"This book is about the fundamentals of R programming. This tutorial manual provides a comprehensive introduction to R, a software package for statistical computing and graphics. Found insideThis book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients. Found inside – Page iiThis second edition is intended for users of S-PLUS 3.3, or later, and covers both Windows and UNIX. Found insideThis practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham’s package development philosophy. This book will serve as a vital resource for both sponsors and producers of systematic reviews of comparative effectiveness research. Found inside – Page 1You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory ... Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Found inside – Page 78A logic high on the R / W input selects the read mode and a logic low selects the write mode . ... BUFFER COLUMN AMP DATA 1/0 A5 * DATATO SRL { ( EXTERNAL ) INTERNAL DATA IN R / W CE CELL MATRIX INTERNAL CONNECTIONS ... Requiring no prior programming experience and packed with practical examples, easy, step-by-step exercises, and sample code, this extremely accessible guide is the ideal introduction to R for complete beginners. Found inside – Page 1By reading this book, you will learn: The difference between an object and its name, and why the distinction is important The important vector data structures, how they fit together, and how you can pull them apart using subsetting The fine ... This book could be used as the main text for a class on reproducible research ..." (The American Statistician) Reproducible Research with R and R Studio, Third Edition brings together the skills and tools needed for doing and presenting ... Found insideNew 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 ... Describes ways to incorporate domain modeling into software development. "This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"-- Found insideIn this book, you will learn Basics: Syntax of Markdown and R code chunks, how to generate figures and tables, and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ... While the text is biased against complex equations, a mathematical background is needed for advanced topics. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Style and approach This book takes a practical, step-by-step approach to explain the concepts of data mining. Practical use-cases involving real-world datasets are used throughout the book to clearly explain theoretical concepts. In addition, the book is highly illustrated with line drawings and photographs which help to reinforce explanations and examples. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. Found insideThis practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company ... Through interviews and examples from data scientists and analytics leaders in a variety of industries . Found insideThis book is about making machine learning models and their decisions interpretable. Describes the features and functions of Apache Hive, the data infrastructure for Hadoop. With ample coverage of emerging rules that have yet to be issued and other matters subject to change, this book outlines fundamental blueprints of the new rules, technological developments, and evolving trends that impact internal audit ... The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. Found insideThis volume is the definitive resource about Mercury for planetary scientists, from students to senior researchers. Found insideRegarded widely as the cornerstone of Keynesian thought, this book challenged the established classical economics and introduced new concepts. ‘The General Theory of Employment, Interest, and Money’ transformed economics and changed the ... Presents case studies and instructions on how to solve data analysis problems using Python. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. The R Book is aimed at undergraduates, postgraduates and professionals in science, engineering and medicine. It is also ideal for students and professionals in statistics, economics, geography and the social sciences. This is the first book on applied econometrics using the R system for statistical computing and graphics. Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. Found insideIdeal for programmers, security professionals, and web administrators familiar with Python, this book not only teaches basic web scraping mechanics, but also delves into more advanced topics, such as analyzing raw data or using scrapers for ... This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. Accessible to seasoned, novice, and occasional R users, this book shows that for many purposes, adding a graphical interface to one’s work is not terribly sophisticated or time consuming. With this book, you’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools. Found insideThis book can also be used as material for courses in both data cleaning and data analysis. Found insideYou 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. Found insideBy using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Provides both rich theory and powerful applications Figures are accompanied by code required to produce them Full color figures This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkison ... This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. Found insideR has been the gold standard in applied machine learning for a long time. Found inside – Page 1This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. Found insideDrawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to ... 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