In previous posts, we had a simple problem to handle - find and sort the 25 most frequent words from a file. Then, we had to comply with different sets of constraints regarding the code.

This week, the constraint is to achieve the goal with the shortest code possible. Of course, this depends a lot on the programming language, and on the API available - whether baked-in or from a standard library.

This is the 3rd post in the Exercises in Programming Style focus series.Other posts include:

The name of this post is a reference found in the Dune book by Frank Herbert, where Kwisatz Haderach means "The Shortening of the Way".

For that, the usage of Kotlin’s stdlib is more than welcome. Without further ado, here’s the code:

fun run ( filename : String ) = ( read ( filename ) . flatMap { it . toLowerCase (). split ( "\\W|_" . toRegex ()) } . filter { it . isNotBlank () && it . length >= 2 } - read ( "stop_words.txt" ). flatMap { it . split ( "," ) }) . groupBy { it } . map { it . key to it . value . size } . sortedBy { it . second } . takeLast ( 25 ) . toMap ()

Let’s analyze the previous snippet.

read(filename).flatMap { it.toLowerCase().split("\\W|_".toRegex()) } Read the file’s content into a list of strings, one element per line. Then, transform each line into lower case, and split those lines along non-word tokens. filter { it.isNotBlank() && it.length >= 2 } Remove blank strings, as well as those that have a length of 1. - read("stop_words.txt").flatMap { it.split(",") } Read the stop words file, and dispatch its single-string comma-separated list into a list of individual stop words. Remove them from the list of words. groupBy { it } Group the words by individual word. The key is the word itself, the value the repeated list of the same word. map { it.key to it.value.size } Transform each entry into a pair, the first item being the key, the second item the list’s size.

I believe the rest of the code to be pretty self-explanatory.

There’s an alternative using fold() , which can replace the groupBy() and map() function calls:

. fold ( mutableMapOf < String , Int >()) { map , word -> map . merge ( word , 1 ) { existing , new -> existing + new } map }. toList ()

Fold’s signature - also called reduce in some languages is:

fun < T , R > Iterable < T >. fold ( initial : R , operation : ( acc : R , T ) -> R ): R

It requires two parameters:

An initial value of type R A function that transforms: a R value

and the next item in the collection

into another R

Here, R is a mutable map. The reason to prefer a mutable data structure over an immutable one is because of the merge() method. This allows to put values in a map, handling the case whether the key already exists or not, in a very concise way.

Conclusion While previous chapters required to think in a different way, this one requires to know one’s language API and libraries. This is a requirement that I’ve observed in other areas domains: Functional Programming in general, every kind of Reactive Programming API, the Clojure language, etc. This is the shortest I could come with, but I welcome you to add your suggestions in the comments.

The complete source code for this post can be found on Github