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!)
حول
نبذه عن الكتاب
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).
الحزم
اختر حزمتك
جميع الحزم تتضمن الكتاب الإلكتروني بالتنسيقات التالية: PDF و EPUB
The Book
الحد الأدنى للسعر
السعر المقترح$20.00مجاناً!
The Book + Code Files + Datasets
الحد الأدنى للسعر
السعر المقترح$25.00This 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.
$20.00
- R Code Files
- Datasets
- HTML Book
المؤلف
عن المؤلفين
Roger D. Peng is a Professor of Statistics and Data Sciences at the University of Texas at Austin. Previously, he was Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and the Co-Director of the Johns Hopkins Data Science Lab. He is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. 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. Roger received a PhD in Statistics from the University of California, Los Angeles. His current research focuses on building analytic design theory for improving the quality of data analyses and on the development of statistical methods for addressing environmental health problems.
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 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.
بودكاست
Podcast Episode
المحتويات
جدول المحتويات
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
احصل على الفصول النموذجية المجانية
Click the buttons to the right to get the free sample in PDF or EPUB, or read the sample online here
أيضا بواسطة المؤلفين
أيضا بواسطة المؤلفين
R Programming for Data Science
The Art of Data Science
Exploratory Data Analysis with R
Executive Data Science
Report Writing for Data Science in R
Developing Data Products in R
The Unix Workbench
Conversations On Data Science
Tidyverse Skills for Data Science in R
Essays on Data Analysis
Advanced Statistical Computing
The Data Science Salon

Time Series Analysis: A Brief Survey
ddp
Unix Tools for Data Scientists
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