[SPARK-18325][SPARKR][ML] SparkR ML wrappers example code and user guide

## What changes were proposed in this pull request?
* Add all R examples for ML wrappers which were added during 2.1 release cycle.
* Split the whole ```ml.R``` example file into individual example for each algorithm, which will be convenient for users to rerun them.
* Add corresponding examples to ML user guide.
* Update ML section of SparkR user guide.

Note: MLlib Scala/Java/Python examples will be consistent, however, SparkR examples may different from them, since R users may use the algorithms in a different way, for example, using R ```formula``` to specify ```featuresCol``` and ```labelCol```.

## How was this patch tested?
Run all examples manually.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #16148 from yanboliang/spark-18325.

15 files changed, 701 insertions, 148 deletions

diff --git a/examples/src/main/r/ml.R b/examples/src/main/r/ml.R
deleted file mode 100644
index a8a1274ac9..0000000000
--- a/examples/src/main/r/ml.R
+++ /dev/null
@@ -1,148 +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.
-#
-
-# To run this example use
-# ./bin/spark-submit examples/src/main/r/ml.R
-
-# Load SparkR library into your R session
-library(SparkR)
-
-# Initialize SparkSession
-sparkR.session(appName = "SparkR-ML-example")
-
-############################ spark.glm and glm ##############################################
-# $example on:glm$
-irisDF <- suppressWarnings(createDataFrame(iris))
-# Fit a generalized linear model of family "gaussian" with spark.glm
-gaussianDF <- irisDF
-gaussianTestDF <- irisDF
-gaussianGLM <- spark.glm(gaussianDF, Sepal_Length ~ Sepal_Width + Species, family = "gaussian")
-
-# Model summary
-summary(gaussianGLM)
-
-# Prediction
-gaussianPredictions <- predict(gaussianGLM, gaussianTestDF)
-showDF(gaussianPredictions)
-
-# Fit a generalized linear model with glm (R-compliant)
-gaussianGLM2 <- glm(Sepal_Length ~ Sepal_Width + Species, gaussianDF, family = "gaussian")
-summary(gaussianGLM2)
-
-# Fit a generalized linear model of family "binomial" with spark.glm
-binomialDF <- filter(irisDF, irisDF$Species != "setosa")
-binomialTestDF <- binomialDF
-binomialGLM <- spark.glm(binomialDF, Species ~ Sepal_Length + Sepal_Width, family = "binomial")
-
-# Model summary
-summary(binomialGLM)
-
-# Prediction
-binomialPredictions <- predict(binomialGLM, binomialTestDF)
-showDF(binomialPredictions)
-# $example off:glm$
-############################ spark.survreg ##############################################
-# $example on:survreg$
-# Use the ovarian dataset available in R survival package
-library(survival)
-
-# Fit an accelerated failure time (AFT) survival regression model with spark.survreg
-ovarianDF <- suppressWarnings(createDataFrame(ovarian))
-aftDF <- ovarianDF
-aftTestDF <- ovarianDF
-aftModel <- spark.survreg(aftDF, Surv(futime, fustat) ~ ecog_ps + rx)
-
-# Model summary
-summary(aftModel)
-
-# Prediction
-aftPredictions <- predict(aftModel, aftTestDF)
-showDF(aftPredictions)
-# $example off:survreg$
-############################ spark.naiveBayes ##############################################
-# $example on:naiveBayes$
-# Fit a Bernoulli naive Bayes model with spark.naiveBayes
-titanic <- as.data.frame(Titanic)
-titanicDF <- createDataFrame(titanic[titanic$Freq > 0, -5])
-nbDF <- titanicDF
-nbTestDF <- titanicDF
-nbModel <- spark.naiveBayes(nbDF, Survived ~ Class + Sex + Age)
-
-# Model summary
-summary(nbModel)
-
-# Prediction
-nbPredictions <- predict(nbModel, nbTestDF)
-showDF(nbPredictions)
-# $example off:naiveBayes$
-############################ spark.kmeans ##############################################
-# $example on:kmeans$
-# Fit a k-means model with spark.kmeans
-irisDF <- suppressWarnings(createDataFrame(iris))
-kmeansDF <- irisDF
-kmeansTestDF <- irisDF
-kmeansModel <- spark.kmeans(kmeansDF, ~ Sepal_Length + Sepal_Width + Petal_Length + Petal_Width,
-                            k = 3)
-
-# Model summary
-summary(kmeansModel)
-
-# Get fitted result from the k-means model
-showDF(fitted(kmeansModel))
-
-# Prediction
-kmeansPredictions <- predict(kmeansModel, kmeansTestDF)
-showDF(kmeansPredictions)
-# $example off:kmeans$
-############################ model read/write ##############################################
-# $example on:read_write$
-irisDF <- suppressWarnings(createDataFrame(iris))
-# Fit a generalized linear model of family "gaussian" with spark.glm
-gaussianDF <- irisDF
-gaussianTestDF <- irisDF
-gaussianGLM <- spark.glm(gaussianDF, Sepal_Length ~ Sepal_Width + Species, family = "gaussian")
-
-# Save and then load a fitted MLlib model
-modelPath <- tempfile(pattern = "ml", fileext = ".tmp")
-write.ml(gaussianGLM, modelPath)
-gaussianGLM2 <- read.ml(modelPath)
-
-# Check model summary
-summary(gaussianGLM2)
-
-# Check model prediction
-gaussianPredictions <- predict(gaussianGLM2, gaussianTestDF)
-showDF(gaussianPredictions)
-
-unlink(modelPath)
-# $example off:read_write$
-############################ fit models with spark.lapply #####################################
-
-# Perform distributed training of multiple models with spark.lapply
-families <- c("gaussian", "poisson")
-train <- function(family) {
-  model <- glm(Sepal.Length ~ Sepal.Width + Species, iris, family = family)
-  summary(model)
-}
-model.summaries <- spark.lapply(families, train)
-
-# Print the summary of each model
-print(model.summaries)
-
-
-# Stop the SparkSession now
-sparkR.session.stop()
diff --git a/examples/src/main/r/ml/als.R b/examples/src/main/r/ml/als.R
new file mode 100644
index 0000000000..383bbba190
--- /dev/null
+++ b/examples/src/main/r/ml/als.R
@@ -0,0 +1,45 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/als.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-als-example")
+
+# $example on$
+# Load training data
+data <- list(list(0, 0, 4.0), list(0, 1, 2.0), list(1, 1, 3.0),
+             list(1, 2, 4.0), list(2, 1, 1.0), list(2, 2, 5.0))
+df <- createDataFrame(data, c("userId", "movieId", "rating"))
+training <- df
+test <- df
+
+# Fit a recommendation model using ALS with spark.als
+model <- spark.als(training, maxIter = 5, regParam = 0.01, userCol = "userId",
+                   itemCol = "movieId", ratingCol = "rating")
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+showDF(predictions)
+# $example off$
diff --git a/examples/src/main/r/ml/gaussianMixture.R b/examples/src/main/r/ml/gaussianMixture.R
new file mode 100644
index 0000000000..54b69acc83
--- /dev/null
+++ b/examples/src/main/r/ml/gaussianMixture.R
@@ -0,0 +1,42 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/gaussianMixture.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-gaussianMixture-example")
+
+# $example on$
+# Load training data
+df <- read.df("data/mllib/sample_kmeans_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# Fit a gaussian mixture clustering model with spark.gaussianMixture
+model <- spark.gaussianMixture(training, ~ features, k = 2)
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+showDF(predictions)
+# $example off$
diff --git a/examples/src/main/r/ml/gbt.R b/examples/src/main/r/ml/gbt.R
new file mode 100644
index 0000000000..be16c2aa66
--- /dev/null
+++ b/examples/src/main/r/ml/gbt.R
@@ -0,0 +1,63 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/gbt.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-gbt-example")
+
+# GBT classification model
+
+# $example on:classification$
+# Load training data
+df <- read.df("data/mllib/sample_libsvm_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# Fit a GBT classification model with spark.gbt
+model <- spark.gbt(training, label ~ features, "classification", maxIter = 10)
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+showDF(predictions)
+# $example off:classification$
+
+# GBT regression model
+
+# $example on:regression$
+# Load training data
+df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# Fit a GBT regression model with spark.gbt
+model <- spark.gbt(training, label ~ features, "regression", maxIter = 10)
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+showDF(predictions)
+# $example off:regression$
diff --git a/examples/src/main/r/ml/glm.R b/examples/src/main/r/ml/glm.R
new file mode 100644
index 0000000000..599071790a
--- /dev/null
+++ b/examples/src/main/r/ml/glm.R
@@ -0,0 +1,57 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/glm.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-glm-example")
+
+# $example on$
+irisDF <- suppressWarnings(createDataFrame(iris))
+# Fit a generalized linear model of family "gaussian" with spark.glm
+gaussianDF <- irisDF
+gaussianTestDF <- irisDF
+gaussianGLM <- spark.glm(gaussianDF, Sepal_Length ~ Sepal_Width + Species, family = "gaussian")
+
+# Model summary
+summary(gaussianGLM)
+
+# Prediction
+gaussianPredictions <- predict(gaussianGLM, gaussianTestDF)
+showDF(gaussianPredictions)
+
+# Fit a generalized linear model with glm (R-compliant)
+gaussianGLM2 <- glm(Sepal_Length ~ Sepal_Width + Species, gaussianDF, family = "gaussian")
+summary(gaussianGLM2)
+
+# Fit a generalized linear model of family "binomial" with spark.glm
+# Note: Filter out "setosa" from label column (two labels left) to match "binomial" family.
+binomialDF <- filter(irisDF, irisDF$Species != "setosa")
+binomialTestDF <- binomialDF
+binomialGLM <- spark.glm(binomialDF, Species ~ Sepal_Length + Sepal_Width, family = "binomial")
+
+# Model summary
+summary(binomialGLM)
+
+# Prediction
+binomialPredictions <- predict(binomialGLM, binomialTestDF)
+showDF(binomialPredictions)
+# $example off$
diff --git a/examples/src/main/r/ml/isoreg.R b/examples/src/main/r/ml/isoreg.R
new file mode 100644
index 0000000000..75dce97ed9
--- /dev/null
+++ b/examples/src/main/r/ml/isoreg.R
@@ -0,0 +1,42 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/isoreg.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-isoreg-example")
+
+# $example on$
+# Load training data
+df <- read.df("data/mllib/sample_isotonic_regression_libsvm_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# Fit an isotonic regression model with spark.isoreg
+model <- spark.isoreg(training, label ~ features, isotonic = FALSE)
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+showDF(predictions)
+# $example off$
diff --git a/examples/src/main/r/ml/kmeans.R b/examples/src/main/r/ml/kmeans.R
new file mode 100644
index 0000000000..043b21b038
--- /dev/null
+++ b/examples/src/main/r/ml/kmeans.R
@@ -0,0 +1,44 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/kmeans.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-kmeans-example")
+
+# $example on$
+# Fit a k-means model with spark.kmeans
+irisDF <- suppressWarnings(createDataFrame(iris))
+kmeansDF <- irisDF
+kmeansTestDF <- irisDF
+kmeansModel <- spark.kmeans(kmeansDF, ~ Sepal_Length + Sepal_Width + Petal_Length + Petal_Width,
+                            k = 3)
+
+# Model summary
+summary(kmeansModel)
+
+# Get fitted result from the k-means model
+showDF(fitted(kmeansModel))
+
+# Prediction
+kmeansPredictions <- predict(kmeansModel, kmeansTestDF)
+showDF(kmeansPredictions)
+# $example off$
diff --git a/examples/src/main/r/ml/kstest.R b/examples/src/main/r/ml/kstest.R
new file mode 100644
index 0000000000..12625f7d3e
--- /dev/null
+++ b/examples/src/main/r/ml/kstest.R
@@ -0,0 +1,39 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/kstest.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-kstest-example")
+
+# $example on$
+# Load training data
+data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25, -1, -0.5))
+df <- createDataFrame(data)
+training <- df
+test <- df
+
+# Conduct the two-sided Kolmogorov-Smirnov (KS) test with spark.kstest
+model <- spark.kstest(df, "test", "norm")
+
+# Model summary
+summary(model)
+# $example off$
diff --git a/examples/src/main/r/ml/lda.R b/examples/src/main/r/ml/lda.R
new file mode 100644
index 0000000000..7b187d155a
--- /dev/null
+++ b/examples/src/main/r/ml/lda.R
@@ -0,0 +1,46 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/lda.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-lda-example")
+
+# $example on$
+# Load training data
+df <- read.df("data/mllib/sample_lda_libsvm_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# Fit a latent dirichlet allocation model with spark.lda
+model <- spark.lda(training, k = 10, maxIter = 10)
+
+# Model summary
+summary(model)
+
+# Posterior probabilities
+posterior <- spark.posterior(model, test)
+showDF(posterior)
+
+# The log perplexity of the LDA model
+logPerplexity <- spark.perplexity(model, test)
+print(paste0("The upper bound bound on perplexity: ", logPerplexity))
+# $example off$
diff --git a/examples/src/main/r/ml/logit.R b/examples/src/main/r/ml/logit.R
new file mode 100644
index 0000000000..a2ac882ed0
--- /dev/null
+++ b/examples/src/main/r/ml/logit.R
@@ -0,0 +1,63 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/logit.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-logit-example")
+
+# Binomial logistic regression
+
+# $example on:binomial$
+# Load training data
+df <- read.df("data/mllib/sample_libsvm_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# Fit an binomial logistic regression model with spark.logit
+model <- spark.logit(training, label ~ features, maxIter = 10, regParam = 0.3, elasticNetParam = 0.8)
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+showDF(predictions)
+# $example off:binomial$
+
+# Multinomial logistic regression
+
+# $example on:multinomial$
+# Load training data
+df <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# Fit a multinomial logistic regression model with spark.logit
+model <- spark.logit(training, label ~ features, maxIter = 10, regParam = 0.3, elasticNetParam = 0.8)
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+showDF(predictions)
+# $example off:multinomial$
diff --git a/examples/src/main/r/ml/ml.R b/examples/src/main/r/ml/ml.R
new file mode 100644
index 0000000000..d601590c22
--- /dev/null
+++ b/examples/src/main/r/ml/ml.R
@@ -0,0 +1,65 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/ml.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-example")
+
+############################ model read/write ##############################################
+# $example on:read_write$
+irisDF <- suppressWarnings(createDataFrame(iris))
+# Fit a generalized linear model of family "gaussian" with spark.glm
+gaussianDF <- irisDF
+gaussianTestDF <- irisDF
+gaussianGLM <- spark.glm(gaussianDF, Sepal_Length ~ Sepal_Width + Species, family = "gaussian")
+
+# Save and then load a fitted MLlib model
+modelPath <- tempfile(pattern = "ml", fileext = ".tmp")
+write.ml(gaussianGLM, modelPath)
+gaussianGLM2 <- read.ml(modelPath)
+
+# Check model summary
+summary(gaussianGLM2)
+
+# Check model prediction
+gaussianPredictions <- predict(gaussianGLM2, gaussianTestDF)
+showDF(gaussianPredictions)
+
+unlink(modelPath)
+# $example off:read_write$
+
+############################ fit models with spark.lapply #####################################
+# Perform distributed training of multiple models with spark.lapply
+costs <- exp(seq(from = log(1), to = log(1000), length.out = 5))
+train <- function(cost) {
+  stopifnot(requireNamespace("e1071", quietly = TRUE))
+  model <- e1071::svm(Species ~ ., data = iris, cost = cost)
+  summary(model)
+}
+
+model.summaries <- spark.lapply(costs, train)
+
+# Print the summary of each model
+print(model.summaries)
+
+# Stop the SparkSession now
+sparkR.session.stop()
diff --git a/examples/src/main/r/ml/mlp.R b/examples/src/main/r/ml/mlp.R
new file mode 100644
index 0000000000..d28fc069bd
--- /dev/null
+++ b/examples/src/main/r/ml/mlp.R
@@ -0,0 +1,48 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/mlp.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-mlp-example")
+
+# $example on$
+# Load training data
+df <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# specify layers for the neural network:
+# input layer of size 4 (features), two intermediate of size 5 and 4
+# and output of size 3 (classes)
+layers = c(4, 5, 4, 3)
+
+# Fit a multi-layer perceptron neural network model with spark.mlp
+model <- spark.mlp(training, label ~ features, maxIter = 100,
+                   layers = layers, blockSize = 128, seed = 1234)
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+showDF(predictions)
+# $example off$
diff --git a/examples/src/main/r/ml/naiveBayes.R b/examples/src/main/r/ml/naiveBayes.R
new file mode 100644
index 0000000000..9c416599b4
--- /dev/null
+++ b/examples/src/main/r/ml/naiveBayes.R
@@ -0,0 +1,41 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/naiveBayes.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-naiveBayes-example")
+
+# $example on$
+# Fit a Bernoulli naive Bayes model with spark.naiveBayes
+titanic <- as.data.frame(Titanic)
+titanicDF <- createDataFrame(titanic[titanic$Freq > 0, -5])
+nbDF <- titanicDF
+nbTestDF <- titanicDF
+nbModel <- spark.naiveBayes(nbDF, Survived ~ Class + Sex + Age)
+
+# Model summary
+summary(nbModel)
+
+# Prediction
+nbPredictions <- predict(nbModel, nbTestDF)
+showDF(nbPredictions)
+# $example off$
diff --git a/examples/src/main/r/ml/randomForest.R b/examples/src/main/r/ml/randomForest.R
new file mode 100644
index 0000000000..d1b96b62a0
--- /dev/null
+++ b/examples/src/main/r/ml/randomForest.R
@@ -0,0 +1,63 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/randomForest.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-randomForest-example")
+
+# Random forest classification model
+
+# $example on:classification$
+# Load training data
+df <- read.df("data/mllib/sample_libsvm_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# Fit a random forest classification model with spark.randomForest
+model <- spark.randomForest(training, label ~ features, "classification", numTrees = 10)
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+showDF(predictions)
+# $example off:classification$
+
+# Random forest regression model
+
+# $example on:regression$
+# Load training data
+df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")
+training <- df
+test <- df
+
+# Fit a random forest regression model with spark.randomForest
+model <- spark.randomForest(training, label ~ features, "regression", numTrees = 10)
+
+# Model summary
+summary(model)
+
+# Prediction
+predictions <- predict(model, test)
+showDF(predictions)
+# $example off:regression$
diff --git a/examples/src/main/r/ml/survreg.R b/examples/src/main/r/ml/survreg.R
new file mode 100644
index 0000000000..f728b8b5d8
--- /dev/null
+++ b/examples/src/main/r/ml/survreg.R
@@ -0,0 +1,43 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/survreg.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-survreg-example")
+
+# $example on$
+# Use the ovarian dataset available in R survival package
+library(survival)
+
+# Fit an accelerated failure time (AFT) survival regression model with spark.survreg
+ovarianDF <- suppressWarnings(createDataFrame(ovarian))
+aftDF <- ovarianDF
+aftTestDF <- ovarianDF
+aftModel <- spark.survreg(aftDF, Surv(futime, fustat) ~ ecog_ps + rx)
+
+# Model summary
+summary(aftModel)
+
+# Prediction
+aftPredictions <- predict(aftModel, aftTestDF)
+showDF(aftPredictions)
+# $example off$