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CRC Press
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Boca Raton, FL 33487-2742

© 2016 by The R Foundation

CRC Press is an imprint of Taylor & Francis Group, an Informa business

Version Date: 20170110

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International Standard Book Number-13: 978-1-4987-7255-6 (Hardback)

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 Contents

Preface xv

Chapter 1  Importing and exporting data 1

1.1 IMPORT AN R DATASET FROM A PACKAGE 1

1.2 LOAD AND SAVE R DATA FILES 2
1.3 READ AND WRITE TEXT FILES 3
1.4 READ AND WRITE CSV FILES 6
1.5 READ DATA FROM THE CLIPBOARD 8
SPREADSHEETS 9
1.6 READ AND WRITE DATA FROM A SPREADSHEET 9
1.7 READ AND WRITE EXCEL FILES 9
STATISTICAL SOFTWARE PROGRAMS 11
1.8 IMPORT AND EXPORT SAS DATASETS 11
1.9 IMPORT AND EXPORT SPSS DATASETS 13
1.10 IMPORT OR EXPORT A STATA DATASET 14
1.11 IMPORT A SYSTAT DATASET 14
DATA EXCHANGE FORMATS AND DATABASES 15
1.12 IMPORT A JSON DATASET 15
1.13 READ DATA FROM A SIMPLE XML FILE 18
1.14 READ DATA FROM AN XML FILE 20
1.15 EXPORT A DATA FRAME TO XML 24
1.16 IMPORT DATA FROM AN HTML TABLE 25
1.17 SCRAPE DATA FROM AN HTML WEB PAGE 26
 1.18 IMPORT FROM A MYSQL/POSTGRESQL DATABASE 29
1.19 READ DATA FROM AN SQL DATABASE USING ODBC 30

Chapter 2  Manipulating data 33

2.1 USE MATHEMATICAL FUNCTIONS 35

2.2 USE COMMON VECTOR OPERATIONS 37
2.3 WORK WITH CHARACTER VECTORS 41
2.4 READ NON-ASCII CHARACTER VECTORS 42
2.5 SORT AND ORDER DATA 43
2.6 TRANSFORM A VARIABLE 45
2.7 FIND THE VALUE OF X CORRESPONDING TO THE
MAXIMUM OR MINIMUM OF Y 46
2.8 CHECK IF ELEMENTS IN ONE OBJECT ARE PRESENT
IN ANOTHER OBJECT 47
2.9 APPLY A FUNCTION TO SUBSETS OF A VECTOR 48
2.10 FILL IN MISSING VALUES WITH PREVIOUS VALUES 48
2.11 CONVERT COMMA AS DECIMAL MARK TO PERIOD 50
2.12 LAG OR SHIFT A VECTOR 50
2.13 CALCULATE THE AREA UNDER A CURVE 52
MATRICES, ARRAYS, AND TABLES 54
2.14 APPLY FUNCTION TO MARGINS OF A MATRIX/AR-
RAY 54
2.15 COMPUTE A MATRIX/ARRAY OF PROPORTIONS 55
2.16 TRANSPOSE A MATRIX (OR DATA FRAME) 56
2.17 CREATE A TABLE OF COUNTS 57
2.18 CONVERT A TABLE OF COUNTS TO A DATA FRAME 59
DATES AND TIMES 61
2.19 PARSING DATES AND TIMES 61
2.20 EXTRACT AND FORMAT DATE/TIME INFORMATION 63
FACTORS 65
2.21 CONVERT A FACTOR TO NUMERIC 65
2.22 CONVERT A FACTOR TO CHARACTER STRINGS 66
 2.23 ADD A NEW LEVEL TO AN EXISTING FACTOR 66
2.24 COMBINE THE LEVELS OF A FACTOR 67
2.25 REMOVE UNUSED LEVELS OF A FACTOR 68
2.26 CHANGE THE REFERENCE LEVEL 69
2.27 CUT A NUMERIC VECTOR INTO A FACTOR 70
DATA FRAMES AND LISTS 70
2.28 SELECT A SUBSET OF A DATA FRAME 70
2.29 SELECT THE COMPLETE CASES OF A DATA FRAME 72
2.30 DELETE A VARIABLE FROM A DATA FRAME 73
2.31 APPLY FUNCTION TO EACH VARIABLE IN A DATA
FRAME 74
2.32 SPLIT DATA FRAME INTO SUBSETS AND APPLY
FUNCTION TO EACH PART 76
2.33 APPLY FUNCTION TO EACH ROW OF A DATA FRAME 77
2.34 COMBINE TWO DATASETS 80
2.35 MERGE DATASETS 81
2.36 ADD NEW OBSERVATIONS TO A DATA FRAME 84
2.37 STACK THE COLUMNS OF A DATA FRAME TO-
GETHER 85
2.38 RESHAPE A DATA FRAME FROM WIDE TO LONG
FORMAT OR VICE VERSA 88
2.39 CONVERT A DATA FRAME TO A VECTOR 94

Chapter 3  Statistical analyses 97

DESCRIPTIVE STATISTICS 100

3.1 COMPUTE SUMMARY STATISTICS 100
3.2 CREATE DESCRIPTIVE TABLE 102
LINEAR MODELS 103
3.3 FIT A LINEAR REGRESSION MODEL 103
3.4 FIT A MULTIPLE LINEAR REGRESSION MODEL 105
3.5 FIT A POLYNOMIAL REGRESSION MODEL 107
3.6 FIT A ONE-WAY ANALYSIS OF VARIANCE 108
3.7 MAKE POST-HOC PAIRWISE COMPARISONS 111
3.8 FIT A TWO-WAY ANALYSIS OF VARIANCE 114
3.9 FIT A LINEAR NORMAL MODEL 117
3.10 FIT A PENALIZED REGRESSION MODEL 121
GENERALIZED LINEAR MODELS 125
3.11 FIT A LOGISTIC REGRESSION MODEL 125
3.12 FIT A CONDITIONAL LOGISTIC REGRESSION MODEL 130
3.13 FIT AN ORDINAL LOGISTIC REGRESSION MODEL 132
3.14 FIT A MULTINOMIAL LOGISTIC REGRESSION MODEL 137
3.15 FIT A POISSON REGRESSION MODEL 140
METHODS FOR ANALYSIS OF REPEATED MEASURE-
MENTS 145
3.16 FIT A LINEAR MIXED-EFFECTS MODEL 145
3.17 FIT A LINEAR MIXED-EFFECTS MODEL WITH SERIAL
CORRELATION 149
3.18 FIT A GENERALIZED LINEAR MIXED MODEL 156
3.19 FIT A GENERALIZED ESTIMATING EQUATION MODEL 160
3.20 ANALYZE TIME SERIES USING AN ARIMA MODEL 165
3.21 DECOMPOSE A TIME SERIES INTO TREND, SEA-
SONAL, AND RESIDUAL COMPONENTS 171
SPECIFIC METHODS 174
3.22 COMPARE POPULATIONS USING T TEST 174
3.23 FIT A NONLINEAR MODEL 176
3.24 FIT A CENSORED REGRESSION MODEL 180
3.25 FIT A ZERO-INFLATED REGRESSION MODEL 182
3.26 FIT A SMOOTH CURVE 185
3.27 FIT A GENERALIZED ADDITIVE MODEL 187
3.28 USE META ANALYSIS TO COMBINE AND SUMMARIZE
THE RESULTS FROM SEVERAL STUDIES 192
3.29 USE RANDOM FOREST FOR CLASSIFICATION AND
REGRESSION 197
3.30 FIT A LINEAR QUANTILE REGRESSION MODEL 203
MODEL VALIDATION 208
3.31 TEST FOR NORMALITY OF A SINGLE SAMPLE 208
3.32 TEST VARIANCE HOMOGENEITY ACROSS GROUPS 210
3.33 VALIDATE A LINEAR OR GENERALIZED LINEAR
MODEL 213
CONTINGENCY TABLES 215
3.34 ANALYZE TWO-DIMENSIONAL CONTINGENCY TA-
BLES 215
3.35 ANALYZE TWO-DIMENSIONAL CONTINGENCY TA-
BLES WITH ORDINAL CATEGORIES 218
3.36 ANALYZE TWO-DIMENSIONAL CONTINGENCY TA-
BLES WITH PAIRED MEASUREMENTS 219
3.37 ANALYZE CONTINGENCY TABLES USING LOG-LINEAR
MODELS 221
AGREEMENT 224
3.38 CREATE A BLAND–ALTMAN PLOT OF AGREEMENT
TO COMPARE TWO QUANTITATIVE METHODS 224
3.39 DETERMINE AGREEMENT AMONG SEVERAL METH-
ODS OF A QUANTITATIVE MEASUREMENT 226
3.40 CALCULATE COHEN’S KAPPA FOR AGREEMENT 231
MULTIVARIATE METHODS 234
3.41 FIT A MULTIVARIATE REGRESSION MODEL 234
3.42 CLUSTER OBSERVATIONS 236
3.43 USE PRINCIPAL COMPONENT ANALYSIS TO REDUCE
DATA DIMENSIONALITY 239
3.44 FIT A PRINCIPAL COMPONENT REGRESSION MODEL 243
3.45 CLASSIFY OBSERVATIONS USING LINEAR DISCRIM-
INANT ANALYSIS 245
3.46 USE PARTIAL LEAST SQUARES REGRESSION FOR
PREDICTION 249
RESAMPLING STATISTICS AND BOOTSTRAPPING 252
3.47 NON-PARAMETRIC BOOTSTRAP ANALYSIS 252
3.48 USE CROSS-VALIDATION TO ESTIMATE THE PER-
FORMANCE OF A MODEL OR ALGORITHM 256
 3.49 CALCULATE POWER OR SAMPLE SIZE FOR SIMPLE
DESIGNS 260
NON-PARAMETRIC METHODS 264
3.50 TEST MEDIAN WITH WILCOXON’S SIGNED RANK
TEST 264
3.51 USE MANN–WHITNEY’S TEST TO COMPARE TWO
GROUPS 267
3.52 COMPARE GROUPS USING KRUSKAL–WALLIS’ TEST 268
3.53 COMPARE GROUPS USING FRIEDMAN’S TEST FOR A
TWO-WAY DESIGN 270
SURVIVAL ANALYSIS 272
3.54 FIT A KAPLAN–MEIER SURVIVAL CURVE TO EVENT
HISTORY DATA 272
3.55 FIT A COX REGRESSION MODEL (PROPORTIONAL
HAZARDS MODEL) 275
3.56 FIT A COX REGRESSION MODEL (PROPORTIONAL
HAZARDS MODEL) WITH TIME-VARYING COVARI-
ATES 280
MISCELLANEOUS 284
3.57 CORRECT P -VALUES FOR MULTIPLE TESTING 284
3.58 USE A BOX–COX TRANSFORMATION TO MAKE NON-
NORMALLY DISTRIBUTED DATA APPROXIMATELY
NORMAL 286

Chapter 4  Graphics 289

4.1 USE GREEK LETTERS AND FORMULAS IN GRAPHS 292

4.2 SET COLORS IN R GRAPHICS 293
4.3 SET COLOR PALETTES IN R GRAPHICS 294
HIGH-LEVEL PLOTS 296
4.4 CREATE A SCATTER PLOT 296
4.5 CREATE A BUBBLE PLOT 298
4.6 CREATE A HISTOGRAM 300
4.7 CREATE A HANGING ROOTOGRAM 301
4.8 CREATE A VIOLIN- OR BOXPLOT 303
4.9 CREATE A BAR PLOT 306
4.10 CREATE A BAR PLOT WITH ERROR BARS 308
4.11 CREATE A PLOT WITH ESTIMATES AND CONFI-
DENCE INTERVALS 310
4.12 CREATE A PIE CHART 312
4.13 CREATE A PYRAMID PLOT 313
4.14 PLOT MULTIPLE SERIES 315
4.15 MAKE A 2D SURFACE PLOT 317
4.16 MAKE A 3D SURFACE PLOT 319
4.17 PLOT A 3D SCATTER PLOT 321
4.18 CREATE A HEAT MAP PLOT 323
4.19 PLOT A CORRELATION MATRIX 325
4.20 MAKE A QUANTILE-QUANTILE PLOT 327
4.21 GRAPHICAL MODEL VALIDATION FOR LINEAR MOD-
ELS 329
4.22 CREATE A VENN OR EULER DIAGRAM 333
TWEAKING GRAPHICS 335
4.23 ADD A BROKEN AXIS TO INDICATE DISCONTINUITY 335
4.24 CREATE A PLOT WITH TWO Y -AXES 336
4.25 ROTATE AXIS LABELS 337
4.26 MULTIPLE PLOTS 339
4.27 ADD A LEGEND TO A PLOT 341
4.28 ADD A TABLE TO A PLOT 342
4.29 LABEL POINTS IN A SCATTER PLOT 344
4.30 IDENTIFY POINTS IN A SCATTER PLOT 346
4.31 VISUALIZE POINTS, SHAPES, AND SURFACES IN 3D
AND INTERACT WITH THEM IN REAL-TIME 347
WORKING WITH GRAPHICS 350
4.32 EXPORTING GRAPHICS 350
4.33 PRODUCE GRAPHICS IN LATEX-READY FORMAT 351
4.34 EMBED FONTS IN POSTSCRIPT OR PDF GRAPHICS 353
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Chapter 5  R 357

GETTING INFORMATION 357

5.1 GETTING HELP 357
5.2 FINDING R SOURCE CODE FOR A FUNCTION 359
5.3 TIMING R COMMANDS AND FUNCTIONS 361
R PACKAGES 362
5.4 INSTALLING R PACKAGES 362
5.5 UPDATE INSTALLED R PACKAGES 364
5.6 UNLOAD A PACKAGE 365
5.7 INSTALL AN R PACKAGE FROM A REPOSITORY 365
5.8 INSTALL A PACKAGE FROM BIOCONDUCTOR 366
5.9 LIST THE INSTALLED PACKAGES 367
5.10 LIST THE CONTENT OF A PACKAGE 367
5.11 UNINSTALL AN R PACKAGE 368
5.12 LIST OR VIEW VIGNETTES 369
5.13 PERMANENTLY CHANGE THE DEFAULT DIRECTORY
WHERE R INSTALLS PACKAGES 371
5.14 AUTOMATICALLY LOAD A PACKAGE AT STARTUP 372
THE R WORKSPACE 373
5.15 LIST OR DELETE OBJECTS 373
5.16 CHANGE THE CURRENT WORKING DIRECTORY 374
5.17 SAVE AND LOAD WORKSPACES 374
5.18 SAVE AND RESTORE HISTORIES 375
5.19 INTERACT WITH THE FILE SYSTEM 377
5.20 LOCATE AND CHOOSE FILES INTERACTIVELY 378
5.21 INTERACT WITH THE OPERATING SYSTEM 379
5.22 GET SESSION INFORMATION 380

Bibliography 385

Index 393
 Preface

A lot has happened to R and in the R community in the five years since
the first edition of The R Primer was published. Changes in R itself,
hundreds of new and improved packages, but most importantly that R
has seen even more widespread acceptance and has been adopted by
more and more research fields as the primary software tool for data
analysis.
In addition, the recent focus in the scientific fields on research docu-
mentation and reproducible research has made R an even more valuable
and ideal tool for data analysis and hence it made sense to update the
current text to reflect some of the changes to R. The majority of the
problems and cases covered in the book have been updated and the text
has grown substantially to cover additional situations and problems.
The second edition of The R Primer follows the same format as the
first edition in that it assumes that the reader knows what he or she
wants and/or what analysis method is appropriate in a given situation,
but that the reader needs help with both coding and interpreting the
output from that model in R. The problems in this second edition have
been — just as for the first edition — heavily inspired by the questions
encountered at the statistical consultancy service at the University of
Copenhagen. During these consultancy sessions we discuss statistical
problems with clients and often provide them with example code that
covers their situation. The examples and problems in the book have
proven valuable as a reference point for the clients in this regard as it is
easy to show them a working example and explain the changes they need
to adapt to get the code working in their situation. Hopefully, others will
also find the text useful as a collection of solutions to common situations
for newcomers and intermediate users of R.

Claus Thorn Ekstrøm

Copenhagen 2016
Preface to the first edition
This book is not about statistical theory, neither is it meant to teach R
programming. This book is intended for readers who know the basics of
R, but find themselves with problems or situations that are commonly
encountered by newcomers to R or for readers who want to see compact
examples of different types of typical statistical analyses. In other words,
if you understand basic statistics and already know a bit about R then
this book is for you.
R has rapidly become the lingua franca of statistical computing; it is
a free statistical programming software and it can be downloaded from
http://cran.r-project.org. Many newcomers to R are often intimi-
dated by the command-line interface, or the sheer number of functions
and packages, or just trying to figure out how to import data and per-
form a simple statistical analysis.
The book consists of a number of examples that illustrate a specific
situation, topic, or problem from data import over data management and
classical statistical analyses to graphics. Each example is self-contained
and provides R code that can be run exactly as shown and the results
from the book can be reproduced. The only change — barring simu-
lated data, machine set-up and small tweaks to make figures suitable
for printing — is that some of the output lines have been removed for
brevity.
This is not a “missing manual” or a thorough exploration of the
functions used. Instead of trying to cover every possible option or special
case that might be of interest, we focus on the common situations that
most beginning users are likely to encounter. Thus we concentrate on
the basics of getting things done and giving examples that can be used
as a starting point for the reader rather than exploring the multitude of
options available with every command and the ever-increasing number
of packages. For most problems — and this is particularly true for a
programming language like R— there is more than one way to solve a
problem. Here, I have provided a single solution to most problems and
have tried to use base R if at all possible. If there are other functions
and/or packages available that cover or extend the same functionality,
then some of them are listed at the end of each example.
The R list of frequently asked questions is highly recommended and
covers a few of the same topics mentioned here. However, it does not
cover examples of statistical analyses and it rarely covers some of the
most basic problems new users encounter.
 Base graphics are used throughout the book. More advanced graph-
ics can be produced with the recent lattice and ggplot2 packages
(see Sarkar (2008), Wickham (2009), or Murrell (2011) for further in-
formation on advanced R graphics). A more complete coverage of R
and/or statistics can be found in the books by Venables and Ripley
(2002), Verzani (2005), Crawley (2007), Dalgaard (2008), and Everitt
and Hothorn (2010). These books have a slightly different target audi-
ence than the present text and are all highly recommended.
The R Primer has a supporting web site at

http://www.rprimer.dk

where additional topics are covered and where the R code used in the
book can be found.
I would like to thank all R developers and package writers for the
enormous work they have done and continue to put into the R program
and extensions. I appreciate all the helpful responses to my enquiries
and suggestions. I am grateful to my colleagues at the Faculty of Life
Sciences, University of Copenhagen, as well as Klaus K. Holst, Duncan
Temple Lang, and Bendix Carstensen for their ideas, comments, sug-
gestions, and encouragement on various stages of the manuscript. Many
thanks to Tina Ekstrøm for once again creating a wonderful cover, and
last, but not least, thanks to Marlene, Ellen, and Anna for bearing with
me through yet another book.

Claus Thorn Ekstrøm

Frederiksberg 2011
 CHAPTER 1
Importing and exporting
data

The first step in most statistical analyses is to get the data into R.
While it is possible to manually type the data directly into R using,
say, the c and data.frame functions, for most serious work the data is
typically stored in a file somewhere locally on the computer, a network,
or somewhere on the Web.
Table 1.1 gives a quick overview of R functions and related packages
for importing common data formats. Specific solutions to importing dif-
ferent types of data are presented in the rest of this chapter.

1.1 IMPORT AN R DATASET FROM A PACKAGE

Problem: You want to import an R dataset from a package
Solution: Many R packages (including those that are part of the default
installation) come with several datasets that are ready to use. R datasets
found in packages are imported with the data function and require that
the package is loaded first.
> library(MESS) # Load the package that contains the data
> data(bees) # Load the data
> head(bees) # Show the first couple of lines of data
Locality Replicate Color Time Type Number id
1 Havreholm A White july1 Bumblebees 1 1
4 Havreholm A Yellow july1 Bumblebees 2 1
7 Havreholm A Blue july1 Bumblebees 0 1
10 Havreholm A White july1 Solitary 1 1
13 Havreholm A Yellow july1 Solitary 4 1
16 Havreholm A Blue july1 Solitary 3 1
2  The R Primer

Table 1.1: Common file formats and R functions to read them

File type Function Package

R data file load or data
Text file read.table
CSV read.csv or read.csv2
JSON fromJSON jsonlite
Excel spreadsheet read_excel readxl
SAS read_sas haven
SPSS .SAV or .POR read_sav or read_por haven
Stata read_stata haven
html read_html rvest
xml xmlTreeParse XML

1.2 LOAD AND SAVE R DATA FILES

Problem: You want to load or save an external R dataset.
Solution: R has its own data file format where files typically have
extension RData or rda and they are imported with the load function.
A single R data files may contain multiple R objects, and the objects can
be any type and not just data frames. The objects imported have the
same name and structure as they have in the file, and we only see a list
of the imported objects if the verbose=TRUE argument is included.
> load("penny.rda") # Load data
> load("penny.rda", verbose=TRUE) # Load data and show objects
Loading objects:
big
bang
theory

Beware that load overwrites existing objects with the same names
without giving any warnings.
The save function is used to save R objects to a file. save takes any
number of R objects as arguments and saves them to an external file,
and the file argument specifies the name of the file.
> x <- c(1, 7, 3, 2)
> df <- data.frame(v1=c("A", "B", "C", "D"), v2=1:4)
> save(x, df, file="mydata.rda") # Save two objects

See also: Problem 5.16 explains how to change the working directory.
 Importing and exporting data  3

1.3 READ AND WRITE TEXT FILES

Problem: You want to work with a dataset stored in a text file.
Solution: Data stored in simple text files can be read into R using
the read.table function. By default, the observations should be listed
in columns where the individual fields are separated by one or more
white space characters (e.g., space, or tabulators), and where each line
in the file corresponds to one row of the data frame. The columns do not
need to be straight or formatted, but multi-word observations like high
income need to be put in quotes or combined into a single word so they
are not interpreted as two columns. If the text file mydata.txt has the
following content
acid digest name
30.3 70.6 NA
29.8 67.5 Eeny
NA 87.0 Meeny
4.1 89.9 Miny
4.4 . Moe
2.8 93.1 .
3.8 96.7 ""
we can read the file into R with the following command:
> indata <- read.table("mydata.txt", header=TRUE)
> indata
acid digest name
1 30.3 70.6 <NA>
2 29.8 67.5 Eeny
3 NA 87.0 Meeny
4 4.1 89.9 Miny
5 4.4 . Moe
6 2.8 93.1 .
7 3.8 96.7

The first argument is the name of the data file, and the second ar-
gument (header=TRUE) is optional and should be used only if the first
line of the text file provides the variable/column names. If the first line
does not contain the column names, the variables will be labeled con-
secutively V1, V2, V3, etc. Each line in the input file must contain the
same number of columns for read.table to work. The sep option can
be set to indicate which character that separates the columns. For ex-
ample, use sep="\t" if the columns are separated by tabs. Data read
with read.table are stored as a data frame within R.
4  The R Primer

The default code for missing observations is the character string NA

which we can see works both for the first and third observation above
(acid is read as a numeric vector and name as a factor). Empty char-
acter fields are read as empty character vectors, unless the argument
na.strings contains the value "" in which case they become missing
values. Empty numeric fields (for example if the columns are separated
by tabs) are automatically considered missing.

> indata <- read.table("mydata.txt", header=TRUE,

+ na.strings=c("NA", ""))
> indata
acid digest name
1 30.3 70.6 <NA>
2 29.8 67.5 Eeny
3 NA 87.0 Meeny
4 4.1 89.9 Miny
5 4.4 . Moe
6 2.8 93.1 .
7 3.8 96.7 <NA>

Note that R considers the variable digest as a factor and not numeric
since the period ‘.’ for observation 5 is read as a character string. If
periods should also be considered missing variables we need to include
that in na.strings vector.

> indata <- read.table("mydata.txt", header=TRUE,

+ na.strings=c("NA", "", "."))
> indata
acid digest name
1 30.3 70.6 <NA>
2 29.8 67.5 Eeny
3 NA 87.0 Meeny
4 4.1 89.9 Miny
5 4.4 NA Moe
6 2.8 93.1 <NA>
7 3.8 96.7 <NA>

Data stored as simple text files can be read by most programs so it is

often desirable to export a data frame as a simple text file. write.table
writes a data frame or a matrix as a text file. The first argument should
be the R object to write and the file argument sets the name of the
output file as shown below.
 Importing and exporting data  5

> data(trees)
> trees$Girth[2] <- NA # Set 2nd obs of Girth to missing
> write.table(trees, file="savedata.txt")

which produces the following file

"Girth" "Height" "Volume"
"1" 8.3 70 10.3
"2" NA 65 10.3
"3" 8.8 63 10.2
"4" 10.5 72 16.4
. . . .
. . . .

There are several important arguments to write.table that change

the output. The quote option defaults to TRUE and determines if char-
acter or factor columns are surrounded by double quotes. quote=FALSE
means that none of the columns are quoted and if set to a numeric vec-
tor then the values determine which columns are to be quoted. sep sets
the field separator string and it defaults to a single white space charac-
ter. na has a default value of NA and the option sets the string used for
missing values in the file. The dec and eol arguments set the character
and character(s) for decimal points and end-of-lines, respectively. The
default arguments to write.table prints out the row names (the line
numbers) as shown in the output above, but that can be circumvented
with the row.names=FALSE argument.
The example below produces a file with comma as decimal point,
where strings are not quoted, where a period represents NA’s, no row
names, and where the end-of-line characters match the format used on
machines running Windows.

> write.table(trees, file="mydata2.txt", row.names=FALSE,

+ dec=",", eol="\r\n", na=".", quote=FALSE)

which results in the following file

Girth Height Volume
8,3 70 10,3
. 65 10,3
8,8 63 10,2
10,5 72 16,4
10,7 81 18,8
. . .
. . .
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