The Art of R Programming: A Tour of Statistical Software Design Author: Norman Matloff | Language: English | ISBN:
B00683GXUO | Format: PDF
The Art of R Programming: A Tour of Statistical Software Design Description
R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.
The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.
Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to:
- Create artful graphs to visualize complex data sets and functions
- Write more efficient code using parallel R and vectorization
- Interface R with C/C++ and Python for increased speed or functionality
- Find new R packages for text analysis, image manipulation, and more
- Squash annoying bugs with advanced debugging techniques
Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.
- File Size: 1234 KB
- Print Length: 400 pages
- Publisher: No Starch Press; 1 edition (September 30, 2011)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00683GXUO
- Text-to-Speech: Enabled
X-Ray:
- Lending: Not Enabled
- Amazon Best Sellers Rank: #54,438 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
- #20
in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Mathematics > Applied > Statistics - #20
in Kindle Store > Kindle eBooks > Nonfiction > Science > Mathematics > Applied > Probability & Statistics - #22
in Kindle Store > Kindle eBooks > Computers & Technology > Software
- #20
in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Mathematics > Applied > Statistics - #20
in Kindle Store > Kindle eBooks > Nonfiction > Science > Mathematics > Applied > Probability & Statistics - #22
in Kindle Store > Kindle eBooks > Computers & Technology > Software
There are hundreds of R books, but this is the best one to address the core problem of learning to *program* in R. As reviewer Jason notes, R is used by several audiences with varying needs, but anyone who uses R for long must come to terms with learning to program it. This is the book for that.
What Matloff does is to lay out the essentials of the R language (or S, if you prefer) in depth but in a readable fashion, with well-chosen examples that reinforce learning about the language itself (as opposed to focusing on statistics or data analysis).
I'm a long-time (12 years) R user, which is my platform for analytics every day, and I have programmed in a variety of languages from C to Perl. I have long missed the fact that there is nothing for R comparable to Kernighan & Ritchie ("K&R", The C Programming Language) or similar programming classics; finally there is. Matloff is not quite as beautiful and elegant as K&R (and to be fair, is not in their position as the language creator) but this book has similar goals and comes reasonably close.
I think there are two primary audiences for this book: those who are learning R from a computer science or programming background; and statisticians and others who use the programming language and want a thorough exposition. In my case, for instance, despite having written perhaps 100k lines of R code over the years, there remained areas where I was uneasy (e.g., exactly how do lists relate to data frames). Matloff sets it all straight, in friendly, readable fashion. Even in rudimentary chapters, I learned shortcuts and miscellaneous functions that are quite useful.
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