Mastering Software Development in R
This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. (Printed copies coming soon!)
This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. (Printed copies coming soon!)
About
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).
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Suggested price25ドル.00This 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.
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- R Code Files
- Datasets
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Contents
Table of Contents
Introduction
- Setup
1.The R Programming Environment
- 1.1Crash Course on R Syntax
- 1.2The Importance of Tidy Data
- 1.3Reading Tabular Data with the
readrPackage - 1.4Reading Web-Based Data
- 1.5Basic Data Manipulation
- 1.6Working with Dates, Times, Time Zones
- 1.7Text Processing and Regular Expressions
- 1.8The Role of Physical Memory
- 1.9Working with Large Datasets
- 1.10Diagnosing Problems
2.Advanced R Programming
- 2.1Control Structures
- 2.2Functions
- 2.3Functional Programming
- 2.4Expressions & Environments
- 2.5Error Handling and Generation
- 2.6Debugging
- 2.7Profiling and Benchmarking
- 2.8Non-standard Evaluation
- 2.9Object Oriented Programming
- 2.10Gaining Your ‘tidyverse’ Citizenship
3.Building R Packages
- 3.1Before You Start
- 3.2R Packages
- 3.3The
devtoolsPackage - 3.4Documentation
- 3.5Data Within a Package
- 3.6Software Testing Framework for R Packages
- 3.7Passing CRAN checks
- 3.8Open Source Licensing
- 3.9Version Control and GitHub
- 3.10Software Design and Philosophy
- 3.11Continuous Integration
- 3.12Cross Platform Development
4.Building Data Visualization Tools
- 4.1Basic Plotting With ggplot2
- 4.2Customizing ggplot2 Plots
- 4.3Mapping
- 4.4htmlWidgets
- 4.5The grid Package
- 4.6Building a New Theme
- 4.7Building New Graphical Elements
About the Authors
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