Interactive And Dynamic Graphics For Data Analysis
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Interactive and Dynamic Graphics for Data Analysis by Dianne Cook,Deborah F. Swayne Pdf
This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapter topics include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The book is augmented by a wealth of online material.
Visual Statistics by Forrest W. Young,Pedro M. Valero-Mora,Michael Friendly Pdf
A visually intuitive approach to statistical data analysis Visual Statistics brings the most complex and advanced statistical methods within reach of those with little statistical training by using animated graphics of the data. Using ViSta: The Visual Statistics System-developed by Forrest Young and Pedro Valero-Mora and available free of charge on the Internet-students can easily create fully interactive visualizations from relevant mathematical statistics, promoting perceptual and cognitive understanding of the data's story. An emphasis is placed on a paradigm for understanding data that is visual, intuitive, geometric, and active, rather than one that relies on convoluted logic, heavy mathematics, systems of algebraic equations, or passive acceptance of results. A companion Web site complements the book by further demonstrating the concept of creating interactive and dynamic graphics. The book provides users with the opportunity to view the graphics in a dynamic way by illustrating how to analyze statistical data and explore the concepts of visual statistics. Visual Statistics addresses and features the following topics: * Why use dynamic graphics? * A history of statistical graphics * Visual statistics and the graphical user interface * Visual statistics and the scientific method * Character-based statistical interface objects * Graphics-based statistical interfaces * Visualization for exploring univariate data This is an excellent textbook for undergraduate courses in data analysis and regression, for students majoring or minoring in statistics, mathematics, science, engineering, and computer science, as well as for graduate-level courses in mathematics. The book is also ideal as a reference/self-study guide for engineers, scientists, and mathematicians. With contributions by highly regarded professionals in the field, Visual Statistics not only improves a student's understanding of statistics, but also builds confidence to overcome problems that may have previously been intimidating.
Interactive and Dynamic Graphics for Data Analysis by Dianne Cook,Deborah F. Swayne Pdf
This book is about using interactive and dynamic plots on a computer screen as part of data exploration and modeling, both alone and as a partner with static graphics and non-graphical computational methods. The area of int- active and dynamic data visualization emerged within statistics as part of research on exploratory data analysis in the late 1960s, and it remains an active subject of research today, as its use in practice continues to grow. It now makes substantial contributions within computer science as well, as part of the growing ?elds of information visualization and data mining, especially visual data mining. The material in this book includes: • An introduction to data visualization, explaining how it di?ers from other types of visualization. • Adescriptionofourtoolboxofinteractiveanddynamicgraphicalmethods. • An approach for exploring missing values in data. • An explanation of the use of these tools in cluster analysis and supervised classi?cation. • An overview of additional material available on the web. • A description of the data used in the analyses and exercises. The book’s examples use the software R and GGobi. R (Ihaka & Gent- man 1996, RDevelopment CoreTeam2006) isafreesoftware environment for statistical computing and graphics; it is most often used from the command line, provides a wide variety of statistical methods, and includes high–quality staticgraphics.RaroseintheStatisticsDepartmentoftheUniversityofAu- land and is now developed and maintained by a global collaborative e?ort.
Interactive Graphics for Data Analysis by Martin Theus,Simon Urbanek Pdf
Interactive Graphics for Data Analysis: Principles and Examples discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses from datasets.Fundamentals of Interactive Statistical GraphicsThe first part of the book summarizes principles and methodology, demons
Interactive Web-Based Data Visualization with R, plotly, and shiny by Carson Sievert Pdf
The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics.
Graphical Data Analysis with R by Antony Unwin Pdf
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.
This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to: produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.
Interactive Visual Data Analysis by Christian Tominski,Heidrun Schumann Pdf
Based on comprehensive taxonomies for both data and tasks Considers three challenging problems: incremental visualization, visual design and guidance Systematically investigates the visualization of multi-faceted data and networks A comprehensive overview on interaction is provided Visualization approaches in innovative display environments (large high-resolution displays, smart environments) are discussed
The essential characteristic of a dynamic graphical method is the direct manipulation of elements of a graph on a computer screen, which in high-performance implementations, the elements change virtually instantaneously on the screen. This book contains a collection of papers about dynamic graphics dating from the late 1960s to 1988. Although technology has advanced considerably, the fundamental ideas about basic graphical principles and data-analytic goals are still relevant today.
Handbook of Data Visualization by Chun-houh Chen,Wolfgang Karl Härdle,Antony Unwin Pdf
Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This book gives an overview of modern data visualization methods, both in theory and practice. It details modern graphical tools such as mosaic plots, parallel coordinate plots, and linked views. Coverage also examines graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well software for graphics.
John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. This is the only advanced programming book on R, written by the author of the S language from which R evolved.
Handbook of Applied Spatial Analysis by Manfred M. Fischer,Arthur Getis Pdf
The Handbook is written for academics, researchers, practitioners and advanced graduate students. It has been designed to be read by those new or starting out in the field of spatial analysis as well as by those who are already familiar with the field. The chapters have been written in such a way that readers who are new to the field will gain important overview and insight. At the same time, those readers who are already practitioners in the field will gain through the advanced and/or updated tools and new materials and state-of-the-art developments included. This volume provides an accounting of the diversity of current and emergent approaches, not available elsewhere despite the many excellent journals and te- books that exist. Most of the chapters are original, some few are reprints from the Journal of Geographical Systems, Geographical Analysis, The Review of Regional Studies and Letters of Spatial and Resource Sciences. We let our contributors - velop, from their particular perspective and insights, their own strategies for m- ping the part of terrain for which they were responsible. As the chapters were submitted, we became the first consumers of the project we had initiated. We gained from depth, breadth and distinctiveness of our contributors’ insights and, in particular, the presence of links between them.
Statistical Challenges in Astronomy by Eric D. Feigelson,Jogesh Babu Pdf
Digital sky surveys, high-precision astrometry from satellite data, deep-space data from orbiting telescopes, and the like have all increased the quantity and quality of astronomical data by orders of magnitude per year for several years. Making sense of this wealth of data requires sophisticated statistical techniques. Fortunately, statistical methodologies have similarly made great strides in recent years. Powerful synergies thus emerge when astronomers and statisticians join in examining astrostatistical problems and approaches. The book begins with an historical overview and tutorial articles on basic cosmology for statisticians and the principles of Bayesian analysis for astronomers. As in earlier volumes in this series, research contributions discussing topics in one field are joined with commentary from scholars in the other. Thus, for example, an overview of Bayesian methods for Poissonian data is joined by discussions of planning astronomical observations with optimal efficiency and nested models to deal with instrumental effects. The principal theme for the volume is the statistical methods needed to model fundamental characteristics of the early universe on its largest scales.
Author : James E. Gentle,Wolfgang Karl Härdle,Yuichi Mori Publisher : Springer Science & Business Media Page : 1180 pages File Size : 41,5 Mb Release : 2012-07-06 Category : Computers ISBN : 9783642215513
Handbook of Computational Statistics by James E. Gentle,Wolfgang Karl Härdle,Yuichi Mori Pdf
The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It begins with "How Computational Statistics became the backbone of modern data science" (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research. The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed. The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications.
Introduction to Statistical Methods for Biosurveillance by Ronald D. Fricker Pdf
Bioterrorism is not a new threat, but in an increasingly interconnected world, the potential for catastrophic outcomes is greater today than ever. The medical and public health communities are establishing biosurveillance systems designed to proactively monitor populations for possible disease outbreaks as a first line of defense. The ideal biosurveillance system should identify trends not visible to individual physicians and clinicians in near-real time. Many of these systems use statistical algorithms to look for anomalies and to trigger epidemiologic investigation, quantification, localization and outbreak management. This book discusses the design and evaluation of statistical methods for effective biosurveillance for readers with minimal statistical training. Weaving public health and statistics together, it presents basic and more advanced methods, with a focus on empirically demonstrating added value. Although the emphasis is on epidemiologic and syndromic surveillance, the statistical methods can be applied to a broad class of public health surveillance problems.