.factor(data$CLASS)
-data$GENDER = as.factor(data$GENDER)
+data$STATE <- as.factor(data$STATE)
+data$CLASS <- as.factor(data$CLASS)
+data$GENDER <- as.factor(data$GENDER)
 
 data.dummy <- dummy.data.frame(data, dummy.class='factor', omit.constants=TRUE);
 write.table(data.dummy, 'autoclaims.csv', sep=',', row.names=F, col.names=F, quote=F)
diff --git a/demo/kaggle-higgs/higgs-train.R b/demo/kaggle-higgs/higgs-train.R
index a9c462ac7..54e36a73f 100644
--- a/demo/kaggle-higgs/higgs-train.R
+++ b/demo/kaggle-higgs/higgs-train.R
@@ -23,9 +23,9 @@ param <- list("objective" = "binary:logitraw",
               "eval_metric" = "ams@0.15",
               "nthread" = 16)
 watchlist <- list("train" = xgmat)
-nrounds = 120
+nrounds <- 120
 print ("loading data end, start to boost trees")
-bst = xgb.train(param, xgmat, nrounds, watchlist );
+bst <- xgb.train(param, xgmat, nrounds, watchlist );
 # save out model
 xgb.save(bst, "higgs.model")
 print ('finish training')
diff --git a/demo/kaggle-higgs/speedtest.R b/demo/kaggle-higgs/speedtest.R
index a3c30c962..12924aa54 100644
--- a/demo/kaggle-higgs/speedtest.R
+++ b/demo/kaggle-higgs/speedtest.R
@@ -6,7 +6,7 @@ require(methods)
 testsize <- 550000
 
 dtrain <- read.csv("data/training.csv", header=TRUE, nrows=350001)
-dtrain$Label = as.numeric(dtrain$Label=='s')
+dtrain$Label <- as.numeric(dtrain$Label=='s')
 # gbm.time = system.time({
 #   gbm.model <- gbm(Label ~ ., data = dtrain[, -c(1,32)], n.trees = 120, 
 #                    interaction.depth = 6, shrinkage = 0.1, bag.fraction = 1,
@@ -24,11 +24,11 @@ sumwpos <- sum(weight * (label==1.0))
 sumwneg <- sum(weight * (label==0.0))
 print(paste("weight statistics: wpos=", sumwpos, "wneg=", sumwneg, "ratio=", sumwneg / sumwpos))
 
-xgboost.time = list()
-threads = c(1,2,4,8,16)
+xgboost.time <- list()
+threads <- c(1,2,4,8,16)
 for (i in 1:length(threads)){
-  thread = threads[i]
-  xgboost.time[[i]] = system.time({
+  thread <- threads[i]
+  xgboost.time[[i]] <- system.time({
     xgmat <- xgb.DMatrix(data, label = label, weight = weight, missing = -999.0)
     param <- list("objective" = "binary:logitraw",
                   "scale_pos_weight" = sumwneg / sumwpos,
@@ -38,9 +38,9 @@ for (i in 1:length(threads)){
                   "eval_metric" = "ams@0.15",
                   "nthread" = thread)
     watchlist <- list("train" = xgmat)
-    nrounds = 120
+    nrounds <- 120
     print ("loading data end, start to boost trees")
-    bst = xgb.train(param, xgmat, nrounds, watchlist );
+    bst <- xgb.train(param, xgmat, nrounds, watchlist );
     # save out model
     xgb.save(bst, "higgs.model")
     print ('finish training')
diff --git a/demo/kaggle-otto/otto_train_pred.R b/demo/kaggle-otto/otto_train_pred.R
index 02989db9b..c60a123eb 100644
--- a/demo/kaggle-otto/otto_train_pred.R
+++ b/demo/kaggle-otto/otto_train_pred.R
@@ -1,20 +1,20 @@
 require(xgboost)
 require(methods)
 
-train = read.csv('data/train.csv',header=TRUE,stringsAsFactors = FALSE)
-test = read.csv('data/test.csv',header=TRUE,stringsAsFactors = FALSE)
-train = train[,-1]
-test = test[,-1]
+train <- read.csv('data/train.csv',header=TRUE,stringsAsFactors = FALSE)
+test <- read.csv('data/test.csv',header=TRUE,stringsAsFactors = FALSE)
+train <- train[,-1]
+test <- test[,-1]
 
-y = train[,ncol(train)]
-y = gsub('Class_','',y)
-y = as.integer(y)-1  # xgboost take features in [0,numOfClass)
+y <- train[,ncol(train)]
+y <- gsub('Class_','',y)
+y <- as.integer(y)-1  # xgboost take features in [0,numOfClass)
 
-x = rbind(train[,-ncol(train)],test)
-x = as.matrix(x)
-x = matrix(as.numeric(x),nrow(x),ncol(x))
-trind = 1:length(y)
-teind = (nrow(train)+1):nrow(x)
+x <- rbind(train[,-ncol(train)],test)
+x <- as.matrix(x)
+x <- matrix(as.numeric(x),nrow(x),ncol(x))
+trind <- 1:length(y)
+teind <- (nrow(train)+1):nrow(x)
 
 # Set necessary parameter
 param <- list("objective" = "multi:softprob",
@@ -23,21 +23,21 @@ param <- list("objective" = "multi:softprob",
               "nthread" = 8)
 
 # Run Cross Validation
-cv.nrounds = 50
-bst.cv = xgb.cv(param=param, data = x[trind,], label = y, 
+cv.nrounds <- 50
+bst.cv <- xgb.cv(param=param, data = x[trind,], label = y, 
                 nfold = 3, nrounds=cv.nrounds)
 
 # Train the model
-nrounds = 50
-bst = xgboost(param=param, data = x[trind,], label = y, nrounds=nrounds)
+nrounds <- 50
+bst <- xgboost(param=param, data = x[trind,], label = y, nrounds=nrounds)
 
 # Make prediction
-pred = predict(bst,x[teind,])
-pred = matrix(pred,9,length(pred)/9)
-pred = t(pred)
+pred <- predict(bst,x[teind,])
+pred <- matrix(pred,9,length(pred)/9)
+pred <- t(pred)
 
 # Output submission
-pred = format(pred, digits=2,scientific=F) # shrink the size of submission
-pred = data.frame(1:nrow(pred),pred)
-names(pred) = c('id', paste0('Class_',1:9))
+pred <- format(pred, digits=2,scientific=F) # shrink the size of submission
+pred <- data.frame(1:nrow(pred),pred)
+names(pred) <- c('id', paste0('Class_',1:9))
 write.csv(pred,file='submission.csv', quote=FALSE,row.names=FALSE)
diff --git a/demo/kaggle-otto/understandingXGBoostModel.Rmd b/demo/kaggle-otto/understandingXGBoostModel.Rmd
index 0f4f82bc0..ac8898f48 100644
--- a/demo/kaggle-otto/understandingXGBoostModel.Rmd
+++ b/demo/kaggle-otto/understandingXGBoostModel.Rmd
@@ -127,7 +127,7 @@ param <- list("objective" = "multi:softprob",
 cv.nrounds <- 5
 cv.nfold <- 3
 
-bst.cv = xgb.cv(param=param, data = trainMatrix, label = y,
+bst.cv <- xgb.cv(param=param, data = trainMatrix, label = y,
                 nfold = cv.nfold, nrounds = cv.nrounds)
 ```
 > As we can see the error rate is low on the test dataset (for a 5mn trained model).
@@ -135,8 +135,8 @@ bst.cv = xgb.cv(param=param, data = trainMatrix, label = y,
 Finally, we are ready to train the real model!!!
 
 ```{r modelTraining}
-nrounds = 50
-bst = xgboost(param=param, data = trainMatrix, label = y, nrounds=nrounds)
+nrounds <- 50
+bst <- xgboost(param=param, data = trainMatrix, label = y, nrounds=nrounds)
 ```
 
 Model understanding
diff --git a/demo/multiclass_classification/train.R b/demo/multiclass_classification/train.R
index 4a07f278d..a9ded8748 100644
--- a/demo/multiclass_classification/train.R
+++ b/demo/multiclass_classification/train.R
@@ -26,7 +26,7 @@ test_x <- test[, 1:34]
 test_y <- test[, V35]
 
 xg_train <- xgb.DMatrix(data = as.matrix(train_x), label = train_y)
-xg_test = xgb.DMatrix(as.matrix(test_x), label = test_y)
+xg_test <- xgb.DMatrix(as.matrix(test_x), label = test_y)
 
 params <- list(
   objective = 'multi:softmax',
@@ -36,7 +36,7 @@ params <- list(
   eta = 0.1
 )
 
-watchlist = list(train = xg_train, test = xg_test)
+watchlist <- list(train = xg_train, test = xg_test)
 
 bst <- xgb.train(
   params = params,
@@ -60,5 +60,5 @@ pred_mat <- matrix(pred_prob, ncol = 6, byrow = TRUE)
 # rowSums(pred_mat)
 
 pred_label <- apply(pred_mat, 1, which.max) - 1L
-error_rate = sum(pred_label != test_y) / length(test_y)
+error_rate <- sum(pred_label != test_y) / length(test_y)
 print(paste("Test error using softprob =", error_rate))