R Programming for Data Science

Table of Contents
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Preface
History and Overview of R
	What is R?
	What is S?
	The S Philosophy
	Back to R
	Basic Features of R
	Free Software
	Design of the R System
	Limitations of R
	R Resources
Getting Started with R
	Installation
	Getting started with the R interface
R Nuts and Bolts
	Entering Input
	Evaluation
	R Objects
	Numbers
	Attributes
	Creating Vectors
	Mixing Objects
	Explicit Coercion
	Matrices
	Lists
	Factors
	Missing Values
	Data Frames
	Names
	Summary
Getting Data In and Out of R
	Reading and Writing Data
	Reading Data Files with read.table()
	Reading in Larger Datasets with read.table
	Calculating Memory Requirements for R Objects
Using the readr Package
Using Textual and Binary Formats for Storing Data
	Using dput() and dump()
	Binary Formats
Interfaces to the Outside World
	File Connections
	Reading Lines of a Text File
	Reading From a URL Connection
Subsetting R Objects
	Subsetting a Vector
	Subsetting a Matrix
	Subsetting Lists
	Subsetting Nested Elements of a List
	Extracting Multiple Elements of a List
	Partial Matching
	Removing NA Values
Vectorized Operations
	Vectorized Matrix Operations
Dates and Times
	Dates in R
	Times in R
	Operations on Dates and Times
	Summary
Managing Data Frames with the dplyr package
	Data Frames
	The dplyr Package
	dplyr Grammar
	Installing the dplyr package
	select()
	filter()
	arrange()
	rename()
	mutate()
	group_by()
	%>%
	Summary
Control Structures
	if-else
	for Loops
	Nested for loops
	while Loops
	repeat Loops
	next, break
	Summary
Functions
	Functions in R
	Your First Function
	Argument Matching
	Lazy Evaluation
	The ... Argument
	Arguments Coming After the ... Argument
	Summary
Scoping Rules of R
	A Diversion on Binding Values to Symbol
	Scoping Rules
	Lexical Scoping: Why Does It Matter?
	Lexical vs. Dynamic Scoping
	Application: Optimization
	Plotting the Likelihood
	Summary
Coding Standards for R
Loop Functions
	Looping on the Command Line
	lapply()
	sapply()
	split()
	Splitting a Data Frame
	tapply
	apply()
	Col/Row Sums and Means
	Other Ways to Apply
	mapply()
	Vectorizing a Function
	Summary
Regular Expressions
	Before You Begin
	Primary R Functions
	grep()
	grepl()
	regexpr()
	sub() and gsub()
	regexec()
	The stringr Package
	Summary
Debugging
	Something’s Wrong!
	Figuring Out What's Wrong
	Debugging Tools in R
	Using traceback()
	Using debug()
	Using recover()
	Summary
Profiling R Code
	Using system.time()
	Timing Longer Expressions
	The R Profiler
	Using summaryRprof()
	Summary
Simulation
	Generating Random Numbers
	Setting the random number seed
	Simulating a Linear Model
	Random Sampling
	Summary
Data Analysis Case Study: Changes in Fine Particle Air Pollution in the U.S.
	Synopsis
	Loading and Processing the Raw Data
	Results
Parallel Computation
	Hidden Parallelism
	Embarrassing Parallelism
	The Parallel Package
	Example: Bootstrapping a Statistic
	Building a Socket Cluster
	Summary
Why I Indent My Code 8 Spaces
About the Author