The key problems to irreproducibility of data in research is missing
documentation, version control and containerization. ContainR
seeks to address these issues by making it easier to create the
environment for a R project.
The package ports the pre-built rocker-versioned Rstudio stacks and basic Docker commands to R. Based on the active working Rstudio project, it allows the user to create a Dockerfile and build a Docker image with Rstudio. Additional settings to include CRAN packages, Github packages, include Python, Julia, jupyter, Pandoc etc. and launch Rstudio container in a browser.
Outputs
Once the image has baked any additional packages into the base Rocker stack, the Dockerfile can be saved for distribution and the image pushed to DockerHub. When a ContainR session is launched, your local config settings can also cloned into the session for previewing before hard coding the package directory into the Docker image.
The completed containr/ folder is created with a Dockerfile, a tar
of the package, a scripts/install_additional.sh file and an optional
JSON file containing the Docker recipe to build the image. The image can
be pushed to Dockerhub and the containr/ file can be version
controlled and/or shared with collaborators.
#> containr
#> ├── ContainR.json
#> ├── Dockerfile
#> ├── containr_0.1.5.9000.tar.gz
#> └── scripts
#> └── install_additional.sh
It is currently a work in progress and welcome any issues and comments.
Read more about the Rocker Project
Docker is required for the
use of this package currently support only R (>= 4.0.0) base images.
You can install the development version of ContainR from
GitHub with:
# install.packages("devtools")
devtools::install_github("psychtek/ContainR")Check if Docker is installed on the system or visit install instructions for OSX, Windows or Linux:
containr::docker_check()Supported
rocker-versioned
stacks can be found by exploring the data_rocker_table inside the
package or visiting their
repository to learn
more.
library(containr)
data_rocker_table |> dplyr::select(-base_image)
#> # A tibble: 5 × 3
#> name image description
#> <chr> <chr> <chr>
#> 1 rstudio rocker/rstudio Rstudio Server
#> 2 tidyverse rocker/tidyverse Adds tidyverse packages & devtools
#> 3 verse rocker/verse Adds tex & publishing-related package
#> 4 geospatial rocker/geospatial Adds geospatial packages
#> 5 binder rocker/binder Adds requirements to run repos on mybinder.org- Open your current working Rstudio project.
- Explore which base Rocker image to use
containr::data_rocker_table. - Create a Dockerfile
containr$new(). - Build a container
build_image(TRUE). - Run the container
start(). - Launch the session in a browser
launch(). - Stop the container
stop()
For example, to create a Dockerfile based on the latest Rocker Rstudio
and install packages that are loaded into the local session, add python
support and install the active project from CMD CHECK archive:
containr <- containr$new(image = "rstudio", name = "project_name",
packages = "loaded", copy = TRUE, include_python = TRUE)The return process from this function results in the creation of a
docker/Dockerfile build recipe with additional scripts in the
docker/scripts/ directory. If any packages require install from
Github, the function takes note of these and attempts to add them to the
install file as well. Any packages already installed on the base Rocker
image will be skipped.
At any point you can check and change what was initialized. This will
clear the docker folder and re-write the folder and scripts according
to your preferences. Calling the print() will give you sanity checks
for your preferences and the Status will update automatically.
The proc() will display the Docker background process:
Setting the build_image(TRUE) flag will then read the newly created
Dockerfile and build the image based on the previous user
requirements. This is saved to the local Docker register and images can
be view with the docker_images() function.
Note that this can take some time depending on your install preferences.
When this finished building the image is now ready to be started. The
start() fun will launch the container in a background process and if
we run the proc() command we see that it is up and running.
Running launch() will open a the session in a new browser window.
Running stop() will stop the container session completely leaving the
image built intact.
Basic Docker functions are also included and additional functionality for the Docker CLI will be added as development continues. These return tibbles to the console instead of the terminal. Currently included are four functions:
docker_check(): Check if Docker is installed and running.
containr::docker_check()
#> ✔ Docker version 20.10.21, build baeda1fdocker_images(): returns a tibble of built images.
containr::docker_images()
#> # A tibble: 4 × 5
#> Repository Tag ID CreatedSince Size
#> <chr> <chr> <chr> <chr> <chr>
#> 1 rstudio_pandoc latest a2feb74b885b 44 minutes ago 2GB
#> 2 rstudio_py latest ed6c79987814 2 hours ago 2.14GB
#> 3 testing_rstudio latest adab64defd77 3 hours ago 1.79GB
#> 4 rocker/rstudio latest d110bab4d154 4 weeks ago 1.79GBdocker_containers(): Returns a tibble of active containers.
containr::docker_containers() |> dplyr::select(Command, CreatedAt, Image, Names, Ports, Status)
#> # A tibble: 1 × 6
#> Command CreatedAt Image Names Ports Status
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 /init 2022-12-02 13:42:07 +1100 AEDT rstudio_pandoc rstudio_pa… 127.… Up 30…docker_search("rstudio"): Returns a tibble of search results.
containr::docker_search("rocker")
#> # A tibble: 25 × 5
#> Name Description StarC…¹ IsOff…² IsAut…³
#> <chr> <chr> <int> <lgl> <chr>
#> 1 rocker/rstudio "RStudio Server image" 402 NA [OK]
#> 2 rocker/shiny "" 172 NA [OK]
#> 3 rocker/tidyverse "Version-stable build of R, rstud… 158 NA [OK]
#> 4 rocker/r-base "Basic R for Rocker And Official … 81 NA [OK]
#> 5 rocker/verse "Adds tex & related publishing pa… 64 NA [OK]
#> 6 rocker/r-ver "Reproducible builds to fixed ver… 50 NA [OK]
#> 7 rocker/shiny-verse "Rocker Shiny image + Tidyverse R… 45 NA [OK]
#> 8 rocker/geospatial "Docker-based Geospatial toolkit … 44 NA [OK]
#> 9 rocker/r-devel "" 22 NA [OK]
#> 10 rocker/ropensci "" 22 NA [OK]
#> # … with 15 more rows, and abbreviated variable names ¹StarCount, ²IsOfficial,
#> # ³IsAutomatedThere are similar packages available such as: dockr, devindocker, harbor containerit and stevedore which provide various levels of functionality. We also recommend checking these out to see if these address your requirements.
- The team at rocker-versioned
- repliCATS Data Management Team