book

Advanced R

Name: Advanced R
Author: Hadley Wickham
ISBN: 9781498759809

by Hadley Wickham

September 2015

Intermediate to advanced

476 pages

7h 43m

English

CRC Press

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Preliminaries
Dedication
Chapman & Hall/CRC: The R Series
Chapter 1 - Introduction
1.1 Who should read this book1.2 What you will get out of this book1.3 Meta-techniques1.4 Recommended reading1.5 Getting help1.6 Acknowledgments1.7 Conventions1.8 Colophon
Section I - Foundations
Chapter 2 - Data structures
QuizOutline2.1 Vectors2.1.1 Atomic vectors2.1.1.1 Types and tests2.1.1.2 Coercion2.1.2 Lists2.1.3 Exercises2.2 Attributes2.2.0.1 Names2.2.1 Factors2.2.2 Exercises2.3 Matrices and arrays 2.3.1 Exercises2.4 Data frames 2.4.1 Creation2.4.2 Testing and coercion2.4.3 Combining data frames2.4.4 Special columns2.4.5 Exercises2.5 Answers
Chapter 3 - Subsetting
QuizOutline3.1 Data types 3.1.1 Atomic vectors3.1.2 Lists3.1.3 Matrices and arrays3.1.4 Data frames3.1.5 S3 objects3.1.6 S4 objects3.1.7 Exercises3.2 Subsetting operators 3.2.1 Simplifying vs. preserving subsetting3.2.2 $3.2.3 Missing/out of bounds indices3.2.4 Exercises3.3 Subsetting and assignment3.4 Applications 3.4.1 Lookup tables (character subsetting)3.4.2 Matching and merging by hand (integer subsetting)3.4.3 Random samples/bootstrap (integer subsetting)3.4.4 Ordering (integer subsetting)3.4.5 Expanding aggregated counts (integer subsetting)3.4.6 Removing columns from data frames (character subsetting)3.4.7 Selecting rows based on a condition (logical subsetting)3.4.8 Boolean algebra vs. sets (logical & integer subsetting)3.4.9 Exercises3.5 Answers
Chapter 4 - Vocabulary
4.1 The basics4.2 Common data structures4.3 Statistics4.4 Working with R4.5 I/O
Chapter 5 - Style guide
5.1 Notation and naming 5.1.1 File names5.1.2 Object names5.2 Syntax 5.2.1 Spacing5.2.2 Curly braces5.2.3 Line length5.2.4 Indentation5.2.5 Assignment5.3 Organisation 5.3.1 Commenting guidelines
Chapter 6 - Functions
QuizOutlinePrerequisites6.1 Function components 6.1.1 Primitive functions6.1.2 Exercises6.2 Lexical scoping 6.2.1 Name masking6.2.2 Functions vs. variables6.2.3 A fresh start6.2.4 Dynamic lookup6.2.5 Exercises6.3 Every operation is a function call6.4 Function arguments 6.4.1 Calling functions6.4.2 Calling a function given a list of arguments6.4.3 Default and missing arguments6.4.4 Lazy evaluation6.4.5 . . .6.4.6 Exercises6.5 Special calls 6.5.1 Infix functions6.5.2 Replacement functions6.5.3 Exercises6.6 Return values 6.6.1 On exit6.6.2 Exercises6.7 Quiz answers

Chapter 7 - OO field guide
PrerequisitesQuizOutline7.1 Base types7.2 S3 7.2.1 Recognising objects, generic functions, and methods7.2.2 Defining classes and creating objects7.2.3 Creating new methods and generics7.2.4 Method dispatch7.2.5 Exercises7.3 S4 7.3.1 Recognising objects, generic functions, and methods7.3.2 Defining classes and creating objects7.3.3 Creating new methods and generics7.3.4 Method dispatch7.3.5 Exercises7.4 RC 7.4.1 Defining classes and creating objects7.4.2 Recognising objects and methods7.4.3 Method dispatch7.4.4 Exercises7.5 Picking a system7.6 Quiz answers
Chapter 8 - Environments
QuizOutlinePrerequisites8.1 Environment basics 8.1.1 Exercises8.2 Recursing over environments 8.2.1 Exercises8.3 Function environments 8.3.1 The enclosing environment8.3.2 Binding environments8.3.3 Execution environments8.3.4 Calling environments8.3.5 Exercises8.4 Binding names to values 8.4.1 Exercises8.5 Explicit environments 8.5.1 Avoiding copies8.5.2 Package state8.5.3 As a hashmap8.6 Quiz answers
Chapter 9 - Debugging, condition handling, and defensive programming
QuizOutline9.1 Debugging techniques9.2 Debugging tools 9.2.1 Determining the sequence of calls9.2.2 Browsing on error9.2.3 Browsing arbitrary code9.2.4 The call stack: traceback(), where, and recover()9.2.5 Other types of failure9.3 Condition handling 9.3.1 Ignore errors with try9.3.2 Handle conditions with tryCatch()9.3.3 withCallingHandlers()9.3.4 Custom signal classes9.3.5 Exercises9.4 Defensive programming 9.4.1 Exercises9.5 Quiz answers
Section II - Functional programming
Chapter 10 - Functional programming
OutlinePrequisites10.1 Motivation10.2 Anonymous functions 10.2.1 Exercises10.3 Closures 10.3.1 Function factories10.3.2 Mutable state10.3.3 Exercises10.4 Lists of functions 10.4.1 Moving lists of functions to the global environment10.4.2 Exercises10.5 Case study: numerical integration 10.5.1 Exercises
Chapter 11 - Functionals
OutlinePrerequisites11.1 My first functional: lapply() 11.1.1 Looping patterns11.1.2 Exercises11.2 For loop functionals: friends of lapply() 11.2.1 Vector output: sapply and vapply11.2.2 Multiple inputs: Map (and mapply)11.2.3 Rolling computations11.2.4 Parallelisation11.2.5 Exercises11.3 Manipulating matrices and data frames 11.3.1 Matrix and array operations11.3.2 Group apply11.3.3 The plyr package11.3.4 Exercises11.4 Manipulating lists 11.4.1 Reduce()11.4.2 Predicate functionals11.4.3 Exercises11.5 Mathematical functionals 11.5.1 Exercises11.6 Loops that should be left as is 11.6.1 Modifying in place11.6.2 Recursive relationships11.6.3 While loops11.7 A family of functions 11.7.1 Exercises
Chapter 12 - Function operators
OutlinePrerequisites12.1 Behavioural FOs 12.1.1 Memoisation12.1.2 Capturing function invocations12.1.3 Laziness12.1.4 Exercises12.2 Output FOs 12.2.1 Minor modifications12.2.2 Changing what a function does12.2.3 Exercises12.3 Input FOs 12.3.1 Prefilling function arguments: partial function application12.3.2 Changing input types12.3.3 Exercises12.4 Combining FOs 12.4.1 Function composition12.4.2 Logical predicates and boolean algebra12.4.3 Exercises
Section III - Computing on the language
Chapter 13 - Non-standard evaluation
OutlinePrerequisites13.1 Capturing expressions 13.1.1 Exercises13.2 Non-standard evaluation in subset 13.2.1 Exercises13.3 Scoping issues 13.3.1 Exercises13.4 Calling from another function 13.4.1 Exercises13.5 Substitute 13.5.1 Adding an escape hatch to substitute13.5.2 Capturing unevaluated . . .13.5.3 Exercises13.6 The downsides of non-standard evaluation 13.6.1 Exercises
Chapter 14 - Expressions
OutlinePrerequisites14.1 Structure of expressions 14.1.1 Exercises14.2 Names 14.2.1 Exercises14.3 Calls 14.3.1 Modifying a call14.3.2 Creating a call from its components14.3.3 Exercises14.4 Capturing the current call 14.4.1 Exercises14.5 Pairlists 14.5.1 Exercises14.6 Parsing and deparsing 14.6.1 Exercises14.7 Walking the AST with recursive functions 14.7.1 Finding F and T14.7.2 Finding all variables created by assignment14.7.3 Modifying the call tree14.7.4 Exercises
Chapter 15 - Domain specific languages
Prerequisites15.1 HTML 15.1.1 Goal15.1.2 Escaping15.1.3 Basic tag functions15.1.4 Tag functions15.1.5 Processing all tags15.1.6 Exercises15.2 LaTeX 15.2.1 LaTeX mathematics15.2.2 Goal15.2.3 to_math15.2.4 Known symbols15.2.5 Unknown symbols15.2.6 Known functions15.2.7 Unknown functions15.2.8 Exercises
Section IV - Performance
Chapter 16 - Performance
16.1 Why is R slow?16.2 Microbenchmarking 16.2.1 Exercises16.3 Language performance 16.3.1 Extreme dynamism16.3.2 Name lookup with mutable environments16.3.3 Lazy evaluation overhead16.3.4 Exercises16.4 Implementation performance 16.4.1 Extracting a single value from a data frame16.4.2 ifelse(), pmin(), and pmax()16.4.3 Exercises16.5 Alternative R implementations
Chapter 17 - Optimising code
OutlinePrerequisites17.1 Measuring performance 17.1.1 Limitations17.2 Improving performance17.3 Code organisation17.4 Has someone already solved the problem? 17.4.1 Exercises17.5 Do as little as possible 17.5.1 Exercises17.6 Vectorise 17.6.1 Exercises17.7 Avoid copies17.8 Byte code compilation17.9 Case study: t-test17.10 Parallelise17.11 Other techniques
Chapter 18 - Memory
OutlinePrerequisitesSources18.1 Object size 18.1.1 Exercises18.2 Memory usage and garbage collection18.3 Memory profiling with lineprof 18.3.1 Exercises18.4 Modification in place 18.4.1 Loops18.4.2 Exercises
Chapter 19 - High performance functions with Rcpp
Outline19.1 Getting started with C++ 19.1.1 No inputs, scalar output19.1.2 Scalar input, scalar output19.1.3 Vector input, scalar output19.1.4 Vector input, vector output19.1.5 Matrix input, vector output19.1.6 Using sourceCpp19.1.7 Exercises19.2 Attributes and other classes 19.2.1 Lists and data frames19.2.2 Functions19.2.3 Other types19.3 Missing values 19.3.1 Scalars 19.3.1.1 Integers19.3.1.2 Doubles19.3.2 Strings19.3.3 Boolean19.3.4 Vectors19.3.5 Exercises19.4 Rcpp sugar 19.4.1 Arithmetic and logical operators19.4.2 Logical summary functions19.4.3 Vector views19.4.4 Other useful functions19.5 The STL 19.5.1 Using iterators19.5.2 Algorithms19.5.3 Data structures19.5.4 Vectors19.5.5 Sets19.5.6 Map19.5.7 Exercises19.6 Case studies 19.6.1 Gibbs sampler19.6.2 R vectorisation vs. C++ vectorisation19.7 Using Rcpp in a package19.8 Learning more19.9 Acknowledgments
Chapter 20 - R's C interface
OutlinePrerequisites20.1 Calling C functions from R20.2 C data structures20.3 Creating and modifying vectors 20.3.1 Creating vectors and garbage collection20.3.2 Missing and non-finite values20.3.3 Accessing vector data20.3.4 Character vectors and lists20.3.5 Modifying inputs20.3.6 Coercing scalars20.3.7 Long vectors20.4 Pairlists20.5 Input validation20.6 Finding the C source code for a function

Content preview from Advanced R

High performance functions with Rcpp

Sometimes R code just isn’t fast enough. You’ve used profiling to figure out where your bottlenecks are, and you’ve done everything you can in R, but your code still isn’t fast enough. In this chapter you’ll learn how to improve performance by rewriting key functions in C++. This magic comes by way of the Rcpp (http://www.rcpp.org/) package, a fantastic tool written by Dirk Eddelbuettel and Romain Francois (with key contributions by Doug Bates, John Chambers, and JJ Allaire). Rcpp makes it very simple to connect C++ to R. While it is possible to write C or Fortran code for use in R, it will be painful by comparison. Rcpp provides a clean, approachable API that lets you write high-performance code, insulated ...

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Advanced R

by Hadley Wickham

High performance functions with Rcpp

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