Introduction
Lately, I’ve begun programming in JS using an increasingly functional style, with the help of Ramda (a functional programming library). What does this mean? At its core, this means writing predominantly pure functions, handling side effects and making use of techniques such as currying, partial application and functional composition. You can choose to take it further than this, however, that’s a story for another day.
The pillars of functional programming in JS
Pure functions
One key area of functional programming is the concept of pure functions. A pure function is one that takes an input and returns an output. It does not depend on external system state and it does not have any side effects. Pure functions will for a given input always return the same output, making them predictable and easy to test.
Side effects
It’s worth mentioning that side effects are sometimes unavoidable, and there are different techniques you can adopt to deal with these. But the key objective here is minimising side effects and handling these away from your pure functions.
Currying
One of the key building blocks of functional programming is the technique of currying. This is where you take a polyadic function (one with multiple arguments) and translate it into a sequence of monadic functions (functions that take a single argument). This works by each function returning its result as the argument to the next function in the sequence. This allows you to partially apply functions by fixing a number of arguments. Importantly, this also enables you to compose functions together which I’ll get onto later.
Example of partial application:
// Function for multiplying two numbers together
const multiplyTogether = (x, y) => {
return x * y;
}
multiplyTogether(2, 5);
// => 10
// Curried multiplication of two numbers
const multiplyTogetherCurried = x => y => {
return x * y;
}
multiplyTogetherCurried(2)(5);
// => 10
// Partial application used to create double number function
const doubleNumber = multiplyTogetherCurried(2);
doubleNumber(5);
// => 10
Composition
Building on currying and adopting another functional discipline of moving data to be the last argument of your function, you can now begin to make use of functional composition, and this is where things start to get pretty awesome.
With functional composition, you can create a sequence of functions which (after the first in the sequence) must be monadic, where each function feeds its returned value into the next function in the sequence as its argument, returning the result at the end of the sequence. We do this in Ramda using compose. Adopting this style can not only make code easier to reason about but also easier to read and write. In my opinion, where this style really shines is in data transformation, allowing you to break down potentially complex transformations into logical steps. Ramda is a big help here, as although you could simply choose to make use of compose and write your own curried monadic functions, it just so happens that Ramda is a library of super useful, (mostly) curried functions, containing functions for mapping over data, reducing data, omitting data based on keys, flattening and unflattening objects and so much more!
Imperative vs functional
Now that you’ve (hopefully) got a better idea of what functional programming is, the question becomes, is following an imperative style wrong? In my opinion, no. When it comes down to choosing between imperative and functional programming in JS, I believe you have to be pragmatic – whilst functional may be your go to choice, there are times when I believe you have to ask yourself if a simple if else statement will do the job. That said, adopting the discipline of writing pure functions where possible and managing side effects, along with handling data transformations using functional composition, will likely make your life as a developer a lot easier and more enjoyable. It sure has for me!
A worked example using Ramda
I’ve included a worked example of a function which I rewrote from a predominantly imperative style to a functional style, as I felt the function was becoming increasing difficult to reason about and with further anticipated additions, I was concerned it would become increasingly brittle.
Original function:
import R from 'ramda';
const dataMapper = (factFindData) => {
const obj = {};
Object.keys(factFindData).forEach(k => {
if (k === 'retirement__pensions') {
obj.retirement__pensions = normalizePensions(factFindData);
return;
}
if (k !== 'db_options' && k !== 'health__applicant__high_blood_pressure_details__readings') {
obj[k] = factFindData[k];
return;
}
if (k === 'health__applicant__high_blood_pressure_details__readings') {
if (factFindData.health__applicant__high_blood_pressure !== 'no') {
obj.health__applicant__high_blood_pressure_details__readings = factFindData[k];
}
}
});
return {
...emptyArrays,
...R.omit(['_id', 'notes', 'created_at', 'updated_at'], obj),
};
};
Refactored function:
import R from 'ramda';
const normalizeForEngine = x => ({ ...emptyArrays, ...x });
const omitNonEngineKeys = R.omit(['_id', 'notes', 'created_at', 'updated_at', 'db_options']);
const normalizeBloodPressure =
R.when(
x => x.health__applicant__high_blood_pressure === 'no',
R.omit(['health__applicant__high_blood_pressure_details__readings'])
);
const mapNormalizedPensions =
R.mapObjIndexed((v, k, o) => k === 'retirement__pensions' ? normalizePensions(o) : v);
const dataMapper =
R.compose(
normalizeForEngine,
omitNonEngineKeys,
normalizeBloodPressure,
mapNormalizedPensions
);
As you can see, when trying to figure out what the data mapper function is doing in the original function, I have to loop through an object, update and maintain the state of a temporary variable (in my head), in each loop checking against multiple conditions, before then taking this result and sticking in into an object, remembering to remove certain keys.
With the refactored function, at a glance I can say that I’m normalising pensions, then normalising blood pressure, then omitting non engine keys, before finally normalising the data for the engine. Doesn’t that feel easier to reason about? If a new requirement came in to normalise let’s say, cholesterol readings, I would simply slot another curried function in after normalizeBloodPressure called for arguments sake normalizeCholesterol.
Conclusion
Functional programming in JS using Ramda can not only reduce your codebase in size, but it can also increase its readability and testability, and make it easier to reason about.
This article was also featured on the Wealth Wizards Engineering Blog, where you can find lots of great content from our team of engineers.