Organised to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks.
Author: Jared P. Lander
Publisher: Addison-Wesley Professional
Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks. Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you'll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R's facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available. Normal 0 false false false EN-US X-NONE X-NONE
R for everyone: Advanced analytics and graphics (2nd ed.). Addison-Wesley Data & Analytics Series. Teetor, P. (2011). R cookbook. O'Reilly. Chapter 2 Basic
Concepts 2.1 Contingency, Square, and Doubly Classified. 1.8 Read File into R
Author: Teck Kiang Tan
This book provides practical applications of doubly classified models by using R syntax to generate the models. It also presents these models in symbolic tables so as to cater to those who are not mathematically inclined, while numerous examples throughout the book illustrate the concepts and their applications. For those who are not aware of this modeling approach, it serves as a good starting point to acquire a basic understanding of doubly classified models. It is also a valuable resource for academics, postgraduate students, undergraduates, data analysts and researchers who are interested in examining square contingency tables.
Scientific Programming and Simulation Using R, Chapman and Hall/CRC. 2nd ed. Boca Raton, Florida: The R Series; 2014. ... LanderJared P, R for Everyone, Advanced Analytics and Graphics, Addison-Wesley Data & Analytics, .
Author: James P Howard, II
Publisher: CRC Press
Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra, interpolation, numerical integration, optimization, and differential equations can be implemented in pure R code. Every algorithm described is given with a complete function implementation in R, along with examples to demonstrate the function and its use. Computational Methods for Numerical Analysis with R is intended for those who already know R, but are interested in learning more about how the underlying algorithms work. As such, it is suitable for statisticians, economists, and engineers, and others with a computational and numerical background.
R-project.org/ package=caret, R package version 6.0-80. Kuhn, M., & Johnson, K.
(2013). Applied predictive modeling. Berlin: Springer. Lander, J. P. (2017). R for everyone: Advanced analytics and graphics (2nd ed.). Boston: Addison-Wesley.
Author: Chris Chapman
The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research. This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications. The 2nd edition increases the book’s utility for students and instructors with the inclusion of exercises and classroom slides. At the same time, it retains all of the features that make it a vital resource for practitioners: non-mathematical exposition, examples modeled on real world marketing problems, intuitive guidance on research methods, and immediately applicable code.
The R book. (2007, second edition 2012). M.J. Crawley John Wiley and Sons. Big
heavy book with lots of (mostly) good stuff. Great on things ... Really a book about
theoretical ecology with examples using R. R for everyone: Advanced Analytics
and Graphics (2013). Jared P. Lander. Addison-Wesley Data & Analytics Series.
Author: Robert Knell
Publisher: Robert Knell
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.
R . Vickery , R . Moorhead , R . Brou , D . Carruth , and S . Doane were funded by
ONR grant N00014 - 001 - 0560 . ... R . Grzeszczuk , C . Henn , and R . Yagel , “ Advanced Geometric Techniques for Ray Casting Volumes , " in SIGGRAPH ' 98
Course Notes , ACM ... The Visualization Toolkit , An Object - Oriented Approach
to 3D Graphics , Prentice - Hall PTR , Upper Saddle River , NJ 07458 , 2nd ed . ,
1998 . ISBN 0 ... 293 – 302 , ACM SIGGRAPH , Addison Wesley , August 1997 .
Author: American Statistical Association. Section on Statistical GraphicsPublish On: 1988
Variable coefficient t - value sig . level Residual Analysis CONSTANT In (
WINAVG ) In ( YEAR ) ln ( PCONTRI ) In ... 4672 R - SQ . ( ADJ . ) = 0 . 5937 SE =
0 . 567901 MAE = 0 . 422835 DurbWat = 2 . 154 176 observations fitted . ... s
performance than the career batting average since the latter does not take into
account the number of bases advanced on a hit . ... Data Analysis and
Regression : A Second Course in Statistics , Addison - wesley . ... SPSS - X User '
s Guide , 2nd Edition .
Author: American Statistical Association. Section on Statistical Graphics
Tufte , E . R . ( 1983 ) , The Visual Display of Quantitative Information , Graphics
Press , Cheshire , CT . ... Tukey , J . W . ( 1977 ) , Exploratory Data Analysis ,
Reading , MA : AddisonWesley . ... Inevitably , not all important arms control
issues are covered . ... This well - written , thorough , and ( so far as I know )
unique book discusses some statistical aspects of verifying compliance with arms
control treaties ...
Calculus with Analytic Geometry Alternate with Late Trigonometry . 4th alt . ed .
Roland E. Larson & Robert P. Hostetler . ... Contemporary Precalculus Through
Applications : Functions , Data Analysis & Matrices . rev . ... Addison - Wesley
Educational Pubs . , Inc. Easy - Way Series , Barron's Educational Series , Inc.
Arithmetic the Easy Way ... Rex Miller & Mark R. Miller . ... ( 0-669-24650-6 ) ;
Macintosh . ( 0-669-24652-2 ) Foundations of Analysis . 2nd ed . Edmund
Landau . 136p . 1960.
This book is organized into short chapters that concentrate on one topic at a time, offering instructors maximum flexibility in planning their courses.
Author: Richard D. De Veaux
Publisher: Pearson College Division
Stats: Data and Models, Third Edition, will intrigue and challenge students by encouraging them to think statistically and by emphasizing how statistics helps us understand the world. Praised by students and instructors alike for its readability and ease of comprehension, this text focuses on statistical thinking and data analysis. The authors draw from their wealth of consulting experience to craft compelling examples, which encourages students to learn how to reason with data. This book is organized into short chapters that concentrate on one topic at a time, offering instructors maximum flexibility in planning their courses. The text is appropriate for a one-or-two semester introductory statistics course and includes advanced topics, such as Analysis of Variance (ANOVA), Multiple Regression, and Nonparametrics.
The programming approach emphasized in this text builds on the readerAs understanding of small-scale programming and extends this knowledge into the realm of large-scale software engineering.
Author: Doug Bell
Publisher: Addison Wesley
Software Engineering: A Programming Approach provides a unique introduction to software engineering for all students of computer science and its related disciplines. It is also ideal for practitioners in the software industry who wish to keep track of new developments in the discipline. The third edition is an update of the original text written by Bell, Morrey and Pugh and further develops the programming approach taken by these authors. The new edition however, being updated by a single author, presents a more coherent and fully integrated text. It also includes recent developments in the field and new chapters include those on: formal development, software management, prototyping, process models and user interface design. The programming approach emphasized in this text builds on the readerAs understanding of small-scale programming and extends this knowledge into the realm of large-scale software engineering. This helps the student to understand the current challenges of software engineering as well as developing an understanding of the broad range of techniques and tools that are currently available in the industry. Particular features of the third edition are: - a pragmatic, non-mathematical approach - an overview of the software development process is included - self-test questions in each chapter ensure understanding of the topic - extensive exercises are provided at the end of each chapter - an accompanying website extends and updates material in the book - use of Java throughout as an illustrative programming language - consistent use of UML as a design notation Douglas Bell is a lecturer at Sheffield Hallam University, England. He hasauthored and co-authored a number of texts including, most recently, Java for Students.