R Exts

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User Manual: R-exts

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Writing R Extensions
Version 3.4.3 (2017-11-30)
R Core Team
This manual is for R, version 3.4.3 (2017-11-30).
Copyright c
1999–2016 R Core Team
Permission is granted to make and distribute verbatim copies of this manual provided
the copyright notice and this permission notice are preserved on all copies.
Permission is granted to copy and distribute modified versions of this manual under
the conditions for verbatim copying, provided that the entire resulting derived work
is distributed under the terms of a permission notice identical to this one.
Permission is granted to copy and distribute translations of this manual into an-
other language, under the above conditions for modified versions, except that this
permission notice may be stated in a translation approved by the R Core Team.
i
Table of Contents
Acknowledgements ................................................. 1
1 Creating R packages ........................................... 2
1.1 Package structure .................................................................. 3
1.1.1 The DESCRIPTION file ......................................................... 4
1.1.2 Licensing ..................................................................... 8
1.1.3 Package Dependencies .........................................................9
1.1.3.1 Suggested packages......................................................11
1.1.4 The INDEX file ............................................................... 12
1.1.5 Package subdirectories ....................................................... 12
1.1.6 Data in packages.............................................................16
1.1.7 Non-R scripts in packages .................................................... 16
1.1.8 Specifying URLs ............................................................. 17
1.2 Configure and cleanup ............................................................ 17
1.2.1 Using Makevars..............................................................20
1.2.1.1 OpenMP support ....................................................... 23
1.2.1.2 Using pthreads ..........................................................25
1.2.1.3 Compiling in sub-directories ............................................. 26
1.2.2 Configure example ........................................................... 26
1.2.3 Using F95 code .............................................................. 28
1.2.4 Using C++11 code...........................................................29
1.2.5 Using C++14 code...........................................................30
1.2.6 Using C++17 code...........................................................31
1.3 Checking and building packages ................................................... 31
1.3.1 Checking packages ........................................................... 32
1.3.2 Building package tarballs.....................................................35
1.3.3 Building binary packages .....................................................37
1.4 Writing package vignettes ......................................................... 37
1.4.1 Encodings and vignettes ..................................................... 39
1.4.2 Non-Sweave vignettes ........................................................ 40
1.5 Package namespaces .............................................................. 41
1.5.1 Specifying imports and exports...............................................41
1.5.2 Registering S3 methods ...................................................... 42
1.5.3 Load hooks .................................................................. 43
1.5.4 useDynLib ................................................................... 43
1.5.5 An example..................................................................45
1.5.6 Namespaces with S4 classes and methods .....................................46
1.6 Writing portable packages.........................................................47
1.6.1 PDF size .................................................................... 53
1.6.2 Check timing ................................................................ 53
1.6.3 Encoding issues .............................................................. 54
1.6.4 Portable C and C++ code ................................................... 55
1.6.5 Binary distribution .......................................................... 58
1.7 Diagnostic messages .............................................................. 59
1.8 Internationalization ............................................................... 60
1.8.1 C-level messages ............................................................. 60
1.8.2 R messages .................................................................. 61
1.8.3 Preparing translations ....................................................... 61
ii
1.9 CITATION files .................................................................. 61
1.10 Package types ................................................................... 62
1.10.1 Frontend ................................................................... 62
1.11 Services ......................................................................... 63
2 Writing R documentation files ............................... 64
2.1 Rd format ........................................................................ 64
2.1.1 Documenting functions .......................................................65
2.1.2 Documenting data sets ....................................................... 69
2.1.3 Documenting S4 classes and methods.........................................70
2.1.4 Documenting packages ....................................................... 71
2.2 Sectioning ........................................................................ 71
2.3 Marking text ..................................................................... 72
2.4 Lists and tables...................................................................74
2.5 Cross-references...................................................................74
2.6 Mathematics......................................................................75
2.7 Figures ........................................................................... 75
2.8 Insertions.........................................................................76
2.9 Indices ........................................................................... 77
2.10 Platform-specific documentation ................................................. 77
2.11 Conditional text ................................................................. 77
2.12 Dynamic pages .................................................................. 78
2.13 User-defined macros ............................................................. 79
2.14 Encoding ........................................................................ 80
2.15 Processing documentation files ................................................... 80
2.16 Editing Rd files..................................................................81
3 Tidying and profiling R code ................................ 82
3.1 Tidying R code ................................................................... 82
3.2 Profiling R code for speed.........................................................82
3.3 Profiling R code for memory use .................................................. 84
3.3.1 Memory statistics from Rprof ................................................ 84
3.3.2 Tracking memory allocations ................................................. 85
3.3.3 Tracing copies of an object ................................................... 85
3.4 Profiling compiled code ........................................................... 85
3.4.1 Linux ........................................................................86
3.4.1.1 sprof .................................................................... 86
3.4.1.2 oprofile and operf ....................................................... 86
3.4.2 Solaris ....................................................................... 89
3.4.3 macOS ...................................................................... 89
4 Debugging ..................................................... 90
4.1 Browsing ......................................................................... 90
4.2 Debugging R code ................................................................ 91
4.3 Checking memory access .......................................................... 95
4.3.1 Using gctorture .............................................................. 95
4.3.2 Using valgrind ............................................................... 96
4.3.3 Using the Address Sanitizer .................................................. 97
4.3.3.1 Using the Leak Sanitizer ................................................ 99
4.3.4 Using the Undefined Behaviour Sanitizer ..................................... 99
4.3.5 Other analyses with ‘clang’ ................................................. 100
4.3.6 Using ‘Dr. Memory’ ........................................................ 100
4.3.7 Fortran array bounds checking .............................................. 101
iii
4.4 Debugging compiled code ........................................................ 101
4.4.1 Finding entry points in dynamically loaded code ............................ 102
4.4.2 Inspecting R objects when debugging ....................................... 103
5 System and foreign language interfaces .................... 105
5.1 Operating system access ......................................................... 105
5.2 Interface functions .C and .Fortran ............................................. 105
5.3 dyn.load and dyn.unload .......................................................107
5.4 Registering native routines.......................................................108
5.4.1 Speed considerations ........................................................ 111
5.4.2 Example: converting a package to use registration ........................... 112
5.4.3 Linking to native routines in other packages ................................. 115
5.5 Creating shared objects .......................................................... 116
5.6 Interfacing C++ code ........................................................... 117
5.6.1 External C++ code .........................................................118
5.7 Fortran I/O ..................................................................... 119
5.8 Linking to other packages ........................................................ 119
5.8.1 Unix-alikes ................................................................. 120
5.8.2 Windows ................................................................... 120
5.9 Handling R objects in C ......................................................... 121
5.9.1 Handling the effects of garbage collection ....................................122
5.9.2 Allocating storage .......................................................... 124
5.9.3 Details of R types...........................................................124
5.9.4 Attributes .................................................................. 125
5.9.5 Classes ..................................................................... 127
5.9.6 Handling lists ...............................................................127
5.9.7 Handling character data .................................................... 128
5.9.8 Finding and setting variables................................................128
5.9.9 Some convenience functions ................................................. 129
5.9.9.1 Semi-internal convenience functions .................................... 129
5.9.10 Named objects and copying ................................................ 129
5.10 Interface functions .Call and .External ....................................... 130
5.10.1 Calling .Call..............................................................130
5.10.2 Calling .External ......................................................... 131
5.10.3 Missing and special values ................................................. 133
5.11 Evaluating R expressions from C................................................133
5.11.1 Zero-finding ............................................................... 135
5.11.2 Calculating numerical derivatives .......................................... 136
5.12 Parsing R code from C ......................................................... 139
5.12.1 Accessing source references ................................................ 140
5.13 External pointers and weak references...........................................140
5.13.1 An example ............................................................... 141
5.14 Vector accessor functions ....................................................... 142
5.15 Character encoding issues.......................................................142
6 The R API: entry points for C code ....................... 144
6.1 Memory allocation...............................................................144
6.1.1 Transient storage allocation ................................................. 144
6.1.2 User-controlled memory .....................................................145
6.2 Error handling...................................................................145
6.2.1 Error handling from FORTRAN ............................................ 146
6.3 Random number generation......................................................146
6.4 Missing and IEEE special values..................................................146
iv
6.5 Printing ......................................................................... 147
6.5.1 Printing from FORTRAN ................................................... 147
6.6 Calling C from FORTRAN and vice versa ........................................147
6.7 Numerical analysis subroutines...................................................148
6.7.1 Distribution functions.......................................................148
6.7.2 Mathematical functions ..................................................... 150
6.7.3 Numerical Utilities..........................................................150
6.7.4 Mathematical constants ..................................................... 152
6.8 Optimization .................................................................... 153
6.9 Integration ...................................................................... 154
6.10 Utility functions ................................................................ 155
6.11 Re-encoding .................................................................... 157
6.12 Allowing interrupts ............................................................. 157
6.13 Platform and version information ............................................... 157
6.14 Inlining C functions ............................................................ 158
6.15 Controlling visibility ............................................................158
6.16 Using these functions in your own C code ....................................... 159
6.17 Organization of header files ..................................................... 160
7 Generic functions and methods............................. 161
7.1 Adding new generics ............................................................. 162
8 Linking GUIs and other front-ends to R................... 163
8.1 Embedding R under Unix-alikes..................................................163
8.1.1 Compiling against the R library ............................................. 165
8.1.2 Setting R callbacks ......................................................... 165
8.1.3 Registering symbols.........................................................168
8.1.4 Meshing event loops ........................................................ 169
8.1.5 Threading issues ............................................................ 169
8.2 Embedding R under Windows ................................................... 170
8.2.1 Using (D)COM ............................................................. 170
8.2.2 Calling R.dll directly........................................................170
8.2.3 Finding R HOME .......................................................... 173
Function and variable index .................................... 175
Concept index ................................................... 178
1
Acknowledgements
The contributions to early versions of this manual by Saikat DebRoy (who wrote the first draft
of a guide to using .Call and .External) and Adrian Trapletti (who provided information on
the C++ interface) are gratefully acknowledged.
2
1 Creating R packages
Packages provide a mechanism for loading optional code, data and documentation as needed.
The R distribution itself includes about 30 packages.
In the following, we assume that you know the library() command, including its lib.loc
argument, and we also assume basic knowledge of the R CMD INSTALL utility. Otherwise, please
look at R’s help pages on
?library
?INSTALL
before reading on.
For packages which contain code to be compiled, a computing environment including a num-
ber of tools is assumed; the “R Installation and Administration” manual describes what is
needed for each OS.
Once a source package is created, it must be installed by the command R CMD INSTALL. See
Section “Add-on-packages” in R Installation and Administration.
Other types of extensions are supported (but rare): See Section 1.10 [Package types], page 62.
Some notes on terminology complete this introduction. These will help with the reading of
this manual, and also in describing concepts accurately when asking for help.
Apackage is a directory of files which extend R, a source package (the master files of a
package), or a tarball containing the files of a source package, or an installed package, the
result of running R CMD INSTALL on a source package. On some platforms (notably macOS and
Windows) there are also binary packages, a zip file or tarball containing the files of an installed
package which can be unpacked rather than installing from sources.
A package is not1alibrary. The latter is used in two senses in R documentation.
A directory into which packages are installed, e.g. /usr/lib/R/library: in that sense it is
sometimes referred to as a library directory or library tree (since the library is a directory
which contains packages as directories, which themselves contain directories).
That used by the operating system, as a shared, dynamic or static library or (especially on
Windows) a DLL, where the second L stands for ‘library’. Installed packages may contain
compiled code in what is known on Unix-alikes as a shared object and on Windows as a DLL.
The concept of a shared library (dynamic library on macOS) as a collection of compiled code
to which a package might link is also used, especially for R itself on some platforms. On
most platforms these concepts are interchangeable (shared objects and DLLs can both be
loaded into the R process and be linked against), but macOS distinguishes between shared
objects (extension .so) and dynamic libraries (extension .dylib).
There are a number of well-defined operations on source packages.
The most common is installation which takes a source package and installs it in a library
using R CMD INSTALL or install.packages.
Source packages can be built. This involves taking a source directory and creating a tarball
ready for distribution, including cleaning it up and creating PDF documentation from any
vignettes it may contain. Source packages (and most often tarballs) can be checked, when
a test installation is done and tested (including running its examples); also, the contents of
the package are tested in various ways for consistency and portability.
Compilation is not a correct term for a package. Installing a source package which contains
C, C++ or Fortran code will involve compiling that code. There is also the possibility of
‘byte’ compiling the R code in a package (using the facilities of package compiler): already
1although this is a persistent mis-usage. It seems to stem from S, whose analogues of R’s packages were officially
known as library sections and later as chapters, but almost always referred to as libraries.
Chapter 1: Creating R packages 3
base and recommended packages are normally byte-compiled and this can be specified for
other packages. So compiling a package may come to mean byte-compiling its R code.
It used to be unambiguous to talk about loading an installed package using library(),
but since the advent of package namespaces this has been less clear: people now often talk
about loading the package’s namespace and then attaching the package so it becomes visible
on the search path. Function library performs both steps, but a package’s namespace can
be loaded without the package being attached (for example by calls like splines::ns).
The concept of lazy loading of code or data is mentioned at several points. This is part of
the installation, always selected for R code but optional for data. When used the R objects of
the package are created at installation time and stored in a database in the Rdirectory of the
installed package, being loaded into the session at first use. This makes the R session start up
faster and use less (virtual) memory. (For technical details, see Section “Lazy loading” in R
Internals.)
CRAN is a network of WWW sites holding the R distributions and contributed code, especially
R packages. Users of R are encouraged to join in the collaborative project and to submit their
own packages to CRAN: current instructions are linked from https://CRAN.R-project.org/
banner.shtml#submitting.
1.1 Package structure
The sources of an R package consists of a subdirectory containing a files DESCRIPTION and
NAMESPACE, and the subdirectories R,data,demo,exec,inst,man,po,src,tests,tools and
vignettes (some of which can be missing, but which should not be empty). The package
subdirectory may also contain files INDEX,configure,cleanup,LICENSE,LICENCE and NEWS.
Other files such as INSTALL (for non-standard installation instructions), README/README.md2, or
ChangeLog will be ignored by R, but may be useful to end users. The utility R CMD build may
add files in a build directory (but this should not be used for other purposes).
Except where specifically mentioned,3packages should not contain Unix-style ‘hidden’
files/directories (that is, those whose name starts with a dot).
The DESCRIPTION and INDEX files are described in the subsections below. The NAMESPACE
file is described in the section on Section 1.5 [Package namespaces], page 41.
The optional files configure and cleanup are (Bourne) shell scripts which are, respec-
tively, executed before and (if option --clean was given) after installation on Unix-alikes, see
Section 1.2 [Configure and cleanup], page 17. The analogues on Windows are configure.win
and cleanup.win.
For the conventions for files NEWS and ChangeLog in the GNU project see https://www.gnu.
org/prep/standards/standards.html#Documentation.
The package subdirectory should be given the same name as the package. Because some file
systems (e.g., those on Windows and by default on OS X) are not case-sensitive, to maintain
portability it is strongly recommended that case distinctions not be used to distinguish different
packages. For example, if you have a package named foo, do not also create a package named
Foo.
To ensure that file names are valid across file systems and supported operating systems, the
ASCII control characters as well as the characters ‘"’, ‘*’, ‘:’, ‘/’, ‘<’, ‘>’, ‘?’, ‘\’, and ‘|’ are not
allowed in file names. In addition, files with names ‘con’, ‘prn’, ‘aux’, ‘clock$’, ‘nul’, ‘com1’ to
2This seems to be commonly used for a file in ‘markdown’ format. Be aware that most users of R will not
know that, nor know how to view such a file: platforms such as macOS and Windows do not have a default
viewer set in their file associations. The CRAN package web pages render such files in HTML: the converter
used expects the file to be encoded in UTF-8.
3currently, top-level files .Rbuildignore and .Rinstignore, and vignettes/.install_extras.
Chapter 1: Creating R packages 4
com9’, and ‘lpt1’ to ‘lpt9’ after conversion to lower case and stripping possible “extensions”
(e.g., ‘lpt5.foo.bar’), are disallowed. Also, file names in the same directory must not differ
only by case (see the previous paragraph). In addition, the basenames of ‘.Rd’ files may be used
in URLs and so must be ASCII and not contain %. For maximal portability filenames should only
contain only ASCII characters not excluded already (that is A-Za-z0-9._!#$%&+,;=@^(){}’[]
— we exclude space as many utilities do not accept spaces in file paths): non-English alphabetic
characters cannot be guaranteed to be supported in all locales. It would be good practice to
avoid the shell metacharacters (){}’[]$~:~is also used as part of ‘8.3’ filenames on Windows.
In addition, packages are normally distributed as tarballs, and these have a limit on path lengths:
for maximal portability 100 bytes.
A source package if possible should not contain binary executable files: they are not portable,
and a security risk if they are of the appropriate architecture. R CMD check will warn about them4
unless they are listed (one filepath per line) in a file BinaryFiles at the top level of the package.
Note that CRAN will not accept submissions containing binary files even if they are listed.
The R function package.skeleton can help to create the structure for a new package: see
its help page for details.
1.1.1 The DESCRIPTION file
The DESCRIPTION file contains basic information about the package in the following format:
 
Package: pkgname
Version: 0.5-1
Date: 2015-01-01
Title: My First Collection of Functions
Authors@R: c(person("Joe", "Developer", role = c("aut", "cre"),
email = "Joe.Developer@some.domain.net"),
person("Pat", "Developer", role = "aut"),
person("A.", "User", role = "ctb",
email = "A.User@whereever.net"))
Author: Joe Developer [aut, cre],
Pat Developer [aut],
A. User [ctb]
Maintainer: Joe Developer <Joe.Developer@some.domain.net>
Depends: R (>= 3.1.0), nlme
Suggests: MASS
Description: A (one paragraph) description of what
the package does and why it may be useful.
License: GPL (>= 2)
URL: https://www.r-project.org, http://www.another.url
BugReports: https://pkgname.bugtracker.url
 
The format is that of a version of a ‘Debian Control File’ (see the help for ‘read.dcf’ and
https://www.debian.org/doc/debian-policy/ch-controlfields.html: R does not require
encoding in UTF-8 and does not support comments starting with ‘#’). Fields start with an
ASCII name immediately followed by a colon: the value starts after the colon and a space.
Continuation lines (for example, for descriptions longer than one line) start with a space or tab.
Field names are case-sensitive: all those used by R are capitalized.
For maximal portability, the DESCRIPTION file should be written entirely in ASCII — if this
is not possible it must contain an ‘Encoding’ field (see below).
Several optional fields take logical values: these can be specified as ‘yes’, ‘true’, ‘no’ or
false’: capitalized values are also accepted.
The ‘Package’, ‘Version’, ‘License’, ‘Description’, ‘Title’, ‘Author’, and ‘Maintainer
fields are mandatory, all other fields are optional. Fields ‘Author’ and ‘Maintainer’ can be
4false positives are possible, but only a handful have been seen so far.
Chapter 1: Creating R packages 5
auto-generated from ‘Authors@R’, and may be omitted if the latter is provided: however if they
are not ASCII we recommend that they are provided.
The mandatory ‘Package’ field gives the name of the package. This should contain only
(ASCII) letters, numbers and dot, have at least two characters and start with a letter and not
end in a dot. If it needs explaining, this should be done in the ‘Description’ field (and not the
Title’ field).
The mandatory ‘Version’ field gives the version of the package. This is a sequence of at
least two (and usually three) non-negative integers separated by single ‘.’ or ‘-’ characters. The
canonical form is as shown in the example, and a version such as ‘0.01’ or ‘0.01.0 will be
handled as if it were ‘0.1-0’. It is not a decimal number, so for example 0.9 < 0.75 since 9 <
75.
The mandatory ‘License’ field is discussed in the next subsection.
The mandatory ‘Title’ field should give a short description of the package. Some package
listings may truncate the title to 65 characters. It should use title case (that is, use capitals
for the principal words: tools::toTitleCase can help you with this), not use any markup,
not have any continuation lines, and not end in a period (unless part of . . . ). Do not repeat
the package name: it is often used prefixed by the name. Refer to other packages and external
software in single quotes, and to book titles (and similar) in double quotes.
The mandatory ‘Description’ field should give a comprehensive description of what the
package does. One can use several (complete) sentences, but only one paragraph. It should be
intelligible to all the intended readership (e.g. for a CRAN package to all CRAN users). It is good
practice not to start with the package name, ‘This package’ or similar. As with the ‘Title’ field,
double quotes should be used for quotations (including titles of books and articles), and single
quotes for non-English usage, including names of other packages and external software. This field
should also be used for explaining the package name if necessary. URLs should be enclosed in
angle brackets, e.g. ‘<https://www.r-project.org>’: see also Section 1.1.8 [Specifying URLs],
page 17.
The mandatory ‘Author’ field describes who wrote the package. It is a plain text field intended
for human readers, but not for automatic processing (such as extracting the email addresses of
all listed contributors: for that use ‘Authors@R’). Note that all significant contributors must be
included: if you wrote an R wrapper for the work of others included in the src directory, you
are not the sole (and maybe not even the main) author.
The mandatory ‘Maintainer’ field should give a single name followed by a valid (RFC 2822)
email address in angle brackets. It should not end in a period or comma. This field is what is
reported by the maintainer function and used by bug.report. For a CRAN package it should
be a person, not a mailing list and not a corporate entity: do ensure that it is valid and will
remain valid for the lifetime of the package.
Note that the display name (the part before the address in angle brackets) should be enclosed
in double quotes if it contains non-alphanumeric characters such as comma or period. (The
current standard, RFC 5322, allows periods but RFC 2822 did not.)
Both ‘Author’ and ‘Maintainer’ fields can be omitted if a suitable ‘Authors@R’ field is given.
This field can be used to provide a refined and machine-readable description of the package
“authors” (in particular specifying their precise roles), via suitable R code. It should create
an object of class "person", by either a call to person or a series of calls (one per “author”)
concatenated by c(): see the example DESCRIPTION file above. The roles can include ‘"aut"
(author) for full authors, ‘"cre"’ (creator) for the package maintainer, and ‘"ctb"’ (contributor)
for other contributors, ‘"cph"’ (copyright holder), among others. See ?person for more infor-
mation. Note that no role is assumed by default. Auto-generated package citation information
Chapter 1: Creating R packages 6
takes advantage of this specification. The ‘Author’ and ‘Maintainer’ fields are auto-generated
from it if needed when building5or installing.
An optional ‘Copyright’ field can be used where the copyright holder(s) are not the authors.
If necessary, this can refer to an installed file: the convention is to use file inst/COPYRIGHTS.
The optional ‘Date’ field gives the release date of the current version of the package. It is
strongly recommended6to use the ‘yyyy-mm-dd’ format conforming to the ISO 8601 standard.
The ‘Depends’, ‘Imports’, ‘Suggests’, ‘Enhances’, ‘LinkingTo’ and
Additional_repositories’ fields are discussed in a later subsection.
Dependencies external to the R system should be listed in the ‘SystemRequirements’ field,
possibly amplified in a separate README file.
The ‘URL’ field may give a list of URLs separated by commas or whitespace, for example
the homepage of the author or a page where additional material describing the software can be
found. These URLs are converted to active hyperlinks in CRAN package listings. See Section 1.1.8
[Specifying URLs], page 17.
The ‘BugReports’ field may contain a single URL to which bug reports about the package
should be submitted. This URL will be used by bug.report instead of sending an email to
the maintainer. A browser is opened for a ‘http://’ or ‘https://URL. As from R 3.4.0,
bug.report will try to extract an email address (preferably from a ‘mailto:’ URL or enclosed
in angle brackets).
Base and recommended packages (i.e., packages contained in the R source distribution or
available from CRAN and recommended to be included in every binary distribution of R) have
a ‘Priority’ field with value ‘base’ or ‘recommended’, respectively. These priorities must not
be used by other packages.
A ‘Collate’ field can be used for controlling the collation order for the R code files in a
package when these are processed for package installation. The default is to collate according to
the ‘C’ locale. If present, the collate specification must list all R code files in the package (tak-
ing possible OS-specific subdirectories into account, see Section 1.1.5 [Package subdirectories],
page 12) as a whitespace separated list of file paths relative to the Rsubdirectory. Paths con-
taining white space or quotes need to be quoted. An OS-specific collation field (‘Collate.unix
or ‘Collate.windows’) will be used in preference to ‘Collate’.
The ‘LazyData’ logical field controls whether the R datasets use lazy-loading. A ‘LazyLoad
field was used in versions prior to 2.14.0, but now is ignored.
The ‘KeepSource’ logical field controls if the package code is sourced using keep.source =
TRUE or FALSE: it might be needed exceptionally for a package designed to always be used with
keep.source = TRUE.
The ‘ByteCompile’ logical field controls if the package code is to be byte-compiled on in-
stallation: the default is currently not to, so this may be useful for a package known to benefit
particularly from byte-compilation (which can take quite a long time and increases the installed
size of the package). It is used for the recommended packages, as they are byte-compiled when R
is installed and for consistency should be byte-compiled when updated. This can be overridden
by installing with flag --no-byte-compile.
The ‘ZipData’ logical field was used to control whether the automatic Windows build would
zip up the data directory or not prior to R 2.13.0: it is now ignored.
The ‘Biarch’ logical field is used on Windows to select the INSTALL option --force-biarch
for this package.
5at least if this is done in a locale which matches the package encoding.
6and required by CRAN, so checked by R CMD check --as-cran.
Chapter 1: Creating R packages 7
The ‘BuildVignettes’ logical field can be set to a false value to stop R CMD build from
attempting to build the vignettes, as well as preventing7R CMD check from testing this. This
should only be used exceptionally, for example if the PDFs include large figures which are not
part of the package sources (and hence only in packages which do not have an Open Source
license).
The ‘VignetteBuilder’ field names (in a comma-separated list) packages that provide an
engine for building vignettes. These may include the current package, or ones listed in ‘Depends’,
Suggests’ or ‘Imports’. The utils package is always implicitly appended. See Section 1.4.2
[Non-Sweave vignettes], page 40, for details.
If the DESCRIPTION file is not entirely in ASCII it should contain an ‘Encoding’ field specifying
an encoding. This is used as the encoding of the DESCRIPTION file itself and of the Rand
NAMESPACE files, and as the default encoding of .Rd files. The examples are assumed to be in
this encoding when running R CMD check, and it is used for the encoding of the CITATION file.
Only encoding names latin1,latin2 and UTF-8 are known to be portable. (Do not specify an
encoding unless one is actually needed: doing so makes the package less portable. If a package
has a specified encoding, you should run R CMD build etc in a locale using that encoding.)
The ‘NeedsCompilation’ field should be set to "yes" if the package contains code which to
be compiled, otherwise "no" (when the package could be installed from source on any platform
without additional tools). This is used by install.packages(type = "both") in R >= 2.15.2
on platforms where binary packages are the norm: it is normally set by R CMD build or the
repository assuming compilation is required if and only if the package has a src directory.
The ‘OS_type’ field specifies the OS(es) for which the package is intended. If present, it
should be one of unix or windows, and indicates that the package can only be installed on a
platform with ‘.Platform$OS.type’ having that value.
The ‘Type’ field specifies the type of the package: see Section 1.10 [Package types], page 62.
One can add subject classifications for the content of the package using the fields
Classification/ACM’ or ‘Classification/ACM-2012’ (using the Computing Classification
System of the Association for Computing Machinery, http: / / www . acm . org / about /
class / ; the former refers to the 1998 version), Classification/JEL’ (the Journal of
Economic Literature Classification System, https://www.aeaweb.org/econlit/jelCodes.
php, or ‘Classification/MSC’ or ‘Classification/MSC-2010’ (the Mathematics Subject
Classification of the American Mathematical Society, http://www.ams.org/msc/; the former
refers to the 2000 version). The subject classifications should be comma-separated lists of the
respective classification codes, e.g., ‘Classification/ACM: G.4, H.2.8, I.5.1’.
A ‘Language’ field can be used to indicate if the package documentation is not in English:
this should be a comma-separated list of standard (not private use or grandfathered) IETF
language tags as currently defined by RFC 5646 (https://tools.ietf.org/html/rfc5646, see
also https://en.wikipedia.org/wiki/IETF_language_tag), i.e., use language subtags which
in essence are 2-letter ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) or 3-letter
ISO 639-3 (https://en.wikipedia.org/wiki/ISO_639-3) language codes.
An ‘RdMacros’ field can be used to hold a comma-separated list of packages from which
the current package will import Rd macro definitions. These will be imported after the system
macros, in the order listed in the ‘RdMacros’ field, before any macro definitions in the current
package are loaded. Macro definitions in individual .Rd files in the man directory are loaded last,
and are local to later parts of that file. In case of duplicates, the last loaded definition will be
used8Both R CMD Rd2pdf and R CMD Rdconv have an optional flag --RdMacros=pkglist. The
option is also a comma-separated list of package names, and has priority over the value given in
DESCRIPTION. Packages using Rd macros should depend on R 3.2.0 or later.
7But it is checked for Open Source packages by R CMD check --as-cran.
8Duplicate definitions may trigger a warning: see Section 2.13 [User-defined macros], page 79.
Chapter 1: Creating R packages 8
Note: There should be no ‘Built’ or ‘Packaged’ fields, as these are added by the
package management tools.
There is no restriction on the use of other fields not mentioned here (but using other capital-
izations of these field names would cause confusion). Fields Note,Contact (for contacting the
authors/developers9) and MailingList are in common use. Some repositories (including CRAN
and R-forge) add their own fields.
1.1.2 Licensing
Licensing for a package which might be distributed is an important but potentially complex
subject.
It is very important that you include license information! Otherwise, it may not even be
legally correct for others to distribute copies of the package, let alone use it.
The package management tools use the concept of ‘free or open source software’ (FOSS, e.g.,
https://en.wikipedia.org/wiki/FOSS) licenses: the idea being that some users of R and its
packages want to restrict themselves to such software. Others need to ensure that there are no
restrictions stopping them using a package, e.g. forbidding commercial or military use. It is a
central tenet of FOSS software that there are no restrictions on users nor usage.
Do not use the ‘License’ field for information on copyright holders: if needed, use a
Copyright’ field.
The mandatory ‘License’ field in the DESCRIPTION file should specify the license of the pack-
age in a standardized form. Alternatives are indicated via vertical bars. Individual specifications
must be one of
One of the “standard” short specifications
GPL-2 GPL-3 LGPL-2 LGPL-2.1 LGPL-3 AGPL-3 Artistic-2.0
BSD_2_clause BSD_3_clause MIT
as made available via https://www.R-project.org/Licenses/ and contained in subdi-
rectory share/licenses of the R source or home directory.
The names or abbreviations of other licenses contained in the license data base in file
share/licenses/license.db in the R source or home directory, possibly (for versioned
licenses) followed by a version restriction of the form ‘(op v)’ with ‘op’ one of the comparison
operators ‘<’, ‘<=’, ‘>’, ‘>=’, ‘==’, or ‘!=’ and ‘v’ a numeric version specification (strings of
non-negative integers separated by ‘.’), possibly combined via ,’ (see below for an example).
For versioned licenses, one can also specify the name followed by the version, or combine
an existing abbreviation and the version with a ‘-’.
Abbreviations GPL and LGPL are ambiguous and usually10 taken to mean any version of the
license: but it is better not to use them.
One of the strings ‘file LICENSE’ or ‘file LICENCE’ referring to a file named LICENSE or
LICENCE in the package (source and installation) top-level directory.
The string ‘Unlimited’, meaning that there are no restrictions on distribution or use other
than those imposed by relevant laws (including copyright laws).
If a package license restricts a base license (where permitted, e.g., using GPL-3 or AGPL-3
with an attribution clause), the additional terms should be placed in file LICENSE (or LICENCE),
and the string ‘+ file LICENSE’ (or ‘+ file LICENCE’, respectively) should be appended to the
corresponding individual license specification. Note that several commonly used licenses do not
permit restrictions: this includes GPL-2 and hence any specification which includes it.
9As from R 3.4.0, bug.report will try to extract an email address from a Contact field if there is no BugReports
field.
10 CRAN expands them to e.g. GPL-2 | GPL-3.
Chapter 1: Creating R packages 9
Examples of standardized specifications include
License: GPL-2
License: LGPL (>= 2.0, < 3) | Mozilla Public License
License: GPL-2 | file LICENCE
License: GPL (>= 2) | BSD_3_clause + file LICENSE
License: Artistic-2.0 | AGPL-3 + file LICENSE
Please note in particular that “Public domain” is not a valid license, since it is not recognized
in some jurisdictions.
Please ensure that the license you choose also covers any dependencies (including system
dependencies) of your package: it is particularly important that any restrictions on the use of
such dependencies are evident to people reading your DESCRIPTION file.
Fields ‘License_is_FOSS’ and ‘License_restricts_use’ may be added by repositories
where information cannot be computed from the name of the license. License_is_FOSS: yes
is used for licenses which are known to be FOSS, and ‘License_restricts_use’ can have values
yes’ or ‘no’ if the LICENSE file is known to restrict users or usage, or known not to. These are
used by, e.g., the available.packages filters.
The optional file LICENSE/LICENCE contains a copy of the license of the package. To avoid
any confusion only include such a file if it is referred to in the ‘License’ field of the DESCRIPTION
file.
Whereas you should feel free to include a license file in your source distribution, please do
not arrange to install yet another copy of the GNU COPYING or COPYING.LIB files but refer to
the copies on https://www.R-project.org/Licenses/ and included in the R distribution (in
directory share/licenses). Since files named LICENSE or LICENCE will be installed, do not use
these names for standard license files. To include comments about the licensing rather than the
body of a license, use a file named something like LICENSE.note.
A few “standard” licenses are rather license templates which need additional information to
be completed via + file LICENSE’.
1.1.3 Package Dependencies
The ‘Depends’ field gives a comma-separated list of package names which this package depends
on. Those packages will be attached before the current package when library or require is
called. Each package name may be optionally followed by a comment in parentheses specifying
a version requirement. The comment should contain a comparison operator, whitespace and a
valid version number, e.g. MASS (>= 3.1-20)’.
The ‘Depends’ field can also specify a dependence on a certain version of R — e.g., if the
package works only with R version 3.0.0 or later, include ‘R (>= 3.0.0)’ in the ‘Depends’ field.
You can also require a certain SVN revision for R-devel or R-patched, e.g. ‘R (>= 2.14.0), R
(>= r56550)’ requires a version later than R-devel of late July 2011 (including released versions
of 2.14.0).
It makes no sense to declare a dependence on Rwithout a version specification, nor on the
package base: this is an R package and package base is always available.
A package or ‘R’ can appear more than once in the ‘Depends’ field, for example to give upper
and lower bounds on acceptable versions.
Both library and the R package checking facilities use this field: hence it is an error to use
improper syntax or misuse the Depends’ field for comments on other software that might be
needed. The R INSTALL facilities check if the version of R used is recent enough for the package
being installed, and the list of packages which is specified will be attached (after checking version
requirements) before the current package.
Chapter 1: Creating R packages 10
The ‘Imports’ field lists packages whose namespaces are imported from (as specified in the
NAMESPACE file) but which do not need to be attached. Namespaces accessed by the ‘::’ and
:::’ operators must be listed here, or in ‘Suggests’ or ‘Enhances’ (see below). Ideally this
field will include all the standard packages that are used, and it is important to include S4-using
packages (as their class definitions can change and the DESCRIPTION file is used to decide which
packages to re-install when this happens). Packages declared in the ‘Depends’ field should not
also be in the ‘Imports’ field. Version requirements can be specified and are checked when the
namespace is loaded (since R >= 3.0.0).
The ‘Suggests’ field uses the same syntax as ‘Depends’ and lists packages that are not neces-
sarily needed. This includes packages used only in examples, tests or vignettes (see Section 1.4
[Writing package vignettes], page 37), and packages loaded in the body of functions. E.g., sup-
pose an example11 from package foo uses a dataset from package bar. Then it is not necessary
to have bar use foo unless one wants to execute all the examples/tests/vignettes: it is useful to
have bar, but not necessary. Version requirements can be specified but should be checked by
the code which uses the package.
Finally, the ‘Enhances’ field lists packages “enhanced” by the package at hand, e.g., by
providing methods for classes from these packages, or ways to handle objects from these packages
(so several packages have ‘Enhances: chron’ because they can handle datetime objects from
chron (https://CRAN.R-project.org/package=chron) even though they prefer R’s native
datetime functions). Version requirements can be specified, but are currently not used. Such
packages cannot be required to check the package: any tests which use them must be conditional
on the presence of the package. (If your tests use e.g. a dataset from another package it should
be in ‘Suggests’ and not ‘Enhances’.)
The general rules are
A package should be listed in only one of these fields.
Packages whose namespace only is needed to load the package using library(pkgname)
should be listed in the ‘Imports’ field and not in the ‘Depends’ field. Packages listed
in imports or importFrom directives in the NAMESPACE file should almost always be in
Imports’ and not ‘Depends’.
Packages that need to be attached to successfully load the package using library(pkgname)
must be listed in the ‘Depends’ field.
All packages that are needed12 to successfully run R CMD check on the package must be
listed in one of ‘Depends’ or ‘Suggests’ or ‘Imports’. Packages used to run examples
or tests conditionally (e.g. via if(require(pkgname))) should be listed in ‘Suggests’ or
Enhances’. (This allows checkers to ensure that all the packages needed for a complete
check are installed.)
In particular, packages providing “only” data for examples or vignettes should be listed in
Suggests’ rather than ‘Depends’ in order to make lean installations possible.
Version dependencies in the ‘Depends’ and ‘Imports’ fields are used by library when it
loads the package, and install.packages checks versions for the ‘Depends’, ‘Imports’ and (for
dependencies = TRUE) ‘Suggests’ fields.
11 even one wrapped in \donttest.
12 This includes all packages directly called by library and require calls, as well as data obtained via
data(theirdata, package = "somepkg") calls: R CMD check will warn about all of these. But there are subtler
uses which it will not detect: e.g. if package A uses package B and makes use of functionality in package B
which uses package C which package B suggests or enhances, then package C needs to be in the ‘Suggests
list for package A. Nor will undeclared uses in included files be reported, nor unconditional uses of packages
listed under ‘Enhances’.
Chapter 1: Creating R packages 11
It is increasingly important that the information in these fields is complete and accurate:
it is for example used to compute which packages depend on an updated package and which
packages can safely be installed in parallel.
This scheme was developed before all packages had namespaces (R 2.14.0 in October 2011),
and good practice changed once that was in place.
Field ‘Depends’ should nowadays be used rarely, only for packages which are intended to
be put on the search path to make their facilities available to the end user (and not to the
package itself): for example it makes sense that a user of package latticeExtra (https: / /
CRAN.R-project.org/package=latticeExtra) would want the functions of package lattice
(https://CRAN.R-project.org/package=lattice) made available.
Almost always packages mentioned in ‘Depends’ should also be imported from in the
NAMESPACE file: this ensures that any needed parts of those packages are available when some
other package imports the current package.
The ‘Imports’ field should not contain packages which are not imported from (via the
NAMESPACE file or :: or ::: operators), as all the packages listed in that field need to be installed
for the current package to be installed. (This is checked by R CMD check.)
R code in the package should call library or require only exceptionally. Such calls are
never needed for packages listed in ‘Depends’ as they will already be on the search path. It used
to be common practice to use require calls for packages listed in ‘Suggests’ in functions which
used their functionality, but nowadays it is better to access such functionality via :: calls.
A package that wishes to make use of header files in other packages needs to declare them as
a comma-separated list in the field ‘LinkingTo’ in the DESCRIPTION file. For example
LinkingTo: link1, link2
The ‘LinkingTo’ field can have a version requirement which is checked at installation.
Specifying a package in ‘LinkingTo’ suffices if these are C++ headers containing source code
or static linking is done at installation: the packages do not need to be (and usually should
not be) listed in the ‘Depends’ or ‘Imports’ fields. This includes CRAN package BH (https://
CRAN.R-project.org/package=BH) and almost all users of RcppArmadillo (https://CRAN.
R-project.org/package=RcppArmadillo) and RcppEigen (https://CRAN.R-project.org/
package=RcppEigen).
For another use of ‘LinkingTo’ see Section 5.4.3 [Linking to native routines in other packages],
page 115.
The ‘Additional_repositories’ field is a comma-separated list of repository URLs where
the packages named in the other fields may be found. It is currently used by R CMD check to
check that the packages can be found, at least as source packages (which can be installed on any
platform).
1.1.3.1 Suggested packages
Note that someone wanting to run the examples/tests/vignettes may not have a suggested
package available (and it may not even be possible to install it for that platform). The
recommendation used to be to make their use conditional via if(require("pkgname")):
this is OK if that conditioning is done in examples/tests/vignettes, although using
if(requireNamespace("pkgname")) is preferred, if possible.
However, using require for conditioning in package code is not good practice as it alters the
search path for the rest of the session and relies on functions in that package not being masked
by other require or library calls. It is better practice to use code like
if (requireNamespace("rgl", quietly = TRUE)) {
rgl::plot3d(...)
} else {
Chapter 1: Creating R packages 12
## do something else not involving rgl.
}
Note the use of rgl:: as that object would not necessarily be visible (and if it is, it need not
be the one from that namespace: plot3d occurs in several other packages). If the intention is
to give an error if the suggested package is not available, simply use e.g. rgl::plot3d.
Note that the recommendation to use suggested packages conditionally in tests does also
apply to packages used to manage test suites: a notorious example was testthat (https://
CRAN.R-project.org/package=testthat) which in version 1.0.0 contained illegal C++ code
and hence could not be installed on standards-compliant platforms.
Some people have assumed that a ‘recommended’ package in Suggests’ can safely be used
unconditionally, but this is not so. (R can be installed without recommended packages, and
which packages are ‘recommended’ may change.)
As noted above, packages in ‘Enhancesmust be used conditionally and hence objects within
them should always be accessed via ::.
1.1.4 The INDEX file
The optional file INDEX contains a line for each sufficiently interesting object in the package,
giving its name and a description (functions such as print methods not usually called explicitly
might not be included). Normally this file is missing and the corresponding information is auto-
matically generated from the documentation sources (using tools::Rdindex()) when installing
from source.
The file is part of the information given by library(help = pkgname).
Rather than editing this file, it is preferable to put customized information about the package
into an overview help page (see Section 2.1.4 [Documenting packages], page 71) and/or a vignette
(see Section 1.4 [Writing package vignettes], page 37).
1.1.5 Package subdirectories
The Rsubdirectory contains R code files, only. The code files to be installed must start with an
ASCII (lower or upper case) letter or digit and have one of the extensions13 .R,.S,.q,.r, or .s.
We recommend using .R, as this extension seems to be not used by any other software. It should
be possible to read in the files using source(), so R objects must be created by assignments.
Note that there need be no connection between the name of the file and the R objects created
by it. Ideally, the R code files should only directly assign R objects and definitely should not
call functions with side effects such as require and options. If computations are required to
create objects these can use code ‘earlier’ in the package (see the ‘Collate’ field) plus functions
in the ‘Depends’ packages provided that the objects created do not depend on those packages
except via namespace imports.
Two exceptions are allowed: if the Rsubdirectory contains a file sysdata.rda (a
saved image of one or more R objects: please use suitable compression as suggested by
tools::resaveRdaFiles, and see also the ‘SysDataCompressionDESCRIPTION field.) this
will be lazy-loaded into the namespace environment – this is intended for system datasets that
are not intended to be user-accessible via data. Also, files ending in ‘.in’ will be allowed in
the Rdirectory to allow a configure script to generate suitable files.
Only ASCII characters (and the control characters tab, formfeed, LF and CR) should be used
in code files. Other characters are accepted in comments14, but then the comments may not
be readable in e.g. a UTF-8 locale. Non-ASCII characters in object names will normally15 fail
13 Extensions .S and .s arise from code originally written for S(-PLUS), but are commonly used for assembler
code. Extension .q was used for S, which at one time was tentatively called QPE.
14 but they should be in the encoding declared in the DESCRIPTION file.
15 This is true for OSes which implement the ‘C’ locale: Windows’ idea of the ‘C’ locale uses the WinAnsi charset.
Chapter 1: Creating R packages 13
when the package is installed. Any byte will be allowed in a quoted character string but \uxxxx
escapes should be used for non-ASCII characters. However, non-ASCII character strings may not
be usable in some locales and may display incorrectly in others.
Various R functions in a package can be used to initialize and clean up. See Section 1.5.3
[Load hooks], page 43.
The man subdirectory should contain (only) documentation files for the objects in the package
in R documentation (Rd) format. The documentation filenames must start with an ASCII (lower
or upper case) letter or digit and have the extension .Rd (the default) or .rd. Further, the names
must be valid in ‘file://’ URLs, which means16 they must be entirely ASCII and not contain
%’. See Chapter 2 [Writing R documentation files], page 64, for more information. Note that all
user-level objects in a package should be documented; if a package pkg contains user-level objects
which are for “internal” use only, it should provide a file pkg-internal.Rd which documents all
such objects, and clearly states that these are not meant to be called by the user. See e.g. the
sources for package grid in the R distribution. Note that packages which use internal objects
extensively should not export those objects from their namespace, when they do not need to be
documented (see Section 1.5 [Package namespaces], page 41).
Having a man directory containing no documentation files may give an installation error.
The man subdirectory may contain a subdirectory named macros; this will contain source for
user-defined Rd macros. (See Section 2.13 [User-defined macros], page 79.) These use the Rd
format, but may not contain anything but macro definitions, comments and whitespace.
The Rand man subdirectories may contain OS-specific subdirectories named unix or windows.
The sources and headers for the compiled code are in src, plus optionally a file Makevars or
Makefile. When a package is installed using R CMD INSTALL,make is used to control compila-
tion and linking into a shared object for loading into R. There are default make variables and
rules for this (determined when R is configured and recorded in R_HOME/etcR_ARCH/Makeconf),
providing support for C, C++, FORTRAN 77, Fortran 9x17, Objective C and Objective C++18
with associated extensions .c,.cc or .cpp,.f,.f90 or .f95,.m, and .mm, respectively. We
recommend using .h for headers, also for C++19 or Fortran 9x include files. (Use of extension
.C for C++ is no longer supported.) Files in the src directory should not be hidden (start with
a dot), and hidden files will under some versions of R be ignored.
It is not portable (and may not be possible at all) to mix all these languages in a single
package, and we do not support using both C++ and Fortran 9x. Because R itself uses it, we
know that C and FORTRAN 77 can be used together and mixing C and C++ seems to be widely
successful.
If your code needs to depend on the platform there are certain defines which can used in C
or C++. On all Windows builds (even 64-bit ones) ‘_WIN32’ will be defined: on 64-bit Windows
builds also ‘_WIN64’, and on macOS ‘__APPLE__’ is defined.20
The default rules can be tweaked by setting macros21 in a file src/Makevars (see Section 1.2.1
[Using Makevars], page 20). Note that this mechanism should be general enough to eliminate the
16 More precisely, they can contain the English alphanumeric characters and the symbols ‘$-_.+!’(),;
= &’.
17 Note that Ratfor is not supported. If you have Ratfor source code, you need to convert it to FORTRAN. Only
FORTRAN 77 (which we write in upper case) is supported on all platforms, but most also support Fortran-95
(for which we use title case). If you want to ship Ratfor source files, please do so in a subdirectory of src and
not in the main subdirectory.
18 either or both of which may not be supported on particular platforms
19 Using .hpp is not guaranteed to be portable.
20 There is also ‘__APPLE_CC__’, but that indicates a compiler with Apple-specific features, not the OS. It is used
in Rinlinedfuns.h.
21 the POSIX terminology, called ‘make variables’ by GNU make.
Chapter 1: Creating R packages 14
need for a package-specific src/Makefile. If such a file is to be distributed, considerable care is
needed to make it general enough to work on all R platforms. If it has any targets at all, it should
have an appropriate first target named ‘all’ and a (possibly empty) target ‘clean’ which removes
all files generated by running make (to be used by ‘R CMD INSTALL --clean’ and ‘R CMD INSTALL
--preclean’). There are platform-specific file names on Windows: src/Makevars.win takes
precedence over src/Makevars and src/Makefile.win must be used. Some make programs
require makefiles to have a complete final line, including a newline.
A few packages use the src directory for purposes other than making a shared object (e.g.
to create executables). Such packages should have files src/Makefile and src/Makefile.win
(unless intended for only Unix-alikes or only Windows).
In very special cases packages may create binary files other than the shared objects/DLLs
in the src directory. Such files will not be installed in a multi-architecture setting since R CMD
INSTALL --libs-only is used to merge multiple sub-architectures and it only copies shared
objects/DLLs. If a package wants to install other binaries (for example executable programs),
it should provide an R script src/install.libs.R which will be run as part of the installation
in the src build directory instead of copying the shared objects/DLLs. The script is run in a
separate R environment containing the following variables: R_PACKAGE_NAME (the name of the
package), R_PACKAGE_SOURCE (the path to the source directory of the package), R_PACKAGE_DIR
(the path of the target installation directory of the package), R_ARCH (the arch-dependent part
of the path, often empty), SHLIB_EXT (the extension of shared objects) and WINDOWS (TRUE on
Windows, FALSE elsewhere). Something close to the default behavior could be replicated with
the following src/install.libs.R file:
files <- Sys.glob(paste0("*", SHLIB_EXT))
dest <- file.path(R_PACKAGE_DIR, paste0(’libs’, R_ARCH))
dir.create(dest, recursive = TRUE, showWarnings = FALSE)
file.copy(files, dest, overwrite = TRUE)
if(file.exists("symbols.rds"))
file.copy("symbols.rds", dest, overwrite = TRUE)
On the other hand, executable programs could be installed along the lines of
execs <- c("one", "two", "three")
if(WINDOWS) execs <- paste0(execs, ".exe")
if ( any(file.exists(execs)) ) {
dest <- file.path(R_PACKAGE_DIR, paste0(’bin’, R_ARCH))
dir.create(dest, recursive = TRUE, showWarnings = FALSE)
file.copy(execs, dest, overwrite = TRUE)
}
Note the use of architecture-specific subdirectories of bin where needed.
The data subdirectory is for data files: See Section 1.1.6 [Data in packages], page 16.
The demo subdirectory is for R scripts (for running via demo()) that demonstrate some of
the functionality of the package. Demos may be interactive and are not checked automatically,
so if testing is desired use code in the tests directory to achieve this. The script files must
start with a (lower or upper case) letter and have one of the extensions .R or .r. If present, the
demo subdirectory should also have a 00Index file with one line for each demo, giving its name
and a description separated by a tab or at least three spaces. (This index file is not generated
automatically.) Note that a demo does not have a specified encoding and so should be an ASCII
file (see Section 1.6.3 [Encoding issues], page 54). Function demo() will use the package encoding
if there is one, but this is mainly useful for non-ASCII comments.
The contents of the inst subdirectory will be copied recursively to the installation directory.
Subdirectories of inst should not interfere with those used by R (currently, R,data,demo,exec,
libs,man,help,html and Meta, and earlier versions used latex,R-ex). The copying of the
Chapter 1: Creating R packages 15
inst happens after src is built so its Makefile can create files to be installed. To exclude
files from being installed, one can specify a list of exclude patterns in file .Rinstignore in the
top-level source directory. These patterns should be Perl-like regular expressions (see the help
for regexp in R for the precise details), one per line, to be matched case-insensitively against
the file and directory paths, e.g. doc/.*[.]png$ will exclude all PNG files in inst/doc based
on the extension.
Note that with the exceptions of INDEX,LICENSE/LICENCE and NEWS, information files at
the top level of the package will not be installed and so not be known to users of Windows and
macOS compiled packages (and not seen by those who use R CMD INSTALL or install.packages
on the tarball). So any information files you wish an end user to see should be included in inst.
Note that if the named exceptions also occur in inst, the version in inst will be that seen in
the installed package.
Things you might like to add to inst are a CITATION file for use by the citation function,
and a NEWS.Rd file for use by the news function. See its help page for the specific format
restrictions of the NEWS.Rd file.
Another file sometimes needed in inst is AUTHORS or COPYRIGHTS to specify the authors or
copyright holders when this is too complex to put in the DESCRIPTION file.
Subdirectory tests is for additional package-specific test code, similar to the specific tests
that come with the R distribution. Test code can either be provided directly in a .R (or .r as
from R 3.4.0) file, or via a.Rin file containing code which in turn creates the corresponding
.R file (e.g., by collecting all function objects in the package and then calling them with the
strangest arguments). The results of running a .R file are written to a .Rout file. If there is a
corresponding22 .Rout.save file, these two are compared, with differences being reported but
not causing an error. The directory tests is copied to the check area, and the tests are run with
the copy as the working directory and with R_LIBS set to ensure that the copy of the package
installed during testing will be found by library(pkg_name). Note that the package-specific
tests are run in a vanilla R session without setting the random-number seed, so tests which use
random numbers will need to set the seed to obtain reproducible results (and it can be helpful
to do so in all cases, to avoid occasional failures when tests are run).
If directory tests has a subdirectory Examples containing a file pkg-Ex.Rout.save, this is
compared to the output file for running the examples when the latter are checked. Reference
output should be produced without having the --timings option set (and note that --as-cran
sets it).
Subdirectory exec could contain additional executable scripts the package needs, typically
scripts for interpreters such as the shell, Perl, or Tcl. NB: only files (and not directories) under
exec are installed (and those with names starting with a dot are ignored), and they are all
marked as executable (mode 755, moderated by ‘umask’) on POSIX platforms. Note too that
this is not suitable for executable programs since some platforms (including Windows) support
multiple architectures using the same installed package directory.
Subdirectory po is used for files related to localization: see Section 1.8 [Internationalization],
page 60.
Subdirectory tools is the preferred place for auxiliary files needed during configuration, and
also for sources need to re-create scripts (e.g. M4 files for autoconf).
22 The best way to generate such a file is to copy the .Rout from a successful run of R CMD check. If you want to
generate it separately, do run R with options --vanilla --slave and with environment variable LANGUAGE=en
set to get messages in English. Be careful not to use output with the option --timings (and note that
--as-cran sets it).
Chapter 1: Creating R packages 16
1.1.6 Data in packages
The data subdirectory is for data files, either to be made available via lazy-loading or for loading
using data(). (The choice is made by the ‘LazyData’ field in the DESCRIPTION file: the default
is not to do so.) It should not be used for other data files needed by the package, and the
convention has grown up to use directory inst/extdata for such files.
Data files can have one of three types as indicated by their extension: plain R code (.R or
.r), tables (.tab,.txt, or .csv, see ?data for the file formats, and note that .csv is not the
standard23 CSV format), or save() images (.RData or .rda). The files should not be hidden
(have names starting with a dot). Note that R code should be “self-sufficient” and not make use
of extra functionality provided by the package, so that the data file can also be used without
having to load the package or its namespace.
Images (extensions .RData24 or .rda) can contain references to the namespaces of packages
that were used to create them. Preferably there should be no such references in data files, and in
any case they should only be to packages listed in the Depends and Imports fields, as otherwise
it may be impossible to install the package. To check for such references, load all the images
into a vanilla R session, and look at the output of loadedNamespaces().
If your data files are large and you are not using ‘LazyData’ you can speed up installation
by providing a file datalist in the data subdirectory. This should have one line per topic that
data() will find, in the format ‘foo’ if data(foo) provides ‘foo’, or ‘foo: bar bah’ if data(foo)
provides ‘bar’ and ‘bah’. R CMD build will automatically add a datalist file to data directories
of over 1Mb, using the function tools::add_datalist.
Tables (.tab,.txt, or .csv files) can be compressed by gzip,bzip2 or xz, optionally with
additional extension .gz,.bz2 or .xz.
If your package is to be distributed, do consider the resource implications of large datasets
for your users: they can make packages very slow to download and use up unwelcome amounts
of storage space, as well as taking many seconds to load. It is normally best to distribute large
datasets as .rda images prepared by save(, compress = TRUE) (the default). Using bzip2 or
xz compression will usually reduce the size of both the package tarball and the installed package,
in some cases by a factor of two or more.
Package tools has a couple of functions to help with data images: checkRdaFiles reports
on the way the image was saved, and resaveRdaFiles will re-save with a different type of
compression, including choosing the best type for that particular image.
Some packages using ‘LazyData’ will benefit from using a form of compression other than
gzip in the installed lazy-loading database. This can be selected by the --data-compress
option to R CMD INSTALL or by using the ‘LazyDataCompression’ field in the DESCRIPTION file.
Useful values are bzip2,xz and the default, gzip. The only way to discover which is best is to
try them all and look at the size of the pkgname/data/Rdata.rdb file.
Lazy-loading is not supported for very large datasets (those which when serialized exceed
2GB, the limit for the format on 32-bit platforms).
The analogue for sysdata.rda is field ‘SysDataCompression’: the default is xz for files
bigger than 1MB otherwise gzip.
1.1.7 Non-R scripts in packages
Code which needs to be compiled (C, C++, FORTRAN, Fortran 95 . . . ) is included in the src
subdirectory and discussed elsewhere in this document.
23 e.g. https://tools.ietf.org/html/rfc4180.
24 People who have trouble with case are advised to use .rda as a common error is to refer to abc.RData as
abc.Rdata!
Chapter 1: Creating R packages 17
Subdirectory exec could be used for scripts for interpreters such as the shell, BUGS,
JavaScript, Matlab, Perl, php (amap (https: / / CRAN . R-project . org / package=amap)),
Python or Tcl (Simile (https://CRAN.R-project.org/package=Simile)), or even R. How-
ever, it seems more common to use the inst directory, for example WriteXLS/inst/Perl,
NMF/inst/m-files,RnavGraph/inst/tcl,RProtoBuf/inst/python and emdbook/inst/BUGS
and gridSVG/inst/js.
Java code is a special case: except for very small programs, .java files should be byte-
compiled (to a .class file) and distributed as part of a .jar file: the conventional location
for the .jar file(s) is inst/java. It is desirable (and required under an Open Source license)
to make the Java source files available: this is best done in a top-level java directory in the
package—the source files should not be installed.
If your package requires one of these interpreters or an extension then this should be declared
in the ‘SystemRequirements’ field of its DESCRIPTION file. (Users of Java most often do so via
rJava (https://CRAN.R-project.org/package=rJava), when depending on/importing that
suffices.)
Windows and Mac users should be aware that the Tcl extensions ‘BWidget’ and ‘Tktable
which are currently included with the R for Windows and in the macOS installers are extensions
and do need to be declared for users of other platforms (and that ‘Tktable’ is less widely available
than it used to be, including not in the main repositories for major Linux distributions).
BWidget’ needs to be installed by the user on other OSes. This is fairly easy to do: first find
the Tcl/Tk search path:
library(tcltk)
strsplit(tclvalue(’auto_path’), " ")[[1]]
then download the sources from http://sourceforge.net/projects/tcllib/files/BWidget/
and at the command line run something like
tar xf bwidget-1.9.8.tar.gz
sudo mv bwidget-1.9.8 /usr/local/lib
substituting a location on the Tcl/Tk search path for /usr/local/lib if needed.
1.1.8 Specifying URLs
URLs in many places in the package documentation will be converted to clickable hyperlinks in
at least some of their renderings. So care is needed that their forms are correct and portable.
The full URL should be given, including the scheme (often ‘http://’ or ‘https://’) and a
final ‘/’ for references to directories.
Spaces in URLs are not portable and how they are handled does vary by HTTP server and
by client. There should be no space in the host part of an ‘http://’ URL, and spaces in the
remainder should be encoded, with each space replaced by ‘%20’.
Other characters may benefit from being encoded: see the help on URLencode().
The canonical URL for a CRAN package is
https://cran.r-project.org/package=pkgname
and not a version starting ‘http://cran.r-project.org/web/packages/pkgname’.
1.2 Configure and cleanup
Note that most of this section is specific to Unix-alikes: see the comments later on about the
Windows port of R.
Chapter 1: Creating R packages 18
If your package needs some system-dependent configuration before installation you can in-
clude an executable (Bourne25) shell script configure in your package which (if present) is
executed by R CMD INSTALL before any other action is performed. This can be a script created
by the Autoconf mechanism, but may also be a script written by yourself. Use this to detect
if any nonstandard libraries are present such that corresponding code in the package can be
disabled at install time rather than giving error messages when the package is compiled or used.
To summarize, the full power of Autoconf is available for your extension package (including
variable substitution, searching for libraries, etc.).
Under a Unix-alike only, an executable (Bourne shell) script cleanup is executed as the last
thing by R CMD INSTALL if option --clean was given, and by R CMD build when preparing the
package for building from its source.
As an example consider we want to use functionality provided by a (C or FORTRAN) library
foo. Using Autoconf, we can create a configure script which checks for the library, sets variable
HAVE_FOO to TRUE if it was found and to FALSE otherwise, and then substitutes this value into
output files (by replacing instances of ‘@HAVE_FOO@’ in input files with the value of HAVE_FOO).
For example, if a function named bar is to be made available by linking against library foo (i.e.,
using -lfoo), one could use
AC_CHECK_LIB(foo, fun, [HAVE_FOO=TRUE], [HAVE_FOO=FALSE])
AC_SUBST(HAVE_FOO)
......
AC_CONFIG_FILES([foo.R])
AC_OUTPUT
in configure.ac (assuming Autoconf 2.50 or later).
The definition of the respective R function in foo.R.in could be
foo <- function(x) {
if(!@HAVE_FOO@)
stop("Sorry, library ’foo’ is not available")
...
From this file configure creates the actual R source file foo.R looking like
foo <- function(x) {
if(!FALSE)
stop("Sorry, library ’foo’ is not available")
...
if library foo was not found (with the desired functionality). In this case, the above R code
effectively disables the function.
One could also use different file fragments for available and missing functionality, respectively.
You will very likely need to ensure that the same C compiler and compiler flags are used in
the configure tests as when compiling R or your package. Under a Unix-alike, you can achieve
this by including the following fragment early in configure.ac (before calling AC_PROG_CC)
25 The script should only assume a POSIX-compliant /bin/sh – see http://pubs.opengroup.org/onlinepubs/
9699919799/utilities/V3_chap02.html. In particular bash extensions must not be used, and not all R
platforms have a bash command, let alone one at /bin/bash. All known shells used with R support the use
of backticks, but not all support ‘$(cmd)’.
Chapter 1: Creating R packages 19
: ${R_HOME=‘R RHOME‘}
if test -z "${R_HOME}"; then
echo "could not determine R_HOME"
exit 1
fi
CC=‘"${R_HOME}/bin/R" CMD config CC‘
CFLAGS=‘"${R_HOME}/bin/R" CMD config CFLAGS‘
CPPFLAGS=‘"${R_HOME}/bin/R" CMD config CPPFLAGS‘
(Using ‘${R_HOME}/bin/R’ rather than just ‘R’ is necessary in order to use the correct version
of R when running the script as part of R CMD INSTALL, and the quotes since ‘${R_HOME}’ might
contain spaces.)
If your code does load checks then you may also need
LDFLAGS=‘"${R_HOME}/bin/R" CMD config LDFLAGS‘
and packages written with C++ need to pick up the details for the C++ compiler and switch the
current language to C++ by something like
CXX=‘"${R_HOME}/bin/R" CMD config CXX‘
CXXFLAGS=‘"${R_HOME}/bin/R" CMD config CXXFLAGS‘
AC_LANG(C++)
The latter is important, as for example C headers may not be available to C++ programs or may
not be written to avoid C++ name-mangling.
You can use R CMD config for getting the value of the basic configuration variables, and also
the header and library flags necessary for linking a front-end executable program against R, see
R CMD config --help for details.
To check for an external BLAS library using the ACX_BLAS macro from the official Autoconf
Macro Archive, one can simply do
F77=‘"${R_HOME}/bin/R" CMD config F77‘
AC_PROG_F77
FLIBS=‘"${R_HOME}/bin/R" CMD config FLIBS‘
ACX_BLAS([], AC_MSG_ERROR([could not find your BLAS library], 1))
Note that FLIBS as determined by R must be used to ensure that FORTRAN 77 code works on
all R platforms. Calls to the Autoconf macro AC_F77_LIBRARY_LDFLAGS, which would overwrite
FLIBS, must not be used (and hence e.g. removed from ACX_BLAS). (Recent versions of Autoconf
in fact allow an already set FLIBS to override the test for the FORTRAN linker flags.)
N.B.: If the configure script creates files, e.g. src/Makevars, you do need a cleanup script
to remove them. Otherwise R CMD build may ship the files that are created. For example,
package RODBC (https://CRAN.R-project.org/package=RODBC) has
#!/bin/sh
rm -f config.* src/Makevars src/config.h
As this example shows, configure often creates working files such as config.log.
If your configure script needs auxiliary files, it is recommended that you ship them in a tools
directory (as R itself does).
You should bear in mind that the configure script will not be used on Windows systems. If
your package is to be made publicly available, please give enough information for a user on a
non-Unix-alike platform to configure it manually, or provide a configure.win script to be used
on that platform. (Optionally, there can be a cleanup.win script. Both should be shell scripts
to be executed by ash, which is a minimal version of Bourne-style sh.) When configure.win
is run the environment variables R_HOME (which uses ‘/’ as the file separator), R_ARCH and Use
Chapter 1: Creating R packages 20
R_ARCH_BIN will be set. Use R_ARCH to decide if this is a 64-bit build (its value there is ‘/x64’)
and to install DLLs to the correct place (${R_HOME}/libs${R_ARCH}). Use R_ARCH_BIN to find
the correct place under the bin directory, e.g. ${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe.
In some rare circumstances, the configuration and cleanup scripts need to know the location
into which the package is being installed. An example of this is a package that uses C code and
creates two shared object/DLLs. Usually, the object that is dynamically loaded by R is linked
against the second, dependent, object. On some systems, we can add the location of this depen-
dent object to the object that is dynamically loaded by R. This means that each user does not
have to set the value of the LD_LIBRARY_PATH (or equivalent) environment variable, but that the
secondary object is automatically resolved. Another example is when a package installs support
files that are required at run time, and their location is substituted into an R data structure at
installation time. The names of the top-level library directory (i.e., specifiable via the ‘-l’ argu-
ment) and the directory of the package itself are made available to the installation scripts via the
two shell/environment variables R_LIBRARY_DIR and R_PACKAGE_DIR. Additionally, the name of
the package (e.g. ‘survival’ or ‘MASS’) being installed is available from the environment variable
R_PACKAGE_NAME. (Currently the value of R_PACKAGE_DIR is always ${R_LIBRARY_DIR}/${R_
PACKAGE_NAME}, but this used not to be the case when versioned installs were allowed. Its main
use is in configure.win scripts for the installation path of external software’s DLLs.) Note
that the value of R_PACKAGE_DIR may contain spaces and other shell-unfriendly characters, and
so should be quoted in makefiles and configure scripts.
One of the more tricky tasks can be to find the headers and libraries of external software. One
tool which is increasingly available on Unix-alikes (but not by default on macOS) to do this is
pkg-config. The configure script will need to test for the presence of the command itself (see
for example package Cairo (https://CRAN.R-project.org/package=Cairo)), and if present it
can be asked if the software is installed, of a suitable version and for compilation/linking flags
by e.g.
$ pkg-config --exists ’QtCore >= 4.0.0’ # check the status
$ pkg-config --modversion QtCore
4.7.1
$ pkg-config --cflags QtCore
-DQT_SHARED -I/usr/include/QtCore
$ pkg-config --libs QtCore
-lQtCore
Note that pkg-config --libs gives the information required to link against the default version
of that library (usually the dynamic one), and pkg-config --static is needed if the static
library is to be used.
Sometimes the name by which the software is known to pkg-config is not what one might
expect (e.g. ‘gtk+-2.0’ even for 2.22). To get a complete list use
pkg-config --list-all | sort
1.2.1 Using Makevars
Sometimes writing your own configure script can be avoided by supplying a file Makevars: also
one of the most common uses of a configure script is to make Makevars from Makevars.in.
AMakevars file is a makefile and is used as one of several makefiles by R CMD SHLIB (which
is called by R CMD INSTALL to compile code in the src directory). It should be written if at all
possible in a portable style, in particular (except for Makevars.win) without the use of GNU
extensions.
The most common use of a Makevars file is to set additional preprocessor options (for example
include paths) for C/C++ files via PKG_CPPFLAGS, and additional compiler flags by setting PKG_
Chapter 1: Creating R packages 21
CFLAGS,PKG_CXXFLAGS,PKG_FFLAGS or PKG_FCFLAGS, for C, C++, FORTRAN or Fortran 9x
respectively (see Section 5.5 [Creating shared objects], page 116).
N.B.: Include paths are preprocessor options, not compiler options, and must be set in
PKG_CPPFLAGS as otherwise platform-specific paths (e.g. ‘-I/usr/local/include’) will take
precedence.
Makevars can also be used to set flags for the linker, for example ‘-L’ and ‘-l’ options, via
PKG_LIBS.
When writing a Makevars file for a package you intend to distribute, take care to ensure that
it is not specific to your compiler: flags such as -O2 -Wall -pedantic (and all other -W flags:
for the Oracle compilers these are used to pass arguments to compiler phases) are all specific to
GCC.
Also, do not set variables such as CPPFLAGS,CFLAGS etc.: these should be settable by users
(sites) through appropriate personal (site-wide) Makevars files. See Section “Customizing pack-
age compilation” in R Installation and Administration,
There are some macros26 which are set whilst configuring the building of R itself and
are stored in R_HOME/etcR_ARCH/Makeconf. That makefile is included as a Makefile after
Makevars[.win], and the macros it defines can be used in macro assignments and make com-
mand lines in the latter. These include
FLIBS A macro containing the set of libraries need to link FORTRAN code. This may
need to be included in PKG_LIBS: it will normally be included automatically if the
package contains FORTRAN source files.
BLAS_LIBS
A macro containing the BLAS libraries used when building R. This may need to
be included in PKG_LIBS. Beware that if it is empty then the R executable will
contain all the double-precision and double-complex BLAS routines, but no single-
precision nor complex routines. If BLAS_LIBS is included, then FLIBS also needs to
be27 included following it, as most BLAS libraries are written at least partially in
FORTRAN.
LAPACK_LIBS
A macro containing the LAPACK libraries (and paths where appropriate) used when
building R. This may need to be included in PKG_LIBS. It may point to a dynamic
library libRlapack which contains the main double-precision LAPACK routines as
well as those double-complex LAPACK routines needed to build R, or it may point
to an external LAPACK library, or may be empty if an external BLAS library also
contains LAPACK.
[libRlapack includes all the double-precision LAPACK routines which were current
in 2003: a list of which routines are included is in file src/modules/lapack/README.
Note that an external LAPACK/BLAS library need not do so, as some were ‘dep-
recated’ (and not compiled by default) in LAPACK 3.6.0 in late 2015.]
For portability, the macros BLAS_LIBS and FLIBS should always be included after
LAPACK_LIBS (and in that order).
SAFE_FFLAGS
A macro containing flags which are needed to circumvent over-optimization of FOR-
TRAN code: it is typically ‘-g -O2 -ffloat-store’ on ‘ix86’ platforms using
26 in POSIX parlance: GNU make calls these ‘make variables’.
27 at least on Unix-alikes: the Windows build currently resolves such dependencies to a static FORTRAN library
when Rblas.dll is built.
Chapter 1: Creating R packages 22
gfortran. Note that this is not an additional flag to be used as part of PKG_
FFLAGS, but a replacement for FFLAGS, and that it is intended for the FORTRAN
77 compiler ‘F77’ and not necessarily for the Fortran 90/95 compiler ‘FC’. See the
example later in this section.
Setting certain macros in Makevars will prevent R CMD SHLIB setting them: in particular if
Makevars sets ‘OBJECTS’ it will not be set on the make command line. This can be useful in
conjunction with implicit rules to allow other types of source code to be compiled and included
in the shared object. It can also be used to control the set of files which are compiled, either by
excluding some files in src or including some files in subdirectories. For example
OBJECTS = 4dfp/endianio.o 4dfp/Getifh.o R4dfp-object.o
Note that Makevars should not normally contain targets, as it is included before the de-
fault makefile and make will call the first target, intended to be all in the default makefile.
If you really need to circumvent that, use a suitable (phony) target all before any actual
targets in Makevars.[win]: for example package fastICA (https://CRAN.R-project.org/
package=fastICA) used to have
PKG_LIBS = @BLAS_LIBS@
SLAMC_FFLAGS=$(R_XTRA_FFLAGS) $(FPICFLAGS) $(SHLIB_FFLAGS) $(SAFE_FFLAGS)
all: $(SHLIB)
slamc.o: slamc.f
$(F77) $(SLAMC_FFLAGS) -c -o slamc.o slamc.f
needed to ensure that the LAPACK routines find some constants without infinite looping. The
Windows equivalent was
all: $(SHLIB)
slamc.o: slamc.f
$(F77) $(SAFE_FFLAGS) -c -o slamc.o slamc.f
(since the other macros are all empty on that platform, and R’s internal BLAS was not used).
Note that the first target in Makevars will be called, but for back-compatibility it is best named
all.
If you want to create and then link to a library, say using code in a subdirectory, use something
like
.PHONY: all mylibs
all: $(SHLIB)
$(SHLIB): mylibs
mylibs:
(cd subdir; $(MAKE))
Be careful to create all the necessary dependencies, as there is no guarantee that the dependencies
of all will be run in a particular order (and some of the CRAN build machines use multiple
CPUs and parallel makes). In particular,
all: mylibs
does not suffice.
Note that on Windows it is required that Makevars[.win] does create a DLL: this is needed
as it is the only reliable way to ensure that building a DLL succeeded. If you want to use the
src directory for some purpose other than building a DLL, use a Makefile.win file.
Chapter 1: Creating R packages 23
It is sometimes useful to have a target ‘clean’ in Makevars or Makevars.win: this will be
used by R CMD build to clean up (a copy of) the package sources. When it is run by build it
will have fewer macros set, in particular not $(SHLIB), nor $(OBJECTS) unless set in the file
itself. It would also be possible to add tasks to the target ‘shlib-clean’ which is run by R CMD
INSTALL and R CMD SHLIB with options --clean and --preclean.
If you want to run R code in Makevars, e.g. to find configuration information, please do
ensure that you use the correct copy of Ror Rscript: there might not be one in the path at all,
or it might be the wrong version or architecture. The correct way to do this is via
"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" filename
"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e ’R expression
where $(R_ARCH_BIN) is only needed currently on Windows.
Environment or make variables can be used to select different macros for 32- and 64-bit code,
for example (GNU make syntax, allowed on Windows)
ifeq "$(WIN)" "64"
PKG_LIBS = value for 64-bit Windows
else
PKG_LIBS = value for 32-bit Windows
endif
On Windows there is normally a choice between linking to an import library or directly to
a DLL. Where possible, the latter is much more reliable: import libraries are tied to a specific
toolchain, and in particular on 64-bit Windows two different conventions have been commonly
used. So for example instead of
PKG_LIBS = -L$(XML_DIR)/lib -lxml2
one can use
PKG_LIBS = -L$(XML_DIR)/bin -lxml2
since on Windows -lxxx will look in turn for
libxxx.dll.a
xxx.dll.a
libxxx.a
xxx.lib
libxxx.dll
xxx.dll
where the first and second are conventionally import libraries, the third and fourth often static
libraries (with .lib intended for Visual C++), but might be import libraries. See for example
https://sourceware.org/binutils/docs-2.20/ld/WIN32.html#WIN32.
The fly in the ointment is that the DLL might not be named libxxx.dll, and in fact on
32-bit Windows there is a libxml2.dll whereas on one build for 64-bit Windows the DLL is
called libxml2-2.dll. Using import libraries can cover over these differences but can cause
equal difficulties.
If static libraries are available they can save a lot of problems with run-time finding of DLLs,
especially when binary packages are to be distributed and even more when these support both
architectures. Where using DLLs is unavoidable we normally arrange (via configure.win) to
ship them in the same directory as the package DLL.
1.2.1.1 OpenMP support
There is some support for packages which wish to use OpenMP28 . The make macros
SHLIB_OPENMP_CFLAGS
28 http:/ / www.openmp.org/,https:/ /en.wikipedia.org/wiki / OpenMP,https://computing.llnl.gov /
tutorials/openMP/
Chapter 1: Creating R packages 24
SHLIB_OPENMP_CXXFLAGS
SHLIB_OPENMP_FCFLAGS
SHLIB_OPENMP_FFLAGS
are available for use in src/Makevars or src/Makevars.win. Include the appropriate macro
in PKG_CFLAGS,PKG_CPPFLAGS and so on, and also in PKG_LIBS. C/C++ code that needs to
be conditioned on the use of OpenMP can be used inside #ifdef _OPENMP: note that some
toolchains used for R (including that of macOS and some others using clang29 ) have no OpenMP
support at all, not even omp.h.
For example, a package with C code written for OpenMP should have in src/Makevars the
lines
PKG_CFLAGS = $(SHLIB_OPENMP_CFLAGS)
PKG_LIBS = $(SHLIB_OPENMP_CFLAGS)
Note that the macro SHLIB_OPENMP_CXXFLAGS applies to the default C++ compiler and not
necessarily to the C++11/14/17 compiler: users of the latter should do their own configure
checks (an example is available in CRAN package ARTP2 (https://CRAN.R-project.org/
package=ARTP2)).
Some care is needed when compilers are from different families which may use different
OpenMP runtimes (e.g. clang vs GCC including gfortran, although it is currently possible to
use the clang runtime with GCC but not vice versa). For a package with Fortran 77 code using
OpenMP the appropriate lines are
PKG_FFLAGS = $(SHLIB_OPENMP_FFLAGS)
PKG_LIBS = $(SHLIB_OPENMP_CFLAGS)
as the C compiler will be used to link the package code (and there is no guarantee that this
will work everywhere). (This does not apply to Fortran 9x code, where SHLIB_OPENMP_FCFLAGS
should be used in both PKG_FCFLAGS and PKG_LIBS.)
For portability, any C/C++ code using the omp_* functions should include the omp.h header:
some compilers (but not all) include it when OpenMP mode is switched on (e.g. via flag
-fopenmp).
There is nothing30 to say what version of OpenMP is supported: version 3.1 (and much
of 4.0) is supported by recent versions31 of the Linux, Windows and Solaris platforms, but
portable packages cannot assume that end users have recent versions.32 macOS currently uses
Apple builds of clang with no OpenMP support (even if invoked as gcc and despite the man
page including the flag -fopenmp for that command). http://www.openmp.org/resources/
openmp-compilers gives some idea of what compilers support what versions.
The performance of OpenMP varies substantially between platforms. The Windows imple-
mentation has substantial overheads33, so is only beneficial if quite substantial tasks are run
in parallel. Also, on Windows new threads are started with the default34 FPU control word,
so computations done on OpenMP threads will not make use of extended-precision arithmetic
which is the default for the main process.
29 Default builds of clang 3.8.0 and later have support for OpenMP, but the libomp run-time library may not
be installed.
30 In most implementations the _OPENMP macro has value a date which can be mapped to an OpenMP version:
for example, value 201307 is the date of version 4.0 (July 2013). However this may be used to denote the
latest version which is partially supported, not that which is fully implemented.
31 GCC since 4.7, hence R builds for Windows since R 3.3.0, which also support OpenMP 4.0.
32 People do use older versions of OSes such as Ubuntu 12.04LTS and Debian Wheezy LTS which have GCC 4.4.
33 as did the GCC-based Apple implementation, but not the Intel/LLVM OpenMP runtime on macOS.
34 Windows default, not MinGW-w64 default.
Chapter 1: Creating R packages 25
Calling any of the R API from threaded code is ‘for experts only’: they will need to read
the source code to determine if it is thread-safe. In particular, code which makes use of the
stack-checking mechanism must not be called from threaded code.
Packages are not standard-alone programs, and an R process could contain more than one
OpenMP-enabled package as well as other components (for example, an optimized BLAS) mak-
ing use of OpenMP. So careful consideration needs to be given to resource usage. OpenMP
works with parallel regions, and for most implementations the default is to use as many threads
as ‘CPUs’ for such regions. Parallel regions can be nested, although it is common to use only
a single thread below the first level. The correctness of the detected number of ‘CPUs’ and the
assumption that the R process is entitled to use them all are both dubious assumptions. The
best way to limit resources is to limit the overall number of threads available to OpenMP in the
R process: this can be done via environment variable OMP_THREAD_LIMIT, where implemented.35
Alternatively, the number of threads per region can be limited by the environment variable OMP_
NUM_THREADS or API call omp_set_num_threads, or, better, for the regions in your code as part
of their specification. E.g. R uses
#pragma omp parallel for num_threads(nthreads) ...
That way you only control your own code and not that of other OpenMP users.
1.2.1.2 Using pthreads
There is no direct support for the POSIX threads (more commonly known as pthreads): by
the time we considered adding it several packages were using it unconditionally so it seems that
nowadays it is universally available on POSIX operating systems (hence not Windows).
For reasonably recent versions of gcc and clang the correct specification is
PKG_CPPFLAGS = -pthread
PKG_LIBS = -pthread
(and the plural version is also accepted on some systems/versions). For other platforms the
specification is
PKG_CPPFLAGS = -D_REENTRANT
PKG_LIBS = -lpthread
(and note that the library name is singular). This is what -pthread does on all known current
platforms (although earlier versions of OpenBSD used a different library name).
For a tutorial see https://computing.llnl.gov/tutorials/pthreads/.
POSIX threads are not normally used on Windows, which has its own native concepts
of threads. However, there are two projects implementing pthreads on top of Windows,
pthreads-w32 and winpthreads (part of the MinGW-w64 project).
Whether Windows toolchains implement pthreads is up to the toolchain provider. A make
variable SHLIB_PTHREAD_FLAGS is available: this should be included in both PKG_CPPFLAGS (or
the Fortran or F9x equivalents) and PKG_LIBS.
The presence of a working pthreads implementation cannot be unambiguously determined
without testing for yourself: however, that ‘_REENTRANT’ is defined36 in C/C++ code is a good
indication.
Note that not all pthreads implementations are equivalent as parts are optional (see http://
pubs. opengroup.org /onlinepubs/ 009695399/basedefs/pthread .h. html): for example,
macOS lacks the ‘Barriers’ option.
See also the comments on thread-safety and performance under OpenMP: on all known R
platforms OpenMP is implemented via pthreads and the known performance issues are in the
latter.
35 Which it was at the time of writing with GCC, Oracle, Intel and Clang compilers.
36 some Windows toolchains had the typo ‘_REENTRANCE’ instead.
Chapter 1: Creating R packages 26
1.2.1.3 Compiling in sub-directories
Package authors fairly often want to organize code in sub-directories of src, for example if they
are including a separate piece of external software to which this is an R interface.
One simple way is simply to set OBJECTS to be all the objects that need to be compiled,
including in sub-directories. For example, CRAN package RSiena (https://CRAN.R-project.
org/package=RSiena) has
SOURCES = $(wildcard data/*.cpp network/*.cpp utils/*.cpp model/*.cpp model/*/*.cpp model/*/*/*.cpp)
OBJECTS = siena07utilities.o siena07internals.o siena07setup.o siena07models.o $(SOURCES:.cpp=.o)
One problem with that approach is that unless GNU make extensions are used, the source
files need to be listed and kept up-to-date. As in the following from CRAN package lossDev
(https://CRAN.R-project.org/package=lossDev):
OBJECTS.samplers = samplers/ExpandableArray.o samplers/Knots.o \
samplers/RJumpSpline.o samplers/RJumpSplineFactory.o \
samplers/RealSlicerOV.o samplers/SliceFactoryOV.o samplers/MNorm.o
OBJECTS.distributions = distributions/DSpline.o \
distributions/DChisqrOV.o distributions/DTOV.o \
distributions/DNormOV.o distributions/DUnifOV.o distributions/RScalarDist.o
OBJECTS.root = RJump.o
OBJECTS = $(OBJECTS.samplers) $(OBJECTS.distributions) $(OBJECTS.root)
Where the subdirectory is self-contained code with a suitable makefile, the best approach is
something like
PKG_LIBS = -LCsdp/lib -lsdp $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
$(SHLIB): Csdp/lib/libsdp.a
Csdp/lib/libsdp.a:
@(cd Csdp/lib && $(MAKE) libsdp.a \
CC="$(CC)" CFLAGS="$(CFLAGS) $(CPICFLAGS)" AR="$(AR)" RANLIB="$(RANLIB)")
Note the quotes: the macros can contain spaces, e.g. CC = "gcc -m64 -std=gnu99". Several
authors have forgotten about parallel makes: the static library in the subdirectory must be
made before the shared object ($(SHLIB)) and so the latter must depend on the former. Others
forget the need37 for position-independent code.
We really do not recommend using src/Makefile instead of src/Makevars, and as the
example above shows, it is not necessary.
1.2.2 Configure example
It may be helpful to give an extended example of using a configure script to create a
src/Makevars file: this is based on that in the RODBC (https:// CRAN.R-project .org/
package=RODBC) package.
The configure.ac file follows: configure is created from this by running autoconf in the
top-level package directory (containing configure.ac).
AC_INIT([RODBC], 1.1.8) dnl package name, version
dnl A user-specifiable option
odbc_mgr=""
AC_ARG_WITH([odbc-manager],
AC_HELP_STRING([--with-odbc-manager=MGR],
[specify the ODBC manager, e.g. odbc or iodbc]),
[odbc_mgr=$withval])
37 A few OSes (AIX, IRIX, Windows) do not need special flags for such code, but most do—although compilers
will often generate PIC code when not asked to do so.
Chapter 1: Creating R packages 27
if test "$odbc_mgr" = "odbc" ; then
AC_PATH_PROGS(ODBC_CONFIG, odbc_config)
fi
dnl Select an optional include path, from a configure option
dnl or from an environment variable.
AC_ARG_WITH([odbc-include],
AC_HELP_STRING([--with-odbc-include=INCLUDE_PATH],
[the location of ODBC header files]),
[odbc_include_path=$withval])
RODBC_CPPFLAGS="-I."
if test [ -n "$odbc_include_path" ] ; then
RODBC_CPPFLAGS="-I. -I${odbc_include_path}"
else
if test [ -n "${ODBC_INCLUDE}" ] ; then
RODBC_CPPFLAGS="-I. -I${ODBC_INCLUDE}"
fi
fi
dnl ditto for a library path
AC_ARG_WITH([odbc-lib],
AC_HELP_STRING([--with-odbc-lib=LIB_PATH],
[the location of ODBC libraries]),
[odbc_lib_path=$withval])
if test [ -n "$odbc_lib_path" ] ; then
LIBS="-L$odbc_lib_path ${LIBS}"
else
if test [ -n "${ODBC_LIBS}" ] ; then
LIBS="-L${ODBC_LIBS} ${LIBS}"
else
if test -n "${ODBC_CONFIG}"; then
odbc_lib_path=‘odbc_config --libs | sed s/-lodbc//‘
LIBS="${odbc_lib_path} ${LIBS}"
fi
fi
fi
dnl Now find the compiler and compiler flags to use
: ${R_HOME=‘R RHOME‘}
if test -z "${R_HOME}"; then
echo "could not determine R_HOME"
exit 1
fi
CC=‘"${R_HOME}/bin/R" CMD config CC‘
CPP=‘"${R_HOME}/bin/R" CMD config CPP‘
CFLAGS=‘"${R_HOME}/bin/R" CMD config CFLAGS‘
CPPFLAGS=‘"${R_HOME}/bin/R" CMD config CPPFLAGS‘
AC_PROG_CC
AC_PROG_CPP
if test -n "${ODBC_CONFIG}"; then
RODBC_CPPFLAGS=‘odbc_config --cflags‘
fi
CPPFLAGS="${CPPFLAGS} ${RODBC_CPPFLAGS}"
dnl Check the headers can be found
AC_CHECK_HEADERS(sql.h sqlext.h)
if test "${ac_cv_header_sql_h}" = no ||
test "${ac_cv_header_sqlext_h}" = no; then
AC_MSG_ERROR("ODBC headers sql.h and sqlext.h not found")
fi
dnl search for a library containing an ODBC function
if test [ -n "${odbc_mgr}" ] ; then
Chapter 1: Creating R packages 28
AC_SEARCH_LIBS(SQLTables, ${odbc_mgr}, ,
AC_MSG_ERROR("ODBC driver manager ${odbc_mgr} not found"))
else
AC_SEARCH_LIBS(SQLTables, odbc odbc32 iodbc, ,
AC_MSG_ERROR("no ODBC driver manager found"))
fi
dnl for 64-bit ODBC need SQL[U]LEN, and it is unclear where they are defined.
AC_CHECK_TYPES([SQLLEN, SQLULEN], , , [# include <sql.h>])
dnl for unixODBC header
AC_CHECK_SIZEOF(long, 4)
dnl substitute RODBC_CPPFLAGS and LIBS
AC_SUBST(RODBC_CPPFLAGS)
AC_SUBST(LIBS)
AC_CONFIG_HEADERS([src/config.h])
dnl and do substitution in the src/Makevars.in and src/config.h
AC_CONFIG_FILES([src/Makevars])
AC_OUTPUT
where src/Makevars.in would be simply
PKG_CPPFLAGS = @RODBC_CPPFLAGS@
PKG_LIBS = @LIBS@
A user can then be advised to specify the location of the ODBC driver manager files by
options like (lines broken for easier reading)
R CMD INSTALL \
--configure-args=’--with-odbc-include=/opt/local/include \
--with-odbc-lib=/opt/local/lib --with-odbc-manager=iodbc’ \
RODBC
or by setting the environment variables ODBC_INCLUDE and ODBC_LIBS.
1.2.3 Using F95 code
R assumes that source files with extension .f are FORTRAN 77, and passes them to the compiler
specified by ‘F77’. On most but not all platforms that compiler will accept Fortran 90/95 code:
some platforms have a separate Fortran 90/95 compiler and a few (by now quite rare38) platforms
have no Fortran 90/95 support.
This means that portable packages need to be written in correct FORTRAN 77, which will
also be valid Fortran 95. See https://developer.R-project.org/Portability.html for
reference resources. In particular, free source form F95 code is not portable.
On some systems an alternative F95 compiler is available: from the gcc family this might
be gfortran or g95. Configuring R will try to find a compiler which (from its name) appears
to be a Fortran 90/95 compiler, and set it in macro ‘FC’. Note that it does not check that such
a compiler is fully (or even partially) compliant with Fortran 90/95. Packages making use of
Fortran 90/95 features should use file extension .f90 or .f95 for the source files: the variable
PKG_FCFLAGS specifies any special flags to be used. There is no guarantee that compiled Fortran
90/95 code can be mixed with any other type of compiled code, nor that a build of R will have
support for such packages.
Some (but not) all compilers specified by the ‘FC’ macro will accept Fortran 2003 or 2008
code: such code should still use file extension .f90 or .f95. For platforms using gfortran, you
may need to include -std=f2003 or -std=f2008 in PKG_FCFLAGS: the default is ‘GNU Fortran’,
Fortran 95 with non-standard extensions. The Oracle f95 compiler ‘accepts some Fortran 2003/8
features’ (search for ‘Oracle Developer Studio 12.5: Fortran User’s Guide’ and look for ^
A§4.6).
38 Cygwin used g77 up to 2011, and some pre-built versions of R for Unix OSes still do.
Chapter 1: Creating R packages 29
Modern versions of Fortran support modules, whereby compiling one source file creates a
module file which is then included in others. (Module files typically have a .mod extension: they
do depend on the compiler used and so should never be included in a package.) This creates a
dependence which make will not know about and often causes installation with a parallel make
to fail. Thus it is necessary to add explicit dependencies to src/Makevars to tell make the
constraints on the order of compilation. For example, if file iface.f90 creates a module ‘iface
used by files cmi.f90 and dmi.f90 then src/Makevars needs to contain something like
cmi.o dmi.o: iface.o
1.2.4 Using C++11 code
R can be built without a C++ compiler although one is available (but not necessarily installed)
on all known R platforms. For full portability across platforms, all that can be assumed is
approximate support for the C++98 standard (the widely used g++ deviates considerably from
the standard). Some compilers have a concept of ‘C++03’ (‘essentially a bug fix’) or ‘C++
Technical Report 1’ (TR1), an optional addition to the ‘C++03’ revision which was published
in 2007. A revised standard was published in 2011 and compilers with pretty much complete
implementations are available. C++11 added all of the C99 features which are not otherwise
implemented in C++, and C++ compilers commonly accept C99 extensions to C++98. A minor
update39 to C++11 (C++14) was published in December 2014. The next standard (C++17) was
approved in Sept 2017 and will be published by ISO in due course.
What standard a C++ compiler aims to support can be hard to determine: the value40 of __
cplusplus may help but some compilers use it to denote a standard which is partially supported
and some the latest standard which is (almost) fully supported. As from version 6, g++ defaults
to C++14 (with GNU extensions): earlier versions aim to support C++03 with many extensions
(including support for TR1). clang with its native41 libc++ headers and library includes most
C++14 features, and does not support TR1. As from version 6.0.0, clang is expected to default
to C++14.
Since version 3.1.0, R has provided support for C++11 in packages in addition to C++98.
This support is not uniform across platforms as it depends on the capabilities of the compiler
(see below). When R is configured, it will determine whether the C++ compiler supports C++11
and which compiler flags, if any, are required to enable C++11 support. For example, recent
versions of g++ or clang++ accept the compiler flag -std=c++11, and earlier versions support a
flag -std=c++0x, but the latter only provided partial support for the C++11 standard (it later
became a deprecated synonym for -std=c++11).
In order to use C++11 code in a package, the package’s Makevars file (or Makevars.win on
Windows) should include the line
CXX_STD = CXX11
Compilation and linking will then be done with the C++11 compiler.
Packages without a src/Makevars or src/Makefile file may specify that they require C++11
for code in the src directory by including ‘C++11’ in the ‘SystemRequirements’ field of the
DESCRIPTION file, e.g.
SystemRequirements: C++11
If a package does have a src/Makevars[.win] file then setting the make variable ‘CXX_STD
is preferred, as it allows R CMD SHLIB to work correctly in the package’s src directory.
39 The changes are linked from https: / / isocpp . org / std / standing-documents /
sd-6-sg10-feature-test-recommendations.
40 Values 199711,201103L and 201402L are most commonly used for C++98, C++11 and C++14 respectively, but
some compilers set 1L.
41 Some distributions, notably Debian, have supplied a build of clang with g++’s headers and library. Conversely,
Apple’s command named g++ is based on clang using libc++.
Chapter 1: Creating R packages 30
Conversely, to ensure that the C++98 standard is assumed even when this is not the compiler
default, use
SystemRequirements: C++98
or
CXX_STD = CXX98
The C++11 compiler will be used systematically by R for all C++ code if the environment
variable USE_CXX11 is defined (with any value). Hence this environment variable should be
defined when invoking R CMD SHLIB in the absence of a Makevars file (or Makevars.win on
Windows) if a C++11 compiler is required.
Further control over compilation of C++11 code can be obtained by specifying the macros
CXX11’ and ‘CXX11STD’ when R is configured42, or in a personal or site Makevars file. See
Section “Customizing package compilation” in R Installation and Administration. If C++11
support is not available then these macros are both empty; if it is available by default, ‘CXX11
defaults to ‘CXX’ and ‘CXX11STD’ is empty . Otherwise, CXX11’ defaults to the same value as
the C++ compiler ‘CXX’ and the flag ‘CXX11STD’ defaults to -std=c++11 or similar. It is possible
to specify ‘CXX11’ to be a distinct compiler just for C++11–using packages, e.g. g++ on Solaris.
Note however that different C++ compilers (and even different versions of the same compiler)
often differ in their ABI so their outputs can rarely be mixed. By setting ‘CXX11STD’ it is also
possible to choose a different dialect of the standard such as -std=c++11.
As noted above, support for C++11 varies across platforms: on some platforms, it may be
possible or necessary to select a different compiler for C++11, via personal or site Makevars files.
There is no guarantee that C++11 can be used in a package in combination with any other
compiled language (even C), as the C++11 compiler may be incompatible with the native com-
pilers for the platform. (There are known problems mixing C++11 with Fortran.)
If a package using C++11 has a configure script it is essential that it selects the correct
compiler, via something like
CXX11=‘"${R_HOME}/bin/R" CMD config CXX11‘
CXX11STD=‘"${R_HOME}/bin/R" CMD config CXX11STD‘
CXX="${CXX11} ${CXX11STD}"
CXXFLAGS=‘"${R_HOME}/bin/R" CMD config CXX11FLAGS‘
AC_LANG(C++)
(paying attention to all the quotes required).
If you want to compile C++11 code in a subdirectory, make sure you pass down the macros
to specify that compiler, e.g. in src/Makevars
sublibs:
@(cd libs && $(MAKE) \
CXX="$(CXX11) $(CXX11STD)" CXXFLAGS="$(CXX11FLAGS) $(CXX11PICFLAGS)")
Note that the mechanisms described here specify C++11 for code compiled by R CMD SHLIB
as used by default by R CMD INSTALL. They do not necessarily apply if there is a src/Makefile
file, nor to compilation done in vignettes or via other packages.
1.2.5 Using C++14 code
Support for a C++14 has been explicitly added to R from version 3.4.0. Similar considerations
to C++11 apply, except that the variables associated with the C++14 compiler use the prefix
CXX14’ instead of ‘CXX11’. Hence to use C++14 code in a package, the package’s Makevars file
(or Makevars.win on Windows) should include the line
CXX_STD = CXX14
42 For details of these and related macros, see file config.site in the R sources.
Chapter 1: Creating R packages 31
In the absence of a Makevars file, C++14 support can also be requested by the line:
SystemRequirements: C++14
in the DESCRIPTION file. Finally, the C++14 compiler can be used systematically by setting the
environment variable USE_CXX14.
Note that code written for C++11 that emulates features of C++14 will not necessarily compile
under a C++14 compiler43, since the emulation typically leads to a namespace clash. In order to
ensure that the code also compiles under C++14, something like the following should be done:
#if __cplusplus >= 201402L
using std::make_unique;
#else
// your emulation
#endif
Code needing C++14 features would do better to test for their presence via ‘SD-6 feature tests’44.
That test could be
#include <memory> // header where this is defined
#if defined(__cpp_lib_make_unique) && (__cpp_lib_make_unique >= 201304)
using std::make_unique;
#else
// your emulation
#endif
The webpage http://en.cppreference.com/w/cpp/compiler_support gives some infor-
mation on which compilers are known to support recent C++ features, including those in the
C++17 drafts (for which feature tests should be used).
1.2.6 Using C++17 code
Experimental support for C++17 has been added to R version 3.4.0. The configure script tests
a subset of C++17 features. At the time of writing (March 2017) both clang 4.0.0 and gcc
7.1 pass these tests (with flags -std=gnu++1z and -std=gnu++17 respectively chosen by the
configure script). Note that the C++17 feature tests are incomplete and are subject to change
in future R versions as compiler support for the standard improves.
The variables associated with the C++17 compiler use the prefix ‘CXX17’. Hence to use C++17
code in a package, the package’s Makevars file (or Makevars.win on Windows) should include
the line
CXX_STD = CXX17
In the absence of a Makevars file, C++17 support can also be requested by the line:
SystemRequirements: C++17
in the DESCRIPTION file. Finally, the C++17 compiler can be used systematically by setting the
environment variable USE_CXX17.
1.3 Checking and building packages
Before using these tools, please check that your package can be installed (which checked it can
be loaded). R CMD check will inter alia do this, but you may get more detailed error messages
doing the install directly.
43 As from R 3.4.0, configure attempts to supply a C++14 compiler only if explicitly requested. However, earlier
versions of R will use the default C++14 mode of g++ 6 and later.
44 See https: / / isocpp . org / std / standing-documents / sd-6-sg10-feature-test-recommendations or
http://en.cppreference.com/w/cpp/experimental/feature_test. It seems a reasonable assumption that
any compiler promising some C++14 conformance will provide these—e.g. g++ 4.9.x did but 4.8.5 did not.
Chapter 1: Creating R packages 32
If your package specifies an encoding in its DESCRIPTION file, you should run these tools in a
locale which makes use of that encoding: they may not work at all or may work incorrectly in
other locales (although UTF-8 locales will most likely work).
Note: R CMD check and R CMD build run R processes with --vanilla in which
none of the user’s startup files are read. If you need R_LIBS set (to find packages
in a non-standard library) you can set it in the environment: also you can use
the check and build environment files (as specified by the environment variables
R_CHECK_ENVIRON and R_BUILD_ENVIRON; if unset, files45 ~/.R/check.Renviron
and ~/.R/build.Renviron are used) to set environment variables when using these
utilities.
Note to Windows users: R CMD build may make use of the Windows toolset (see the
“R Installation and Administration” manual) if present and in your path, and it is
required for packages which need it to install (including those with configure.win
or cleanup.win scripts or a src directory) and e.g. need vignettes built.
You may need to set the environment variable TMPDIR to point to a suitable writable
directory with a path not containing spaces – use forward slashes for the separators.
Also, the directory needs to be on a case-honouring file system (some network-
mounted file systems are not).
1.3.1 Checking packages
Using R CMD check, the R package checker, one can test whether source R packages work cor-
rectly. It can be run on one or more directories, or compressed package tar archives with
extension .tar.gz,.tgz,.tar.bz2 or .tar.xz.
It is strongly recommended that the final checks are run on a tar archive prepared by R CMD
build.
This runs a series of checks, including
1. The package is installed. This will warn about missing cross-references and duplicate aliases
in help files.
2. The file names are checked to be valid across file systems and supported operating system
platforms.
3. The files and directories are checked for sufficient permissions (Unix-alikes only).
4. The files are checked for binary executables, using a suitable version of file if available46.
(There may be rare false positives.)
5. The DESCRIPTION file is checked for completeness, and some of its entries for correctness.
Unless installation tests are skipped, checking is aborted if the package dependencies cannot
be resolved at run time. (You may need to set R_LIBS in the environment if dependent
packages are in a separate library tree.) One check is that the package name is not that of
a standard package, nor one of the defunct standard packages (‘ctest’, ‘eda’, ‘lqs’, ‘mle’,
modreg’, ‘mva’, ‘nls’, ‘stepfun’ and ‘ts’). Another check is that all packages mentioned
in library or requires or from which the NAMESPACE file imports or are called via :: or
::: are listed (in ‘Depends’, ‘Imports’, ‘Suggests’): this is not an exhaustive check of the
actual imports.
6. Available index information (in particular, for demos and vignettes) is checked for com-
pleteness.
45 On systems which use sub-architectures, architecture-specific versions such as ~/.R/check.Renviron.i386
take precedence.
46 A suitable file.exe is part of the Windows toolset: it checks for gfile if a suitable file is not found: the
latter is available in the OpenCSW collection for Solaris at http://www.opencsw.org. The source repository
is ftp://ftp.astron.com/pub/file/.
Chapter 1: Creating R packages 33
7. The package subdirectories are checked for suitable file names and for not being empty. The
checks on file names are controlled by the option --check-subdirs=value. This defaults to
default’, which runs the checks only if checking a tarball: the default can be overridden
by specifying the value as ‘yes’ or ‘no’. Further, the check on the src directory is only
run if the package does not contain a configure script (which corresponds to the value
yes-maybe’) and there is no src/Makefile or src/Makefile.in.
To allow a configure script to generate suitable files, files ending in ‘.in’ will be allowed
in the Rdirectory.
A warning is given for directory names that look like R package check directories – many
packages have been submitted to CRAN containing these.
8. The R files are checked for syntax errors. Bytes which are non-ASCII are reported as
warnings, but these should be regarded as errors unless it is known that the package will
always be used in the same locale.
9. It is checked that the package can be loaded, first with the usual default packages and then
only with package base already loaded. It is checked that the namespace this can be loaded
in an empty session with only the base namespace loaded. (Namespaces and packages can
be loaded very early in the session, before the default packages are available, so packages
should work then.)
10. The R files are checked for correct calls to library.dynam. Package startup functions are
checked for correct argument lists and (incorrect) calls to functions which modify the search
path or inappropriately generate messages. The R code is checked for possible problems
using codetools (https://CRAN.R-project.org/package=codetools). In addition, it is
checked whether S3 methods have all arguments of the corresponding generic, and whether
the final argument of replacement functions is called ‘value’. All foreign function calls (.C,
.Fortran,.Call and .External calls) are tested to see if they have a PACKAGE argument,
and if not, whether the appropriate DLL might be deduced from the namespace of the
package. Any other calls are reported. (The check is generous, and users may want to
supplement this by examining the output of tools::checkFF("mypkg", verbose=TRUE),
especially if the intention were to always use a PACKAGE argument)
11. The Rd files are checked for correct syntax and metadata, including the presence of the
mandatory fields (\name,\alias,\title and \description). The Rd name and title are
checked for being non-empty, and there is a check for missing cross-references (links).
12. A check is made for missing documentation entries, such as undocumented user-level objects
in the package.
13. Documentation for functions, data sets, and S4 classes is checked for consistency with the
corresponding code.
14. It is checked whether all function arguments given in \usage sections of Rd files are docu-
mented in the corresponding \arguments section.
15. The data directory is checked for non-ASCII characters and for the use of reasonable levels
of compression.
16. C, C++ and FORTRAN source and header files47 are tested for portable (LF-only) line
endings. If there is a Makefile or Makefile.in or Makevars or Makevars.in file under the
src directory, it is checked for portable line endings and the correct use of ‘$(BLAS_LIBS)
and ‘$(LAPACK_LIBS)
Compiled code is checked for symbols corresponding to functions which might terminate
R or write to stdout/stderr instead of the console. Note that the latter might give false
positives in that the symbols might be pulled in with external libraries and could never
47 An exception is made for subdirectories with names starting ‘win’ or ‘Win’.
Chapter 1: Creating R packages 34
be called. Windows48 users should note that the Fortran and C++ runtime libraries are
examples of such external libraries.
17. Some checks are made of the contents of the inst/doc directory. These always include
checking for files that look like leftovers, and if suitable tools (such as qpdf) are available,
checking that the PDF documentation is of minimal size.
18. The examples provided by the package’s documentation are run. (see Chapter 2 [Writing
R documentation files], page 64, for information on using \examples to create executable
example code.) If there is a file tests/Examples/pkg-Ex.Rout.save, the output of running
the examples is compared to that file.
Of course, released packages should be able to run at least their own examples. Each
example is run in a ‘clean’ environment (so earlier examples cannot be assumed to have
been run), and with the variables Tand Fredefined to generate an error unless they are set
in the example: See Section “Logical vectors” in An Introduction to R.
19. If the package sources contain a tests directory then the tests specified in that direc-
tory are run. (Typically they will consist of a set of .R source files and target output
files .Rout.save.) Please note that the comparison will be done in the end user’s lo-
cale, so the target output files should be ASCII if at all possible. (The command line
option --test-dir=foo may be used to specify tests in a non-standard location. For ex-
ample, unusually slow tests could be placed in inst/slowTests and then R CMD check
--test-dir=inst/slowTests would be used to run them. Other names that have been
suggested are, for example, inst/testWithOracle for tests that require Oracle to be in-
stalled, inst/randomTests for tests which use random values and may occasionally fail by
chance, etc.)
20. The code in package vignettes (see Section 1.4 [Writing package vignettes], page 37) is
executed, and the vignette PDFs re-made from their sources as a check of completeness of
the sources (unless there is a ‘BuildVignettes’ field in the package’s DESCRIPTION file with
a false value). If there is a target output file .Rout.save in the vignette source directory,
the output from running the code in that vignette is compared with the target output file
and any differences are reported (but not recorded in the log file). (If the vignette sources
are in the deprecated location inst/doc, do mark such target output files to not be installed
in .Rinstignore.)
If there is an error49 in executing the R code in vignette foo.ext, a log file foo.ext.log
is created in the check directory. The vignette PDFs are re-made in a copy of the package
sources in the vign_test subdirectory of the check directory, so for further information on
errors look in directory pkgname/vign_test/vignettes. (It is only retained if there are
errors or if environment variable _R_CHECK_CLEAN_VIGN_TEST_ is set to a false value.)
21. The PDF version of the package’s manual is created (to check that the Rd files can be
converted successfully). This needs L
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installed. See Section “Making the manuals” in R Installation and Administration.
All these tests are run with collation set to the Clocale, and for the examples and tests with
environment variable LANGUAGE=en: this is to minimize differences between platforms.
Use R CMD check --help to obtain more information about the usage of the R package
checker. A subset of the checking steps can be selected by adding command-line options. It
also allows customization by setting environment variables _R_CHECK_*_ as described in Section
“Tools” in R Internals: a set of these customizations similar to those used by CRAN can be
48 on most other platforms such runtime libraries are dynamic, but static libraries are currently used on Windows
because the toolchain is not a standard part of the OS.
49 or if option --use-valgrind is used or environment variable _R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_ is set
to a true value or if there are differences from a target output file
Chapter 1: Creating R packages 35
selected by the option --as-cran (which works best if Internet access is available). Some Win-
dows users may need to set environment variable R_WIN_NO_JUNCTIONS to a non-empty value.
The test of cyclic declarations50in DESCRIPTION files needs repositories (including CRAN) set:
do this in ~/.Rprofile, by e.g.
options(repos = c(CRAN="https://cran.r-project.org"))
One check customization which can be revealing is
_R_CHECK_CODETOOLS_PROFILE_="suppressLocalUnused=FALSE"
which reports unused local assignments. Not only does this point out computations which are
unnecessary because their results are unused, it also can uncover errors. (Two such are to intend
to update an object by assigning a value but mistype its name or assign in the wrong scope,
for example using <- where <<- was intended.) This can give false positives, most commonly
because of non-standard evaluation for formulae and because the intention is to return objects
in the environment of a function for later use.
Complete checking of a package which contains a file README.md needs pandoc installed: see
http://johnmacfarlane.net/pandoc/installing.html. This should be reasonably current:
at the time of writing CRAN used version 1.12.4.2 to process these files.
You do need to ensure that the package is checked in a suitable locale if it contains non-ASCII
characters. Such packages are likely to fail some of the checks in a Clocale, and R CMD check
will warn if it spots the problem. You should be able to check any package in a UTF-8 locale
(if one is available). Beware that although a Clocale is rarely used at a console, it may be the
default if logging in remotely or for batch jobs.
Multiple sub-architectures: On systems which support multiple sub-architectures
(principally Windows), R CMD check will install and check a package which con-
tains compiled code under all available sub-architectures. (Use option --force-
multiarch to force this for packages without compiled code, which are otherwise
only checked under the main sub-architecture.) This will run the loading tests, ex-
amples and tests directory under each installed sub-architecture in turn, and give
an error if any fail. Where environment variables (including perhaps PATH) need to
be set differently for each sub-architecture, these can be set in architecture-specific
files such as R_HOME/etc/i386/Renviron.site.
An alternative approach is to use R CMD check --no-multiarch to check the pri-
mary sub-architecture, and then to use something like R --arch=x86_64 CMD check
--extra-arch or (Windows) /path/to/R/bin/x64/Rcmd check --extra-arch to
run for each additional sub-architecture just the checks51 which differ by sub-
architecture. (This approach is required for packages which are installed by R CMD
INSTALL --merge-multiarch.)
Where packages need additional commands to install all the sub-architectures these
can be supplied by e.g. --install-args=--force-biarch.
1.3.2 Building package tarballs
Packages may be distributed in source form as “tarballs” (.tar.gz files) or in binary form.
The source form can be installed on all platforms with suitable tools and is the usual form for
Unix-like systems; the binary form is platform-specific, and is the more common distribution
form for the Windows and macOS platforms.
Using R CMD build, the R package builder, one can build R package tarballs from their sources
(for example, for subsequent release).
50 For example, in early 2014 gdata (https://CRAN.R-project.org/package=gdata) declared ‘Imports: gtools
and gtools (https://CRAN.R-project.org/package=gtools) declared ‘Imports: gdata’.
51 loading, examples, tests, running vignette code
Chapter 1: Creating R packages 36
Prior to actually building the package in the standard gzipped tar file format, a few diagnostic
checks and cleanups are performed. In particular, it is tested whether object indices exist and
can be assumed to be up-to-date, and C, C++ and FORTRAN source files and relevant makefiles
in a src directory are tested and converted to LF line-endings if necessary.
Run-time checks whether the package works correctly should be performed using R CMD check
prior to invoking the final build procedure.
To exclude files from being put into the package, one can specify a list of exclude patterns in
file .Rbuildignore in the top-level source directory. These patterns should be Perl-like regular
expressions (see the help for regexp in R for the precise details), one per line, to be matched
case-insensitively against the file and directory names relative to the top-level package source
directory. In addition, directories from source control systems52 or from eclipse53, directories
with names ending .Rcheck or Old or old and files GNUMakefile54,Read-and-delete-me or
with base names starting with ‘.#’, or starting and ending with ‘#’, or ending in ‘~’, ‘.bak’ or
.swp’, are excluded by default. In addition, those files in the R,demo and man directories which
are flagged by R CMD check as having invalid names will be excluded.
Use R CMD build --help to obtain more information about the usage of the R package
builder.
Unless R CMD build is invoked with the --no-build-vignettes option (or the package’s
DESCRIPTION contains ‘BuildVignettes: no’ or similar), it will attempt to (re)build the vi-
gnettes (see Section 1.4 [Writing package vignettes], page 37) in the package. To do so it installs
the current package into a temporary library tree, but any dependent packages need to be
installed in an available library tree (see the Note: at the top of this section).
Similarly, if the .Rd documentation files contain any \Sexpr macros (see Section 2.12 [Dy-
namic pages], page 78), the package will be temporarily installed to execute them. Post-execution
binary copies of those pages containing build-time macros will be saved in build/partial.rdb.
If there are any install-time or render-time macros, a .pdf version of the package manual will
be built and installed in the build subdirectory. (This allows CRAN or other repositories to
display the manual even if they are unable to install the package.) This can be suppressed by
the option --no-manual or if package’s DESCRIPTION contains ‘BuildManual: no’ or similar.
One of the checks that R CMD build runs is for empty source directories. These are in
most (but not all) cases unintentional, if they are intentional use the option --keep-empty-
dirs (or set the environment variable _R_BUILD_KEEP_EMPTY_DIRS_ to ‘TRUE’, or have a
BuildKeepEmpty’ field with a true value in the DESCRIPTION file).
The --resave-data option allows saved images (.rda and .RData files) in the data directory
to be optimized for size. It will also compress tabular files and convert .R files to saved images.
It can take values no,gzip (the default if this option is not supplied, which can be changed
by setting the environment variable _R_BUILD_RESAVE_DATA_) and best (equivalent to giving it
without a value), which chooses the most effective compression. Using best adds a dependence
on R (>= 2.10) to the DESCRIPTION file if bzip2 or xz compression is selected for any of the
files. If this is thought undesirable, --resave-data=gzip (which is the default if that option is
not supplied) will do what compression it can with gzip. A package can control how its data
is resaved by supplying a ‘BuildResaveData’ field (with one of the values given earlier in this
paragraph) in its DESCRIPTION file.
The --compact-vignettes option will run tools::compactPDF over the PDF files in
inst/doc (and its subdirectories) to losslessly compress them. This is not enabled by default
(it can be selected by environment variable _R_BUILD_COMPACT_VIGNETTES_) and needs qpdf
(http://qpdf.sourceforge.net/) to be available.
52 called CVS or .svn or .arch-ids or .bzr or .git (but not files called .git) or .hg.
53 called .metadata.
54 which is an error: GNU make uses GNUmakefile.
Chapter 1: Creating R packages 37
It can be useful to run R CMD check --check-subdirs=yes on the built tarball as a final
check on the contents.
Where a non-POSIX file system is in use which does not utilize execute permissions, some
care is needed with permissions. This applies on Windows and to e.g. FAT-formatted drives and
SMB-mounted file systems on other OSes. The ‘mode’ of the file recorded in the tarball will be
whatever file.info() returns. On Windows this will record only directories as having execute
permission and on other OSes it is likely that all files have reported ‘mode’ 0777. A particular
issue is packages being built on Windows which are intended to contain executable scripts such as
configure and cleanup:R CMD build ensures those two are recorded with execute permission.
Directory build of the package sources is reserved for use by R CMD build: it contains infor-
mation which may not easily be created when the package is installed, including index informa-
tion on the vignettes and, rarely, information on the help pages and perhaps a copy of the PDF
reference manual (see above).
1.3.3 Building binary packages
Binary packages are compressed copies of installed versions of packages. They contain compiled
shared libraries rather than C, C++ or Fortran source code, and the R functions are included
in their installed form. The format and filename are platform-specific; for example, a binary
package for Windows is usually supplied as a .zip file, and for the macOS platform the default
binary package file extension is .tgz.
The recommended method of building binary packages is to use
R CMD INSTALL --build pkg where pkg is either the name of a source tarball (in the usual
.tar.gz format) or the location of the directory of the package source to be built. This operates
by first installing the package and then packing the installed binaries into the appropriate binary
package file for the particular platform.
By default, R CMD INSTALL --build will attempt to install the package into the default library
tree for the local installation of R. This has two implications:
If the installation is successful, it will overwrite any existing installation of the same package.
The default library tree must have write permission; if not, the package will not install and
the binary will not be created.
To prevent changes to the present working installation or to provide an install location with
write access, create a suitably located directory with write access and use the -l option to build
the package in the chosen location. The usage is then
R CMD INSTALL -l location --build pkg
where location is the chosen directory with write access. The package will be installed as a
subdirectory of location, and the package binary will be created in the current directory.
Other options for R CMD INSTALL can be found using R CMD INSTALL --help, and platform-
specific details for special cases are discussed in the platform-specific FAQs.
Finally, at least one web-based service is available for building binary packages from (checked)
source code: WinBuilder (see https://win-builder.R-project.org/) is able to build Win-
dows binaries. Note that this is intended for developers on other platforms who do not have
access to Windows but wish to provide binaries for the Windows platform.
1.4 Writing package vignettes
In addition to the help files in Rd format, R packages allow the inclusion of documents in
arbitrary other formats. The standard location for these is subdirectory inst/doc of a source
package, the contents will be copied to subdirectory doc when the package is installed. Pointers
from package help indices to the installed documents are automatically created. Documents
Chapter 1: Creating R packages 38
in inst/doc can be in arbitrary format, however we strongly recommend providing them in
PDF format, so users on almost all platforms can easily read them. To ensure that they can be
accessed from a browser (as an HTML index is provided), the file names should start with an
ASCII letter and be comprised entirely of ASCII letters or digits or hyphen or underscore.
A special case is package vignettes. Vignettes are documents in PDF or HTML format ob-
tained from plain text literate source files from which R knows how to extract R code and
create output (in PDF/HTML or intermediate L
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X). Vignette engines do this work, using
“tangle” and “weave” functions respectively. Sweave, provided by the R distribution, is the
default engine. Since R version 3.0.0, other vignette engines besides Sweave are supported; see
Section 1.4.2 [Non-Sweave vignettes], page 40.
Package vignettes have their sources in subdirectory vignettes of the package sources. Note
that the location of the vignette sources only affects R CMD build and R CMD check: the tarball
built by R CMD build includes in inst/doc the components intended to be installed.
Sweave vignette sources are normally given the file extension .Rnw or .Rtex, but for historical
reasons extensions55 .Snw and .Stex are also recognized. Sweave allows the integration of L
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documents: see the Sweave help page in R and the Sweave vignette in package utils for details
on the source document format.
Package vignettes are tested by R CMD check by executing all R code chunks they contain
(except those marked for non-evaluation, e.g., with option eval=FALSE for Sweave). The R
working directory for all vignette tests in R CMD check is a copy of the vignette source directory.
Make sure all files needed to run the R code in the vignette (data sets, . . .) are accessible
by either placing them in the inst/doc hierarchy of the source package or by using calls to
system.file(). All other files needed to re-make the vignettes (such as L
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input files and files for any figures not created by running the code in the vignette) must be in
the vignette source directory. R CMD check will check that vignette production has succeeded by
comparing modification times of output files in inst/doc with the source in vignettes.
R CMD build will automatically56 create the (PDF or HTML versions of the) vignettes in
inst/doc for distribution with the package sources. By including the vignette outputs in the
package sources it is not necessary that these can be re-built at install time, i.e., the package au-
thor can use private R packages, screen snapshots and L
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on his machine.57
By default R CMD build will run Sweave on all Sweave vignette source files in vignettes. If
Makefile is found in the vignette source directory, then R CMD build will try to run make after
the Sweave runs, otherwise texi2pdf is run on each .tex file produced.
The first target in the Makefile should take care of both creation of PDF/HTML files and
cleaning up afterwards (including after Sweave), i.e., delete all files that shall not appear in the
final package archive. Note that if the make step runs R it needs to be careful to respect the
environment values of R_LIBS and R_HOME58 . Finally, if there is a Makefile and it has a ‘clean:
target, make clean is run.
All the usual caveats about including a Makefile apply. It must be portable (no GNU
extensions), use LF line endings and must work correctly with a parallel make: too many authors
have written things like
## BAD EXAMPLE
all: pdf clean
55 and to avoid problems with case-insensitive file systems, lower-case versions of all these extensions.
56 unless inhibited by using ‘BuildVignettes: no’ in the DESCRIPTION file.
57 provided the conditions of the package’s license are met: many, including CRAN, see the omission of source
components as incompatible with an Open Source license.
58 R_HOME/bin is prepended to the PATH so that references to Ror Rscript in the Makefile do make use of the
currently running version of R.
Chapter 1: Creating R packages 39
pdf: ABC-intro.pdf ABC-details.pdf
%.pdf: %.tex
texi2dvi --pdf $*
clean:
rm *.tex ABC-details-*.pdf
which will start removing the source files whilst pdflatex is working.
Metadata lines can be placed in the source file, preferably in L
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One such is a \VignetteIndexEntry of the form
%\VignetteIndexEntry{Using Animal}
Others you may see are \VignettePackage (currently ignored), \VignetteDepends
and \VignetteKeyword (which replaced \VignetteKeywords). These are processed at
package installation time to create the saved data frame Meta/vignette.rds, but only
the \VignetteIndexEntry and \VignetteKeyword statements are currently used. The
\VignetteEngine statement is described in Section 1.4.2 [Non-Sweave vignettes], page 40.
At install time an HTML index for all vignettes in the package is automatically cre-
ated from the \VignetteIndexEntry statements unless a file index.html exists in directory
inst/doc. This index is linked from the HTML help index for the package. If you do supply a
inst/doc/index.html file it should contain relative links only to files under the installed doc
directory, or perhaps (not really an index) to HTML help files or to the DESCRIPTION file, and
be valid HTML as confirmed via the W3C Markup Validation Service (https://validator.w3.
org) or Validator.nu (https://validator.nu/).
Sweave/Stangle allows the document to specify the split=TRUE option to create a single R
file for each code chunk: this will not work for vignettes where it is assumed that each vignette
source generates a single file with the vignette extension replaced by .R.
Do watch that PDFs are not too large – one in a CRAN package was 72MB! This is usually
caused by the inclusion of overly detailed figures, which will not render well in PDF viewers.
Sometimes it is much better to generate fairly high resolution bitmap (PNG, JPEG) figures and
include those in the PDF document.
When R CMD build builds the vignettes, it copies these and the vignette sources from direc-
tory vignettes to inst/doc. To install any other files from the vignettes directory, include
a file vignettes/.install_extras which specifies these as Perl-like regular expressions on one
or more lines. (See the description of the .Rinstignore file for full details.)
1.4.1 Encodings and vignettes
Vignettes will in general include descriptive text, R input, R output and figures, L
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clude files and bibliographic references. As any of these may contain non-ASCII characters, the
handling of encodings can become very complicated.
The vignette source file should be written in ASCII or contain a declaration of the encoding
(see below). This applies even to comments within the source file, since vignette engines process
comments to look for options and metadata lines. When an engine’s weave and tangle functions
are called on the vignette source, it will be converted to the encoding of the current R session.
Stangle() will produce an R code file in the current locale’s encoding: for a non-ASCII
vignette what that is is recorded in a comment at the top of the file.
Sweave() will produce a .tex file in the current encoding, or in UTF-8 if that is declared.
Non-ASCII encodings need to be declared to L
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\usepackage[utf8]{inputenc}
Chapter 1: Creating R packages 40
(It is also possible to use the more recent ‘inputenx’ L
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X package.) For files where this line
is not needed (e.g. chapters included within the body of a larger document, or non-Sweave
vignettes), the encoding may be declared using a comment like
%\VignetteEncoding{UTF-8}
If the encoding is UTF-8, this can also be declared using the declaration
%\SweaveUTF8
If no declaration is given in the vignette, it will be assumed to be in the encoding declared for
the package. If there is no encoding declared in either place, then it is an error to use non-ASCII
characters in the vignette.
In any case, be aware that L
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X may require the ‘usepackage’ declaration.
Sweave() will also parse and evaluate the R code in each chunk. The R output will also
be in the current locale (or UTF-8 if so declared), and should be covered by the ‘inputenc
declaration. One thing people often forget is that the R output may not be ASCII even for
ASCII R sources, for many possible reasons. One common one is the use of ‘fancy’ quotes: see
the R help on sQuote: note carefully that it is not portable to declare UTF-8 or CP1252 to
cover such quotes, as their encoding will depend on the locale used to run Sweave(): this can
be circumvented by setting options(useFancyQuotes="UTF-8") in the vignette.
The final issue is the encoding of figures – this applies only to PDF figures and not PNG
etc. The PDF figures will contain declarations for their encoding, but the Sweave option
pdf.encoding may need to be set appropriately: see the help for the pdf() graphics device.
As a real example of the complexities, consider the fortunes (https://CRAN.R-project.org/
package=fortunes) package version ‘1.4-0’. That package did not have a declared encoding,
and its vignette was in ASCII. However, the data it displays are read from a UTF-8 CSV file
and will be assumed to be in the current encoding, so fortunes.tex will be in UTF-8 in any
locale. Had read.table been told the data were UTF-8, fortunes.tex would have been in the
locale’s encoding.
1.4.2 Non-Sweave vignettes
Vignettes in formats other than Sweave are supported via “vignette engines”. For example knitr
(https://CRAN.R-project.org/package=knitr) version 1.1 or later can create .tex files from
a variation on Sweave format, and .html files from a variation on “markdown” format. These
engines replace the Sweave() function with other functions to convert vignette source files into
L
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X files for processing into .pdf, or directly into .pdf or .html files. The Stangle() function
is replaced with a function that extracts the R source from a vignette.
R recognizes non-Sweave vignettes using filename extensions specified by the engine. For
example, the knitr (https: //CRAN.R-project . org / package=knitr) package supports the
extension .Rmd (standing for “R markdown”). The user indicates the vignette engine within the
vignette source using a \VignetteEngine line, for example
%\VignetteEngine{knitr::knitr}
This specifies the name of a package and an engine to use in place of Sweave in processing the
vignette. As Sweave is the only engine supplied with the R distribution, the package providing
any other engine must be specified in the ‘VignetteBuilder’ field of the package DESCRIPTION
file, and also specified in the ‘Suggests’, ‘Imports’ or ‘Depends’ field (since its namespace must
be available to build or check your package). If more than one package is specified as a builder,
they will be searched in the order given there. The utils package is always implicitly appended
to the list of builder packages, but may be included earlier to change the search order.
Note that a package with non-Sweave vignettes should always have a ‘VignetteBuilder
field in the DESCRIPTION file, since this is how R CMD check recognizes that there are vignettes
to be checked: packages listed there are required when the package is checked.
Chapter 1: Creating R packages 41
The vignette engine can produce .tex,.pdf, or .html files as output. If it produces .tex
files, R will call texi2pdf to convert them to .pdf for display to the user (unless there is a
Makefile in the vignettes directory).
Package writers who would like to supply vignette engines need to register those engines in
the package .onLoad function. For example, that function could make the call
tools::vignetteEngine("knitr", weave = vweave, tangle = vtangle,
pattern = "[.]Rmd$", package = "knitr")
(The actual registration in knitr (https:// CRAN.R-project .org/ package=knitr) is more
complicated, because it supports other input formats.) See the ?tools::vignetteEngine help
topic for details on engine registration.
1.5 Package namespaces
R has a namespace management system for code in packages. This system allows the package
writer to specify which variables in the package should be exported to make them available to
package users, and which variables should be imported from other packages.
The namespace for a package is specified by the NAMESPACE file in the top level package
directory. This file contains namespace directives describing the imports and exports of the
namespace. Additional directives register any shared objects to be loaded and any S3-style
methods that are provided. Note that although the file looks like R code (and often has R-
style comments) it is not processed as R code. Only very simple conditional processing of if
statements is implemented.
Packages are loaded and attached to the search path by calling library or require. Only the
exported variables are placed in the attached frame. Loading a package that imports variables
from other packages will cause these other packages to be loaded as well (unless they have already
been loaded), but they will not be placed on the search path by these implicit loads. Thus code
in the package can only depend on objects in its own namespace and its imports (including the
base namespace) being visible59.
Namespaces are sealed once they are loaded. Sealing means that imports and exports cannot
be changed and that internal variable bindings cannot be changed. Sealing allows a simpler
implementation strategy for the namespace mechanism. Sealing also allows code analysis and
compilation tools to accurately identify the definition corresponding to a global variable reference
in a function body.
The namespace controls the search strategy for variables used by functions in the package.
If not found locally, R searches the package namespace first, then the imports, then the base
namespace and then the normal search path.
1.5.1 Specifying imports and exports
Exports are specified using the export directive in the NAMESPACE file. A directive of the form
export(f, g)
specifies that the variables fand gare to be exported. (Note that variable names may be
quoted, and reserved words and non-standard names such as [<-.fractions must be.)
For packages with many variables to export it may be more convenient to specify the names
to export with a regular expression using exportPattern. The directive
exportPattern("^[^\\.]")
exports all variables that do not start with a period. However, such broad patterns are not
recommended for production code: it is better to list all exports or use narrowly-defined
59 Note that lazy-loaded datasets are not in the package’s namespace so need to be accessed via ::, e.g.
survival::survexp.us.
Chapter 1: Creating R packages 42
groups. (This pattern applies to S4 classes.) Beware of patterns which include names start-
ing with a period: some of these are internal-only variables and should never be exported, e.g.
.__S3MethodsTable__.’ (and the code nowadays excludes known cases).
Packages implicitly import the base namespace. Variables exported from other packages
with namespaces need to be imported explicitly using the directives import and importFrom.
The import directive imports all exported variables from the specified package(s). Thus the
directives
import(foo, bar)
specifies that all exported variables in the packages foo and bar are to be imported. If only
some of the exported variables from a package are needed, then they can be imported using
importFrom. The directive
importFrom(foo, f, g)
specifies that the exported variables fand gof the package foo are to be imported. Using
importFrom selectively rather than import is good practice and recommended notably when
importing from packages with more than a dozen exports.
To import every symbol from a package but for a few exceptions, pass the except argument
to import. The directive
import(foo, except=c(bar, baz))
imports every symbol from foo except bar and baz. The value of except should evaluate to
something coercible to a character vector, after substituting each symbol for its corresponding
string.
It is possible to export variables from a namespace which it has imported from other name-
spaces: this has to be done explicitly and not via exportPattern.
If a package only needs a few objects from another package it can use a fully qualified variable
reference in the code instead of a formal import. A fully qualified reference to the function fin
package foo is of the form foo::f. This is slightly less efficient than a formal import and also
loses the advantage of recording all dependencies in the NAMESPACE file (but they still need to be
recorded in the DESCRIPTION file). Evaluating foo::f will cause package foo to be loaded, but
not attached, if it was not loaded already—this can be an advantage in delaying the loading of
a rarely used package.
Using foo:::f instead of foo::f allows access to unexported objects. This is generally not
recommended, as the semantics of unexported objects may be changed by the package author
in routine maintenance.
1.5.2 Registering S3 methods
The standard method for S3-style UseMethod dispatching might fail to locate methods defined
in a package that is imported but not attached to the search path. To ensure that these methods
are available the packages defining the methods should ensure that the generics are imported
and register the methods using S3method directives. If a package defines a function print.foo
intended to be used as a print method for class foo, then the directive
S3method(print, foo)
ensures that the method is registered and available for UseMethod dispatch, and the function
print.foo does not need to be exported. Since the generic print is defined in base it does not
need to be imported explicitly.
(Note that function and class names may be quoted, and reserved words and non-standard
names such as [<- and function must be.)
It is possible to specify a third argument to S3method, the function to be used as the method,
for example
S3method(print, check_so_symbols, .print.via.format)
Chapter 1: Creating R packages 43
when print.check_so_symbols is not needed.
1.5.3 Load hooks
There are a number of hooks called as packages are loaded, attached, detached, and unloaded.
See help(".onLoad") for more details.
Since loading and attaching are distinct operations, separate hooks are provided for each.
These hook functions are called .onLoad and .onAttach. They both take arguments60 libname
and pkgname; they should be defined in the namespace but not exported.
Packages can use a .onDetach or .Last.lib function (provided the latter is exported from
the namespace) when detach is called on the package. It is called with a single argument,
the full path to the installed package. There is also a hook .onUnload which is called when
the namespace is unloaded (via a call to unloadNamespace, perhaps called by detach(unload
= TRUE)) with argument the full path to the installed package’s directory. .onUnload and
.onDetach should be defined in the namespace and not exported, but .Last.lib does need to
be exported.
Packages are not likely to need .onAttach (except perhaps for a start-up banner); code to
set options and load shared objects should be placed in a .onLoad function, or use made of the
useDynLib directive described next.
User-level hooks are also available: see the help on function setHook.
These hooks are often used incorrectly. People forget to export .Last.lib. Compiled
code should be loaded in .onLoad (or via auseDynLb directive: see below) and unloaded in
.onUnload. Do remember that a package’s namespace can be loaded without the namespace
being attached (e.g. by pkgname::fun) and that a package can be detached and re-attached
whilst its namespace remains loaded.
1.5.4 useDynLib
ANAMESPACE file can contain one or more useDynLib directives which allows shared objects that
need to be loaded.61 The directive
useDynLib(foo)
registers the shared object foo62 for loading with library.dynam. Loading of registered ob-
ject(s) occurs after the package code has been loaded and before running the load hook func-
tion. Packages that would only need a load hook function to load a shared object can use the
useDynLib directive instead.
The useDynLib directive also accepts the names of the native routines that are to be used in
Rvia the .C,.Call,.Fortran and .External interface functions. These are given as additional
arguments to the directive, for example,
useDynLib(foo, myRoutine, myOtherRoutine)
By specifying these names in the useDynLib directive, the native symbols are resolved when
the package is loaded and R variables identifying these symbols are added to the package’s
namespace with these names. These can be used in the .C,.Call,.Fortran and .External
calls in place of the name of the routine and the PACKAGE argument. For instance, we can call
the routine myRoutine from R with the code
.Call(myRoutine, x, y)
rather than
.Call("myRoutine", x, y, PACKAGE = "foo")
60 they will be called with two unnamed arguments, in that order.
61 NB: this will only be read in all versions of R if the package contains R code in a Rdirectory.
62 Note that this is the basename of the shared object, and the appropriate extension (.so or .dll) will be
added.
Chapter 1: Creating R packages 44
There are at least two benefits to this approach. Firstly, the symbol lookup is done just
once for each symbol rather than each time the routine is invoked. Secondly, this removes any
ambiguity in resolving symbols that might be present in several compiled DLLs. However, this
approach is nowadays deprecated in favour of supplying registration information (see below).
In some circumstances, there will already be an R variable in the package with the same name
as a native symbol. For example, we may have an R function in the package named myRoutine.
In this case, it is necessary to map the native symbol to a different R variable name. This can
be done in the useDynLib directive by using named arguments. For instance, to map the native
symbol name myRoutine to the R variable myRoutine_sym, we would use
useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)
We could then call that routine from R using the command
.Call(myRoutine_sym, x, y)
Symbols without explicit names are assigned to the R variable with that name.
In some cases, it may be preferable not to create R variables in the package’s namespace
that identify the native routines. It may be too costly to compute these for many routines
when the package is loaded if many of these routines are not likely to be used. In this case,
one can still perform the symbol resolution correctly using the DLL, but do this each time the
routine is called. Given a reference to the DLL as an R variable, say dll, we can call the routine
myRoutine using the expression
.Call(dll$myRoutine, x, y)
The $operator resolves the routine with the given name in the DLL using a call to
getNativeSymbol. This is the same computation as above where we resolve the symbol when the
package is loaded. The only difference is that this is done each time in the case of dll$myRoutine.
In order to use this dynamic approach (e.g., dll$myRoutine), one needs the reference to the
DLL as an R variable in the package. The DLL can be assigned to a variable by using the
variable = dllName format used above for mapping symbols to R variables. For example, if
we wanted to assign the DLL reference for the DLL foo in the example above to the variable
myDLL, we would use the following directive in the NAMESPACE file:
myDLL = useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)
Then, the R variable myDLL is in the package’s namespace and available for calls such as
myDLL$dynRoutine to access routines that are not explicitly resolved at load time.
If the package has registration information (see Section 5.4 [Registering native routines],
page 108), then we can use that directly rather than specifying the list of symbols again in
the useDynLib directive in the NAMESPACE file. Each routine in the registration information
is specified by giving a name by which the routine is to be specified along with the address
of the routine and any information about the number and type of the parameters. Using the
.registration argument of useDynLib, we can instruct the namespace mechanism to create R
variables for these symbols. For example, suppose we have the following registration information
for a DLL named myDLL:
static R_NativePrimitiveArgType foo_t[] = {
REALSXP, INTSXP, STRSXP, LGLSXP
};
static const R_CMethodDef cMethods[] = {
{"foo", (DL_FUNC) &foo, 4, foo_t},
{"bar_sym", (DL_FUNC) &bar, 0},
{NULL, NULL, 0, NULL}
};
Chapter 1: Creating R packages 45
static const R_CallMethodDef callMethods[] = {
{"R_call_sym", (DL_FUNC) &R_call, 4},
{"R_version_sym", (DL_FUNC) &R_version, 0},
{NULL, NULL, 0}
};
Then, the directive in the NAMESPACE file
useDynLib(myDLL, .registration = TRUE)
causes the DLL to be loaded and also for the R variables foo,bar_sym,R_call_sym and R_
version_sym to be defined in the package’s namespace.
Note that the names for the R variables are taken from the entry in the registration informa-
tion and do not need to be the same as the name of the native routine. This allows the creator
of the registration information to map the native symbols to non-conflicting variable names in
R, e.g. R_version to R_version_sym for use in an R function such as
R_version <- function()
{
.Call(R_version_sym)
}
Using argument .fixes allows an automatic prefix to be added to the registered symbols,
which can be useful when working with an existing package. For example, package KernSmooth
(https://CRAN.R-project.org/package=KernSmooth) has
useDynLib(KernSmooth, .registration = TRUE, .fixes = "F_")
which makes the R variables corresponding to the FORTRAN symbols F_bkde and so on, and
so avoid clashes with R code in the namespace.
NB: Using these arguments for a package which does not register native symbols merely slows
down the package loading (although at the time of writing 90 CRAN packages did so). Once
symbols are registered, check that the corresponding R variables are not accidentally exported
by a pattern in the NAMESPACE file.
1.5.5 An example
As an example consider two packages named foo and bar. The R code for package foo in file
foo.R is
 
x <- 1
f <- function(y) c(x,y)
foo <- function(x) .Call("foo", x, PACKAGE="foo")
print.foo <- function(x, ...) cat("<a foo>\n")
 
Some C code defines a C function compiled into DLL foo (with an appropriate extension). The
NAMESPACE file for this package is
 
useDynLib(foo)
export(f, foo)
S3method(print, foo)
 
The second package bar has code file bar.R
Chapter 1: Creating R packages 46
 
c <- function(...) sum(...)
g <- function(y) f(c(y, 7))
h <- function(y) y+9
 
and NAMESPACE file
 
import(foo)
export(g, h)
 
Calling library(bar) loads bar and attaches its exports to the search path. Package foo is also
loaded but not attached to the search path. A call to gproduces
> g(6)
[1] 1 13
This is consistent with the definitions of cin the two settings: in bar the function cis defined
to be equivalent to sum, but in foo the variable crefers to the standard function cin base.
1.5.6 Namespaces with S4 classes and methods
Some additional steps are needed for packages which make use of formal (S4-style) classes and
methods (unless these are purely used internally). The package should have Depends: methods
in its DESCRIPTION file63 and import(methods) or importFrom(methods, ...) plus any classes
and methods which are to be exported need to be declared in the NAMESPACE file. For example,
the stats4 package has
export(mle) # exporting methods implicitly exports the generic
importFrom("graphics", plot)
importFrom("stats", optim, qchisq)
## For these, we define methods or (AIC, BIC, nobs) an implicit generic:
importFrom("stats", AIC, BIC, coef, confint, logLik, nobs, profile,
update, vcov)
exportClasses(mle, profile.mle, summary.mle)
## All methods for imported generics:
exportMethods(coef, confint, logLik, plot, profile, summary,
show, update, vcov)
## implicit generics which do not have any methods here
export(AIC, BIC, nobs)
All S4 classes to be used outside the package need to be listed in an exportClasses direc-
tive. Alternatively, they can be specified using exportClassPattern64 in the same style as
for exportPattern. To export methods for generics from other packages an exportMethods
directive can be used.
Note that exporting methods on a generic in the namespace will also export the generic, and
exporting a generic in the namespace will also export its methods. If the generic function is not
local to this package, either because it was imported as a generic function or because the non-
generic version has been made generic solely to add S4 methods to it (as for functions such as
plot in the example above), it can be declared via either or both of export or exportMethods,
but the latter is clearer (and is used in the stats4 example above). In particular, for primitive
functions there is no generic function, so export would export the primitive, which makes no
sense. On the other hand, if the generic is local to this package, it is more natural to export the
function itself using export(), and this must be done if an implicit generic is created without
setting any methods for it (as is the case for AIC in stats4).
63 This was necessary at least prior to R 3.0.2 as the methods package looked for its own R code on the search
path.
64 This defaults to the same pattern as exportPattern: use something like exportClassPattern("^$") to
override this.
Chapter 1: Creating R packages 47
A non-local generic function is only exported to ensure that calls to the function will dispatch
the methods from this package (and that is not done or required when the methods are for
primitive functions). For this reason, you do not need to document such implicitly created
generic functions, and undoc in package tools will not report them.
If a package uses S4 classes and methods exported from another package, but does not
import the entire namespace of the other package65, it needs to import the classes and methods
explicitly, with directives
importClassesFrom(package, ...)
importMethodsFrom(package, ...)
listing the classes and functions with methods respectively. Suppose we had two small packages
Aand Bwith Busing A. Then they could have NAMESPACE files
 
export(f1, ng1)
exportMethods("[")
exportClasses(c1)
 
and  
importFrom(A, ng1)
importClassesFrom(A, c1)
importMethodsFrom(A, f1)
export(f4, f5)
exportMethods(f6, "[")
exportClasses(c1, c2)
 
respectively.
Note that importMethodsFrom will also import any generics defined in the namespace on
those methods.
It is important if you export S4 methods that the corresponding generics are available. You
may for example need to import plot from graphics to make visible a function to be converted
into its implicit generic. But it is better practice to make use of the generics exported by stats4
as this enables multiple packages to unambiguously set methods on those generics.
1.6 Writing portable packages
This section contains advice on writing packages to be used on multiple platforms or for distri-
bution (for example to be submitted to a package repository such as CRAN).
Portable packages should have simple file names: use only alphanumeric ASCII characters
and period (.), and avoid those names not allowed under Windows which are mentioned above.
Many of the graphics devices are platform-specific: even X11() (aka x11()) which although
emulated on Windows may not be available on a Unix-alike (and is not the preferred screen
device on OS X). It is rarely necessary for package code or examples to open a new device, but
if essential,66 use dev.new().
Use R CMD build to make the release .tar.gz file.
R CMD check provides a basic set of checks, but often further problems emerge when people
try to install and use packages submitted to CRAN – many of these involve compiled code. Here
are some further checks that you can do to make your package more portable.
65 if it does, there will be opaque warnings about replacing imports if the classes/methods are also imported.
66 People use dev.new() to open a device at a particular size: that is not portable but using dev.new(noRStudioGD
= TRUE) helps.
Chapter 1: Creating R packages 48
If your package has a configure script, provide a configure.win script to be used on
Windows (an empty file if no actions are needed).
If your package has a Makevars or Makefile file, make sure that you use only portable
make features. Such files should be LF-terminated67 (including the final line of the file)
and not make use of GNU extensions. (The POSIX specification is available at http://
pubs.opengroup. org / onlinepubs / 9699919799/utilities/make . html; anything not
documented there should be regarded as an extension to be avoided.) Commonly misused
GNU extensions are conditional inclusions (ifeq and the like), ${shell ...},${wildcard
...} and similar, and the use of +=68 and :=. Also, the use of $< other than in implicit rules
is a GNU extension, as is the $^ macro. Unfortunately makefiles which use GNU extensions
often run on other platforms but do not have the intended results.
The use of ${shell ...} can be avoided by using backticks, e.g.
PKG_CPPFLAGS = ‘gsl-config --cflags‘
which works in all versions of make known69 to be used with R.
If you really must require GNU make, declare it in the DESCRIPTION file by
SystemRequirements: GNU make
and ensure that you use the value of environment variable MAKE (and not just make) in your
scripts. (On some platforms GNU make is available under a name such as gmake, and there
SystemRequirements is used to set MAKE.)
If you only need GNU make for parts of the package which are rarely needed (for example
to create bibliography files under vignettes), use a file called GNUmakefile rather than
Makefile as GNU make (only) will use the former.
Since the only viable make for Windows is GNU make, it is permissible to use GNU exten-
sions in files Makevars.win or Makefile.win.
Bash extensions also need to be avoided in shell scripts, including expressions in Makefiles
(which are passed to the shell for processing). Some R platforms use strict70 Bourne shells:
the R toolset on Windows and some Unix-alike OSes use ash (https://en.wikipedia.
org/wiki/Almquist_shell), a rather minimal shell with few builtins. Beware of assuming
that all the POSIX command-line utilities are available, especially on Windows where only a
minimal set is provided for use with R. (See Section “The command line tools” in R Installa-
tion and Administration.) One particular issue is the use of echo, for which two behaviours
are allowed (http://pubs.opengroup.org/onlinepubs/9699919799/utilities/echo.
html) and both occur as defaults on R platforms: portable applications should not use -n
(as the first argument) nor escape sequences. Another common issue is the construction
export FOO=value
which is bash-specific (first set the variable then export it by name).
Make use of the abilities of your compilers to check the standards-conformance of your
code. For example, gcc and gfortran71 can be used with options -Wall -pedantic to
alert you to potential problems. This is particularly important for C++, where g++ -Wall
-pedantic will alert you to the use of some of the GNU extensions which fail to compile
67 Solaris make does not accept CRLF-terminated Makefiles; Solaris warns about and some other makes ignore
incomplete final lines.
68 This was apparently introduced in SunOS 4, and is available elsewhere provided it is surrounded by spaces.
69 GNU make, BSD make formerly in FreeBSD and macOS, AT&T make as implemented on Solaris, pmake in
FreeBSD, ‘Distributed Make’ (dmake), part of Oracle Studio and available in other versions.
70 For example, test options -a and -e are not portable, and not supported in the AT&T Bourne shell used on
Solaris 10/11, even though they are in the 2008 POSIX standard. Nor does Solaris support ‘$(cmd)’.
71 http://fortranwiki.org/fortran/show/Modernizing+Old+Fortran may help explain some of the warnings
from gfortran -Wall -pedantic.
Chapter 1: Creating R packages 49
on most other C++ compilers. If R was not configured accordingly, one can achieve this via
personal Makevars files. See Section “Customizing package compilation” in R Installation
and Administration,
Portable C++ code needs to follow the 1998 standard (and not use features from C99), or
to specify a C++11 compiler (see Section 1.2.4 [Using C++11 code], page 29) where available
(which is not the case on all R platforms).
If you use FORTRAN 77, ftnchek (http://www.dsm.fordham.edu/~ftnchek/) provides
thorough testing of conformance to the standard.
If using Fortran 9x with the GNU compiler, use the flags -std=f95 -Wall -pedantic which
reject most GNU extensions and features from later standards.
R has tested that DOUBLE COMPLEX works (although an extension to the Fortran stan-
dards) and so is preferred to COMPLEX*16. (Fortran 9x code can use something like
COMPLEX(KIND=KIND(0.0D0))72.)
Not all common R platforms conform to the expected standards, e.g. C99 for C code. One
common area of problems is the *printf functions where Windows does not support %lld,
%Lf and similar formats (and has its own formats such as %I64d for 64-bit integers). It
is very rare to need to output such types, and 64-bit integers can usually be converted to
doubles for output.
R CMD check performs some checks for non-portable compiler/linker flags in src/Makevars.
However, it cannot check the meaning of such flags, and some are commonly accepted but
with compiler-specific meanings. There are other non-portable flags which are not checked,
nor are src/Makefile files and makefiles in sub-directories. As a comment in the code says
It is hard to think of anything apart from -I* and -D* that is safe for general
use . . .
although -pthread is pretty close to portable. (Option -U is portable but little use on the
command line as it will only cancel built-in defines (not portable) and those defined earlier
on the command line (R does not use any).)
Do be very careful with passing arguments between R, C and FORTRAN code. In particular,
long in C will be 32-bit on some R platforms (including 64-bit Windows), but 64-bit on
most modern Unix and Linux platforms. It is rather unlikely that the use of long in C code
has been thought through: if you need a longer type than int you should use a configure test
for a C99/C++11 type such as int_fast64_t (and failing that, long long73) and typedef
your own type to be long or long long, or use another suitable type (such as size_t).
It is not safe to assume that long and pointer types are the same size, and they are not on
64-bit Windows. If you need to convert pointers to and from integers use the C99/C++11
integer types intptr_t and uintptr_t (which are defined in the header <stdint.h> and
are not required to be implemented by the C99 standard but are used in C code by R itself).
Note that integer in FORTRAN corresponds to int in C on all R platforms.
Under no circumstances should your compiled code ever call abort or exit74 : these termi-
nate the user’s R process, quite possibly including all his unsaved work. One usage that
could call abort is the assert macro in C or C++ functions, which should never be active in
production code. The normal way to ensure that is to define the macro NDEBUG, and R CMD
INSTALL does so as part of the compilation flags. If you wish to use assert during devel-
opment. you can include -UNDEBUG in PKG_CPPFLAGS. Note that your own src/Makefile
or makefiles in sub-directories may also need to define NDEBUG.
72 See http://people.ds.cam.ac.uk/nmm1/fortran/paper_07.pdf.
73 but note that long long is not a standard C++98 type, and C++ compilers set up for strict checking will reject
it.
74 or where supported the variants _Exit and _exit.
Chapter 1: Creating R packages 50
This applies not only to your own code but to any external software you compile in or link
to.
Compiled code should not write to stdout or stderr and C++ and Fortran I/O should not
be used. As with the previous item such calls may come from external software and may
never be called, but package authors are often mistaken about that.
Compiled code should not call the system random number generators such as rand,drand48
and random75, but rather use the interfaces to R’s RNGs described in Section 6.3 [Random
numbers], page 146. In particular, if more than one package initializes the system RNG
(e.g. via srand), they will interfere with each other.
Nor should the C++11 random number library be used, nor any other third-party random
number generators such as those in GSL.
Errors in memory allocation and reading/writing outside arrays are very common causes
of crashes (e.g., segfaults) on some machines. See Section 4.3 [Checking memory access],
page 95, for tools which can be used to look for this.
Many platforms will allow unsatisfied entry points in compiled code, but will crash the
application (here R) if they are ever used. Some (notably Windows) will not. Looking at
the output of
nm -pg mypkg.so
and checking if any of the symbols marked Uis unexpected is a good way to avoid this.
Linkers have a lot of freedom in how to resolve entry points in dynamically-loaded code,
so the results may differ by platform. One area that has caused grief is packages including
copies of standard system software such as libz (especially those already linked into R).
In the case in point, entry point gzgets was sometimes resolved against the old version
compiled into the package, sometimes against the copy compiled into R and sometimes
against the system dynamic library. The only safe solution is to rename the entry points
in the copy in the package. We have even seen problems with entry point name myprintf,
which is a system entry point76 on some Linux systems.
Conflicts between symbols in DLLs are handled in very platform-specific ways. Good ways
to avoid trouble are to make as many symbols as possible static (check with nm -pg), and
to use names which are clearly tied to your package (which also helps users if anything does
go wrong). Note that symbol names starting with R_ are regarded as part of R’s namespace
and should not be used in packages.
It is good practice for DLLs to register their symbols (see Section 5.4 [Registering native
routines], page 108), restrict visibility (see Section 6.15 [Controlling visibility], page 158)
and not allow symbol search (see Section 5.4 [Registering native routines], page 108). It
should be possible for a DLL to have only one visible symbol, R_init_pkgname, on suitable
platforms77, which would completely avoid symbol conflicts.
It is not portable to call compiled code in R or other packages via .Internal,.C,.Fortran,
.Call or .External, since such interfaces are subject to change without notice and will
probably result in your code terminating the R process.
Do not use (hard or symbolic) file links in your package sources. Where possible R CMD
build will replace them by copies.
If you do not yourself have a Windows system, consider submitting your source package to
WinBuilder (https://win-builder.r-project.org/) before distribution.
75 This and srandom are in any case not portable. They are in POSIX but not in the C99 standard, and not
available on Windows.
76 in libselinux.
77 At least Linux and Windows, but not macOS.
Chapter 1: Creating R packages 51
It is bad practice for package code to alter the search path using library,require or
attach and this often does not work as intended. For alternatives, see Section 1.1.3.1
[Suggested packages], page 11, and with.
Examples can be run interactively via example as well as in batch mode when checking.
So they should behave appropriately in both scenarios, conditioning by interactive() the
parts which need an operator or observer. For instance, progress bars78 are only appropriate
in interactive use, as is displaying help pages or calling View() (see below).
Be careful with the order of entries in macros such as PKG_LIBS. Some linkers will re-order
the entries, and behaviour can differ between dynamic and static libraries. Generally -L
options should precede79 the libraries (typically specified by -l options) to be found from
those directories, and libraries are searched once in the order they are specified. Not all
linkers allow a space after -L .
Care is needed with the use of LinkingTo. This puts one or more directories on the include
search path ahead of system headers but (prior to R 3.4.0) after those specified in the
CPPFLAGS macro of the R build (which normally includes -I/usr/local/include, but
most platforms ignore that and include it with the system headers).
Any confusion would be avoided by having LinkingTo headers in a directory named after
the package. In any case, name conflicts of headers and directories under package include
directories should be avoided, both between packages and between a package and system
and third-party software.
The ar utility is often used in makefiles to make static libraries. Its modifier uis defined
by POSIX but is disabled in GNU ar on some recent Linux distributions which use ‘deter-
ministic mode’. The safest way to make a static library is to first remove any existing file of
that name then use ar -cr and then ranlib if needed (which is system-dependent: on most
systems80 ar always maintains a symbol table). The POSIX standard says options should
be preceded by a hyphen (as in -cr), although most OSes accept them without. Note that
on some systems ar -cr must have at least one file specified.
Some people have a need to set a locale. Locale names are not portable, and e.g.
fr_FR.utf8’ is commonly used on Linux but not accepted on either Solaris or macOS.
fr_FR.UTF-8’ is more portable, being accepted on recent Linux, AIX, FreeBSD, macOS
and Solaris (at least). However, some Linux distributions micro-package, so locales defined
by glibc (including these examples) may not be installed.
Avoid spaces in file names, not least as they can cause difficulties for external tools. A recent
example was a package with a knitr (https: //CRAN .R-project. org/package=knitr)
vignette that used spaces in plot names: this caused some versions of pandoc to fail with a
baffling error message.
Non-ASCII filenames can also cause problems (particularly in non-UTF-8 locales).
Make sure that any version requirement for Java code is both declared in the
SystemRequirements’ field and tested at runtime (not least as the Java installation when
the package is installed might not be the same as when the package is run and will not
be for binary packages). Java 8 (aka 1.8) is available for fewer platforms than Java 7. A
suitable test for packages using rJava (https://CRAN.R-project.org/package=rJava)
would be
.jinit()
jv <- .jcall("java/lang/System", "S", "getProperty", "java.runtime.version")
if(substr(jv, 1L, 1L) == "1") {
78 except perhaps the simplest kind as used by download.file() in non-interactive use.
79 Whereas the GNU linker reorders so -L options are processed first, the Solaris one does not.
80 some versions of macOS did not.
Chapter 1: Creating R packages 52
jvn <- as.numeric(paste0(strsplit(jv, "[.]")[[1L]][1:2], collapse = "."))
if(jvn < 1.8) stop("Java 8 is needed for this package but not available")
}
(Java 9 changed the format of this string.)
Some packages have stated a requirement on a particular JDK, but a package should only
be requiring a JRE unless providing its own Java interface.
A package with a hard-to-satisfy system requirement is by definition not portable, an-
noyingly so if this is not declared in the ‘SystemRequirements’ field. The most common
example is the use of pandoc, which is only available for a very limited range of platforms
(and has onerous requirements to install from source) and has capabilities81 that vary by
build but are not documented.
An external command can be an optional requirement for an imported package but needed
for examples or tests in the package itself. Such usage should always be conditional on a test
for existence (perhaps using Sys.which), as well as declared in the ‘SystemRequirements
field.
Be sure to use portable encoding names: none of utf8,mac and macroman are. See the help
for file for more details.
Do not invoke R by plain R,Rscript or (on Windows) Rterm in your examples, tests,
vignettes, makefiles or other scripts. As pointed out in several places earlier in this manual,
use something like
"$(R_HOME)/bin/Rscript"
"$(R_HOME)/bin$(R_ARCH_BIN)/Rterm"
with appropriate quotes (as, although not recommended, R_HOME can contain spaces).
Do be careful in what your tests (and examples) actually test. Bad practice seen in distributed
packages include:
It is not reasonable to test the time taken by a command: you cannot know how fast or
how heavily loaded an R platform might be. At best you can test a ratio of times, and even
that is fraught with difficulties.
Do not test the exact format of R messages (from R itself or from other packages): They
change, and they can be translated.
Packages have even tested the exact format of system error messages, which are platform-
dependent and perhaps locale-dependent.
If you use functions such as View, remember that in testing there is no one to look at the
output. It is better to use something like one of
if(interactive()) View(obj) else print(head(obj))
if(interactive()) View(obj) else str(obj)
Only test the accuracy of results if you have done a formal error analysis. Things such
as checking that probabilities numerically sum to one are silly: numerical tests should
always have a tolerance. That the tests on your platform achieve a particular tolerance says
little about other platforms. R is configured by default to make use of long doubles where
available, but they may not be available or be too slow for routine use. Most R platforms
use ‘ix86’ or ‘x86_64’ CPUs: these use extended precision registers on some but not all of
their FPU instructions. Thus the achieved precision can depend on the compiler version
and optimization flags—our experience is that 32-bit builds tend to be less precise than
64-bit ones. But not all platforms use those CPUs, and not all82 which use them configure
81 For example, the ability to handle ‘https://’ URLs, which even the build in some major Linux distributions
in 2017 did not possess.
82 Not doing so is the default on Windows, overridden for the R executables. It is also the default on some
Solaris compilers.
Chapter 1: Creating R packages 53
them to allow the use of extended precision. In particular, ARM CPUs do not (currently)
have extended precision nor long doubles, and long double was 64-bit on HP/PA Linux.
If you must try to establish a tolerance empirically, configure and build R with --disable-
long-double and use appropriate compiler flags (such as -ffloat-store and -fexcess-
precision=standard for gcc, depending on the CPU type83 ) to mitigate the effects of
extended-precision calculations.
Tests which involve random inputs or non-deterministic algorithms should normally set a
seed or be tested for many seeds.
1.6.1 PDF size
There are a several tools available to reduce the size of PDF files: often the size can be reduced
substantially with no or minimal loss in quality. Not only do large files take up space: they can
stress the PDF viewer and take many minutes to print (if they can be printed at all).
qpdf (http://qpdf.sourceforge.net/) can compress losslessly. It is fairly readily available
(e.g. it has binaries for Windows and packages in Debian/Ubuntu/Fedora, and is installed as
part of the CRAN macOS distribution of R). R CMD build has an option to run qpdf over PDF
files under inst/doc and replace them if at least 10Kb and 10% is saved. The full path to the
qpdf command can be supplied as environment variable R_QPDF (and is on the CRAN binary
of R for macOS). It seems MiKTeX does not use PDF object compression and so qpdf can
reduce considerably the files it outputs: MiKTeX can be overridden by code in the preamble of
an Sweave or L
A
T
E
X file — see how this is done for the R reference manual at https://svn.
r-project.org/R/trunk/doc/manual/refman.top.
Other tools can reduce the size of PDFs containing bitmap images at excessively high reso-
lution. These are often best re-generated (for example Sweave defaults to 300 ppi, and 100–150
is more appropriate for a package manual). These tools include Adobe Acrobat (not Reader),
Apple’s Preview84 and Ghostscript (which converts PDF to PDF by
ps2pdf options -dAutoRotatePages=/None in.pdf out.pdf
and suitable options might be
-dPDFSETTINGS=/ebook
-dPDFSETTINGS=/screen
; see http://www.ghostscript.com/doc/current/Ps2pdf.htm for more such and consider all
the options for image downsampling). There have been examples in CRAN packages for which
Ghostscript 9.06 and later produced much better reductions than 9.05 or earlier.
We come across occasionally large PDF files containing excessively complicated figures using
PDF vector graphics: such figures are often best redesigned or failing that, output as PNG files.
Option --compact-vignettes to R CMD build defaults to value ‘qpdf’: use ‘both’ to
try harder to reduce the size, provided you have Ghostscript available (see the help for
tools::compactPDF).
1.6.2 Check timing
There are several ways to find out where time is being spent in the check process. Start by setting
the environment variable _R_CHECK_TIMINGS_ to ‘0’. This will report the total CPU times (not
Windows) and elapsed times for installation and running examples, tests and vignettes, under
each sub-architecture if appropriate. For tests and vignettes, it reports the time for each as well
as the total.
83 These are not needed for the default compiler settings on ‘x86_64’ but are likely to be needed on ‘ix86’.
84 Select ‘Save as’, and select ‘Reduce file size’ from the ‘Quartz filter’ menu’: this can be accessed in other ways,
for example by Automator.
Chapter 1: Creating R packages 54
Setting _R_CHECK_TIMINGS_ to a positive value sets a threshold (in seconds elapsed time) for
reporting timings.
If you need to look in more detail at the timings for examples, use option --timings
to R CMD check (this is set by --as-cran). This adds a summary to the check output for
all the examples with CPU or elapsed time of more than 5 seconds. It produces a file
mypkg.Rcheck/mypkg-Ex.timings containing timings for each help file: it is a tab-delimited
file which can be read into R for further analysis.
Timings for the tests and vignette runs are given at the bottom of the corresponding log
file: note that log files for successful vignette runs are only retained if environment variable
_R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_ is set to a true value.
1.6.3 Encoding issues
Care is needed if your package contains non-ASCII text, and in particular if it is intended to be
used in more than one locale. It is possible to mark the encoding used in the DESCRIPTION file
and in .Rd files, as discussed elsewhere in this manual.
First, consider carefully if you really need non-ASCII text. Many users of R will only be
able to view correctly text in their native language group (e.g. Western European, Eastern
European, Simplified Chinese) and ASCII.85. Other characters may not be rendered at all,
rendered incorrectly, or cause your R code to give an error. For .Rd documentation, marking
the encoding and including ASCII transliterations is likely to do a reasonable job. The set of
characters which is commonly supported is wider than it used to be around 2000, but non-Latin
alphabets (Greek, Russian, Georgian, . . .) are still often problematic and those with double-
width characters (Chinese, Japanese, Korean) often need specialist fonts to render correctly.
Several CRAN packages have messages in their R code in French (and a few in German). A
better way to tackle this is to use the internationalization facilities discussed elsewhere in this
manual.
Function showNonASCIIfile in package tools can help in finding non-ASCII bytes in files.
There is a portable way to have arbitrary text in character strings (only) in your R code,
which is to supply them in Unicode as \uxxxx escapes. If there are any characters not in the
current encoding the parser will encode the character string as UTF-8 and mark it as such.
This applies also to character strings in datasets: they can be prepared using \uxxxx escapes or
encoded in UTF-8 in a UTF-8 locale, or even converted to UTF-8 via ‘iconv()’. If you do this,
make sure you have ‘R (>= 2.10)’ (or later) in the ‘Depends’ field of the DESCRIPTION file.
R sessions running in non-UTF-8 locales will if possible re-encode such strings for display
(and this is done by RGui on Windows, for example). Suitable fonts will need to be selected
or made available86 both for the console/terminal and graphics devices such as ‘X11()’ and
windows()’. Using ‘postscript’ or ‘pdf’ will choose a default 8-bit encoding depending on the
language of the UTF-8 locale, and your users would need to be told how to select the ‘encoding
argument.
If you want to run R CMD check on a Unix-alike over a package that sets a package encoding
in its DESCRIPTION file and do not use a UTF-8 locale you may need to specify a suitable locale
via environment variable R_ENCODING_LOCALES. The default is equivalent to the value
"latin1=en_US:latin2=pl_PL:UTF-8=en_US.UTF-8:latin9=fr_FR.iso885915@euro"
(which is appropriate for a system based on glibc: macOS requires latin9=fr_FR.ISO8859-15)
except that if the current locale is UTF-8 then the package code is translated to UTF-8 for syntax
checking, so it is strongly recommended to check in a UTF-8 locale.
85 except perhaps some special characters such as backslash and hash which may be taken over for currency
symbols.
86 Typically on a Unix-alike this is done by telling fontconfig where to find suitable fonts to select glyphs from.
Chapter 1: Creating R packages 55
1.6.4 Portable C and C++ code
Writing portable C and C++ code is mainly a matter of observing the standards (C99, C++98 or
where declared C++11/14) and testing that extensions (such as POSIX functions) are supported.
Note that the ‘TR1’ C++ extensions are not part of any of these standards and the <tr1/name>
headers are not supplied by some of the compilers used for R, including on macOS. (Use the
C++11 versions instead.)
Note too that the POSIX standards only require recently-defined functions to be declared if
certain macros are defined with large enough values, and on some compiler/OS combinations87
they are not declared otherwise. So you may need to include something like one of88
#define _XOPEN_SOURCE 500
or
#ifdef __GLIBC__
# define _POSIX_C_SOURCE 200809L
#endif
before any headers. (strdup and strncasecmp are two such functions.)
However, some common errors are worth pointing out here. It can be helpful to look up
functions at http://www.cplusplus.com/reference/ or http://en.cppreference.com/w/
and compare what is defined in the various standards.
Both the compiler and OS (via system header files, which may differ by architecture even
for nominally the same OS) affect the compilability of C/C++ code. Compilers from the GCC,
clang, Intel and Oracle Studio suites are routinely used with R, and both clang and Oracle
have more than one implementation of C++ headers and library. The range of possibilities makes
comprehensive empirical checking impossible, and regrettably compilers are patchy at best on
warning about non-standard code.
Mathematical functions such as sqrt are defined in C++ for floating-point arguments. It
is legitimate in C++ to overload these with versions for types float,double,long double
and possibly more. This means that calling sqrt on an integer type may have ‘overloading
ambiguity’ as it could be promoted to any of the supported floating-point types: this
is commonly seen on Solaris, but for pow also seen on macOS. (C++98 has an overload
for std::pow(<double>, <int>), but this may not be visible from the main namespace.
C++11 requires additional overloads for integer types, and ambiguous overloads are more
common in C++11 (and later) compiler modes.)
A not-uncommonly-seen problem is to mistakenly call floor(x/y) or ceil(x/y) for int
arguments xand y. Since x/y does integer division, the result is an int and ‘overloading
ambiguity’ may be reported. Some people have (pointlessly) called floor and ceil on
integer arguments, which may have an ‘overloading ambiguity’.
A surprising common misuse is things like pow(10, -3): this should be the constant 1e-3.
Function fabs is defined only for floating-point types, except in C++11 which has overloads
for std::fabs in <cmath> for integer types. Function abs is defined in C99’s <stdlib.h>
for int and in C++98’s <cstdlib> for integer types, overloaded in <cmath> for floating-point
types. C++11 has additional overloads for std::abs in <cmath> for integer types. The effect
of calling abs with a floating-point type is implementation-specific: it may truncate to an
integer.
87 This is seen on Linux, Solaris and FreeBSD, although each has other ways to turn on all extensions, e.g.
defining _GNU_SOURCE,__EXTENSIONS__ or _BSD_SOURCE: the GCC compilers by default define _GNU_SOURCE
unless a strict standard such as -std=c99 is used. On macOS extensions are declared unless one of these
macros is given too small a value.
88 Solaris 10 does not recognize this value of _POSIX_C_SOURCE, nor values of _XOPEN_SOURCE beyond 600.
Chapter 1: Creating R packages 56
Functions/macros such as isnan,isinf and isfinite are not required by C++98: where
compilers support them they may be only in the std namespace or only in the main name-
space. There is no way to make use of these functions which works with all C++ compilers
currently in use on R platforms: use R’s versions such as ISNAN and R_FINITE instead.
If you must use them in C++11, beware that some compilers89 provide both std::isnan
and ::isnan, so using
using namespace std;
may cause ‘overloading ambiguity’ and you must use std::isnan etc explicitly.
It is an error (and make little sense, although has been seen) to call these functions for
integer arguments: a few compilers give a compilation error.
The GNU C/C++ compilers support a large number of non-portable extensions. For exam-
ple, INFINITY (which is in C99 but not C++98), for which R provides the portable R_PosInf
(and R_NegInf for -INFINITY). And NAN is just one NaN value: in R code NA_REAL is usually
what is intended, but R_NaN is also available.
Some (but not all) extensions are listed at https: / / gcc . gnu . org / onlinedocs /
gcc / C-Extensions . html and https: / / gcc . gnu . org / onlinedocs / gcc /
C_002b_002b-Extensions.html.
Other GNU extensions which have bitten package writers is the use of non-portable char-
acters such as ‘$’ in identifiers and use of C++ headers under ext.
The GNU Fortran compiler also supports a large number of non-portable extensions, the
most commonly encountered one being ISNAN90. Some are listed at https://gcc.gnu.
org/onlinedocs/gfortran/Extensions-implemented-in-GNU-Fortran.html. One that
frequently catches package writers is that it allows out-of-order declarations: in standard-
conformant Fortran variables must be declared (explicitly or implicitly) before use in other
declarations such as dimensions.
Including C-style headers in C++ code is not portable. Including the legacy header91 math.h
in C++ code may conflict with cmath which may be included by other headers. This is
particularly problematic with C++11 compilers, as functions like sqrt and isnan are defined
for double arguments in math.h and for a range of types including double in cmath. Similar
issues have been seen for stdlib.h and cstdlib. Including the C++ version first used to
be a sufficient workaround but for some 2016 compilers only one could be included.
Variable-length arrays are C99, not supported by C++98 nor by the C++ compilers in use
with R on some platforms.
The restrict qualifier is C99/C11 but not part of C++11 and not supported by some C++
compilers used with R.
Be careful to include the headers which define the functions you use. Some compilers/OSes
include other system headers in their headers which are not required by the standards,
and so code may compile on such systems and not on others. (A prominent example is
the C++11 header <random> which is indirectly included by <algorithm> by g++. Another
issue is the C header <time.h> which is included by other headers on Linux and Windows
but not macOS nor Solaris.)
Note that malloc,calloc,realloc and free are defined by C99 in the header stdlib.h
and (in the std:: namespace) by C++ header cstdlib. Some earlier implementations used
a header malloc.h, but that is not portable and does not exist on macOS.
89 E.g. gcc 5.3 in C++11 mode.
90 There is a portable way to do this in Fortran 2003 (ieee_is_nan() in module ieee_arithmetic), but ironically
that is not supported in the commonly-used versions 4.x of GNU Fortran. A pretty robust alternative is to
test if(my_var /= my_var).
91 which often is the same as the header included by the C compiler, but some compilers have wrappers for some
of the C headers.
Chapter 1: Creating R packages 57
This also applies to types such as ssize_t. The POSIX standards say that is declared in
headers unistd.h and sys/types.h, and the latter is often included indirectly by other
headers on some but not all systems.
Similarly for constants: for example SIZE_MAX is defined in stdint.h alongside size_t
(according to the C99 standard: it is not part of C++98).
For C++ code, be careful to specify namespaces where needed. Many functions are defined
by the standards to be in the std namespace, but g++ puts many such also in the C++ main
namespace. One way to do so is to use declarations such as
using std::floor;
but it is usually preferable to use explicit namespace prefixes in the code.
Examples seen in CRAN packages include
abs acos atan calloc ceil div exp fabs floor fmod free log malloc memcpy
memset pow printf qsort round sin sprintf sqrt strcmp strcpy strerror
strlen strncmp strtol tan trunc
Some C++ compilers refuse to compile constructs such as
if(ptr > 0) { ....}
which compares a pointer to the integer 0. This could just use if(ptr) (pointer addresses
cannot be negative) but if needed pointers can be tested against nullptr (C++11 and later)
or NULL.
Note that although nullptr was only introduced in C++11, some compilers accept it in
C++98 mode (but most do not).
Macros defined by the compiler/OS can cause problems. Identifiers starting with an under-
score followed by an upper-case letter or another underscore are reserved for system macros
and should not be used in portable code (including not as guards in C/C++ headers). Other
macros, typically upper-case, may be defined by the compiler or system headers and can
cause problems. The most common issue involves the names of the Intel CPU registers
such as CS,DS,ES,FS,GS and SS (and more with longer abbreviations) defined on i586/x64
Solaris in <sys/regset.h> and often included indirectly by <stdlib.h> and other core
headers. Further examples are ERR,LITTLE_ENDIAN,zero and I(which is defined in So-
laris’ <complex.h> as a compiler intrinsic for the imaginary unit). Some of these can be
avoided by defining _POSIX_C_SOURCE before including any system headers, but it is better
to only use all-upper-case names which have a unique prefix such as the package name.
typedefs in OS headers can conflict with those in the package: examples include ulong on
several OSes and index_t and single on Solaris. (Note that these may conflict with other
uses as identifiers, e.g. defining a C++ function called single.)
If you use OpenMP, check carefully that you have followed the advice in the subsection on
Section 1.2.1.1 [OpenMP support], page 23. In particular, any use of OpenMP in C/C++
code will need to use
#ifdef _OPENMP
# include <omp.h>
#endif
Any use of OpenMP functions, e.g. omp_set_num_threads also needs to be conditioned.
And do not hardcode -lgomp: not only is that specific to the GCC family of compilers,
using the correct linker flag often sets up the run-time path to the library.
Package authors commonly assume things are part of C99 when they are not: the most
common example is POSIX function strdup. The most common C library on Linux, glibc,
will hide the declarations of such extensions unless a ‘feature-test macro’ is defined before
(almost) any system header is included. So for strdup you need
#define _POSIX_C_SOURCE 200809L
Chapter 1: Creating R packages 58
...
#include <string.h>
...
strdup call(s)
where the appropriate value can be found by man strdup on Linux. (Use of strncasecmp
is similar.)
However, modes of gcc with ‘GNU EXTENSIONS’ (which are the default, either
-std=gnu99 or -std=gnu11) declare enough macros to ensure that missing declarations
are rarely seen.
This applies also to constants such as M_PI and M_LN2, which are part of the X/Open
standard: to use these define _XOPEN_SOURCE before including any headers, or include the
R header Rmath.h.
Similarly, package authors commonly assume things are part of C++ when they were intro-
duced in C++11 if at all. Recent examples from CRAN packages include the C99/C++11
functions
erf expm1 fmin fmax lgamma lround loglp round snprintf strcasecmp trunc
(all of which are in the std namespace in C++11) and the POSIX functions strdup and
strncasecmp and constants M_PI and M_LN2 (see the previous item). R has long provided
fmax2,fmin2,fround,ftrunc,lgammafn and many of the X/Open constants, declared in
header Rmath.h. Uses of erf can be replaced by pnorm (see the R help page for the latter).
Using alloca portably is tricky: it is neither an ISO C nor a POSIX function. An adequately
portable preamble is
#ifdef __GNUC__
/* Includes GCC, clang and Intel compilers */
# undef alloca
# define alloca(x) __builtin_alloca((x))
#elif defined(__sun) || defined(_AIX)
/* this is necessary (and sufficient) for Solaris 10 and AIX 6: */
# include <alloca.h>
#endif
Some additional information for C++ is available at http: / / journal . r-project . org /
archive/2011-2/RJournal_2011-2_Plummer.pdf by Martyn Plummer.
1.6.5 Binary distribution
If you want to distribute a binary version of a package on Windows or OS X, there are further
checks you need to do to check it is portable: it is all too easy to depend on external software
on your own machine that other users will not have.
For Windows, check what other DLLs your package’s DLL depends on (‘imports’ from in the
DLL tools’ parlance). A convenient GUI-based tool to do so is ‘Dependency Walker’ (http://
www.dependencywalker.com/) for both 32-bit and 64-bit DLLs – note that this will report
as missing links to R’s own DLLs such as R.dll and Rblas.dll. For 32-bit DLLs only, the
command-line tool pedump.exe -i (in Rtools*.exe) can be used, and for the brave, the objdump
tool in the appropriate toolchain will also reveal what DLLs are imported from. If you use a
toolchain other than one provided by the R developers or use your own makefiles, watch out in
particular for dependencies on the toolchain’s runtime DLLs such as libgfortran,libstdc++
and libgcc_s.
For macOS, using R CMD otool -L on the package’s shared object(s) in the libs direc-
tory will show what they depend on: watch for any dependencies in /usr/local/lib or
/usr/local/gfortran/lib, notably libgfortran.?.dylib and libquadmath.0.dylib.
Chapter 1: Creating R packages 59
Many people (including the CRAN package repository) will not accept source packages con-
taining binary files as the latter are a security risk. If you want to distribute a source package
which needs external software on Windows or macOS, options include
To arrange for installation of the package to download the additional software from a URL,
as e.g. package Cairo (https://CRAN.R-project.org/package=Cairo) does.
(For CRAN.) To negotiate with Uwe Ligges to host the additional components on Win-
Builder, and write a configure.win file to install them.
Be aware that license requirements will need to be met so you may need to supply the sources
for the additional components (and will if your package has a GPL-like license).
1.7 Diagnostic messages
Diagnostic messages can be made available for translation, so it is important to write them in
a consistent style. Using the tools described in the next section to extract all the messages can
give a useful overview of your consistency (or lack of it). Some guidelines follow.
Messages are sentence fragments, and not viewed in isolation. So it is conventional not to
capitalize the first word and not to end with a period (or other punctuation).
Try not to split up messages into small pieces. In C error messages use a single format
string containing all English words in the messages.
In R error messages do not construct a message with paste (such messages will not be
translated) but via multiple arguments to stop or warning, or via gettextf.
Do not use colloquialisms such as “can’t” and “don’t”.
Conventionally single quotation marks are used for quotations such as
’ord’ must be a positive integer, at most the number of knots
and double quotation marks when referring to an R character string or a class, such as
’format’ must be "normal" or "short" - using "normal"
Since ASCII does not contain directional quotation marks, it is best to use ‘’ and let the
translator (including automatic translation) use directional quotations where available. The
range of quotation styles is immense: unfortunately we cannot reproduce them in a portable
texinfo document. But as a taster, some languages use ‘up’ and ‘down’ (comma) quotes
rather than left or right quotes, and some use guillemets (and some use what Adobe calls
‘guillemotleft’ to start and others use it to end).
In R messages it is also possible to use sQuote or dQuote as in
stop(gettextf("object must be of class %s or %s",
dQuote("manova"), dQuote("maov")),
domain = NA)
Occasionally messages need to be singular or plural (and in other languages there may be
no such concept or several plural forms – Slovenian has four). So avoid constructions such
as was once used in library
if((length(nopkgs) > 0) && !missing(lib.loc)) {
if(length(nopkgs) > 1)
warning("libraries ",
paste(sQuote(nopkgs), collapse = ", "),
" contain no packages")
else
warning("library ", paste(sQuote(nopkgs)),
" contains no package")
}
Chapter 1: Creating R packages 60
and was replaced by
if((length(nopkgs) > 0) && !missing(lib.loc)) {
pkglist <- paste(sQuote(nopkgs), collapse = ", ")
msg <- sprintf(ngettext(length(nopkgs),
"library %s contains no packages",
"libraries %s contain no packages",
domain = "R-base"),
pkglist)
warning(msg, domain=NA)
}
Note that it is much better to have complete clauses as here, since in another language one
might need to say ‘There is no package in library %s’ or ‘There are no packages in libraries
%s’.
1.8 Internationalization
There are mechanisms to translate the R- and C-level error and warning messages. There are
only available if R is compiled with NLS support (which is requested by configure option
--enable-nls, the default).
The procedures make use of msgfmt and xgettext which are part of GNU gettext and this
will need to be installed: Windows users can find pre-compiled binaries at https://www.stats.
ox.ac.uk/pub/Rtools/goodies/gettext-tools.zip.
1.8.1 C-level messages
The process of enabling translations is
In a header file that will be included in all the C (or C++ or Objective C/C++) files containing
messages that should be translated, declare
#include <R.h> /* to include Rconfig.h */
#ifdef ENABLE_NLS
#include <libintl.h>
#define _(String) dgettext ("pkg", String)
/* replace pkg as appropriate */
#else
#define _(String) (String)
#endif
For each message that should be translated, wrap it in _(...), for example
error(_("’ord’ must be a positive integer"));
If you want to use different messages for singular and plural forms, you need to add
#ifndef ENABLE_NLS
#define dngettext(pkg, String, StringP, N) (N > 1 ? StringP : String)
#endif
and mark strings by
dngettext("pkg", <singular string>, <plural string>, n)
In the package’s src directory run
xgettext --keyword=_ -o pkg.pot *.c
The file src/pkg.pot is the template file, and conventionally this is shipped as po/pkg.pot.
Chapter 1: Creating R packages 61
1.8.2 R messages
Mechanisms are also available to support the automatic translation of R stop,warning and
message messages. They make use of message catalogs in the same way as C-level messages,
but using domain R-pkg rather than pkg. Translation of character strings inside stop,warning
and message calls is automatically enabled, as well as other messages enclosed in calls to gettext
or gettextf. (To suppress this, use argument domain=NA.)
Tools to prepare the R-pkg.pot file are provided in package tools:xgettext2pot will prepare
a file from all strings occurring inside gettext/gettextf,stop,warning and message calls.
Some of these are likely to be spurious and so the file is likely to need manual editing. xgettext
extracts the actual calls and so is more useful when tidying up error messages.
The R function ngettext provides an interface to the C function of the same name: see exam-
ple in the previous section. It is safest to use domain="R-pkg"explicitly in calls to ngettext,
and necessary for earlier versions of R unless they are calls directly from a function in the
package.
1.8.3 Preparing translations
Once the template files have been created, translations can be made. Conventional translations
have file extension .po and are placed in the po subdirectory of the package with a name that
is either ‘ll.po’ or ‘R-ll.po’ for translations of the C and R messages respectively to language
with code ‘ll’.
See Section “Localization of messages” in R Installation and Administration, for details of
language codes.
There is an R function, update_pkg_po in package tools, to automate much of the mainte-
nance of message translations. See its help for what it does in detail.
If this is called on a package with no existing translations, it creates the directory pkgdir/po,
creates a template file of R messages, pkgdir/po/R-pkg.pot, within it, creates the ‘en@quot
translation and installs that. (The ‘en@quot’ pseudo-language interprets quotes in their direc-
tional forms in suitable (e.g. UTF-8) locales.)
If the package has C source files in its src directory that are marked for translation, use
touch pkgdir/po/pkg.pot
to create a dummy template file, then call update_pkg_po again (this can also be done before
it is called for the first time).
When translations to new languages are added in the pkgdir/po directory, running the same
command will check and then install the translations.
If the package sources are updated, the same command will update the template files, merge
the changes into the translation .po files and then installed the updated translations. You
will often see that merging marks translations as ‘fuzzy’ and this is reported in the coverage
statistics. As fuzzy translations are not used, this is an indication that the translation files need
human attention.
The merged translations are run through tools::checkPofile to check that C-style formats
are used correctly: if not the mismatches are reported and the broken translations are not
installed.
This function needs the GNU gettext-tools installed and on the path: see its help page.
1.9 CITATION files
An installed file named CITATION will be used by the citation() function. (It should be in the
inst subdirectory of the package sources.)
Chapter 1: Creating R packages 62
The CITATION file is parsed as R code (in the package’s declared encoding, or in ASCII if none
is declared). If no such file is present, citation auto-generates citation information from the
package DESCRIPTION metadata, and an example of what that would look like as a CITATION file
can be seen in recommended package nlme (https://CRAN.R-project.org/package=nlme) (see
below): recommended packages boot (https://CRAN.R-project.org/package=boot), cluster
(https://CRAN.R-project.org/package=cluster) and mgcv (https://CRAN.R-project.
org/package=mgcv) have further examples.
ACITATION file will contain calls to function bibentry.
Here is that for nlme (https://CRAN.R-project.org/package=nlme):
year <- sub("-.*", "", meta$Date)
note <- sprintf("R package version %s", meta$Version)
bibentry(bibtype = "Manual",
title = "{nlme}: Linear and Nonlinear Mixed Effects Models",
author = c(person("Jose", "Pinheiro"),
person("Douglas", "Bates"),
person("Saikat", "DebRoy"),
person("Deepayan", "Sarkar"),
person("R Core Team")),
year = year,
note = note,
url = "https://CRAN.R-project.org/package=nlme")
Note the way that information that may need to be updated is picked up from object meta,
a parsed version of the DESCRIPTION file – it is tempting to hardcode such information, but it
normally then gets outdated. See ?bibentry for further details of the information which can be
provided.
In case a bibentry contains L
A
T
E
X markup (e.g., for accented characters or mathematical
symbols), it may be necessary to provide a text representation to be used for printing via the
textVersion argument to bibentry. E.g., earlier versions of nlme (https://CRAN.R-project.
org/package=nlme) additionally used
textVersion =
paste0("Jose Pinheiro, Douglas Bates, Saikat DebRoy,",
"Deepayan Sarkar and the R Core Team (",
year,
"). nlme: Linear and Nonlinear Mixed Effects Models. ",
note, ".")
The CITATION file should itself produce no output when source-d.
It is desirable (and essential for CRAN) that the CITATION file does not contain calls to
functions such as packageDescription which assume the package is installed in a library tree
on the package search path.
1.10 Package types
The DESCRIPTION file has an optional field Type which if missing is assumed to be Package’,
the sort of extension discussed so far in this chapter. Currently one other type is recognized;
there used also to be a ‘Translation’ type.
1.10.1 Frontend
This is a rather general mechanism, designed for adding new front-ends such as the former
gnomeGUI package (see the Archive area on CRAN). If a configure file is found in the top-
level directory of the package it is executed, and then if a Makefile is found (often generated by
Chapter 1: Creating R packages 63
configure), make is called. If R CMD INSTALL --clean is used make clean is called. No other
action is taken.
R CMD build can package up this type of extension, but R CMD check will check the type and
skip it.
Many packages of this type need write permission for the R installation directory.
1.11 Services
Several members of the R project have set up services to assist those writing R packages,
particularly those intended for public distribution.
win-builder.r-project.org (https: / / win-builder . r-project . org) offers the automated
preparation of (32/64-bit) Windows binaries from well-tested source packages.
R-Forge (R-Forge.r-project.org (https: / / R-Forge . r-project . org)) and RForge
(www.rforge.net (https://www.rforge.net)) are similar services with similar names. Both
provide source-code management through SVN, daily building and checking, mailing lists
and a repository that can be accessed via install.packages (they can be selected by
setRepositories and the GUI menus that use it). Package developers have the opportunity
to present their work on the basis of project websites or news announcements. Mailing lists,
forums or wikis provide useRs with convenient instruments for discussions and for exchanging
information between developers and/or interested useRs.
64
2 Writing R documentation files
2.1 Rd format
R objects are documented in files written in “R documentation” (Rd) format, a simple markup
language much of which closely resembles (La)T
E
X, which can be processed into a variety of
formats, including L
A
T
E
X, HTML and plain text. The translation is carried out by functions in
the tools package called by the script Rdconv in R_HOME/bin and by the installation scripts for
packages.
The R distribution contains more than 1300 such files which can be found in the
src/library/pkg/man directories of the R source tree, where pkg stands for one of the
standard packages which are included in the R distribution.
As an example, let us look at a simplified version of src/library/base/man/load.Rd which
documents the R function load.
 
% File src/library/base/man/load.Rd
\name{load}
\alias{load}
\title{Reload Saved Datasets}
\description{
Reload the datasets written to a file with the function
\code{save}.
}
\usage{
load(file, envir = parent.frame())
}
\arguments{
\item{file}{a connection or a character string giving the
name of the file to load.}
\item{envir}{the environment where the data should be
loaded.}
}
\seealso{
\code{\link{save}}.
}
\examples{
## save all data
save(list = ls(), file= "all.RData")
## restore the saved values to the current environment
load("all.RData")
## restore the saved values to the workspace
load("all.RData", .GlobalEnv)
}
\keyword{file}
 
An Rd file consists of three parts. The header gives basic information about the name of
the file, the topics documented, a title, a short textual description and R usage information for
the objects documented. The body gives further information (for example, on the function’s
arguments and return value, as in the above example). Finally, there is an optional footer with
keyword information. The header is mandatory.
Information is given within a series of sections with standard names (and user-defined sections
are also allowed). Unless otherwise specified1these should occur only once in an Rd file (in any
1e.g. \alias,\keyword and \note sections.
Chapter 2: Writing R documentation files 65
order), and the processing software will retain only the first occurrence of a standard section in
the file, with a warning.
See “Guidelines for Rd files” (https://developer.r-project.org/Rds.html) for guidelines
for writing documentation in Rd format which should be useful for package writers. The R generic
function prompt is used to construct a bare-bones Rd file ready for manual editing. Methods
are defined for documenting functions (which fill in the proper function and argument names)
and data frames. There are also functions promptData,promptPackage,promptClass, and
promptMethods for other types of Rd file.
The general syntax of Rd files is summarized below. For a detailed technical discussion of
current Rd syntax, see “Parsing Rd files” (https://developer.r-project.org/parseRd.pdf).
Rd files consist of four types of text input. The most common is L
A
T
E
X-like, with the backslash
used as a prefix on markup (e.g. \alias), and braces used to indicate arguments (e.g. {load}).
The least common type of text is ‘verbatim’ text, where no markup other than the comment
marker (%) is processed. There is also a rare variant of ‘verbatim’ text (used in \eqn,\deqn,
\figure, and \newcommand) where comment markers need not be escaped. The final type is
R-like, intended for R code, but allowing some embedded macros. Quoted strings within R-like
text are handled specially: regular character escapes such as \n may be entered as-is. Only
markup starting with \l (e.g. \link) or \v (e.g. \var) will be recognized within quoted strings.
The rarely used vertical tab \v must be entered as \\v.
Each macro defines the input type for its argument. For example, the file initially uses
L
A
T
E
X-like syntax, and this is also used in the \description section, but the \usage section
uses R-like syntax, and the \alias macro uses ‘verbatim’ syntax. Comments run from a percent
symbol %to the end of the line in all types of text except the rare ‘verbatim’ variant (as on the
first line of the load example).
Because backslashes, braces and percent symbols have special meaning, to enter them into
text sometimes requires escapes using a backslash. In general balanced braces do not need to
be escaped, but percent symbols always do, except in the ‘verbatim’ variant. For the complete
list of macros and rules for escapes, see “Parsing Rd files” (https://developer.r-project.
org/parseRd.pdf).
2.1.1 Documenting functions
The basic markup commands used for documenting R objects (in particular, functions) are given
in this subsection.
\name{name}
name typically2is the basename of the Rd file containing the documentation. It
is the “name” of the Rd object represented by the file and has to be unique in a
package. To avoid problems with indexing the package manual, it may not contain
!’ ‘|’ nor ‘@’, and to avoid possible problems with the HTML help system it should
not contain ‘/’ nor a space. (L
A
T
E
X special characters are allowed, but may not be
collated correctly in the index.) There can only be one \name entry in a file, and it
must not contain any markup. Entries in the package manual will be in alphabetic3
order of the \name entries.
\alias{topic}
The \alias sections specify all “topics” the file documents. This information is
collected into index data bases for lookup by the on-line (plain text and HTML)
2There can be exceptions: for example Rd files are not allowed to start with a dot, and have to be uniquely
named on a case-insensitive file system.
3in the current locale, and with special treatment for L
A
T
E
X special characters and with any ‘pkgname-package
topic moved to the top of the list.
Chapter 2: Writing R documentation files 66
help systems. The topic can contain spaces, but (for historical reasons) leading and
trailing spaces will be stripped. Percent and left brace need to be escaped by a
backslash.
There may be several \alias entries. Quite often it is convenient to document
several R objects in one file. For example, file Normal.Rd documents the density,
distribution function, quantile function and generation of random variates for the
normal distribution, and hence starts with
\name{Normal}
\alias{Normal}
\alias{dnorm}
\alias{pnorm}
\alias{qnorm}
\alias{rnorm}
Also, it is often convenient to have several different ways to refer to an R object,
and an \alias does not need to be the name of an object.
Note that the \name is not necessarily a topic documented, and if so desired it needs
to have an explicit \alias entry (as in this example).
\title{Title}
Title information for the Rd file. This should be capitalized and not end in a period;
try to limit its length to at most 65 characters for widest compatibility.
Markup is supported in the text, but use of characters other than English text and
punctuation (e.g., ‘<’) may limit portability.
There must be one (and only one) \title section in a help file.
\description{...}
A short description of what the function(s) do(es) (one paragraph, a few lines only).
(If a description is too long and cannot easily be shortened, the file probably tries to
document too much at once.) This is mandatory except for package-overview files.
\usage{fun(arg1,arg2, ...)}
One or more lines showing the synopsis of the function(s) and variables documented
in the file. These are set in typewriter font. This is an R-like command.
The usage information specified should match the function definition exactly (such
that automatic checking for consistency between code and documentation is possi-
ble).
It is no longer advisable to use \synopsis for the actual synopsis and show modified
synopses in the \usage. Support for \synopsis will be removed in \R 3.1.0. To
indicate that a function can be used in several different ways, depending on the
named arguments specified, use section \details. E.g., abline.Rd contains
\details{
Typical usages are
\preformatted{abline(a, b, untf = FALSE, \dots)
......
}
Use \method{generic}{class}to indicate the name of an S3 method for the generic
function generic for objects inheriting from class "class". In the printed versions,
this will come out as generic (reflecting the understanding that methods should not
be invoked directly but via method dispatch), but codoc() and other QC tools
always have access to the full name.
For example, print.ts.Rd contains
Chapter 2: Writing R documentation files 67
\usage{
\method{print}{ts}(x, calendar, \dots)
}
which will print as
Usage:
## S3 method for class ’ts’:
print(x, calendar, ...)
Usage for replacement functions should be given in the style of dim(x) <- value
rather than explicitly indicating the name of the replacement function ("dim<-" in
the above). Similarly, one can use \method{generic}{class}(arglist) <- value
to indicate the usage of an S3 replacement method for the generic replacement
function "generic<-" for objects inheriting from class "class".
Usage for S3 methods for extracting or replacing parts of an object, S3 methods for
members of the Ops group, and S3 methods for user-defined (binary) infix opera-
tors (‘%xxx%’) follows the above rules, using the appropriate function names. E.g.,
Extract.factor.Rd contains
\usage{
\method{[}{factor}(x, \dots, drop = FALSE)
\method{[[}{factor}(x, \dots)
\method{[}{factor}(x, \dots) <- value
}
which will print as
Usage:
## S3 method for class ’factor’:
x[..., drop = FALSE]
## S3 method for class ’factor’:
x[[...]]
## S3 replacement method for class ’factor’:
x[...] <- value
\S3method is accepted as an alternative to \method.
\arguments{...}
Description of the function’s arguments, using an entry of the form
\item{arg_i}{Description of arg_i.}
for each element of the argument list. (Note that there is no whitespace between
the three parts of the entry.) There may be optional text outside the \item entries,
for example to give general information about groups of parameters.
\details{...}
A detailed if possible precise description of the functionality provided, extending
the basic information in the \description slot.
\value{...}
Description of the function’s return value.
If a list with multiple values is returned, you can use entries of the form
\item{comp_i}{Description of comp_i.}
for each component of the list returned. Optional text may precede4this list (see
for example the help for rle). Note that \value is implicitly a \describe environ-
4Text between or after list items is discouraged.
Chapter 2: Writing R documentation files 68
ment, so that environment should not be used for listing components, just individual
\item{}{} entries.
\references{...}
A section with references to the literature. Use \url{} or \href{}{} for web point-
ers.
\note{...}
Use this for a special note you want to have pointed out. Multiple \note sections
are allowed, but might be confusing to the end users.
For example, pie.Rd contains
\note{
Pie charts are a very bad way of displaying information.
The eye is good at judging linear measures and bad at
judging relative areas.
......
}
\author{...}
Information about the author(s) of the Rd file. Use \email{} without extra delim-
iters (such as ‘( )’ or ‘< >’) to specify email addresses, or \url{} or \href{}{} for
web pointers.
\seealso{...}
Pointers to related R objects, using \code{\link{...}} to refer to them (\code is
the correct markup for R object names, and \link produces hyperlinks in output
formats which support this. See Section 2.3 [Marking text], page 72, and Section 2.5
[Cross-references], page 74).
\examples{...}
Examples of how to use the function. Code in this section is set in typewriter font
without reformatting and is run by example() unless marked otherwise (see below).
Examples are not only useful for documentation purposes, but also provide test code
used for diagnostic checking of R code. By default, text inside \examples{} will
be displayed in the output of the help page and run by example() and by R CMD
check. You can use \dontrun{} for text that should only be shown, but not run,
and \dontshow{} for extra commands for testin