Mastering Scientific Computing with R

432 Pages·2015·3.973 MB·English

by  Paul Gerrard, Radia M. Johnson

Mastering Scientific Computing
with R
Employ professional quantitative methods to answer
scientific questions with a powerful open source data
analysis environment
Paul Gerrard
Radia M. Johnson
BIRMINGHAM - MUMBAI
Mastering Scientific Computing with R
Copyright © 2015 Packt Publishing
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First published: January 2015
Production reference: 1270115
Published by Packt Publishing Ltd.
Livery Place
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Birmingham B3 2PB, UK.
ISBN 978-1-78355-525-3
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Cover image by Jason Dupuis Mayer ([e-mail])
Credits
Authors Project Coordinator
Paul Gerrard Mary Alex
Radia M. Johnson
Proofreaders
Simran Bhogal
Reviewers
Laurent Drouet Martin Diver
Ratanlal Mahanta Ameesha Green
Mzabalazo Z. Ngwenya Paul Hindle
Donato Teutonico Bernadette Watkins
Commissioning Editor Indexer
Kartikey Pandey Priya Subramani
Acquisition Editor Graphics
Greg Wild Sheetal Aute
Disha Haria
Content Development Editor Abhinash Sahu
Akshay Nair
Production Coordinator
Technical Editors Conidon Miranda
Rosmy George
Ankita Thakur
Cover Work
Conidon Miranda
Copy Editors
Shivangi Chaturvedi
Pranjali Chury
Puja Lalwani
Adithi Shetty
About the Authors
Paul Gerrard is a physician and healthcare researcher who is based out of Portland,
Maine, where he currently serves as the medical director of the cardiopulmonary
rehabilitation program at New England Rehabilitation Hospital of Portland. He
studied business economics in college. After completing medical school, he did a
residency in physical medicine and rehabilitation at Harvard Medical School and
Spaulding Rehabilitation Hospital, where he served as chief resident and stayed
on as faculty at Harvard before moving to Portland. He continues to collaborate on
research projects with researchers at other academic institutions within the Boston
area and around the country. He has published and presented research on a range
of topics, including traumatic brain injury, burn rehabilitation, health outcomes, and
the epidemiology of disabling medical conditions.
I would like to thank my beautiful wife, Deirdre, and my son,
Patrick. My work on this book is dedicated to the loving memory
of Fiona.
Radia M. Johnson has a doctorate degree in immunology and currently works
as a research scientist at the Institute for Research in Immunology and Cancer at
the Université de Montréal, where she uses genomics and bioinformatics to identify
and characterize the molecular changes that contribute to cancer development. She
routinely uses R and other computer programming languages to analyze large data
sets from ongoing collaborative projects. Since obtaining her PhD at the University of
Toronto, she has also worked as a research associate at the University of Cambridge in
Hematology, where she gained experience using system biology to study blood cancer.
I would like to thank Dr. Charlie Massie for teaching me to love
programming in R and Dr. Phil Kousis for all his support through
the years. You are both excellent mentors and wonderful friends!
About the Reviewers
Laurent Drouet holds a PhD in economics and social sciences from the University
of Geneva, Switzerland, and a master's degree in applied mathematics from the
Institute of Applied Mathematics of Angers, France. He was also a postdoctoral
research fellow at the Research Lab of Economics and Environmental Management
at the Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. He was also
a researcher at the Public Research Center Tudor, Luxembourg. He is currently a
senior researcher at Fondazione Eni Enrico Mattei (FEEM) and a research affiliate
at Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Italy.
His main research is related to integrated assessment modeling and energy
modeling. For more than a decade, he designed scientific tools to perform data
analysis for this type of modeling. He also built optimization frameworks to couple
models of many kinds (such as climate models, air quality models, and economy
models). He created and developed the bottom-up techno-economic energy model
ETEM to study optimal energy policies at urban or national levels.
I want to thank my wife for her support every day both in my
private life and professional life.
Ratanlal Mahanta holds an MSc in computational finance. He is currently
working at GPSK Investment Group as a senior quantitative analyst. He has 4
years of experience in quantitative trading and strategies developments for sell
side and risk consulting firms. He is an expert in high frequency and algorithmic
trading. He has expertise in these areas: quantitative trading (FX, equities, futures
and options, and engineering on derivatives); algorithms—partial differential
equations, stochastic differential equations, the finite difference method, Monte
Carlo, and Machine Learning; code—R programming, C++, MATLAB, HPC, and
scientific computing; data analysis—Big Data analytic [EOD to TBT], Bloomberg,
Quandl, and Quantopian; and strategies—vol-arbitrage, vanilla and exotic options
modeling, trend following, mean reversion, co-integration, Monte Carlo simulations,
ValueatRisk, stress testing, buy side trading strategies with high Sharpe ratio, credit
risk modeling, and credit rating.
He has reviewed Mastering R for Quantitative Finance, Packt Publishing. He is currently
reviewing two other books for Packt Publishing: Mastering Python for Data Science and
Machine Learning with R Cookbook.
Mzabalazo Z. Ngwenya holds a postgraduate degree in mathematical statistics
from the University of Cape Town. He has worked extensively in the field of
statistical consulting, wherein he utilized varied statistical software including R. His
area of interest are primarily centered around statistical computing. Previously, he
was involved in reviewing Learning RStudio for R Statistical Computing, Mark P.J. van
der Loo and Edwin de Jonge; R Statistical Application Development Example Beginner's
Guide, Prabhanjan Narayanachar Tattar; Machine Learning with R, Brett Lantz; R Graph
Essentials, David Alexandra Lillis, and R Object-oriented Programming, Kelly Black,
all by Packt Publishing. He currently works as a biometrician.
Donato Teutonico has several years of experience in modeling and the simulation
of drug effects and clinical trials in industrial and academic settings. He received
his PharmD degree from the University of Turin, Italy, specializing in chemical and
pharmaceutical technology, and his PhD in pharmaceutical sciences from Paris-Sud
University, France.
He is the author of two R packages for pharmacometrics, CTStemplate and
panels-for-pharmacometrics, which are both available on Google Code. He is
also the author of Instant R Starter, Packt Publishing.
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Table of Contents
Preface 1
Chapter 1: Programming with R 7
Data structures in R 10
Atomic vectors 10
Operations on vectors 14
Lists 15
Attributes 19
Factors 21
Multidimensional arrays 22
Matrices 23
Data frames 25
Loading data into R 28
Saving data frames 31
Basic plots and the ggplot2 package 33
Flow control 43
The for() loop 43
The apply() function 44
The if() statement 46
The while() loop 46
The repeat{} and break statement 47
Functions 48
General programming and debugging tools 52
Summary 55
Chapter 2: Statistical Methods with R 57
Descriptive statistics 59
Data variability 61
Confidence intervals 62
Probability distributions 63