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Beginning R: The Statistical Programming Language Dr. Mark Gardener

Beginning R: The Statistical Programming Language

Dr. Mark Gardener

Published March 22nd 2013
ISBN :
Paperback
504 pages
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 About the Book 

Key Features This book will be light enough to appeal to beginning users, yet robust enough for a seasoned user to learn from it because it: Appeals to programmers wanting to narrow the skills gap (McKinsey Consulting states nearly 200,000 people will be needed with R knowledge in the US alone). Guides the reader in cutting cost in whatever industry they work in. Teaches the basics of the R language rather than just recipes by providing simple data examples that allow users to see what is happening while permitting complex analysis. It can be used as a complete reference to perform simple to complex tasks in R. About the Book: Beginning R: The Statistical Programming Language R, an open source statistical programming language is a computer program that does statistics, as well as produce publication quality graphs. It currently has an estimate user base of 2 million users. It is unlike many computer programs in that the user must type commands using the keyboard. Because it is a complex language, the user needs to learn to speak R. This book is about data analysis and the programming language called R. This is rapidly becoming the de-facto standard amongst professionals and is used in every conceivable discipline from science and medicine to business and engineering. This book delves into the language of R and makes it accessible using simple data examples to explore its power and versatility. In learning how to speak R you will unlock its potential and gain better insights into tackling even the most complex of data analysis tasks. Content Introduction Chapter 1: Introducing R: What It Is and How to Get It Chapter 2: Starting Out: Becoming Familiar with R Chapter 3: Starting Out: Working with Objects Chapter 4: Data: Descriptive Statistics and Tabulation Chapter 5: Data: Distribution Chapter 6: Simple Hypothesis Testing Chapter 7: Introduction to Graphical Analysis Chapter 8: Formula Notation and Complex Statistics Chapter 9: Manipulating Data and Extracting