[SPARK-19639][SPARKR][Example]:Add spark.svmLinear example and update vignettes

R/pkg/vignettes/sparkr-vignettes.Rmd

@@ -469,6 +469,8 @@ SparkR supports the following machine learning models and algorithms.
 
 #### Classification
 
+* Linear Support Vector Machine (SVM) Classifier
+
 * Logistic Regression
 
 * Multilayer Perceptron (MLP)
@@ -532,6 +534,26 @@ head(carsDF_test)
 
 ### Models and Algorithms
 
+#### Linear Support Vector Machine (SVM) Classifier
+
+[Linear Support Vector Machine (SVM)](https://en.wikipedia.org/wiki/Support_vector_machine#Linear_SVM) classifier is an SVM classifier with linear kernels.
+This is a binary classifier. We use a simple example to show how to use `spark.svmLinear`
+for binary classification.
+
+```{r}
+# load training data and create a DataFrame
+t <- as.data.frame(Titanic)
+training <- createDataFrame(t)
+# fit a Linear SVM classifier model
+model <- spark.svmLinear(training,  Survived ~ ., regParam = 0.01, maxIter = 10)
+summary(model)
+```
+
+Predict values on training data
+```{r}
+prediction <- predict(model, training)
+```
+
 #### Logistic Regression
 
 [Logistic regression](https://en.wikipedia.org/wiki/Logistic_regression) is a widely-used model when the response is categorical. It can be seen as a special case of the [Generalized Linear Predictive Model](https://en.wikipedia.org/wiki/Generalized_linear_model).

examples/src/main/r/ml/survreg.R

@@ -43,3 +43,4 @@ head(aftPredictions)
 # $example off$
 
 sparkR.session.stop()
+

examples/src/main/r/ml/svmLinear.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/svmLinear.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-svmLinear-example")
+
+# $example on$
+# load training data
+t <- as.data.frame(Titanic)
+training <- createDataFrame(t)
+
+# fit Linear SVM model
+model <- spark.svmLinear(training,  Survived ~ ., regParam = 0.01, maxIter = 10)
+
+# Model summary
+summary(model)
+
+# Prediction
+prediction <- predict(model, training)
+showDF(prediction)
+# $example off$
+sparkR.session.stop()