Operations on arrays in R programming are used to create, modify, and delete arrays. Array operations are very useful as they allow you to work on array-like data structures. They also have the advantage of being very fast. Here are some of the most commonly used array operations in R programming.

## Introduction to Arrays in R

Arrays are multidimensional R objects that contain atoms belonging to the same data type. There are a fixed number of rows and columns in each matrix. The number of rows, columns, and the total number of such matrices are called the dimensions of the matrix. There are a large number of operations that can be performed on arrays in R programming:

## Modification of an item in the array

The array value can be accessed in R by specifying the dimensions, that is, the row, column, and matrix index, respectively. This value can then be reassigned to a new value by simply using the assignment operator (**** operators in r****). The value is replaced with a new instance.

#creating data vec = 1:24 #creating array #creating 2 matrices with dimensions 4x3 arr = array(vec, dim = c(4,3,2)) #printing the array values print("Original Array") print(arr) #accessing the element at specified position orignal_val = arr[2,3,1] #printing the element at specified position cat("Original Element value : ", orignal_val) #reassigning a new value at this index arr[2,3,1] = 1000 #printing the array values print("Modified Array") print(arr) modified_val = arr[2,3,1] cat("Modified Element value : ", modified_val)

##### Output

[1] "Original Array" , , 1 [,1] [,2] [,3] [1,] 1 5 9 [2,] 2 6 10 [3,] 3 7 11 [4,] 4 8 12 , , 2 [,1] [,2] [,3] [1,] 13 17 21 [2,] 14 18 22 [3,] 15 19 23 [4,] 16 20 24 Original Element value : 10 [1] "Modified Array" , , 1 [,1] [,2] [,3] [1,] 1 5 9 [2,] 2 6 1000 [3,] 3 7 11 [4,] 4 8 12 , , 2 [,1] [,2] [,3] [1,] 13 17 21 [2,] 14 18 22 [3,] 15 19 23 [4,] 16 20 24 Modified Element value : 1000

## Accessing the dimensions of the array

The `dim()`

the method in R can be used to display the dimensions of the two-dimensional matrices and the number of such matrices in R. The following code can be used to gather information :

#creating data vec = 1:24 #creating array #creating 2 matrices with dimensions 4x3 arr = array(vec, dim = c(4,3,2)) #printing the dimensions of the array cat("Dimensions ", dim(arr))

##### Output

Dimensions 4 3 2

## Check for the existence of an element in the array

The element can be checked if it exists in the array or not by using the` %in%`

operator. This operator is used to return a boolean value depending on whether the data element is present in the specified object or not. The syntax to check for element in the array is:

### Syntax

ele %in% obj

### Example

#creating data vec = 1:24 #creating array #creating 2 matrices with dimensions 4x3 arr = array(vec, dim = c(4,3,2)) #printing the array values print("Original Array") print(arr) #check for presence of value 23 flag1 = 23 %in% arr cat("23 present : ", flag1) #check for presence of value 1000 flag2 = 1000 %in% arr cat("1000 present : ", flag2)

##### Output

[1] "Original Array" , , 1 [,1] [,2] [,3] [1,] 1 5 9 [2,] 2 6 10 [3,] 3 7 11 [4,] 4 8 12 , , 2 [,1] [,2] [,3] [1,] 13 17 21 [2,] 14 18 22 [3,] 15 19 23 [4,] 16 20 24 23 present : TRUE 1000 present : FALSE

## Applying functions over arrays

Functions can be applied over arrays by using the `apply()`

method, which takes as arguments the function to be applied over the array. The method has the following syntax :

apply (data , margin , fun )

- Where, data - The data over which the function is to be applied
- margin - The dataset to be used
- fun - the function to be applied

In the following example, the sum is computed row-wise for both matrices, that is, the first value of the output is the summation of the values in row1 of matrix1 as well as matrix2.

#creating data vec = 1:24 #creating array #creating 2 matrices with dimensions 4x3 arr = array(vec, dim = c(4,3,2)) #printing the array values print("Original Array") print(arr) #applying function over array res <- apply(arr,c(1),sum) #printing the output of function print(res)

##### Output

[1] "Original Array" , , 1 [,1] [,2] [,3] [1,] 1 5 9 [2,] 2 6 10 [3,] 3 7 11 [4,] 4 8 12 , , 2 [,1] [,2] [,3] [1,] 13 17 21 [2,] 14 18 22 [3,] 15 19 23 [4,] 16 20 24 [1] 66 72 78 84