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R Programming for Data Science

About the Book

Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world.

This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.

If you are interested in a printed copy of this book, you can purchase one at Lulu.

About the Author

Roger D. Peng

Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health where his research focuses on the development of statistical methods for addressing environmental health problems. 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.

Packages

The Book

This package contains just the book in PDF, EPUB, or MOBI formats.

PDF
EPUB
MOBI
WEB
English

The Book + Datasets + R Code Files

This package contains the book and R code files corresponding to each of the chapters in the book. The package also contains the datasets used in all of the chapters so that the code can be fully executed.

Includes:

Datasets
R Code Files

PDF
EPUB
MOBI
WEB
English

The Book + Lecture Videos (HD) + Datasets + R Code Files

This package includes the book, high definition lecture video files (720p), datasets and R code files for all chapters. The collection also contains live demonstrations of how to use various aspects of R that could not be included in the book. The videos are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license.

Includes:

Datasets
R Code Files
Lecture Videos (HD)

PDF
EPUB
MOBI
WEB
English

1. Stay in Touch!
2. Preface
3. History and Overview of R
- 3.1 What is R?
- 3.2 What is S?
- 3.3 The S Philosophy
- 3.4 Back to R
- 3.5 Basic Features of R
- 3.6 Free Software
- 3.7 Design of the R System
- 3.8 Limitations of R
- 3.9 R Resources
4. Getting Started with R
- 4.1 Installation
- 4.2 Getting started with the R interface
5. R Nuts and Bolts
- 5.1 Entering Input
- 5.2 Evaluation
- 5.3 R Objects
- 5.4 Numbers
- 5.5 Attributes
- 5.6 Creating Vectors
- 5.7 Mixing Objects
- 5.8 Explicit Coercion
- 5.9 Matrices
- 5.10 Lists
- 5.11 Factors
- 5.12 Missing Values
- 5.13 Data Frames
- 5.14 Names
- 5.15 Summary
6. Getting Data In and Out of R
- 6.1 Reading and Writing Data
- 6.2 Reading Data Files with read.table()
- 6.3 Reading in Larger Datasets with read.table
- 6.4 Calculating Memory Requirements for R Objects
7. Using the readr Package
8. Using Textual and Binary Formats for Storing Data
- 8.1 Using dput() and dump()
- 8.2 Binary Formats
9. Interfaces to the Outside World
- 9.1 File Connections
- 9.2 Reading Lines of a Text File
- 9.3 Reading From a URL Connection
10. Subsetting R Objects
- 10.1 Subsetting a Vector
- 10.2 Subsetting a Matrix
- 10.3 Subsetting Lists
- 10.4 Subsetting Nested Elements of a List
- 10.5 Extracting Multiple Elements of a List
- 10.6 Partial Matching
- 10.7 Removing NA Values
11. Vectorized Operations
- 11.1 Vectorized Matrix Operations
12. Dates and Times
- 12.1 Dates in R
- 12.2 Times in R
- 12.3 Operations on Dates and Times
- 12.4 Summary
13. Managing Data Frames with the dplyr package
- 13.1 Data Frames
- 13.2 The dplyr Package
- 13.3 dplyr Grammar
- 13.4 Installing the dplyr package
- 13.5 select()
- 13.6 filter()
- 13.7 arrange()
- 13.8 rename()
- 13.9 mutate()
- 13.10 group_by()
- 13.11 %>%
- 13.12 Summary
14. Control Structures
- 14.1 if-else
- 14.2 for Loops
- 14.3 Nested for loops
- 14.4 while Loops
- 14.5 repeat Loops
- 14.6 next, break
- 14.7 Summary
15. Functions
- 15.1 Functions in R
- 15.2 Your First Function
- 15.3 Argument Matching
- 15.4 Lazy Evaluation
- 15.5 The ... Argument
- 15.6 Arguments Coming After the ... Argument
- 15.7 Summary
16. Scoping Rules of R
- 16.1 A Diversion on Binding Values to Symbol
- 16.2 Scoping Rules
- 16.3 Lexical Scoping: Why Does It Matter?
- 16.4 Lexical vs. Dynamic Scoping
- 16.5 Application: Optimization
- 16.6 Plotting the Likelihood
- 16.7 Summary
17. Coding Standards for R
18. Loop Functions
- 18.1 Looping on the Command Line
- 18.2 lapply()
- 18.3 sapply()
- 18.4 split()
- 18.5 Splitting a Data Frame
- 18.6 tapply
- 18.7 apply()
- 18.8 Col/Row Sums and Means
- 18.9 Other Ways to Apply
- 18.10 mapply()
- 18.11 Vectorizing a Function
- 18.12 Summary
19. Regular Expressions
- 19.1 Before You Begin
- 19.2 Primary R Functions
- 19.3 grep()
- 19.4 grepl()
- 19.5 regexpr()
- 19.6 sub() and gsub()
- 19.7 regexec()
- 19.8 The stringr Package
- 19.9 Summary
20. Debugging
- 20.1 Something's Wrong!
- 20.2 Figuring Out What's Wrong
- 20.3 Debugging Tools in R
- 20.4 Using traceback()
- 20.5 Using debug()
- 20.6 Using recover()
- 20.7 Summary
21. Profiling R Code
- 21.1 Using system.time()
- 21.2 Timing Longer Expressions
- 21.3 The R Profiler
- 21.4 Using summaryRprof()
- 21.5 Summary
22. Simulation
- 22.1 Generating Random Numbers
- 22.2 Setting the random number seed
- 22.3 Simulating a Linear Model
- 22.4 Random Sampling
- 22.5 Summary
23. Data Analysis Case Study: Changes in Fine Particle Air Pollution in the U.S.
- 23.1 Synopsis
- 23.2 Loading and Processing the Raw Data
- 23.3 Results
24. Parallel Computation
- 24.1 Hidden Parallelism
- 24.2 Embarrassing Parallelism
- 24.3 The Parallel Package
- 24.4 Example: Bootstrapping a Statistic
- 24.5 Building a Socket Cluster
- 24.6 Summary
25. Why I Indent My Code 8 Spaces
26. About the Author

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