build.sbt

resolvers += "Pellucid Bintray" at "http://dl.bintray.com/pellucid/maven"

libraryDependencies += "com.pellucid" %% "framian" % "0.3.3"

example.scala

package example

import scala.collection.mutable.ArrayBuffer

import framian._
import framian.csv._
import spire.implicits._

object Main extends App {

  // Populate the array buffers with some values. I made values an
  // `ArrayBuffer[Double]` instead of `ArrayBuffer[String]` to show that you
  // can mix types in a Frame.

  val values =  ArrayBuffer[Double](1.2, 3.4, 5.6)
  val times = ArrayBuffer[String]("1", "3", "2")
  val ids = ArrayBuffer[String]("Alice", "Bob", "Charlie")

  // Loading values into a Frame as the columns. We use `mergeColumns` here.
  // This method takes a tuple of column / series pairs. In this case, we are
  // providing a 3-tuple with each of our columns converted to a Series[Int,
  // Double] or Series[Int, String]. The keys in this case are just the index
  // into the array buffer of the value.

  val frame: Frame[Int, String] = Frame.mergeColumns(
    "id" -> Series(ids: _*),
    "time" -> Series(times: _*),
    "value" -> Series(values: _*)
  )

  // Extracting the *values* as a Double. Cols by default will return values as
  // a Rec, which is rarely what we want. Instead, we use the `.as[T]` method to
  // have the values returned as type `Double` (via `.as[Double]`).
  //
  // Also, we use foreachDense here so that we only get non-missing values (as
  // type `Double`), instead of cells (as type `Cell[Double]`).

  println("Values:")
  frame.get(Cols("value").as[Double]).foreachDense { (row, value) =>
    println(s"$row:$value")
  }
  println()

  // We can reindex the Frame by the IDs if we prefer. Here we'll just print
  // the values again as above, but with the ID instead of the row number.

  val frame2: Frame[String, String] = frame.reindex(Cols("id").as[String])
  println("Values:")
  frame2.get(Cols("value").as[Double]).foreachDense { (id, value) =>
    println(s"$id:$value")
  }
  println()

  // Converting to a CSV. We add a header to the CSV, since the default CSV
  // format does not.

  val fmt = CsvFormat.CSV.withHeader(true)
  val csv = Csv.fromFrame(fmt)(frame)
  println("Csv:")
  println(csv.toString)
}

output

Values:
0:1.2
1:3.4
2:5.6

Values:
Alice:1.2
Bob:3.4
Charlie:5.6

Csv:
id,time,value
Alice,1,1.2
Bob,3,3.4
Charlie,2,5.6