Sliding Window on Arrays in DSA Javascript - Build from Scratch

Sliding Window on Arrays

📖 Scenario: You are analyzing daily temperatures recorded over a week. You want to find the highest average temperature over any 3-day period.

🎯 Goal: Build a program that uses the sliding window technique to find the maximum average temperature over any 3 consecutive days.

📋 What You'll Learn

Create an array called temperatures with exactly these values: [70, 72, 68, 74, 76, 73, 75]

Create a variable called windowSize and set it to 3

Use a for loop with variable i to slide over the array and calculate the sum of each 3-day window

Find and store the maximum sum of any 3-day window in a variable called maxSum

Calculate the maximum average temperature by dividing maxSum by windowSize

Print the maximum average temperature rounded to 2 decimal places

💡 Why This Matters

🌍 Real World

Analyzing temperature trends helps in weather forecasting and planning outdoor activities.

💼 Career

Sliding window technique is widely used in data analysis, signal processing, and performance optimization tasks in software development.

1

Create the temperature data array

Create an array called temperatures with these exact values: [70, 72, 68, 74, 76, 73, 75]

DSA Javascript

// Create the temperatures array
// Your code here

Hint

Use const temperatures = [70, 72, 68, 74, 76, 73, 75]; to create the array.

2

Set the sliding window size

Create a variable called windowSize and set it to 3

DSA Javascript

const temperatures = [70, 72, 68, 74, 76, 73, 75];
// Create windowSize variable and set it to 3
// Your code here

Hint

Use const windowSize = 3; to set the window size.

3

Calculate the maximum sum of any 3-day window

Use a for loop with variable i to slide over temperatures and calculate the sum of each 3-day window. Store the maximum sum in a variable called maxSum. Initialize maxSum with the sum of the first 3 elements.

DSA Javascript

const temperatures = [70, 72, 68, 74, 76, 73, 75];
const windowSize = 3;
// Calculate maxSum using sliding window
// Your code here

Hint

Start maxSum with the sum of the first 3 temperatures. Then slide the window from index 1 to the end, updating maxSum if you find a bigger sum.

4

Calculate and print the maximum average temperature

Calculate the maximum average temperature by dividing maxSum by windowSize. Then print the result rounded to 2 decimal places using console.log.

DSA Javascript

const temperatures = [70, 72, 68, 74, 76, 73, 75];
const windowSize = 3;

let maxSum = temperatures[0] + temperatures[1] + temperatures[2];

for (let i = 1; i  maxSum) {
        maxSum = currentSum;
    }
}

// Calculate max average and print rounded to 2 decimals
// Your code here

Hint

Divide maxSum by windowSize and use toFixed(2) to round to 2 decimals before printing.

Practice

1. What is the main purpose of the sliding window technique when working with arrays?

easy

A. To find the maximum element in the entire array

B. To sort the array elements in ascending order

C. To reverse the array elements in place

D. To efficiently process subarrays of fixed size without recomputing everything

Solution

1. Step 1: Understand the sliding window concept

   The sliding window technique moves a fixed-size window over the array to process subarrays efficiently.

2. Step 2: Compare with other options

   Sorting, reversing, or finding max in the entire array are different operations not related to sliding window.

3. Final Answer:

   To efficiently process subarrays of fixed size without recomputing everything -> Option D

4. Quick Check:

   Sliding window = efficient fixed-size subarray processing [OK]

Hint: Sliding window moves fixed-size subarray to save work [OK]

Common Mistakes:

• Confusing sliding window with sorting or reversing
• Thinking it processes the whole array at once
• Assuming it finds max without window size

2. Which of the following is the correct syntax to initialize a sliding window sum for the first k elements in JavaScript?

easy

A. let sum = arr.slice(0, k).reduce((a, b) => a + b, 0);

B. let sum = arr.reduce((a, b) => a + b, 0);

C. let sum = arr.slice(k).reduce((a, b) => a + b, 0);

D. let sum = arr.splice(0, k).reduce((a, b) => a + b, 0);

Solution

1. Step 1: Understand how to get first k elements

   Using arr.slice(0, k) extracts the first k elements without modifying the array.

2. Step 2: Sum the elements correctly

   Using reduce((a, b) => a + b, 0) sums all elements in the sliced array.

3. Final Answer:

   let sum = arr.slice(0, k).reduce((a, b) => a + b, 0); -> Option A

4. Quick Check:

   slice(0,k) + reduce sum = correct initialization [OK]

Hint: Use slice(0,k) to get first window, then reduce to sum [OK]

Common Mistakes:

• Using reduce on whole array instead of first k elements
• Using splice which modifies the original array
• Slicing from k to end instead of 0 to k

3. What will be the output of the following code?

const arr = [1, 3, 2, 6, -1, 4, 1, 8, 2];
const k = 5;
let maxSum = 0;
let windowSum = 0;

// Initialize first window sum
for(let i = 0; i < k; i++) {
  windowSum += arr[i];
}
maxSum = windowSum;

// Slide the window
for(let i = k; i < arr.length; i++) {
  windowSum += arr[i] - arr[i - k];
  if(windowSum > maxSum) maxSum = windowSum;
}
console.log(maxSum);

medium

A. 18

B. 17

C. 16

D. 19

Solution

1. Step 1: Calculate initial window sum for first 5 elements

   Sum of [1,3,2,6,-1] = 1+3+2+6-1 = 11

2. Step 2: Slide window and update sums

   Next windows and sums:
   Remove 1, add 4: 11 - 1 + 4 = 14
   Remove 3, add 1: 14 - 3 + 1 = 12
   Remove 2, add 8: 12 - 2 + 8 = 18 (maxSum updated to 18)
   Remove 6, add 2: 18 - 6 + 2 = 14

3. Step 3: Find maxSum

   Max sum found is 18

4. Final Answer:

   18 -> Option A

5. Quick Check:

   Max sliding window sum = 18 [OK]

Hint: Update sum by subtracting left, adding right element [OK]

Common Mistakes:

• Not updating maxSum correctly
• Adding instead of subtracting old element
• Wrong initial sum calculation

4. Identify the bug in the following sliding window code that finds the maximum sum of subarrays of size k:

function maxSlidingWindowSum(arr, k) {
  let maxSum = 0;
  let windowSum = 0;
  for(let i = 0; i <= k; i++) {
    windowSum += arr[i];
  }
  maxSum = windowSum;
  for(let i = k; i < arr.length; i++) {
    windowSum += arr[i] - arr[i - k];
    if(windowSum > maxSum) maxSum = windowSum;
  }
  return maxSum;
}

medium

A. The subtraction in the second loop is incorrect

B. The first loop should run while i < k, not i <= k

C. maxSum should be initialized to Number.MIN_VALUE

D. The function does not handle empty arrays

Solution

1. Step 1: Check the first loop range

   The first loop runs from i = 0 to i <= k, which means it accesses arr[k], an out-of-bound index for window size k.

2. Step 2: Correct the loop condition

   The loop should run while i < k to sum exactly k elements starting from index 0.

3. Final Answer:

   The first loop should run while i < k, not i <= k -> Option B

4. Quick Check:

   Loop range for first window = i < k [OK]

Hint: Loop for first window sums exactly k elements: i < k [OK]

Common Mistakes:

• Using <= k causes out-of-bounds error
• Ignoring array length checks
• Incorrect initialization of maxSum

5. Given an array of integers and a number k, how would you find the length of the longest subarray with sum less than or equal to k using the sliding window technique?

hard

A. Calculate prefix sums and find max subarray sum using Kadane's algorithm

B. Sort the array and use binary search to find subarrays with sum ≤ k

C. Expand the window by moving right pointer and shrink from left when sum exceeds k, track max length

D. Use nested loops to check all subarrays and pick the longest with sum ≤ k

Solution

1. Step 1: Use two pointers to create a sliding window

   Start with both pointers at the beginning, expand right pointer to add elements to sum.

2. Step 2: Shrink window from left when sum exceeds k

   If sum > k, move left pointer forward to reduce sum until sum ≤ k again, tracking max window length.

3. Step 3: Track the maximum length of valid windows

   Keep updating max length whenever sum ≤ k during expansion and shrinking.

4. Final Answer:

   Expand the window by moving right pointer and shrink from left when sum exceeds k, track max length -> Option C

5. Quick Check:

   Sliding window with two pointers and sum check = Expand the window by moving right pointer and shrink from left when sum exceeds k, track max length [OK]

Hint: Move right to expand, left to shrink when sum > k [OK]

Common Mistakes:

• Using sorting or binary search which doesn't apply here
• Using nested loops causing O(n²) time
• Confusing Kadane's algorithm with sum limit problem