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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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Graph implementations


Let us create an ADT (Graph_ADT) for the implementation of functions on a given graph. The key features of ADT for a given graph analysis are the following:

  • Fixed number of vertices

  • Provision for addition and deletion of edges

  • Provision to support a mark array, which can assist algorithms in traversing along the graph

The vertices are denoted using non-zero integer values, and can additionally store vertex names or some kind of application-based predetermined values.

The following are some ADT functions that are widely used for implementing graph functions:

  • num_vert: This function returns the number of vertices for a given graph.

  • num_edge: This function returns the number of edges for a given graph.

  • weightEdge: This function returns the weight of an edge connecting two adjacent vertices. Its input is a pair of two connected vertices and its output is a numeric value indicating its weight.

  • assignEdge: This function is used to assign weight to a given edge of a graph. The...

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