How to use this image

All examples in this guide use the public image. If you've mirrored the repository for your own use (for example, to your Docker Hub namespace), update your commands to reference the mirrored image instead of the public one.

For example:

• Public image: dhi.io/<repository>:<tag>
• Mirrored image: <your-namespace>/dhi-<repository>:<tag>

For the examples, you must first use docker login dhi.io to authenticate to the registry to pull the images.

What's included in this r-base image

This Docker Hardened r-base image includes:

• R — the R interpreter and interactive console.
• Rscript — front end for running R scripts and one-liners non-interactively.
• The recommended packages that ship with the upstream R release, including MASS, Matrix, lattice, survival, boot, nlme, cluster, and rpart. Unlike Debian's packaging, which splits these into separate r-cran-* packages, they are installed with the interpreter.
• r — littler, for running R from the command line and in #! scripts.
• install.r, install2.r, installBioc.r, installDeps.r, installGithub.r, and testInstalled.r — littler's helper scripts, available on PATH from /usr/local/bin.
• OpenBLAS, so linear algebra is multithreaded rather than falling back to reference BLAS.
• Cairo-backed bitmap and SVG devices (png(), jpeg(), tiff(), svg()), plus R's native pdf() and X11 devices.
• Tcl/Tk support, so capabilities("tcltk") is TRUE and CRAN packages that depend on tcltk install normally.

As on any headless Linux host, library(tcltk) loads and Tcl works, but Tk itself is only initialized when a DISPLAY is set, so tktoplevel() and other widget calls need an X server or X forwarding. For the same reason capabilities("X11") reports FALSE without a display even though the X11 device is built in. Both behaviors match upstream r-base on the same R release.

Start an r-base image

Print the R version to confirm the image runs:

$ docker run --rm dhi.io/r-base:<tag> R --version

The image's default command is R, which starts the interactive console. To use it interactively, allocate a TTY:

$ docker run --rm -it dhi.io/r-base:<tag>

Common r-base use cases

Evaluate an expression without writing a file

Rscript -e runs R code straight from the command line, which is the quickest way to smoke-test the image or to run a small calculation inside a pipeline.

$ docker run --rm dhi.io/r-base:<tag> Rscript -e 'cat(sum(1:100), "\n")'
5050

Run an analysis script from the host

Mount your script into the container and run it with Rscript. Use a working directory the nonroot user can read.

Given analysis.R in the current directory:

data <- data.frame(x = 1:10, y = (1:10)^2)
fit <- lm(y ~ x, data = data)
cat("slope:", coef(fit)[["x"]], "\n")

Run it:

$ docker run --rm -v "$PWD/analysis.R:/work/analysis.R:ro" -w /work \
    dhi.io/r-base:<tag> Rscript analysis.R
slope: 11

Render a plot to a file

The image ships Cairo-backed graphics devices, so plotting needs no X server. Write the output to a mounted directory. The runtime image runs as a nonroot user (UID 65532), so create the output directory first and run the container with your host UID to keep the mounted directory writable:

$ mkdir -p out
$ docker run --rm -u "$(id -u):$(id -g)" -v "$PWD/out:/out" dhi.io/r-base:<tag> \
    Rscript -e 'png("/out/plot.png", width = 800, height = 600); plot(1:10, (1:10)^2, type = "b"); dev.off()'

Install CRAN packages in a build stage

Installing from CRAN compiles C, C++, and FORTRAN sources, so it needs the dev variant's toolchain. Use a multi-stage build and copy the installed library into the runtime stage.

FROM dhi.io/r-base:<tag>-dev AS build
RUN Rscript -e 'install.packages("jsonlite", repos = "https://cloud.r-project.org", lib = "/usr/local/lib/R/site-library")'

FROM dhi.io/r-base:<tag>
COPY --from=build /usr/local/lib/R/site-library /usr/local/lib/R/site-library
COPY analysis.R /work/analysis.R
WORKDIR /work
CMD ["Rscript", "analysis.R"]

For reproducible dependency management across a larger project, including lockfiles and per-project libraries, see the renv documentation⁠ and the CRAN package installation docs⁠.

Non-hardened images vs. Docker Hardened Images

This image is built from the upstream R release rather than from Debian's r-base source package, which leads to a few differences from r-base and rocker/r-base:

• Recommended packages are installed with the interpreter rather than through a separate r-recommended package, so there is nothing extra to install for MASS, Matrix, and the rest.
• R_HOME is /usr/lib/R, matching upstream r-base. littler is available as /usr/bin/r, its helper scripts are reachable from /usr/local/bin, and /usr/local/lib/R/site-library is first on R_LIBS_SITE.
• No docker user or staff group. Upstream r-base adds a docker user in the staff group so that group can write to the site library. Runtime variants here run as the standard hardened nonroot user instead.
• Runtime variants run as a nonroot user and contain no package manager. Unlike most hardened runtime images they do ship a minimal shell (/bin/sh, dash): R's front end /usr/lib/R/bin/R is a POSIX shell script, so a shell is required for R to start at all. Use the dev variant for anything that installs packages or compiles code.
• FIPS variants use the validated OpenSSL module for R's TLS network operations. The HTTPS transport behind download.file() and install.packages() reaches OpenSSL through libcurl. R's built-in hashing functions, including tools::md5sum(), use R's bundled implementations and continue to work in FIPS variants.

Image variants

Docker Hardened Images come in different variants depending on their intended use. Image variants are identified by their tag.

• Runtime variants are designed to run your application in production. These images are intended to be used either directly or as the FROM image in the final stage of a multi-stage build. These images typically:

  • Run as a nonroot user
  • Do not include a shell or a package manager
  • Contain only the minimal set of libraries needed to run the app

• Build-time variants typically include dev in the tag name and are intended for use in the first stage of a multi-stage Dockerfile. These images typically:

  • Run as the root user
  • Include a shell and package manager
  • Are used to build or compile applications

• FIPS variants include fips in the variant name and tag. They come in both runtime and build-time variants. These variants use cryptographic modules that have been validated under FIPS 140, a U.S. government standard for secure cryptographic operations. For example, usage of MD5 fails in FIPS variants.

To view the image variants and get more information about them, select the Tags tab for this repository, and then select a tag.

Migrate to a Docker Hardened Image

To migrate your application to a Docker Hardened Image, you must update your Dockerfile. At minimum, you must update the base image in your existing Dockerfile to a Docker Hardened Image. This and a few other common changes are listed in the following table of migration notes.

The following steps outline the general migration process.

1. Find hardened images for your app.

   A hardened image may have several variants. Inspect the image tags and find the image variant that meets your needs.

2. Update the base image in your Dockerfile.

   Update the base image in your application's Dockerfile to the hardened image you found in the previous step. For framework images, this is typically going to be an image tagged as dev because it has the tools needed to install packages and dependencies.

3. For multi-stage Dockerfiles, update the runtime image in your Dockerfile.

   To ensure that your final image is as minimal as possible, you should use a multi-stage build. All stages in your Dockerfile should use a hardened image. While intermediary stages will typically use images tagged as dev, your final runtime stage should use a non-dev image variant.

4. Install additional packages

   Docker Hardened Images contain minimal packages in order to reduce the potential attack surface. You may need to install additional packages in your Dockerfile. Inspect the image variants to identify which packages are already installed.

   Only images tagged as dev typically have package managers. You should use a multi-stage Dockerfile to install the packages. Install the packages in the build stage that uses a dev image. Then, if needed, copy any necessary artifacts to the runtime stage that uses a non-dev image.

   For Alpine-based images, you can use apk to install packages. For Debian-based images, you can use apt-get to install packages.

Troubleshooting migration

The following are common issues that you may encounter during migration.

General debugging

The hardened images intended for runtime don't contain a shell nor any tools for debugging. The recommended method for debugging applications built with Docker Hardened Images is to use Docker Debug⁠ to attach to these containers. Docker Debug provides a shell, common debugging tools, and lets you install other tools in an ephemeral, writable layer that only exists during the debugging session.

Permissions

By default image variants intended for runtime, run as the nonroot user. Ensure that necessary files and directories are accessible to the nonroot user. You may need to copy files to different directories or change permissions so your application running as the nonroot user can access them.

Privileged ports

Non-dev hardened images run as a nonroot user by default. As a result, applications in these images can't bind to privileged ports (below 1024) when running in Kubernetes or in Docker Engine versions older than 20.10. To avoid issues, configure your application to listen on port 1025 or higher inside the container, even if you map it to a lower port on the host. For example, docker run -p 80:8080 my-image will work because the port inside the container is 8080, and docker run -p 80:81 my-image won't work because the port inside the container is 81.

No shell

By default, image variants intended for runtime don't contain a shell. Use dev images in build stages to run shell commands and then copy any necessary artifacts into the runtime stage. In addition, use Docker Debug to debug containers with no shell.

Entry point

Docker Hardened Images may have different entry points than images such as Docker Official Images. Use docker inspect to inspect entry points for Docker Hardened Images and update your Dockerfile if necessary.