# Writing R Extensions

Writing R Extensions Version 3.0.2 (2013-09-25) R Core Team This manual is for R, version 3.0.2 (2013-09-25). Copyright c 1999–2013 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 ..................................................................... 7 1.1.3 Package Dependencies ........................................................ 8 1.1.4 The INDEX file ............................................................... 10 1.1.5 Package subdirectories ....................................................... 11 1.1.6 Data in packages ............................................................ 14 1.1.7 Non-R scripts in packages ................................................... 15 1.2 Configure and cleanup............................................................ 15 1.2.1 Using Makevars ............................................................. 18 1.2.1.1 OpenMP support ....................................................... 21 1.2.1.2 Using pthreads ......................................................... 22 1.2.1.3 Compiling in sub-directories ............................................ 22 1.2.2 Configure example........................................................... 23 1.2.3 Using F95 code.............................................................. 25 1.3 Checking and building packages .................................................. 25 1.3.1 Checking packages........................................................... 26 1.3.2 Building package tarballs .................................................... 29 1.3.3 Building binary packages .................................................... 30 1.4 Writing package vignettes ........................................................ 31 1.4.1 Encodings and vignettes ..................................................... 33 1.4.2 Non-Sweave vignettes........................................................ 33 1.5 Package namespaces .............................................................. 34 1.5.1 Specifying imports and exports .............................................. 35 1.5.2 Registering S3 methods...................................................... 35 1.5.3 Load hooks.................................................................. 36 1.5.4 useDynLib .................................................................. 36 1.5.5 An example ................................................................. 38 1.5.6 Namespaces with S4 classes and methods .................................... 39 1.6 Writing portable packages ........................................................ 40 1.6.1 PDF size .................................................................... 43 1.6.2 Check timing ................................................................ 43 1.6.3 Encoding issues ............................................................. 44 1.6.4 Binary distribution .......................................................... 44 1.7 Diagnostic messages .............................................................. 45 1.8 Internationalization .............................................................. 46 1.8.1 C-level messages............................................................. 46 1.8.2 R messages .................................................................. 47 1.8.3 Preparing translations ....................................................... 47 1.9 CITATION files .................................................................. 48 1.10 Package types ................................................................... 48 1.10.1 Frontend ................................................................... 49 1.11 Services ......................................................................... 49 ii 2 Writing R documentation files .............................. 50 2.1 Rd format........................................................................ 50 2.1.1 Documenting functions ...................................................... 51 2.1.2 Documenting data sets ...................................................... 55 2.1.3 Documenting S4 classes and methods ........................................ 56 2.1.4 Documenting packages....................................................... 56 2.2 Sectioning........................................................................ 57 2.3 Marking text ..................................................................... 57 2.4 Lists and tables .................................................................. 59 2.5 Cross-references .................................................................. 60 2.6 Mathematics ..................................................................... 60 2.7 Figures........................................................................... 61 2.8 Insertions ........................................................................ 61 2.9 Indices ........................................................................... 62 2.10 Platform-specific documentation................................................. 62 2.11 Conditional text................................................................. 63 2.12 Dynamic pages.................................................................. 63 2.13 User-defined macros ............................................................. 64 2.14 Encoding ....................................................................... 64 2.15 Processing documentation files .................................................. 65 2.16 Editing Rd files ................................................................. 66 3 Tidying and profiling R code................................ 67 3.1 Tidying R code................................................................... 67 3.2 Profiling R code for speed ........................................................ 67 3.3 Profiling R code for memory use .................................................. 69 3.3.1 Memory statistics from Rprof ............................................... 69 3.3.2 Tracking memory allocations................................................. 70 3.3.3 Tracing copies of an object .................................................. 70 3.4 Profiling compiled code........................................................... 70 3.4.1 Linux ....................................................................... 71 3.4.1.1 sprof ................................................................... 71 3.4.1.2 oprofile ................................................................. 71 3.4.2 Solaris ...................................................................... 72 3.4.3 OS X........................................................................ 72 4 Debugging .................................................... 73 4.1 Browsing......................................................................... 73 4.2 Debugging R code................................................................ 74 4.3 Using gctorture and memory access............................................... 78 4.3.1 Using gctorture.............................................................. 78 4.3.2 Using valgrind............................................................... 78 4.3.3 Using AddressSanitizer ...................................................... 80 4.3.4 Fortran array bounds checking ............................................... 80 4.4 Debugging compiled code......................................................... 81 4.4.1 Finding entry points in dynamically loaded code ............................. 82 4.4.2 Inspecting R objects when debugging ........................................ 82 iii 5 System and foreign language interfaces..................... 85 5.1 Operating system access.......................................................... 85 5.2 Interface functions .C and .Fortran .............................................. 85 5.3 dyn.load and dyn.unload ....................................................... 87 5.4 Registering native routines ....................................................... 88 5.4.1 Speed considerations......................................................... 90 5.4.2 Linking to native routines in other packages.................................. 91 5.5 Creating shared objects .......................................................... 92 5.6 Interfacing C++ code ............................................................ 93 5.7 Fortran I/O ...................................................................... 94 5.8 Linking to other packages ........................................................ 95 5.8.1 Unix-alikes .................................................................. 95 5.8.2 Windows .................................................................... 96 5.9 Handling R objects in C.......................................................... 97 5.9.1 Handling the effects of garbage collection .................................... 98 5.9.2 Allocating storage ........................................................... 99 5.9.3 Details of R types ........................................................... 99 5.9.4 Attributes.................................................................. 100 5.9.5 Classes ..................................................................... 102 5.9.6 Handling lists .............................................................. 102 5.9.7 Handling character data .................................................... 103 5.9.8 Finding and setting variables ............................................... 103 5.9.9 Some convenience functions................................................. 104 5.9.9.1 Semi-internal convenience functions .................................... 105 5.9.10 Named objects and copying ............................................... 105 5.10 Interface functions .Call and .External ....................................... 106 5.10.1 Calling .Call ............................................................. 106 5.10.2 Calling .External ........................................................ 106 5.10.3 Missing and special values ................................................. 108 5.11 Evaluating R expressions from C ............................................... 108 5.11.1 Zero-finding ............................................................... 110 5.11.2 Calculating numerical derivatives .......................................... 111 5.12 Parsing R code from C ......................................................... 114 5.12.1 Accessing source references ................................................ 115 5.13 External pointers and weak references .......................................... 116 5.13.1 An example ............................................................... 117 5.14 Vector accessor functions ....................................................... 117 5.15 Character encoding issues ...................................................... 118 6 The R API: entry points for C code....................... 119 6.1 Memory allocation .............................................................. 119 6.1.1 Transient storage allocation................................................. 119 6.1.2 User-controlled memory .................................................... 120 6.2 Error handling .................................................................. 121 6.2.1 Error handling from FORTRAN ............................................ 121 6.3 Random number generation ..................................................... 121 6.4 Missing and IEEE special values ................................................. 122 6.5 Printing......................................................................... 122 6.5.1 Printing from FORTRAN .................................................. 122 6.6 Calling C from FORTRAN and vice versa ....................................... 123 6.7 Numerical analysis subroutines .................................................. 123 6.7.1 Distribution functions ...................................................... 124 6.7.2 Mathematical functions..................................................... 125 iv 6.7.3 Numerical Utilities ......................................................... 125 6.7.4 Mathematical constants .................................................... 127 6.8 Optimization.................................................................... 127 6.9 Integration ...................................................................... 128 6.10 Utility functions ............................................................... 129 6.11 Re-encoding ................................................................... 131 6.12 Allowing interrupts ............................................................ 131 6.13 Platform and version information............................................... 132 6.14 Inlining C functions ............................................................ 132 6.15 Controlling visibility ........................................................... 132 6.16 Using these functions in your own C code....................................... 133 6.17 Organization of header files .................................................... 134 7 Generic functions and methods ............................ 135 7.1 Adding new generics ............................................................ 136 8 Linking GUIs and other front-ends to R .................. 137 8.1 Embedding R under Unix-alikes ................................................. 137 8.1.1 Compiling against the R library ............................................ 139 8.1.2 Setting R callbacks ......................................................... 139 8.1.3 Registering symbols ........................................................ 142 8.1.4 Meshing event loops ........................................................ 142 8.1.5 Threading issues ........................................................... 143 8.2 Embedding R under Windows ................................................... 144 8.2.1 Using (D)COM............................................................. 144 8.2.2 Calling R.dll directly ....................................................... 144 8.2.3 Finding R HOME .......................................................... 147 Function and variable index.................................... 148 Concept index................................................... 151 Acknowledgements 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. Chapter 1: Creating R packages 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 48. 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. A package 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 OS X 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 not1 a library. 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 OS X) 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 OS X 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 1 although 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 the base and recommended packages are byte-compiled. At some future time this might be done routinely, so compiling a package may come to mean 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 R directory 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 http://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,3 packages 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 34. The optional files configure and cleanup are (Bourne shell) script files which are, re- spectively, executed before and (provided that option --clean was given) after installation on Unix-alikes, see Section 1.2 [Configure and cleanup], page 15. The analogues on Windows are configure.win and cleanup.win. For the conventions for files NEWS and ChangeLog in the GNU project see http://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$’, ‘nul’, ‘com1’ to ‘com9’, and ‘lpt1’ to ‘lpt9’ after conversion to lower case and stripping possible “extensions” 2 This seems to be commonly used for a file in ‘markdown’ format. Be aware that most users of R will not know that, nor know how to view such a file: platforms such as OS X and Windows do not have a default viewer set in their file associations. The CRAN package web pages render such files in HTML. 3 currently, top-level files .Rbuildignore and .Rinstignore, and vignettes/.install_extras. Chapter 1: Creating R packages 4 (e.g., ‘lpt5.foo.bar’), are disallowed. Also, file names in the same directory must not differ only by case (see the previous paragraph). In addition, the basenames of ‘.Rd’ files may be used in URLs and so must be ASCII and not contain %. For maximal portability filenames should only contain only ASCII characters not excluded already (that is A-Za-z0-9._!#$%&+,;=@^(){}’[] — 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: 2004-01-01 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 and Pat Developer, with contributions from A. User Maintainer: Joe Developer Depends: R (>= 1.8.0), nlme Suggests: MASS Description: A short (one paragraph) description of what the package does and why it may be useful. License: GPL (>= 2) URL: http://www.r-project.org, http://www.another.url BugReports: http://pkgname.bugtracker.url ©The format is that of a version of a ‘Debian Control File’ (see the help for ‘read.dcf’ and http://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 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. 4 false positives are possible, but only a handful have been seen so far. Chapter 1: Creating R packages 5 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. 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 ‘Description’ field should give a comprehensive description of what the package does. One can use several (complete) sentences, but only one paragraph. The mandatory ‘Title’ field should give a short description of the package. Some package listings may truncate the title to 65 characters. It should be capitalized, not use any markup, not have any continuation lines, and not end in a period. 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 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. 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. 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 information. Note that no role is assumed by default. Auto-generated package citation information takes advantage of this specification. The ‘Author’ and ‘Maintainer’ fields are auto-generated from it if needed when building5 or 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 ‘Date’ field gives the release date of the current version of the package. It is strongly recommended to use the yyyy-mm-dd format conforming to the ISO 8601 standard. The ‘Depends’, ‘Imports’, ‘Suggests’, ‘Enhances’ and ‘LinkingTo’ 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. 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.reports instead of sending an email to the maintainer. 5 at least if this is done in a locale which matches the package encoding. Chapter 1: Creating R packages 6 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 11) as a whitespace separated list of file paths relative to the R subdirectory. 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. (Introduced in R 3.0.0.) 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 preventing6 R 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 33 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 R and 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 the repository assuming compilation is required if and only if the package has a src directory. 6 But it is checked for Open Source packages by R CMD check --as-cran. Chapter 1: Creating R packages 7 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 48. One can add subject classifications for the content of the package using the fields ‘Classification/ACM’ (using the Computing Classification System of the Association for Computing Machinery, http: / / www . acm . org / class / ), ‘Classification/JEL’ (the Journal of Economic Literature Classification System, http://www.aeaweb.org/journal/ jel_class_system.html), or ‘Classification/MSC’ (the Mathematics Subject Classification of the American Mathematical Society, http: / / www . ams . org / msc / ). 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 (http://tools.ietf.org/html/rfc5646, see also http://en.wikipedia.org/wiki/IETF_language_tag), i.e., use language subtags which in essence are 2-letter ISO 639-1 (http://en.wikipedia.org/wiki/ISO_639-1) or 3-letter ISO 639-3 (http://en.wikipedia.org/wiki/ISO_639-3) language codes. 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/developers) 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., http://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 http://www.R-project.org/Licenses/ and contained in subdirec- tory 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 Chapter 1: Creating R packages 8 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 usually 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. Examples of standardized specifications include License: GPL-2 License: GPL (>= 2) | BSD License: LGPL (>= 2.0, < 3) | Mozilla Public License License: GPL-2 | file LICENCE 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 http://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)’. Chapter 1: Creating R packages 9 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 R without 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. 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 31), and packages loaded in the body of functions. E.g., sup- pose an example 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, and will be used by R CMD check. Note that someone wanting to run the examples/tests/vignettes may not have a sug- gested package available (and it may not even be possible to install it for that platform), so it is desirable that the use of suggested packages is made conditional via if(require(pkgname))). 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 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 needed7 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 7 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 Chapter 1: Creating R packages 10 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. 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 would want the functions of 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 As from R 3.0.2 the ‘LinkingTo’ field can have a version requirement which is checked at installation. (In earlier versions of R it would cause the specification to be ignored.) 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. For another use of ‘LinkingTo’ see Section 5.4.2 [Linking to native routines in other packages], page 91. 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. 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 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 56) and/or a vignette (see Section 1.4 [Writing package vignettes], page 31). 1.1.5 Package subdirectories The R subdirectory 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 extensions8 .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 R subdirectory contains a file sysdata.rda (a saved image of one or more R objects: please use suitable compression as suggested by tools::resaveRdaFiles) 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 R directory 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 comments, but then the comments may not be readable in e.g. a UTF-8 locale. Non-ASCII characters in object names will normally9 fail 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 36. 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 means10 they must be entirely ASCII and not contain ‘%’. See Chapter 2 [Writing R documentation files], page 50, 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 34). Having a man directory containing no documentation files may give an installation error. The R and 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- 8 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. 9 This is true for OSes which implement the ‘C’ locale: Windows’ idea of the ‘C’ locale uses the WinAnsi charset. 10 More precisely, they can contain the English alphanumeric characters and the symbols ‘$-_.+!’(),; = &’. Chapter 1: Creating R packages 12 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 9x11, Objective C and Objective C++12 with associated extensions .c,.cc or .cpp,.f,.f90 or .f95,.m, and .mm, respectively. We recommend using .h for headers, also for C++13 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 OS X ‘__APPLE__’ is defined.14 The default rules can be tweaked by setting macros15 in a file src/Makevars (see Section 1.2.1 [Using Makevars], page 18). Note that this mechanism should be general enough to eliminate the 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-arch setting since R CMD INSTALL --libs-only is used to merge multiple architectures and it only copies shared objects/DLLs. If a package wants to install other binaries (for example executable programs), it should pro- vide 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), 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(paste("*", SHLIB_EXT, sep=’’)) libarch <- if (nzchar(R_ARCH)) paste(’libs’, R_ARCH, sep=’’) else ’libs’ dest <- file.path(R_PACKAGE_DIR, libarch) dir.create(dest, recursive = TRUE, showWarnings = FALSE) 11 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. 12 either or both of which may not be supported on particular platforms 13 Using .hpp is not guaranteed to be portable. 14 There is also ‘__APPLE_CC__’, but that indicates a compiler with Apple-specific features, not the OS. It is used in Rinlinedfuns.h. 15 the POSIX terminology, called ‘make variables’ by GNU make. Chapter 1: Creating R packages 13 file.copy(files, dest, overwrite = TRUE) if(file.exists("symbols.rds")) file.copy("symbols.rds", dest, overwrite = TRUE) The data subdirectory is for data files: See Section 1.1.6 [Data in packages], page 14. 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 white space. (Note that it is not possible to generate this index file 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 44). As from R 3.0.0 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 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 matched16 against the file and directory paths, e.g. doc/.*[.]png$ will exclude all PNG files in inst/doc based on the (lower-case) 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 OS X 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. 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 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 corresponding17 .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). 16 case-insensitively on Windows. 17 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. Chapter 1: Creating R packages 14 If 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. Subdirectory exec could contain additional executable scripts the package needs, typically scripts for interpreters such as the shell, Perl, or Tcl. This mechanism is currently used only by a very few packages. 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 may not be suitable for executable programs since some platforms (including OS X and 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 46. 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 standard18 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 .RData 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. 18 e.g http://tools.ietf.org/html/rfc4180. Chapter 1: Creating R packages 15 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 and all platforms prior to R 3.0.0). 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. Subdirectory exec could be used for scripts for interpreters such as the shell (e.g. arulesSequences), BUGS, Java, JavaScript, Matlab, Perl (FEST), php (amap), Python or Tcl, or even R. However, it seems more common to use the inst directory, for example AMA/inst/java, WriteXLS/inst/Perl, Amelia/inst/tklibs, NMF/inst/matlab and emdbook/inst/BUGS. If your package requires one of these interpreters or an extension then this should be declared in the ‘SystemRequirements’ field of its DESCRIPTION file. Windows and Mac users should be aware that the Tcl extensions ‘BWidget’ and ‘Tktable’ which are currently included with the R for Windows and OS X installers are extensions and do need to be declared. ‘Tktable’ did ship as part of the X11-based Tcl/Tk provided on CRAN for OS X prior to R 3.0.0, but you will need to tell your users how to make use of it: > addTclPath(’/usr/local/lib/Tktable2.9’) > tclRequire(’Tktable’) 2.9 It should work with no further user action as from R 3.0.0. ‘BWidget’ needs to be installed by the user for OS X with R 2.x.y and 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 tar xf bwidget-1.9.6.tar.gz sudo mv bwidget-1.9.6 /usr/local/lib substituting a location on the Tcl/Tk search path for /usr/local/lib if needed. 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. If your package needs some system-dependent configuration before installation you can in- clude an executable (Bourne shell) script configure in your package which (if present) is ex- ecuted 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 Chapter 1: Creating R packages 16 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 : ${R_HOME=‘R RHOME‘} if test -z "${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 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 Chapter 1: Creating R packages 17 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.) Note bene: If the configure script creates files, e.g. src/Makevars, you do need a cleanup script to remove them. Otherwise if the package has vignettes, R CMD build will ship the files that are created. For example, 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 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 dependent 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. (This happens with the Java Archive files in the Omegahat SJava package.) The names of the top-level library directory (i.e., specifiable via the ‘-l’ argument) 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 OS X) 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), and if present it can be asked if the software is installed, of a suitable version and for compilation/linking flags by e.g. $pkg-config --exists ’QtCore >= 4.0.0’ # check the status Chapter 1: Creating R packages 18$ 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. A Makevars 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_ CFLAGS, PKG_CXXFLAGS, PKG_FFLAGS or PKG_FCFLAGS, for C, C++, FORTRAN or Fortran 9x respectively (see Section 5.5 [Creating shared objects], page 92). NB: 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 prece- dence. 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 Solaris compiler 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 macros19 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 19 in POSIX parlance: GNU make calls these ‘make variables’. Chapter 1: Creating R packages 19 contain all the double-precision and double-complex BLAS routines, but no single- precision or complex routines. If BLAS_LIBS is included, then FLIBS also needs to be20 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 all the double-precision LAPACK routines as well as those double-complex LAPACK and BLAS 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. [There is no guarantee that the LAPACK library will provide more than all the double-precision and double-complex routines, and some do not provide all the aux- iliary routines.] 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 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 default 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 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 20 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 20 $(F77)$(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 a no guarantee that the depen- dencies of all will be run in a particular order (and some of the CRAN build machines use multiple CPUs and parallel makes). 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. 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 R or 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 Chapter 1: Creating R packages 21 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 http://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 OpenMP21. The make macros SHLIB_OPENMP_CFLAGS 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 SUPPORT_OPENMP, a macro defined in the header Rconfig.h (see Section 6.13 [Platform and version information], page 132) or _OPENMP: note that some toolchains used for R (e.g. clang) 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) There is nothing to say what version of OpenMP is supported: version 3.0 (May 2008) is supported by recent versions of the Linux, Windows and Solaris platforms, but portable packages cannot assume that end users have recent versions. The compilers currently used on OS X have partial support for OpenMP 2.5, but this is not enabled in the CRAN build. (Apple have discontinued support for those compilers, and their preferred alternative, clang, has no OpenMP support.) The performance of OpenMP varies substantially between platforms. Both the Windows and OS X (where available) implementations have substantial overheads and are only beneficial if quite substantial tasks are run in parallel. 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 21 http://www.openmp.org/, http://en.wikipedia.org/wiki/OpenMP, https://computing.llnl.gov/ tutorials/openMP/ Chapter 1: Creating R packages 22 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.22 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 (a recent part of the MinGW-w64 project). Whether Windows toolchains implement pthreads is up to the toolchain provider: the cur- rently recommended toolchain does by default provide it. A make variable SHLIB_PTHREAD_ FLAGS is available: this should be included in both PKG_CPPFLAGS (or the Fortran or F9x equiv- alents) and PKG_LIBS. The presence of a working pthreads implementation cannot be unambiguously determined without testing for yourself: however, that ‘_REENTRANT’ is defined23 in C/C++ code is a good indication. 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. 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 has 22 Which it was at the time of writing with GCC, Solaris Studio and Intel compilers. 23 some Windows toolchains have the typo ‘_REENTRANCE’ instead. Chapter 1: Creating R packages 23 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: 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 need 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 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=$withval]) if test "$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${odbc_include_path}" else Chapter 1: Creating R packages 24 if test [ -n "${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 "${odbc_mgr}" ] ; then AC_SEARCH_LIBS(SQLTables,${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 ]) dnl for unixODBC header AC_CHECK_SIZEOF(long, 4) dnl substitute RODBC_CPPFLAGS and LIBS AC_SUBST(RODBC_CPPFLAGS) Chapter 1: Creating R packages 25 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 rare24) 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 http://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 Solaris f95 compiler ‘accepts some Fortran 2003 features’. Note that the compiler used for OS X is gfortran 4.2.3 which has limited Fortran 2003 support (http://gcc.gnu.org/onlinedocs/gcc-4.2.3/gfortran/). 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. 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 24 Cygwin used g77 up to 2011, and some pre-built versions of R for Unix OSes still do. Chapter 1: Creating R packages 26 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, files25 ~/.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 available26. (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. 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. 25 On systems which use sub-architectures, architecture-specific versions such as ~/.R/check.Renviron.i386 take precedence. 26 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. Chapter 1: Creating R packages 27 To allow a configure script to generate suitable files, files ending in ‘.in’ will be allowed in the R directory. 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. 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 files27 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 be called. Windows28 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. 27 An exception is made for subdirectories with names starting ‘win’ or ‘Win’. 28 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. Chapter 1: Creating R packages 28 18. The examples provided by the package’s documentation are run. (see Chapter 2 [Writing R documentation files], page 50, 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 T and F redefined 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 directory 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 locale, so the target output files should be ASCII if at all possible. 20. The code in package vignettes (see Section 1.4 [Writing package vignettes], page 31) 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 error29 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 LATEX and suitable fonts and LATEX packages to be installed. See Section “Making the manuals” in R Installation and Administration. All these tests are run with collation set to the C locale, 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 selected by the option --as-cran (which works best if Internet access is available30). 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 C locale, 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 C locale 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 29 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 30 Windows users behind proxies may want to set environment variable R_WIN_INTERNET2 to a non-empty value, e.g. in ~/.R/check_environ. Some Windows users may need to set R_WIN_NO_JUNCTIONS to a non-empty value. Chapter 1: Creating R packages 29 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 checks31 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 OS X platforms. Using R CMD build, the R package builder, one can build R package tarballs from their sources (for example, for subsequent release). 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 matched32 against the file and directory names relative to the top-level package source directory. In addition, directories from source control systems33 or from eclipse34, directories with names ending .Rcheck or Old or old and files GNUMakefile, 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 31) 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 63), the package will be temporarily installed to execute them. Post-execution 31 loading, examples, tests, vignettes 32 case-insensitively on Windows. 33 called CVS or .svn or .arch-ids or .bzr or .git (but not files called .git) or .hg. 34 called .metadata. Chapter 1: Creating R packages 30 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. 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. 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 OS X 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. Chapter 1: Creating R packages 31 • 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 (e.g. handling Fortran sources on OS X) are discussed in the platform-specific FAQs. In much earlier versions of R, R CMD build --binary could build a binary version of a package, but this approach is now deprecated in favour of R CMD INSTALL --build. Finally, at least one web-based service is available for building binary packages from (checked) source code: WinBuilder (see http://win-builder.R-project.org/) is able to build Windows 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 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 PDF documents with sources in Sweave, which we call package vignettes. (Since R 3.0.0, other vignette formats are supported; see Section 1.4.2 [Non-Sweave vignettes], page 33.) The preferred location for the sources is the subdirectory vignettes of the source package, but pro tem35 for compatibility with the layout before R 2.14.0, vignette sources will be looked for in inst/doc if subdirectory vignettes does not exist. 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 components36 intended to be installed. Vignette sources are normally given the file extension .Rnw or .Rtex, but for historical reasons extensions37 .Snw and .Stex are also recognized as vignettes. Sweave allows the integration of LATEX documents: see the Sweave help page in R and the Sweave vignette in package utils for details on the document format. Package vignettes are tested by R CMD check by executing all R code chunks they contain (except those with option eval=FALSE). 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 vignette PDFs (such as LATEX style files, BiBTeX input files and files for any figures not created by running the code in the vignette) must be in the vignette source directory. 35 This is expected to be removed in R 3.1.0. 36 at least the PDF documents and the Sweave files so these can be tangled to produce the R code. 37 and to avoid problems with case-insensitive file systems, lower-case versions of all these extensions. Chapter 1: Creating R packages 32 R CMD build will automatically38 create the (PDF or HTML versions of the) vignettes in inst/doc for distribution with the package sources. By including the vignettes in the package sources it is not necessary that these can be re-built at install time, i.e., the package author can use private R packages, screen snapshots and LATEX extensions which are only available on his machine.39 By default R CMD build will run Sweave on all Sweave vignette source files in vignettes, or pro tem if that does not exist, inst/doc (but not in sub-directories). If no Makefile is found in directory inst/doc, then tools::texi2pdf is run on all processed vignette sources. Whenever a Makefile is found in the vignette source directory, then R CMD build will try to run make after the Sweave runs. 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_HOME40. 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 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. Note that it is pointless (and potentially misleading since the files might be outdated) to include in inst/doc R code files which would be generated from vignette sources, as these will be re-generated when the package is built (unless the vignette does not generate any R code, in which case it is also pointless/misleading). Metadata lines can be placed in the source file, preferably in LATEX comments in the preamble. 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 33. 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. 38 unless inhibited by using ‘BuildVignettes: no’ in the DESCRIPTION file. 39 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. 40 R_HOME/bin is prepended to the PATH so that references to R or Rscript in the Makefile do make use of the currently running version of R. Chapter 1: Creating R packages 33 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, LATEX in- 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 recorded in a comment at the top of the file. Sweave() will produce a .tex file in the current locale’s encoding. That needs to be declared to LATEX via a line like \usepackage[utf8]{inputenc} (It is also possible to use the more recent ‘inputenx’LATEX package.) R CMD check will warn about any non-ASCII vignettes it finds which do not have such a declaration. Sweave() will also parse and evaluate the R code in each chunk. The R output will also be in the current locale, 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 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 R 3.0.0 and later allow vignettes in formats other than Sweave by means of “vignette engines”. For example 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 LATEX 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. Chapter 1: Creating R packages 34 R recognizes non-Sweave vignettes using filename extensions specified by the engine. For example, the 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. The vignette engine can produce .tex,.pdf, or .html files as output. If it produces .tex files, R will call pdflatex to convert them to .pdf for display to the user. 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 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 mechanism for specifying a namespace for a package is to place a 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. 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. Prior to R 2.14.0, namespaces were optional in packages: a default namespace was generated on installation in 2.14.x and 2.15.x. As from 3.0.0 a namespace is mandatory. Chapter 1: Creating R packages 35 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 f and g are 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 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 f and g of the package foo are to be imported. Using importFrom selectively rather than import is good practice. 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 f in 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. Chapter 1: Creating R packages 36 (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) when print.check_so_symbols is not needed. There used to be a limit on the number of S3method directives: it was 500 prior to R 3.0.2. 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 arguments41 libname and pkgname; they should be defined in the namespace but not exported. Packages can use a .onDetach (as from R 3.0.0) or .Last.lib function (provided the lat- ter 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 a useDynLb 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 A NAMESPACE file can contain one or more useDynLib directives which allows shared objects that need to be loaded.42 The directive useDynLib(foo) registers the shared object foo43 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 R via the .C,.Call,.Fortran and .External interface functions. These are given as additional arguments to the directive, for example, useDynLib(foo, myRoutine, myOtherRoutine) 41 they will be called with two unnamed arguments, in that order. 42 NB: this will only be read in all versions of R if the package contains R code in a R directory. 43 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 37 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") 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. 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 88), 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: R_CMethodDef cMethods[] = { {"foo", (DL_FUNC) &foo, 4, {REALSXP, INTSXP, STRSXP, LGLSXP}}, Chapter 1: Creating R packages 38 {"bar_sym", (DL_FUNC) &bar, 0}, {NULL, NULL, 0} }; 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 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. 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("\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 ¨c <- function(...) sum(...) g <- function(y) f(c(y, 7)) h <- function(y) y+9 © Chapter 1: Creating R packages 39 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 g produces > g(6) [1] 1 13 This is consistent with the definitions of c in the two settings: in bar the function c is defined to be equivalent to sum, but in foo the variable c refers to the standard function c in 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 file44 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 exportClassPattern45 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). 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 44 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. 45 This defaults to the same pattern as exportPattern: use something like exportClassPattern("^$") to override this. Chapter 1: Creating R packages 40 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 package, 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 A and B with B using 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 ., 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, 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. • 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-terminated (including the final line of the file) and Chapter 1: Creating R packages 41 not make use of GNU extensions. Commonly misused GNU extensions are conditional inclusions (ifeq and the like),${shell ...} and ${wildcard ...}, and the use of += and :=. Also, the use of$< 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 known46 to be used with R. If you really must assume GNU make, declare it in the DESCRIPTION file by SystemRequirements: GNU make 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. Moreover, Bash extensions also need to be avoided in shell scripts, including expres- sion in Makefiles (which are passed to the shell for processing). Some R platforms use strictly POSIX-conformant Bourne shells, and Windows and some Unix-alike OSes use ash (http://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, espe- cially on Windows where only a minimal set is provided for use with R. (See Section “The command line tools” in R Installation 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) or escape sequences. • Make use of the abilities of your compilers to check the standards-conformance of your code. For example, gcc 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 GNU extensions which fail to compile 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, Although there is a 2011 version of the C++ standard, it is not yet fully implemented (nor is it likely to be widely available for some years) and portable C++ code needs to follow the 1998 standard (and not use features from C99). Similarly, the 2011 C standard is unlikely to be widely implemented for several years. If you use FORTRAN 77, ftnchek (http://www.dsm.fordham.edu/~ftnchek/) provides thorough testing of conformance to the standard. • 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 type such as int_fast64_t (and failing that, long long47) 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 integer types intptr_t and uintptr_t (which are defined in the header and are not required to be implemented by the C99 standard). Note that integer in FORTRAN corresponds to int in C on all R platforms. 46 GNU make, BSD make as in FreeBSD, AT&T make as implemented on Solaris. 47 but note that long long is not a standard C++ type, and C++ compilers set up for strict checking will reject it. Chapter 1: Creating R packages 42 • Under no circumstances should your compiled code ever call abort or exit: these terminate 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 development. you can include -UNDEBUG in PKG_CPPFLAGS. Note that your own src/Makefile or makefiles in sub-directories may also need to define NDEBUG. 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. • Errors in memory allocation and reading/writing outside arrays are very common causes of crashes (e.g., segfaults) on some machines. See Section 4.3.2 [Using valgrind], page 78 for a tool 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 # or other extension such as .sl and checking if any of the symbols marked U is unexpected is a good way to avoid this. • 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). • 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 (http://win-builder.r-project.org/) before distribution. Do be careful in what your tests 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 error messages: they change, and they can be translated. • 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. 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 all48 which use them configure them allow the use of extended precision. 48 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 43 If you must try to establish a tolerance empirically, configure and build R with --disable- long-doubles and use appropriate compiler flags (such as -ffloat-store for gcc) to mitigate the effects of extended-precision calculations. 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 17, and is installed as part of the CRAN OS X 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 OS X). 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 LATEX 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 Preview49 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/9.07/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 dramatically 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. 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 implied by --as-cran as from R 3.0.2). 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. 49 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 44 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.50. 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 available51 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 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) except that if the current locale is UTF-8 then the package code is translated to UTF-8 for syntax checking. 1.6.4 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 50 except perhaps some special characters such as backslash and hash which may be taken over for currency symbols. 51 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 45 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 OS X, using R CMD otool -L on the package’s shared objects in the libs directory will show what they depend on: watch for any dependencies in /usr/local/lib, notably libgfortran.2.dylib. 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 OS X, options include • To arrange for installation of the package to download the additional software from a URL, as e.g. 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. There used to be many examples, e.g. package rgdal (however nowadays CRAN prefers to use a uniform cross-compilation approach for software such as GDAL). 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) Chapter 1: Creating R packages 46 • 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") } 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 http://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 /* to include Rconfig.h */ #ifdef ENABLE_NLS #include #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")); Chapter 1: Creating R packages 47 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", , , 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. 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 Chapter 1: Creating R packages 48 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. (To be installed, it needed to be in the inst subdirectory of the package sources.) 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 (see below): recommended packages boot, cluster and mgcv have further examples. A CITATION file will contain calls to function bibentry. Here is that for nlme, re-formatted: citHeader("To cite package ’nlme’ in publications use:") year <- sub(".*(2[[:digit:]]{3})-.*", "\\1", meta$Date, perl = TRUE) vers <- paste("R package version", meta$Version) citEntry(entry = "Manual", title = "nlme: Linear and Nonlinear Mixed Effects Models", author = personList(as.person("Jose Pinheiro"), as.person("Douglas Bates"), as.person("Saikat DebRoy"), as.person("Deepayan Sarkar"), person("R Core Team")), year = year, note = vers, textVersion = paste0("Jose Pinheiro, Douglas Bates, Saikat DebRoy,", "Deepayan Sarkar and the R Core Team (", year, "). nlme: Linear and Nonlinear Mixed Effects Models. ", vers, ".")) Note the way that information that may need to be updated is picked up from 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. The CITATION file should itself produce no output when source-d. 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. Chapter 1: Creating R packages 49 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 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 offers the automated preparation of (32/64-bit) Windows binaries from well-tested source packages. R-Forge (R-Forge.r-project.org) and RForge (www.rforge.net) are similar services with sim- ilar 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. Chapter 2: Writing R documentation files 50 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)TEX, which can be processed into a variety of formats, including LATEX, 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 specified1 these should occur only once in an Rd file (in any 1 e.g. \alias,\keyword and \note sections. Chapter 2: Writing R documentation files 51 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” 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”. Rd files consists of three types of text input. The most common is LATEX-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 is processed. The third 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 LATEX-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 (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. For the complete list of macros and rules for escapes, see “Parsing Rd files”. 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 typically2 is 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. (LATEX 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) 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, 2 There 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. 3 in the current locale, and with special treatment for LATEX special characters and with any ‘pkgname-package’ topic moved to the top of the list. Chapter 2: Writing R documentation files 52 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 \usage{ \method{print}{ts}(x, calendar, \dots) } which will print as Chapter 2: Writing R documentation files 53 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 precede4 this list (see for example the help for rle). Note that \value is implicitly a \describe environ- ment, so that environment should not be used for listing components, just individual \item{}{} entries. 4 Text between or after list items is discouraged. Chapter 2: Writing R documentation files 54 \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 57, and Section 2.5 [Cross-references], page 60). \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 testing that should not be shown to users, but will be run by example(). (Previously this was called \testonly, and that is still accepted.) Text inside \dontrun{} is verbatim, but the other parts of the \examples section are R-like text. For example, x <- runif(10) # Shown and run. \dontrun{plot(x)} # Only shown. \dontshow{log(x)} # Only run. Thus, example code not included in \dontrun must be executable! In addition, it should not use any system-specific features or require special facilities (such as In- ternet access or write permission to specific directories). Text included in \dontrun is indicated by comments in the processed help files: it need not be valid R code but the escapes must still be used for %,\ and unpaired braces as in other verbatim text. Example code must be capable of being run by example, which uses source. This means that it should not access stdin, e.g. to scan() data from the example file. Data needed for making the examples executable can be obtained by random number generation (for example, x <- rnorm(100)), or by using standard data sets listed by data() (see ?data for more info). Chapter 2: Writing R documentation files 55 Finally, there is \donttest, used (at the beginning of a separate line) to mark code that should be run by examples() but not by R CMD check. This should be needed only occasionally but can be used for code which might fail in circumstances that are hard to test for, for example in some locales. (Use e.g. capabilities() to test for features needed in the examples wherever possible, and you can also use try() or tryCatch().) \keyword{key} There can be zero or more \keyword sections per file. Each \keyword section should specify a single keyword, preferably one of the standard keywords as listed in file KEYWORDS in the R documentation directory (default R_HOME/doc). Use e.g. RShowDoc("KEYWORDS") to inspect the standard keywords from within R. There can be more than one \keyword entry if the R object being documented falls into more than one category, or none. The special keyword ‘internal’ marks a page of internal objects that are not part of the package’s API. If the help page for object foo has keyword ‘internal’, then help(foo) gives this help page, but foo is excluded from several object indices, including the alphabetical list of objects in the HTML help system. help.search() can search by keyword, including user-defined values: however the ‘Search Engine & Keywords’ HTML page accessed via help.start() provides single- click access only to a pre-defined list of keywords. 2.1.2 Documenting data sets The structure of Rd files which document R data sets is slightly different. Sections such as \arguments and \value are not needed but the format and source of the data should be ex- plained. As an example, let us look at src/library/datasets/man/rivers.Rd which documents the standard R data set rivers. ¨\name{rivers} \docType{data} \alias{rivers} \title{Lengths of Major North American Rivers} \description{ This data set gives the lengths (in miles) of 141 \dQuote{major} rivers in North America, as compiled by the US Geological Survey. } \usage{rivers} \format{A vector containing 141 observations.} \source{World Almanac and Book of Facts, 1975, page 406.} \references{ McNeil, D. R. (1977) \emph{Interactive Data Analysis}. New York: Wiley. } \keyword{datasets} ©This uses the following additional markup commands. \docType{...} Indicates the “type” of the documentation object. Always ‘data’ for data sets, and ‘package’ for pkg-package.Rd overview files. Documentation for S4 methods and classes uses ‘methods’ (from promptMethods()) and ‘class’ (from promptClass()). Chapter 2: Writing R documentation files 56 \format{...} A description of the format of the data set (as a vector, matrix, data frame, time series, . . .). For matrices and data frames this should give a description of each column, preferably as a list or table. See Section 2.4 [Lists and tables], page 59, for more information. \source{...} Details of the original source (a reference or URL). In addition, section \references could give secondary sources and usages. Note also that when documenting data set bar, • The \usage entry is always bar or (for packages which do not use lazy-loading of data) data(bar). (In particular, only document a single data object per Rd file.) • The \keyword entry should always be ‘datasets’. If bar is a data frame, documenting it as a data set can be initiated via prompt(bar). Otherwise, the promptData function may be used. 2.1.3 Documenting S4 classes and methods There are special ways to use the ‘?’ operator, namely ‘class?topic’ and ‘methods?topic’, to access documentation for S4 classes and methods, respectively. This mechanism depends on conventions for the topic names used in \alias entries. The topic names for S4 classes and methods respectively are of the form class-class generic,signature_list-method where signature list contains the names of the classes in the signature of the method (without quotes) separated by ‘,’ (without whitespace), with ‘ANY’ used for arguments without an explicit specification. E.g., ‘genericFunction-class’ is the topic name for documentation for the S4 class "genericFunction", and ‘coerce,ANY,NULL-method’ is the topic name for documentation for the S4 method for coerce for signature c("ANY", "NULL"). Skeletons of documentation for S4 classes and methods can be generated by using the func- tions promptClass() and promptMethods() from package methods. If it is necessary or desired to provide an explicit function declaration (in a \usage section) for an S4 method (e.g., if it has “surprising arguments” to be mentioned explicitly), one can use the special markup \S4method{generic}{signature_list}(argument_list) (e.g., ‘\S4method{coerce}{ANY,NULL}(from, to)’). To make full use of the potential of the on-line documentation system, all user-visible S4 classes and methods in a package should at least have a suitable \alias entry in one of the package’s Rd files. If a package has methods for a function defined originally somewhere else, and does not change the underlying default method for the function, the package is responsible for documenting the methods it creates, but not for the function itself or the default method. An S4 replacement method is documented in the same way as an S3 one: see the description of \method in Section 2.1.1 [Documenting functions], page 51. See help("Documentation", package = "methods") for more information on using and cre- ating on-line documentation for S4 classes and methods. 2.1.4 Documenting packages Packages may have an overview help page with an \alias pkgname-package, e.g. ‘utils-package’ for the utils package, when package?pkgname will open that help page. If a topic named pkgname does not exist in another Rd file, it is helpful to use this as an additional \alias. Chapter 2: Writing R documentation files 57 Skeletons of documentation for a package can be generated using the function promptPackage(). If the final = TRUE argument is used, then the Rd file will be generated in final form, containing the information that would be produced up to library(help = pkgname). Otherwise (the default) comments will be inserted giving suggestions for content. Apart from the mandatory \name and \title and the pkgname-package alias, the only requirement for the package overview page is that it include a \docType{package} statement. All other content is optional. We suggest that it should be a short overview, to give a reader unfamiliar with the package enough information to get started. More extensive documentation is better placed into a package vignette (see Section 1.4 [Writing package vignettes], page 31) and referenced from this page, or into individual man pages for the functions, datasets, or classes. 2.2 Sectioning To begin a new paragraph or leave a blank line in an example, just insert an empty line (as in (La)TEX). To break a line, use \cr. In addition to the predefined sections (such as \description{},\value{}, etc.), you can “define” arbitrary ones by \section{section_title}{...}. For example \section{Warning}{ You must not call this function unless ... } For consistency with the pre-assigned sections, the section name (the first argument to \section) should be capitalized (but not all upper case). Whitespace between the first and second braced expressions is not allowed. Markup (e.g. \code) within the section title may cause problems with the latex conversion (depending on the version of macro packages such as ‘hyperref’) and so should be avoided. The \subsection macro takes arguments in the same format as \section, but is used within a section, so it may be used to nest subsections within sections or other subsections. There is no predefined limit on the nesting level, but formatting is not designed for more than 3 levels (i.e. subsections within subsections within sections). Note that additional named sections are always inserted at a fixed position in the output (before \note,\seealso and the examples), no matter where they appear in the input (but in the same order amongst themselves as in the input). 2.3 Marking text The following logical markup commands are available for emphasizing or quoting text. \emph{text} \strong{text} Emphasize text using italic and bold font if possible; \strong is regarded as stronger (more emphatic). \bold{text} Set text in bold font if possible. \sQuote{text} \dQuote{text} Portably single or double quote text (without hard-wiring the characters used for quotation marks). Each of the above commands takes LATEX-like input, so other macros may be used within text. Chapter 2: Writing R documentation files 58 The following logical markup commands are available for indicating specific kinds of text. Except as noted, these take verbatim text input, and so other macros may not be used within them. Some characters will need to be escaped (see Section 2.8 [Insertions], page 61). \code{text} Indicate text that is a literal example of a piece of an R program, e.g., a fragment of R code or the name of an R object. Text is entered in R-like syntax, and displayed using typewriter font if possible. Macros \var and \link are interpreted within text. \preformatted{text} Indicate text that is a literal example of a piece of a program. Text is displayed using typewriter font if possible. Formatting, e.g. line breaks, is preserved. (Note that this includes a line break after the initial {, so typically text should start on the same line as the command.) Due to limitations in LATEX as of this writing, this macro may not be nested within other markup macros other than \dQuote and \sQuote, as errors or bad formatting may result. \kbd{keyboard-characters} Indicate keyboard input, using slanted typewriter font if possible, so users can distinguish the characters they are supposed to type from computer output. Text is entered verbatim. \samp{text} Indicate text that is a literal example of a sequence of characters, entered verbatim. No wrapping or reformatting will occur. Displayed using typewriter font if possible. \verb{text} Indicate text that is a literal example of a sequence of characters, with no interpre- tation of e.g. \var, but which will be included within word-wrapped text. Displayed using typewriter font if possible. \pkg{package_name} Indicate the name of an R package. LATEX-like. \file{file_name} Indicate the name of a file. Text is LATEX-like, so backslash needs to be escaped. Displayed using a distinct font if possible. \email{email_address} Indicate an electronic mail address. LATEX-like, will be rendered as a hyperlink in HTML and PDF conversion. Displayed using typewriter font if possible. \url{uniform_resource_locator} Indicate a uniform resource locator (URL) for the World Wide Web. The argument is handled verbatim, and rendered as a hyperlink in HTML and PDF conversion. Displayed using typewriter font if possible. \href{uniform_resource_locator}{text} Indicate a hyperlink to the World Wide Web. The first argument is handled verba- tim, and is used as the URL in the hyperlink, with the second argument of LATEX-like text displayed to the user. \var{metasyntactic_variable} Indicate a metasyntactic variable. In some cases this will be rendered distinctly, e.g. in italic, but not in all5.LATEX-like. 5 Currently it is rendered differently only in HTML conversions, and LATEX conversion outside ‘\usage’ and ‘\examples’ environments. Chapter 2: Writing R documentation files 59 \env{environment_variable} Indicate an environment variable. Verbatim. Displayed using typewriter font if possible \option{option} Indicate a command-line option. Verbatim. Displayed using typewriter font if possible. \command{command_name} Indicate the name of a command. LATEX-like, so \var is interpreted. Displayed using typewriter font if possible. \dfn{term} Indicate the introductory or defining use of a term. LATEX-like. \cite{reference} Indicate a reference without a direct cross-reference via \link (see Section 2.5 [Cross-references], page 60), such as the name of a book. LATEX-like. \acronym{acronym} Indicate an acronym (an abbreviation written in all capital letters), such as GNU. LATEX-like. 2.4 Lists and tables The \itemize and \enumerate commands take a single argument, within which there may be one or more \item commands. The text following each \item is formatted as one or more para- graphs, suitably indented and with the first paragraph marked with a bullet point (\itemize) or a number (\enumerate). Note that unlike argument lists, \item in these formats is followed by a space and the text (not enclosed in braces). For example \enumerate{ \item A database consists of one or more records, each with one or more named fields. \item Regular lines start with a non-whitespace character. \item Records are separated by one or more empty lines. } \itemize and \enumerate commands may be nested. The \describe command is similar to \itemize but allows initial labels to be specified. Each \item takes two arguments, the label and the body of the item, in exactly the same way as an argument or value \item.\describe commands are mapped to

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