Chapter 11 Building app with {golem}

Now that the application is prototyped inside a {golem} (Guyader et al. 2020) skeleton, you can work on its integration. In this step of the workflow, you will be linking the back-end and front-end together, and working on the global engineering of the application, i.e:

  • add and organize dependencies
  • creating and including sub-modules if necessary
  • organize utility functions and link them to the module they are used in
  • adding testing infrastructure
  • linking to CI / CD services

Note that some concepts introduced here will be more extensively explored in the following chapters: the present chapter is a walkthrough of what you will find inside the 02_dev scripts.

11.1 Add dependencies

11.1.1 Package dependencies

When you are building a {shiny} (Chang et al. 2020) application, you will have to deal with dependencies. Well, at least with one dependency, {shiny}. But chances are that you will not only be using {shiny} inside your application: you will probably call functions from other packages, for example from {ggplot2} (Wickham, Chang, et al. 2020) for plotting, {DT} (Xie, Cheng, and Tan 2020) for interactive tables, or any other package that is necessary for your application to work.

If you are building your application using {golem}, you will have 3 default dependencies: {golem} itself, {shiny}, and {config}. If you call golem::use_recommended_deps() in the first workflow script, you will also have {shiny}, {DT}, {attempt}, {glue}, {htmltools}, and {golem} as dependencies to your package33 . But what about other dependencies like {ggplot2} or {DT}? These ones need to be added by hand.

Here is how to process for a new dependency:

  • Open the dev/02_dev.R script,
  • Call the use_package() function from {usethis}: usethis::use_package("")
  • Detail import mechanism in the related R files

11.1.2 Importing packages and functions

There are two places where the dependencies of your application need to be managed34 : the DESCRIPTION file and the NAMESPACE

  • The DESCRIPTION file, which dictates which packages have to be installed when your application is installed. With R, you can not install just a subset of a package (i.e when you install {golem} on your machine, you install the whole package, not only a subset of functions), and you have to list somewhere these packages you need: this is what is done when your are listing in the DESCRIPTION file with usethis::use_package(). What that implies is that every package listed as a dependency will be fully installed on the machine when your application is installed. For example, if you list {DT} inside the DESCRIPTION file, the whole package will be installed when your app is installed.

  • The NAMESPACE file, which describe how your app interacts with the R session at run time, i.e when your application is launched. What this NAMESPACE file allows to do is to specify only a subset of functions to import from other packages: for example you can choose to import only renderDT() and DTOutput() from {DT}, instead of importing all the functions. The idea with selective import is to avoid namespace conflicts: if you remember correctly, we described a few chapters ago a namespace conflict occurring between jsonlite::flatten() and purrr::flatten()35 : using a selective importation, i.e only importing the function we need from {purrr}, will prevent our code from failing because it is looking for the wrong function. To do so, we will need to go to every script that defines one or several function/s, and add a {roxygen2} (Wickham, Danenberg, et al. 2020) tag, in the following form : #' @importFrom purrr map map_df partial. Note that you can also use explicit namespacing, i.e the pkg::function() notation inside your code.

Also, if you need a little help to identify dependencies, all the explicitly namespaced calls (pkg::function()) can be scraped using the {attachment} (Guyader and Rochette 2020) package:


If you are using a development package (for example one installed from GitHub), you can add it to the DESCRIPTION using the use_dev_package() function from {usethis}. This will add another field to the DESCRIPTION file, Remotes, with the location where the package is available.

All of this can seem a little bit daunting at first, but that is for the best:

Having a high quality namespace helps encapsulate your package and makes it self-contained. This ensures that other packages won’t interfere with your code, that your code won’t interfere with other packages, and that your package works regardless of the environment in which it’s run.

R Packages, (Wickham and Bryan 2020a)

To learn more about the details of dependencies, DESCRIPTION and NAMESPACE, here are some resources:

11.2 Submodules and utility functions

When building a large application, you will be splitting your code base into smaller pieces. We have developed in the Structuring your Project chapter that these utilitarian functions should be defined in files that are prefixed with a specific term. In the {golem} world, these are utils_* and fct_* files:

  • utils_* files contain small functions that might be used several times in the application
  • fct_* files contain larger functions that are more central to the application

Two functions can be called to create these files:

golem::add_fct( "helpers" ) 
golem::add_utils( "helpers" )
  • The first will create a R/fct_helpers.R file
  • The second will create a R/utils_helpers.R file

The idea, as explained before, is that as soon as you open a {golem} based project, you are able to identify what the files contain, without having to open them36 .

For example, the {hexmake} app has two of these files R/utils_ui.R and R/utils_server.R, in which you will find small functions that are reused throughout the app.

The fct_* files are to be used with larger functions, which are more central to the application, but that might not fit into a specific module. For example, in {hexmake}, you will find R/fct_mongo.R, that is used to handle all the things related to connecting and interacting with the Mongodb database.

As you can see, the difference is that fct_* file are more “topic centered”, in the sense that they gather functions that relate to a specific feature of the application (here, the database), while utils_* files are more used as a place where to put miscellaneous functions.

Note that when building a module with golem::add_module(), you can add a module specific fct_* or utils_* file:

golem::add_module("rendering", fct = "connect", utils = "wrapper")

Will create:

  • R/mod_rendering.R
  • R/mod_rendering_fct_connect.R
  • R/mod_rendering_utils_wrapper.R

And this can also be done the other way around, by specifying the module you want to link your file to:

golem::add_utils("wrapper", module = "rendering")

11.3 Add tests

No piece of software should go into production if it has not been sufficiently tested. In this part of the building process, you will be setting tests for the application you are building. We will get back to the how, why and what of testing in an upcoming chapter, but as we are currently going through the 02_dev.R script, we mention here the line that allows you to add a test skeleton to your app.

If you have followed every steps from the 01_start.R file, you already have a full testing infrastructure ready, with a set of recommended tests inserted by {golem}. But as it is hard to find tests that are relevant to all applications (as every application is unique), you will have to add and fill manually the tests that will check your app. And right now, to add a new testing file, you can call:


More on testing in the Build yourself a safety net chapter.

11.4 Documentation and Code Coverage

11.4.1 Vignette

Vignettes are long format documentation for your application, they are the one users see when they are running browseVignettes(), when they look at the documentation in the Help pane from RStudio, when they are browsing a webpage on CRAN, and it is also the files that are used when the {pkgdown} websites are built. The good news is that if you have been using our “Rmd first” method, you already have most of the Vignettes built: they are the Markdown files describing how the back-end of your application works. Depending on how you applied this principle, these Rmd files might live inside the inst/ folder, or already as package Vignettes. If you need to add a new Vignette, be it for adding an Rmd describing the back-end or a global documentation about the application, you can call the use_vignette() function from {usethis}.


Then, you can build all the Vignettes with:


11.4.2 Code Coverage & Continuous Integration Code Coverage

Code coverage is a way to detect the volume of code that is covered by unit testing. You can do this locally, or you can use online services like Appveyor, an online platform that computes and tracks the code coverage of your repository.

To add it to your application, call the use_coverage() function from the {usethis} package:


At the time of writing these lines, this function supports two services: CodeCov and coveralls.

Note that you can also perform code coverage locally, using the {covr} (Hester 2020b) package, and the package_coverage() function.

code_coverage <- covr::package_coverage()

For example, here is the output of running the package_coverage() function on the {golem} package on the 2020-04-29 on the dev branch:

{golem} code coverage results

FIGURE 11.1: {golem} code coverage results

As you can see, we reach a code coverage of almost 70%. Deciding what the perfect percentage of coverage should be is not an easy task, and setting for an arbitrary coverage is not a smart move either, as it very much depends on the type of project you are working on. For example, in {golem}, the addins.R file is not tested (0% code coverage), and that is for a good reason: these addins are linked to RStudio and are not meant to be tested/used in a non-interactive environment, and (at least at the time of writing these lines) there are no automated way to test for RStudio addins. Another thing to keep in mind while computing code coverage is that it counts the number of lines that are run when the tests are run, which means that if you write your whole function on one single line, you will have a 100% code coverage. Another example is writing your if/else statement on one line if (this) that else that: your code coverage will count this line as covered, even if your test suite only runs the if(this) and not the else ; in other words, even if your code coverage is good here, you are still not testing this algorithm extensively.

Note that you can also identify files with zero code coverage using the covr::zero_coverage(covr::package_coverage()) function, which, instead of printing back a metric of coverage for each file, will point to all the lines that are not covered by tests inside your package:

{golem} files with zero code coverage

FIGURE 11.2: {golem} files with zero code coverage

So, to sum up: do not set an arbitrary code coverage percentage goal, but rather use it as a general metric all along your project. With CodeCov, you can get a timeline of the evolution of code coverage: a good tool for judging when you need to write more tests. For example, here is the general tendency for the code coverage of the {tibble} package over the last 6 months (November 2019 - April 2020): results for the {tibble} package

FIGURE 11.3: results for the {tibble} package

Perfect for getting a general feeling about the code coverage during the life of the project!

Note also that if you want to add the code coverage of your application inside a Vignette, you can use the {covrpage} (Sidi 2020) package, that bundles the results of {covr} coverage report into an interactive, human readable Vignette, that you can later on use as package documentation, or as an article inside your package website. {covrpage} can be installed from GitHub with remotes::install_github('metrumresearchgroup/covrpage'). Continuous Integration

Continuous Integration, on the other hand, is ensuring the software is still working whenever a change is made by one of the developers. The idea is to add to the centralized version control system (for example Git)37 a service like Travis CI, GitHub Action (if you are on GitHub) or GitLab CI (for GitLab) that runs a series of commands whenever something is integrated to the repository, i.e every time a change to the codebase is made. In other words, every time a new piece of code is sent to the central repository, a service runs regression tests that check that the software is still in a valid, working state.

You can set up various continuous integration services automatically by using functions from the {usethis} package:

  • Travis CI is set up with usethis::use_travis()
  • AppVeyor with usethis::use_appveyor()
  • GitLab CI with use_gitlab_ci()
  • Circle CI with use_circleci()
  • GitHub Actions with use_github_actions()
  • Jenkins with use_jenkins()

If ever you want to add badges to your README files for these services, {usethis} also comes with a series of functions to do just that: use_travis_badge(), use_appveyor_badge(), use_circleci_badge() and use_github_actions_badge().

CI services can do a lot more, like for example deploying the application, build a container and send it to a container registry, compile RMarkdown files38 , etc. The possibilities are almost limitless!

11.5 Using {golem} dev functions

When building an application, you will want it to behave differently depending on where it is run, and notably, if it is run in development or in production. We have seen that to do that, you can use the golem-config.yml file, or by passing arguments to run_app(). A third option is to use the dev functions from {golem}.

There is a series of tools to make your app behave differently whether it is in “dev” or “prod” mode. Notably, the app_prod() and app_dev() function look for the value of options( "" ), or return TRUE if this option does not exist. In other words, by setting options( "" ) to TRUE, you will make the functions that depend on this option behave in a specific way.

Some functions pre-exist in {golem}, for example if you need to print a message to the console only during dev, you can do it with cat_dev().

options( "" = FALSE)
golem::cat_dev("In dev\n")
In dev
options( "" = TRUE)
golem::cat_dev("In dev\n")

Of course, chances are you do not only need to print things, you might want to use other functions. Good news! You can make any function being “dev-dependent” with the make_dev() function:

log_dev <- golem::make_dev(log)
options( "" = FALSE)
[1] 2.303
options( "" = TRUE)

That way, you can use functions in your back-end for development purpose, that will be ignore in production.


Chang, Winston, Joe Cheng, JJ Allaire, Yihui Xie, and Jonathan McPherson. 2020. Shiny: Web Application Framework for R.

Guyader, Vincent, Colin Fay, Sébastien Rochette, and Cervan Girard. 2020. Golem: A Framework for Robust Shiny Applications.

Guyader, Vincent, and Sébastien Rochette. 2020. Attachment: Deal with Dependencies.

Hester, Jim. 2020b. Covr: Test Coverage for Packages.

Sidi, Jonathan. 2020. Covrpage: Create a Readme Summary Page for Github Testthat and Covr Outputs.

Wickham, Hadley, and Jennifer Bryan. 2020a. R Packages.

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2020. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics.

Wickham, Hadley, Peter Danenberg, Gábor Csárdi, and Manuel Eugster. 2020. Roxygen2: In-Line Documentation for R.

Xie, Yihui, Joe Cheng, and Xianying Tan. 2020. DT: A Wrapper of the Javascript Library ’Datatables’.

  1. The idea with this function is to provide a shortcut for adding commonly used dependencies, so that you don’t have to do it by hand↩︎

  2. This is not {shiny} nor {golem} specific, but a requirement for any package↩︎

  3. which can be pretty common as {jsonlite} might import JSON files as list, and {purrr} has pretty powerful tools for manipulating lists↩︎

  4. The utils_* convention is a pretty common one: a lot of R packages contain a file call utils.R that bundles a series of small functions that are used throughout the package.↩︎

  5. We will get back to Version Control in the Version Control chapter↩︎

  6. For example, the online version of this book is compiled to HTML every time something is merged into the master branch on GitHub↩︎

ThinkR Website