Book Image

R Data Structures and Algorithms

By : PKS Prakash, Achyutuni Sri Krishna Rao
Book Image

R Data Structures and Algorithms

By: PKS Prakash, Achyutuni Sri Krishna Rao

Overview of this book

In this book, we cover not only classical data structures, but also functional data structures. We begin by answering the fundamental question: why data structures? We then move on to cover the relationship between data structures and algorithms, followed by an analysis and evaluation of algorithms. We introduce the fundamentals of data structures, such as lists, stacks, queues, and dictionaries, using real-world examples. We also cover topics such as indexing, sorting, and searching in depth. Later on, you will be exposed to advanced topics such as graph data structures, dynamic programming, and randomized algorithms. You will come to appreciate the intricacies of high performance and scalable programming using R. We also cover special R data structures such as vectors, data frames, and atomic vectors. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. We will also explore the application of binary search and will go in depth into sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort.
Table of Contents (17 chapters)
R Data Structures and Algorithms
Credits
About the Authors
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface

Getting started with data structure


Data structure is a critical component in any algorithm. Before we go into details; let's illustrate this with an example; a sorting algorithm for positive integer for a finite length needs to be programmed using user input, and the output is to be displayed in ascending order. The sorting algorithm, which acts as a connector between the user-defined input and user-desired output can be approached in multiple ways:

  • Bubble sort and shell sort, which are simple variants of sorting, but are highly inefficient

  • Insertion sort and selection sort, primarily used for sorting small datasets

  • Merge sort, heap sort, and quick sort, which are efficient ways of sorting based on the complexities involved in an average system runtime

  • Distributed sorts such as counting sort, bucket sort, and radix sort, which can handle both runtime and memory usage

Each of these options can, in turn, handle a particular set of instances more effectively. This essentially reduces the concept...