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.

Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.

This step-by-step guide shows you how to use R to get data analysis right. The book explores the entire process of analysis, covering key steps from preparing your data to putting your analysis together and writing up your findings. It helps you get to grips with doing different statistical techniques in R and: Equips you with practical data visualisation tools to create graphs and tables. Shows you how to prepare and present your research for assessment, publication and dissemination. Covers key issues facing today’s social scientists, such as making research reproducible. Features include an introduction to each chapter, and end-of-chapter exercises to check your understanding of the material. The online resources for this text include data sets that you can perform your own analysis on, and links to publications that are relevant to programming with R. A good starting point for any postgraduate student conducting a research project, this book will help you develop your statistics and programming knowledge and get quickly up to speed.

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

This book offers state-of-the-art ‘tools for thinking’ for urban designers, planners and decision-makers. Thematically it focuses on the contexts of problems in urban design and places community spaces at the heart of urban design research. The book provides practicable tools for network modelling and visualization in urban design research. Step-by-step examples take readers through methods for tracing the evolution of road networks, and their impacts on contemporary community spaces. Easy-to-follow guides to programming show how to process and plot community data sets as network graphs. They reveal how these can help to observe and represent the different ways in which community spaces are inter-connected. This book places these technological methods in the context of current theories of community formations. It considers how these cutting-edge tools for thinking in urban design research – comprising both theories and methods – could transform our understanding of community spaces as being complex, inter-dependent and socially meaningful assets. This book is pioneering in its analysis of the urban contexts to community formations, and in its argument for professional integration between urban and knowledge practitioners. Academics and professionals within the fields of design research, urban studies, spatial analysis, urban geography and sociology will benefit from reading this book.

Research, development, and applications in computer graphics have dramatically expanded in recent years. Because of decreasing prices, superior hardware is now being used and image quality is better than ever. Many people now require image-synthesis techniques and software for their applicaions. Moreover, the techniques of computer ani mation have become very popular. In this book, we present a wide range of applications of computer graphics. This book is a collection of 44 papers in various areas of computer graphics selected from papers presented at Graphics Interface '85. Graphics Interface '85, held from May 27 to 31 in Montreal, was the first truly international computer graphics conference in Canada. This year, for the first time, the conference was presented jointly by the Com puter Graphics Society and the Canadian Man-Computer Communications Society. This new arrangement gave the conference international scope. The conference was spon sored by the Department of Communications in Ottawa, the Department of Science and Technology in Quebec, Supply and Services Canada, the Natural Sciences and Engineer ing Research Council of Canada, Hydro-Quebec, the "Association Canadienne Fran«aise pour I' Avancement des Sciences", and the Canadian Broadcasting Corpora tion. Graphics Interface '85 was organized by "1'Ecole des Hautes Etudes Commerciales" of the University of Montreal. Over 100 papers were submitted to the conference , but 64 were selected by the inter national program committee for presentation. This book contains new expanded versions of the papers.

The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.

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