R Arrays

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Arrays are a powerful data structure in R, allowing users to store and manipulate data across more than two dimensions. In this article, we will delve into the world of arrays, looking at how they are structured, created, and modified. We’ll also explore the various operations that can be performed on arrays, their practical use-cases, the benefits they offer, as well as potential challenges users may encounter. Practical examples will be provided to facilitate a comprehensive understanding.

What is an Array in R?

In R, an array is a multi-dimensional data structure that can store elements of the same type (numeric, character, or logical) across more than two dimensions. While matrices in R are confined to two dimensions (rows and columns), arrays break this limitation by adding a third dimension (and more, if needed). Each dimension in an array is termed as an “array dimension”.

Creating Arrays in R

Creating an array in R is facilitated through the array() function. This function takes a vector as input and converts it into a specified number of dimensions. The syntax for creating an array is as follows:

my_array <- array(vector, dim = c(dim1, dim2, ..., dimN))

For example:

# Create a numeric array
my_array <- array(1:12, dim = c(3, 2, 2))

This creates a 3-dimensional array with 3 rows, 2 columns, and 2 slices.

Operations on Arrays

Once an array is created, various operations can be performed on it, including arithmetic operations, indexing and slicing, reshaping, and applying functions.

Arithmetic Operations

Similar to vectors and matrices, arithmetic operations on arrays in R are carried out element-wise. That is, the operation is applied to each corresponding pair of elements from the two arrays:

# Define two 3x2x2 arrays
array1 <- array(1:12, dim = c(3, 2, 2))
array2 <- array(13:24, dim = c(3, 2, 2))

# Perform addition
result <- array1 + array2

Indexing and Slicing Arrays

Indexing (or slicing) arrays in R is straightforward. We use square brackets [] with indices for each dimension:

# Access the element at the 2nd row, 1st column, and 1st slice
element <- my_array[2, 1, 1]

# Access the entire 2nd row, 1st slice
row <- my_array[2, , 1]

# Access the entire 1st column, 2nd slice
column <- my_array[, 1, 2]

Reshaping Arrays

Arrays can be reshaped using the aperm() function, which permutes the dimensions of the array:

# Permute the dimensions of a 3x2x2 array to 2x3x2
reshaped_array <- aperm(my_array, c(2, 1, 3))

Applying Functions to Arrays

Similar to matrices, functions can be applied to arrays. However, the apply() function needs to be used instead of the rowSums(), colSums(), rowMeans(), and colMeans() functions:

# Compute the sum of each row in each slice
row_sums <- apply(my_array, c(1, 3), sum)

# Compute the mean of each column in each slice
col_means <- apply(my_array, c(2, 3), mean)

Practical Use-Cases of Arrays

Arrays are extensively used in various fields including:

  1. Multivariate Data Analysis: Arrays are used when dealing with multivariate data, where each dimension represents a different variable.
  2. Image Processing: In image processing, a colored image can be represented as a 3D array, where the first two dimensions represent the pixel coordinates, and the third dimension represents the color channels (Red, Green, Blue).
  3. Tensor Computations: In machine learning and data science, higher-dimensional arrays, also known as tensors, are often used. In these contexts, arrays in R can be used for tensor computations.

Benefits and Drawbacks of Arrays


  1. Multi-dimensionality: Arrays can store data in more than two dimensions, making them suitable for complex data structures.
  2. Homogeneous data storage: Arrays provide an efficient way to store and manipulate large amounts of homogeneous data.


  1. Homogeneity: Like matrices, arrays can only hold elements of the same data type.
  2. Complexity: Handling arrays, especially high-dimensional ones, can be complex and may require a good understanding of the data structure.

In conclusion, arrays are an extremely flexible and powerful data structure in R. Understanding their capabilities and knowing how to manipulate them effectively is a key skill for any R programmer, especially those working in areas like multivariate data analysis, image processing, and machine learning.

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