Reconstructing Reproducible R Computational Environments.
rang 
The goal of rang (Reconstructing Ancient Number-crunching Gears) [1] is to obtain the dependency graph of R packages at a specific time point.
Although this package can also be used to ensure the current R computational environment can be reconstructed by future researchers, this package gears towards reconstructing historical R computational environments which have not been completely declared. For the former purpose, packages such as renv, groundhog, miniCRAN, and Require should be used. One can think of rang as an archaeological tool.
To reconstruct a historical R computational environment, this package assumes only the availability of source packages online. The reconstruction procedures have been tested in several vintage versions of R.
Please cite this package as:
Chan CH, Schoch D (2023) rang: Reconstructing reproducible R computational environments. PLOS ONE https://doi.org/10.1371/journal.pone.0286761
Installation
You can install the development version of rang like so:
remotes::install_github("gesistsa/rang")
Or the stable CRAN version
install.packages("rang")
Example
To obtain the dependency graph of R packages, use resolve. Currently, this package supports CRAN, Bioconductor, GitHub, and local packages.
library(rang)
x <- resolve(pkgs = c("sna", "schochastics/rtoot", "S4Vectors"), snapshot_date = "2022-11-30")
graph <- resolve(pkgs = c("openNLP", "LDAvis", "topicmodels", "quanteda"),
snapshot_date = "2020-01-16")
graph
#> resolved: 4 package(s). Unresolved package(s): 0
#> $`cran::openNLP`
#> The latest version of `openNLP` [cran] at 2020-01-16 was 0.2-7, which has 3 unique dependencies (2 with no dependencies.)
#>
#> $`cran::LDAvis`
#> The latest version of `LDAvis` [cran] at 2020-01-16 was 0.3.2, which has 2 unique dependencies (2 with no dependencies.)
#>
#> $`cran::topicmodels`
#> The latest version of `topicmodels` [cran] at 2020-01-16 was 0.2-9, which has 7 unique dependencies (5 with no dependencies.)
#>
#> $`cran::quanteda`
#> The latest version of `quanteda` [cran] at 2020-01-16 was 1.5.2, which has 63 unique dependencies (33 with no dependencies.)
graph$sysreqs
#> [1] "apt-get install -y default-jdk" "apt-get install -y libxml2-dev"
#> [3] "apt-get install -y make" "apt-get install -y zlib1g-dev"
#> [5] "apt-get install -y libpng-dev" "apt-get install -y libgsl0-dev"
#> [7] "apt-get install -y libicu-dev" "apt-get install -y python3"
graph$r_version
#> [1] "3.6.2"
The resolved result is an S3 object called rang and can be exported as an installation script. The installation script can be execute on a vanilla R installation.
export_rang(graph, "rang.R")
However, the execution of the installation script often fails (now) due to missing system dependencies and incompatible R versions. Therefore, the approach outlined below should be used.
Recreate the computational environment via Rocker
A rang object can be used to recreate the computational environment via Rocker. Please note that the oldest R version one can get from Rocker is R 3.1.0.
dockerize(graph, "~/rocker_test")
Now, you can build and run the Docker container.
cd ~/rocker_test
docker build -t rang .
docker run --rm --name "rangtest" -ti rang
Using the above example, sessionInfo() outputs the following. You have successfully gone back to the pre-pandemic time.
R version 3.6.2 (2019-12-12)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 10 (buster)
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.3.5.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
[7] LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
[9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] topicmodels_0.2-9 LDAvis_0.3.2 openNLP_0.2-7 quanteda_1.5.2
loaded via a namespace (and not attached):
[1] NLP_0.2-0 Rcpp_1.0.3 pillar_1.4.3
[4] compiler_3.6.2 tools_3.6.2 stopwords_1.0
[7] lubridate_1.7.4 lifecycle_0.1.0 tibble_2.1.3
[10] gtable_0.3.0 lattice_0.20-38 pkgconfig_2.0.3
[13] rlang_0.4.2 Matrix_1.2-18 fastmatch_1.1-0
[16] parallel_3.6.2 openNLPdata_1.5.3-4 rJava_0.9-11
[19] xml2_1.2.2 stringr_1.4.0 stats4_3.6.2
[22] grid_3.6.2 data.table_1.12.8 R6_2.4.1
[25] ggplot2_3.2.1 spacyr_1.2 magrittr_1.5
[28] scales_1.1.0 modeltools_0.2-22 colorspace_1.4-1
[31] stringi_1.4.5 RcppParallel_4.4.4 lazyeval_0.2.2
[34] munsell_0.5.0 tm_0.7-7 slam_0.1-47
[37] crayon_1.3.4
Caching R packages
One can also cache (or archive) the R packages from CRAN and Github at the time dockerize is executed. The cached R packages will then transfer to the container. Please note that system requirements (i.e. deb packages) are not cached.
dockerize(graph, "~/rocker_test", cache = TRUE)
Using alternative Rocker images
One can also select other Rocker versioned images: rstudio, tidyverse, verse, geospatial.
dockerize(graph, "~/rocker_test", image = "rstudio")
tidyverse, verse, and geospatial are similar to the default (r-ver). For rstudio, one needs to build and launch it with:
cd ~/rocker_test
docker build -t rang .
docker run -p 8787:8787 -e PASSWORD=abc123 --rm --name "rangtest" -ti rang
With any browser, go to: local:8787. The default username is rstudio, password is as specified.
Using Apptainer/Singularity containers
A rang object can be used to recreate the computational environment via Rocker. Instead of Docker you can also use Apptainer/Singularity. Please note that the oldest R version one can get from Rocker is R 3.1.0.
apptainerize(graph, "~/rocker_test")
# singularize(graph, "~/rocker_test") # same function, as so far Apptainer is identical to Singularity
Now, you can build and run the Apptainer/Singularity container.
For Apptainer installation:
cd ~/rocker_test
apptainer build container.sif container.def
apptainer run container.sif R
For Singularity installation:
cd ~/rocker_test
sudo singularity build container.sif container.def
singularity run container.sif R
Using the above example, sessionInfo() outputs the following. You have successfully gone back to the pre-pandemic.
R version 3.6.2 (2019-12-12) -- "Dark and Stormy Night"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 10 (buster)
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.3.5.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_3.6.2
apptainerize()/singularize() functions work exactly the same as dockerize(), except you cannot cache Linux distribution rootfs.
Apptainer/Singularity with RStudio IDE
To run RStudio IDE in Apptainer/Singularity container, some writeable folders and a config file have to be created locally:
mkdir -p run var-lib-rstudio-server .rstudio
printf 'provider=sqlite\ndirectory=/var/lib/rstudio-server\n' > database.conf
After that, you can run the container (do not run as root user, otherwise you will not be able to login to RStudio IDE).
Start instance (on default RSTUDIO port 8787):
apptainer instance start \
--bind run:/run,var-lib-rstudio-server:/var/lib/rstudio-server,database.conf:/etc/rstudio/database.conf,.rstudio:/home/rstudio/.rstudio/ \
container.sif \
rangtest
Now open a browser and go to localhost:8787. The default username is your local username, default password is ‘set_your_password’ (if you are using container generated by rang).
List running instances:
apptainer instance list
Stop instance:
apptainer instance stop rangtest
Start instance with custom port (e.g. 8080) and password:
apptainer instance start \
--env RPORT=8080
--env PASSWORD='set_your_password' \
--bind run:/run,var-lib-rstudio-server:/var/lib/rstudio-server,database.conf:/etc/rstudio/database.conf,.rstudio:/home/rstudio/.rstudio/ \
container.sif \
rangtest
Run container with custom rserver command line:
apptainer exec \
--env PASSWORD='set_your_password' \
--bind run:/run,var-lib-rstudio-server:/var/lib/rstudio-server,database.conf:/etc/rstudio/database.conf,.rstudio:/home/rstudio/.rstudio/ \
container.sif \
/usr/lib/rstudio-server/bin/rserver \
--auth-none=0 --auth-pam-helper-path=pam-helper \
--server-user=$(whoami) --www-port=8787
If you run the container using apptainer exec command, you will have to kill the rserver process manually or Cmd/Ctrl+C from the running container to stop the server.
Recreate the computational environment for R < 3.1.0
rang can still be used to recreate computational environments for R < 3.1.0. The Dockerfile generated is based on Debian Lenny (5.0) and the requested version of R is compiled from source. As of writing, this method works for R < 3.1.0 but not R < 1.3.1. The image parameter is ignored in this case.
rang_rio <- resolve("rio", snapshot_date = "2013-08-28") ## R 3.0.1
dockerize(rang_rio, output_dir = "~/old_renviron")
Acknowledgment
The logo of rang is a remix of this public domain image. The two dogs should be Presa Canario, the native dog breed on the islands of Gran Canaria and Tenerife.
- It stands for “R Archiving Nerds at GESIS”. The package was previously named
gran, but we decided to rename it torangbecause there is another package named gRAN.