Why Number of Islands BFS and DFS in DSA Javascript?

The Big Idea

What if you could count all islands on a map perfectly without missing a single one or counting twice?

The Scenario

Imagine you have a big map made of squares, where some squares are water and some are land. You want to count how many separate islands (groups of connected land squares) are on the map. Doing this by looking at each square and trying to remember which islands you already counted can get very confusing and slow.

The Problem

Trying to count islands by checking each square manually is slow and easy to mess up. You might count the same island twice or miss some parts because it's hard to keep track of which land squares belong together. This makes the task error-prone and frustrating.

The Solution

Using BFS (Breadth-First Search) or DFS (Depth-First Search) helps you explore each island fully once you find a piece of land. These methods let you visit all connected land squares systematically, marking them as visited so you don't count them again. This way, you can count islands quickly and correctly.

Before vs After

✗ Before

let count = 0;
for (let i = 0; i < grid.length; i++) {
  for (let j = 0; j < grid[0].length; j++) {
    if (grid[i][j] === '1') count++;
  }
}

✓ After

function bfs(grid, i, j) {
  // explore all connected land
}

let count = 0;
for (let i = 0; i < grid.length; i++) {
  for (let j = 0; j < grid[0].length; j++) {
    if (grid[i][j] === '1') {
      bfs(grid, i, j);
      count++;
    }
  }
}

What It Enables

This lets you quickly and accurately find how many islands are on any map, no matter how big or complex.

Real Life Example

Think about a drone flying over a flooded area trying to count how many separate dry patches (islands) are left. BFS or DFS helps the drone's software count these patches without missing or double-counting any.

Key Takeaways

Manual counting is slow and error-prone.

BFS and DFS explore connected land fully to avoid double counting.

This method works well for any size or shape of map.

Practice

1. What does the "Number of Islands" problem ask you to find in a grid of '1's and '0's?

easy

A. Count how many groups of connected '1's (islands) are in the grid

B. Find the largest island size in the grid

C. Calculate the total number of '0's in the grid

D. Replace all '1's with '0's in the grid

Solution

1. Step 1: Understand the grid representation

   The grid contains '1's representing land and '0's representing water.

2. Step 2: Identify what an island means

   An island is a group of connected '1's horizontally or vertically.

3. Final Answer:

   Count how many groups of connected '1's (islands) are in the grid -> Option A

4. Quick Check:

   Number of islands = count of connected '1' groups [OK]

Hint: Islands = connected '1's groups, count them [OK]

Common Mistakes:

• Confusing island count with largest island size
• Counting '0's instead of '1's
• Ignoring connectivity rules (only horizontal/vertical)

2. Which of the following is the correct way to start a DFS function in JavaScript for the Number of Islands problem?

easy

A. function dfs(grid, r, c) { if (r < 0 || c < 0) return; }

B. function dfs(grid, r, c) { if (r <= 0 || c <= 0 || r > grid.length || c > grid[0].length) return; }

C. function dfs(grid, r, c) { if (r > grid.length || c > grid[0].length) return; }

D. function dfs(grid, r, c) { if (r < 0 || c < 0 || r >= grid.length || c >= grid[0].length) return; }

Solution

1. Step 1: Check boundary conditions for DFS

   We must stop DFS if row or column is outside grid bounds.

2. Step 2: Write correct boundary check

   Row or column less than 0 or greater or equal to grid size means out of bounds.

3. Final Answer:

   function dfs(grid, r, c) { if (r < 0 || c < 0 || r >= grid.length || c >= grid[0].length) return; } -> Option D

4. Quick Check:

   Boundary check includes all invalid indices [OK]

Hint: Check all out-of-bound indices with < 0 or >= length [OK]

Common Mistakes:

• Using > instead of >= for boundary check
• Missing some boundary conditions
• Using <= 0 which excludes valid index 0

3. What will be the output of the following code snippet for the grid below?

const grid = [ ['1','1','0','0'], ['1','0','0','1'], ['0','0','1','1'], ['0','0','0','0'] ]; function numIslands(grid) { let count = 0; const dfs = (r, c) => { if (r < 0 || c < 0 || r >= grid.length || c >= grid[0].length || grid[r][c] === '0') return; grid[r][c] = '0'; dfs(r + 1, c); dfs(r - 1, c); dfs(r, c + 1); dfs(r, c - 1); }; for (let r = 0; r < grid.length; r++) { for (let c = 0; c < grid[0].length; c++) { if (grid[r][c] === '1') { dfs(r, c); count++; } } } return count; } console.log(numIslands(grid));

medium

A. 1

B. 3

C. 2

D. 4

Solution

1. Step 1: Identify islands in the grid

   Islands are groups of connected '1's. Here, islands are: top-left (3 cells: (0,0),(0,1),(1,0)), single cell at (1,3), and bottom cluster (2 cells: (2,2),(2,3)). Total islands = 3.

2. Step 2: Count islands using DFS

   DFS marks visited '1's as '0'. After visiting each island, count increments. Total islands counted = 3.

3. Final Answer:

   3 -> Option B

4. Quick Check:

   Count connected '1' groups = 3 [OK]

Hint: Count connected '1' groups by marking visited [OK]

Common Mistakes:

• Counting diagonal connections as islands
• Not marking visited cells, causing overcount
• Misreading grid layout

4. The following BFS code for Number of Islands has a bug. What is the bug?

function bfs(grid, r, c) { const queue = [[r, c]]; while (queue.length > 0) { const [row, col] = queue.shift(); if (row < 0 || col < 0 || row >= grid.length || col >= grid[0].length || grid[row][col] === '0') continue; grid[row][col] = '0'; queue.push([row + 1, col]); queue.push([row - 1, col]); queue.push([row, col + 1]); queue.push([row, col - 1]); } }

medium

A. It does not check if a cell is already visited before pushing neighbors

B. It marks visited cells after adding neighbors to queue

C. It uses queue.shift() which is inefficient for large grids

D. It uses recursion instead of iteration

Solution

1. Step 1: Analyze neighbor addition in BFS

   The code adds neighbors to the queue without checking if they are visited or water.

2. Step 2: Identify consequence of missing check

   This causes multiple visits to the same cell, increasing runtime and possible infinite loops.

3. Final Answer:

   It does not check if a cell is already visited before pushing neighbors -> Option A

4. Quick Check:

   Check before enqueueing neighbors to avoid repeats [OK]

Hint: Check visited before enqueueing neighbors in BFS [OK]

Common Mistakes:

• Thinking queue.shift() inefficiency is the bug here
• Marking visited after enqueue instead of before
• Confusing recursion with iteration

5. Given a grid where diagonal connections also count as connected land, how would you modify the DFS to count islands correctly?

hard

A. Ignore diagonal neighbors and count only vertical connections

B. Only check horizontal neighbors (r,c+1) and (r,c-1)

C. Add diagonal neighbors (r+1,c+1), (r+1,c-1), (r-1,c+1), (r-1,c-1) to DFS calls

D. Use BFS instead of DFS without changing neighbors

Solution

1. Step 1: Understand connectivity rules

   Diagonal connections mean neighbors include diagonals, not just up/down/left/right.

2. Step 2: Modify DFS to include diagonal neighbors

   Add DFS calls for (r+1,c+1), (r+1,c-1), (r-1,c+1), (r-1,c-1) along with existing neighbors.

3. Final Answer:

   Add diagonal neighbors (r+1,c+1), (r+1,c-1), (r-1,c+1), (r-1,c-1) to DFS calls -> Option C

4. Quick Check:

   Include diagonal neighbors for diagonal connectivity [OK]

Hint: Add diagonal neighbors to DFS for diagonal island counting [OK]

Common Mistakes:

• Ignoring diagonal neighbors when required
• Switching BFS/DFS without changing neighbor logic
• Counting only horizontal or vertical neighbors