trait
Roller[I] extends AnyRef
Type Members
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abstract
type
K
Abstract Value Members
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abstract
def
apply(in: I): TraversableOnce[K]
Concrete Value Members
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final
def
!=(arg0: AnyRef): Boolean
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: AnyRef): Boolean
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
Inherited from AnyRef
Inherited from Any
Given a TupleN, produces a sequence of (N + 1) tuples each of arity N such that, for all k from 0 to N, there is a tuple with k Somes followed by (N - k) Nones.
This is useful for comparing some metric across multiple layers of some hierarchy. For example, suppose we have some climate data represented as case class Data(continent: String, country: String, city: String, temperature: Double) and we want to know the average temperatures of
Here we desire the (continent, country) and (continent, country, city) pair because, for example, if we grouped by city instead of by (continent, country, city), we would accidentally combine the results for Paris, Texas and Paris, France.
Then we could do > import com.twitter.algebird.macros.Roller.roller > val data: List[Data] > val averageTemps: Map[(Option[String], Option[String], Option[String]), Double] = > data.flatMap { d => roller((d.continent, d.country, d.city)).map((_, d)) } > .groupBy(_._1) > .mapValues { xs => val temps = xs.map(_.temperature); temps.sum / temps.length }