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Compendium content

First, create a new empty RStudio project. Let’s called it comp. To create a new compendium structure, run rcompendium::new_compendium().

By default, the following content is created:

comp/                             # Root of the compendium
├── comp.Rproj                    # RStudio project (created by user, optional)
├── .git/                         # GIT tracking folder
├── .gitignore                    # List of files/folders to be ignored by GIT
|                                 # (specific to R language)
├── R/                            # R functions location
│   ├── fun-demo.R                # Example of an R function (to remove)
│   └── comp-package.R            # Dummy R file for high-level documentation
├── man/                          # R functions helps (automatically updated)
│   ├── print_msg.Rd              # Documentation of the demo R function
│   └── pkg-package.Rd            # High-level documentation
├── DESCRIPTION                   # Project metadata                              [*]
├──                    # Content of the GPL (>= 2) license (default)
├── NAMESPACE                     # Automatically generated
├── .Rbuildignore                 # List of files/folders to be ignored while 
│                                 # checking/installing the package
├──                     # GitHub README (automatically generated)
├── README.Rmd                    # GitHub README                                 [*]
├── data/                         # User raw data (.csv, .gpkg, etc.)
│   ├── raw-data/                 # Read-only files
│   └── derived-data/             # Modified data derived from raw data
├── analyses/                     # R scripts (not function) to run analyses
├── outputs/                      # Outputs (R objects, .csv, etc.)
├── figures/                      # Figures (.png, .pdf, etc.)
└── make.R                        # Main R script to source all R scripts 
                                  # stored in analyses/

[*] These files are automatically created but user needs to manually add 
    some information.

If create_repo = TRUE (default), a new GitHub repository will be created directly from R. It will be available at:{{account}}/comp/ (where {{account}} is either your GitHub account or a GitHub organization). This repository can be private if private = TRUE.

Compendium metadata


The DESCRIPTION file contains important compendium metadata. By default rcompendium creates the following file:

Package: comp
Type: Package
Title: The Title of the Project                                              [*]
Authors@R: c(
    person(given   = "John",
           family  = "Doe",
           role    = c("aut", "cre", "cph"),
           email   = "",
           comment = c(ORCID = "9999-9999-9999-9999")))
Description: A paragraph providing a full description of the project (on     [*] 
    several lines...)
License: GPL (>= 2)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.2

[*] Title and Description must be adapted by user.

This DESCRIPTION file is specific to R package but it can be used to work with research compendia (see below). For further information on how to edit this file, please read


The is the homepage of your repository on GitHub. Its purpose is to help visitor to understand your project. Always edit the README.Rmd (not the .md version).

For further information, please read


  • Keep the root of the project as clean as possible
  • Store your raw data in data/raw-data/
  • Document raw data modifications (with R scripts and/or R functions)
  • Export modified raw data in data/derived-data/ (or outputs/)
  • Store only R functions in R/
  • Store only R scripts and/or Rmd in analyses/
  • Control your project with the make.R file (add lines to source R scripts)
  • Built relative paths using here::here()
  • Call external functions as package::function()
  • Alternatively add #' @import package (or #' @importFrom package function) in R/comp-package.R to call external functions as function()
  • Use devtools::document() to update the NAMESPACE
  • Use rcompendium::add_dependencies(".") to update the list of required dependencies in DESCRIPTION
  • Do not use install.packages() but remotes::install_deps() (this will install required dependencies listed in DESCRIPTION)
  • Do not use library() but devtools::load_all() (this will load required dependencies listed in DESCRIPTION and R functions stored in R/)
  • Do not source your functions but use instead devtools::load_all()
  • And document everything!

Advanced features

Using renv

The default structure created by rcompendium::new_compendium() is a good starting point for making analyses reproducible. You can increase reproducibility by using the package renv.

renv will freeze the exact package versions you depend on (in renv.lock). This ensures that each collaborator (or you in the future) will use the exact same versions of these packages. Moreover renv provides to each project its own private package library making each project isolated from others.

To initialize renv for your compendium, use renv = TRUE in rcompendium::new_compendium() or call the function rcompendium::add_renv() after rcompendium::new_compendium().

The make.R will also be updated (replacement of remotes::install_deps() by renv::restore())

Working with renv

  1. Work as usual
  2. Update NAMESPACE with devtools::document()
  3. Update required dependencies in DESCRIPTION with rcompendium::add_dependencies(".")
  4. Install required dependencies locally with renv::install()
  5. Save the local environment with renv::snapshot()

Using docker

Docker is a tool that creates a self-contained environment (containers) within which the operating system, system libraries and software (i.e. R, RStudio server) are frozen and ready-to-use. The use of a container requires a recipe (Dockerfile) that describes the steps needed to create the environment. From this recipe, a Docker image is built: this is a template from which a container will be created.

More information on Docker for R users:

To add a Dockerfile to your compendium, use dockerfile = TRUE in rcompendium::new_compendium() or call the function rcompendium::add_dockerfile() after rcompendium::new_compendium().

By default rcompendium creates the following Dockerfile:

FROM rocker/rstudio:4.1.3


## Install system dependencies (for devtools) ----

RUN sudo apt update -yq \
 && sudo apt install --no-install-recommends libxml2-dev -yq \
 && sudo apt clean all \
 && sudo apt purge \
 && sudo rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*

## Copy local project ----

ENV folder="/home/rstudio/"

COPY . $folder
RUN chown -R rstudio:rstudio $folder

## Set working directory ----

WORKDIR $folder

## Install R packages ----

RUN R -e "install.packages('remotes', repos = c(CRAN = ''))" \
 && R -e "remotes::install_deps(upgrade = 'never')"

If you use renv with Docker (recommended) the last step will look like:

## Install R packages ----


RUN R -e "install.packages('remotes', repos = c(CRAN = ''))" \
 && R -e "remotes::install_github('rstudio/renv@${RENV_VERSION}')" \
 && sudo -u rstudio R -e "renv::restore()"

By default the Docker image is based on rocker/rstudio. You can customize this Dockerfile (e.g. adding system dependencies) and use a different default Docker image (i.e. tidyverse, verse, geospatial, etc.). For more information:

The versions of R and renv (if applicable) specified in the Dockerfile are the same as the local system.

When the image will be built, the whole project will be added to the image. When a container will be launched the default working directory will be the root of the project.

Once the project is ready to be shared, the collaborator (or you) must follow these steps:

  1. Clone the repository
  2. Build the Docker image (in a terminal) from the Dockerfile by running: docker build -t "image_name" . (this can take time)
  3. Start a container (in a terminal) from this new Docker image image_name by running: docker run --rm -p -e DISABLE_AUTH=true image_name
  4. Open a Web browser and visit a new instance of RStudio Server is available with everything ready-to-use (data, code, packages, etc.)
  5. To reproduce the analysis, run: source("make.R")