[Minor][SQL] Little refactor DataFrame related codes

Simplify some codes related to DataFrame.

*  Calling `toAttributes` instead of a `map`.
*  Original `createDataFrame` creates the `StructType` and its attributes in a redundant way. Refactored it to create `StructType` and call `toAttributes` on it directly.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #4298 from viirya/refactor_df and squashes the following commits:

1d61c64 [Liang-Chi Hsieh] Revert it.
f36efb5 [Liang-Chi Hsieh] Relax the constraint of toDataFrame.
2c9f370 [Liang-Chi Hsieh] Just refactor DataFrame codes.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala

@@ -98,7 +98,7 @@ trait ScalaReflection {
   /** Returns a Sequence of attributes for the given case class type. */
   def attributesFor[T: TypeTag]: Seq[Attribute] = schemaFor[T] match {
     case Schema(s: StructType, _) =>
-      s.fields.map(f => AttributeReference(f.name, f.dataType, f.nullable, f.metadata)())
+      s.toAttributes
   }
 
   /** Returns a catalyst DataType and its nullability for the given Scala Type using reflection. */

sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala

@@ -168,8 +168,8 @@ class SQLContext(@transient val sparkContext: SparkContext)
    */
   implicit def createDataFrame[A <: Product: TypeTag](rdd: RDD[A]): DataFrame = {
     SparkPlan.currentContext.set(self)
-    val attributeSeq = ScalaReflection.attributesFor[A]
-    val schema = StructType.fromAttributes(attributeSeq)
+    val schema = ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType]
+    val attributeSeq = schema.toAttributes
     val rowRDD = RDDConversions.productToRowRdd(rdd, schema)
     new DataFrame(this, LogicalRDD(attributeSeq, rowRDD)(self))
   }