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Metaprogramming in R: Advanced Statistical Programming for Data Science, Analysis and Finance

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From the Back Cover

Learn how to manipulate functions and expressions to modify how the R language interprets itself. This book is an introduction to metaprogramming in the R language, so you will write programs to manipulate other programs. Metaprogramming in R shows you how to treat code as data that you can generate, analyze, or modify. R is a very high-level language where all operations are functions and all functions are data that can be manipulated. This book shows you how to leverage R's natural flexibility in how function calls and expressions are evaluated, to create small domain-specific languages to extend R within the R language itself. You will:Find out about the anatomy of a function in R Look inside a function callWork with R expressions and environmentsManipulate expressions in RUse substitutions 

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About the Author

Thomas Mailund is an associate professor in bioinformatics at Aarhus University, Denmark. He has a background in math and computer science.  For the last decade, his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.  He has published Beginning Data Science in R and Functional Programming in R with Apress as well as other books out there.  

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Product details

Paperback: 120 pages

Publisher: Apress; 1st ed. edition (June 2, 2017)

Language: English

ISBN-10: 1484228804

ISBN-13: 978-1484228807

Product Dimensions:

6.1 x 0.3 x 9.2 inches

Shipping Weight: 1.6 ounces (View shipping rates and policies)

Average Customer Review:

5.0 out of 5 stars

1 customer review

Amazon Best Sellers Rank:

#720,131 in Books (See Top 100 in Books)

This book is what I have been looking for as far as understanding the more difficult R concepts which involve A) environments-closures and B) computing on the language. I have been using R and writing R code for a long time ( probably 10 years on and off ) but the questions that come to the R-list involving environments, expressions, bquote, quote, calls, etc have always left me longing for a good explanation of them. Then this book arrived. It is full of great examples that really go into deep details about what's going on in regard to the mentioned topics. The examples need to be read really carefully and often more than twice but I came away with a much better understanding of the topics mentioned. Three things to note:1) I have the book "Advanced R" by Hadley Wickham but have not read it. I bet Hadley covers a lot of the same topics, so, if you already have his book, I can't guaranteee that there won't be some amount of overlap.2) This is the review of "Meta Programming in R" ( without the data-science, finance in the title ) book that is no longer available. I am unclear of exactly what in the book has changed but, from looking at the table of contents, it doesn't look like much has changed.3) There are typos in the explanations and the book could use a professional proofreader but these issues did not hinder me from understanding the intended meanings of the explanations.Overall, a tremendous addition to your library if you're looking to understand the more difficult aspects of R.

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Metaprogramming in R: Advanced Statistical Programming for Data Science, Analysis and Finance PDF

Metaprogramming in R: Advanced Statistical Programming for Data Science, Analysis and Finance PDF

Metaprogramming in R: Advanced Statistical Programming for Data Science, Analysis and Finance PDF

Metaprogramming in R: Advanced Statistical Programming for Data Science, Analysis and Finance PDF