# Description

R is now the most widely used statistical software in academic science and it is rapidly expanding into other fields such as finance. R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating. Introductory R is written for the novice user who knows a little about statistics but who hasn't yet got to grips with the ways of R. This new edition is completely revised and greatly expanded with new chapters on the basics of descriptive statistics and statistical testing, considerably more information on statistics and six new chapters on programming in R. Topics covered include:

A

R

Expanding your analysis and plotting capacities with add-in R

A set of simple rules to follow to make sure you

An introduction to the

A detailed introduction to drawing

How to understand the

Basic

The

Thorough coverage of the basics of data analysis in R with chapters on using

What the assumptions behind the analyses mean and how to test them using

Explanations of the

Writing

Using

Using

Writing longer R

The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.

A

**walkthrough**of the basics of R's command line interface**Data structures**including vectors, matrices and data framesR

**functions**and how to use themExpanding your analysis and plotting capacities with add-in R

**packages**A set of simple rules to follow to make sure you

**import your data**properlyAn introduction to the

**script editor**and advice on workflowA detailed introduction to drawing

**publication-standard graphs**in RHow to understand the

**help files**and how to deal with some of the most common errors that you might encounter.Basic

**descriptive statistics**The

**theory behind statistical testing**and how to interpret the output of statistical testsThorough coverage of the basics of data analysis in R with chapters on using

**chi-squared tests**,**t-tests**,**correlation**analysis,**regression**,**ANOVA**and**general linear models**What the assumptions behind the analyses mean and how to test them using

**diagnostic plots**Explanations of the

**summary tables**produced for statistical analyses such as regression and ANOVAWriting

**your own functions**in RUsing

**table operations**to manipulate matrices and data framesUsing

**conditional statements**and**loops**in R programmes.Writing longer R

**programmes**.The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.