Refined module

kantan.csv comes with a refined module that can be used by adding the following dependency to your build.sbt:

libraryDependencies += "com.nrinaudo" %% "kantan.csv-refined" % "0.7.0"

You then need to import the corresponding package:

import kantan.csv.refined._

And that’s pretty much it. You can now encode and decode refined types directly.

Let’s first set our types up:

import eu.timepit.refined.api.Refined
import eu.timepit.refined.numeric.Positive
import kantan.csv._
import kantan.csv.ops._

type PositiveInt = Int Refined Positive

We can then simply write the following:

"1,2".readCsv[List, List[PositiveInt]](rfc)
// res0: List[ReadResult[List[PositiveInt]]] = List(Right(value = List(1, 2)))

"1,-2".readCsv[List, List[PositiveInt]](rfc)
// res1: List[ReadResult[List[PositiveInt]]] = List(
//   Left(
//     value = TypeError(message = "Not acceptable: 'Predicate failed: (-2 > 0).'")
//   )
// )

• Decoding rows as collections
• Decoding cells as arbitrary types
• Decoding rows as tuples
• Decoding rows as case classes
• Decoding rows as arbitrary types
• Decoding CSV data into a collection
• Decoding CSV data one row at a time
• What can be parsed as CSV data?
• Error handling
• Encoding collections as rows
• Encoding arbitrary types as cells
• Encoding tuples as rows
• Encoding case classes as rows
• Encoding arbitrary types as rows
• Encoding entire collections
• Encoding one row at a time
• What can CSV data be written to?
• Encoders, decoders and codecs
• Using header values instead of indexes
• Default instances
• External CSV libraries
• Cats module
• Scalaz module
• Generic module
• Java 8 dates and times
• Refined module
• Enumeratum module
• Libra module
• Working with BOMs (and MS Excel)