Reproduced example from https://darrenjw.wordpress.com/2015/08/21/data-frames-and-tables-in-scala/

DataFramesAndTablesInScalaArticle.scala

import gallia._ // see https://github.com/galliaproject/gallia-core/blob/init/README.md#dependencies

// ===========================================================================
object DataFramesAndTablesInScalaArticle {
  // https://darrenjw.wordpress.com/2015/08/21/data-frames-and-tables-in-scala/

  // ---------------------------------------------------------------------------	
  def main(args: Array[String]): Unit = {    
    "/data/misc/cars93.csv".stream()
        .filterBy("EngineSize").matches(_ <= 4.0)
        .generate("WeightKG").from("Weight").using(_ * 0.453592)
      .write("/tmp/out.csv")
  }
  
  // ---------------------------------------------------------------------------
  /*
    from article (minus the print statements):
  
      // R:
      
      df=read.csv("cars93.csv")
      df = df[df$EngineSize<=4.0,]
      df$WeightKG = df$Weight*0.453592
      write.csv(df,"cars93m.csv",row.names=FALSE)
      
      
      // ---------------------------------------------------------------------------
      // Saddle:
      
      val file = CsvFile("cars93.csv")
      val df = CsvParser.parse(file).withColIndex(0)
      val df2 = df.rfilter(_("EngineSize").mapValues(CsvParser.parseDouble).at(0)<=4.0)
      val wkg=df2.col("Weight").mapValues(CsvParser.parseDouble).mapValues(_*0.453592).setColIndex(Index("WeightKG"))
      val df3=df2.joinPreserveColIx(wkg.mapValues(_.toString))
      df3.writeCsvFile("saddle-out.csv")
      
      
      // ---------------------------------------------------------------------------
      // Scala-datatable:
      
      val colTypes=Map("DriveTrain" -> StringCol, "Min.Price" -> Double, [...]) // full schema
      val df=readCsv("Cars93",new FileReader("cars93.csv"),colTypes)
      val df2=df.filter(row=>row.as[Double]("EngineSize")<=4.0).toDataTable 
      val oldCol=df2.columns("Weight").as[Int]
      val newCol=new DataColumn[Double]("WeightKG",oldCol.data.map{_.toDouble*0.453592})
      val df3=df2.columns.add(newCol).get
      writeCsv(df3,new File("out.csv"))
      
      
      // ---------------------------------------------------------------------------
      // Framian:
      
      val df=Csv.parseFile(new File("cars93.csv")).labeled.toFrame
      val df2=df.filter(Cols("EngineSize").as[Double])( _ <= 4.0 )
      val df3=df2.map(Cols("Weight").as[Int],"WeightKG")(r=>r.toDouble*0.453592)
      val csv = Csv.fromFrame(new CsvFormat(",", header = true))(df3)
      new PrintWriter("out.csv") { write(csv.toString); close }
      
      
      // --------------------------------------------------------------------------- 
      // Spark:

      val df = sqlContext.read.format("com.databricks.spark.csv").option("header", "true"). option("inferSchema","true").load("cars93.csv")
      val df2=df.filter("EngineSize <= 4.0")
      val col=df2.col("Weight")*0.453592
      val df3=df2.withColumn("WeightKG",col)
      df3.write.format("com.databricks.spark.csv").option("header","true").save("out-csv")
	*/
}

// ===========================================================================