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R Data Structures and Algorithms
R Data Structures and Algorithms

R Data Structures and Algorithms: Increase speed and performance of your applications with effi cient data structures and algorithms

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Profile Icon PKS Prakash Profile Icon Sri Krishna Rao
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€32.99
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Paperback Nov 2016 276 pages 1st Edition
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Arrow left icon
Profile Icon PKS Prakash Profile Icon Sri Krishna Rao
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€32.99
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Paperback Nov 2016 276 pages 1st Edition
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R Data Structures and Algorithms

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...

Getting started with data structure

Data structure is a critical component in any algorithm. Before we go into details; let's illustrate this with an example; a sorting algorithm for positive integer for a finite length needs to be programmed using user input, and the output is to be displayed in ascending order. The sorting algorithm, which acts as a connector between the user-defined input and user-desired output can be approached in multiple ways:

  • Bubble sort and shell sort, which are simple variants of sorting, but are highly inefficient
  • Insertion sort and selection sort, primarily used for sorting small datasets
  • Merge sort, heap sort, and quick sort, which are efficient ways of sorting based on the complexities involved in an average system runtime
  • Distributed sorts such as counting sort, bucket sort, and radix sort, which can handle both runtime and memory usage

Each of these options can, in turn, handle a particular set of instances more effectively. This essentially reduces...

Memory management in R

Memory management primarily deals with the administration of available memory and the prediction of additional memory required for smoother and faster execution of functions. The current section will cover the concept of memory allocation, which deals with storage of an object in the R environment.

During memory allocation R allocates memory differently to different objects in its environment. Memory allocation can be determined using the object_size function from the pryr package. The pryr package can be installed from the CRAN repository using install.packages("pryr"). The package is available for ‎R (≥ 3.1.0). The object_size function in pryr is similar to the object.size function in the base package. However, it is more accurate as it takes into account the:

  • Environment size associated with the current object
  • Shared elements within a given object under consideration.

The following are examples of using the object_size function in R to evaluate...

Exercises

  1. The following are some growth-rate functional forms. Can you arrange them in the order of slower to faster performance?
    • 10n3
    • 3(log e n)2
    • 10n
    • 100n
    • Log2n2
    • Log2n3
    • Log3n2
    • Log3n3
    • n1.5
  2. Answer the following questions:
    • How can we evaluate the total memory currently being used by a given R environment? What is the purpose of garbage collection (GC) in the context of R?
    • Which occupies more size-a matrix with 10 numbers of categorical attributes, or a dataframe with 10 numbers of corresponding factors?
    • Can you evaluate and plot the memory allocation for dataframes and matrices with an increment of five observations for a fixed number of attributes (15 columns)?
    • Why does data.table occupy more memory than data.frame?
  3. Is data.table scalable in terms of performance (faster execution of operations) related to data pre-processing and transformations?

    (Hint: microbenchmark using large number of variables and observations with a higher number of iterations for each scenario).

  4. What are the best, worst, and average...

Summary

This chapter summarizes the basic concepts and nuances of evaluating algorithms in R. We covered the conceptual theory of memory management and system runtime in R. We discussed the best, worst, and average-case scenarios to evaluate the performance of algorithms. In addition, we also looked into the trade-off between a computer's configuration and algorithm's system runtime, algorithm asymptotic analysis, simplifying and classifying rules, and computational evaluation of programs. The next chapter will cover fundamental data structure and the concepts of lists in R.

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Key benefits

  • • See how to use data structures such as arrays, stacks, trees, lists, and graphs through real-world examples
  • • Find out about important and advanced data structures such as searching and sorting algorithms
  • • Understand important concepts such as big-o notation, dynamic programming, and functional data structured

Description

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.

Who is this book for?

This book is for R developers who want to use data structures efficiently. Basic knowledge of R is expected.

What you will learn

  • • Understand the rationality behind data structures and algorithms
  • • Understand computation evaluation of a program featuring asymptotic and empirical algorithm analysis
  • • Get to know the fundamentals of arrays and linked-based data structures
  • • Analyze types of sorting algorithms
  • • Search algorithms along with hashing
  • • Understand linear and tree-based indexing
  • • Be able to implement a graph including topological sort, shortest path problem, and Prim's algorithm
  • • Understand dynamic programming (Knapsack) and randomized algorithms
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Length: 276 pages
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Table of Contents

10 Chapters
1. Getting Started Chevron down icon Chevron up icon
2. Algorithm Analysis Chevron down icon Chevron up icon
3. Linked Lists Chevron down icon Chevron up icon
4. Stacks and Queues Chevron down icon Chevron up icon
5. Sorting Algorithms Chevron down icon Chevron up icon
6. Exploring Search Options Chevron down icon Chevron up icon
7. Indexing Chevron down icon Chevron up icon
8. Graphs Chevron down icon Chevron up icon
9. Programming and Randomized Algorithms Chevron down icon Chevron up icon
10. Functional Data Structures Chevron down icon Chevron up icon

Customer reviews

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Amazon Customer Jan 02, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The 1st book on Data Structures in R available in the market !Has well explained examples and R codes not available anywhere else on the internet.
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