diff --git a/R-package/R/slice.xgb.DMatrix.R b/R-package/R/slice.xgb.DMatrix.R
index d8ef8cb9c..4d9854a85 100644
--- a/R-package/R/slice.xgb.DMatrix.R
+++ b/R-package/R/slice.xgb.DMatrix.R
@@ -34,8 +34,8 @@ setMethod("slice", signature = "xgb.DMatrix",
               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 2c7c74fc3..fa2d6524c 100644
--- a/R-package/R/utils.R
+++ b/R-package/R/utils.R
@@ -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")
@@ -251,7 +251,7 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
       # 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]])
       }
@@ -310,7 +310,7 @@ 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)
+    cuts <- floor(length(y) / k)
     if(cuts < 2) cuts <- 2
     if(cuts > 5) cuts <- 5
     y <- cut(y,
diff --git a/R-package/R/xgb.cv.R b/R-package/R/xgb.cv.R
index af79bde4e..7122f2480 100644
--- a/R-package/R/xgb.cv.R
+++ b/R-package/R/xgb.cv.R
@@ -130,7 +130,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
     # 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$eval_metric)=='function') {
+        if (class(params$eval_metric) == 'function') {
             feval <- params$eval_metric
             params[['eval_metric']] <- NULL
         }
@@ -158,7 +158,7 @@ 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.')
     }
   
@@ -187,19 +187,19 @@ 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)) {
                 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
diff --git a/R-package/R/xgb.importance.R b/R-package/R/xgb.importance.R
index 14c5bbd44..8800c4c22 100644
--- a/R-package/R/xgb.importance.R
+++ b/R-package/R/xgb.importance.R
@@ -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.")
   }
 
@@ -110,7 +110,7 @@ xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = N
       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 b0f5ee279..281806d16 100644
--- a/R-package/R/xgb.model.dt.tree.R
+++ b/R-package/R/xgb.model.dt.tree.R
@@ -86,7 +86,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model
     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
 
@@ -96,15 +96,15 @@ xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model
 
   allTrees <- data.table()
 
-  anynumber_regex<-"[-+]?[0-9]*\\.?[0-9]+([eE][-+]?[0-9]+)?"
+  anynumber_regex <- "[-+]?[0-9]*\\.?[0-9]+([eE][-+]?[0-9]+)?"
   for(i in 1:n_round){
   
-    tree <- text[(position[i]+1):(position[i+1]-1)]
+    tree <- text[(position[i] + 1):(position[i + 1] - 1)]
   
     # avoid tree made of a leaf only (no split)
-    if(length(tree) <2) next
+    if(length(tree) < 2) next
   
-    treeID <- i-1
+    treeID <- i - 1
   
     notLeaf <- str_match(tree, "leaf") %>% is.na
     leaf <- notLeaf %>% not %>% tree[.]
@@ -128,7 +128,7 @@ 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)
   }
diff --git a/R-package/R/xgb.plot.importance.R b/R-package/R/xgb.plot.importance.R
index f126dfe46..d469005dd 100644
--- a/R-package/R/xgb.plot.importance.R
+++ b/R-package/R/xgb.plot.importance.R
@@ -44,9 +44,9 @@ xgb.plot.importance <- function(importance_matrix = NULL, numberOfClusters = c(1
   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$cluster %>% as.character]
+  importance_matrix[,"Cluster" := clusters$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() )
+  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)  
 }
diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index 4bf1d36f6..7bb7bbf87 100644
--- a/R-package/R/xgb.train.R
+++ b/R-package/R/xgb.train.R
@@ -151,14 +151,14 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
   if (!is.null(params$objective) && !is.null(obj))
     stop("xgb.train: cannot assign two different objectives")
   if (!is.null(params$objective))
-    if (class(params$objective)=='function') {
+    if (class(params$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$eval_metric)=='function') {
+    if (class(params$eval_metric) == 'function') {
       feval <- params$eval_metric
       params$eval_metric <- NULL
     }
@@ -188,7 +188,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
     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.')
   }
   
@@ -200,17 +200,17 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
     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)) {
+        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
diff --git a/R-package/tests/testthat/test_basic.R b/R-package/tests/testthat/test_basic.R
index 88bd905ca..2e4e54902 100644
--- a/R-package/tests/testthat/test_basic.R
+++ b/R-package/tests/testthat/test_basic.R
@@ -18,7 +18,7 @@ test_that("early stopping", {
   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)
+  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)
diff --git a/R-package/tests/testthat/test_custom_objective.R b/R-package/tests/testthat/test_custom_objective.R
index 9fcbeca4d..6fd9c6d6d 100644
--- a/R-package/tests/testthat/test_custom_objective.R
+++ b/R-package/tests/testthat/test_custom_objective.R
@@ -13,14 +13,14 @@ test_that("custom objective works", {
   
   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))
   }
   
@@ -34,13 +34,13 @@ test_that("custom objective works", {
   
   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 9cef61c49..668c16c5d 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_lint.R b/R-package/tests/testthat/test_lint.R
index 2f2a07d54..38d1b0ec0 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 5d3d78e27..acf2916bc 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