Jack Franklin

Adventures with ReasonML

If you follow me on Twitter, or have read this blog for a while, you'll probably know that I'm a big fan of Elm. It's a functional, strictly typed language that compiles to JavaScript and is a great alternative to JavaScript for building web applications.

That said, it's not the only contender in this space. Reason is also a very popular option that has gained a lot of traction recently. I've always been interested in trying it out, and Advent of Code, a series of coding challenges posted each day leading up to Christmas, gave me a great excuse.

If you're into Elm, you might also be interested to know that I've done two videos completing Advent of Code challenges in Elm that you can find on Youtube.

If you're eager to skip ahead into the code, you can find it all on GitHub. In the rest of this post I'll talk you through my approach to getting up and running with Reason, and my thoughts on the language after trying it. I am not a Reason expert, so if you spot any errors or things I've misunderstood, please let me know! Equally, there might be better ways of solving the task, so if you have any suggestions please get in touch.

The first part of this blog post talks through my approach and how I solved the problem, and then we end with a list of my good and bad parts of trying Reason.

Getting started

I followed the official Installation and getting started guide to get easily up and running. It involved installing the compiler, BuckleScript, which is what takes Reason and produces JavaScript.

That let me run:

bsb -init my-new-project -theme basic-reason

To get a basic project up and running! I also installed reason-vscode so that I had nice error highlighting and type hinting as I coded. I find this particularly useful when working with a new language/framework that I'm not super familiar with.

Writing tests

I didn't want to build a UI to solve the Advent of Code problem; so I did a bit of googling to see if I could use Reason to write some unit tests, and solve the problem in a TDD style. I managed to find bs-jest, a library that adds bindings to BuckleScript to the JS testing framework Jest. This lets us write Reason, but have it compiled into JavaScript that we can then run with Jest as normal. So we'll write a tests.re file, have it compiled into tests.js, and then run jest tests.js. Setting this up was just a case of following the instructions in the README, and it worked perfectly.

The Advent of Code challenge

I was taking on Day Two, and for this exercise only completed Part One. I'll leave Part Two as an exercise for you!

The first part of the exercise needed me to take a string, such as bababc, and calculate the frequencies that letters occur. So for this string, we'd end up with:

{ a: 2, b: 3, c: 1 }

So that was the first thing I set out to write. I discovered that BuckleScript provides a Js.Dict module that is the equivalent of a native JS object, and I could use that. It also provides Js.Array, and Js.String. Using a combination of methods from these modules, I could split my input, and loop over it, updating a dict with new frequencies as I go through each letter.

I decided to store the frequencies in a dictionary. In Reason you have to decide what the types of the values are in a dictionary, so I went with integers, given we're counting frequencies.

I first set out to write a function that could take a dictionary and a letter, and update the frequency for that letter:

Defining this function looks very similar to JavaScript:

let incrementOrSetFrequency =
(frequencies: Js.Dict.t(int), letter: string): Js.Dict.t(int) => {

The bit that Reason adds is the type annotations. After each of the two arguments, we declare the types. We don't have to do this - Reason will be able to infer them for us - but I find it helps me work with code if I've documented the type, and very rarely the compiler can infer a type slightly differently to what you actually want it to be.

The type annotation above says that frequencies is a Js.Dict.t(int), which means a dictionary where each value is an int type. letter is a string. After the arguments we have the return type, which is also a dict, as we want to take the dict, update it, and then return it again.

The first thing we need to do is check to see if letter is in the dictionary, and we can use Js.Dict.get(frequencies, letter) to do this. It doesn't return the value or undefined though, like you would expect in JavaScript. Instead, it returns something that's an Option type. This is Reason's way of trying to avoid unexpected undefined or nulls in your application. You can read more about Option on the Reason docs.

When you have a function that returns an Option type, you can use pattern matching to see what the value is, and act accordingly. So if we look in our dictionary for our letter and it returns None, we need to add the letter. If it returns Some(int), we want to increment it by one:

let incrementOrSetFrequency =
(frequencies: Js.Dict.t(int), letter: string): Js.Dict.t(int) => {
switch (Js.Dict.get(frequencies, letter)) {
| Some(x) =>
Js.Dict.set(frequencies, letter, x + 1);
| None =>
Js.Dict.set(frequencies, letter, 1);

Getting our first test passing

At this point I decided I'd figured out enough Reason to be dangerous, and wanted to write a test so I could work towards getting it passing. I created __tests__/daytwo_test.re:

open Jest;
describe("DayTwo", () => {
open Expect;
test("letterFrequencies", () =>
|> toEqual(Js.Dict.fromList([("b", 3), ("a", 2), ("c", 1)]))

If you've written JS tests with Jest, you'll probably find the above quite intuitive, and I was able to use Js.Dict.fromList to take a list of tuples and create the dictionary that I needed for the test. The compiler compiled this into a JS file that I could run using the regular Jest CLI. This was one thing I liked about Reason; I can use the regular Jest CLI, rather than having to use a special one specifically for Reason. Jest's CLI is so good that it makes total sense to work on top of it rather than creating a language specific one from scratch.

To get the test passing we needed to take our input string, split it into a list of letters, and run each one through our incrementOrSetFrequency function:

let letterFrequencies = (input: string): Js.Dict.t(int) => {
let frequencies = Js.Dict.empty();
|> Js.String.split("")
|> Js.Array.reduce(
(acc, currentValue) => incrementOrSetFrequency(acc, currentValue),

And with that the test is passing!

Getting frequencies for our entire puzzle input

Next we need to take our full puzzle input, which is a series of strings, and run the above function on each of them, so we can start to work towards the final answer that we need.

Once again, I start by writing a test. I replicate the input that the real puzzle provides by putting each entry on its own line. I want to make sure we get the logic for splitting lines works properly.

Note that {|string here|} allows us to define a multi-line string.

test("checksum", () => {
   let puzzleInput = {|

   expect(DayTwo.checksum(puzzleInput)) |> toEqual(12);

We can use the familiar Js.String.split once again here, but pass it "\n" as the thing to split on. We then map the resulting lines over String.trim, which trims any whitespace and removes it. Note that we're not using Js.String.trim here, this is the ReasonML module String, not the BuckleScript Js.String module. This was one of the things I found most confusing when learning Reason. It wasn't clear why some of the functions we use are Reason modules, and others are provided by BuckleScript.

If you're familiar with Reason and can clarify the above confusion, I'd love to talk it through and update the blog post to include it.

So, the first part of the checksum function is to take the multi-line input, split it, and then ensure that we don't have any blanks:

let checksum = (input: string): int => {
|> Js.String.split("\n")
|> Js.Array.map(String.trim)
|> Js.Array.filter(s => String.length(s) > 0)
// note: this is invalid (we're not returning an int)

Once I've split the lines and given them a trim, I then use Js.Array.filter to remove any strings that are entirely empty. Now we are working with an array of letter frequencies that looks something like this:


So we want to take each one and pass it into the letterFrequencies function that we have defined:

let checksum = (input: string): int => {
|> Js.String.split("\n")
|> Js.Array.map(String.trim)
|> Js.Array.filter(s => String.length(s) > 0)
|> Js.Array.map(letterFrequencies)
// note: this is invalid (we're not returning an int)

Now we've turned that list of strings into a list of frequencies. This code sample highlights one of my favourite Reason features (I'm biased as it's also a favourite feature of mine from other functional languages like Elm and Elixir), the pipeline operator. The pipeline operator takes the thing on the left and passes it as the last argument to the function on the right. It means fewer parentheses around everything and lends itself to creating really readable code.

Calculating frequency occurrences

Now we have a list of frequency dictionaries, we need to take them and figure out:

The result for each of those is what we'll need to multiply together to get our checksum, which is the solution to our puzzle.

What I'd like to do is take our list of frequencies and map it into a list of Reason objects that contain two properties, twice and thrice. These will be booleans and correspond to if a word contains a letter twice or thrice. To help the compiler give me good type errors if I make a mistake, I create a custom type:

type twiceAndThriceFrequency = {
twice: bool,
thrice: bool,

This declares a type, twiceAndThriceFrequency, which is an object with two properties that are both booleans. I can then create a function that will take a frequencies dictionary and convert it into one of these objects. Now I have this custom type, I can use it in the type annotation too:

let findTwicesAndThrices = (frequencies: Js.Dict.t(int)): twiceAndThriceFrequency => {
{twice: true, thrice: true }

For now I've hardcoded the values to both be true, we will fill those in shortly. Notice how having the custom type defined makes the type annotation read really nicely and clearly.

To figure out the value of the twice and thrice keys, we need to see if the frequencies dictionary has any values of 2 or 3 in it. For this problem, we don't actually care about which letter occurs two or three times, we just need to know if any of them do.

We can use Js.Dict.values, which takes a dictionary and returns an array of the values inside it. It's just like Object.values() in JavaScript. We can then use Js.Array.some, which takes an array and a function and tells us if any items in the array satisfy it. Therefore, we can define the functions hasTwices and hasThrices like so:

let hasTwices = (frequencies: Js.Dict.t(int)): bool => {
frequencies |> Js.Dict.values |> Js.Array.some(v => v === 2);

let hasThrices = (frequencies: Js.Dict.t(int)): bool => {
frequencies |> Js.Dict.values |> Js.Array.some(v => v === 3);

Note that in this solution I'm not worrying about performance. If I was, we'd be doing this differently to reduce the number of times we iterate over the frequencies array. I'll leave it as an exercise to the reader to improve that.

Mapping to our twiceAndThriceFrequency type

Now we have these functions, we can define a function that will take a frequencies dictionary and return a twiceAndThriceFrequency type:

let findTwicesAndThrices = (frequencies: Js.Dict.t(int)): twiceAndThriceFrequency => {
{twice: hasTwices(frequencies), thrice: hasThrices(frequencies)};

Notice that we don't need the return keyword in Reason. The last expression in a function is automatically returned for you.

And once we have this function, we can update our main checksum function:

let checksum = (input: string): int => {
|> Js.String.split("\n")
|> Js.Array.map(String.trim)
|> Js.Array.filter(s => String.length(s) > 0)
|> Js.Array.map(letterFrequencies)
|> Js.Array.map(findTwicesAndThrices)
// note: this is invalid (we're not returning an int)

Calculating our checksum

At this point we are working with a list of objects that have { twice: true/false, thrice: true/false } within them. We want to go through this list and reduce it down to two values: the number of times that we have a letter occurring twice, and the number of times we have a letter occurring three times. So if we have this list:

  { twice: true, thrice: false },
  { twice: false, thrice: false },
  { twice: true, thrice: true },

We want to end up with:

{ twice: 2, thrice: 1 }

It's then these two numbers that we multiply to find our checksum.

We can use Js.Array.reduce to do this. It will take our array and loop through each value in turn, allowing us to check the values of twice and thrice and increment our accumulator accordingly. Our starting accumulator will be an object, which I also define a type for:

type twiceAndThriceCounter = {
twice: int,
thrice: int,

And now we can start planning our reduce call:

|> Js.Array.reduce(
(acc: twiceAndThriceCounter, currentValue: twiceAndThriceFrequency) => acc
{twice: 0, thrice: 0},

Inside the body of the callback function, we need to check the currentValue and check the values of twice and thrice.

This is a case where Reason's pattern matching comes in really handy. We can write code that pattern matches against the object and its values:

switch (currentValue) {
| {twice: true, thrice: true} => {
twice: acc.twice + 1,
thrice: acc.thrice + 1,
| {twice: true, thrice: false} => {
twice: acc.twice + 1,
thrice: acc.thrice,
| {twice: false, thrice: true} => {
twice: acc.twice,
thrice: acc.thrice + 1,
| {twice: false, thrice: false} => acc

Each case that we're matching against starts with the pipe (|) and then we match against the twice and thrice values within currentValue. So the first will match only if currentValue has both values set to true, in which case we increment both of our counters. In the case of one of twice or thrice being true, we increment the appropriate counter and if both values are false, we do nothing.

Pattern matching is my favourite feature of Reason (it's also one of my favourite parts of Elm), and it leads to some really nice, expressive code. What's also nice is that if we don't write code that deals with every possible case, we get a compiler error. In the example below, I've removed the case that deals with both values being true. You can see the compiler spot this and tell me:

  Warning number 8
/Users/jackfranklin/git/advent-of-code/day-two-reason-ml/src/DayTwo.re 55:10-65:10

53|> Js.Array.reduce(
54(acc: twiceAndThriceCounter, currentValue: twiceAndThriceFrequenc
y) =>
55switch (currentValue) {
56| {twice: true, thrice: false} => {
64| {twice: false, thrice: false} => acc
66{twice: 0, thrice: 0},

You forgot to handle a possible value here, for example:
{twice=true; thrice=true}

This means you can never end up with code in production that doesn't deal with all possible cases, which is fantastic. It also means if you refactor and now your pattern matching is out of date, the compiler will tell you.

Once we have this reduce done, it's going to end up turning our array of frequencies into one object with two values. The solution to the puzzle (and what we need to get our test passing) is to take these values and multiply them. We can do this by piping our object into an anonymous function that does just this:

|> result => result.twice * result.thrice

And with this, our tests are back to green!

 PASS  __tests__/daytwo_test.bs.js
    ✓ letterFrequencies (6ms)
    ✓ checksum (1ms)

There's one small refactor we can make here though. Much like JavaScript and its ES2015 destructuring, we can destructure an object into the keys when it's passed into a function. So we can rewrite our final line as:

|> (({twice, thrice}) => twice * thrice)

Which I think reads a bit more clearly. And with that, our puzzle is solved!


This was literally the first time I'd written Reason and after finishing the Advent of Code challenge I took a moment to think through what I found good, and what I struggled with, from the perspective of a beginner using a new language.

It's also worth noting that my experience with Elm almost certainly makes it easier for me to learn Reason, there are similarities between the two.

Things I liked

Things I found confusing

Please remember that I am writing this as a Reason beginner, not an authority! If I've misunderstood something or made a mistake, please let me know and I'd be happy to update the blog post and give credit accordingly.

All in all, I'd really recommend giving Reason a go! I'm excited to see where the language and ecosystem goes in 2019 and beyond, and I'll definitely be playing with it some more, maybe next time on an actual frontend project, rather than just a coding exercise.