From 2ad031be67c7a0f0c4895c084c891330a9ec935e Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 11 Jul 2016 14:31:11 -0700
Subject: [SPARKR][DOC] SparkR ML user guides update for 2.0

## What changes were proposed in this pull request?
* Update SparkR ML section to make them consistent with SparkR API docs.
* Since #13972 adds labelling support for the ```include_example``` Jekyll plugin, so that we can split the single ```ml.R``` example file into multiple line blocks with different labels, and include them in different algorithms/models in the generated HTML page.

## How was this patch tested?
Only docs update, manually check the generated docs.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #14011 from yanboliang/r-user-guide-update.
---
 examples/src/main/r/ml.R | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

(limited to 'examples')

diff --git a/examples/src/main/r/ml.R b/examples/src/main/r/ml.R
index 65242e68b3..a8a1274ac9 100644
--- a/examples/src/main/r/ml.R
+++ b/examples/src/main/r/ml.R
@@ -24,9 +24,8 @@ library(SparkR)
 # Initialize SparkSession
 sparkR.session(appName = "SparkR-ML-example")
 
-# $example on$
 ############################ spark.glm and glm ##############################################
-
+# $example on:glm$
 irisDF <- suppressWarnings(createDataFrame(iris))
 # Fit a generalized linear model of family "gaussian" with spark.glm
 gaussianDF <- irisDF
@@ -55,8 +54,9 @@ 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)
 
@@ -72,9 +72,9 @@ 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])
@@ -88,9 +88,9 @@ 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
@@ -107,9 +107,9 @@ 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
@@ -120,7 +120,7 @@ gaussianGLM <- spark.glm(gaussianDF, Sepal_Length ~ Sepal_Width + Species, famil
 modelPath <- tempfile(pattern = "ml", fileext = ".tmp")
 write.ml(gaussianGLM, modelPath)
 gaussianGLM2 <- read.ml(modelPath)
-# $example off$
+
 # Check model summary
 summary(gaussianGLM2)
 
@@ -129,7 +129,7 @@ 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
-- 
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