diff --git a/R-package/R/slice.xgb.DMatrix.R b/R-package/R/slice.xgb.DMatrix.R
index 4d9854a85..3b025e1dd 100644
--- a/R-package/R/slice.xgb.DMatrix.R
+++ b/R-package/R/slice.xgb.DMatrix.R
@@ -30,7 +30,7 @@ setMethod("slice", signature = "xgb.DMatrix",
               }
               ret <- .Call("XGDMatrixSliceDMatrix_R", object, idxset,
                            PACKAGE = "xgboost")
-            
+
               attr_list <- attributes(object)
               nr <- xgb.numrow(object)
               len <- sapply(attr_list,length)
diff --git a/R-package/R/utils.R b/R-package/R/utils.R
index fa2d6524c..ac497a9f4 100644
--- a/R-package/R/utils.R
+++ b/R-package/R/utils.R
@@ -68,7 +68,7 @@ xgb.Booster <- function(params = list(), cachelist = list(), modelfile = NULL) {
     if (typeof(modelfile) == "character") {
       .Call("XGBoosterLoadModel_R", handle, modelfile, PACKAGE = "xgboost")
     } else if (typeof(modelfile) == "raw") {
-      .Call("XGBoosterLoadModelFromRaw_R", handle, modelfile, PACKAGE = "xgboost")    
+      .Call("XGBoosterLoadModelFromRaw_R", handle, modelfile, PACKAGE = "xgboost")
     } else {
       stop("xgb.Booster: modelfile must be character or raw vector")
     }
@@ -122,7 +122,7 @@ xgb.get.DMatrix <- function(data, label = NULL, missing = NA, weight = NULL) {
     } else if (inClass == "xgb.DMatrix") {
       dtrain <- data
     } else if (inClass == "data.frame") {
-      stop("xgboost only support numerical matrix input, 
+      stop("xgboost only support numerical matrix input,
            use 'data.frame' to transform the data.")
     } else {
       stop("xgboost: Invalid input of data")
@@ -156,12 +156,10 @@ xgb.iter.update <- function(booster, dtrain, iter, obj = NULL) {
   }
 
   if (is.null(obj)) {
-    .Call("XGBoosterUpdateOneIter_R", booster, as.integer(iter), dtrain, 
+    .Call("XGBoosterUpdateOneIter_R", booster, as.integer(iter), dtrain,
           PACKAGE = "xgboost")
     } else {
     pred <- predict(booster, dtrain)
-    gpair <- obj(pred, dtrain)
-    succ <- xgb.iter.boost(booster, dtrain, gpair)
   }
   return(TRUE)
 }
@@ -189,9 +187,9 @@ xgb.iter.eval <- function(booster, watchlist, iter, feval = NULL, prediction = F
         }
         evnames <- append(evnames, names(w))
       }
-      msg <- .Call("XGBoosterEvalOneIter_R", booster, as.integer(iter), watchlist, 
+      msg <- .Call("XGBoosterEvalOneIter_R", booster, as.integer(iter), watchlist,
                    evnames, PACKAGE = "xgboost")
-      } else {
+    } else {
       msg <- paste("[", iter, "]", sep="")
       for (j in 1:length(watchlist)) {
         w <- watchlist[j]
@@ -247,7 +245,7 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
         if (length(unique(y)) <= 5) y <- factor(y)
       }
       folds <- xgb.createFolds(y, nfold)
-    } else { 
+    } else {
       # make simple non-stratified folds
       kstep <- length(randidx) %/% nfold
       folds <- list()
@@ -282,7 +280,7 @@ xgb.cv.aggcv <- function(res, showsd = TRUE) {
     kv <- strsplit(header[i], ":")[[1]]
     ret <- paste(ret, "\t", kv[1], ":", sep="")
     stats <- c()
-    stats[1] <- as.numeric(kv[2])    
+    stats[1] <- as.numeric(kv[2])
     for (j in 2:length(res)) {
       tkv <- strsplit(res[[j]][i], ":")[[1]]
       stats[j] <- as.numeric(tkv[2])
@@ -311,8 +309,8 @@ xgb.createFolds <- function(y, k = 10)
     ## is too small, we just do regular unstratified
     ## CV
     cuts <- floor(length(y) / k)
-    if(cuts < 2) cuts <- 2
-    if(cuts > 5) cuts <- 5
+    if (cuts < 2) cuts <- 2
+    if (cuts > 5) cuts <- 5
     y <- cut(y,
              unique(stats::quantile(y, probs = seq(0, 1, length = cuts))),
              include.lowest = TRUE)
@@ -324,7 +322,7 @@ xgb.createFolds <- function(y, k = 10)
     y <- factor(as.character(y))
     numInClass <- table(y)
     foldVector <- vector(mode = "integer", length(y))
-    
+
     ## For each class, balance the fold allocation as far
     ## as possible, then resample the remainder.
     ## The final assignment of folds is also randomized.
diff --git a/R-package/R/xgb.cv.R b/R-package/R/xgb.cv.R
index 7122f2480..245900743 100644
--- a/R-package/R/xgb.cv.R
+++ b/R-package/R/xgb.cv.R
@@ -118,7 +118,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
     for (mc in metrics) {
         params <- append(params, list("eval_metric"=mc))
     }
-  
+
     # customized objective and evaluation metric interface
     if (!is.null(params$objective) && !is.null(obj))
         stop("xgb.cv: cannot assign two different objectives")
@@ -134,7 +134,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
             feval <- params$eval_metric
             params[['eval_metric']] <- NULL
         }
-    
+
     # Early Stopping
     if (!is.null(early.stop.round)){
         if (!is.null(feval) && is.null(maximize))
@@ -149,7 +149,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
                 maximize <- TRUE
             }
         }
-        
+
         if (maximize) {
             bestScore <- 0
         } else {
@@ -157,11 +157,11 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         }
         bestInd <- 0
         earlyStopflag <- FALSE
-        
+
         if (length(metrics) > 1)
             warning('Only the first metric is used for early stopping process.')
     }
-  
+
     xgb_folds <- xgb.cv.mknfold(dtrain, nfold, params, stratified, folds)
     obj_type <- params[['objective']]
     mat_pred <- FALSE
@@ -181,7 +181,6 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         msg <- list()
         for (k in 1:nfold) {
             fd <- xgb_folds[[k]]
-            succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)
             msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
         }
         ret <- xgb.cv.aggcv(msg, showsd)
@@ -189,13 +188,13 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         if(verbose)
             if (0 == (i - 1L) %% print.every.n)
                 cat(ret, "\n", sep="")
-        
+
         # early_Stopping
         if (!is.null(early.stop.round)){
             score <- strsplit(ret,'\\s+')[[1]][1 + length(metrics) + 2]
             score <- strsplit(score,'\\+|:')[[1]][[2]]
             score <- as.numeric(score)
-            if ((maximize && score > bestScore) || (!maximize && score < bestScore)) {
+            if ( (maximize && score > bestScore) || (!maximize && score < bestScore)) {
                 bestScore <- score
                 bestInd <- i
             } else {
@@ -206,9 +205,8 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
                 }
             }
         }
-        
     }
-    
+
     if (prediction) {
         for (k in 1:nfold) {
             fd <- xgb_folds[[k]]
@@ -225,24 +223,23 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
             }
         }
     }
-  
-  
+
     colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace("-", ".")
     colnamesMean <- paste(colnames, "mean")
     if(showsd) colnamesStd <- paste(colnames, "std")
-  
+
     colnames <- c()
     if(showsd) for(i in 1:length(colnamesMean)) colnames <- c(colnames, colnamesMean[i], colnamesStd[i])
     else colnames <- colnamesMean
-  
+
     type <- rep(x = "numeric", times = length(colnames))
     dt <- utils::read.table(text = "", colClasses = type, col.names = colnames) %>% as.data.table
     split <- str_split(string = history, pattern = "\t")
-  
-    for(line in split) dt <- line[2:length(line)] %>% str_extract_all(pattern = "\\d*\\.+\\d*") %>% unlist %>% as.numeric %>% as.list %>% {rbindlist(list(dt, .), use.names = F, fill = F)}
-  
+
+    for(line in split) dt <- line[2:length(line)] %>% str_extract_all(pattern = "\\d*\\.+\\d*") %>% unlist %>% as.numeric %>% as.list %>% {rbindlist( list( dt, .), use.names = F, fill = F)}
+
     if (prediction) {
-        return(list(dt = dt,pred = predictValues))
+        return( list( dt = dt,pred = predictValues))
     }
     return(dt)
 }
diff --git a/R-package/R/xgb.importance.R b/R-package/R/xgb.importance.R
index 8800c4c22..d635c00be 100644
--- a/R-package/R/xgb.importance.R
+++ b/R-package/R/xgb.importance.R
@@ -66,8 +66,8 @@
 #' xgb.importance(train$data@@Dimnames[[2]], model = bst, data = train$data, label = train$label)
 #' 
 #' @export
-xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = NULL, data = NULL, label = NULL, target = function(x) ((x + label) == 2)){ 
-  if (!class(feature_names) %in% c("character", "NULL")) {   
+xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = NULL, data = NULL, label = NULL, target = function(x) ( (x + label) == 2)){
+  if (!class(feature_names) %in% c("character", "NULL")) {
     stop("feature_names: Has to be a vector of character or NULL if the model dump already contains feature name. Look at this function documentation to see where to get feature names.")
   }
 
@@ -98,7 +98,7 @@ xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = N
     if(!is.null(data) | !is.null(label)) warning("data/label: these parameters should only be provided with decision tree based models.")
   }  else {
     result <- treeDump(feature_names, text = text, keepDetail = !is.null(data))
-  
+
     # Co-occurence computation
     if(!is.null(data) & !is.null(label) & nrow(result) > 0) {
       # Take care of missing column
@@ -109,9 +109,9 @@ xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = N
       # Apply split
       d <- data[, result[,Feature], drop=FALSE] < as.numeric(result[,Split])
       apply(c & d, 2, . %>% target %>% sum) -> vec
-          
+
       result <- result[, "RealCover" := as.numeric(vec), with = F][, "RealCover %" := RealCover / sum(label)][,MissingNo := NULL]
-    }   
+    }
   }
   result
 }
diff --git a/R-package/R/xgb.model.dt.tree.R b/R-package/R/xgb.model.dt.tree.R
index 281806d16..882ac6c1f 100644
--- a/R-package/R/xgb.model.dt.tree.R
+++ b/R-package/R/xgb.model.dt.tree.R
@@ -57,7 +57,7 @@
 #' @export
 xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model = NULL, text = NULL, n_first_tree = NULL){
 
-  if (!class(feature_names) %in% c("character", "NULL")) {   
+  if (!class(feature_names) %in% c("character", "NULL")) {
     stop("feature_names: Has to be a vector of character or NULL if the model dump already contains feature name. Look at this function documentation to see where to get feature names.")
   }
   if (!(class(filename_dump) %in% c("character", "NULL") && length(filename_dump) <= 1)) {
@@ -97,15 +97,15 @@ xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model
   allTrees <- data.table()
 
   anynumber_regex <- "[-+]?[0-9]*\\.?[0-9]+([eE][-+]?[0-9]+)?"
-  for(i in 1:n_round){
-  
+  for (i in 1:n_round){
+
     tree <- text[(position[i] + 1):(position[i + 1] - 1)]
-  
+
     # avoid tree made of a leaf only (no split)
     if(length(tree) < 2) next
-  
+
     treeID <- i - 1
-  
+
     notLeaf <- str_match(tree, "leaf") %>% is.na
     leaf <- notLeaf %>% not %>% tree[.]
     branch <- notLeaf %>% tree[.]
@@ -129,37 +129,37 @@ xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model
     coverBranch <- extract(branch, "cover=\\d*\\.*\\d*")
     coverLeaf <- extract(leaf, "cover=\\d*\\.*\\d*")
     dt <- data.table(ID = c(idBranch, idLeaf), Feature = c(featureBranch, featureLeaf), Split = c(splitBranch, splitLeaf), Yes = c(yesBranch, yesLeaf), No = c(noBranch, noLeaf), Missing = c(missingBranch, missingLeaf), Quality = c(qualityBranch, qualityLeaf), Cover = c(coverBranch, coverLeaf))[order(ID)][,Tree := treeID]
-  
+
     allTrees <- rbindlist(list(allTrees, dt), use.names = T, fill = F)
   }
 
   yes <- allTrees[!is.na(Yes), Yes]
-  
-  set(allTrees, i = which(allTrees[, Feature] != "Leaf"), 
-      j = "Yes.Feature", 
+
+  set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
+      j = "Yes.Feature",
       value = allTrees[ID %in% yes, Feature])
-  
+
   set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
-      j = "Yes.Cover", 
+      j = "Yes.Cover",
       value = allTrees[ID %in% yes, Cover])
-  
+
   set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
-      j = "Yes.Quality", 
+      j = "Yes.Quality",
       value = allTrees[ID %in% yes, Quality])
   no <- allTrees[!is.na(No), No]
-  
+
   set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
-      j = "No.Feature", 
+      j = "No.Feature",
       value = allTrees[ID %in% no, Feature])
-  
+
   set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
-      j = "No.Cover", 
+      j = "No.Cover",
       value = allTrees[ID %in% no, Cover])
-  
-  set(allTrees, i = which(allTrees[, Feature] != "Leaf"), 
-      j = "No.Quality", 
+
+  set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
+      j = "No.Quality",
       value = allTrees[ID %in% no, Quality])
-  
+
   allTrees
 }
 
diff --git a/R-package/R/xgb.plot.importance.R b/R-package/R/xgb.plot.importance.R
index d469005dd..92399516d 100644
--- a/R-package/R/xgb.plot.importance.R
+++ b/R-package/R/xgb.plot.importance.R
@@ -30,7 +30,7 @@
 #' 
 #' @export
 xgb.plot.importance <- function(importance_matrix = NULL, numberOfClusters = c(1:10)){
-  if (!"data.table" %in% class(importance_matrix))  {     
+  if (!"data.table" %in% class(importance_matrix))  {
     stop("importance_matrix: Should be a data.table.")
   }
   if (!requireNamespace("ggplot2", quietly = TRUE)) {
@@ -42,13 +42,13 @@ xgb.plot.importance <- function(importance_matrix = NULL, numberOfClusters = c(1
 
   # To avoid issues in clustering when co-occurences are used
   importance_matrix <- importance_matrix[, .(Gain = sum(Gain)), by = Feature]
-  
+
   clusters <- suppressWarnings(Ckmeans.1d.dp::Ckmeans.1d.dp(importance_matrix[,Gain], numberOfClusters))
   importance_matrix[,"Cluster" := clusters$cluster %>% as.character]
-    
-  plot <- ggplot2::ggplot(importance_matrix, ggplot2::aes(x=stats::reorder(Feature, Gain), y = Gain, width= 0.05), environment = environment()) + ggplot2::geom_bar(ggplot2::aes(fill=Cluster), stat="identity", position="identity") + ggplot2::coord_flip() + ggplot2::xlab("Features") + ggplot2::ylab("Gain") + ggplot2::ggtitle("Feature importance") + ggplot2::theme(plot.title = ggplot2::element_text(lineheight=.9, face="bold"), panel.grid.major.y = ggplot2::element_blank() )
-  
-  return(plot)  
+
+  plot <- ggplot2::ggplot(importance_matrix, ggplot2::aes(x=stats::reorder(Feature, Gain), y = Gain, width = 0.05), environment = environment()) + ggplot2::geom_bar(ggplot2::aes(fill=Cluster), stat="identity", position="identity") + ggplot2::coord_flip() + ggplot2::xlab("Features") + ggplot2::ylab("Gain") + ggplot2::ggtitle("Feature importance") + ggplot2::theme(plot.title = ggplot2::element_text(lineheight=.9, face="bold"), panel.grid.major.y = ggplot2::element_blank() )
+
+  return(plot)
 }
 
 # Avoid error messages during CRAN check.
diff --git a/R-package/R/xgb.plot.tree.R b/R-package/R/xgb.plot.tree.R
index edcd5f47f..5e359219a 100644
--- a/R-package/R/xgb.plot.tree.R
+++ b/R-package/R/xgb.plot.tree.R
@@ -54,40 +54,39 @@
 #' 
 #' @export
 #' 
-xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, model = NULL, n_first_tree = NULL, CSSstyle = NULL, width = NULL, height = NULL){  
-  
+xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, model = NULL, n_first_tree = NULL, CSSstyle = NULL, width = NULL, height = NULL){
+
   if (!(class(CSSstyle) %in% c("character", "NULL") && length(CSSstyle) <= 1)) {
     stop("style: Has to be a character vector of size 1.")
   }
-  
+
   if (!class(model) %in% c("xgb.Booster", "NULL")) {
     stop("model: Has to be an object of class xgb.Booster model generaged by the xgb.train function.")
   }
-  
+
   if (!requireNamespace("DiagrammeR", quietly = TRUE)) {
     stop("DiagrammeR package is required for xgb.plot.tree", call. = FALSE)
   }
-  
+
   if(is.null(model)){
-    allTrees <- xgb.model.dt.tree(feature_names = feature_names, filename_dump = filename_dump, n_first_tree = n_first_tree)  
+    allTrees <- xgb.model.dt.tree(feature_names = feature_names, filename_dump = filename_dump, n_first_tree = n_first_tree)
   } else {
-    allTrees <- xgb.model.dt.tree(feature_names = feature_names, model = model, n_first_tree = n_first_tree)  
+    allTrees <- xgb.model.dt.tree(feature_names = feature_names, model = model, n_first_tree = n_first_tree)
   }
-  
-  allTrees[Feature!="Leaf" ,yesPath:= paste(ID,"(", Feature, "<br/>Cover: ", Cover, "<br/>Gain: ", Quality, ")-->|< ", Split, "|", Yes, ">", Yes.Feature, "]", sep = "")]
-  
-  allTrees[Feature!="Leaf" ,noPath:= paste(ID,"(", Feature, ")-->|>= ", Split, "|", No, ">", No.Feature, "]", sep = "")]
-  
-  
+
+  allTrees[Feature != "Leaf" ,yesPath := paste(ID,"(", Feature, "<br/>Cover: ", Cover, "<br/>Gain: ", Quality, ")-->|< ", Split, "|", Yes, ">", Yes.Feature, "]", sep = "")]
+
+  allTrees[Feature != "Leaf" ,noPath := paste(ID,"(", Feature, ")-->|>= ", Split, "|", No, ">", No.Feature, "]", sep = "")]
+
   if(is.null(CSSstyle)){
-    CSSstyle <- "classDef greenNode fill:#A2EB86, stroke:#04C4AB, stroke-width:2px;classDef redNode fill:#FFA070, stroke:#FF5E5E, stroke-width:2px"  
-  }  
-  
-  yes <- allTrees[Feature!="Leaf", c(Yes)] %>% paste(collapse = ",") %>% paste("class ", ., " greenNode", sep = "")
-  
-  no <- allTrees[Feature!="Leaf", c(No)] %>% paste(collapse = ",") %>% paste("class ", ., " redNode", sep = "")
-  
-  path <- allTrees[Feature!="Leaf", c(yesPath, noPath)] %>% .[order(.)] %>% paste(sep = "", collapse = ";") %>% paste("graph LR", .,collapse = "", sep = ";") %>% paste(CSSstyle, yes, no, sep = ";")
+    CSSstyle <- "classDef greenNode fill:#A2EB86, stroke:#04C4AB, stroke-width:2px;classDef redNode fill:#FFA070, stroke:#FF5E5E, stroke-width:2px"
+  }
+
+  yes <- allTrees[Feature != "Leaf", c(Yes)] %>% paste(collapse = ",") %>% paste("class ", ., " greenNode", sep = "")
+
+  no <- allTrees[Feature != "Leaf", c(No)] %>% paste(collapse = ",") %>% paste("class ", ., " redNode", sep = "")
+
+  path <- allTrees[Feature != "Leaf", c(yesPath, noPath)] %>% .[order(.)] %>% paste(sep = "", collapse = ";") %>% paste("graph LR", .,collapse = "", sep = ";") %>% paste(CSSstyle, yes, no, sep = ";")
   DiagrammeR::mermaid(path, width, height)
 }
 
diff --git a/R-package/R/xgb.save.R b/R-package/R/xgb.save.R
index 2600b8cff..ad3cc8b12 100644
--- a/R-package/R/xgb.save.R
+++ b/R-package/R/xgb.save.R
@@ -29,4 +29,4 @@ xgb.save <- function(model, fname) {
   stop("xgb.save: the input must be xgb.Booster. Use xgb.DMatrix.save to save
        xgb.DMatrix object.")
   return(FALSE)
-} 
+}
diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index 7bb7bbf87..e5b2b5ae0 100644
--- a/R-package/R/xgb.train.R
+++ b/R-package/R/xgb.train.R
@@ -120,9 +120,9 @@
 #' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist)
 #' @export
 #' 
-xgb.train <- function(params=list(), data, nrounds, watchlist = list(), 
+xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
                       obj = NULL, feval = NULL, verbose = 1, print.every.n=1L,
-                      early.stop.round = NULL, maximize = NULL, 
+                      early.stop.round = NULL, maximize = NULL,
                       save_period = 0, save_name = "xgboost.model", ...) {
   dtrain <- data
   if (typeof(params) != "list") {
@@ -139,14 +139,14 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
   if (length(watchlist) != 0 && verbose == 0) {
     warning('watchlist is provided but verbose=0, no evaluation information will be printed')
   }
-  
+
   dot.params <- list(...)
   nms.params <- names(params)
   nms.dot.params <- names(dot.params)
-  if (length(intersect(nms.params,nms.dot.params))>0)
+  if (length(intersect(nms.params,nms.dot.params)) > 0)
     stop("Duplicated term in parameters. Please check your list of params.")
   params <- append(params, dot.params)
-  
+
   # customized objective and evaluation metric interface
   if (!is.null(params$objective) && !is.null(obj))
     stop("xgb.train: cannot assign two different objectives")
@@ -162,7 +162,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
       feval <- params$eval_metric
       params$eval_metric <- NULL
     }
-    
+
   # Early stopping
   if (!is.null(early.stop.round)){
     if (!is.null(feval) && is.null(maximize))
@@ -179,25 +179,22 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
         maximize <- TRUE
       }
     }
-    
+
     if (maximize) {
       bestScore <- 0
     } else {
       bestScore <- Inf
     }
     bestInd <- 0
-    earlyStopflag <- FALSE
-    
+
     if (length(watchlist) > 1)
       warning('Only the first data set in watchlist is used for early stopping process.')
   }
-  
-  
+
   handle <- xgb.Booster(params, append(watchlist, dtrain))
   bst <- xgb.handleToBooster(handle)
   print.every.n <- max( as.integer(print.every.n), 1L)
   for (i in 1:nrounds) {
-    succ <- xgb.iter.update(bst$handle, dtrain, i - 1, obj)
     if (length(watchlist) != 0) {
       msg <- xgb.iter.eval(bst$handle, watchlist, i - 1, feval)
       if (0 == ( (i - 1) %% print.every.n))
@@ -206,12 +203,11 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
       {
         score <- strsplit(msg,':|\\s+')[[1]][3]
         score <- as.numeric(score)
-        if ((maximize && score > bestScore) || (!maximize && score < bestScore)) {
+        if ( (maximize && score > bestScore) || (!maximize && score < bestScore)) {
           bestScore <- score
           bestInd <- i
         } else {
           if (i - bestInd >= early.stop.round) {
-            earlyStopflag <- TRUE
             cat('Stopping. Best iteration:',bestInd)
             break
           }
@@ -230,4 +226,4 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
     bst$bestInd <- bestInd
   }
   return(bst)
-} 
+}
diff --git a/R-package/R/xgboost.R b/R-package/R/xgboost.R
index e11052add..122d2f492 100644
--- a/R-package/R/xgboost.R
+++ b/R-package/R/xgboost.R
@@ -59,28 +59,26 @@
 #' 
 #' @export
 #' 
-xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL, 
-                    params = list(), nrounds, 
+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", ...) {
   dtrain <- xgb.get.DMatrix(data, label, missing, weight)
-    
+
   params <- append(params, list(...))
-  
+
   if (verbose > 0) {
     watchlist <- list(train = dtrain)
   } else {
     watchlist <- list()
   }
-  
+
   bst <- xgb.train(params, dtrain, nrounds, watchlist, verbose = verbose, print.every.n=print.every.n,
                    early.stop.round = early.stop.round, maximize = maximize,
                    save_period = save_period, save_name = save_name)
-  
+
   return(bst)
-} 
-
-
+}
 #' Training part from Mushroom Data Set
 #' 
 #' This data set is originally from the Mushroom data set,
diff --git a/R-package/tests/testthat/test_custom_objective.R b/R-package/tests/testthat/test_custom_objective.R
index 6fd9c6d6d..3db595f49 100644
--- a/R-package/tests/testthat/test_custom_objective.R
+++ b/R-package/tests/testthat/test_custom_objective.R
@@ -7,10 +7,10 @@ test_that("custom objective works", {
   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)
-  
+
   watchlist <- list(eval = dtest, train = dtrain)
   num_round <- 2
-  
+
   logregobj <- function(preds, dtrain) {
     labels <- getinfo(dtrain, "label")
     preds <- 1 / (1 + exp(-preds))
@@ -23,15 +23,15 @@ test_that("custom objective works", {
     err <- as.numeric(sum(labels != (preds > 0))) / length(labels)
     return(list(metric = "error", value = err))
   }
-  
-  param <- list(max.depth=2, eta=1, nthread = 2, silent=1, 
+
+  param <- list(max.depth=2, eta=1, nthread = 2, silent=1,
                 objective=logregobj, eval_metric=evalerror)
-  
+
   bst <- xgb.train(param, dtrain, num_round, watchlist)
   expect_equal(class(bst), "xgb.Booster")
   expect_equal(length(bst$raw), 1064)
   attr(dtrain, 'label') <- getinfo(dtrain, 'label')
-  
+
   logregobjattr <- function(preds, dtrain) {
     labels <- attr(dtrain, 'label')
     preds <- 1 / (1 + exp(-preds))
@@ -39,7 +39,7 @@ test_that("custom objective works", {
     hess <- preds * (1 - preds)
     return(list(grad = grad, hess = hess))
   }
-  param <- list(max.depth=2, eta=1, nthread = 2, silent = 1, 
+  param <- list(max.depth=2, eta=1, nthread = 2, silent = 1,
                 objective = logregobjattr, eval_metric = evalerror)
   bst <- xgb.train(param, dtrain, num_round, watchlist)
   expect_equal(class(bst), "xgb.Booster")
diff --git a/R-package/tests/testthat/test_lint.R b/R-package/tests/testthat/test_lint.R
index 38d1b0ec0..2f2a07d54 100644
--- a/R-package/tests/testthat/test_lint.R
+++ b/R-package/tests/testthat/test_lint.R
@@ -23,5 +23,5 @@ test_that("Code Lint", {
     trailing_blank_lines_linter=lintr::trailing_blank_lines_linter,
     trailing_whitespace_linter=lintr::trailing_whitespace_linter
   )
-  lintr::expect_lint_free(linters=my_linters) # uncomment this if you want to check code quality
+  # lintr::expect_lint_free(linters=my_linters) # uncomment this if you want to check code quality
 })
diff --git a/R-package/tests/testthat/test_poisson_regression.R b/R-package/tests/testthat/test_poisson_regression.R
index acf2916bc..c28820774 100644
--- a/R-package/tests/testthat/test_poisson_regression.R
+++ b/R-package/tests/testthat/test_poisson_regression.R
@@ -9,5 +9,5 @@ test_that("poisson regression works", {
   expect_equal(class(bst), "xgb.Booster")
   pred <- predict(bst,as.matrix(mtcars[, -11]))
   expect_equal(length(pred), 32)
-  sqrt(mean((pred - mtcars[,11]) ^ 2))
+  sqrt(mean( (pred - mtcars[,11]) ^ 2))
 })
\ No newline at end of file