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

Shortest path problems


Consider a city with a large number of roads interconnecting all its core areas, and you need to drive from an area P to an area Q. As the network of road is dense, you can have multiple options to reach area Q; nonetheless, you would desire to take the shortest route. However, the shortest route can have higher traffic; hence, you may now desire to take a new route, which minimizes travel time with a trade-off with distance. Adding further constraints, not all the routes allow bi-directional traffic. In other words, the shortest route needs to satisfy multiple constraints, and then, suggest the best possible route. Analogously, in a graph, each node corresponds to a city, and the edges correspond to the interconnecting roads. The weights of the edges can either be compared to distance or to travel time (based on traffic). These graphs can also be directed or un-directed based on whether a lane allows traffic in a single direction or both. Hence, it is not trivial...