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. arXiv preprint:2303.04758
You can install the development version of rang like so:
remotes::install_github("chainsawriot/rang")Or the stable CRAN version
install.packages("rang")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.
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 rangUsing 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 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)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 rangWith any browser, go to: local:8787. The default
username is rstudio, password is as specified.
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")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.
gran, but we decided to rename it to
rang because there is another package named gRAN.