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Mastering Software Development in R

Roger D. Peng, Sean Kross, and Brooke Anderson

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!)

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About

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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    Author

    About the Authors

    Roger D. Peng

    Roger D. Peng is a Professor of Statistics and Data Sciences at the University of Texas, Austin. Previously, he was Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research focuses on the development of statistical methods for addressing environmental health problems and on developing tools for doing better data analysis. He is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. He can be found on Twitter and GitHub at @rdpeng.

    Leanpub Podcast

    Episode 16

    An Interview with Roger D. Peng

    Sean Kross

    Sean Kross is a software developer in the department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Sean's professional interests range between metagenomics, cybersecurity, and human-computer interaction. He is also the lead developer of the swirl, a software package designed to teach programming, statistics, and data science in an authentic programming environment. You can find Sean on Twitter and GitHub at @seankross.

    Brooke Anderson

    Brooke Anderson is an Assistant Professor at Colorado State University in the Department of Environmental & Radiological Health Sciences, as well as a Faculty Associate in the Department of Statistics. She is also a member of the university’s Partnership of Air Quality, Climate, and Health and is a member of the editorial boards of Epidemiology and Environmental Health Perspectives. Previously, she completed a postdoctoral appointment in Biostatistics at Johns Hopkins Bloomberg School of Public and a PhD in Engineering at Yale University. Her research focuses on the health risks associated with climate-related exposures, including heat waves and air pollution, for which she has conducted several national-level studies. As part of her research, she has also published a number of open source R software packages to facilitate environmental epidemiologic research. 

    Contents

    Table of Contents

    Introduction

    1. Setup

    1.The R Programming Environment

    1. 1.1Crash Course on R Syntax
    2. 1.2The Importance of Tidy Data
    3. 1.3Reading Tabular Data with the readr Package
    4. 1.4Reading Web-Based Data
    5. 1.5Basic Data Manipulation
    6. 1.6Working with Dates, Times, Time Zones
    7. 1.7Text Processing and Regular Expressions
    8. 1.8The Role of Physical Memory
    9. 1.9Working with Large Datasets
    10. 1.10Diagnosing Problems

    2.Advanced R Programming

    1. 2.1Control Structures
    2. 2.2Functions
    3. 2.3Functional Programming
    4. 2.4Expressions & Environments
    5. 2.5Error Handling and Generation
    6. 2.6Debugging
    7. 2.7Profiling and Benchmarking
    8. 2.8Non-standard Evaluation
    9. 2.9Object Oriented Programming
    10. 2.10Gaining Your ‘tidyverse’ Citizenship

    3.Building R Packages

    1. 3.1Before You Start
    2. 3.2R Packages
    3. 3.3The devtools Package
    4. 3.4Documentation
    5. 3.5Data Within a Package
    6. 3.6Software Testing Framework for R Packages
    7. 3.7Passing CRAN checks
    8. 3.8Open Source Licensing
    9. 3.9Version Control and GitHub
    10. 3.10Software Design and Philosophy
    11. 3.11Continuous Integration
    12. 3.12Cross Platform Development

    4.Building Data Visualization Tools

    1. 4.1Basic Plotting With ggplot2
    2. 4.2Customizing ggplot2 Plots
    3. 4.3Mapping
    4. 4.4htmlWidgets
    5. 4.5The grid Package
    6. 4.6Building a New Theme
    7. 4.7Building New Graphical Elements

    About the Authors

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