fp-data-transforms

Practical Data Transformations

This skill covers the data transformations you do every day: working with arrays, reshaping objects, normalizing API responses, grouping data, and safely accessing nested values. Each section shows the imperative approach first, then the functional equivalent, with honest assessments of when each approach shines.

When to Use

• You need to transform arrays, objects, grouped data, or nested values in TypeScript.
• The task involves reshaping API responses, null-safe access, aggregation, or normalization.
• You want practical functional patterns for everyday data work instead of low-level loops.

Table of Contents

1. [Array Operations](#1-array-operations)
2. [Object Transformations](#2-object-transformations)
3. [Data Normalization](#3-data-normalization)
4. [Grouping and Aggregation](#4-grouping-and-aggregation)
5. [Null-Safe Access](#5-null-safe-access)
6. [Real-World Examples](#6-real-world-examples)
7. [When to Use What](#7-when-to-use-what)

1. Array Operations

Array operations are the bread and butter of data transformation. Let's replace verbose loops with expressive, chainable operations.

Map: Transform Every Element

The Task: Convert an array of prices from cents to dollars.

Imperative Approach

const pricesInCents = [999, 1499, 2999, 4999];

function convertToDollars(prices: number[]): number[] {
  const result: number[] = [];
  for (let i = 0; i < prices.length; i++) {
    result.push(prices[i] / 100);
  }
  return result;
}

const dollars = convertToDollars(pricesInCents);
// [9.99, 14.99, 29.99, 49.99]

Functional Approach

const pricesInCents = [999, 1499, 2999, 4999];

const toDollars = (cents: number): number => cents / 100;

const dollars = pricesInCents.map(toDollars);
// [9.99, 14.99, 29.99, 49.99]

Why functional is better here: The intent is immediately clear. map says "transform each element." The transformation logic (toDollars) is named and reusable. No index management, no manual array building.

Filter: Keep What Matches

The Task: Get all active users from a list.

Imperative Approach

interface User {
  id: string;
  name: string;
  isActive: boolean;
}

function getActiveUsers(users: User[]): User[] {
  const result: User[] = [];
  for (const user of users) {
    if (user.isActive) {
      result.push(user);
    }
  }
  return result;
}

Functional Approach

const isActive = (user: User): boolean => user.isActive;

const activeUsers = users.filter(isActive);

// Or inline for simple predicates
const activeUsers = users.filter(user => user.isActive);

Why functional is better here: The predicate (isActive) is separated from the iteration logic. You can reuse, test, and compose predicates independently.

Reduce: Accumulate Into Something New

The Task: Calculate the total price of items in a cart.

Imperative Approach

interface CartItem {
  name: string;
  price: number;
  quantity: number;
}

function calculateTotal(items: CartItem[]): number {
  let total = 0;
  for (const item of items) {
    total += item.price * item.quantity;
  }
  return total;
}

Functional Approach

const calculateTotal = (items: CartItem[]): number =>
  items.reduce(
    (total, item) => total + item.price * item.quantity,
    0
  );

// Or break out the line total calculation
const lineTotal = (item: CartItem): number => item.price * item.quantity;

const calculateTotal = (items: CartItem[]): number =>
  items.map(lineTotal).reduce((a, b) => a + b, 0);

Honest assessment: For simple sums, the imperative loop is actually quite readable. The functional version shines when you need to compose the accumulation with other transformations, or when the reduction logic is complex enough to benefit from being named.

Chaining: Combine Operations

The Task: Get the names of all active premium users, sorted alphabetically.

Imperative Approach

interface User {
  id: string;
  name: string;
  isActive: boolean;
  tier: 'free' | 'premium';
}

function getActivePremiumNames(users: User[]): string[] {
  const result: string[] = [];
  for (const user of users) {
    if (user.isActive && user.tier === 'premium') {
      result.push(user.name);
    }
  }
  result.sort((a, b) => a.localeCompare(b));
  return result;
}

Functional Approach

const getActivePremiumNames = (users: User[]): string[] =>
  users
    .filter(user => user.isActive)
    .filter(user => user.tier === 'premium')
    .map(user => user.name)
    .sort((a, b) => a.localeCompare(b));

// Or with named predicates for reuse
const isActive = (user: User): boolean => user.isActive;
const isPremium = (user: User): boolean => user.tier === 'premium';
const getName = (user: User): string => user.name;
const alphabetically = (a: string, b: string): number => a.localeCompare(b);

const getActivePremiumNames = (users: User[]): string[] =>
  users
    .filter(isActive)
    .filter(isPremium)
    .map(getName)
    .sort(alphabetically);

Why functional is better here: Each step in the chain has a single responsibility. You can read the transformation as a series of steps: "filter active, filter premium, get names, sort." Adding or removing a step is trivial.

Using fp-ts Array Module

fp-ts provides additional array utilities with better composition support:

import * as A from 'fp-ts/Array';
import * as O from 'fp-ts/Option';
import { pipe } from 'fp-ts/function';

// Safe head (first element)
const first = pipe(
  [1, 2, 3],
  A.head
); // Some(1)

const firstOfEmpty = pipe(
  [] as number[],
  A.head
); // None

// Safe lookup by index
const third = pipe(
  ['a', 'b', 'c', 'd'],
  A.lookup(2)
); // Some('c')

// Find with predicate
const found = pipe(
  users,
  A.findFirst(user => user.id === 'abc123')
); // Option<User>

// Partition into two groups
const [inactive, active] = pipe(
  users,
  A.partition(user => user.isActive)
);

// Take first N elements
const topThree = pipe(
  sortedScores,
  A.takeLeft(3)
);

// Unique values
const uniqueTags = pipe(
  allTags,
  A.uniq({ equals: (a, b) => a === b })
);

2. Object Transformations

Objects need reshaping constantly: picking fields, omitting sensitive data, merging settings, and updating nested values.

Pick: Select Specific Fields

The Task: Extract only the public fields from a user object.

Imperative Approach

interface User {
  id: string;
  name: string;
  email: string;
  passwordHash: string;
  internalNotes: string;
}

function getPublicUser(user: User): { id: string; name: string; email: string } {
  return {
    id: user.id,
    name: user.name,
    email: user.email,
  };
}

Functional Approach

// Generic pick utility
const pick = <T extends object, K extends keyof T>(
  keys: K[]
) => (obj: T): Pick<T, K> =>
  keys.reduce(
    (result, key) => {
      result[key] = obj[key];
      return result;
    },
    {} as Pick<T, K>
  );

const getPublicUser = pick<User, 'id' | 'name' | 'email'>(['id', 'name', 'email']);

const publicUser = getPublicUser(user);

Why functional is better here: The pick utility is reusable across your codebase. Type safety ensures you can only pick keys that exist.

Omit: Remove Specific Fields

The Task: Remove sensitive fields before logging.

Imperative Approach

function sanitizeForLogging(user: User): Omit<User, 'passwordHash' | 'internalNotes'> {
  const { passwordHash, internalNotes, ...safe } = user;
  return safe;
}

Functional Approach

// Generic omit utility
const omit = <T extends object, K extends keyof T>(
  keys: K[]
) => (obj: T): Omit<T, K> => {
  const result = { ...obj };
  for (const key of keys) {
    delete result[key];
  }
  return result as Omit<T, K>;
};

const