《高效R编程》：Colin Gillespie与Robin Lovelace的实战指南

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《高效R语言编程》是由Colin Gillespie和Robin Lovelace合著的一本实践指南，专为提升R编程效率而设计。这本书是英文版，旨在帮助R语言开发者掌握更智能、更高效的编程策略。两位作者在书中分享了他们在R语言领域的专业知识和经验，为读者提供了深入理解R语言特性和最佳实践的机会。全书分为实用章节，涵盖了R语言的基础和进阶内容，包括数据处理、统计分析、函数编程、性能优化、内存管理以及高级编程技巧等。作者强调了代码的可读性、复用性和维护性，提倡编写清晰、高效的代码，以避免常见的编程陷阱和性能瓶颈。本书不仅提供理论指导，还配以丰富的实例和实战项目，让读者能够在实际操作中学习和应用所学知识。此外，作者也关注到了现代软件开发的最佳实践，如版本控制、测试驱动开发(TDD)和持续集成(CI/CD)，帮助读者将R编程技能与现代开发流程相结合。《高效R编程》适合R语言的初学者和已有一定基础的用户，无论是想要提高工作效率，还是寻求优化现有代码的解决方案，都能从中获益匪浅。对于教育机构和个人开发者来说，它是一本不可多得的参考资料，能够提升R语言编程的技能和项目管理能力。值得注意的是，版权信息表明该书于2017年出版，并获得了Colin Gillespie和Robin Lovelace的共同授权。O'Reilly Media公司负责发行，包括印刷版和在线版，读者可以通过访问http://oreilly.com/safari获取更多资源。本书在编排上由Nicole Tache编辑，Nicolas Adams担任生产编辑，Gillian McGarvey负责校对，Christina Edwards负责最终的校对工作，同时还得到了WordCo Indexing Services的索引服务，以及David Futato、Randy Comer和Rebecca Demarest的专业设计支持。《高效R编程》是一本全面且实用的R语言学习工具，对于提升R语言编程技能和开发效率具有重要的参考价值。无论你是R语言的新手还是经验丰富的程序员，这本书都将帮助你在R的世界里走得更远，更快。

CHAPTER 1

Introduction

This chapter describes the wide range of people this book was written for, in terms of

R and programming experience, and how you can get the most out of it. Anyone set‐

ting out to improve efficiency should have an understanding of precisely what they

mean by the term, and this is discussed with reference to algorithmic and programmer

efficiency in “What Is Efficiency?” on page 4, and with reference to R in particular in

“What Is Efficient R Programming?” on the same page. It may seem obvious, but it’s

also worth thinking about why anyone would bother with efficient code now that

powerful computers are cheap and accessible. This is covered in “Why Efficiency?”

on page 6.

This book happily is not completely R-specific. Non R–programming skills that are

needed for efficient R programming, which you will develop during the course of fol‐

lowing this book, are covered in “Cross-Transferable Skills for Efficiency” on page 7.

Atypically for a book about programming, this section introduces touch typing and

consistency, cross-transferable skills that should improve your efficiency beyond pro‐

gramming. However, this is first and foremost a book about programming and it

wouldn’t be so without code examples in every chapter. Despite being more concep‐

tual and discursive, this opening chapter is no exception: its penultimate section

(“Benchmarking and Profiling” on page 9) describes two essential tools in the effi‐

cient R programmer’s toolbox and how to use them with a couple of illustrative

examples. The final thing to say at the outset is how to use this book in conjunction

with the book’s associated package and its source code. This is covered in “Book

Resources” on page 14.

Prerequisites

As emphasized in the next section, it’s useful to run code and experiment as you read.

This section, found at the beginning of each chapter, ensures that you have the neces‐

sary packages for each chapter. The prerequisites for this chapter are:

• A working installation of R on your computer (see

“Installing and Updating

RStudio” on page 35).

• Install and load the microbenchmark, profvis, and ggplot2 packages (see

“Installing R Packages” on page 23 for tips on installing packages and keeping

them up-to-date). You can ensure that these packages are installed by loading

them as follows:

library("microbenchmark")

library("profvis")

library("ggplot2")

The prerequisites needed to run the code contained in the entire book are covered in

“Book Resources” on page 14 at the end of this chapter.

Who This Book Is for and How to Use It

This book is for anyone who wants to make their R code faster to type, faster to run,

and more scalable. These considerations generally come aer learning the very basics

of R for data analysis; we assume you are either accustomed to R or proficient at pro‐

gramming in other languages, although this book could still be of use for beginners.

Thus the book should be useful to people with a range of skill levels, who can broadly

be divided into three groups:

For programmers with little experience with R

This book will help you navigate the quirks of R to make it work efficiently: it is

easy to write slow R code if you treat it as if it were another language.

For R users with little experience in programming

This book will show you many concepts and tricks of the trade, some of which

are borrowed from computer science, that will make your work more time

effective.

For R beginners with little experience in programming

This book can steer you to get things right (or at least less wrong) at the outset.

Bad habits are easy to gain but hard to lose. Reading this book at the outset of

your programming career could save you many hours in the future searching the

web for issues covered in this book.

2 | Chapter 1: Introduction

Identifying which group you best fit into will help you get the most out of it. For

everyone, we recommend reading Ecient R Programming while you have an active R

project on the go, whether it’s a collaborative task at work or simply a personal project

at home. Why? The scope of this book is wider than most programming textbooks

(Chapter 4 covers project management, for example) and working on a project out‐

side the confines of it will help put the concepts, recommendations, and code into

practice. Going directly from words into action in this way will help ensure that the

information is consolidated: learn by doing.

If you’re an R novice and fit into the final category, we recommend that this active R

project not be an important deliverable, but another R resource. Though this book is

generic, it is likely that your usage of R will be largely domain-specific. For this rea‐

son, we recommend reading it alongside teaching material in your chosen area. Fur‐

thermore, we advocate that all readers use this book alongside other R resources such

as the numerous vignettes, tutorials, and online articles that the R community has

produced (described in the following tip). At a bare minimum, you should be familiar

with data frames, looping, and simple plots, which you will learn from these resour‐

ces.

Resources for Learning R

There are many places to find generic and domain-specific R teaching materials. For

complete beginners, there are a number of introductory resources, such as the excel‐

lent Student’s Guide to R and the more technical IcebreakeR tutorial.

R also comes preinstalled with guidance, revealed by entering help.start() into the

R console, including the classic official guide An Introduction to R, which is excellent,

but daunting to many. Entering vignette() will display a list of guides packaged

within your R installation (and hence do not require an internet connection). To see

the vignette for a specific topic, just enter the vignette’s name into the same command

(e.g., vignette(package = "dplyr", "introduction")) to see the introductory

vignette for the dplyr package.

Another early port of call should be the Comprehensive R Archive Network (CRAN)

website. The Contributed Documentation page contains a list of contributed resour‐

ces, mainly tutorials, on subjects ranging from map making to econometrics. The new

bookdown website contains a list of complete (or near complete) books that cover

domains such as R for Data Science and Authoring Books with R Markdown. We rec‐

ommend keeping your eye on the R-o-sphere via the R-Bloggers website, popular

Twitter feeds, and CRAN-affiliated email lists for up-to-date materials that can be

used in conjunction with this book.

Who This Book Is for and How to Use It | 3

What Is Eciency?

In everyday life, efficiency roughly means working well. An efficient vehicle goes far

without guzzling gas. An efficient worker gets the job done fast without stress. And

an efficient light shines brightly with a minimum of energy consumption. In this final

sense, efficiency (η) has a formal definition as the ratio of work done (W, light out‐

put) per unit effort (Q, energy consumption in this case):

η =

How does this translate into programming? Efficient code can be defined narrowly or

broadly. The first, more narrow definition is algorithmic eciency: how fast the com‐

puter can undertake a piece of work given a particular piece of code. This concept

dates back to the very origins of computing, as illustrated by the following quote by

Ada Lovelace (1842) in her notes on the work of Charles Babbage:

In almost every computation a great variety of arrangements for the succession of the

processes is possible, and various considerations must influence the selections amongst

them for the purposes of a calculating engine. One essential object is to choose that

arrangement which shall tend to reduce to a minimum the time necessary for complet‐

ing the calculation.

The second, broader definition of efficient computing is programmer productivity.

This is the amount of useful work a person (not a computer) can do per unit time. It

may be possible to rewrite your code base in C to make it 100 times faster. But if this

takes 100 human hours, it may not be worth it. Computers can chug away day and

night. People cannot. Human productivity is the subject of Chapter 4.

By the end of this book, you should know how to write code that is efficient from

both algorithmic and productivity perspectives. Efficient code is also concise, elegant,

and easy to maintain, which is vital when working on large projects. But this raises

the wider question: what is different about efficient R code compared with efficient

code in any other language?

What Is Ecient R Programming?

The issue flagged by Ada of having a great variety of ways to solve a problem is key to

understanding how efficient R programming differs from efficient programming in

other languages. R is notorious for allowing users to solve problems in many ways.

This is due to R’s inherent flexibility, in which almost “anything can be modified after

it is created” (Wickham 2014). R’s inventors, Ross Ihaka and Robert Gentleman,

designed it to be this way: a cell in a data frame can be selected in multiple ways in

base R alone (three of which are illustrated later in this chapter, in “Benchmarking

Example” on page 10). This is useful because it allows programmers to use the lan‐

4 | Chapter 1: Introduction

guage as best suits their needs, but it can be confusing for people looking for the right

way of doing things and can cause inefficiencies if you don’t fully understand the lan‐

guage.

R’s notoriety for being able to solve a problem in multiple ways has grown with the

proliferation of community-contributed packages. In this book, we focus on the best

way of solving problems from an efficiency perspective. Often it is instructive to dis‐

cover why a certain way of doing things is faster than other ways. However, if your

aim is simply to get

stu done, you only need to know what is likely to be the most

efficient way. In this way, R’s flexibility can be inefficient: although it may be easier to

find a way of solving any given problem in R than other languages, solving the prob‐

lem with R may make it harder to find the best way to solve that problem, as there are

so many. This book tackles this issue head on by recommending what we believe are

the most efficient approaches. We hope you trust our views, based on years of using

and teaching R, but we also hope that you challenge them at times and test them with

benchmarks if you suspect there’s a better way of doing things (thanks to R’s flexibility

and ability to interface with other languages, there may well be).

It is well known that R code can lack algorithmic efficiency compared with low-level

languages for certain tasks, especially if it was written by someone who doesn’t fully

understand the language. But it is worth highlighting the numerous ways that R

encourages and guides efficiency, especially programmer efficiency:

• R is not compiled, but it calls compiled code. This means that you get the best of

both worlds: thankfully, R removes the laborious stage of compiling your code

before being able to run it, but provides impressive speed gains by calling com‐

piled C, FORTRAN, and other language behind the scenes.

• R is a functional and object-orientated language (Wickham 2014). This means

that it is possible to write complex and flexible functions in R that get a huge

amount of work done with a single line of code.

• R uses RAM for memory. This may seem obvious, but it’s worth saying: RAM is

much faster than any hard disk system. Compared with databases, R is therefore

very fast at common data manipulation, processing, and modeling operations.

RAM is now cheaper than ever, meaning the potential downsides of this feature

are further away than ever.

• R is supported by excellent integrated development environments (IDEs). The

environment in which you program can have a huge impact on programmer effi‐

ciency as it can provide help quickly, allow for interactive plotting, and allow

your R projects to be tightly integrated with other aspects of your project such as

file management, version management, and interactive visualization systems, as

discussed in “RStudio” on page 34.

What Is Ecient R Programming? | 5

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