diff --git a/R-package/R/xgb.DMatrix.R b/R-package/R/xgb.DMatrix.R
index 9c7ba2684845..9d1d53c12197 100644
--- a/R-package/R/xgb.DMatrix.R
+++ b/R-package/R/xgb.DMatrix.R
@@ -257,8 +257,6 @@ setinfo.xgb.DMatrix <- function(object, name, info, ...) {
     return(TRUE)
   }
   if (name == "weight") {
-    if (length(info) != nrow(object))
-      stop("The length of weights must equal to the number of rows in the input data")
     .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
     return(TRUE)
   }
diff --git a/R-package/tests/testthat/test_ranking.R b/R-package/tests/testthat/test_ranking.R
new file mode 100644
index 000000000000..7a352bea2ec0
--- /dev/null
+++ b/R-package/tests/testthat/test_ranking.R
@@ -0,0 +1,51 @@
+require(xgboost)
+require(Matrix)
+
+context('Learning to rank')
+
+test_that('Test ranking with unweighted data', {
+  X <- sparseMatrix(i = c(2, 3, 7, 9, 12, 15, 17, 18),
+                    j = c(1, 1, 2, 2,  3,  3,  4,  4),
+                    x = rep(1.0, 8), dims = c(20, 4))
+  y <- c(0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0)
+  group <- c(5, 5, 5, 5)
+  dtrain <- xgb.DMatrix(X, label = y, group = group)
+
+  params <- list(eta = 1, tree_method = 'exact', objective = 'rank:pairwise', max_depth = 1,
+                 eval_metric = 'auc', eval_metric = 'aucpr')
+  bst <- xgb.train(params, dtrain, nrounds = 10, watchlist = list(train = dtrain))
+  # Check if the metric is monotone increasing
+  expect_true(all(diff(bst$evaluation_log$train_auc) >= 0))
+  expect_true(all(diff(bst$evaluation_log$train_aucpr) >= 0))
+})
+
+test_that('Test ranking with weighted data', {
+  X <- sparseMatrix(i = c(2, 3, 7, 9, 12, 15, 17, 18),
+                    j = c(1, 1, 2, 2,  3,  3,  4,  4),
+                    x = rep(1.0, 8), dims = c(20, 4))
+  y <- c(0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0)
+  group <- c(5, 5, 5, 5)
+  weight <- c(1.0, 2.0, 3.0, 4.0)
+  dtrain <- xgb.DMatrix(X, label = y, group = group, weight = weight)
+
+  params <- list(eta = 1, tree_method = 'exact', objective = 'rank:pairwise', max_depth = 1,
+                 eval_metric = 'auc', eval_metric = 'aucpr')
+  bst <- xgb.train(params, dtrain, nrounds = 10, watchlist = list(train = dtrain))
+  # Check if the metric is monotone increasing
+  expect_true(all(diff(bst$evaluation_log$train_auc) >= 0))
+  expect_true(all(diff(bst$evaluation_log$train_aucpr) >= 0))
+  for (i in 1:10) {
+    pred <- predict(bst, newdata = dtrain, ntreelimit = i)
+    # is_sorted[i]: is i-th group correctly sorted by the ranking predictor?
+    is_sorted <- lapply(seq(1, 20, by = 5),
+      function (k) {
+        ind <- order(-pred[k:(k + 4)])
+        z <- y[ind + (k - 1)]
+        all(diff(z) <= 0)  # Check if z is monotone decreasing
+      })
+    # Since we give weights 1, 2, 3, 4 to the four query groups,
+    # the ranking predictor will first try to correctly sort the last query group
+    # before correctly sorting other groups.
+    expect_true(all(diff(as.numeric(is_sorted)) >= 0))
+  }
+})