The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.
See What’s New in the Second Edition:
The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.
See What’s New in the Second Edition:
- Increased emphasis on more idiomatic R provides a grounding in the functionality of base R.
- Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible.
- Use of knitr package makes code easier to read and therefore easier to reason about.
- Additional information on computer-intensive approaches motivates the traditional approach.
- Updated examples and data make the information current and topical.
The book has an accompanying package, Using R, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package=’Using R’)), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text.
The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.
