R packages are the fundamental units of reproducible code in R.1 Docker is a virtualization technology that can be used to bundle an application and all its dependencies in a virtual container that can be distributed and deployed to run reproducibly on any Windows, Linux or MacOS operating system. When used in tandem, these tools can help developers deliver software with an inherently reproducible set of dependencies, including specific dependent R packages. An R package developer may consider building a docker image that contains their R package. This approach can be useful in various scenarios, one of which is a case where the R package includes functions to pre- and post-process data that is also processed by a domain-specific tool written in another language (i.e., something that couldn’t be included as an R package dependency).
We developed pracpac
with a goal of providing intuitive
functions for developers to use custom R packages and Docker together.
The pracpac
package is conceptually inspired by packages
like devtools
and usethis
, which dramatically
reduce the technical burden of R package development. With
pracpac
, users can easily create templates of the necessary
files and directory structure to build a Docker image that contains
their R package and specific dependency packages, with versions
optionally frozen via renv
.
It may be useful to clarify Docker terminology used throughout:
For more information on Docker installation, terminology, and usage see https://docs.docker.com/.
pracpac
The pracpac
package is designed to do two things:
These features are delivered in the use_docker()
and
build_image()
functions.
use_docker()
The pracpac
package includes individual functions to add
a template Dockerfile, build the source of an R package to be added to
the Docker image, and define dependencies for that package in an
renv
lock file. All files created are moved to the Docker
directory specified by the user, which as a default is set to a
docker/
subdirectory of the R package. For convenience, the
pracpac
functionality is wrapped into the
use_docker()
function.
The example that follows uses the hellow
R package that
ships with pracpac
. With pracpac
installed,
the hellow
source code can be found with the following
command:
To motivate the basic usage we will demonstrate how to use the
example package copied to a tempdir()
.
NOTE: In practice, it is likely more convenient to
use pracpac
functions within the flow of R package
development (i.e., with the working directory at the package root). As
such, the file copying here may not be necessary for most usage.
library(pracpac)
library(fs)
## specify the temp directory
tmp <- tempdir()
## create a subdirectory of temp called "example"
dir_create(path = path(tmp, "example"))
## copy the example hellow package to the temp directory
dir_copy(path = system.file("hellow", package = "pracpac"), new_path = path(tmp, "example"))
The contents of the hellow
package source are structured
as follows:
├── DESCRIPTION
├── LICENSE
├── LICENSE.md
├── NAMESPACE
├── R
│ └── hello.R
├── hellow.Rproj
└── man
└── isay.Rd
To create the template for a Docker image that contains the
hellow
R package the developer can use
use_docker()
:
├── DESCRIPTION
├── LICENSE
├── LICENSE.md
├── NAMESPACE
├── R
│ └── hello.R
├── docker
│ ├── Dockerfile
│ ├── hellow_0.1.0.tar.gz
│ └── renv.lock
├── hellow.Rproj
└── man
└── isay.Rd
With defaults set, this function will create a
Dockerfile
with the following contents:
FROM rocker/r-ver:latest
## copy the renv.lock into the image
COPY renv.lock /renv.lock
## install renv
RUN Rscript -e 'install.packages(c("renv"))'
## set the renv path var to the renv lib
ENV RENV_PATHS_LIBRARY renv/library
## restore packages from renv.lock
RUN Rscript -e 'renv::restore(lockfile = "/renv.lock", repos = NULL)'
## copy in built R package
COPY hellow_0.1.0.tar.gz /hellow_0.1.0.tar.gz
## run script to install built R package from source
RUN Rscript -e 'install.packages("/hellow_0.1.0.tar.gz", type='source', repos=NULL)'
And an renv.lock
with the dependencies of
hellow
(in this case just the praise
package):
{
"R": {
"Version": "4.0.2",
"Repositories": [
{
"Name": "CRAN",
"URL": "https://cran.rstudio.com"
}
]
},
"Packages": {
"praise": {
"Package": "praise",
"Version": "1.0.0",
"Source": "Repository",
"Repository": "CRAN",
"Hash": "a555924add98c99d2f411e37e7d25e9f",
"Requirements": []
}
}
}
The use_docker()
defaults will produce the behavior
described above. However, the functionality can be customized further.
For example, the user can optionally specify a use case to create
variants of template files (described in more detail in other
vignettes). Another option is to specify an img_path
defining where the files used to build the Docker image should be
written, which may be useful for developers who prefer not to build
images within the R package root. The following shows how this could be
used to write the Docker template files to the directory above the
package root:
├── Dockerfile
├── hellow
│ ├── DESCRIPTION
│ ├── LICENSE
│ ├── LICENSE.md
│ ├── NAMESPACE
│ ├── R
│ │ └── hello.R
│ ├── hellow.Rproj
│ └── man
│ └── isay.Rd
├── hellow_0.1.0.tar.gz
For a full list of options see ?use_docker
.
build_image()
The use_docker()
function includes an option to “build”.
By default this parameter is set to FALSE
. The
pracpac
templates are likely to require some editing by the
developer. However, after editing the Dockerfile
and any
constituent files to be added the user can call
build_image()
to build the Docker image:
Note that if the user has specified a different img_path
in use_docker()
, then the same path needs to be used with
build_image()
.
By default the image will be built and tagged with the name of the R package and a “latest” and version suffix:
hellow 0.1.0 e1a9bc2ebbb5 15 seconds ago 828MB
hellow latest e1a9bc2ebbb5 15 seconds ago 828MB
The tagging scheme can be altered with the “tag” argument. The
build_image()
function also includes a parameter to
leverage the Docker build “cache” feature. For more details see
?build_image
. To use additional build parameters the user
can call the Docker daemon directly on the host or use a client like stevedore
.
Hadley Wickham and Jenny Bryan. R Packages (2e). https://r-pkgs.org/.↩︎