A walkthrough of the basics of R's command line interface
Data structures including vectors, matrices and data frames
R functions and how to use them
Expanding your analysis and plotting capacities with add-in R packages
A set of simple rules to follow to make sure you import your data properly
An introduction to the script editor and advice on workflow
A detailed introduction to drawing publication-standard graphs in R
How 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 tests
Thorough 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 ANOVA
Writing your own functions in R
Using table operations to manipulate matrices and data frames
Using 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.