First (and Second) Steps in Statistics, Daniel B Wright and Kamala London

• 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, UsingR, available from CRAN, R's repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), 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.

Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.

New to the Second Edition

• The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
• New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
• New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
• New chapter on simulation that includes examples of data generated from complex models and distributions
• A detailed discussion of the philosophy and use of the knitr and markdown packages for R
• New packages that extend the functionality of R and facilitate sophisticated analyses
• Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots

Easily Find Your Desired Task

Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.

Expanded with over 100 more pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition presents the right balance of data examples, statistical theory, and computing to teach introductory statistics to students in the life sciences. This popular textbook covers the mathematics underlying classical statistical analysis, the modeling aspects of statistical analysis and the biological interpretation of results, and the application of statistical software in analyzing real-world problems and datasets.

New to the Second Edition

• A new chapter on non-linear regression models
• A new chapter that contains examples of complete data analyses, illustrating how a full-fledged statistical analysis is undertaken
• Additional exercises in most chapters
• A summary of statistical formulas related to the specific designs used to teach the statistical concepts

This text provides a computational toolbox that enables students to analyze real datasets and gain the confidence and skills to undertake more sophisticated analyses. Although accessible with any statistical software, the text encourages a reliance on R. For those new to R, an introduction to the software is available in an appendix. The book also includes end-of-chapter exercises as well as an entire chapter of case exercises that help students apply their knowledge to larger datasets and learn more about approaches specific to the life sciences.