R Programming for Data Science
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.
This package contains just the book in PDF, EPUB, or MOBI formats.
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.
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.
- 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
- 6.3 Reading in Larger Datasets with read.table
- 6.4 Calculating Memory Requirements for R Objects
7. Using the
8. Using Textual and Binary Formats for Storing Data
- 8.2 Binary Formats
- 8.1 Using
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
- 13.1 Data Frames
13.4 Installing the
- 13.12 Summary
14. Control Structures
- 14.7 Summary
- 15.1 Functions in R
- 15.2 Your First Function
- 15.3 Argument Matching
- 15.4 Lazy Evaluation
15.6 Arguments Coming After the
- 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
- 18.5 Splitting a Data Frame
- 18.6 tapply
- 18.8 Col/Row Sums and Means
- 18.9 Other Ways to Apply
- 18.11 Vectorizing a Function
- 18.12 Summary
19. Regular Expressions
- 19.1 Before You Begin
- 19.2 Primary R Functions
- 19.9 Summary
- 20.1 Something’s Wrong!
- 20.2 Figuring Out What’s Wrong
- 20.3 Debugging Tools in R
- 20.7 Summary
21. Profiling R Code
- 21.2 Timing Longer Expressions
- 21.3 The R Profiler
- 21.5 Summary
- 21.1 Using
- 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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