fp-data-transforms

name	fp-data-transforms
description	Everyday data transformations using functional patterns - arrays, objects, grouping, aggregation, and null-safe access
risk	unknown
source	community
version	1.0.0
author	Claude
tags	["functional-programming","typescript","data-transformation","fp-ts","arrays","objects","grouping","aggregation","null-safety"]

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.

---

Table of Contents

1. Array Operations
2. Object Transformations
3. Data Normalization
4. Grouping and Aggregation
5. Null-Safe Access
6. Real-World Examples
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
    .(isActive)
    .(isPremium)
    .(getName)
    .(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
 topThree = (
  sortedScores,
  A.()
);


 uniqueTags = (
  allTags,
  A.({ :  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 sanitizeForLogging = omit<User, 'passwordHash' | 'internalNotes'>([
  'passwordHash',
  'internalNotes',
]);

Honest assessment: For one-off omits, destructuring (the imperative approach) is perfectly fine and very readable. The functional omit utility pays off when you have many such transformations or need to compose them.

Merge: Combine Objects

The Task: Merge user settings with defaults.

Imperative Approach

interface Settings {
  theme: 'light' | 'dark';
  fontSize: number;
  notifications: boolean;
  language: string;
}

function mergeSettings(
  defaults: Settings,
  userSettings: Partial<Settings>
): Settings {
  return {
    theme: userSettings.theme !== undefined ? userSettings.theme : defaults.theme,
    fontSize: userSettings.fontSize !== undefined ? userSettings.fontSize : defaults.fontSize,
    notifications: userSettings.notifications !== undefined
      ? userSettings.notifications
      : defaults.notifications,
    language: userSettings.language !== undefined ? userSettings.language : defaults.language,
  };
}

Functional Approach

const mergeSettings = (
  defaults: Settings,
  userSettings: Partial<Settings>
): Settings => ({
  ...defaults,
  ...userSettings,
});

// Usage
const defaults: Settings = {
  theme: 'light',
  fontSize: 14,
  notifications: true,
  language: 'en',
};

const userPrefs: Partial<Settings> = {
  theme: 'dark',
  fontSize: 16,
};

const finalSettings = mergeSettings(defaults, userPrefs);
// { theme: 'dark', fontSize: 16, notifications: true, language: 'en' }

Why functional is better here: Spread syntax is concise and handles any number of keys. Later spreads override earlier ones, giving you natural "defaults with overrides" behavior.

Deep Merge: Nested Object Combination

The Task: Merge nested configuration objects.

Imperative Approach

interface Config {
  api: {
    baseUrl: string;
    timeout: number;
    retries: number;
  };
  ui: {
    theme: string;
    animations: boolean;
  };
}

function deepMerge(
  target: Config,
  source: Partial<Config>
): Config {
  const result = { ...target };

  if (source.api) {
    result.api = { ...target.api, ...source.api };
  }
  if (source.ui) {
    result.ui = { ...target.ui, ...source.ui };
  }

  return result;
}

Functional Approach

// Generic deep merge for one level of nesting
const deepMerge = <T extends Record<string, object>>(
  target: T,
  source: { [K in keyof T]?: Partial<T[K]> }
): T => {
  const result = { ...target };

  for (const key of Object.keys(source) as Array<keyof T>) {
    if (source[key] !== undefined) {
      result[key] = { ...target[key], ...source[key] };
    }
  }

  return result;
};

// Usage
const defaultConfig: Config = {
  api: { baseUrl: 'https://api.example.com', timeout: 5000, retries: 3 },
  ui: { theme: 'light', animations: true },
};

const customConfig = deepMerge(defaultConfig, {
  api: { timeout: 10000 },
  ui: { theme: 'dark' },
});
// api.baseUrl preserved, api.timeout overridden
// ui.theme overridden, ui.animations preserved

Immutable Updates: Change Nested Values

The Task: Update a deeply nested value without mutation.

Imperative (Mutating) Approach

interface State {
  user: {
    profile: {
      settings: {
        theme: string;
      };
    };
  };
}

function updateTheme(state: State, newTheme: string): void {
  state.user.profile.settings.theme = newTheme; // Mutation!
}

Functional (Immutable) Approach

// Manual spread nesting
const updateTheme = (state: State, newTheme: string): State => ({
  ...state,
  user: {
    ...state.user,
    profile: {
      ...state.user.profile,
      settings: {
        ...state.user.profile.settings,
        theme: newTheme,
      },
    },
  },
});

// With a lens-like helper
const updatePath = <T, V>(
  obj: T,
  path: string[],
  value: V
): T => {
  if (path.length === 0) return value as unknown as T;

  const [head, ...rest] = path;
  return {
    ...obj,
    [head]: updatePath((obj as Record<string, unknown>)[head], rest, value),
  } as T;
};

const newState = updatePath(state, ['user', 'profile', 'settings', 'theme'], 'dark');

Honest assessment: The spread nesting is verbose but explicit. For deeply nested updates, consider using a library like immer or fp-ts lenses. The verbosity of the functional approach is the price of immutability.

---

3. Data Normalization

API responses rarely match the shape your app needs. Normalization transforms nested, denormalized data into flat, indexed structures.

API Response to App State

The Task: Transform a nested API response into a normalized state.

API Response (What You Get)

interface ApiResponse {
  orders: Array<{
    id: string;
    customerId: string;
    customerName: string;
    customerEmail: string;
    items: Array<{
      productId: string;
      productName: string;
      quantity: number;
      price: number;
    }>;
    total: number;
    status: string;
  }>;
}

App State (What You Need)

interface NormalizedState {
  orders: {
    byId: Record<string, Order>;
    allIds: string[];
  };
  customers: {
    byId: Record<string, Customer>;
    allIds: string[];
  };
  products: {
    byId: Record<string, Product>;
    allIds: string[];
  };
}

interface Order {
  id: string;
  customerId: string;
  itemIds: string[];
  total: number;
  status: string;
}

interface Customer {
  id: string;
  name: string;
  email: string;
}

interface Product {
  id: string;
  name: string;
  price: number;
}

Imperative Approach

function normalizeApiResponse(response: ApiResponse): NormalizedState {
  const state: NormalizedState = {
    orders: { byId: {}, allIds: [] },
    customers: { byId: {}, allIds: [] },
    products: { byId: {}, allIds: [] },
  };

  for (const order of response.orders) {
    // Extract customer
    if (!state.customers.byId[order.customerId]) {
      state.customers.byId[order.customerId] = {
        id: order.customerId,
        name: order.customerName,
        email: order.customerEmail,
      };
      state.customers.allIds.push(order.customerId);
    }

    // Extract products and build item IDs
    const itemIds: string[] = [];
    for (const item of order.items) {
      if (!state.products.byId[item.]) {
        state..[item.] = {
          : item.,
          : item.,
          : item.,
        };
        state...(item.);
      }
      itemIds.(item.);
    }

    
    state..[order.] = {
      : order.,
      : order.,
      itemIds,
      : order.,
      : order.,
    };
    state...(order.);
  }

   state;
}

Functional Approach

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

// Helper to create normalized collection
interface NormalizedCollection<T extends { id: string }> {
  byId: Record<string, T>;
  allIds: string[];
}

const createNormalizedCollection = <T extends { id: string }>(
  items: T[]
): NormalizedCollection<T> => ({
  byId: pipe(
    items,
    A.reduce({} as Record<string, T>, (acc, item) => ({
      ...acc,
      [item.id]: item,
    }))
  ),
  allIds: items.map(item => item.id),
});

// Extract entities
const extractCustomers = (orders: ApiResponse['orders']): Customer[] =>
  pipe(
    orders,
    A.map( => ({
      : order.,
      : order.,
      : order.,
    })),
    A.({ :  a. === b. })
  );

 extractProducts = (: []): [] =>
  (
    orders,
    A.( order.),
    A.( ({
      : item.,
      : item.,
      : item.,
    })),
    A.({ :  a. === b. })
  );

 extractOrders = (: []): [] =>
  orders.( ({
    : order.,
    : order.,
    : order..( item.),
    : order.,
    : order.,
  }));


 normalizeApiResponse = (: ):  ({
  : ((response.)),
  : ((response.)),
  : ((response.)),
});

Why functional is better here: Each extraction is independent and testable. The createNormalizedCollection helper is reusable. Adding a new entity type means adding one new extraction function.

Transform API Response to UI-Ready Data

The Task: Convert API data to what your components need.

// API gives you this
interface ApiUser {
  user_id: string;
  first_name: string;
  last_name: string;
  email_address: string;
  created_at: string; // ISO string
  avatar_url: string | null;
}

// Components need this
interface DisplayUser {
  id: string;
  fullName: string;
  email: string;
  memberSince: string; // "Jan 2024"
  avatarUrl: string; // With fallback
}

Functional Approach

const formatDate = (isoString: string): string => {
  const date = new Date(isoString);
  return date.toLocaleDateString('en-US', { month: 'short', year: 'numeric' });
};

const DEFAULT_AVATAR = 'https://example.com/default-avatar.png';

const toDisplayUser = (apiUser: ApiUser): DisplayUser => ({
  id: apiUser.user_id,
  fullName: `${apiUser.first_name} ${apiUser.last_name}`,
  email: apiUser.email_address,
  memberSince: formatDate(apiUser.created_at),
  avatarUrl: apiUser.avatar_url ?? DEFAULT_AVATAR,
});

// Transform array of users
const toDisplayUsers = (apiUsers: ApiUser[]): DisplayUser[] =>
  apiUsers.map(toDisplayUser);

---

4. Grouping and Aggregation

Grouping and aggregating data is essential for reports, dashboards, and analytics.

GroupBy: Organize by Key

The Task: Group orders by customer.

Imperative Approach

interface Order {
  id: string;
  customerId: string;
  total: number;
  date: string;
}

function groupByCustomer(orders: Order[]): Record<string, Order[]> {
  const result: Record<string, Order[]> = {};

  for (const order of orders) {
    if (!result[order.customerId]) {
      result[order.customerId] = [];
    }
    result[order.customerId].push(order);
  }

  return result;
}

Functional Approach

// Generic groupBy utility
const groupBy = <T, K extends string | number>(
  getKey: (item: T) => K
) => (items: T[]): Record<K, T[]> =>
  items.reduce(
    (groups, item) => {
      const key = getKey(item);
      return {
        ...groups,
        [key]: [...(groups[key] || []), item],
      };
    },
    {} as Record<K, T[]>
  );

// Usage
const groupByCustomer = groupBy<Order, string>(order => order.customerId);
const ordersByCustomer = groupByCustomer(orders);

// Or inline
const ordersByStatus = groupBy((order: Order) => order.status)(orders);

Using fp-ts NonEmptyArray.groupBy:

import * as NEA from 'fp-ts/NonEmptyArray';
import { pipe } from 'fp-ts/function';

// NEA.groupBy guarantees non-empty arrays in result
const ordersByCustomer = pipe(
  orders as NEA.NonEmptyArray<Order>, // Must be non-empty
  NEA.groupBy(order => order.customerId)
); // Record<string, NonEmptyArray<Order>>

CountBy: Count Occurrences

The Task: Count orders by status.

Imperative Approach

function countByStatus(orders: Order[]): Record<string, number> {
  const counts: Record<string, number> = {};

  for (const order of orders) {
    counts[order.status] = (counts[order.status] || 0) + 1;
  }

  return counts;
}

Functional Approach

// Generic countBy utility
const countBy = <T, K extends string>(
  getKey: (item: T) => K
) => (items: T[]): Record<K, number> =>
  items.reduce(
    (counts, item) => {
      const key = getKey(item);
      return {
        ...counts,
        [key]: (counts[key] || 0) + 1,
      };
    },
    {} as Record<K, number>
  );

// Usage
const orderCountByStatus = countBy((order: Order) => order.status)(orders);
// { pending: 5, shipped: 12, delivered: 8 }

SumBy: Aggregate Numeric Values

The Task: Calculate total revenue per product category.

Imperative Approach

interface Sale {
  productId: string;
  category: string;
  amount: number;
}

function sumByCategory(sales: Sale[]): Record<string, number> {
  const totals: Record<string, number> = {};

  for (const sale of sales) {
    totals[sale.category] = (totals[sale.category] || 0) + sale.amount;
  }

  return totals;
}

Functional Approach

// Generic sumBy utility
const sumBy = <T, K extends string>(
  getKey: (item: T) => K,
  getValue: (item: T) => number
) => (items: T[]): Record<K, number> =>
  items.reduce(
    (totals, item) => {
      const key = getKey(item);
      return {
        ...totals,
        [key]: (totals[key] || 0) + getValue(item),
      };
    },
    {} as Record<K, number>
  );

// Usage
const revenueByCategory = sumBy(
  (sale: Sale) => sale.category,
  (sale: Sale) => sale.amount
)(sales);
// { electronics: 15000, clothing: 8500, books: 3200 }

Complex Aggregation Example

The Task: Calculate totals from line items with quantity and unit price.

interface LineItem {
  productId: string;
  productName: string;
  quantity: number;
  unitPrice: number;
}

interface Invoice {
  id: string;
  lineItems: LineItem[];
  taxRate: number;
}

Functional Approach

const lineTotal = (item: LineItem): number =>
  item.quantity * item.unitPrice;

const subtotal = (items: LineItem[]): number =>
  items.reduce((sum, item) => sum + lineTotal(item), 0);

const calculateTax = (amount: number, rate: number): number =>
  amount * rate;

const calculateInvoiceTotal = (invoice: Invoice): {
  subtotal: number;
  tax: number;
  total: number;
} => {
  const sub = subtotal(invoice.lineItems);
  const tax = calculateTax(sub, invoice.taxRate);

  return {
    subtotal: sub,
    tax,
    total: sub + tax,
  };
};

// With fp-ts pipe for clarity
import { pipe } from 'fp-ts/function';

const calculateInvoiceTotal = (invoice: Invoice) => {
   sub = (
    invoice.,
    A.(lineTotal),
    A.(,  a + b)
  );

   {
    : sub,
    : sub * invoice.,
    : sub * ( + invoice.),
  };
};

---

5. Null-Safe Access

Stop writing if (x && x.y && x.y.z). Safely navigate nested structures without runtime errors.

The Problem

interface Config {
  database?: {
    connection?: {
      host?: string;
      port?: number;
    };
    pool?: {
      max?: number;
    };
  };
  features?: {
    experimental?: {
      enabled?: boolean;
    };
  };
}

Imperative (Verbose) Approach

function getDatabaseHost(config: Config): string {
  if (
    config.database &&
    config.database.connection &&
    config.database.connection.host
  ) {
    return config.database.connection.host;
  }
  return 'localhost';
}

Optional Chaining (Modern TypeScript)

const getDatabaseHost = (config: Config): string =>
  config.database?.connection?.host ?? 'localhost';

Honest assessment: For simple access patterns, optional chaining (?.) is perfect. It's built into the language and very readable. Use fp-ts Option when you need to compose operations on potentially missing values.

When to Use Option Instead

Use fp-ts Option when:

• You need to chain multiple operations on potentially missing values
• You want to distinguish "missing" from other falsy values
• You're building a pipeline of transformations

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

// Safe property access that returns Option
const prop = <T, K extends keyof T>(key: K) =>
  (obj: T | null | undefined): O.Option<T[K]> =>
    obj != null && key in obj
      ? O.some(obj[key] as T[K])
      : O.none;

// Chain accesses with flatMap
const getDatabaseHost = (config: Config): O.Option<string> =>
  pipe(
    O.some(config),
    O.flatMap(prop('database')),
    O.flatMap(prop('connection')),
    O.flatMap(prop('host'))
  );

// Extract with default
const host = pipe(
  getDatabaseHost(config),
  O.getOrElse(() => 'localhost')
);

Safe Array Access

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

// Imperative: throws if array is empty
const first = items[0]; // Could be undefined!

// Safe: returns Option
const first = A.head(items); // Option<Item>

// Get first item's name, or default
const firstName = pipe(
  items,
  A.head,
  O.map(item => item.name),
  O.getOrElse(() => 'No items')
);

// Safe lookup by index
const third = pipe(
  items,
  A.lookup(2),
  O.map(item => item.name),
  O.getOrElse(() => 'Not found')
);

Safe Record/Dictionary Access

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

const users: Record<string, User> = {
  'user-1': { name: 'Alice', email: '[e-mail]' },
  'user-2': { name: 'Bob', email: '[e-mail]' },
};

// Imperative: could be undefined
const user = users['user-3']; // User | undefined

// Safe: returns Option
const user = R.lookup('user-3')(users); // Option<User>

// Get user email or default
const email = pipe(
  users,
  R.lookup('user-3'),
  O.map(u => u.email),
  O.getOrElse(() => '[e-mail]')
);

Combining Multiple Optional Values

The Task: Get a user's display name, which requires both first and last name.

interface Profile {
  firstName?: string;
  lastName?: string;
  nickname?: string;
}

// Imperative
function getDisplayName(profile: Profile): string {
  if (profile.firstName && profile.lastName) {
    return `${profile.firstName} ${profile.lastName}`;
  }
  if (profile.nickname) {
    return profile.nickname;
  }
  return 'Anonymous';
}

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

const getDisplayName = (profile: Profile): string =>
  pipe(
    // Try full name first
    O.Do,
    O.bind('first', () => O.fromNullable(profile.firstName)),
    O.bind('last', () => O.(profile.)),
    O.( ),
    
    O.( O.(profile.)),
    
    O.( )
  );

---

6. Real-World Examples

Example 1: Transform API Response to UI-Ready Data

// API response
interface ApiOrder {
  order_id: string;
  customer: {
    id: string;
    full_name: string;
  };
  line_items: Array<{
    product_id: string;
    product_name: string;
    qty: number;
    unit_price: number;
  }>;
  order_date: string;
  status: 'pending' | 'processing' | 'shipped' | 'delivered';
}

// What the UI needs
interface OrderSummary {
  id: string;
  customerName: string;
  itemCount: number;
  total: number;
  formattedTotal: string;
  date: string;
  statusLabel: string;
  statusColor: string;
}

// Transformation
const STATUS_CONFIG: Record<string, { label: string; :  }> = {
  : { : , :  },
  : { : , :  },
  : { : , :  },
  : { : , :  },
};

 formatCurrency = (: ): 
  ;

 formatDate = (: ): 
   (iso).(, {
    : ,
    : ,
    : ,
  });

 toOrderSummary = (: ):  {
   total = order..(
     sum + item. * item.,
    
  );

   status = [order.] ?? .;

   {
    : order.,
    : order..,
    : order..( sum + item., ),
    total,
    : (total),
    : (order.),
    : status.,
    : status.,
  };
};


 toOrderSummaries = (: []): [] =>
  orders.(toOrderSummary);

Example 2: Merge User Settings with Defaults

interface AppSettings {
  theme: {
    mode: 'light' | 'dark' | 'system';
    primaryColor: string;
    fontSize: 'small' | 'medium' | 'large';
  };
  notifications: {
    email: boolean;
    push: boolean;
    sms: boolean;
    frequency: 'immediate' | 'daily' | 'weekly';
  };
  privacy: {
    showProfile: boolean;
    showActivity: boolean;
    allowAnalytics: boolean;
  };
}

type DeepPartial<T> = {
  [P in keyof T]?: T[P] extends object ? DeepPartial<T[P]> : T[P];
};

const DEFAULT_SETTINGS: AppSettings = {
  theme: {
    mode: 'system',
    primaryColor: '#007bff',
    fontSize: 'medium',
  },
  notifications: {
    email: true,
    push: true,
    : ,
    : ,
  },
  : {
    : ,
    : ,
    : ,
  },
};

 deepMergeSettings = (
  : ,
  : <>
):  ({
  : { ...defaults., ...user. },
  : { ...defaults., ...user. },
  : { ...defaults., ...user. },
});


 : <> = {
  : { :  },
  : { : , :  },
};

 finalSettings = (, userPreferences);

Example 3: Group Orders by Customer with Totals

interface Order {
  id: string;
  customerId: string;
  customerName: string;
  items: Array<{ name: string; price: number; quantity: number }>;
  date: string;
}

interface CustomerOrderSummary {
  customerId: string;
  customerName: string;
  orderCount: number;
  totalSpent: number;
  orders: Order[];
}

const calculateOrderTotal = (order: Order): number =>
  order.items.reduce((sum, item) => sum + item.price * item.quantity, 0);

const groupOrdersByCustomer = (orders: Order[]): CustomerOrderSummary[] => {
  const grouped = groupBy((order: Order) => order.customerId)(orders);

   .(grouped).( ({
    customerId,
    : customerOrders[].,
    : customerOrders.,
    : customerOrders.(
       sum + (order),
      
    ),
    : customerOrders,
  }));
};

Example 4: Safely Access Deeply Nested Config

interface AppConfig {
  services?: {
    api?: {
      endpoints?: {
        users?: string;
        orders?: string;
        products?: string;
      };
      auth?: {
        type?: 'bearer' | 'basic' | 'oauth';
        token?: string;
      };
    };
    database?: {
      primary?: {
        host?: string;
        port?: number;
        name?: string;
      };
    };
  };
}

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

// Create a type-safe config accessor
const getConfigValue = <T>(
  config: AppConfig,
  path: (config: AppConfig) => T | undefined,
  defaultValue: T
): T => path(config) ?? defaultValue;

// Usage with optional chaining (simplest)
const apiUsersEndpoint = getConfigValue(
  config,
   c.?.?.?.,
  
);


 getEndpoint = (: , :  |  | ): 
  (
    O.(config.),
    O.( O.(s.)),
    O.( O.(a.)),
    O.( O.(e[name])),
    O.( )
  );


  = () => ({
  : config.?.?.?. ?? ,
  : config.?.?.?. ?? ,
  : config.?.?.?. ?? ,
});

---

7. When to Use What

Use Native Methods When:

• Simple transformations: .map(), .filter(), .reduce() are perfectly good
• No composition needed: You're doing a one-off transformation
• Team familiarity: Everyone knows native methods
• Optional chaining suffices: obj?.prop?.value ?? default handles your null-safety needs

// Native is fine here
const activeUserNames = users
  .filter(u => u.isActive)
  .map(u => u.name);

Use fp-ts When:

• Chaining operations that might fail: Multiple steps where each can return nothing
• Composing transformations: Building reusable transformation pipelines
• Type-safe error handling: You want the compiler to track potential failures
• Complex data pipelines: Many steps that benefit from explicit composition

// fp-ts shines here
const result = pipe(
  users,
  A.findFirst(u => u.id === userId),
  O.flatMap(u => O.fromNullable(u.profile)),
  O.flatMap(p => O.fromNullable(p.settings)),
  O.map(s => s.theme),
  O.getOrElse(() => 'default')
);

Use Custom Utilities When:

• Domain-specific operations: groupBy, countBy, sumBy for your data
• Repeated patterns: You find yourself writing the same transformation many times
• Team conventions: Establishing consistent patterns across the codebase

// Custom utility pays off when used repeatedly
const revenueByRegion = sumBy(
  (sale: Sale) => sale.region,
  (sale: Sale) => sale.amount
)(sales);

Performance Considerations

• Chaining creates intermediate arrays: arr.filter().map() creates one array, then another
• For hot paths, consider reduce: One pass through the data
• Measure before optimizing: The readability cost of optimization is often not worth it

// If performance matters (and you've measured!)
const result = items.reduce((acc, item) => {
  if (item.isActive) {
    acc.push(item.name.toUpperCase());
  }
  return acc;
}, [] as string[]);

// vs the more readable (but 2-pass) version
const result = items
  .filter(item => item.isActive)
  .map(item => item.name.toUpperCase());

---

Summary

Task	Imperative	Functional	Recommendation
Transform array elements	for loop with push	.map()	Use map
Filter array	for loop with condition	.filter()	Use filter
Accumulate values	for loop with accumulator	.reduce()	Use reduce for complex, loop for simple
Group by key	for loop with object	groupBy utility	Create reusable utility
Pick object fields	manual property copy	pick utility	Use spread for one-off, utility for repeated
Merge objects	property-by-property	spread syntax	Use spread
Deep merge	nested conditionals	recursive utility	Use utility or library
Null-safe access	if (x && x.y)	?. or Option	Use ?. for simple, Option for composition
Normalize API data	nested loops	extraction functions	Break into composable functions

The functional approach is better when:

• You need to compose operations
• You want reusable transformations
• You value explicit data flow over implicit state
• Type safety for missing values matters

The imperative approach is acceptable when:

• The transformation is a one-off
• The logic is simple and linear
• Performance is critical and you've measured
• The team is more comfortable with it