Building R Packages
10 minute read
Motivation for building R packages
- Consolidate functions with related utilties in single place
- Interdepencies among less complex functions make coding more efficient
- Minimizes duplications
- Help page infrastructure improves documentation of functions
- Big picture of utilties provided by package vignettes (manuals)
- Package can be easier shared with colleagues and public
- Increases code accessibilty for other users
- Makes software more extensible and maintainable
Package development environments
This page introduces two approaches for building R packages:
R Baseand related functionalities
devtoolsand related packages (e.g.
The sample code provided below creates for each of the two methods a simple test package that can be installed and loaded on a user’s system. The instructions for the second appoach are more detailed since it is likely to provide the most practical solution for newer users of R.
1. R Base Approach
R packages can be built with the
package.skeleton function. The most
comprehensive documentation on package development is provided by the Writing
page on CRAN. The basic workflow example below will create a directory named
mypackage containing the skeleton of the package for all functions, methods
and classes defined in the R script(s) passed on to the
The basic structure of the package directory is described
The package directory will also contain a file named
instructions for completing the package:
1.1 Create package skeleton
## Download R script (here pkg_build_fct.R) containing two sample functions download.file("https://raw.githubusercontent.com/tgirke/GEN242/main/content/en/tutorials/rpackages/helper_functions/pkg_build_fct.R", "pkg_build_fct.R") ## Build package skeleton based on functions in pkg_build_fct.R package.skeleton(name="mypackage", code_files=c("pkg_build_fct.R"))
The given example will create a directory named
mypackage containing the
skeleton of the package for all functions, methods and classes defined in the R
script(s) passed on to the
code_files argument. The basic structure of the
package directory is described here.
The package directory will also contain a file named ‘Read-and-delete-me’ with the following
instructions for completing the package:
- Edit the help file skeletons in man, possibly combining help files for multiple functions.
- Edit the exports in NAMESPACE, and add necessary imports.
- Put any C/C++/Fortran code in src.
- If you have compiled code, add a
- Run R CMD build to build the package tarball.
- Run R CMD check to check the package tarball.
- Read Writing R Extensions for more information.
1.2 Build package
Once a package skeleton is available one can build the package from the
command-line (Linux/OS X), or from within R by executing the command-line calls
system("...") command. For instance, the command-line call
R CMD build ...
can be run from within R with
system("R CMD build ..."). The following examples
refer to the R console.
system("R CMD build mypackage")
1.3 Check package
This will create a tarball of the package with its version number encoded in
the file name (here
mypackage_1.0.tar.gz). Subsequently, the package tarball
needs to be checked for errors with
R CMD check.
system("R CMD check mypackage_1.0.tar.gz")
All issues in a package’s source code and documentation should be addressed
R CMD check returns no error or warning messages anymore.
1.4 Install package
Install package from source on Linux or OS X systems.
install.packages("mypackage_1.0.tar.gz", repos=NULL) # On OS X include the argument type="source"
Windows requires a zip archive for installing R packages, which can be most
conveniently created from the command-line (Linux/OS X) by installing the
package in a local directory (here
tempdir) and then creating a zip archive
from the installed package directory:
$ mkdir tempdir $ R CMD INSTALL -l tempdir mypackage_1.0.tar.gz $ cd tempdir $ zip -r mypackage mypackage ## The resulting mypackage.zip archive can be installed from R under Windows like this: install.packages("mypackage.zip", repos=NULL)
1.5 Maintain package
Several useful helper utilities exist for maintaing and extending packages. Typical package development routines include:
- Adding new functions, methods and classes to the script files in the ./R directory in your package
- Adding their names to the NAMESPACE file of the package
.Rdhelp templates can be generated with the
prompt()function family like this:
source("myscript.R") # imports functions, methods and classes from myscript.R prompt(myfct) # writes help file myfct.Rd promptClass("myclass") # writes file myclass-class.Rd promptMethods("mymeth") # writes help file mymeth.Rd
.Rd help files can be edited in a text editor, and properly rendered and viewed from within R with help of
the following functions.
library(tools) Rd2txt("./mypackage/man/myfct.Rd") # renders *.Rd files as they look in final help pages checkRd("./mypackage/man/myfct.Rd") # checks *.Rd help file for problems
1.6 Submit package to public repository
The best way of sharing an R package with the community is to submit it to one of the main R package repositories, such as CRAN or Bioconductor. The details about the submission process are given on the corresponding repository submission pages:
Several package develpment routines of the traditional method outlined above
are manual, such as updating the NAMESPACE file and documenting functions in
separate help (
*.Rd) files. This process can be simplified and partially
automated by taking advantage of a more recent R package development
environment composed of several helper packages including
sinew (Wickham and Bryan, n.d.). Many books and web sites document this process
in more detail. Here is a small selection of useful online documentation about
R package development:
- Book: R Packages by Hadley Wickham and Jenny Bryan
- My First R Package by Fong Chun Chan
- How to Creat an R Package, Easy Mode by Amit Kohli
- Package Development Cheat Sheet
sinewby Jonathan Sidi: Blog and CRAN
Workflow for building R packages
The following outlines the basic workflow for building, testing and extending R packages with the package development environment functionalities outlined above.
2.1 Create package skeleton
library("devtools"); library("roxygen2"); library("usethis"); library(sinew) # If not availble install these packages with 'install.packages(...)' create("myfirstpkg") # Creates package skeleton. The chosen name (here myfirstpkg) will be the name of the package. setwd("myfirstpkg") # Set working directory of R session to package directory 'myfirstpkg' use_mit_license() # Add license information to description file (here MIT). To look up alternatives, do ?use_mit_license
2.2 Add R functions
Next, R functions can be added to
*.R file(s) under the R directory of the new
package. Several functions can be organized in one
*.R file, each in its own
file or any combination. For demonstration purposes, the following will
download an R file (
pkg_build_fct.R from here) defining two functions (named:
talkToMe) and save it to the R directory of the package.
2.3 Auto-generate roxygen comment lines
makeOxygen function from the
sinew package creates
skeletons based on the information from each function (below for
The roxygen comment lines need to be added above the code of each function.
This can be done by copy and paste from the R console or by writing the output
to a temporary file (below via
writeLines). Alternatively, the
function can be used to create a roxygenized copy of an R source file, where the
roxygen comment lines have been added above all functions automatically. Next,
the default text in the comment lines needs to be replaced by meaningful text
describing the utility and usage of each function. This editing process of documentation
can be completed and/or revised any time.
load_all() # Loads package in a simulated way without installing it. writeLines(makeOxygen(myMAcomp), "myroxylines") # This creates a 'myroxylines' file in current directory. Delete this file after adding its content to the corresponding functions.
2.4 Autogenerate help files
document function autogenerates for each function one
*.Rd file in the
man directory of the package. The content in the
*.Rd help files is based
on the information in the roxygen comment lines generated in the
previous step. In addition, all relevant export/import instructions are added
NAMESPACE file. Importantly, when using roxygen-based documentation in a package
*.Rd files should not be manually edited since this information
will be lost during the automation routines provided by
document() # Auto-generates/updates *.Rd files under man directory (here: myMAcomp.Rd and talkToMe.Rd) tools::Rd2txt("man/myMAcomp.Rd") # Renders Rd file from source tools::checkRd("man/myMAcomp.Rd") # Checks Rd file for problems
2.5 Add a vignette
A vignette template can be auto-generated with the
use_vignette function from
usethis package. The
*.Rmd source file of the vignette will be located
under a new
vignette directory. Additional vignettes can be manually added to
this directory as needed.
use_vignette("introduction", title="Introduction to this package")
2.6 Check, install and build package
Now the package can be checked for problems. All warnings and errors should be
addressed prior to submission to a public repository. After this it can be
installed on a user’s system with the
install command. In addition, the
build function allows to assemble the package in a
*.tar.gz file. The
latter is often important for sharing packages and/or submitting them to public
setwd("..") # Redirect R session to parent directory check("myfirstpkg") # Check package for problems, when in pkg dir one can just use check() # remove.packages("myfirstpkg") # Optional. Removes test package if already installed install("myfirstpkg", build_vignettes=TRUE) # Installs package build("myfirstpkg") # Creates *.tar.gz file for package required to for submission to CRAN/Bioc
2.7 Using the new package
After installing and loading the package its functions, help files and vignettes can be accessed as follows.
library("myfirstpkg") library(help="myfirstpkg") ?myMAcomp vignette("introduction", "myfirstpkg")
Another very useful development function is
test for evaluating the test code of a package.
2.8 Share package on GitHub
To host and share the new package
myfirstpkg on GitHub, one can use the following steps:
- Create an empty target GitHub repos online (e.g. named
mypkg_repos) as outlined here.
- Clone the new GitHub repos to local system with
git clone https://github.com/<github_username>/<repo name>(here from command-line)
- Copy the root directory of the package into the downloaded repos with
cp -r myfirstpkg mypkg_repos
mypkg_repos, and then add all files and directories of the package to the staging area with
git add -A :/.
- Commit and push the changes to GitHub with:
git commit -am "first commit"; git push.
- After this the package should be life on the corresponding GitHub web page.
- Assuming the package is public, it can be installed directly from GitHub by anyone as shown below (from within R). Installs of
private packages require a personal access token (PAT) that needs to be assigned to the
auth_tokenargument. PATs can be created here.
devtools::install_github("<github_user_name>/<mypkg_repos>", subdir="myfirstpkg") # If the package is in the root directory of the repos, then the 'subdir' argument can be dropped.
## R version 4.2.0 (2022-04-22) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Debian GNU/Linux 11 (bullseye) ## ## Matrix products: default ## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0 ## ## locale: ##  LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 ##  LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 ##  LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C ##  LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: ##  stats graphics grDevices utils datasets methods base ## ## other attached packages: ##  ggplot2_3.3.6 limma_3.52.0 BiocStyle_2.24.0 ## ## loaded via a namespace (and not attached): ##  bslib_0.3.1 compiler_4.2.0 pillar_1.7.0 BiocManager_1.30.17 ##  jquerylib_0.1.4 tools_4.2.0 digest_0.6.29 jsonlite_1.8.0 ##  evaluate_0.15 lifecycle_1.0.1 tibble_3.1.7 gtable_0.3.0 ##  pkgconfig_2.0.3 rlang_1.0.2 DBI_1.1.2 cli_3.3.0 ##  yaml_2.3.5 blogdown_1.9 xfun_0.30 fastmap_1.1.0 ##  withr_2.5.0 dplyr_1.0.9 stringr_1.4.0 knitr_1.39 ##  generics_0.1.2 sass_0.4.1 vctrs_0.4.1 tidyselect_1.1.2 ##  grid_4.2.0 glue_1.6.2 R6_2.5.1 fansi_1.0.3 ##  rmarkdown_2.14 bookdown_0.26 purrr_0.3.4 magrittr_2.0.3 ##  codetools_0.2-18 scales_1.2.0 htmltools_0.5.2 ellipsis_0.3.2 ##  assertthat_0.2.1 colorspace_2.0-3 utf8_1.2.2 stringi_1.7.6 ##  munsell_0.5.0 crayon_1.5.1