[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.

R/pkg/R/mllib.R

@@ -55,8 +55,9 @@ setClass("KMeansModel", representation(jobj = "jobj"))
 
 #' Generalized Linear Models
 #'
-#' Fits generalized linear model against a Spark DataFrame. Users can print, make predictions on the
-#' produced model and save the model to the input path.
+#' Fits generalized linear model against a Spark DataFrame.
+#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
 #'
 #' @param data SparkDataFrame for training.
 #' @param formula A symbolic description of the model to be fitted. Currently only a few formula
@@ -270,7 +271,8 @@ setMethod("summary", signature(object = "NaiveBayesModel"),
 #' K-Means Clustering Model
 #'
 #' Fits a k-means clustering model against a Spark DataFrame, similarly to R's kmeans().
-#' Users can print, make predictions on the produced model and save the model to the input path.
+#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
 #'
 #' @param data SparkDataFrame for training
 #' @param formula A symbolic description of the model to be fitted. Currently only a few formula

docs/sparkr.md

@@ -355,32 +355,39 @@ head(teenagers)
 
 # Machine Learning
 
-SparkR supports the following Machine Learning algorithms.
+SparkR supports the following machine learning algorithms currently: `Generalized Linear Model`, `Accelerated Failure Time (AFT) Survival Regression Model`, `Naive Bayes Model` and `KMeans Model`.
+Under the hood, SparkR uses MLlib to train the model.
+Users can call `summary` to print a summary of the fitted model, [predict](api/R/predict.html) to make predictions on new data, and [write.ml](api/R/write.ml.html)/[read.ml](api/R/read.ml.html) to save/load fitted models.
+SparkR supports a subset of the available R formula operators for model fitting, including ‘~’, ‘.’, ‘:’, ‘+’, and ‘-‘.
 
-* Generalized Linear Regression Model [spark.glm()](api/R/spark.glm.html)
-* Naive Bayes [spark.naiveBayes()](api/R/spark.naiveBayes.html)
-* KMeans [spark.kmeans()](api/R/spark.kmeans.html)
-* AFT Survival Regression [spark.survreg()](api/R/spark.survreg.html)
+## Algorithms
 
-[Generalized Linear Regression](api/R/spark.glm.html) can be used to train a model from a specified family. Currently the Gaussian, Binomial, Poisson and Gamma families are supported. We support a subset of the available R formula operators for model fitting, including '~', '.', ':', '+', and '-'.
+### Generalized Linear Model
 
-The [summary()](api/R/summary.html) function gives the summary of a model produced by different algorithms listed above.
-It produces the similar result compared with R summary function.
+[spark.glm()](api/R/spark.glm.html) or [glm()](api/R/glm.html) fits generalized linear model against a Spark DataFrame.
+Currently "gaussian", "binomial", "poisson" and "gamma" families are supported.
+{% include_example glm r/ml.R %}
 
-## Model persistence
+### Accelerated Failure Time (AFT) Survival Regression Model
+
+[spark.survreg()](api/R/spark.survreg.html) fits an accelerated failure time (AFT) survival regression model on a SparkDataFrame.
+Note that the formula of [spark.survreg()](api/R/spark.survreg.html) does not support operator '.' currently.
+{% include_example survreg r/ml.R %}
+
+### Naive Bayes Model
 
-* [write.ml](api/R/write.ml.html) allows users to save a fitted model in a given input path
-* [read.ml](api/R/read.ml.html) allows users to read/load the model which was saved using write.ml in a given path
+[spark.naiveBayes()](api/R/spark.naiveBayes.html) fits a Bernoulli naive Bayes model against a SparkDataFrame. Only categorical data is supported.
+{% include_example naiveBayes r/ml.R %}
 
-Model persistence is supported for all Machine Learning algorithms for all families.
+### KMeans Model
 
-The examples below show how to build several models:
-* GLM using the Gaussian and Binomial model families
-* AFT survival regression model
-* Naive Bayes model
-* K-Means model
+[spark.kmeans()](api/R/spark.kmeans.html) fits a k-means clustering model against a Spark DataFrame, similarly to R's kmeans().
+{% include_example kmeans r/ml.R %}
+
+## Model persistence
 
-{% include_example r/ml.R %}
+The following example shows how to save/load a MLlib model by SparkR.
+{% include_example read_write r/ml.R %}
 
 # R Function Name Conflicts
 

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