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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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Randomized algorithms

In scenarios where computations are very expensive, introduction of randomness can help reduce computational effort at the expense of accuracy. The algorithms can be classified into the following and are depicted in Figure 9.7:

  • Deterministic algorithms
  • Randomized algorithm

    Randomized algorithms

    Figure 9.7: Different types of algorithm structures

Deterministic algorithms solve the problem correctly where computational effort required is a polynomial of the size of the input, whereas random algorithms take random sources as input and make their own choices while executing.

Randomized algorithms for finding large values

The computational cost of finding the largest value in an unsorted list is O(n). Any deterministic algorithm will require O(n) effort to determine the maximum value. However, in scenarios where time is essence, and n is very large, approximation algorithms are used, which, instead of finding the actual solution, determine the solution that is closer to the actual solution...

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