Lattice

Multivariate Data Visualization with R
Author: Deepayan Sarkar
Publisher: Springer Science & Business Media
ISBN: 9780387759692
Category: Mathematics
Page: 268
View: 3114
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Written by the author of the lattice system, this book describes lattice in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed. The book contains close to 150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics.

R in a Nutshell


Author: Joseph Adler
Publisher: O'Reilly Germany
ISBN: 3897216507
Category: Computers
Page: 768
View: 6203
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Wozu sollte man R lernen? Da gibt es viele Gründe: Weil man damit natürlich ganz andere Möglichkeiten hat als mit einer Tabellenkalkulation wie Excel, aber auch mehr Spielraum als mit gängiger Statistiksoftware wie SPSS und SAS. Anders als bei diesen Programmen hat man nämlich direkten Zugriff auf dieselbe, vollwertige Programmiersprache, mit der die fertigen Analyse- und Visualisierungsmethoden realisiert sind – so lassen sich nahtlos eigene Algorithmen integrieren und komplexe Arbeitsabläufe realisieren. Und nicht zuletzt, weil R offen gegenüber beliebigen Datenquellen ist, von der einfachen Textdatei über binäre Fremdformate bis hin zu den ganz großen relationalen Datenbanken. Zudem ist R Open Source und erobert momentan von der universitären Welt aus die professionelle Statistik. R kann viel. Und Sie können viel mit R machen – wenn Sie wissen, wie es geht. Willkommen in der R-Welt: Installieren Sie R und stöbern Sie in Ihrem gut bestückten Werkzeugkasten: Sie haben eine Konsole und eine grafische Benutzeroberfläche, unzählige vordefinierte Analyse- und Visualisierungsoperationen – und Pakete, Pakete, Pakete. Für quasi jeden statistischen Anwendungsbereich können Sie sich aus dem reichen Schatz der R-Community bedienen. Sprechen Sie R! Sie müssen Syntax und Grammatik von R nicht lernen – wie im Auslandsurlaub kommen Sie auch hier gut mit ein paar aufgeschnappten Brocken aus. Aber es lohnt sich: Wenn Sie wissen, was es mit R-Objekten auf sich hat, wie Sie eigene Funktionen schreiben und Ihre eigenen Pakete schnüren, sind Sie bei der Analyse Ihrer Daten noch flexibler und effektiver. Datenanalyse und Statistik in der Praxis: Anhand unzähliger Beispiele aus Medizin, Wirtschaft, Sport und Bioinformatik lernen Sie, wie Sie Daten aufbereiten, mithilfe der Grafikfunktionen des lattice-Pakets darstellen, statistische Tests durchführen und Modelle anpassen. Danach werden Ihnen Ihre Daten nichts mehr verheimlichen.

Introduction to Scientific Programming and Simulation Using R


Author: Owen Jones,Robert Maillardet,Andrew Robinson
Publisher: CRC Press
ISBN: 9781420068740
Category: Mathematics
Page: 472
View: 1553
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Known for its versatility, the free programming language R is widely used for statistical computing and graphics, but is also a fully functional programming language well suited to scientific programming. An Introduction to Scientific Programming and Simulation Using R teaches the skills needed to perform scientific programming while also introducing stochastic modelling. Stochastic modelling in particular, and mathematical modelling in general, are intimately linked to scientific programming because the numerical techniques of scientific programming enable the practical application of mathematical models to real-world problems. Following a natural progression that assumes no prior knowledge of programming or probability, the book is organised into four main sections: Programming In R starts with how to obtain and install R (for Windows, MacOS, and Unix platforms), then tackles basic calculations and program flow, before progressing to function based programming, data structures, graphics, and object-oriented code A Primer on Numerical Mathematics introduces concepts of numerical accuracy and program efficiency in the context of root-finding, integration, and optimization A Self-contained Introduction to Probability Theory takes readers as far as the Weak Law of Large Numbers and the Central Limit Theorem, equipping them for point and interval estimation Simulation teaches how to generate univariate random variables, do Monte-Carlo integration, and variance reduction techniques In the last section, stochastic modelling is introduced using extensive case studies on epidemics, inventory management, and plant dispersal. A tried and tested pedagogic approach is employed throughout, with numerous examples, exercises, and a suite of practice projects. Unlike most guides to R, this volume is not about the application of statistical techniques, but rather shows how to turn algorithms into code. It is for those who want to make tools, not just use them.

Guidebook to R Graphics Using Microsoft Windows


Author: Kunio Takezawa
Publisher: John Wiley & Sons
ISBN: 1118270150
Category: Mathematics
Page: 278
View: 7851
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Introduces the graphical capabilities of R to readers new to thesoftware Due to its flexibility and availability, R has become thecomputing software of choice for statistical computing andgenerating graphics across various fields of research. Guidebook toR Graphics Using Microsoft® Windows offers a uniquepresentation of R, guiding new users through its many benefits,including the creation of high-quality graphics. Beginning with getting the program up and running, this booktakes readers step by step through the process of creatinghistograms, boxplots, strip charts, time series graphs,steam-and-leaf displays, scatterplot matrices, and map graphs. Inaddition, the book presents: Tips for establishing, saving, and printing graphs along withessential base-package plotting functions Interactive R programs for carrying out common tasks such asinputting values, moving data on a natural spline, adjustingthree-dimensional graphs, and understanding simple and local linearregression Various external packages for R that help to create more complexgraphics like rimage, gplots, ggplot2, tripack, rworldmap, andplotrix packages Throughout the book, concise explanations of key concepts of Rgraphics assist readers in carrying out the presented procedures,and any coverage of functions is clearly written out and displayedin the text as demos. The discussed techniques are accompanied by awealth of screenshots and graphics with related R code available onthe book's FTP site, and numerous exercises allow readers to testtheir understanding of the presented material. Guidebook to R Graphics Using Microsoft® Windows is avaluable resource for researchers in the fields of statistics,public health, business, and the life and social sciences who useor would like to learn how to use R to create visualrepresentations of data. The book can also be used as a supplementfor courses on statistical analysis at the upper-undergraduatelevel.

Grundlagen der Datenanalyse mit R

Eine anwendungsorientierte Einführung
Author: Daniel Wollschläger
Publisher: Springer-Verlag
ISBN: 9783642122286
Category: Social Science
Page: 412
View: 8018
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Dieses Buch liefert eine anwendungsorientierte Einführung in die Grundlagen der Datenauswertung mit dem freien Statistikpaket R. Es behandelt deskriptive Auswertungen ebenso wie inferenzstatistische Analysen. Neben den geläufigsten univariaten Verfahren berücksichtigt es nonparametrische sowie ausgewählte multivariate Methoden. Zudem deckt es die vielfältigen Möglichkeiten ab, Diagramme zu erstellen und Daten mit anderen Programmen auszutauschen. Die statistischen Verfahren werden an Beispielen erläutert und an vielen Stellen mit Diagrammen illustriert. Hinzu kommen manuelle Kontrollrechnungen, um die Ergebnisse von R Schritt für Schritt nachvollziehbar zu machen. Das Buch richtet sich an alle, die R kennenlernen und in konkreten Aufgabenstellungen einsetzen möchten, ohne bereits über Vorerfahrungen mit befehlsgesteuerten Programmen oder Programmiersprachen zu verfügen.

R für Dummies


Author: Andrie de Vries,Joris Meys
Publisher: John Wiley & Sons
ISBN: 3527812520
Category: Computers
Page: 414
View: 9107
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Wollen Sie auch die umfangreichen Möglichkeiten von R nutzen, um Ihre Daten zu analysieren, sind sich aber nicht sicher, ob Sie mit der Programmiersprache wirklich zurechtkommen? Keine Sorge - dieses Buch zeigt Ihnen, wie es geht - selbst wenn Sie keine Vorkenntnisse in der Programmierung oder Statistik haben. Andrie de Vries und Joris Meys zeigen Ihnen Schritt für Schritt und anhand zahlreicher Beispiele, was Sie alles mit R machen können und vor allem wie Sie es machen können. Von den Grundlagen und den ersten Skripten bis hin zu komplexen statistischen Analysen und der Erstellung aussagekräftiger Grafiken. Auch fortgeschrittenere Nutzer finden in diesem Buch viele Tipps und Tricks, die Ihnen die Datenauswertung erleichtern.

Introductory R: A Beginner's Guide to Data Visualisation, Statistical Analysis and Programming in R


Author: Robert Knell
Publisher: Robert Knell
ISBN: 0957597118
Category:
Page: 531
View: 1786
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R is now the most widely used statistical software in academic science and it is rapidly expanding into other fields such as finance. R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating. Introductory R is written for the novice user who knows a little about statistics but who hasn't yet got to grips with the ways of R. This new edition is completely revised and greatly expanded with new chapters on the basics of descriptive statistics and statistical testing, considerably more information on statistics and six new chapters on programming in R. Topics covered include: A walkthrough of the basics of R's command line interface Data structures including vectors, matrices and data frames R functions and how to use them Expanding your analysis and plotting capacities with add-in R packages A set of simple rules to follow to make sure you import your data properly An introduction to the script editor and advice on workflow A detailed introduction to drawing publication-standard graphs in R How to understand the help files and how to deal with some of the most common errors that you might encounter. Basic descriptive statistics The theory behind statistical testing and how to interpret the output of statistical tests Thorough coverage of the basics of data analysis in R with chapters on using chi-squared tests, t-tests, correlation analysis, regression, ANOVA and general linear models What the assumptions behind the analyses mean and how to test them using diagnostic plots Explanations of the summary tables produced for statistical analyses such as regression and ANOVA Writing your own functions in R Using table operations to manipulate matrices and data frames Using conditional statements and loops in R programmes. Writing longer R programmes. The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.

Option Pricing and Estimation of Financial Models with R


Author: Stefano M. Iacus
Publisher: John Wiley & Sons
ISBN: 9781119990208
Category: Business & Economics
Page: 472
View: 4441
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Presents inference and simulation of stochastic process in the field of model calibration for financial times series modelled by continuous time processes and numerical option pricing. Introduces the bases of probability theory and goes on to explain how to model financial times series with continuous models, how to calibrate them from discrete data and further covers option pricing with one or more underlying assets based on these models. Analysis and implementation of models goes beyond the standard Black and Scholes framework and includes Markov switching models, Lévy models and other models with jumps (e.g. the telegraph process); Topics other than option pricing include: volatility and covariation estimation, change point analysis, asymptotic expansion and classification of financial time series from a statistical viewpoint. The book features problems with solutions and examples. All the examples and R code are available as an additional R package, therefore all the examples can be reproduced.

Learning R

A Step-by-Step Function Guide to Data Analysis
Author: Richard Cotton
Publisher: "O'Reilly Media, Inc."
ISBN: 1449357180
Category: Computers
Page: 400
View: 3540
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Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts. The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you’ve learned, and concludes with exercises, most of which involve writing R code. Write a simple R program, and discover what the language can do Use data types such as vectors, arrays, lists, data frames, and strings Execute code conditionally or repeatedly with branches and loops Apply R add-on packages, and package your own work for others Learn how to clean data you import from a variety of sources Understand data through visualization and summary statistics Use statistical models to pass quantitative judgments about data and make predictions Learn what to do when things go wrong while writing data analysis code

R Graphics Essentials for Great Data Visualization

200 Practical Examples You Want to Know for Data Science
Author: Alboukadel Kassambara
Publisher: STHDA
ISBN: 1979748101
Category: Computers
Page: 153
View: 8613
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Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics. With this book, you 'll learn: - How to quickly create beautiful graphics using ggplot2 packages - How to properly customize and annotate the plots - Type of graphics for visualizing categorical and continuous variables - How to add automatically p-values to box plots, bar plots and alternatives - How to add marginal density plots and correlation coefficients to scatter plots - Key methods for analyzing and visualizing multivariate data - R functions and packages for plotting time series data - How to combine multiple plots on one page to create production-quality figures.

Hurricane Climatology

A Modern Statistical Guide Using R
Author: James B. Elsner,Thomas H. Jagger
Publisher: Oxford University Press
ISBN: 0199324069
Category: Science
Page: 390
View: 5952
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Hurricanes are nature's most destructive storms and they are becoming more powerful as the globe warms. Hurricane Climatology explains how to analyze and model hurricane data to better understand and predict present and future hurricane activity. It uses the open-source and now widely used R software for statistical computing to create a tutorial-style manual for independent study, review, and reference. The text is written around the code that when copied will reproduce the graphs, tables, and maps. The approach is different from other books that use R. It focuses on a single topic and explains how to make use of R to better understand the topic. The book is organized into two parts, the first of which provides material on software, statistics, and data. The second part presents methods and models used in hurricane climate research.

Graphics for Statistics and Data Analysis with R


Author: Kevin J. Keen
Publisher: CRC Press
ISBN: 0429632215
Category: Mathematics
Page: 590
View: 7468
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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.

Zeitreihenmodelle


Author: Andrew C. Harvey
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3486786741
Category: Business & Economics
Page: 396
View: 7465
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Gegenstand des Werkes sind Analyse und Modellierung von Zeitreihen. Es wendet sich an Studierende und Praktiker aller Disziplinen, in denen Zeitreihenbeobachtungen wichtig sind.

Programmieren mit R


Author: Uwe Ligges
Publisher: Springer-Verlag
ISBN: 3540267328
Category: Mathematics
Page: 237
View: 4289
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R ist eine objekt-orientierte und interpretierte Sprache und Programmierumgebung für Datenanalyse und Grafik - frei erhältlich unter der GPL. Ziel dieses Buches ist es, nicht nur ausführlich in die Grundlagen der Sprache R einzuführen, sondern auch ein Verständnis der Struktur der Sprache zu vermitteln. Leicht können so eigene Methoden umgesetzt, Objektklassen definiert und ganze Pakete aus Funktionen und zugehöriger Dokumentation zusammengestellt werden. Die enormen Grafikfähigkeiten von R werden detailliert beschrieben. Das Buch richtet sich an alle, die R als flexibles Werkzeug zur Datenenalyse und -visualisierung einsetzen möchten: Studierende, die Daten in Projekten oder für ihre Diplomarbeit analysieren möchten, Forschende, die neue Methoden ausprobieren möchten, und diejenigen, die in der Wirtschaft täglich Daten aufbereiten, analysieren und anderen in komprimierter Form präsentieren.

CSS kurz & gut


Author: Eric A. Meyer
Publisher: O'Reilly Germany
ISBN: 3868991751
Category: Computers
Page: 256
View: 677
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Cascading Style Sheets sind für viele Designer und Webentwickler zu einem unersetzlichen Mittel für effizientes, standardkonformes und browserübergreifendes Webdesign geworden. Angesichts der Fülle an Selektoren und Eigenschaften ist es aber nicht immer leicht, den Überblick über den Standard zu behalten. Darüber hinaus sind mit CSS3 viele spannende Design-Möglichkeiten hinzugekommen, die einen genaueren Blick wert sind. CSS – kurz & gut beinhaltet eine vollständige Referenz aller CSS-Selektoren, Eigenschaften und Werte, die in den Standards CSS2, CSS 2.1 und CSS3 definiert sind. Darüber hinaus enthält der Band eine kurze Einführung in die Funktionsweise von Cascading Style Sheets und berücksichtigt auch neue Konzepte wie beispielsweise Media-Queries, die in CSS3 neu eingeführt wurden.

Graphische Semiologie

Diagramme, Netze, Karten
Author: Jacques Bertin
Publisher: Walter de Gruyter
ISBN: 3110834901
Category: Science
Page: 430
View: 8598
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California Cooperative Oceanic Fisheries Investigations Reports


Author: N.A
Publisher: N.A
ISBN: N.A
Category: Fisheries
Page: N.A
View: 8451
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Visualize This!


Author: Nathan Yau
Publisher: John Wiley & Sons
ISBN: 3527760229
Category: Statistics / Graphic methods / Data processing
Page: 422
View: 8403
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A guide on how to visualise and tell stories with data, providing practical design tips complemented with step-by-step tutorials.

Flächenland

ein mehrdimensionaler Roman
Author: Edwin Abbott Abbott
Publisher: N.A
ISBN: 9783950101102
Category: Fourth dimension
Page: 94
View: 5376
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Displaying Time Series, Spatial, and Space-Time Data with R


Author: Oscar Perpinan Lamigueiro
Publisher: CRC Press
ISBN: 1466565209
Category: Mathematics
Page: 208
View: 3391
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Code and Methods for Creating High-Quality Data Graphics A data graphic is not only a static image, but it also tells a story about the data. It activates cognitive processes that are able to detect patterns and discover information not readily available with the raw data. This is particularly true for time series, spatial, and space-time datasets. Focusing on the exploration of data with visual methods, Displaying Time Series, Spatial, and Space-Time Data with R presents methods and R code for producing high-quality graphics of time series, spatial, and space-time data. Practical examples using real-world datasets help you understand how to apply the methods and code. The book illustrates how to display a dataset starting with an easy and direct approach and progressively adding improvements that involve more complexity. Each of the book’s three parts is devoted to different types of data. In each part, the chapters are grouped according to the various visualization methods or data characteristics. Web Resource Along with the main graphics from the text, the author’s website offers access to the datasets used in the examples as well as the full R code. This combination of freely available code and data enables you to practice with the methods and modify the code to suit your own needs.