diff --git a/examples/src/main/r/ml/glm.R b/examples/src/main/r/ml/glm.R
index 23141b57df14321a372228189ac7d6ac7909d40c..68787f9aa9dca181114514f3305f53bbe6563d4e 100644
--- a/examples/src/main/r/ml/glm.R
+++ b/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")