tugboat

A simple R package to generate a Dockerfile and corresponding Docker image from an analysis directory. tugboat also prepares your analysis directory to be shared via Binder.

tugboat uses the renv package to automatically detect all the packages necessary to replicate your analysis and will generate a Dockerfile that contains an exact copy of your entire directory with all the packages installed. tugboat transforms an unstructured analysis folder into a renv.lock file and constructs a Docker image that includes all your essential R packages based on this lockfile.

tugboat may be of use, for example, when preparing a replication package for research. With tugboat, you can take a directory on your local computer and quickly generate a corresponding Dockerfile and Docker image that contains all the code and the necessary software to reproduce your findings.

Installation

Install tugboat from CRAN:

install.packages("tugboat")

Or install the development version from GitHub:

# install.packages("pak")
pak::pkg_install("dmolitor/tugboat")

Usage

tugboat only has three exported functions; one to create a Dockerfile from your analysis directory, one to build the corresponding Docker image, and one to make your project ready to share and run in an online, interactive compute environment via Binder.

Create the Dockerfile

The primary function from tugboat is create(). This function converts your analysis directory into a Dockerfile that includes all your code and essential R packages.

This function scans all files in the current analysis directory, attempts to detect all R packages, and installs these packages in the resulting Docker image. It also copies the entire contents of the analysis directory into the Docker image. For example, if your analysis directory is named incredible_analysis, the corresponding location of your code and data files in the generated Docker image will be /incredible_analysis.

For the most common use-cases, there are a couple of arguments in this function that are particularly important:

• project: This argument tells tugboat which directory is the one to generate the Dockerfile from. You can set this value yourself, or you can just use the default value. By default, tugboat uses the here::here function to determine what directory is the analysis directory. To get a detailed understanding of exactly how this works take a look at the here package. In general, this “just works”!
• as: This argument tells tugboat where to save the Dockerfile. In general you don’t need to set this and tugboat will just save the Dockerfile in the project directory from above.
• exclude: A vector of files or sub-directories in your analysis directory that should NOT be included in the Docker image. This is particularly important when you have, for example, a sub-directory with large data files that would make the resulting Docker image extremely large if included. You can tell tugboat to exclude this sub-directory and then simply mount it to a Docker container as needed.

Below I’ll outline a couple examples.

library(tugboat)

# The simplest scenario where your analysis directory is your current
# active project, you are fine with the default base "r-base:latest"
# Docker image, and you want to include all files/directories:
create()

# Suppose your analysis directory is actually a sub-directory of your
# main project directory:
create(project = here::here("sub-directory"))

# Suppose that you specifically need a Docker base image that has RStudio
# installed so that you can interact with your analysis within a Docker 
# container. To do this, we will explicitly specify a different Docker
# base image using the `FROM` argument.
create(FROM = "rocker/rstudio")

# Finally, suppose that we want to include all files except a couple
# particularly data-heavy sub-directories:
create(exclude = c("data/big_directory_1", "data/big_directory_2"))

Build the Docker image

Once the Dockerfile has been created, we can build the Docker image with the build() function. By default this will infer the Dockerfile directory using here::here. This function assumes a little knowledge about Docker; if you aren’t sure where to start, this is a great starting point.

The following example will do the simplest thing and will build the image locally.

build(image_name = "awesome_analysis")

Suppose that, like above, your analysis directory is a sub-directory of your main project directory:

build(
  dockerfile = here::here("sub-directory"),
  image_name = "awesome_analysis"
)

Push to DockerHub

If, instead of just building the Docker image locally, you want to build the image and then push to DockerHub, you can make a couple small additions to the code above:

build(
  image_name = "awesome_analysis",
  push = TRUE,
  dh_username = Sys.getenv("DH_USERNAME"),
  dh_password = Sys.getenv("DH_PASSWORD")
)

Note: If you choose to push, you also need to provide your DockerHub username and password. Typically you don’t want to pass these in directly and should instead use environment variables (or a similar method) instead.

Share your project via Binder

Binder lets others instantly launch and interact with your R project in a live, cloud-based environment with no local setup required. tugboat will prepare your project to be shared with Binder. The process is simple:

• First, create the Dockerfile from your analysis directory:

  create(
    project = here::here(),
    exclude = c("data/big_directory_1", "data/big_directory_2")
  )

• Then, prep your directory for Binder. Your analysis directory must be a GitHub repository:

  binderize(branch = "main")

  By default this will add a Binder badge to your README.Rmd or README.md file. If your README file already has a section for badges, it will add the new badge automatically:

  ✔ Adding "Launch RStudio Binder badge" to README.md.

  If your README file does not have a section for badges, it will automatically save the badge to your clipboard and you will need to manually insert it into the README.

  ☐ Copy and paste the following lines into README.md:
    <!-- badges: start -->
    [![Launch RStudio Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/{username}/{repo}/{branch}?urlpath=rstudio)
    <!-- badges: end -->
    [Copied to clipboard]

After running binderize() you will see the following message:

Your repository has been configured for Binder.
[x] Commit and push all changes
[x] Launch Binder at: https://mybinder.org/v2/gh/{username}/{repo}/{branch}?urlpath=rstudio

You must commit and push all changes before visiting the Binder link, otherwise it will likely fail. Binder can automatically detect changes to the repository and will rebuild as necessary, ensuring that the Binder repository stays up to date.

Why tugboat? 🚢

There are a few available packages with similar goals, so why tugboat? tugboat is minimal and builds directly on top of renv and pak. Each of these packages is actively maintained and provides specific utilities that the tugboat utilizes for maximum convenience. tugboat aims to leverage packages that are likely to remain actively maintained and handle dependency management as seamlessly as possible.

• containerit is a robust package that is directly comparable to tugboat. However, it implements its own method for discovering R package dependencies instead of using renv. It also relies on sysreqsdb for system dependency discovery, which has been archived in favor of r-system-requirements, which pak is built on. It also isn’t super actively maintained and isn’t on CRAN.

• holepunch is related but focuses on making GitHub repositories Binder-compatible. It currently relies on MRAN, which is now obsolete, and does not use pak for system dependency management. It is also not actively maintained and is not on CRAN.

• automagic focuses on automatically detecting and installing R package dependencies but uses its own method rather than relying on renv. automagic also has no utilities for creating/building Docker images.

Examples

For some worked examples of how to use tugboat in practice, see the examples/ directory.