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diff --git a/R/pkg/inst/tests/test_mllib.R b/R/pkg/inst/tests/test_mllib.R
deleted file mode 100644
index e0667e5e22..0000000000
--- a/R/pkg/inst/tests/test_mllib.R
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@@ -1,115 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-library(testthat)
-
-context("MLlib functions")
-
-# Tests for MLlib functions in SparkR
-
-sc <- sparkR.init()
-
-sqlContext <- sparkRSQL.init(sc)
-
-test_that("glm and predict", {
-  training <- createDataFrame(sqlContext, iris)
-  test <- select(training, "Sepal_Length")
-  model <- glm(Sepal_Width ~ Sepal_Length, training, family = "gaussian")
-  prediction <- predict(model, test)
-  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
-
-  # Test stats::predict is working
-  x <- rnorm(15)
-  y <- x + rnorm(15)
-  expect_equal(length(predict(lm(y ~ x))), 15)
-})
-
-test_that("glm should work with long formula", {
-  training <- createDataFrame(sqlContext, iris)
-  training$LongLongLongLongLongName <- training$Sepal_Width
-  training$VeryLongLongLongLonLongName <- training$Sepal_Length
-  training$AnotherLongLongLongLongName <- training$Species
-  model <- glm(LongLongLongLongLongName ~ VeryLongLongLongLonLongName + AnotherLongLongLongLongName,
-               data = training)
-  vals <- collect(select(predict(model, training), "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-})
-
-test_that("predictions match with native glm", {
-  training <- createDataFrame(sqlContext, iris)
-  model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
-  vals <- collect(select(predict(model, training), "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-})
-
-test_that("dot minus and intercept vs native glm", {
-  training <- createDataFrame(sqlContext, iris)
-  model <- glm(Sepal_Width ~ . - Species + 0, data = training)
-  vals <- collect(select(predict(model, training), "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-})
-
-test_that("feature interaction vs native glm", {
-  training <- createDataFrame(sqlContext, iris)
-  model <- glm(Sepal_Width ~ Species:Sepal_Length, data = training)
-  vals <- collect(select(predict(model, training), "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-})
-
-test_that("summary coefficients match with native glm", {
-  training <- createDataFrame(sqlContext, iris)
-  stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training, solver = "normal"))
-  coefs <- unlist(stats$coefficients)
-  devianceResiduals <- unlist(stats$devianceResiduals)
-
-  rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
-  rCoefs <- unlist(rStats$coefficients)
-  rDevianceResiduals <- c(-0.95096, 0.72918)
-
-  expect_true(all(abs(rCoefs - coefs) < 1e-5))
-  expect_true(all(abs(rDevianceResiduals - devianceResiduals) < 1e-5))
-  expect_true(all(
-    rownames(stats$coefficients) ==
-    c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
-})
-
-test_that("summary coefficients match with native glm of family 'binomial'", {
-  df <- createDataFrame(sqlContext, iris)
-  training <- filter(df, df$Species != "setosa")
-  stats <- summary(glm(Species ~ Sepal_Length + Sepal_Width, data = training,
-    family = "binomial"))
-  coefs <- as.vector(stats$coefficients[,1])
-
-  rTraining <- iris[iris$Species %in% c("versicolor","virginica"),]
-  rCoefs <- as.vector(coef(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
-    family = binomial(link = "logit"))))
-
-  expect_true(all(abs(rCoefs - coefs) < 1e-4))
-  expect_true(all(
-    rownames(stats$coefficients) ==
-    c("(Intercept)", "Sepal_Length", "Sepal_Width")))
-})
-
-test_that("summary works on base GLM models", {
-  baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris)
-  baseSummary <- summary(baseModel)
-  expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4)
-})