16  Building R Packages

16.1 R Packages

Most R users are familiar with loading and utilizing packages in their work. And they know how rich CRAN is in providing for many conceivable needs. Most people have never created a package for their own work, and most think the process is too complicated. Really it’s pretty straighforward and super useful in your personal work. Creating packages serves two main use cases:

  • Mechanism to redistribute reusable code (even if just for yourself)
  • Mechanism to reproducibly document analysis and models and their results

Even if you don’t plan on writing a package with such broad appeal such as, say, ggplot2 or dplyr, you still might consider creating a package to contain:

  • Useful utility functions you write (i.e. a Personal Package). Having a place to put these functions makes it much easier to find and use them later.
  • A set of shared routines for your lab or research group, making it easier to remain consistent within your team and also to save time.
  • The analysis accompanying a thesis or manuscript, making it all that much easier for others to reproduce your results.
Packages for Creating and Maintaining Packages

The usethis, devtools and roxygen2 packages make creating and maintining a package to be a straightforward experience.

16.1.1 Create a Basic Package

To create a package we’re going to use the following packages:

  • devtools: Provides R functions that make package development easier by expediting common development tasks.
  • usethis: Commonly referred to as a “workflow package” and provides functions that automate common tasks in project setup and development for both R packages and non-package projects.
  • roxygen2: Provides a structure for describing your functions in the scripts you’re creating them in. It will additionally process the source code and the documentation within it to automatically create the necessary files for the documentation to appear in your R Package.

Thanks to the great usethis package, it only takes one function call to create the skeleton of an R package using create_package(). Which eliminates pretty much all reasons for procrastination. To create a package called mytools, all you do is:

usethis::create_package("~/mytools")
✔ Creating '/home/dolinh/mytools/'
✔ Setting active project to '/home/dolinh/mytools'
✔ Creating 'R/'
✔ Writing 'DESCRIPTION'
Package: mytools
Title: What the Package Does (One Line, Title Case)
Version: 0.0.0.9000
Authors@R (parsed):
    * First Last <first.last@example.com> [aut, cre] (YOUR-ORCID-ID)
Description: What the package does (one paragraph).
License: `use_mit_license()`, `use_gpl3_license()` or friends to
    pick a license
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
✔ Writing 'NAMESPACE'
✔ Writing 'mytools.Rproj'
✔ Adding '^mytools\\.Rproj$' to '.Rbuildignore'
✔ Adding '.Rproj.user' to '.gitignore'
✔ Adding '^\\.Rproj\\.user$' to '.Rbuildignore'
✔ Opening '/home/dolinh/mytools/' in new RStudio session
✔ Setting active project to '<no active project>'
What did the create_package() function do?
  1. Open a new project called mytools (the name of the package) in a new RStduio session.
  2. Create a top-level directory structure, including a number of critical files under the standard R package structure:
    1. DESCRIPTIONfile: The most important file, which provides metadata about your package. Edit this file to provide reasonable values for each of the fields, including your contact information.
    2. NAMESPACE file declares the functions your package exports for external use and the external functions your package imports from other packages.
    3. R/ directory is where you save all your function scripts and other .R files.
    4. .Rbuildignore lists files that we need to have around but that should not be included when building the R package from source.
    5. .Rproj.user is a directory used internally by RStudio.
  3. Add the Build Tab to the Environment Pane.

16.1.2 Add a License

Information about choosing a LICENSE is provided in the R Package (2e) book Chapter 12: Licensing.

The DESCRIPTION file expects the license to be chose from a predefined list, but you can use its various utility methods for setting a specific license file, such as the MIT license or the Apache 2 license:

usethis::use_apache_license()
✔ Setting License field in DESCRIPTION to 'Apache License (>= 2.0)'
✔ Writing 'LICENSE.md'
✔ Adding '^LICENSE\\.md$' to '.Rbuildignore'

Once your license has been chosen, and you’ve edited your DESCRIPTION file with your contact information, a title, and a description, it will look like this:

Package: mytools
Title: Halina Do-Linh's Utility R Functions
Version: 0.0.0.9000
Authors@R: 
    person("Halina", "Do-Linh", email = "dolinh@nceas.ucsb.edu", role = c("aut", "cre"),
           comment = c(ORCID = "YOUR-ORCID-ID"))
Description: A collection of useful R functions that I use for general utilities.
License: Apache License (>= 2)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3

16.1.3 Add Code

The skeleton package created contains a directory R which should contain your source files. Add your functions and classes in files to this directory, attempting to choose names that don’t conflict with existing packages. For example, you might add a file custom_theme that contains a function custom_theme() that you might want to reuse. The usethis::use_r() function will help set up you files in the right places. For example, running:

usethis::use_r("custom_theme")
✔ Setting active project to '/home/dolinh/mytools'
• Modify 'R/custom_theme.R'
• Call `use_test()` to create a matching test file

creates the file R/custom_theme and stores it in the R directory, which you can then modify as needed:

custom_theme <- function(base_size = 9) {
    ggplot2::theme(
      axis.ticks       = ggplot2::element_blank(),
      text             = ggplot2::element_text(family = 'Helvetica', 
                                               color = 'gray30', 
                                               size = base_size),
      plot.title       = ggplot2::element_text(size = ggplot2::rel(1.25), 
                                               hjust = 0.5, 
                                               face = 'bold'),
      panel.background = ggplot2::element_blank(),
      legend.position  = 'right',
      panel.border     = ggplot2::element_blank(),
      panel.grid.minor = ggplot2::element_blank(),
      panel.grid.major = ggplot2::element_line(colour = 'grey90', 
                                               linewidth = .25),
      legend.key       = ggplot2::element_rect(colour = NA, 
                                               fill = NA),
      axis.line        = ggplot2::element_blank()
      )
}
Power of Packages

Remember when we created custom_theme() from the Functions Lesson Section 15.1.4? Now that we’ve added it to our mytools package, we don’t have to worry about coyping the code from another file, sourcing the file from another directory, or copying the script from an R Project.

Instead we can leverage the portable functionality of a package to easily access our custom functions and maintain the code in one location.

16.1.4 Add Dependencies

If your R code depends on functions from another package, you must declare it. In the Imports section in the DESCRIPTION file, list all the packages your functions depend upon.

In our custom_theme() function, we depend on the ggplot2 package, and so we need to list it as a dependency.

Once again, usethis provides a handy helper method:

usethis::use_package("ggplot2")
✔ Adding 'ggplot2' to Imports field in DESCRIPTION
• Refer to functions with `ggplot2::fun()`

Take a look at the DESCRIPTION file again, and you’ll see the Imports section has been added, with ggplot2 underneath.

Package: mytools
Title: Halina Do-Linh's Utility R Functions
Version: 0.0.0.9000
Authors@R: 
    person("Halina", "Do-Linh", email = "dolinh@nceas.ucsb.edu", role = c("aut", "cre"),
           comment = c(ORCID = "YOUR-ORCID-ID"))
Description: A collection of useful R functions that I use for general utilities.
License: Apache License (>= 2)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
Imports: 
    ggplot2

16.1.5 Add Documentation

Documentation is crucial to add to each of your functions. In the Functions Lesson, we did this using the roxygen2 package and that same package and approach can be used for packages.

The roxygen2 approach allows us to add comments in the source code, where are then converted into Help pages that we can access by typing ?function_name in the Console.

Let’s add documentation for the custom_theme() function.

#' My custom ggplot theme
#'
#' @param base_size Numeric value of font size of all text elements in plot
#'
#' @return A theme used for ggplot point or line plots
#' @export
#'
#' @examples
#' library(ggplot2)
#' 
#'   ggplot(data = mtcars, aes(x = mpg, y = disp)) +
#'     geom_point() +
#'     custom_theme(base_size = 30)
custom_theme <- function(base_size = 9) {
  ggplot2::theme(
    axis.ticks       = ggplot2::element_blank(),
    text             = ggplot2::element_text(family = 'Helvetica',
                                             color = 'gray30',
                                             size = base_size),
    plot.title       = ggplot2::element_text(size = ggplot2::rel(1.25),
                                             hjust = 0.5,
                                             face = 'bold'),
    panel.background = ggplot2::element_blank(),
    legend.position  = 'right',
    panel.border     = ggplot2::element_blank(),
    panel.grid.minor = ggplot2::element_blank(),
    panel.grid.major = ggplot2::element_line(colour = 'grey90',
                                             linewidth = .25),
    legend.key       = ggplot2::element_rect(colour = NA,
                                             fill = NA),
    axis.line        = ggplot2::element_blank()
  )
}

Once your files are documented, you can then process the documentation using devtools::document() to generate the appropriate .Rd files that your package needs. The .Rd files will appear in the man/ directory, which is automatically created by devtools::document().

devtools::document()
ℹ Updating mytools documentation
ℹ Loading mytools
Writing custom_theme.Rd

We now have a package that we can check() and install() and release(). These functions come from the devtools package, but first let’s do some testing.

16.1.6 Testing

You can test your code using the testthat package’s testing framework. The ussethis::use_testthat() function will set up your package for testing, and then you can use the use_test() function to setup individual test files. For example, in the Functions Lesson we created some tests for our fahr_to_celsius functions but ran them line by line in the console.

First, lets add that function to our package. Run the use_r function in the console:

usethis::use_r("fahr_to_celsius")

Then copy the function and documentation into the R script that opens and save the file.

#' Convert temperature values from Fahrenheit to Celsius
#'
#' @param fahr Numeric or numeric vector in degrees Fahrenheit
#' 
#' @return Numeric or numeric vector in degrees Celsius
#' @export
#' 
#' @examples
#' fahr_to_celsius(32)
#' fahr_to_celsius(c(32, 212, 72))

fahr_to_celsius <- function(fahr) {
  celsius <- (fahr-32)*5/9
  return(celsius)
}

Now, set up your package for testing:

usethis::use_testthat()
✔ Setting active project to '/home/dolinh/mytools'
✔ Adding 'testthat' to Suggests field in DESCRIPTION
✔ Setting Config/testthat/edition field in DESCRIPTION to '3'
✔ Creating 'tests/testthat/'
✔ Writing 'tests/testthat.R'
• Call `use_test()` to initialize a basic test file and open it for editing.

Then write a test for fahr_to_celsius:

usethis::use_test("fahr_to_celsius")
✔ Writing 'tests/testthat/test-fahr_to_celsius.R'
• Modify 'tests/testthat/test-fahr_to_celsius.R'

You can now add tests to the test-fahr_to_celsius.R, and you can run all of the tests using devtools::test(). For example, if you add a test to the test-fahr_to_celsius.R file:

test_that("fahr_to_celsius works", {
  expect_equal(fahr_to_celsius(32), 0)
  expect_equal(fahr_to_celsius(212), 100)
})

Then you can run the tests to be sure all of your functions are working using devtools::test():

devtools::test()
ℹ Testing mytools
✔ | F W S  OK | Context
✔ |         2 | fahr_to_celsius [0.2s]                                                                                             

══ Results ════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════
Duration: 0.4 s

[ FAIL 0 | WARN 0 | SKIP 0 | PASS 2 ]

Yay, all tests passed!

16.1.7 Checking and Installing

Now that you’ve completed testing your package, you can check it for consistency and completeness using devtools::check().

devtools::check()
══ Documenting ════════════════════════════════════════════════════════════════════════════════════════════════════════════════════
ℹ Updating mytools documentation
ℹ Loading mytools

══ Building ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════
Setting env vars:
• CFLAGS    : -Wall -pedantic -fdiagnostics-color=always
• CXXFLAGS  : -Wall -pedantic -fdiagnostics-color=always
• CXX11FLAGS: -Wall -pedantic -fdiagnostics-color=always
• CXX14FLAGS: -Wall -pedantic -fdiagnostics-color=always
• CXX17FLAGS: -Wall -pedantic -fdiagnostics-color=always
• CXX20FLAGS: -Wall -pedantic -fdiagnostics-color=always
── R CMD build ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
✔  checking for file ‘/home/dolinh/mytools/DESCRIPTION’ (610ms)
─  preparing ‘mytools’:
✔  checking DESCRIPTION meta-information (338ms)
─  checking for LF line-endings in source and make files and shell scripts
─  checking for empty or unneeded directories
─  building ‘mytools_0.0.0.9000.tar.gz’
   
══ Checking ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════
Setting env vars:
• _R_CHECK_CRAN_INCOMING_REMOTE_               : FALSE
• _R_CHECK_CRAN_INCOMING_                      : FALSE
• _R_CHECK_FORCE_SUGGESTS_                     : FALSE
• _R_CHECK_PACKAGES_USED_IGNORE_UNUSED_IMPORTS_: FALSE
• NOT_CRAN                                     : true
── R CMD check ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
─  using log directory ‘/tmp/Rtmp1UgqFD/file6d79323df6fae/mytools.Rcheck’ (649ms)
─  using R version 4.2.2 (2022-10-31)
─  using platform: x86_64-pc-linux-gnu (64-bit)
─  using session charset: UTF-8
─  using options ‘--no-manual --as-cran’
✔  checking for file ‘mytools/DESCRIPTION’
─  this is package ‘mytools’ version ‘0.0.0.9000’
─  package encoding: UTF-8
✔  checking package namespace information
✔  checking package dependencies (2.1s)
✔  checking if this is a source package
✔  checking if there is a namespace
✔  checking for executable files
✔  checking for hidden files and directories
✔  checking for portable file names
✔  checking for sufficient/correct file permissions
✔  checking serialization versions
✔  checking whether package ‘mytools’ can be installed (3.2s)
✔  checking installed package size
✔  checking package directory
✔  checking for future file timestamps (412ms)
✔  checking DESCRIPTION meta-information (584ms)
✔  checking top-level files ...
✔  checking for left-over files
✔  checking index information
✔  checking package subdirectories ...
✔  checking R files for non-ASCII characters ...
✔  checking R files for syntax errors ...
✔  checking whether the package can be loaded (481ms)
✔  checking whether the package can be loaded with stated dependencies ...
✔  checking whether the package can be unloaded cleanly ...
✔  checking whether the namespace can be loaded with stated dependencies ...
✔  checking whether the namespace can be unloaded cleanly (450ms)
✔  checking loading without being on the library search path (522ms)
✔  checking dependencies in R code (1.2s)
✔  checking S3 generic/method consistency (1s)
✔  checking replacement functions ...
✔  checking foreign function calls ...
✔  checking R code for possible problems (5.2s)
✔  checking Rd files (449ms)
✔  checking Rd metadata ...
✔  checking Rd line widths ...
✔  checking Rd cross-references ...
✔  checking for missing documentation entries ...
✔  checking for code/documentation mismatches (885ms)
✔  checking Rd \usage sections (1.3s)
✔  checking Rd contents ...
✔  checking for unstated dependencies in examples ...
✔  checking examples (2.7s)
✔  checking for unstated dependencies in ‘tests’ ...
─  checking tests (418ms)
✔  Running ‘testthat.R’ (1.4s)
✔  checking for non-standard things in the check directory
✔  checking for detritus in the temp directory
   
   
── R CMD check results ──────────────────────────────────────────────────────────────────────────────────── mytools 0.0.0.9000 ────
Duration: 27.3s

0 errors ✔ | 0 warnings ✔ | 0 notes ✔

Then you can install it locally using devtools::install(), which needs to be run from the parent directory of your module

devtools::install()
── R CMD build ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
✔  checking for file ‘/home/dolinh/mytools/DESCRIPTION’ (541ms)
─  preparing ‘mytools’:
✔  checking DESCRIPTION meta-information ...
─  checking for LF line-endings in source and make files and shell scripts
─  checking for empty or unneeded directories
─  building ‘mytools_0.0.0.9000.tar.gz’
   
Running /opt/R/4.2.2/lib/R/bin/R CMD INSTALL /tmp/Rtmp1UgqFD/mytools_0.0.0.9000.tar.gz --install-tests 
* installing to library ‘/home/dolinh/R/x86_64-pc-linux-gnu-library/4.2’
* installing *source* package ‘mytools’ ...
** using staged installation
** R
** tests
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (mytools)

After installing, your package is now available for use in your local environment, yay!

Check out the Build Tab

Remember when we ran usethis::create_package() and after we ran it we saw the Build Tab added to the Environment pane?

In the Build Tab, each of the buttons correspond with one of the devtools functions we ran, meaning:

  • Test button is equivalent to running devtools::test() in the Console
  • Check button is equivalent to running devtools::check() in the Console
  • Install button is equivalent to running devtools::install() in the Console

16.1.8 Sharing and Releasing

  • GitHub: The simplest way to share your package with others is to upload it to a GitHub repository, which allows others to install your package using the install_github('mytools','github_username') function from devtools.

  • CRAN: If your package might be broadly useful, also consider releasing it to CRAN, using the release() method from devtools(). Releasing a package to CRAN requires a significant amount of work to ensure it follows the standards set by the R community, but it is entirely tractable and a valuable contribution to the science community. If you are considering releasing a package more broadly, you may find that the supportive community at ROpenSci provides incredible help and valuable feeback through their onboarding process.

  • R-Universe: A newer approach is to link your package release to R-Universe, which is an effective way to make it easy to test and maintain packages so that many people can install them using the familiar install.pacakges() function in R. In R-Universe, people and organizations can create their own universe of packages, which represent a collection of packages that appear as a CRAN-compatible repository in R. For example, for DataONE we maintain the DataONE R-Universe, which lists the packages we actively maintain as an organization. So, any R-user that wants to install these packages can do so by adding our universe to their list of repositories, and then installing packages as normal. For example, to install the codyn package, one could use:

install.packages('codyn', repos = c('https://dataoneorg.r-universe.dev', 'https://cloud.r-project.org'))

16.1.9 Exercise: Add More Functions

Add additional temperature conversion functions to the mytools package and:

  • Add full documentation for each function
  • Write tests to ensure the functions work properly
  • Rebuild the package using document(), check(), and install()
Don’t forget to update the version number before you install!

Version information is located in the DESCRIPTION file and when you first create a package the version is 0.0.0.9000.

This version number follows the format major.minor.patch.dev. The different parts of the version represent different things:

  • Major: A significant change to the package that would be expected to break users code. This is updated very rarely when the package has been redesigned in some way.
  • Minor: A minor version update means that new functionality has been added to the package. It might be new functions to improvements to existing functions that are compatible with most existing code.
  • Patch: Patch updates are bug fixes. They solve existing issues but don’t do anything new.
  • Dev: Dev versions are used during development and this part is missing from release versions. For example you might use a dev version when you give someone a beta version to test. A package with a dev version can be expected to change rapidly or have undiscovered issues.

After you’ve made some changes to a package, but before you install run the code:

usethis::use_version()
Current version is 0.0.0.9000.
What should the new version be? (0 to exit) 

1: major --> 1.0.0
2: minor --> 0.1.0
3: patch --> 0.0.1
4:   dev --> 0.0.0.9001

Since we’re adding new functions, we can consider this a minor change and can select option 2.

Selection: 2
✔ Setting Version field in DESCRIPTION to '0.1.0'

Source: COMBINE’s R package workshop, Ch 9: Versioning

16.1.10 Additional Resources