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

Exercises


  1. The following are some growth-rate functional forms. Can you arrange them in the order of slower to faster performance?

    • 10n3

    • 3(log e n)2

    • 10n

    • 100n

    • Log2n2

    • Log2n3

    • Log3n2

    • Log3n3

    • n1.5

  2. Answer the following questions:

    • How can we evaluate the total memory currently being used by a given R environment? What is the purpose of garbage collection (GC) in the context of R?

    • Which occupies more size-a matrix with 10 numbers of categorical attributes, or a dataframe with 10 numbers of corresponding factors?

    • Can you evaluate and plot the memory allocation for dataframes and matrices with an increment of five observations for a fixed number of attributes (15 columns)?

    • Why does data.table occupy more memory than data.frame?

  3. Is data.table scalable in terms of performance (faster execution of operations) related to data pre-processing and transformations?

    (Hint: microbenchmark using large number of variables and observations with a higher number of iterations for each scenario).

  4. What are the...