Books: **128**

Cover | Title | Year |
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The Statistical Programming Language R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields. This book examines this complex language using simple statistical examples, showing how R operates in a user-friendly context. Both students and workers in fields that more » require extensive statistical analysis will find this book helpful as they learn to use R for simple summary statistics, hypothesis testing, creating graphs, regression, and much more. It covers formula notation, complex statistics, manipulating data and extracting components, and rudimentary programming. « less | 2012 | |

An Introduction to Statistical Programming Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. R is a powerful open-source implementation of the statistical language S, which was developed more » by AT&T. R has eclipsed S and the commercially-available S-Plus language, and has become the de facto standard for doing, teaching, and learning computational statistics. « less | 2012 | |

Discover the world around you through programming If you're curious about how things work, this fun and intriguing guide will help you find real answers to everyday problems. By using fundamental math and doing simple programming with the Ruby and R languages, you'll learn how to model a problem and work toward a solution.
All you need is a basic more » understanding of programming. After a quick introduction to Ruby and R, you'll explore a wide range of questions by learning how to assemble, process, simulate, and analyze the available data. You'll learn to see everyday things in a different perspective through simple programs and common sense logic. Once you finish this book, you can begin your own journey of exploration and discovery. « less | 2012 | |

A complete practical tutorial for RStudio, designed keeping in mind the needs of analysts and R developers alike. Step-by-step examples that apply the principles of reproducible research and good programming practices to R projects. Learn to effectively generate reports, create graphics, and perform more » analysis, and even build R-packages with RStudio. « less | 2012 | |

Practical Recipes for Visualizing Data This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R's graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and more » why the recipe works.
Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started. « less | 2012 | |

If you're considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You'll learn how to write R functions and use R packages to help you prepare, visualize, more » and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports.
Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop. « less | 2012 | |

A Case Study in Real-World Data Analysis How do you use R to import, manage, visualize, and analyze real-world data? With this short, hands-on tutorial, you learn how to collect online data, massage it into a reasonable form, and work with it using R facilities to interact with web servers, parse HTML and XML, and more. Rather than use canned more » sample data, you'll plot and analyze current home foreclosure auctions in Philadelphia.
This practical mashup exercise shows you how to access spatial data in several formats locally and over the Web to produce a map of home foreclosures. It's an excellent way to explore how the R environment works with R packages and performs statistical analysis. « less | 2011 | |

With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with more » R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.
Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process. « less | 2011 | |

Learn to draw any type of graph or visual data representation in R. Filled with practical tips and techniques for creating any type of graph you need; not just theoretical explanations. All examples are accompanied with the corresponding graph images, so you know what the results look like. Each recipe more » is independent and contains the complete explanation and code to perform the task as efficiently as possible. « less | 2011 | |

Data Analysis and Graphics with R R in Action is the first book to present both the R system and the use cases that make it such a compelling package for business developers. The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical more » statistics and covers elegant methods for dealing with messy and incomplete data using features of R.
R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data. « less | 2011 |