[SPARK-20258][DOC][SPARKR] Fix SparkR logistic regression example in...

[SPARK-20258][DOC][SPARKR] Fix SparkR logistic regression example in programming guide (did not converge)

## What changes were proposed in this pull request?

SparkR logistic regression example did not converge in programming guide (for IRWLS). All estimates are essentially zero:

```
training2 <- read.df("data/mllib/sample_binary_classification_data.txt", source = "libsvm")
df_list2 <- randomSplit(training2, c(7,3), 2)
binomialDF <- df_list2[[1]]
binomialTestDF <- df_list2[[2]]
binomialGLM <- spark.glm(binomialDF, label ~ features, family = "binomial")

17/04/07 11:42:03 WARN WeightedLeastSquares: Cholesky solver failed due to singular covariance matrix. Retrying with Quasi-Newton solver.

> summary(binomialGLM)

Coefficients:
                 Estimate
(Intercept)    9.0255e+00
features_0     0.0000e+00
features_1     0.0000e+00
features_2     0.0000e+00
features_3     0.0000e+00
features_4     0.0000e+00
features_5     0.0000e+00
features_6     0.0000e+00
features_7     0.0000e+00
```

Author: actuaryzhang <actuaryzhang10@gmail.com>

Closes #17571 from actuaryzhang/programGuide2.

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

@@ -27,7 +27,7 @@ sparkR.session(appName = "SparkR-ML-glm-example")
 # $example on$
 training <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
 # Fit a generalized linear model of family "gaussian" with spark.glm
-df_list <- randomSplit(training, c(7,3), 2)
+df_list <- randomSplit(training, c(7, 3), 2)
 gaussianDF <- df_list[[1]]
 gaussianTestDF <- df_list[[2]]
 gaussianGLM <- spark.glm(gaussianDF, label ~ features, family = "gaussian")
@@ -44,8 +44,9 @@ gaussianGLM2 <- glm(label ~ features, gaussianDF, family = "gaussian")
 summary(gaussianGLM2)
 
 # Fit a generalized linear model of family "binomial" with spark.glm
-training2 <- read.df("data/mllib/sample_binary_classification_data.txt", source = "libsvm")
-df_list2 <- randomSplit(training2, c(7,3), 2)
+training2 <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
+training2 <- transform(training2, label = cast(training2$label > 1, "integer"))
+df_list2 <- randomSplit(training2, c(7, 3), 2)
 binomialDF <- df_list2[[1]]
 binomialTestDF <- df_list2[[2]]
 binomialGLM <- spark.glm(binomialDF, label ~ features, family = "binomial")