Bøger i Chapman & Hall/CRC: The R Series serien

Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of the methodology, this book brings together the theoretical and methodological aspects of MFA. It also covers principal component analysis, multiple correspondence analysis, factor analysis for mixed data, hierarchical MFA, and more. The book also includes examples of applications and details on how to implement MFA using an R package, with the data and R scripts available online.

This book is designed for users who already know R or MATLAB® and now need to learn the other platform. The author covers essential tasks, such as working with matrices and vectors, writing functions and other programming concepts, graphics, numerical computing, and file input/output. He highlights important differences between the two platforms and explores common mistakes that are easy to make when transitioning from one platform to the other.

This is one of the first parallel computing books to focus exclusively on parallel data structures, algorithms, software tools, and applications in data science. The book prepares readers to write effective parallel code in various languages and learn more about different R packages and other tools. It covers the classic "n observations, p variables" matrix format and common data structures. Many examples illustrate the range of issues encountered in parallel programming.

Quickly and Easily Write Dynamic Documents Suitable for both beginners and advanced users, Dynamic Documents with R and knitr, Second Edition makes writing statistical reports easier by integrating computing directly with reporting. Reports range from homework, projects, exams, books, blogs, and web pages to virtually any documents related to statistical graphics, computing, and data analysis. The book covers basic applications for beginners while guiding power users in understanding the extensibility of the knitr package. New to the Second Edition A new chapter that introduces R Markdown v2 Changes that reflect improvements in the knitr package New sections on generating tables, defining custom printing methods for objects in code chunks, the C/Fortran engines, the Stan engine, running engines in a persistent session, and starting a local server to serve dynamic documents Boost Your Productivity in Statistical Report Writing and Make Your Scientific Computing with R Reproducible Like its highly praised predecessor, this edition shows you how to improve your efficiency in writing reports. The book takes you from program output to publication-quality reports, helping you fine-tune every aspect of your report.

This book focuses on why one draws graphics to display data and which graphics to draw (and uses R to do so). 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. All the datasets are available in R or one of its packages and the R code is available online. Color graphics are used throughout the book.