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| 008 | 190708s2017 caua ob 001 0 eng d | |
| 020 | ▼a 9781484231432 | |
| 020 | ▼a 9781484231449 (e-book) | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 050 | 4 | ▼a QA76.6-76.66 |
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| 084 | ▼a 005.11 ▼2 DDCK | |
| 090 | ▼a 005.11 | |
| 100 | 1 | ▼a Mailund, Thomas. |
| 245 | 1 0 | ▼a Functional data structures in R ▼h [electronic resource] : ▼b advanced statistical programming in R / ▼c Thomas Mailund. |
| 260 | ▼a Berkeley, CA : ▼b Apress, ▼c c2017. | |
| 300 | ▼a 1 online resource (xii, 256 p.) : ▼b ill. | |
| 500 | ▼a Title from e-Book title page. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 520 | ▼a 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. | |
| 530 | ▼a Issued also as a book. | |
| 538 | ▼a Mode of access: World Wide Web. | |
| 650 | 0 | ▼a Computer science. |
| 650 | 0 | ▼a Data structures (Computer science.). |
| 856 | 4 0 | ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-1-4842-3144-9 |
| 945 | ▼a KLPA | |
| 991 | ▼a E-Book(소장) |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/e-Book 컬렉션/ | 청구기호 CR 005.11 | 등록번호 E14014941 | 도서상태 대출불가(열람가능) | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
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.
What You'll Learn
- 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
- Build new versions of traditional data structures that are known
Who This Book Is For
Experienced or advanced programmers with at least a comfort level with R. Some experience with data structures recommended.
New feature
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
정보제공 :
목차
CONTENTS About the Author = vii About the Technical Reviewer = ix Introduction = xi Chapter 1 Introduction = 1 Chapter 2 Abstract Data Structures = 3 Structure on Data = 4 Abstract Data Structures in R = 6 Implementing Concrete Data Structures in R = 9 Asymptotic Running Time = 11 Experimental Evaluation of Algorithms = 15 Chapter 3 Immutable and Persistent Data = 25 Persistent Data Structures = 26 List Functions = 28 Trees = 37 Random Access Lists = 56 Chapter 4 Bags, Stacks, and Queues = 67 Bags = 68 Stacks = 73 Queues = 74 Side Effects Through Environments = 77 Side Effects Through Closures = 79 A Purely Functional Queue = 82 Time Comparisons = 84 Amortized Time Complexity and Persistent Data Structures = 85 Double-Ended Queues = 87 Lazy Queues = 95 Implementing Lazy Evaluation = 96 Lazy Lists = 98 Amortized Constant Time, Logarithmic Worst-Case, Lazy Queues = 107 Constant Time Lazy Queues = 118 Explicit Rebuilding Queue = 124 Chapter 5 Heaps = 135 Leftist Heaps = 140 Binomial Heaps = 144 Splay Heaps = 157 Plotting Heaps = 178 Heaps and Sorting = 183 Chapter 6 Sets and Search Trees = 189 Search Trees = 190 Red-Black Search Trees = 192 Insertion = 195 Deletion = 203 Visualizing Red-Black Trees = 226 Splay Trees = 231 Conclusions = 247 Acknowledgements = 248 Bibliography = 249 Index = 251