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bookdown Authoring Books and Technical Documents with R Markdown

bookdown Authoring Books and Technical Documents with R Markdown

bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown and extends R Markdown for technical writing so that you can make better use of document elements such as figures tables equations theorems citations and references. Similar to LaTeX you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats including LaTeX/PDF HTML EPUB and Word thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers reports dissertations course handouts study notes and even novels. You do not have to use R either. Other choices of computing languages include Python C C plus plus SQL Bash Stan JavaScript and so on although R is best supported. You can also leave out computing for example to write a fiction. This book itself is an example of publishing with bookdown and R Markdown and its source is fully available on GitHub. | bookdown Authoring Books and Technical Documents with R Markdown

GBP 89.99
1

CRC Standard Mathematical Tables and Formulas

Data Science for Wind Energy

Algebra & Geometry An Introduction to University Mathematics

A Course in Categorical Data Analysis

A Course in Categorical Data Analysis

Categorical data-comprising counts of individuals objects or entities in different categories-emerge frequently from many areas of study including medicine sociology geology and education. They provide important statistical information that can lead to real-life conclusions and the discovery of fresh knowledge. Therefore the ability to manipulate understand and interpret categorical data becomes of interest-if not essential-to professionals and students in a broad range of disciplines. Although t-tests linear regression and analysis of variance are useful valid methods for analysis of measurement data categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. Developed from long experience in teaching categorical analysis to a multidisciplinary mix of undergraduate and graduate students A Course in Categorical Data Analysis presents the easiest most straightforward ways of extracting real-life conclusions from contingency tables. The author uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets. Although he offers S-PLUS routines through the Internet readers do not need full knowledge of a statistical software package. In this unique text the author chooses methods and an approach that nurtures intuitive thinking. He trains his readers to focus not on finding a model that fits the data but on using different models that may lead to meaningful conclusions. The book offers some simple innovative techniques not highighted in other texts that help make the book accessible to a broad interdisciplinary audience. A Course in Categorical Data Analysis enables readers to quickly use its offering of tools for drawing scientific medical or real-life conclusions from categorical data sets.

GBP 170.00
1