learning about arrays

Author: GameOfCode64

Diff for: day3.js renamed to Array/day3.js

@@ -0,0 +1,75 @@
+// ! Day 4 of learning DSA in
+
+//  Todo: Array Data Starcher
+
+// * Q1, Second Largest Number
+// * Given an array Arr of size of N  Print the Second largest Number
+
+// ? Example
+// * Input --> [3,5,6,7,89,33,143]; ---> output: 89
+
+// * The Time Complexity of This Function is O(n log n) (bad Time Time complexity)
+function FindSecond(array) {
+  let newArray = Array.from(new Set(array));
+  newArray.sort((a, b) => {
+    return b - a;
+  });
+  if (newArray.length > 2) {
+    return newArray[1];
+  } else {
+    return -1;
+  }
+}
+const arr = [3, 5, 6, 7, 89, 33, 143];
+console.log(FindSecond(arr));
+
+// * The Time Complexity of This Function is O(n) (Liner Search) (bad Time Time complexity)
+function Optimise(array) {
+  let largest = Number.NEGATIVE_INFINITY;
+  let SLargest = Number.NEGATIVE_INFINITY;
+
+  for (let i = 0; i < array.length; i++) {
+    if (array[i] > largest) {
+      SLargest = largest;
+      largest = array[i];
+    } else if (array[i] != largest && array[i] > SLargest) {
+      SLargest = array[i];
+    }
+  }
+  return SLargest;
+}
+console.log(Optimise(arr));
+
+console.clear();
+
+// * Q2, Rotate Array by K
+// * Given an integer array num rotate the array to the right by K steps
+
+// ? Example
+// *  Input  -->  [1,2,3,4,5,6,7], k = 3,  Output --> [5,6,7,1,2,3,4]
+// * [1,2,3,4,5,6,7] -> [7,1,2,3,4,5,6] -> [6,7,1,2,3,4,5] -> [5,6,7,1,2,3,4]
+
+// Todo: My Solution (Wrong)
+function rotateArray(arr, k) {
+  if (k > arr.length) {
+    return -1;
+  } else {
+    for (let i = 0; i < k; i++) {
+      var Array = [];
+      Array.unshift(arr[arr.length - 1], ...arr);
+      arr.pop();
+    }
+    return Array;
+  }
+}
+
+// *
+function rotateArray1(arr, k) {
+  let size = arr.length;
+  if (size > k) {
+    k = k % size;
+  }
+  const rotated = arr.splice(size - k, size);
+  arr.unshift(...rotated);
+  return arr;
+}

Diff for: Array/day4.js

@@ -2,7 +2,7 @@
 
 // Todo: Topic 1 (Big O Notation)
 
-// Todo: O(n) -> ("signifies that the execution time of the algorithm grows linearly in proportion to the size of the input data(n) ")  (Linear Search) (OK Time Complexity)
+// Todo: O(n) -> ("signifies that the execution time of the algorithm grows linearly in proportion to the size of the input data(n) ")  (Linear Search) (Good Complexity)
 
 const groceryList = ["Milk", "Eggs", "Bread", "Butter", "Cheese", "Tomatoes"];