Domain-Specific Languages in R: Advanced Statistical Programming

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Domain-Specific Languages in R: Advanced Statistical Programming Front Cover

Domain-Specific Languages in R: Advanced Statistical Programming

by Thomas Mailund
  • Length: 257 pages
  • Edition: 1st ed.
  • Publisher: Apress
  • Publication Date: 2018-10-02
  • ISBN-10: 1484235878
  • ISBN-13: 9781484235874
  • Sales Rank: #1959371 (See Top 100 Books)

Gain an accelerated introduction to domain-specific languages in R, including coverage of regular expressions. This compact, in-depth book shows you how DSLs are programming languages specialized for a particular purpose, as opposed to general purpose programming languages. Along the way, you’ll learn to specify tasks you want to do in a precise way and achieve programming goals within a domain-specific context.

Domain-Specific Languages in R includes examples of DSLs including large data sets or matrix multiplication; pattern matching DSLs for application in computer vision; and DSLs for continuous time Markov chains and their applications in data science. After reading and using this book, you’ll understand how to write DSLs in R and have skills you can extrapolate to other programming languages.

What You’ll Learn

  • Program with domain-specific languages using R
  • Discover the components of DSLs
  • Carry out large matrix expressions and multiplications
  • Implement metaprogramming with DSLs
  • Parse and manipulate expressions

Who This Book Is For

Those with prior programming experience. R knowledge is helpful but not required.

Table of Contents

Chapter 1: Introduction
Chapter 2: Matrix Expressions
Chapter 3: Components of a Programming Language
Chapter 4: Functions, Classes, and Operators
Chapter 5: Parsing and Manipulating Expressions
Chapter 6: Lambda Expressions
Chapter 7: Environments and Expressions
Chapter 8: Tidy Evaluation
Chapter 9: List Comprehension
Chapter 10: Continuous-Time Markov Chains
Chapter 11: Pattern Matching
Chapter 12: Dynamic Programming
Chapter 13: Conclusion

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