Cool thanks!
On Monday, August 4, 2014 8:58 AM, kriskalish <k...@kalish.net> wrote: Hey Ron, It was pretty much exactly as Sean had depicted. I just needed to provide count an anonymous function to tell it which elements to count. Since I wanted to count them all, the function is simply "true". val grouped = rdd.groupByKey().mapValues { mcs => val values = mcs.map(_.foo.toDouble) val n = values.count(x => true) val sum = values.sum val sumSquares = values.map(x => x * x).sum val stddev = math.sqrt(n * sumSquares - sum * sum) / n print("stddev: " + stddev) stddev } I hope that helps -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Computing-mean-and-standard-deviation-by-key-tp11192p11334.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org