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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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Sorting terminology and notation


In this chapter, the input for any algorithm is a vector of elements (key values) unless stated otherwise. These elements can be of any type: numeric, character, logical, or complex.

Consider an input vector V of elements i1,i2,...,in . These elements are said to be sorted provided their corresponding values satisfy a particular order. In other words, the elements of vector V are said to be sorted in non-decreasing order provided their values satisfy the condition i1<i2<,...,<in .

All the algorithms presented in this chapter can handle the special case of sorting, that is, duplicate elements in a given input vector; however, only some of them perform it optimally. An algorithm is said to be performing optimally provided it retains the original position of duplicate elements without redundantly ordering them, thereby reducing computation time.

The simplest way to compare performances of two algorithms is by assessing their computational system runtime...

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