Getting started with R. An Introduction for Biologists

242 Pages·2017·4.243 MB·English

by  Andrew Beckerman, Dylan Childs, Owen Petchey

Getting Started
with R
An Introduction for
Biologists
Second Edition
ANDREW P. BECKERMAN
DYLAN Z. CHILDS
DepartmentofAnimalandPlantSciences,
UniversityofSheffield
OWEN L. PETCHEY
DepartmentofEvolutionaryBiology
andEnvironmentalStudies,
UniversityofZurich
3
3
GreatClarendonStreet,Oxford,OX26DP,
UnitedKingdom
©AndrewBeckerman,DylanChilds,&OwenPetchey2017
Themoralrightsoftheauthorshavebeenasserted
FirstEditionpublishedin2012
SecondEditionpublishedin2017
Impression:1
PublishedintheUnitedStatesofAmericabyOxfordUniversityPress
198MadisonAvenue,NewYork,NY10016,UnitedStatesofAmerica
BritishLibraryCataloguinginPublicationData
Dataavailable
LibraryofCongressControlNumber:2016946804
ISBN978–0–19–878783–9(hbk.)
ISBN978–0–19–878784–6(pbk.)
DOI10.1093/acprof:oso/9780198787839.001.0001
Printedandboundby
CPILitho(UK)Ltd,Croydon,CR04YY
Contents
Preface ix
Introductiontothesecondedition ix
Whatthisbookisabout xii
Howthebookisorganized xiv
WhyR? xvi
Updates xviii
Acknowledgements xviii
Chapter1: GettingandGettingAcquaintedwithR 1
1.1 Gettingstarted 1
1.2 GettingR 2
1.3 GettingRStudio 5
1.4 Let’splay 6
1.5 UsingRasagiantcalculator(thesizeofyourcomputer) 8
1.6 Yourfirstscript 15
1.7 Intermezzoremarks 21
1.8 Importantfunctionality:packages 21
1.9 Gettinghelp 24
1.10 Amini-practical—somein-depthplay 26
1.11 Somemoretoptipsandhintsforasuccessfulfirst
(andmore)Rexperience 28
Appendix1aMini-tutorialsolutions 29
Appendix1bFileextensionsandoperatingsystems 30
Chapter2: GettingYourDataintoR 35
2.1 GettingdatareadyforR 35
2.2 GettingyourdataintoR 40
2.3 Checkingthatyourdataareyourdata 45
2.4 Basictroubleshootingwhileimportingdata 48
2.5 Summingup 49
AppendixAdvancedactivity:dealingwithuntidydata 50
Chapter3: DataManagement,Manipulation,andExploration
withdplyr 57
3.1 Summarystatisticsforeachvariable 58
3.2 dplyrverbs 59
3.3 Subsetting 60
3.4 Transforming 67
3.5 Sorting 68
3.6 Mini-summaryandtwotoptips 69
3.7 Calculatingsummarystatisticsaboutgroupsofyourdata 70
3.8 Whathaveyoulearned...lots 73
Appendix3aComparingclassicmethodsanddplyr 73
Appendix3bAdvanceddplyr 74
Chapter4: VisualizingYourData 79
4.1 Thefirststepineverydataanalysis—makingapicture 79
4.2 ggplot2:agrammarforgraphics 80
4.3 Box-and-whiskerplots 85
4.4 Distributions:makinghistogramsofnumericvariables 87
4.5 Savingyourgraphsforpresentation,documents,etc. 90
4.6 Closingremarks 91
Chapter5: IntroducingStatisticsinR 93
5.1 GettingstarteddoingstatisticsinR 93
5.2 χ2contingencytableanalysis 95
5.3 Two-samplet-test 103
5.4 Introducing...linearmodels 108
5.5 Simplelinearregression 109
5.6 Analysisofvariance:theone-wayANOVA 118
5.7 Wrappingup 128
AppendixGettingpackagesnotonCRAN 128
Chapter6: AdvancingYourStatisticsinR 131
6.1 Gettingstartedwithmoreadvancedstatistics 131
6.2 Thetwo-wayANOVA 131
6.3 Analysisofcovariance(ANCOVA) 145
6.4 Overview:ananalysisworkflow 164
Chapter7: GettingStartedwithGeneralizedLinearModels 167
7.1 Introduction 167
7.2 Countsandrates—PoissonGLMs 170
7.3 Doingitwrong 173
7.4 Doingitright—thePoissonGLM 177
7.5 WhenaPoissonGLMisn’tgoodforcounts 194
7.6 Summary,andbeyondsimplePoissonregression 201
Chapter8: PimpingYourPlots:ScalesandThemesinggplot2 203
8.1 Whatyoualreadyknowaboutgraphs 203
8.2 Preparation 204
8.3 Whatyoumaywanttocustomize 206
8.4 Axislabels,axislimits,andannotation 207
8.5 Scales 209
8.6 Thetheme 212
8.7 Summingup 218
Chapter9: ClosingRemarks:FinalCommentsand
Encouragement 219
GeneralAppendices 223
Appendix1DataSources 223
Appendix2FurtherReading 224
Appendix3RMarkdown 225
Index 227
Preface
Introductiontothesecondedition
ThisisabookabouthowtouseR,anopensourceprogramminglanguage
andenvironmentforstatistics.Itisnotabookaboutstatisticsperse,buta
bookaboutgettingstartedusingR.Itisabookthatwehopewillteachyou
howusingRcanmakeyourlife(researchcareer)easier.
Several years ago we published the first edition of this book, aiming to
help people move from ‘hearing about R’ to ‘using R’. We had realized
thattherewerelotsofbooksaboutexploringdataanddoingstatisticswith
R, but none specifically designed for people that didn’t have a lot of ex-
perience or confidence in using much more than a spreadsheet, people
thatdidn’thavealotoftime,andpeoplethatappreciatedanengagingand
sometimeshumorousinitialjourneyintoR.Thefirsteditionwasalsode-
signedforpeoplewhodidknowstatisticsandotherpackages,butwanted
a quick ‘getting started’ guide, because, well, it is hard to get started with
R in some ways. Overall, we aimed to make the somewhat steep learning
curvemoreofawalkinthepark.
Over the past five years much has changed. Most significantly, R has
evolved as a platform for doing data analysis, for managing data, and for
producing figures. Other things have not changed. People still seem to
needandappreciatehelpinnavigatingtheprocessofgettingstartedwork-
ingwithR.Thus,thisnewversionofthebookdoestwothings.Itretains
ourfocusonhelpingyougetstartedusingR.Welovedoingthisandwe’ve
beenteachingthisfor15years.Notsurprisingly,manyofyouarealsofind-
ingthatthisgetting-startedbookisgreatforundergraduateandgraduate
teaching.Wethankyouallforyourfeedback!
Second,wehavesubstantiallyrevisedhowweuse,andthussuggestyou
use,R.Ourchangesandsuggestionstakeadvantageofsomenewandvery
cool,efficient,andstraightforwardtools.Wethinkthesechangeswillhelp
youfocusevenmoreonyourdataandquestions.Thisisgood.
If you compare this second edition with the first, you will find sev-
eral differences. We no longer rely on base R tools and graphics for data
manipulation and figure making, instead introducing dplyr and ggplot2.
We’vealsoexpandedthesetofbasicstatisticsweintroducetoyou,includ-
ing new examples of a simple regression and a one-way and a two-way
ANOVA,inadditiontotheoldANCOVAexample.Third,weprovidean
entirenewchapteronthegeneralizedlinearmodel.Oh,yes,andwehave
addedanauthor,Dylan.
WHAT’S SO DIFFERENT FROM THE FIRST EDITION?
We teach a particular workflow for quantitative problem solving: have a
clear question, get the right data for that question, inspect and visualize
thedata,usethevisualizationtorevealtheanswertothequestion,makea
statisticalmodelthatreflectsyourquestion,checktheassumptionsofthe
model, interpret the model to confirm or refute your answer, and clearly
andbeautifullycommunicateyouranswerinafigure.
In R there are many different tools, and combinations of these tools,
foraccomplishingthisworkflow.Inthefirsteditionofthisbookweintro-
duced a set of ‘classic’ R tools drawn from the base R installation. These
classic tools worked and, importantly, continue to work very well. We
taught them in our courses for years. We used them in our research for
years.Westillusethemsometimes.AndasyoustarttouseR,andinteract
with people using R, and perhaps share code, you will find many people
usingtheseclassictoolsandmethods.
Butthetoolsandtheirsyntaxweredesignedalongtimeago.Manyem-
ployaratheridiosyncraticsetofsymbolsandsyntaxtoaccomplishtasks.
For example, square brackets are used for selecting parts of datasets, and
dollarsignsforreferringtoparticularvariables.Sometimesdifferenttools
thatperformsimilartasksworkinverydifferentways.Thismakesforra-
theridiosyncraticinstructionsthatarenotsoeasyforpeopletoreadorto
rememberhowtowrite.
So after much deliberation, and some good experiences, we decided
that in this second edition we would introduce a popular and new set of
tools contributed by Sir1 Hadley Wickham and many key collaborators
(http://had.co.nz).Thesenewtoolsintroduceasetofquitestand-
ardized and coherent syntax and exist in a set of add-on packages—you
willlearnexactlywhattheseareandhow tousethemlater.Andyouwill
alsolearnsomebaseR.Infact,youwilllearnagreatdealofbaseR.
WedecidedtoteachthisnewwayofusingRbecause:
• The tools use a more ‘natural language’ that is easier for humans to
workwith.
• Thestandardizationandcoherenceamongthetoolsmakethemeasy
tolearnanduse.
• Thetoolsworkverywellforsimpleandsmallproblems,butalsoscale
veryintuitivelyandnaturallytoquitecomplexandlargeproblems.
• Therearetoolsforeverypartoftheworkflow,fromdatamanagement
tostatisticalanalysisandmakingbeautifulgraphs.
• Each of us independently migrated to this new set of tools, giving
us greater confidence that it’s the way forward. (Well, Andrew was
forcedabit.)
Thoughweareconfidentthatteachingnewcomersthesenewtoolsisthe
rightthingtodo,therearesomerisksand,inparticular,peopletaughtonly
thesenewtoolsmaynotbeabletoworkeasilywithpeopleorcodeusing
1Unofficial knighthood for contributions to making our R-lives so much easier and
beautiful.
the classic way. Furthermore, some colleagues have questioned the wis-
domofteachingthis‘modern’approachtoentry-levelstudents(i.e.those
withnoorlittlepreviousexperiencewithR),especiallyiftaughtintheab-
sence of the classic approach (funnily enough, many of these ‘concerned’
colleaguesdon’tuseRatall!).Certainlytherisksmentionedabovearereal,
andforthatreasonweprovideashortappendixinChapter3(thechapter
onDatamanagement)thatlinkstheclassicandnewmethods.Theclassic
waycanstillsometimesbethebestway.Andolddogsdon’toftenagreeto
learningnewtricks.
Another concern voiced asks why we’re teaching ‘advanced R’ at entry
level, with the idea that the use of new tools and add-on packages im-
plies ‘advanced’. After all, why wouldn’t the ‘base’ R distribution contain
everything an entry-level user needs? Well, it does, but we’ve found the
standardizationandsyntaxintheadd-onpackagestobevaluableevenfor
usasseasonedusers.Andoneshouldnotread‘base’Rdistributionas‘ba-
sic’ R distribution, or ‘add-on’ package as ‘advanced’ package. The ‘base’
distribution contains many advanced tools, and many add-on packages
containverybasictools.
WehopeyouenjoythisnewGettingStartedwithR.
Whatthisbookisabout
WeloveR.Weusestatisticsinoureverydaylifeasresearchersandteach-
ers.Sometimesevenmore:Owenusedittoexplorethenursingbehaviour
of his firstborn. We are first and foremost evolutionary and community
ecologists, but over the past 15 years we have developed, first in parallel
and then together, an affinity for R. We want to share our 40+ years of
combined experience using R to show you how easy, important, and ex-
citingitcanbe.Thisbookisbasedon3–5-daycourseswegiveinvarious
guises around the world. The courses are designed to give students and
staffalikeaboostupthesteepinitiallearningcurveassociatedwithR.
We assume that course participants, and you as readers, already use
somespreadsheet,statistical,andgraphingprograms(suchasExcel,SPSS,
Minitab, SAS, JMP, Statistica, and SigmaPlot). Most participants, and we
hope you, have some grasp of common statistical methods, including
the chi-squared test, the t-test, and ANOVA. In return for a few days
of their lives, we give participants knowledge about how to easily use R,
and R only, to manage data, make figures, and do statistics. R changed
our researchlives, and many participants agree that it has done the same
forthem.
The efforts we put into developing the course and this book are, how-
ever, minuscule compared with the efforts of the R Core Development
Team. Please remember to acknowledge them and package contributors
whenyouuseRtoanalyseandpublishyouramazingfindings.
WHAT YOU NEED TO KNOW TO MAKE THIS BOOK WORK
FOR YOU
Thereareafewthingsthatyouneedtoknowtomakethisbook,andour
ideas, work for you. Many of you already know how to do most of these
things, having been in the Internet age for long enough now, but just to
besure:
1. YouneedtoknowhowtodownloadthingsfromtheInternet.Ifyou
useWindows,Macintosh,orLinux,theprinciplesarethesame,but
the details are different. Know your operating system. Know your
browserandknowyourmouse/trackpad.
2. Youneedtoknowhowtomakefoldersonyourcomputerandsave
filestothem.Thisisessentialforbeingorganizedandefficient.
3. It is useful, though not essential, to understand what a ‘path’ is on
yourcomputer.Thisistheaddressofafolderorafile(i.e.thepath
toafile).OnWindows,dependingonthetypeyouareusing,thisin-
volvesadrivename,acolon(:),andslashes(\or/).OnaMacintosh
and Linux/Unix, this requires the names of your hard drive, the
nameofyourhomedirectory,atilde(~),thenamesoffolders,and
slashes(/).