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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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Chapter 2. Algorithm Analysis

An algorithm can be defined as a set of step-by-step instructions which govern the outline of a program that needs to be executed using computational resources. The execution can be in any programming language such as R, Python, and Java. Data is an intricate component of any program, and depending on how data is organized (data structure), your execution time can vary drastically. That's why data structure is such a critical component of any good algorithm implementation. This book will concentrate primarily on running time or time complexity and partly on memory utilization and their relationship during program execution. The current chapter will cover following topics in detail:

  • Best, worst, and average cases
  • Computer versus algorithm
  • Algorithm asymptotic analysis
    • Upper bounds evaluation
    • Lower bounds evaluation
    • Big Θ notation
    • Simplifying rules
    • Classifying functions
  • Computation evaluation of a program
  • Analyzing problems
  • Space bounds
  • Empirical analysis...
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R Data Structures and Algorithms
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