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

Minimum-cost spanning tree


A Minimum Spanning Tree (MST) works on graphs with directed and weighted (non-negative costs) edges. Consider a graph G with n vertices. The spanning tree is a subgraph of graph G with all its n vertices connected to each other using n-1 edges. Thus, there is no possibility of a cycle with the subgraph. If the spanning tree does have a cycle, then it is advisable to remove any one edge, probably the one with the highest cost. The spanning tree with the least sum of edge weights is termed as a MST. It is widely used in applications such as laying of power cables across the city, connecting all houses using the least length of power cables. Here, the weight of each edge is the length of the cable, and the vertices are houses in the city. The most common algorithms to find the minimum cost spanning tree are Prim's algorithm and Kruskal's algorithm. Figure 8.11 shows the minimum cost spanning tree for an undirected-weighted graph.

Figure 8.11: Illustration of an undirected...