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zwoV__q?LS@Ahyv9&w@t-d9HMjgPnEgDa-A>c9&RW%G_)S51}MCW-kvsJx>WnZj-4qs^wfT{+GxXlCw?!l~|Aqzf-?sE0x!VGG)jtfMV2zNpKs; z{!=0=0mGGEd-Ee@47kG;=d@q)3u(dzpsKg<3+C|!fOu2XAPUEYXuO|7H+K7iE@x1n zl~88`(IAlG9M5PF){4r!1=ksTe1T}xFRvTY;6he2z^EIaT!&a|io`hpY^5wUVVpCg z7u=NY4Ar>cHTv1qitV+@FB*mQ1zlyEOf;q#KxMsQ?w9cU9E>K`PjXMWe9qYYi`XyD z`8(0_1xNQQT3$B)Au#v5Y!r)Y?HvkQx8WOb1fb z= 3.1, the images from the Rocker project are used [@boettiger:2017:IR]. For R < 3.1 but >= 1.3.1, a custom image based on Debian is used. As of writing, `rang` does not support R < 1.3.1, i.e. snapshot date earlier than 2001-08-31 (which is 13 years earlier than all solutions depend on MRAN). There are two features of `dockerize()` that are important for future reproducibility. +For R >= 3.1, the images from the Rocker project are used [@boettiger:2017:IR]. For R < 3.1 but >= 1.3.1, a custom image based on Debian is used. As of writing, `rang` does not support R < 1.3.1, i.e. snapshot date earlier than 2001-08-31 (which is 13 years earlier than all solutions depending on MRAN). There are two features of `dockerize()` that are important for future reproducibility. -1. By default, the container building process downloads source packages from their sources and then compiles them. This step depends on the future availability of R packages on CRAN (which is extremely likely to be the case in the near future, given the continuous availability since 1997-04-23) [^1]m Bioconductor, and Github. However, it is also possible to cache (or archive) the source packages now. The archived R packages can then be used instead during the building process. The significance of this step in terms of long-term computational reproducibility will be discussed in Section 4. +1. By default, the container building process downloads source packages from their sources and then compiles them. This step depends on the future availability of R packages on CRAN (which is extremely likely to be the case in the near future, given the continuous availability since 1997-04-23) [^1], Bioconductor, and Github. However, it is also possible to cache (or archive) the source packages now. The archived R packages can then be used instead during the building process. The significance of this step in terms of long-term computational reproducibility will be discussed in Section 4. [^1]: https://stat.ethz.ch/pipermail/r-announce/1997/000001.html @@ -239,7 +239,7 @@ cd materials Rscript fn_5.R ``` -The same file can also be "rescued" by `rang`. +The same file can thus also be "rescued" by `rang`. [^groundhog]: http://datacolada.org/100 @@ -266,7 +266,7 @@ The software paper of the R package `ptproc` was published in 2003 and introduce [^taskview]: https://cran.r-project.org/web/views/SpatioTemporal.html -Even with this over-a-decade removal and new packages with similar functionalities have been created, there is evidence that `ptproc` is still being sought for. As late as 2017, there are blog posts on how to install the long obsolete package on modern R [^blog]. The package is extremely challenging to install on a modern R system because the package was written before the introduction of name space management in R 1.7.0 [@RN-2003-001]. In other words, the available tarball file from the original author's website does not contain a `NAMESPACE` file as all other modern R packages do. +Even with this over-a-decade removal and new packages with similar functionalities have been created, there is evidence that `ptproc` is still being sought for. As late as 2017, there are blog posts on how to install the long obsolete package on modern versions of R [^blog]. The package is extremely challenging to install on a modern R system because the package was written before the introduction of name space management in R 1.7.0 [@RN-2003-001]. In other words, the available tarball file from the original author's website does not contain a `NAMESPACE` file as all other modern R packages do. The oldest version of R that `rang` can support, as of writing, is R 1.3.1. `rang` is probably the only solution available that can support the 1.x series of R (i.e. before 2004-10-04). Similar to the case of `maxent` above, a Dockerfile to assemble a Docker image with `ptproc` installed can be generated with two lines of code. @@ -322,9 +322,9 @@ The file `peng.Rout` contains the execution results of the script from inside th ## Recover a removed Bioconductor package -Also similar to CRAN, packages can get removed over time on Bioconductor. The Bioconductor package `Sushi` has been deprecated by the original authors and is removed from Bioconductor version 3.16 (2022-11-02). `Sushi` is a data visualization tool for genomic data and was used in many online tutorials and scientific papers, including the original paper announcing the package by the original authors [@phanstiel:2014:S]. +Similar to CRAN, packages can also be removed over time from Bioconductor. The Bioconductor package `Sushi` has been deprecated by the original authors and is removed from Bioconductor version 3.16 (2022-11-02). `Sushi` is a data visualization tool for genomic data and was used in many online tutorials and scientific papers, including the original paper announcing the package by the original authors [@phanstiel:2014:S]. -`rang` has native support for Bioconductor packages since 0.2. We obtained the R script `"PaperFigure.R"` from the Github repo of `Sushi` [^sushi], which generates the figure in @phanstiel:2014:S. Similar to the above case of `ptproc`, we made a completely automated BASH script to run `"PaperFigure.R"` and get the generated figure out of the container (@fig-figure2). We made no modification to `"PaperFigure.R"`. +`rang` has native support for Bioconductor packages since version 0.2. We obtained the R script `"PaperFigure.R"` from the Github repository of `Sushi` [^sushi], which generates the figure in @phanstiel:2014:S. Similar to the above case of `ptproc`, we made a completely automated BASH script to run `"PaperFigure.R"` and get the generated figure out of the container (@fig-figure2). We made no modification to `"PaperFigure.R"`. ```sh Rscript -e "require(rang); dockerize(resolve('Sushi', '2014-06-05'), @@ -349,7 +349,7 @@ knitr::include_graphics("sushi_figure1.pdf", dpi = 300) # Preparing research compendia with long-term computational reproducibility -The above six examples show how useful it is for `rang` reconstructing tricky computational environments which have not been completely declared in the literature. Although we position `rang` mostly as an archaeological tool, we also think that `rang` can also be used to prepare research compendia of current research. We can't predict the future but research compendia generated by `rang` would probably have long-term computational reproducibility. +The above six examples show how powerful `rang` is to reconstruct tricky computational environments which have not been completely declared in the literature. Although we position `rang` mostly as an archaeological tool, we think that `rang` can also be used to prepare research compendia of current research. We can't predict the future but research compendia generated by `rang` would probably have long-term computational reproducibility. To demonstrate this point, we took the recent paper by @oser:2022:HPE. This paper was selected because 1) the paper was published in *Political Communication*, a high impact journal that awards Open Science Badges; 2) shared data and R code are available; and most importantly, 3) the shared R code is well-written. In the repository of this paper, we based on the materials shared by @oser:2022:HPE and prepared a research compendium that should have long-term computational reproducibility. The research compendium is similar to the Executable Compendium suggested by the Turing way. @@ -405,9 +405,9 @@ oserdocker/ oserimg.tar.gz ``` -In this executable compendium, only the first four elements are essential. The directory `oserdocker` (116 MB) contains cached R packages, Dockerfile, and a verbatim copy of the directory `meta-analysis/` to be transferred into the Docker image. That can be regenerated by running `make resolve`. However, having this directory preserved insures against the situations that some R packages used in the project were no longer available or any of the information providers used by `rang` for resolving the dependency relationships were not available. (Or in the rare circumstance of `rang` is no longer available.) +In this executable compendium, only the first four elements are essential. The directory `oserdocker` (116 MB) contains cached R packages, a Dockerfile, and a verbatim copy of the directory `meta-analysis/` to be transferred into the Docker image. That can be regenerated by running `make resolve`. However, having this directory preserved insures against the situations that some R packages used in the project were no longer available or any of the information providers used by `rang` for resolving the dependency relationships were not available. (Or in the rare circumstance of `rang` is no longer available.) -`oserimg.tar.gz` (667 MB) is a backup copy of the Docker image. This can be regenerated by running `make export`. Preserving this file insurces against all the situations mentioned above, but also the situations of Docker Hub and the software repositories used by the dockerized operating system being not available. When `oserimg.tar.gz` is available, it is possible to run `make rebuild` and `make render` even without internet access (provided that Docker and `make` have been preinstalled). Of course, there is still an extremely rare situation where Docker (the program) itself is no longer available [^make]. However, it is possible to convert the image file for use on other containerization solutions such as Singularity [^singularity], if Docker is really not available anymore. +`oserimg.tar.gz` (667 MB) is a backup copy of the Docker image. This can be regenerated by running `make export`. Preserving this file insures against all the situations mentioned above, but also the situations of Docker Hub and the software repositories used by the dockerized operating system being not available. When `oserimg.tar.gz` is available, it is possible to run `make rebuild` and `make render` even without internet access (provided that Docker and `make` have been installed before). Of course, there is still an extremely rare situation where Docker (the program) itself is no longer available [^make]. However, it is possible to convert the image file for use on other containerization solutions such as Singularity [^singularity], if Docker is really not available anymore. Sharing of research artifacts less than 1G is not as challenging as it used to be. Zenodo, for example, allows the sharing of 50G of files. Therefore, sharing of the last two components of the executable compendium prepared with `rang` is at least possible on Zenodo. However, for data repositories with more restrictions on data size, sharing the executable compendium without the last two parts could be considered sufficient. For that, run `make` will make the default target `all` and generate all the things needed for reproducing the analysis inside a container. @@ -421,10 +421,10 @@ The above `Makefile` is general enough that one can reuse it by just modifying h # Concluding remarks -This paper presents `rang`, a solution to (re)construct R computational environments based on Docker. As the six examples in Section 3 shown, `rang` can be used archaeologically to rerun some old code, many of them not executable without the analytic and reconstruction processes facilitated by `rang`. These retrospectively use cases demonstrate how versatile `rang` is. `rang` is also helpful for prospective usage, as demonstrated in Section 4 whereby an executable compendium is created. +This paper presents `rang`, a solution to (re)construct R computational environments based on Docker. As the six examples in Section 3 show, `rang` can be used archaeologically to rerun old code, many of them not executable without the analytic and reconstruction processes facilitated by `rang`. These retrospective use cases demonstrate how versatile `rang` is. `rang` is also helpful for prospective usage, as demonstrated in Section 4 whereby an executable compendium is created. There are still many features that we did not mention in this paper. `rang` is built with interoperability in mind. As of writing, `rang` is interoperable with existing R packages such as `renv` and R built-in `sessionInfo()`. Also, the `rang` object can be used for network analysis with R packages such as `igraph`. -Computational reproducibility is a complex topic and as in all of these complex topic, there is no silver bullet [@canon:2019:CPR]. All solutions have their trade-offs. The (re)construction process based on `rang` takes notably more time than other solutions because all packages are compiled from source. `rang` trades computational efficiency of this often one-off (re)constructing process for correctness, backward compatibility and independence from any commercial backups of software repositories such as MRAN. There are also other limitations. In the Vignette of `rang` (https://cran.r-project.org/web/packages/rang/vignettes/faq.html), we list out all of these limitations as well as possible mitigation. +Computational reproducibility is a complex topic and as in all of these complex topic, there is no silver bullet [@canon:2019:CPR]. All solutions have their trade-offs. The (re)construction process based on `rang` takes notably more time than other solutions because all packages are compiled from source. `rang` trades computational efficiency of this often one-off (re)constructing process for correctness, backward compatibility and independence from any commercial backups of software repositories such as MRAN. There are also other limitations. In the Vignette of `rang` (https://cran.r-project.org/web/packages/rang/vignettes/faq.html), we list all of these limitations as well as possible mitigation. # References