diff --git a/R-package/R/predict.xgb.Booster.R b/R-package/R/predict.xgb.Booster.R
index 9cc1867da..432581e76 100644
--- a/R-package/R/predict.xgb.Booster.R
+++ b/R-package/R/predict.xgb.Booster.R
@@ -48,7 +48,7 @@ setMethod("predict", signature = "xgb.Booster",
       stop("predict: ntreelimit must be equal to or greater than 1")
     }
   }
-  option = 0
+  option <- 0
   if (outputmargin) {
     option <- option + 1
   }
diff --git a/R-package/R/slice.xgb.DMatrix.R b/R-package/R/slice.xgb.DMatrix.R
index d8ef8cb9c..3b025e1dd 100644
--- a/R-package/R/slice.xgb.DMatrix.R
+++ b/R-package/R/slice.xgb.DMatrix.R
@@ -30,12 +30,12 @@ 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)
-              ind <- which(len==nr)
-              if (length(ind)>0) {
+              ind <- which(len == nr)
+              if (length(ind) > 0) {
                   nms <- names(attr_list)[ind]
                   for (i in 1:length(ind)) {
                     attr(ret,nms[i]) <- attr(object,nms[i])[idxset]
diff --git a/R-package/R/utils.R b/R-package/R/utils.R
index 459eb068e..b4f4a371f 100644
--- a/R-package/R/utils.R
+++ b/R-package/R/utils.R
@@ -1,4 +1,4 @@
-#' @importClassesFrom Matrix dgCMatrix dgeMatrix
+  #' @importClassesFrom Matrix dgCMatrix dgeMatrix
 #' @import methods
 
 # depends on matrix
@@ -15,14 +15,14 @@ xgb.setinfo <- function(dmat, name, info) {
     stop("xgb.setinfo: first argument dtrain must be xgb.DMatrix")
   }
   if (name == "label") {
-    if (length(info)!=xgb.numrow(dmat))
+    if (length(info) != xgb.numrow(dmat))
       stop("The length of labels must equal to the number of rows in the input data")
     .Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info),
           PACKAGE = "xgboost")
     return(TRUE)
   }
   if (name == "weight") {
-    if (length(info)!=xgb.numrow(dmat))
+    if (length(info) != xgb.numrow(dmat))
       stop("The length of weights must equal to the number of rows in the input data")
     .Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info),
           PACKAGE = "xgboost")
@@ -36,7 +36,7 @@ xgb.setinfo <- function(dmat, name, info) {
     return(TRUE)
   }
   if (name == "group") {
-    if (sum(info)!=xgb.numrow(dmat))
+    if (sum(info) != xgb.numrow(dmat))
       stop("The sum of groups must equal to the number of rows in the input data")
     .Call("XGDMatrixSetInfo_R", dmat, name, as.integer(info),
           PACKAGE = "xgboost")
@@ -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")
@@ -156,7 +156,7 @@ 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)
@@ -189,9 +189,9 @@ 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]
@@ -247,11 +247,11 @@ 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()
-      for (i in 1:(nfold-1)) {
+      for (i in 1:(nfold - 1)) {
         folds[[i]] <- randidx[1:kstep]
         randidx <- setdiff(randidx, folds[[i]])
       }
@@ -261,7 +261,7 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
   ret <- list()
   for (k in 1:nfold) {
     dtest <- slice(dall, folds[[k]])
-    didx = c()
+    didx <- c()
     for (i in 1:nfold) {
       if (i != k) {
         didx <- append(didx, folds[[i]])
@@ -282,7 +282,7 @@ 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])
@@ -310,9 +310,9 @@ xgb.createFolds <- function(y, k = 10)
     ## At most, we will use quantiles. If the sample
     ## is too small, we just do regular unstratified
     ## CV
-    cuts <- floor(length(y)/k)
-    if(cuts < 2) cuts <- 2
-    if(cuts > 5) cuts <- 5
+    cuts <- floor(length(y) / k)
+    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 +324,7 @@ 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 3f1be704f..5f964c4f8 100644
--- a/R-package/R/xgb.cv.R
+++ b/R-package/R/xgb.cv.R
@@ -118,23 +118,23 @@ 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$objective) && !is.null(obj))
         stop("xgb.cv: cannot assign two different objectives")
     if (!is.null(params$objective))
         if (class(params$objective) == 'function') {
-            obj = params$objective
-            params[['objective']] = NULL
+            obj <- params$objective
+            params[['objective']] <- NULL
         }
     # if (!is.null(params$eval_metric) && !is.null(feval))
     #  stop("xgb.cv: cannot assign two different evaluation metrics")
     if (!is.null(params$eval_metric))
-        if (class(params$eval_metric)=='function') {
-            feval = params$eval_metric
-            params[['eval_metric']] = NULL
+        if (class(params$eval_metric) == 'function') {
+            feval <- params$eval_metric
+            params[['eval_metric']] <- NULL
         }
-    
+
     # Early Stopping
     if (!is.null(early.stop.round)){
         if (!is.null(feval) && is.null(maximize))
@@ -144,12 +144,12 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         if (is.null(maximize))
         {
             if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
-                maximize = FALSE
+                maximize <- FALSE
             } else {
-                maximize = TRUE
+                maximize <- TRUE
             }
         }
-        
+
         if (maximize) {
             bestScore <- 0
         } else {
@@ -157,26 +157,26 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         }
         bestInd <- 0
         earlyStopflag <- FALSE
-        
-        if (length(metrics)>1)
+
+        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
     if (!is.null(obj_type) && obj_type == 'multi:softprob')
     {
-        num_class = params[['num_class']]
+        num_class <- params[['num_class']]
         if (is.null(num_class))
             stop('must set num_class to use softmax')
         predictValues <- matrix(0,xgb.numrow(dtrain),num_class)
-        mat_pred = TRUE
+        mat_pred <- TRUE
     }
     else
         predictValues <- rep(0,xgb.numrow(dtrain))
     history <- c()
-    print.every.n = max(as.integer(print.every.n), 1L)
+    print.every.n <- max(as.integer(print.every.n), 1L)
     for (i in 1:nrounds) {
         msg <- list()
         for (k in 1:nfold) {
@@ -187,28 +187,27 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         ret <- xgb.cv.aggcv(msg, showsd)
         history <- c(history, ret)
         if(verbose)
-            if (0 == (i-1L)%%print.every.n)
+            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(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 {
-                if (i-bestInd >= early.stop.round) {
+                if (i - bestInd >= early.stop.round) {
                     earlyStopflag <- TRUE
                     cat('Stopping. Best iteration:',bestInd)
                     break
                 }
             }
         }
-        
     }
-    
+
     if (prediction) {
         for (k in 1:nfold) {
             fd <- xgb_folds[[k]]
@@ -225,24 +224,23 @@ 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 0b0703587..478438a79 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.")
   }
 
@@ -79,7 +79,7 @@ xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = N
     stop("model: Has to be an object of class xgb.Booster model generaged by the xgb.train function.")
   }
 
-  if((is.null(data) & !is.null(label)) |(!is.null(data) & is.null(label))) {
+  if((is.null(data) & !is.null(label)) | (!is.null(data) & is.null(label))) {
     stop("data/label: Provide the two arguments if you want co-occurence computation or none of them if you are not interested but not one of them only.")
   }
 
@@ -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 <- result[, "RealCover" := as.numeric(vec), with = F][, "RealCover %" := RealCover / sum(label)][,MissingNo := NULL]
+    }
   }
   result
 }
@@ -119,13 +119,13 @@ xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = N
 treeDump <- function(feature_names, text, keepDetail){
   if(keepDetail) groupBy <- c("Feature", "Split", "MissingNo") else groupBy <- "Feature"
 
-  result <- xgb.model.dt.tree(feature_names = feature_names, text = text)[,"MissingNo":= Missing == No ][Feature!="Leaf",.(Gain = sum(Quality), Cover = sum(Cover), Frequence = .N), by = groupBy, with = T][,`:=`(Gain = Gain/sum(Gain), Cover = Cover/sum(Cover), Frequence = Frequence/sum(Frequence))][order(Gain, decreasing = T)]
+  result <- xgb.model.dt.tree(feature_names = feature_names, text = text)[,"MissingNo" := Missing == No ][Feature != "Leaf",.(Gain = sum(Quality), Cover = sum(Cover), Frequence = .N), by = groupBy, with = T][,`:=`(Gain = Gain / sum(Gain), Cover = Cover / sum(Cover), Frequence = Frequence / sum(Frequence))][order(Gain, decreasing = T)]
 
   result
 }
 
 linearDump <- function(feature_names, text){
-  which(text == "weight:") %>% {a=.+1;text[a:length(text)]} %>% as.numeric %>% data.table(Feature = feature_names, Weight = .)
+  which(text == "weight:") %>% {a =. + 1; text[a:length(text)]} %>% as.numeric %>% data.table(Feature = feature_names, Weight = .)
 }
 
 # Avoid error messages during CRAN check.
diff --git a/R-package/R/xgb.model.dt.tree.R b/R-package/R/xgb.model.dt.tree.R
index cef988962..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)) {
@@ -81,12 +81,12 @@ xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model
   }
 
   if(!is.null(model)){
-    text = xgb.dump(model = model, with.stats = T)
+    text <- xgb.dump(model = model, with.stats = T)
   } else if(!is.null(filename_dump)){
     text <- readLines(filename_dump) %>% str_trim(side = "both")
   }
 
-  position <- str_match(text, "booster") %>% is.na %>% not %>% which %>% c(length(text)+1)
+  position <- str_match(text, "booster") %>% is.na %>% not %>% which %>% c(length(text) + 1)
 
   extract <- function(x, pattern)  str_extract(x, pattern) %>% str_split("=") %>% lapply(function(x) x[2] %>% as.numeric) %>% unlist
 
@@ -96,16 +96,16 @@ 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){
-  
-    tree <- text[(position[i]+1):(position[i+1]-1)]
-  
+  anynumber_regex <- "[-+]?[0-9]*\\.?[0-9]+([eE][-+]?[0-9]+)?"
+  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
-  
+    if(length(tree) < 2) next
+
+    treeID <- i - 1
+
     notLeaf <- str_match(tree, "leaf") %>% is.na
     leaf <- notLeaf %>% not %>% tree[.]
     branch <- notLeaf %>% tree[.]
@@ -128,38 +128,38 @@ xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model
     qualityLeaf <- extract(leaf, paste0("leaf=",anynumber_regex))
     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]
-  
+    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 f126dfe46..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$cluster %>% as.character]
-    
-  plot <- ggplot2::ggplot(importance_matrix, ggplot2::aes(x=stats::reorder(Feature, Gain), y = Gain, width= 0.05), environment = environment())+  ggplot2::geom_bar(ggplot2::aes(fill=Cluster), stat="identity", position="identity") + ggplot2::coord_flip() + ggplot2::xlab("Features") + ggplot2::ylab("Gain") + ggplot2::ggtitle("Feature importance") + ggplot2::theme(plot.title = ggplot2::element_text(lineheight=.9, face="bold"), panel.grid.major.y = ggplot2::element_blank() )
-  
-  return(plot)  
+  importance_matrix[,"Cluster" := clusters$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)
 }
 
 # 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 b1d79d866..8e839af5c 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") {
@@ -139,30 +139,30 @@ 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)
+
+  dot.params <- list(...)
+  nms.params <- names(params)
+  nms.dot.params <- names(dot.params)
+  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)
-  
+  params <- append(params, dot.params)
+
   # customized objective and evaluation metric interface
   if (!is.null(params$objective) && !is.null(obj))
     stop("xgb.train: cannot assign two different objectives")
   if (!is.null(params$objective))
-    if (class(params$objective)=='function') {
-      obj = params$objective
-      params$objective = NULL
+    if (class(params$objective) == 'function') {
+      obj <- params$objective
+      params$objective <- NULL
     }
   if (!is.null(params$eval_metric) && !is.null(feval))
     stop("xgb.train: cannot assign two different evaluation metrics")
   if (!is.null(params$eval_metric))
-    if (class(params$eval_metric)=='function') {
-      feval = params$eval_metric
-      params$eval_metric = NULL
+    if (class(params$eval_metric) == 'function') {
+      feval <- params$eval_metric
+      params$eval_metric <- NULL
     }
-    
+
   # Early stopping
   if (!is.null(early.stop.round)){
     if (!is.null(feval) && is.null(maximize))
@@ -174,44 +174,43 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
     if (is.null(maximize))
     {
       if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
-        maximize = FALSE
+        maximize <- FALSE
       } else {
-        maximize = TRUE
+        maximize <- TRUE
       }
     }
-    
+
     if (maximize) {
-      bestScore = 0
+      bestScore <- 0
     } else {
-      bestScore = Inf
+      bestScore <- Inf
     }
-    bestInd = 0
+    bestInd <- 0
     earlyStopflag = FALSE
-    
-    if (length(watchlist)>1)
+
+    if (length(watchlist) > 1)
       warning('Only the first data set in watchlist is used for early stopping process.')
   }
-  
-  
+
   handle <- xgb.Booster(params, append(watchlist, dtrain))
   bst <- xgb.handleToBooster(handle)
-  print.every.n=max( as.integer(print.every.n), 1L)
+  print.every.n <- max( as.integer(print.every.n), 1L)
   for (i in 1:nrounds) {
     succ <- xgb.iter.update(bst$handle, dtrain, i - 1, obj)
     if (length(watchlist) != 0) {
       msg <- xgb.iter.eval(bst$handle, watchlist, i - 1, feval)
-      if (0== ( (i-1) %% print.every.n))
-	    cat(paste(msg, "\n", sep=""))
+      if (0 == ( (i - 1) %% print.every.n))
+	    cat(paste(msg, "\n", sep = ""))
       if (!is.null(early.stop.round))
       {
-        score = strsplit(msg,':|\\s+')[[1]][3]
-        score = as.numeric(score)
-        if ((maximize && score>bestScore) || (!maximize && score<bestScore)) {
-          bestScore = score
-          bestInd = i
+        score <- strsplit(msg,':|\\s+')[[1]][3]
+        score <- as.numeric(score)
+        if ( (maximize && score > bestScore) || (!maximize && score < bestScore)) {
+          bestScore <- score
+          bestInd <- i
         } else {
-          if (i-bestInd>=early.stop.round) {
-            earlyStopflag = TRUE
+          earlyStopflag = TRUE
+          if (i - bestInd >= early.stop.round) {
             cat('Stopping. Best iteration:',bestInd)
             break
           }
@@ -226,8 +225,8 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
   }
   bst <- xgb.Booster.check(bst)
   if (!is.null(early.stop.round)) {
-    bst$bestScore = bestScore
-    bst$bestInd = bestInd
+    bst$bestScore <- bestScore
+    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_basic.R b/R-package/tests/testthat/test_basic.R
index 791f1246c..2e4e54902 100644
--- a/R-package/tests/testthat/test_basic.R
+++ b/R-package/tests/testthat/test_basic.R
@@ -4,30 +4,30 @@ context("basic functions")
 
 data(agaricus.train, package='xgboost')
 data(agaricus.test, package='xgboost')
-train = agaricus.train
-test = agaricus.test
+train <- agaricus.train
+test <- agaricus.test
 
 test_that("train and predict", {
-  bst = xgboost(data = train$data, label = train$label, max.depth = 2,
+  bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
                 eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
-  pred = predict(bst, test$data)
+  pred <- predict(bst, test$data)
 })
 
 
 test_that("early stopping", {
-  res = xgb.cv(data = train$data, label = train$label, max.depth = 2, nfold = 5,
+  res <- xgb.cv(data = train$data, label = train$label, max.depth = 2, nfold = 5,
                eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
                early.stop.round = 3, maximize = FALSE)
-  expect_true(nrow(res)<20)
-  bst = xgboost(data = train$data, label = train$label, max.depth = 2,
+  expect_true(nrow(res) < 20)
+  bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
                 eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
                 early.stop.round = 3, maximize = FALSE)
-  pred = predict(bst, test$data)
+  pred <- predict(bst, test$data)
 })
 
 test_that("save_period", {
-  bst = xgboost(data = train$data, label = train$label, max.depth = 2,
+  bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
                 eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
                 save_period = 10, save_name = "xgb.model")
-  pred = predict(bst, test$data)
+  pred <- predict(bst, test$data)
 })
diff --git a/R-package/tests/testthat/test_custom_objective.R b/R-package/tests/testthat/test_custom_objective.R
index 9fcbeca4d..3db595f49 100644
--- a/R-package/tests/testthat/test_custom_objective.R
+++ b/R-package/tests/testthat/test_custom_objective.R
@@ -7,40 +7,40 @@ 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))
+    preds <- 1 / (1 + exp(-preds))
     grad <- preds - labels
     hess <- preds * (1 - preds)
     return(list(grad = grad, hess = hess))
   }
   evalerror <- function(preds, dtrain) {
     labels <- getinfo(dtrain, "label")
-    err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
+    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$raw), 1064)
   attr(dtrain, 'label') <- getinfo(dtrain, 'label')
-  
+
   logregobjattr <- function(preds, dtrain) {
     labels <- attr(dtrain, 'label')
-    preds <- 1/(1 + exp(-preds))
+    preds <- 1 / (1 + exp(-preds))
     grad <- preds - labels
     hess <- preds * (1 - preds)
     return(list(grad = grad, hess = hess))
   }
-  param <- list(max.depth=2, eta=1, nthread = 2, silent=1, 
-                objective=logregobjattr, eval_metric=evalerror)
+  param <- list(max.depth=2, eta=1, nthread = 2, silent = 1,
+                objective = logregobjattr, eval_metric = evalerror)
   bst <- xgb.train(param, dtrain, num_round, watchlist)
   expect_equal(class(bst), "xgb.Booster")
   expect_equal(length(bst$raw), 1064)
diff --git a/R-package/tests/testthat/test_helpers.R b/R-package/tests/testthat/test_helpers.R
index 4d80146e3..0ac6b388e 100644
--- a/R-package/tests/testthat/test_helpers.R
+++ b/R-package/tests/testthat/test_helpers.R
@@ -8,11 +8,11 @@ require(vcd)
 data(Arthritis)
 data(agaricus.train, package='xgboost')
 df <- data.table(Arthritis, keep.rownames = F)
-df[,AgeDiscret:= as.factor(round(Age/10,0))]
-df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))]
-df[,ID:=NULL]
-sparse_matrix = sparse.model.matrix(Improved~.-1, data = df)
-output_vector = df[,Y:=0][Improved == "Marked",Y:=1][,Y]
+df[,AgeDiscret := as.factor(round(Age / 10,0))]
+df[,AgeCat := as.factor(ifelse(Age > 30, "Old", "Young"))]
+df[,ID := NULL]
+sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
+output_vector <- df[,Y := 0][Improved == "Marked",Y := 1][,Y]
 bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
                eta = 1, nthread = 2, nround = 10,objective = "binary:logistic")
 
diff --git a/R-package/tests/testthat/test_poisson_regression.R b/R-package/tests/testthat/test_poisson_regression.R
index 5d3d78e27..c28820774 100644
--- a/R-package/tests/testthat/test_poisson_regression.R
+++ b/R-package/tests/testthat/test_poisson_regression.R
@@ -4,10 +4,10 @@ require(xgboost)
 
 test_that("poisson regression works", {
   data(mtcars)
-  bst = xgboost(data=as.matrix(mtcars[,-11]),label=mtcars[,11],
-                objective='count:poisson',nrounds=5)
+  bst <- xgboost(data = as.matrix(mtcars[,-11]),label = mtcars[,11],
+                objective = 'count:poisson', nrounds=5)
   expect_equal(class(bst), "xgb.Booster")
-  pred = predict(bst,as.matrix(mtcars[,-11]))
+  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