Praise for the First Edition
"The main strength of this book is that it provides a unified framework of graphical tools for data analysis, especially for univariate and low-dimensional multivariate data. In addition, it is clearly written in plain language and the inclusion of R code is particularly useful to assist readers’ understanding of the graphical techniques discussed in the book. … It not only summarises graphical techniques, but it also serves as a practical reference for researchers and graduate students with an interest in data display." -Han Lin Shang, Journal of Applied Statistics
Graphics for Statistics and Data Analysis with R, Second Edition, presents the basic principles of graphical design and applies these principles to engaging examples using the graphics and lattice packages in R. It offers a wide array of modern graphical displays for data visualization and representation. Added in the second edition are coverage of the ggplot2 graphics package, material on human visualization and color rendering in R, on screen, and in print.
Features
- Emphasizes the fundamentals of statistical graphics and best practice guidelines for producing and choosing among graphical displays in R
- Presents technical details on topics such as: the estimation of quantiles, nonparametric and parametric density estimation; diagnostic plots for the simple linear regression model; polynomial regression, splines, and locally weighted polynomial regression for producing a smooth curve; Trellis graphics for multivariate data
- Provides downloadable R code and data for figures at www.graphicsforstatistics.com
Kevin J. Keen is a Professor of Mathematics and Statistics at the University of Northern British Columbia (Prince George, Canada) and an Accredited Professional StatisticianTM by the Statistical Society of Canada and the American Statistical Association.
Introduction. The Graphical Display of Information. A Single Discrete Variable. Basic Charts for the Distribution of a Single Discrete Variable. Advanced Charts for the Distribution of a Single Discrete Variable. A Single Continuous Variable. Exploratory Plots for the Distribution of a Single Continuous Variable. Diagnostic Plots for the Distribution of a Continuous Variable. Nonparametric Density Estimation for a Single Continuous Variable. Parametric Density Estimation for a Single Continuous Variable. Two Variables. Depicting the Distribution of Two Discrete Variables. Depicting the Distribution of One Continuous Variable and One Discrete Variable. Depicting the Distribution of Two Continuous Variables. Statistical Models for Two or More Variables. Graphical Displays for Simple Linear Regression. Graphical Displays for Polynomial Regression. Visualizing Multivariate Data. References. Index.
Biography
Kevin J. Keen is a Professor of Mathematics and Statistics at the University of Northern British Columbia (Prince George, Canada) and an Accredited Professional StatisticianTM by the Statistical Society of Canada and the American Statistical Association.
"A leading expert wrote the book. The book is an exposition of statistical methodology that focuses on ideas and concepts and makes extensive use of graphical presentation, but readers should have some prior experience of statistical methodology. The chapters also contain many exercises with solutions and hints presented in the Appendix. The R codes are available for download on the website. The book presents data and Programmes to replicate the models developed, offers new methods that are ready to use, and explores graphical statistics in its entirety from the fundamentals of modern methods. The book is also a complete reference manual and should be considered a must-have companion for the interested advanced audience."
~International Society for Clinical Biostatistics
". . . this is a book I can recommend for consideration in a course or as a course supplement. It is generally clear and well-written, and the statistical aspects of some of these methods are explained in sufficient detail to put these in context."
~Michael Friendly, Journal of Agricultural, Biological, and Environmental Statistics
