Dataframe.map need to result with more than the rows in dataset - scala

Question

I am using scala and spark and have a simple dataframe.map to produce the required transformation on data. However I need to provide an additional row of data with the modified original. How can I use the dataframe.map to give out this.
ex:
dataset from:
id, name, age
1, john, 23
2, peter, 32
if age < 25 default to 25.
dataset to:
id, name, age
1, john, 25
1, john, -23
2, peter, 32

Answer 1

Would a 'UnionAll' handle it?
eg.
df1 = original dataframe
df2 = transformed df1
df1.unionAll(df2)
EDIT: implementation using unionAll()
val df1=sqlContext.createDataFrame(Seq( (1,"john",23) , (2,"peter",32) )).
toDF( "id","name","age")
def udfTransform= udf[Int,Int] { (age) => if (age<25) 25 else age }
val df2=df1.withColumn("age2", udfTransform($"age")).
where("age!=age2").
drop("age2")
df1.withColumn("age", udfTransform($"age")).
unionAll(df2).
orderBy("id").
show()
+---+-----+---+
| id| name|age|
+---+-----+---+
| 1| john| 25|
| 1| john| 23|
| 2|peter| 32|
+---+-----+---+
Note: the implementation differs a bit from the originally proposed (naive) solution. The devil is always in the detail!
EDIT 2: implementation using nested array and explode
val df1=sx.createDataFrame(Seq( (1,"john",23) , (2,"peter",32) )).
toDF( "id","name","age")
def udfArr= udf[Array[Int],Int] { (age) =>
if (age<25) Array(age,25) else Array(age) }
val df2=df1.withColumn("age", udfArr($"age"))
df2.show()
+---+-----+--------+
| id| name| age|
+---+-----+--------+
| 1| john|[23, 25]|
| 2|peter| [32]|
+---+-----+--------+
df2.withColumn("age",explode($"age") ).show()
+---+-----+---+
| id| name|age|
+---+-----+---+
| 1| john| 23|
| 1| john| 25|
| 2|peter| 32|
+---+-----+---+