• Title A Step-by-Step R Tutorial: An Introduction into R Applications and Programming
• Author(s) Emmanuel Paradis
• Publisher: CRAN-R Project
• Hardcover/Paperback N/A
• eBook: PDF (245 pages, 11.8 MB)
• Language: English
• ISBN-10: N/A
• ISBN-13: 978-8740309911
• Share This:  

Book Description

A Step-by-Step Tutorial in R has a two-fold aim: to learn the basics of R and to acquire basic skills for programming efficiently in R. Emphasis is on converting ideas about analysing data into useful R programs. It is stressed throughout that programming starts first by getting a clear understanding of the problem. Once the problem is well formulated the next phase is to write step-by-step code for execution by the R evaluator.

Although A Step-by-Step Tutorial in R is primarily intended as a course directed by an instructor, it can also be used with a little more effort as a self-teaching option. The first 11 chapters form the core and deal with management of R objects, workspaces, functions, graphics, data structures, subscripting, search paths, evaluation environments, vectorised programming, mapping functions, loops, error tracing and statistical modelling. The optional final chapters take a closer look at analysis of variance and covariance and optimization techniques.

• Niel le Roux is an Emeritus Professor of Statistics at Stellenbosch University

• Amazon
• Amazon (R for Excel Users)

• The R Programming Language
• Statistics, and SAS Programming
• Data Analysis and Data Mining
• Geographic Information System (GIS) and Web Mapping

• A Step-by-Step R Tutorial: An Introduction into R Applications and Programming
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF
• The Mirror Site (3) - PDF

• R for Beginners (Sasha D. Hafner)

  The objective of this book is to introduce participants to the use of R for data manipulation and analysis. It is intented for individuals with little or no prior experience in R. The topics that are covered are the most important for getting started with R.

• Just Enough R: Learn Data Analysis with R in a Day (S. Raman)

  Learn R programming for data analysis in a single day. The book aims to teach data analysis using R within a single day to anyone who already knows some programming in any other language.

• The R Inferno (Patrick Burns)

  This book is an essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. If you are using spreadsheets to understand data, switch to R.

• The Art of R Programming: A Tour of Statistical Software Design

  A guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.

• Geocomputation with R (Robin Lovelace, et al.)

  This book is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities.

• Hands-On Programming with R: Functions and Simulations

  This book not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.

• R Programming for Data Science (Roger D. Peng)

  This book is about the fundamentals of R programming. Get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. You will have a solid foundation on data science toolbox.

• R Graphics Cookbook: Practical Recipes for Visualizing Data

  This cookbook provides more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quickly - without having to comb through all the details of R's graphing systems.

• An Introduction to R (Alex Douglas, et al.)

  The main aim of this book is to help you climb the initial learning curve and provide you with the basic skills and experience (and confidence!) to enable you to further your experience in using R.

• Advanced R, Second Edition (Hadley Wickham)

  This book helps you understand how R works at a fundamental level. Designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages to understand what makes R different and special.

• Advanced R Solutions (Malte Grosser, et al)

  This book offers solutions to the exercises from Advanced R, 2nd Edition by Hadley Wickham. It is work in progress and under active development. The 2nd edition of Advanced R is in print now and we hope to provide most of the answers.

• R for Data Science: Visualize, Model, Transform, Tidy, Import

  This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

• R Packages: Organize, Test, Document, and Share Your Code

  Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author's package development philosophy.

• Forecasting, Principles and Practice, Using R (R. J. Hyndman)

  This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

• Introduction to Data Science: Data Analysis and Algorithms with R

  Introduces concepts and skills that can help tackling real-world data analysis challenges. Covers concepts from probability, statistical inference, linear regression, and machine learning. Helps developing skills such as R programming, data wrangling, etc.

• Regression Models for Data Science in R (Brian Caffo)

  The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.

• Efficient R Programming: Practical Guide to Smarter Programming

  This book is about increasing the amount of work you can do with R in a given amount of time. It's about both computational and programmer efficiency. This book is for anyone who wants to make their use of R more reproducible, scalable, and faster.

• Cookbook for R: Best R Programming TIPs (Winston Chang)

  The goal of this cookbook is to provide solutions to common tasks and problems in analyzing data. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

• Advanced R (Florian Prive)

  This book aims at giving a wide understanding of many aspects of R. Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R's functionality.

• Applied Statistics with R (David Dalpiaz)

  This book provides an integrated treatment of statistical inference techniques in data science using the R Statistical Software. It provides a much-needed, easy-to-follow introduction to statistics and the R programming language.

• An Introduction to Statistical Learning: with Applications in R

  It provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

• An Introduction to Bayesian Thinking using R

  It provides an introduction to Bayesian Inference in decision making without requiring calculus. It may be used on its own as an open-access introduction to Bayesian inference using R Programming for anyone interested in learning about Bayesian statistics.