Authors:
- Learn to program with domain-specific languages using R
- Carry out large matrix expressions and multiplications
- Implement metaprogramming with DSLs
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Table of contents (13 chapters)
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Front Matter
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Back Matter
About this book
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.
Authors and Affiliations
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Aarhus N, Denmark
Thomas Mailund
About the author
Bibliographic Information
Book Title: Domain-Specific Languages in R
Book Subtitle: Advanced Statistical Programming
Authors: Thomas Mailund
DOI: https://doi.org/10.1007/978-1-4842-3588-1
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Thomas Mailund 2018
Softcover ISBN: 978-1-4842-3587-4Published: 24 June 2018
eBook ISBN: 978-1-4842-3588-1Published: 23 June 2018
Edition Number: 1
Number of Pages: IX, 257
Number of Illustrations: 10 b/w illustrations
Topics: Programming Languages, Compilers, Interpreters, Artificial Intelligence, Programming Techniques, Probability and Statistics in Computer Science, Big Data