本书解释了为什么软件开发是一种精神活动，思考如何解决问题，并就开发人员如何能更好地开发软件进行了评论。书中不仅给出了一些理论上的答案，同时提供了大量实践技术和窍门。 本书供各层次软件开发人员阅读。

Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data.

R Markdown: The Definitive Guide is the first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages. In this book, you will learn Basics: Syntax of Markdown and R code chunks, how to generate figures and tables, and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ioslides/Slidy/Beamer/PowerPoint presentations Extensions and applications: Dashboards, Tufte handouts, xaringan/reveal.js presentations, websites, books, journal articles, and interactive tutorials Advanced topics: Parameterized reports, HTML widgets, document templates, custom output formats, and Shiny documents. Yihui Xie is a software engineer at RStudio. He has authored and co-authored several R packages, including knitr, rmarkdown, bookdown, blogdown, shiny, xaringan, and animation. He has published three other books, Dynamic Documents with R and knitr, bookdown: Authoring Books and Technical Documents with R Markdown, and blogdown: Creating Websites with R Markdown. J.J. Allaire is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown ecosystem including rmarkdown, flexdashboard, learnr, and radix. Garrett Grolemund is the co-author of R for Data Science and author of Hands-On Programming with R. He wrote the lubridate R package and works for RStudio as an advocate who trains engineers to do data science with R and the Tidyverse. Instructors We provide complimentary e-inspection copies of primary textbooks to instructors considering our books for course adoption.

Features · Provides an authoritative and comprehensive guide to the knitr package in R · Emphasizes reproducible research · Covers both simple examples and full applications, from homework to websites to books · Describes a wide range of options, useful tricks, and solutions · Explains the internal design and extensions of the knitr package · Offers demos and other information about the package on the author's website Summary Quickly and Easily Write Dynamic Documents Suitable for both beginners and advanced users, Dynamic Documents with R and knitr, Second Edition makes writing statistical reports easier by integrating computing directly with reporting. Reports range from homework, projects, exams, books, blogs, and web pages to virtually any documents related to statistical graphics, computing, and data analysis. The book covers basic applications for beginners while guiding power users in understanding the extensibility of the knitr package. New to the Second Edition · A new chapter that introduces R Markdown v2 · Changes that reflect improvements in the knitr package · New sections on generating tables, defining custom printing methods for objects in code chunks, the C/Fortran engines, the Stan engine, running engines in a persistent session, and starting a local server to serve dynamic documents Boost Your Productivity in Statistical Report Writing and Make Your Scientific Computing with R Reproducible Like its highly praised predecessor, this edition shows you how to improve your efficiency in writing reports. The book takes you from program output to publication-quality reports, helping you fine-tune every aspect of your report.

在线阅读本书 Widely considered one of the best practical guides to programming, Steve McConnells original CODE COMPLETE has been helping developers write better software for more than a decade. Now this classic book has been fully updated and revised with leading-edge practicesand hundreds of new code samplesillustrating the art and science of software construction. Capturing the body of knowledge available from research, academia, and everyday commercial practice, McConnell synthesizes the most effective techniques and must-know principles into clear, pragmatic guidance. No matter what your experience level, development environment, or project size, this book will inform and stimulate your thinkingand help you build the highest quality code. Discover the timeless techniques and strategies that help you: Design for minimum complexity and maximum creativity Reap the benefits of collaborative development Apply defensive programming techniques to reduce and flush out errors Exploit opportunities to refactoror evolvecode, and do it safely Use construction practices that are right-weight for your project Debug problems quickly and effectively Resolve critical construction issues early and correctly Build quality into the beginning, middle, and end of your project 点击链接进入中文版： 代码大全(第2版)

With the same insight and authority that made their book The Unix Programming Environment a classic, Brian Kernighan and Rob Pike have written The Practice of Programming to help make individual programmers more effective and productive. This book is full of practical advice and real-world examples in C, C++, Java, and a variety of special-purpose languages. Kernighan and Pike have distilled years of experience writing programs, teaching, and working with other programmers to create this book. Anyone who writes software will profit from its principles and guidance.

The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data Mining with R: Learning with Case Studies uses practical examples to illustrate the power of R and data mining. Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools. To present the main data mining processes and techniques, the author takes a hands-on approach that utilizes a series of detailed, real-world case studies: Predicting algae blooms Predicting stock market returns Detecting fraudulent transactions Classifying microarray samples With these case studies, the author supplies all necessary steps, code, and data. Web Resource A supporting website mirrors the do-it-yourself approach of the text. It offers a collection of freely available R source files that encompass all the code used in the case studies. The site also provides the data sets from the case studies as well as an R package of several functions.

Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way. Expanded from Tyler Akidau’s popular blog posts "Streaming 101" and "Streaming 102", this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You’ll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax. You’ll explore: How streaming and batch data processing patterns compare The core principles and concepts behind robust out-of-order data processing How watermarks track progress and completeness in infinite datasets How exactly-once data processing techniques ensure correctness How the concepts of streams and tables form the foundations of both batch and streaming data processing The practical motivations behind a powerful persistent state mechanism, driven by a real-world example How time-varying relations provide a link between stream processing and the world of SQL and relational algebra

Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.

Writing better software: 30 years of UNIX development wisdom In this book, five years in the making, the author encapsulates three decades of unwritten, hard-won software engineering wisdom. Raymond brings together for the first time the philosophy, design patterns, tools, culture, and traditions that make UNIX home to the world's best and most innovative software, and shows how these are carried forward in Linux and today's open-source movement. Using examples from leading open-source projects, he shows UNIX and Linux programmers how to apply this wisdom in building software that's more elegant, more portable, more reusable, and longer-lived.

Elements of Programming provides a different understanding of programming than is presented elsewhere. Its major premise is that practical programming, like other areas of science and engineering, must be based on a solid mathematical foundation. The book shows that algorithms implemented in a real programming language, such as C++, can operate in the most general mathematical setting. For example, the fast exponentiation algorithm is defined to work with any associative operation. Using abstract algorithms leads to efficient, reliable, secure, and economical software. This is not an easy book. Nor is it a compilation of tips and tricks for incremental improvements in your programming skills. The book’s value is more fundamental and, ultimately, more critical for insight into programming. To benefit fully, you will need to work through it from beginning to end, reading the code, proving the lemmas, doing the exercises. When finished, you will see how the application of the deductive method to your programs assures that your system’s software components will work together and behave as they must. Following key definitions, the book describes a number of algorithms and requirements for types on which they are defined that exemplify its abstract mathematical approach. The code for these descriptions—also available on the Web—is written in a small subset of C++ meant to be accessible to any experienced programmer. This subset is defined in a special language appendix coauthored by Sean Parent and Bjarne Stroustrup. Whether you are a software developer, or any other professional for whom programming is an important activity, or a committed student, you will come to understand what the book’s experienced authors have been teaching and demonstrating for years—that mathematics is good for programming, that theory is good for practice.

New! Java versions of all C++ programs in the book at http: //bit.ly/epi-programs Get a PDF sampler of EPI from http: //bit.ly/epi-sampler Have you ever... Wanted to work at an exciting futuristic company? Struggled with an interview problem that could have been solved in 15 minutes? Wished you could study real-world computing problems? If so, you need to read Elements of Programming Interviews (EPI). The core of EPI is a collection of 300 problems with detailed solutions, including over 100 figures and 250 tested programs. The problems are challenging, well-motivated, and accessible. They are representative of the questions asked at interviews at the most exciting companies. The book begins with a summary of patterns for data structure, algorithms, and problem solving that will help you solve the most challenging interview problems. This is followed by chapters on basic and advanced data structures, algorithm design, concurrency, system design, probability and discrete mathematics. Each chapter starts with a brief review of key concepts and results followed by a deep and wide set of questions. EPI concludes with a summary of the nontechnical aspects of interviewing, including common mistakes, strategies for a great interview, perspectives from across the table, negotiating the best offer, and much more. "This book is the best compilation of programming related problems I have seen. It is a great resource for a diverse set of topics when preparing for technical interviews, as a quick refresher in a subject area or when you are just looking for a brain teaser to challenge yourself." Shashank Gupta / Scaligent, formerly Engineering Manager, Amazon.com, Senior Engineering Manager, Yahoo!, Manager of Software Development, Cisco Systems

Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: -The fundamentals of R, including standard data types and functions -Functional programming as a useful framework for solving wide classes of problems -The positives and negatives of metaprogramming -How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.

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For Computer Organization and Architecture and Computer Systems courses in CS and EE and ECE departments. Developed out of an introductory course at Carnegie Mellon University, this text explains the important and enduring concepts underlying all computer systems, and shows the concrete ways that these ideas affect the correctness, performance, and utility of application programs. The text's concrete and hands-on approach will help students understand what is going on "under the hood" of a computer system. Few students studying computer science or computer engineering will ever have the opportunity to build a computer system. On the other hand, most students will be required to use and program computers on a near daily basis. 'Computer Systems' introduces the important and enduring concepts that underlie application programs.

As programmers, we've all seen source code that's so ugly and buggy it makes our brain ache. And let's be honest, we've all written code like that. With this book, you'll learn to write code that's easy to read and understand. You'll have more fun and your coworkers will love you. The Art of Coding focuses on the nuts and bolts of programming, with simple and practical techniques you can use every time you sit down to write code. You'll find tips throughout the book, with easy-to-digest code examples, helpful illustrations, and cartoons for fun. * Learn to pick variable names that are "dense with information" * Organize your loops and conditionals so they're easy to understand * Make your comments short and sweet * Recognize when your code is doing too many things at once * Write tests that are concise, but thorough * Master the art of breaking hard problems into many smaller ones

Writing code is the easy part of your work as a software developer. This practical book lets you explore the other 90%—everything from requirements discovery and rapid prototyping to business analysis and designing for maintainability. Instead of providing neatly packaged advice from on high, author Gregory Brown presents detailed examples of the many problems developers encounter, including the thought process it takes to solve them.He does this in an unusual and entertaining fashion by making you the main character in a series of chapter-length stories. As these stories progress, the examples become more complex, and your responsibilities increase. Together, these stories take you on a journey that will make you question and refine the way you think about, and work on, software projects.Steps in this unique journey include:Using prototypes to explore project ideasSpotting hidden dependencies in incremental changesIdentifying the pain points of service integrationsDeveloping a rigorous approach towards problem-solvingDesigning software from the bottom upData modeling in an imperfect worldGradual process improvement as an antidote for over-commitmentThe future of software development