So far, we have understood the runtime model of SparkR and the basic data abstractions that provide the fault tolerance and scalability. We have understood how to access the Spark API from R shell or R studio. It's time to try out some basic and familiar operations:

> 
> //Open the shell 
> 
> //Try help(package=SparkR) if you want to more information 
> 
> df <- createDataFrame(iris) //Create a Spark DataFrame 
> df    //Check the type. Notice the column renaming using underscore 
SparkDataFrame[Sepal_Length:double, Sepal_Width:double, Petal_Length:double, Petal_Width:double, Species:string] 
> 
> showDF(df,4) //Print the contents of the Spark DataFrame 
+------------+-----------+------------+-----------+-------+ 
|Sepal_Length|Sepal_Width|Petal_Length|Petal_Width|Species| 
+------------+-----------+------------+-----------+-------+ 
|         5.1|        3.5|         1.4|        0.2| setosa| 
|         4.9|        3.0|         1.4| ...