diff --git a/R-package/R/callbacks.R b/R-package/R/callbacks.R
index 0ce732e8a..80a6c66ca 100644
--- a/R-package/R/callbacks.R
+++ b/R-package/R/callbacks.R
@@ -127,7 +127,7 @@ cb.evaluation.log <- function() {
       # rearrange col order from _mean,_mean,...,_std,_std,...
       # to be _mean,_std,_mean,_std,...
       len <- length(mnames)
-      means <- mnames[1:(len/2)]
+      means <- mnames[seq_len(len/2)]
       stds <- mnames[(len/2 + 1):len]
       cnames <- numeric(len)
       cnames[c(TRUE, FALSE)] <- means
@@ -320,9 +320,9 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
     
     metric_name <<- eval_names[metric_idx]
     
-    # maximixe is usually NULL when not set in xgb.train and built-in metrics
+    # maximize is usually NULL when not set in xgb.train and built-in metrics
     if (is.null(maximize))
-      maximize <<- ifelse(grepl('(_auc|_map|_ndcg)', metric_name), TRUE, FALSE)
+      maximize <<- grepl('(_auc|_map|_ndcg)', metric_name)
 
     if (verbose && NVL(env$rank, 0) == 0)
       cat("Will train until ", metric_name, " hasn't improved in ", 
@@ -597,7 +597,7 @@ has.callbacks <- function(cb_list, query_names) {
     return(FALSE)
   if (!is.list(cb_list) ||
       any(sapply(cb_list, class) != 'function')) {
-    stop('`cb_list`` must be a list of callback functions')
+    stop('`cb_list` must be a list of callback functions')
   }
   cb_names <- callback.names(cb_list)
   if (!is.character(cb_names) ||
diff --git a/R-package/R/utils.R b/R-package/R/utils.R
index ce93b8235..3a4b59f7f 100644
--- a/R-package/R/utils.R
+++ b/R-package/R/utils.R
@@ -185,7 +185,7 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
          "\tConsider providing pre-computed CV-folds through the 'folds=' parameter.\n")
   }
   # shuffle
-  rnd_idx <- sample(1:nrows)
+  rnd_idx <- sample.int(nrows)
   if (stratified &&
       length(label) == length(rnd_idx)) {
     y <- label[rnd_idx]
@@ -211,9 +211,9 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
     # make simple non-stratified folds
     kstep <- length(rnd_idx) %/% nfold
     folds <- list()
-    for (i in 1:(nfold - 1)) {
-      folds[[i]] <- rnd_idx[1:kstep]
-      rnd_idx <- rnd_idx[-(1:kstep)]
+    for (i in seq_len(nfold - 1)) {
+      folds[[i]] <- rnd_idx[seq_len(kstep)]
+      rnd_idx <- rnd_idx[-seq_len(kstep)]
     }
     folds[[nfold]] <- rnd_idx
   }
@@ -254,15 +254,15 @@ xgb.createFolds <- function(y, k = 10)
     ## For each class, balance the fold allocation as far
     ## as possible, then resample the remainder.
     ## The final assignment of folds is also randomized.
-    for (i in 1:length(numInClass)) {
+    for (i in seq_along(numInClass)) {
       ## create a vector of integers from 1:k as many times as possible without
       ## going over the number of samples in the class. Note that if the number
       ## of samples in a class is less than k, nothing is producd here.
-      seqVector <- rep(1:k, numInClass[i] %/% k)
+      seqVector <- rep(seq_len(k), numInClass[i] %/% k)
       ## add enough random integers to get  length(seqVector) == numInClass[i]
-      if (numInClass[i] %% k > 0) seqVector <- c(seqVector, sample(1:k, numInClass[i] %% k))
+      if (numInClass[i] %% k > 0) seqVector <- c(seqVector, sample.int(k, numInClass[i] %% k))
       ## shuffle the integers for fold assignment and assign to this classes's data
-      foldVector[which(y == dimnames(numInClass)$y[i])] <- sample(seqVector)
+      foldVector[y == dimnames(numInClass)$y[i]] <- sample(seqVector)
     }
   } else {
     foldVector <- seq(along = y)
diff --git a/R-package/R/xgb.Booster.R b/R-package/R/xgb.Booster.R
index 8e72486bd..3bdc8a58b 100644
--- a/R-package/R/xgb.Booster.R
+++ b/R-package/R/xgb.Booster.R
@@ -2,7 +2,7 @@
 # internal utility function
 xgb.Booster.handle <- function(params = list(), cachelist = list(), modelfile = NULL) {
   if (typeof(cachelist) != "list" ||
-      !all(sapply(cachelist, inherits, 'xgb.DMatrix'))) {
+      !all(vapply(cachelist, inherits, logical(1), what = 'xgb.DMatrix'))) {
     stop("cachelist must be a list of xgb.DMatrix objects")
   }
 
@@ -296,9 +296,9 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
     } else if (n_group == 1) {
       matrix(ret, nrow = n_row, byrow = TRUE, dimnames = dnames)
     } else {
-      grp_mask <- rep(1:n_col1, n_row) +
-        rep((0:(n_row - 1)) * n_col1 * n_group, each = n_col1)
-      lapply(1:n_group, function(g) {
+      grp_mask <- rep(seq_len(n_col1), n_row) +
+        rep((seq_len(n_row) - 1) * n_col1 * n_group, each = n_col1)
+      lapply(seq_len(n_group), function(g) {
         matrix(ret[grp_mask + n_col1 * (g - 1)], nrow = n_row, byrow = TRUE, dimnames = dnames)
       })
     }
diff --git a/R-package/R/xgb.DMatrix.R b/R-package/R/xgb.DMatrix.R
index 8b589f0d4..cb975cf06 100644
--- a/R-package/R/xgb.DMatrix.R
+++ b/R-package/R/xgb.DMatrix.R
@@ -305,7 +305,7 @@ slice.xgb.DMatrix <- function(object, idxset, ...) {
   ind <- which(len == nr)
   if (length(ind) > 0) {
     nms <- names(attr_list)[ind]
-    for (i in 1:length(ind)) {
+    for (i in seq_along(ind)) {
       attr(ret, nms[i]) <- attr(object, nms[i])[idxset]
     }
   }
diff --git a/R-package/R/xgb.cv.R b/R-package/R/xgb.cv.R
index 68d50e0e3..4b6a699d6 100644
--- a/R-package/R/xgb.cv.R
+++ b/R-package/R/xgb.cv.R
@@ -178,7 +178,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   
   # create the booster-folds
   dall <- xgb.get.DMatrix(data, label, missing)
-  bst_folds <- lapply(1:length(folds), function(k) {
+  bst_folds <- lapply(seq_along(folds), function(k) {
     dtest  <- slice(dall, folds[[k]])
     dtrain <- slice(dall, unlist(folds[-k]))
     handle <- xgb.Booster.handle(params, list(dtrain, dtest))
diff --git a/R-package/R/xgb.importance.R b/R-package/R/xgb.importance.R
index e6cfb1af5..5c08d4d0e 100644
--- a/R-package/R/xgb.importance.R
+++ b/R-package/R/xgb.importance.R
@@ -115,7 +115,7 @@ xgb.importance <- function(feature_names = NULL, model = NULL, trees = NULL,
     } else {
       data.table(Feature = rep(feature_names, each = num_class),
                  Weight = weights,
-                 Class = 0:(num_class - 1))[order(Class, -abs(Weight))]
+                 Class = seq_len(num_class) - 1)[order(Class, -abs(Weight))]
     }
   } else { 
   # tree model
diff --git a/R-package/R/xgb.model.dt.tree.R b/R-package/R/xgb.model.dt.tree.R
index 12706ec55..6a00797fe 100644
--- a/R-package/R/xgb.model.dt.tree.R
+++ b/R-package/R/xgb.model.dt.tree.R
@@ -65,7 +65,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
                               trees = NULL, use_int_id = FALSE, ...){
   check.deprecation(...)
   
-  if (!inherits(model, "xgb.Booster") & !is.character(text)) {
+  if (!inherits(model, "xgb.Booster") && !is.character(text)) {
     stop("Either 'model' must be an object of class xgb.Booster\n",
          "  or 'text' must be a character vector with the result of xgb.dump\n",
          "  (or NULL if 'model' was provided).")
@@ -121,7 +121,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
       # skip some indices with spurious capture groups from anynumber_regex
       xtr <- stri_match_first_regex(t, branch_rx)[, c(2,3,5,6,7,8,10), drop = FALSE]
       xtr[, 3:5] <- add.tree.id(xtr[, 3:5], Tree)
-      lapply(1:ncol(xtr), function(i) xtr[,i])
+      lapply(seq_len(ncol(xtr)), function(i) xtr[,i])
     }]
   # assign feature_names when available
   if (!is.null(feature_names)) {
@@ -136,7 +136,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
   td[isLeaf == TRUE,
      (leaf_cols) := {
       xtr <- stri_match_first_regex(t, leaf_rx)[, c(2,4)]
-      c("Leaf", lapply(1:ncol(xtr), function(i) xtr[,i]))
+      c("Leaf", lapply(seq_len(ncol(xtr)), function(i) xtr[,i]))
     }]
   
   # convert some columns to numeric
diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index 7712e8775..a644055ca 100644
--- a/R-package/R/xgb.train.R
+++ b/R-package/R/xgb.train.R
@@ -251,7 +251,7 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
     stop("second argument dtrain must be xgb.DMatrix")
   if (length(watchlist) > 0) {
     if (typeof(watchlist) != "list" ||
-        !all(sapply(watchlist, inherits, 'xgb.DMatrix')))
+        !all(vapply(watchlist, inherits, logical(1), what = 'xgb.DMatrix')))
       stop("watchlist must be a list of xgb.DMatrix elements")
     evnames <- names(watchlist)
     if (is.null(evnames) || any(evnames == ""))