Programming with Data: A Guide to the S Language [Paperback]

John M. Chambers (Author)

Book Description

ISBN-10: 0387985034 | ISBN-13: 978-0387985039 | Publication Date: June 19, 1998 | Edition: Corrected

Here is a thorough and authoritative guide to the latest version of the S language and its programming environment. Programming With Data describes a new and greatly extended version of S, written by the chief designer of the language itself. It is a guide to the complete programming process, starting from simple, interactive use, and continuing through ambitious software projects. The focus is on the needs of the programmer/user, with the aim of turning ideas into software, quickly and faithfully. The new version of S provides a powerful class/method structure, new techniques to deal with large objects, extended interfaces to other languages and files, object-based documentation compatible with HTML, and powerful new interactive programming techniques. This version of S underlies the S-Plus system, versions 5.0 and higher.

Editorial Reviews

About the Author

John Chambers has been a member of technical staff in reserach at Bell Laboratories since 1966. In 1977, he became the first statistician to be named a Bell Labs Fellow, cited for "pioneering contributions to the field of statistical computing." His research has touched on nearly all aspects of computing with data but he is best known for the design of the S language. He is the author or co-author of seven books.

Product Details

• Paperback: 484 pages
• Publisher: Springer; Corrected edition (June 19, 1998)
• Language: English
• ISBN-10: 0387985034
• ISBN-13: 978-0387985039
• Product Dimensions: 9.1 x 6.1 x 1.2 inches
• Shipping Weight: 1.4 pounds (View shipping rates and policies)
• Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #218,948 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

40 of 41 people found the following review helpful:

4.0 out of 5 stars A good tutorial on the S programming language., October 27, 1999

By A Customer

This review is from: Programming with Data: A Guide to the S Language (Paperback)

This is the first book I've found that approaches the S programming language as more than an inconvenient detail that you have to learn to get to the cool statistics functions in "S-Plus." It's much more accessible than most of the books on S or S-Plus for people who aren't actually statisticians, but need to use the language for some reason.

16 of 19 people found the following review helpful:

5.0 out of 5 stars Definitive book on S language, February 15, 2005

By 

Random Thoughts (Maryland) - See all my reviews

This review is from: Programming with Data: A Guide to the S Language (Paperback)

This book was originally published in 1998 by MathSoft, and now with more demand I'm glad that it will be redistributed by Springer. As co-developer of the orginal S language, John Chambers new book is supposed to provide the most authorative reference on defining new, object-oriented (OOP)-style S for the current version of S-PLUS, and R. I think it may not have reached its full promise yet, partly, maybe because of its terse style and not-so-easy read for beginners. I have to admit myself I have ignored of it for quite a while until recently when I see its full power realized in the now fast developing R project, particularly Bioconductor project Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health). I think it may be a dream work for software developers to have such a flexible and extensible computing environment in R and S so that much involved data objects and methods can be built so easily for many complex data mining and bioinformatics tasks. I think a big benefit of the open source approach to software developments is to enable researchers to reach quickly to users and to deliver the final promised land for collaboration and sharing of ideas and data. I think John Chambers truly deserves all the credit he gets for this work and for inspiring the R project, which is now beginning to gain widespread acceptance by the data mining and bioinformatic research communities.