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Functional data structures in R : advanced statistical programming in R

Author: Thomas Mailund
Publisher: Berkeley, CA : Apress, 2017.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Get an introduction to functional data structures using R and write more effective code and gain performance for your programs. This book teaches you workarounds because data in functional languages is not mutable: for example you'll learn how to change variable-value bindings by modifying environments, which can be exploited to emulate pointers and implement traditional data structures. You'll also see how, by  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Mailund, Thomas.
Functional Data Structures in R : Advanced Statistical Programming in R.
Berkeley, CA : Apress, ©2017
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Thomas Mailund
ISBN: 9781484231449 1484231449 1484231430 9781484231432
OCLC Number: 1012884016
Description: 1 online resource (262 pages)
Contents: 1: Abstract Data Structures2: Immutable and Persistent Data3: Bags, Stacks, and Queues4: Heaps5: Sets and Search Trees6: ConclusionsBibliography
Responsibility: Thomas Mailund.

Abstract:

Get an introduction to functional data structures using R and write more effective code and gain performance for your programs. This book teaches you workarounds because data in functional languages is not mutable: for example you'll learn how to change variable-value bindings by modifying environments, which can be exploited to emulate pointers and implement traditional data structures. You'll also see how, by abandoning traditional data structures, you can manipulate structures by building new versions rather than modifying them. You'll discover how these so-called functional data structures are different from the traditional data structures you might know, but are worth understanding to do serious algorithmic programming in a functional language such as R. By the end of Functional Data Structures in R, you'll understand the choices to make in order to most effectively work with data structures when you cannot modify the data itself. These techniques are especially applicable for algorithmic development important in big data, finance, and other data science applications. You will: Carry out algorithmic programming in R Use abstract data structures Work with both immutable and persistent data Emulate pointers and implement traditional data structures in R Implement data structures in C/C++ with some wrapper code in R Build new versions of traditional data structures that are known.

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