R in Action, 2nd Edition: Data analysis and graphics with R

608 Pages·2015·0.88 MB·English

by  Robert I. Kabacoff

SECOND EDITION
IN ACTION
Data analysis and graphics with R
Robert I. Kabacoff
M A N N I N G
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Praise for the First Edition
Lucid and engaging—this is without doubt the fun way to learn R!
—Amos A. Folarin, University College London
Be prepared to quickly raise the bar with the sheer quality that R can produce.
—Patrick Breen, Rogers Communications Inc.
An excellent introduction and reference on R from the author of the best R website.
—Christopher Williams, University of Idaho
Thorough and readable. A great R companion for the student or researcher.
—Samuel McQuillin, University of South Carolina
Finally, a comprehensive introduction to R for programmers.
—Philipp K. Janert, Author of Gnuplot in Action
Essential reading for anybody moving to R for the first time.
—Charles Malpas, University of Melbourne
One of the quickest routes to R proficiency. You can buy the book on Friday and
have a working program by Monday.
—Elizabeth Ostrowski, Baylor College of Medicine
One usually buys a book to solve the problems they know they have. This book
solves problems you didn't know you had.
—Carles Fenollosa, Barcelona Supercomputing Center
Clear, precise, and comes with a lot of explanations and examples…the book can
be used by beginners and professionals alike, and even for teaching R!
—Atef Ouni, Tunisian National Institute of Statistics
A great balance of targeted tutorials and in-depth examples.
—Landon Cox, 360VL Inc.
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R in Action
SECOND EDITION
Data analysis and graphics with R
ROBERT I. KABACOFF
MANNING
SHELTER ISLAND
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ISBN: 9781617291388
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brief contents
PART 1 GETTING STARTED ...................................................... 1
1 ■ Introduction to R 3
2 ■ Creating a dataset 20
3 ■ Getting started with graphs 46
4 ■ Basic data management 71
5 ■ Advanced data management 89
PART 2 BASIC METHODS ...................................................... 115
6 ■ Basic graphs 117
7 ■ Basic statistics 137
PART 3 INTERMEDIATE METHODS ........................................ 165
8 ■ Regression 167
9 ■ Analysis of variance 212
10 ■ Power analysis 239
11 ■ Intermediate graphs 255
12 ■ Resampling statistics and bootstrapping 279
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vi BRIEF CONTENTS
PART 4 ADVANCED METHODS ............................................... 299
13 ■ Generalized linear models 301
14 ■ Principal components and factor analysis 319
15 ■ Time series 340
16 ■ Cluster analysis 369
17 ■ Classification 389
18 ■ Advanced methods for missing data 414
PART 5 EXPANDING YOUR SKILLS ......................................... 435
19 ■ Advanced graphics with ggplot2 437
20 ■ Advanced programming 463
21 ■ Creating a package 491
22 ■ Creating dynamic reports 513
23 ■ Advanced graphics with the lattice package 1 online only
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contents
preface xvii
acknowledgments xix
about this book xxi
about the cover illustration xxvii
PART 1 GETTING STARTED ........................................... 1
1 Introduction to R 3
1.1 Why use R? 5
1.2 Obtaining and installing R 7
1.3 Working with R 7
Getting started 8 ■ Getting help 10 ■ The workspace 11
Input and output 13
1.4 Packages 15
What are packages? 15 ■ Installing a package 15
Loading a package 15 ■ Learning about a
package 16
1.5 Batch processing 16
1.6 Using output as input: reusing results 17
1.7 Working with large datasets 17
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viii CONTENTS
1.8 Working through an example 18
1.9 Summary 19
2 Creating a dataset 20
2.1 Understanding datasets 21
2.2 Data structures 22
Vectors 22 ■ Matrices 23 ■ Arrays 24 ■ Data frames 25
Factors 28 ■ Lists 30
2.3 Data input 32
Entering data from the keyboard 33 ■ Importing data from a
delimited text file 34 ■ Importing data from Excel 37
Importing data from XML 38 ■ Importing data from the
web 38 ■ Importing data from SPSS 38 ■ Importing data
from SAS 39 ■ Importing data from Stata 40 ■ Importing
data from NetCDF 40 ■ Importing data from HDF5 40
Accessing database management systems (DBMSs) 40
Importing data via Stat/Transfer 42
2.4 Annotating datasets 43
Variable labels 43 ■ Value labels 43
2.5 Useful functions for working with data objects 43
2.6 Summary 44
3 Getting started with graphs 46
3.1 Working with graphs 47
3.2 A simple example 49
3.3 Graphical parameters 50
Symbols and lines 51 ■ Colors 52 ■ Text characteristics 53
Graph and margin dimensions 54
3.4 Adding text, customized axes, and legends 56
Titles 56 ■ Axes 57 ■ Reference lines 60 ■ Legend 60
Text annotations 61 ■ Math annotations 63
3.5 Combining graphs 64
Creating a figure arrangement with fine control 68
3.6 Summary 70
4 Basic data management 71
4.1 A working example 71
4.2 Creating new variables 73
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CONTENTS ix
4.3 Recoding variables 75
4.4 Renaming variables 76
4.5 Missing values 77
Recoding values to missing 78 ■ Excluding missing values
from analyses 78
4.6 Date values 79
Converting dates to character variables 81 ■ Going
further 81
4.7 Type conversions 81
4.8 Sorting data 82
4.9 Merging datasets 83
Adding columns to a data frame 83 ■ Adding rows to
a data frame 84
4.10 Subsetting datasets 84
Selecting (keeping) variables 84 ■ Excluding (dropping)
variables 84 ■ Selecting observations 85 ■ The subset()
function 86 ■ Random samples 87
4.11 Using SQL statements to manipulate data
frames 87
4.12 Summary 88
5 Advanced data management 89
5.1 A data-management challenge 90
5.2 Numerical and character functions 91
Mathematical functions 91 ■ Statistical functions 92
Probability functions 94 ■ Character functions 97
Other useful functions 98 ■ Applying functions to matrices
and data frames 99
5.3 A solution for the data-management challenge 101
5.4 Control flow 105
Repetition and looping 105 ■ Conditional
execution 106
5.5 User-written functions 107
5.6 Aggregation and reshaping 109
Transpose 110 ■ Aggregating data 110 ■ The reshape2
package 111
5.7 Summary 113
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