FUNCTIONAL PROGRAMMING IN R 4 : advanced statistical programming for data science,... analysis, and finance

Master functions and discover how to write functional programs in R. In this book, updated for R 4, you'll learn to make your functions pure by avoiding side effects, write functions that manipulate other functions, and construct complex functions using simpler functions as building blocks. In Functional Programming in R 4 , you’ll see how to replace loops, which can have side-effects, with recursive functions that can more easily avoid them. In addition, the book covers why you shouldn't use recursion when loops are more efficient and how you can get the best of both worlds. Functional programming is a style of programming, like object-oriented programming, but one that focuses on data transformations and calculations rather than objects and state. Where in object-oriented programming you model your programs by describing which states an object can be in and how methods will reveal or modify that state, in functional programming you model programs by describing how functions translate input data to output data. Functions themselves are considered to be data you can manipulate and much of the strength of functional programming comes from manipulating functions; that is, building more complex functions by combining simpler functions. What You'll Learn Write functions in R 4, including infix operators and replacement functions Create higher order functions Pass functions to other functions and start using functions as data you can manipulate Use Filer, Map and Reduce functions to express the intent behind code clearly and safely Build new functions from existing functions without necessarily writing any new functions, using point-free programming Create functions that carry data along with them Who This Book Is For Those with at least some experience with programming in R. Table of Contents About the Author About the Technical Reviewer Acknowledgments Chapter 1: Introduction Chapter 2: Functions in R Writing Functions in R Named Parameters and Default Parameters The “Gobble Up Everything Else” Parameter: “...” Lazy Evaluation Functions Don’t Have Names Vectorized Functions Infix Operators Replacement Functions Untitled Chapter 3: Pure Functional Programming Writing Pure Functions Recursion As Loops The Structure of a Recursive Function Tail-Recursion Runtime Considerations Chapter 4: Scope and Closures Environments and Functions Environment Chains, Scope, and Function Calls Scopes, Lazy Evaluation, and Default Parameters Nested Functions and Scopes Closures Reaching Outside Your Innermost Scope Lexical and Dynamic Scope Chapter 5: Higher-Order Functions Currying A Parameter Binding Function Continuation-Passing Style Thunks and Trampolines Chapter 6: Filter, Map, and Reduce The General Sequence Object in R Is a List Filtering Sequences Mapping over Sequences Reducing Sequences Bringing the Functions Together The Apply Family of Functions sapply, vapply, and lapply The apply Function The tapply Function Functional Programming in purrr Filter-like Functions Map-like Functions Reduce-like Functions Chapter 7: Point-Free Programming Function Composition Pipelines Chapter 8: Conclusions Index