Graphical Data Analysis with R: (Chapman & Hall/CRC The R Series)

See How Graphics Reveal Information Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA. Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.

Table of Contents:
Setting the Scene Graphics in action Introduction What is graphical data analysis (GDA)? Using this book, the R code in it, and the book’s webpage Brief Review of the Literature and Background Materials Literature review Interactive graphics Other graphics software Websites Datasets Statistical texts Examining Continuous Variables Introduction What features might continuous variables have? Looking for features Comparing distributions by subgroups What plots are there for individual continuous variables? Plot options Modelling and testing for continuous variables Displaying Categorical Data Introduction What features might categorical variables have? Nominal data—no fixed category order Ordinal data—fixed category order Discrete data—counts and integers Formats, factors, estimates, and barcharts Modelling and testing for categorical variables Looking for Structure: Dependency Relationships and Associations Introduction What features might be visible in scatterplots? Looking at pairs of continuous variables Adding models: lines and smooths Comparing groups within scatterplots Scatterplot matrices for looking at many pairs of variables Scatterplot options Modelling and testing for relationships between variables Investigating Multivariate Continuous Data Introduction What is a parallel coordinate plot (pcp)? Features you can see with parallel coordinate plots Interpreting clustering results Parallel coordinate plots and time series Parallel coordinate plots for indices Options for parallel coordinate plots Modelling and testing for multivariate continuous data Parallel coordinate plots and comparing model results Studying Multivariate Categorical Data Introduction Data on the sinking of the Titanic What is a mosaicplot? Different mosaicplots for different questions of interest Which mosaicplot is the right one? Additional options Modelling and testing for multivariate categorical data Getting an Overview Introduction Many individual displays Multivariate overviews Multivariate overviews for categorical variables Graphics by group Modelling and testing for overviews Graphics and Data Quality: How Good Are the Data? Introduction Missing values Outliers Modelling and testing for data quality Comparisons, Comparisons, Comparisons Introduction Making comparisons Making visual comparisons Comparing group effects graphically Comparing rates visually Graphics for comparing many subsets Graphics principles for comparisons Modelling and testing for comparisons Graphics for Time Series Introduction Graphics for a single time series Multiple series Special features of time series Alternative graphics for time series R classes and packages for time series Modelling and testing time series Ensemble Graphics and Case Studies Introduction What is an ensemble of graphics? Combining different views—a case study example Case studies Some Notes on Graphics with R Graphics systems in R Loading datasets and packages for graphical analysis Graphics conventions in statistics What is a graphic anyway? Options for all graphics Some R graphics advice and coding tips Other graphics Large datasets Perfecting graphics Summary Data analysis and graphics Key features of GDA Strengths and weaknesses of GDA Recommendations for GDA References General Index Datasets Index

About the Author :
Antony Unwin is a professor of computer-oriented statistics and data analysis at the University of Augsburg. He is a fellow of the American Statistical Society, co-author of Graphics of Large Datasets, and co-editor of the Handbook of Data Visualization. His research focuses on data visualisation, especially in interactive graphics. His research group has developed several pieces of interactive graphics software and written packages for R.

Review :
". . . the book follows a learning-by-doing approach.With numerous examples, the author shows how important qualitative aspects of data can be detected by means of simple plots, and how a few simple changes in a graph may uncover relevant information not visible before, setting aside the more technical aspects of plots in R. Still, for each graph, the respective R-code is provided in the book, and complete programme codes for the examples ae available on the book’s webpage. Thus, by copy and paste, one can easily rescale all graphs, change the aspect ratio and apply other modifications to the original plot. This blended-learning approach facilitates exploring the data graphically without requiring too much knowledge of R syntax. This book is therefore well suited for students and novice data analysts who want to learn from examples. It could also supplement theoretical statistics courses, and help statistics teachers in finding suitable graphical displays for various purposes." —Jasmin Wachter, Universität Klagenfurt "Overall, the book is a very good introduction to the practical side of graphical data analysis using R. The presentation of R code and graphics output is excellent, with colours used when required. The book appears to be free of typographical and other errors, and its index is useful. Also, the book is well written and neatly structured. I enjoyed reading the book and can recommend it to anyone who wants to learn more about their data through graphics using R. It will also be a valuable asset for a library and as part of an undergraduate course in applied statistics." —Journal of the Royal Statistical Society, Series A "Throughout, the book follows a learning-by-doing approach. With numerous examples, the author shows how important qualitative aspects of data can be detected by means of simple plots, and how a few simple changes in a graph may uncover relevant information not visible before, setting aside the more technical aspects of plots in R. Still, for each graph, the respective R-code is provided in the book, and complete programme codes for the examples ae available on the book’s webpage. … This blended-learning approach facilitates exploring the data graphically without requiring too much knowledge of R syntax. This book is therefore well suited for students and novice data analysts who want to learn from examples. It could also supplement theoretical statistics courses, and help statistics teachers in finding suitable graphical displays for various purposes." —Statistical Papers, 2017 "… an attractive addition to the current statistical graphics texts as it demonstrates what can be learned through graphs." —Significance Magazine, February 2016 "… the strength of this book lies in the profound introduction to the topic of graphical data analysis. The comprehensive sectional introductions and overviews along with the `how-to’ might well be regarded as the modern update to Tukey’s 1977 landmark book." —Biometrical Journal, December 2015 "Antony Unwin’s very clever new book … is well written, clearly by a practitioner with wide experience, gives generally good (though sometimes opinionated) advice, and includes R code for nearly all examples, as well as nice collections of additional exercises for each chapter … Beyond the content, Unwin also does an admirable job of conveying enthusiasm for data graphics." —Journal of Educational and Behavioral Statistics, December 2015 "This text has the potential of bringing sophisticated visualization to a broad audience without resorting to mathematical formalizations or the skills of a graphics artist. It engages the reader with interesting graphics right from the start and overall is clear and unintimidating. Code for all examples is provided in the text and is available on a supporting website. What’s more, the code works as is, rather unusual and refreshing." —Journal of Statistical Software, November 2015 "For statisticians and experts in data analysis, the book is without doubt the new reference work on the subject." —Thomas Rahlf, datendesign-r.de ...would also be an excellent suggested additional reading for a pragmatic graphical data analysis-oriented course. —Reijo Sund, Centre for Research Methods, University of Helsinki