Graphics for Statistics and Data Analysis with R

Graphics for Statistics and Data Analysis with R

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Graphics for Statistics and Data Analysis with R presents the basic principles of sound graphical design and applies these principles to engaging examples using the graphical functions available in R. It offers a wide array of graphical displays for the presentation of data, including modern tools for data visualization and representation. The book considers graphical displays of a single discrete variable, a single continuous variable, and then two or more of each of these. It includes displays and the R code for producing the displays for the dot chart, bar chart, pictographs, stemplot, boxplot, and variations on the quantile-quantile plot. The author discusses nonparametric and parametric density estimation, diagnostic plots for the simple linear regression model, polynomial regression, and locally weighted polynomial regression for producing a smooth curve through data on a scatterplot. The last chapter illustrates visualizing multivariate data with examples using Trellis graphics. Showing how to use graphics to display or summarize data, this text provides best practice guidelines for producing and choosing among graphical displays. It also covers the most effective graphing functions in R. R code is available for download on the book's more

Product details

  • Hardback | 489 pages
  • 160.02 x 236.22 x 27.94mm | 816.46g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 200 black & white illustrations, 36 black & white tables
  • 1584880872
  • 9781584880875
  • 1,403,194

Review quote

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. ... I enjoyed reading this 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. ... it is a recommended purchase for any statistical reference library. -Han Lin Shang, Journal of Applied Statistics, April 2012 This textbook is intended for graduate students in statistics seeking to learn the basic principles of graphical design for the presentation of data. Experienced statisticians can also find this book very useful as it has a comprehensive discussion of graphical displays as well as R commands listed for many of the textbook's examples. ... This textbook is also a handy reference for graphical analysis and contains engaging examples of real-world data and exercises for those seeking reference on statistical graphics at the end of each chapter. -Vanessa Narayanassamy, Significance, December 2011 The chapter exercises are the highlight of this book, and a valuable resource for teachers. The problems are well conceived, methodical, and reinforce objectives discussed in the chapter. ... Aspects of the text may be a valuable resource for an introductory statistics course which uses R. The extensive chapter exercises should give students ample practice. ... For the experienced R user, the book is a handy reference to create sophisticated graphical summaries for one- and two-variable visualization problems. -Samuel J. Frame, The American Statistician, August 2011 The author states that it can be used as a textbook for a dedicated course on graphical data analysis or as a supplementary text in courses in statistics and data analysis. My general reaction is that it succeeds in these goals. ... The book is impressive for the sheer number of graphs ... these graphs are quite well done ... -Biometrics, 67, September 2011 The emphasis in the book is on graphs for single discrete and continuous variables and bivariate relationships thereof, and it is very thorough in that regard. ... The author also gives a lot of attention to detail ... quite suitable as a course textbook, either as a supplementary text for a regular statistics course or as the main text for a specialized course on graphical methods. ... The book certainly achieves its goal, increasing graphicacy, that is, the ability to display and exchange information with graphics. I thoroughly enjoyed reading the book and would recommend it to anyone interested in learning more about graphical displays of quantitative information. -Statistics in Medicine, 2011, 30 This is a textbook for graduate students in statistics and a helpful resource for practitioners. It provides a comprehensive discussion about methods for data representation and graphical display, and a guideline on when, which and how they can be applied... I found the examples particularly useful. The author thoroughly evaluates competing graphical methods in terms of effective display when applied to the same data. There is a series of exercises at the end of each chapter, which complements its worked examples. ... a valuable resource for practitioners in seeking a reference on statistical graphics. -Journal of the Royal Statistical Society, Series A, April 2011 The book is methodical and complete ... Reading this book will give you the ability to recognize and create the majority of the named graphics of statistics ... . I would recommend this book if you were interested in a detailed survey of 1D and 2D graphics ... . -Journal of Statistical Software, September 2010, Volume 36show more

About Kevin J. Keen

Kevin J. Keen is a statistician and an associate professor of mathematics at the University of Northern British Columbia in Prince George, more

Table of contents

INTRODUCTION The Graphical Display of Information Introduction Know the Intended Audience Principles of Effective Statistical Graphs Graphicacy Graphical Statistics A SINGLE DISCRETE VARIABLE Basic Charts for the Distribution of a Single Discrete Variable Introduction An Example from the United Nations The Dot Chart The Bar Chart The Pie Chart Advanced Charts for the Distribution of a Single Discrete Variable Introduction The Stacked Bar Chart The Pictograph Variations on the Dot and Bar Charts Frames, Grid Lines, and Order A SINGLE CONTINUOUS VARIABLE Exploratory Plots for the Distribution of a Single Continuous Variable Introduction The Dotplot The Stemplot The Boxplot The EDF Plot Diagnostic Plots for the Distribution of a Continuous Variable Introduction The Quantile-Quantile Plot The Probability Plot Estimation of Quartiles and Percentiles Nonparametric Density Estimation for a Single Continuous Variable Introduction The Histogram Kernel Density Estimation Spline Density Estimation Choosing a Plot for a Continuous Variable Parametric Density Estimation for a Single Continuous Variable Introduction Normal Density Estimation Transformations to Normality Pearson's Curves Gram-Charlier Series Expansion TWO VARIABLES Depicting the Distribution of Two Discrete Variables Introduction The Grouped Dot Chart The Grouped Dot-Whisker Chart The Two-Way Dot Chart The Multi-Valued Dot Chart The Side-by-Side Bar Chart The Side-by-Side Bar-Whisker Chart The Side-by-Side Stacked Bar Chart The Side-by-Side Pie Chart The Mosaic Chart Depicting the Distribution of One Continuous Variable and One Discrete Variable Introduction The Side-by-Side Dotplot The Side-by-Side Boxplot The Notched Boxplot The Variable-Width Boxplot The Back-to-Back Stemplot The Side-by-Side Stemplot The Side-by-Side Dot-Whisker Plot The Trellis Kernel Density Estimate Depicting the Distribution of Two Continuous Variables Introduction The Scatterplot The Sunflower Plot The Bagplot The Two-Dimensional Histogram Two-Dimensional Kernel Density Estimation STATISTICAL MODELS FOR TWO OR MORE VARIABLES Graphical Displays for Simple Linear Regression Introduction The Simple Linear Regression Model Residual Analysis Influence Analysis Graphical Displays for Polynomial Regression Introduction The Polynomial Regression Model Splines Locally Weighted Polynomial Regression Visualizing Multivariate Data Introduction Three or More Discrete Variables One Discrete and Two or More Continuous Variables Observations of Multiple Variables The Multiple Linear Regression Model References Index Exercises appear at the end of each more

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