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

Memory management in R


Memory management primarily deals with the administration of available memory and the prediction of additional memory required for smoother and faster execution of functions. The current section will cover the concept of memory allocation, which deals with storage of an object in the R environment.

During memory allocation R allocates memory differently to different objects in its environment. Memory allocation can be determined using the object_size function from the pryr package. The pryr package can be installed from the CRAN repository using install.packages("pryr"). The package is available for ‎R (≥ 3.1.0). The object_size function in pryr is similar to the object.size function in the base package. However, it is more accurate as it takes into account the:

  • Environment size associated with the current object

  • Shared elements within a given object under consideration.

The following are examples of using the object_size function in R to evaluate memory allocation:


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