Book Image

R Data Structures and Algorithms

By : PKS Prakash, Achyutuni Sri Krishna Rao
Book Image

R Data Structures and Algorithms

By: PKS Prakash, Achyutuni 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 (17 chapters)
R Data Structures and Algorithms
Credits
About the Authors
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface

Linked list


A list can be defined as a collection of a finite number, or as sequence of data items known as elements. Each element of a list will have a specific data type-in the simplest scenario, all elements of a list have the same data type. In R, list implementation is essentially arrays of R objects (SEXP). The array-based implementation of lists will be discussed in the next section. To implement the list data structure, we will use environments, also known as objects in R:

# Example list with array, data.frame, matrix, and character 
> elist <- list(vec=1:4,df=data.frame(a=1:3, b=4:6),mat=matrix(1:4,    nrow=2), name="pks")
> elist[["vec"]]
[1] 1 2 3 4

In a linked list, each item holds a relative position with respect to the others. In a list, there is no requirement for contiguous memory, thus, data can have non-contiguous allocation. For example, Figure 3.6 shows the implementation of contiguous and non-contiguous memory allocation:

Figure 3.10: Memory allocation

In non...