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R Data Structures and Algorithms

R Data Structures and Algorithms

By : PKS Prakash, Sri Krishna Rao
4.5 (2)
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R Data Structures and Algorithms

R Data Structures and Algorithms

4.5 (2)
By: PKS Prakash, 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 (11 chapters)
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An empirical comparison of sorting algorithms


Empirical comparison analysis intends to evaluate the performance of algorithms based on the system runtime. Many algorithms might possess the same asymptote complexity, but their performance might differ based on the size of the input vector. Empirical analysis is performed on the underlying assumption that the system properties and configuration remain the same for all the running algorithms under consideration.

Table 5.2 shows the system runtime for actual implementation of sorting algorithms measured using microbenchmark in R:

Table 5.2: Empirical comparison of sorting algorithms using system configuration of 2.8-GHz Intel i7 CPU running Windows. The system runtime is shown in milliseconds

The input used for empirical analysis is a random vector of integers of various lengths ranging from 10, 100, 1,000 to 10,000. The input for the best-case scenario is an increasing sorted vector of length 1,000. Similarly, the input for the worst-case scenario...

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