Calculate date difference for a specific column ID Scala - scala

Question

I need to calculate a date difference for a column, considering a specific ID shown in a different column and the first date for that specific ID, using Scala.
I have the following dataset:
The column ID shows the specific ID previously mentioned, the column date shows the date of the event and the column rank shows the chronological positioning of the different event dates for each specific ID.
I need to calculate for ID 1, the date difference for ranks 2 and 3 compared to rank 1 for that same ID, the same for ID 2 and so forth.
The expected result is the following:
Does somebody know how to do it?
Thanks!!!

Answer 1

Outside of using a library like Spark to reason about your data in SQL-esque terms, this can be accomplished using the Collections API by first finding the minimum date for each ID and then comparing the dates in the original collection:
# import java.time.temporal.ChronoUnit.DAYS
import java.time.temporal.ChronoUnit.DAYS
# import java.time.LocalDate
import java.time.LocalDate
# case class Input(id : Int, date : LocalDate, rank : Int)
defined class Input
# case class Output(id : Int, date : LocalDate, rank : Int, diff : Long)
defined class Output
# val inData = Seq(Input(1, LocalDate.of(2020, 12, 10), 1),
Input(1, LocalDate.of(2020, 12, 12), 2),
Input(1, LocalDate.of(2020, 12, 16), 3),
Input(2, LocalDate.of(2020, 12, 11), 1),
Input(2, LocalDate.of(2020, 12, 13), 2),
Input(2, LocalDate.of(2020, 12, 14), 3))
inData: Seq[Input] = List(
Input(1, 2020-12-10, 1),
Input(1, 2020-12-12, 2),
Input(1, 2020-12-16, 3),
Input(2, 2020-12-11, 1),
Input(2, 2020-12-13, 2),
Input(2, 2020-12-14, 3)
# val minDates = inData.groupMapReduce(_.id)(identity){(a, b) =>
a.date.isBefore(b.date) match {
case true => a
case false => b
}}
minDates: Map[Int, Input] = Map(1 -> Input(1, 2020-12-10, 1), 2 -> Input(2, 2020-12-11, 1))
# val outData = inData.map(a => Output(a.id, a.date, a.rank, DAYS.between(minDates(a.id).date, a.date)))
outData: Seq[Output] = List(
Output(1, 2020-12-10, 1, 0L),
Output(1, 2020-12-12, 2, 2L),
Output(1, 2020-12-16, 3, 6L),
Output(2, 2020-12-11, 1, 0L),
Output(2, 2020-12-13, 2, 2L),
Output(2, 2020-12-14, 3, 3L)

Answer 2

You can get the required output by performing the steps as done below :
//Creating the Sample data
import org.apache.spark.sql.types._
val sampledf = Seq((1,"2020-12-10",1),(1,"2020-12-12",2),(1,"2020-12-16",3),(2,"2020-12-08",1),(2,"2020-12-11",2),(2,"2020-12-13",3))
.toDF("ID","Date","Rank").withColumn("Date",$"Date".cast("Date"))
//adding column with just the value for the rank = 1 column
import org.apache.spark.sql.functions._
val df1 = sampledf.withColumn("Basedate",when($"Rank" === 1 ,$"Date"))
//Doing GroupBy based on ID and basedate column and filtering the records with null basedate
val groupedDF = df1.groupBy("ID","basedate").min("Rank").filter($"min(Rank)" === 1)
//joining the two dataframes and selecting the required columns.
val joinedDF = df1.join(groupedDF.as("t"), Seq("ID"),"left").select("ID","Date","Rank","t.basedate")
//Applying the inbuilt datediff function to get the required output.
val finalDF = joinedDF.withColumn("DateDifference", datediff($"Date",$"basedate"))
finalDF.show(false)
//If using databricks you can use display method.
display(finalDF)