Data structures represent a way to organize and access particular data efficiently. They are critical to any problem solving and provide a complete solution to implement reusable codes. R Data Structures and Algorithms aims at strengthening the data structure skills among R users working in the analytics and intelligence domains. R is a well-designed language and environment for statistical computing and graphics developed at Bell Laboratories (formerly AT&T, now Lucent Technologies). This book will allow users to design optimized algorithms from the computational efficiency and resource usage perspective of an algorithm. This book puts forward the processes of building algorithms by introducing several data structures and their relationship with algorithms, followed by their analysis and evaluation. This book intends to cover not only the classical data structures, but also understand the integrities of functional data structures. We will cover the fundamentals of data structures, such as lists, stacks, queues, and dictionaries, followed by topics such as indexing, sorting, and searching in depth. Readers will also be exposed to advanced topics such as graphs, dynamic programming, and randomized algorithms.
The objective of this book is to build data structure concepts using R.
Chapter 1, Getting Started, builds a background for the aspects of data structures that are important to develop basics of R, as well as why they are important.
Chapter 2, Algorithm Analysis, talks about motivation, basic notation, and fundamental techniques for algorithm analysis.
Chapter 3, Linked Lists, builds a foundation of linked lists and will cover multiple variants of linked lists, such as linear linked lists, doubly linked lists, and circular linked lists.
Chapter 4, Stacks and Queues, introduces you to array-based and linked list-based stacks and queues and their implementation in R.
Chapter 5, Sorting Algorithms, explains various sorting algorithms, such as insertion sort, bubble sort, selection sort, and shell sort, and provides an empirical comparison between different algorithms.
Chapter 6, Exploring Search Options, provides details about search operations carried out on both vectors and lists, including linked lists. It also introduces you to self-organizing lists and hashing concepts.
Chapter 7, Indexing, covers indexing concepts, which are essential in file structuring, and organize large amounts of data on disk. It will also cover ISAM, 2-3 trees, B-tree, and B+ tree in detail.
Chapter 8, Graphs, builds a foundation for the graph data structure and its implementation. It also covers various algorithms for traversals, shortest-paths problems, and minimum-cost spanning trees in detail.
Chapter 9, Programming and Randomized Algorithms, extends the concept of a static data structure to randomize data structure, such as randomized skip lists. The chapter will also introduce programming concepts and several applications of it.
Chapter 10, Functional Data Structures, introduces you to functional data structures and lazy evaluation. It will also cover functional stacks and queues in R.