Mastering Software Development in R
Mastering Software Development in R
About the Book
The world of R has evolved substantially since its early days as a statistical computing language. As the field of data science has rocketed to the forefront of all areas of scientific and industry work, R has become the centerpiece language for doing data science. Through the contributions of a vibrant and highly active developer community, R has evolved to the point where it can be considered a software development language for developing robust, modular, and highly reusable software tools.
We begin by providing a rigorous introduction to the R language, and quickly move on to more advanced aspects like functional programming, object-oriented programming, building R packages, and software maintainence. We also discuss the development of custom visualization tools through packages like ggplot2 and ggmap.
This book is about using R to develop the tools for doing data science. Whether you are on a data science team or working by yourself as part of a community of developers or data scientists, you will find this book useful as a reference for the software development process in R. Throughout, we focus on the aspects of the R language that are relevant to developing code and tools that will be used by others.
Printed copies of the book are available from Lulu (coming soon).
Packages
The Book
PDF
EPUB
WEB
English
The Book + Code Files + Datasets
This package provides in convenient form the code executed in the book as well as datasets needed to reproduce the examples. In addition, we include a complete HTML version of the book for browsing locally on your computer.
Includes:
R Code Files
Datasets
HTML Book
PDF
EPUB
WEB
English
Table of Contents
-
Introduction
- Setup
-
1. The R Programming Environment
- 1.1 Crash Course on R Syntax
- 1.2 The Importance of Tidy Data
-
1.3 Reading Tabular Data with the
readrPackage - 1.4 Reading Web-Based Data
- 1.5 Basic Data Manipulation
- 1.6 Working with Dates, Times, Time Zones
- 1.7 Text Processing and Regular Expressions
- 1.8 The Role of Physical Memory
- 1.9 Working with Large Datasets
- 1.10 Diagnosing Problems
-
2. Advanced R Programming
- 2.1 Control Structures
- 2.2 Functions
- 2.3 Functional Programming
- 2.4 Expressions & Environments
- 2.5 Error Handling and Generation
- 2.6 Debugging
- 2.7 Profiling and Benchmarking
- 2.8 Non-standard Evaluation
- 2.9 Object Oriented Programming
- 2.10 Gaining Your ‘tidyverse’ Citizenship
-
3. Building R Packages
- 3.1 Before You Start
- 3.2 R Packages
-
3.3 The
devtoolsPackage - 3.4 Documentation
- 3.5 Data Within a Package
- 3.6 Software Testing Framework for R Packages
- 3.7 Passing CRAN checks
- 3.8 Open Source Licensing
- 3.9 Version Control and GitHub
- 3.10 Software Design and Philosophy
- 3.11 Continuous Integration
- 3.12 Cross Platform Development
-
4. Building Data Visualization Tools
- 4.1 Basic Plotting With ggplot2
- 4.2 Customizing ggplot2 Plots
- 4.3 Mapping
- 4.4 htmlWidgets
- 4.5 The grid Package
- 4.6 Building a New Theme
- 4.7 Building New Graphical Elements
- About the Authors
Other books by these authors
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