A Quick Introduction to liftr

Introduction

In essence, liftr aims to solve the problem of persistent reproducible reporting. To achieve this goal, it extends the R Markdown metadata format, and uses Docker to containerize and render R Markdown documents.

Metadata for containerization

To containerize your R Markdown document, the first step is adding liftr fields to the YAML metadata section of the document. For example:

---
title: "The Missing Example of liftr"
author: "Author Name"
date: "2024-03-11"
output: rmarkdown::html_document
liftr:
  maintainer: "Maintainer Name"
  email: "name@example.com"
  from: "rocker/r-base:latest"
  pandoc: true
  texlive: false
  sysdeps:
    - gfortran
  cran:
    - glmnet
  bioc:
    - Gviz/3.9
  remotes:
    - "nanxstats/liftr"
  include: "DockerfileSnippet"
---

All available metadata fields are expained below.

Required metadata

maintainer

Maintainer’s name for the Dockerfile.
email

Maintainer’s email address for the Dockerfile.

Optional metadata

from

Base image for building the docker image. Default is "rocker/r-base:latest". For R users, the images offered by the rocker project and Bioconductor can be considered first.
pandoc

Should we install pandoc in the container? Default is true.

If pandoc was already installed in the base image, this should be set to false to avoid potential errors. For example, for rocker/rstudio images and bioconductor/... images, this option will be automatically set to false since they already have pandoc installed.
texlive

Is TeX environment needed when rendering the document? Default is false. Should be true particularly when the output format is PDF.
sysdeps

Debian/Ubuntu system software packages depended in the document.

Please also include software packages depended by the R packages below. For example, here gfortran is required for compiling glmnet.
cran

CRAN packages depended in the document.

If only pkgname is provided, liftr will install the latest version of the package on CRAN. To improve reproducibility, we recommend to use the package name with a specified version number: pkgname/pkgversion (e.g. ggplot2/1.0.0), even if the version is the current latest version. Note: pkgversion must be provided to install the archived versions of packages.
bioc

Bioconductor packages depended in the document. If used, the first package’s name must be followed by the desired Bioconductor version (e.g. Gviz/3.9). All the packages used must be installed from the same Bioconductor version.
remotes

Remote R packages that are not available from CRAN or Bioconductor.

The remote package naming specification from devtools is adopted here. Packages can be installed from GitHub, Bitbucket, Git/SVN servers, URLs, etc.
include

The path to a text file that contains custom Dockerfile snippet. The snippet will be included in the generated Dockerfile. This can be used to install additional software packages or further configure the system environment.

Note that this file should be in the same directory as the input R Markdown file.

Containerize the document

After adding proper liftr metadata to the document YAML data block, we can use lift() to parse the document and generate a Dockerfile.

We will use a minimal example included in the liftr package. First, we create a new directory and copy the R Markdown document into the directory:

path = "~/liftr-minimal/"
dir.create(path)
file.copy(system.file("examples/liftr-minimal.Rmd", package = "liftr"), path)

Then, we use lift() to parse the document and generate the Dockerfile:

library("liftr")

input = paste0(path, "liftr-minimal.Rmd")
lift(input)

After successfully running lift(), the Dockerfile will be in the ~/liftr-minimal/ directory.

Render the document

Now we can use render_docker() to render the document into an HTML file, under a Docker container:

render_docker(input)

The function render_docker() will parse the Dockerfile, build a new Docker image, and run a Docker container to render the input document. If successfully rendered, the output liftr-minimal.html will be in the ~/liftr-minimal/ directory. You can also pass additional arguments in rmarkdown::render to this function.

In order to share the dockerized R Markdown document, simply share the .Rmd file. Other users can use the lift() and render_docker() functions to render the document as above.

Housekeeping

Normally, the argument prune is set to TRUE in render_docker(). This means any dangling containers or images due to unsuccessful builds will be automatically cleaned.

To clean up the dangling containers, images, and everything without specifying names, please use prune_container_auto(), prune_image_auto(), and prune_all_auto().

If you wish to manually remove the Docker container or image (whose information will be stored in an output YAML file) after sucessful rendering, use prune_container() and prune_image():

purge_image(paste0(path, "liftr-minimal.docker.yml"))

The above input YAML file contains the basic information of the Docker container, image, and commands to render the document. It is generated by setting purge_info = TRUE (default) in render_docker().

Install Docker

Docker is an essential system requirement when using liftr to render the R Markdown documents. install_docker() will help you find the proper guide to install and set up Docker in your system. To check if Docker is correctly installed, use check_docker_install(); to check if the Docker daemon is running, use check_docker_running(). In particular, Linux users should configure Docker to run without sudo.

Nan Xiao <https://nanx.me>