Table Of ContentR High Performance
Programming
Overcome performance difficulties in R with a range
of exciting techniques and solutions
Aloysius Lim
William Tjhi
BIRMINGHAM - MUMBAI
R High Performance Programming
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First published: January 2015
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Credits
Authors Project Coordinator
Aloysius Lim Milton Dsouza
William Tjhi
Proofreaders
Ameesha Green
Reviewers
Richard Cotton Clyde Jenkins
Kirill Müller Jonathan Todd
John Silberholz
Indexer
Tejal Soni
Commissioning Editor
Kunal Parikh
Graphics
Sheetal Aute
Acquisition Editor
Richard Brookes-Bland Valentina D'silva
Content Development Editor Production Coordinator
Susmita Sabat Komal Ramchandani
Technical Editor Cover Work
Shiny Poojary Komal Ramchandani
Copy Editor
Neha Vyas
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About the Authors
Aloysius Lim has a knack for translating complex data and models into
easy-to-understand insights. As cofounder of About People, a data science and
design consultancy, he loves solving problems and helping others to find practical
solutions to business challenges using data. His breadth of experience—7 years in
the government, education, and retail industries—equips him with unique
perspectives to find creative solutions.
My deepest thanks go to God for the opportunity to write this
book and share the knowledge that I have been given. My lovely
wife, Bethany, has been a tremendous source of support and
encouragement throughout this project. Thank you dear, for all
your love. Many thanks to my partner William for his wonderful
friendship. He has been a source of inspiration and insights
throughout this journey.
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William Tjhi is a data scientist with years of experience working in academia,
government, and industry. He began his data science journey as a PhD candidate
researching new algorithms to improve the robustness of high-dimensional data
clustering. Upon receiving his doctorate, he moved from basic to applied research,
solving problems among others in molecular biology and epidemiology using
machine learning. He published some of his research in peer-reviewed journals and
conferences. With the rise of Big Data, William left academia for industry, where he
started practicing data science in both business and public sector settings. William
is passionate about R and has been using it as his primary analysis tool since his
research days. He was once part of Revolution Analytics, and there he contributed
to make R more suitable for Big Data.
I would like to thank my coauthor, Aloysius. Your hard work,
patience, and determination made this book possible.
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About the Reviewers
Richard Cotton is a data scientist with a mixed background in proteomics, debt
collection, and chemical health and safety, and he has worked extensively on tools
to give nontechnical users access to statistical models. He is the author of the book
Learning R, O'Reilly, and has created a number of popular R packages, including
assertive, regex, pathological, and sig. He works for Weill Cornell Medical College
in Qatar.
Kirill Müller holds a diploma in computer science and currently works as a
research assistant at the Institute for Transport Planning and Systems of the Swiss
Federal Institute of Technology (ETHZ) in Zurich. He is an avid R user and has
contributed to several R packages.
John Silberholz is a fourth year PhD student at the MIT Operations Research
Center, working under advisor Dimitris Bertsimas. His thesis research focuses on
data-driven approaches to design novel chemotherapy regimens for advanced cancer
and approaches to identify effective population screening strategies for cancer. His
research interests also include analytical applications in the fields of bibliometrics
and heuristic evaluation. John codeveloped 15.071x: The Analytics Edge, a massive
open online course (MOOC), which teaches machine learning and optimization
using R and spreadsheet solvers.
Before coming to MIT, John completed his BS degree in mathematics and computer
science from the University of Maryland. He completed internships as a software
developer at Microsoft and Google, and he cofounded Enertaq, an electricity
grid reliability start-up.
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Table of Contents
Preface 1
Chapter 1: Understanding R's Performance – Why Are R
Programs Sometimes Slow? 9
Three constraints on computing performance – CPU, RAM,
and disk I/O 10
R is interpreted on the fly 12
R is single-threaded 13
R requires all data to be loaded into memory 14
Algorithm design affects time and space complexity 15
Summary 18
Chapter 2: Profiling – Measuring Code's Performance 19
Measuring total execution time 20
Measuring execution time with system.time() 20
Repeating time measurements with rbenchmark 22
Measuring distribution of execution time with microbenchmark 24
Profiling the execution time 24
Profiling a function with Rprof() 25
The profiling results 27
Profiling memory utilization 28
Monitoring memory utilization, CPU utilization, and disk I/O using
OS tools 31
Identifying and resolving bottlenecks 32
Summary 35
Chapter 3: Simple Tweaks to Make R Run Faster 37
Vectorization 38
Use of built-in functions 39
Preallocating memory 41
