The goal of checkpoint is to solve the problem of package reproducibility in R. Specifically, checkpoint solve the problems that occur when you don’t have the correct versions of R packages. Since packages get updated on CRAN all the time, it can be difficult to recreate an environment where all your packages are consistent with some earlier state.
To solve this, checkpoint allows you to install packages from a specific snapshot date. In other words, checkpoint makes it possible to install package versions from a specific date in the past, as if you had a CRAN time machine.
Version 1.0 of checkpoint is a major refactoring/rewrite, aimed at resolving many long-standing issues. You can provide feedback by opening an issue or by contacting me.
With the checkpoint package, you can easily:
The checkpoint package has 3 main functions.
The create_checkpoint function
~/.checkpoint by default, but you can change its location.library() and require() calls, as well as the namespacing operators :: and :::.The use_checkpoint function
options(repos)create_checkpoint, i.e. modifies .libPaths()This means the remainder of your script will run with the packages from your specified date.
Finally, the checkpoint function serves as a unified interface to create_checkpoint and use_checkpoint. It looks for a pre-existing checkpoint directory, and if not found, creates it with create_checkpoint. It then calls use_checkpoint to put the checkpoint into use.
Sharing a script to be reproducible is as easy as placing the following snippet at the top:
Then send this script to your collaborators. When they run this script on their machine for the first time, checkpoint will perform the same steps of scanning for package dependencies, creating the checkpoint directory, installing the necessary packages, and setting your session to use the checkpoint. On subsequent runs, checkpoint will find and use the created checkpoint, so the packages don’t have to be installed again.
If you have more than one script in your project, you can place the above snippet in every standalone script. Alternatively, you can put it in a script of its own, and run it before running any other script.
The checkpoint package is designed to be used with projects, which are directories that contain the R code and output associated with the tasks you’re working on. If you use RStudio, you will probably be aware of the concept, but the same applies for many other programming editors and IDEs including Visual Studio Code, Notepad++ and Sublime Text.
When it is run, create_checkpoint scans all R files inside a given project to determine what packages your code requires. The default project is the current directory ".".
If you do not have an actual project open, this will usually expand to your R user directory (~/<username> on Unix/Linux and MacOS, or C:\Users\<username>\Documents on Windows). For most people, this means that the function will scan through all the projects they have on their machine, which can lead to checkpointing a very large number of packages. Because of this, you should ensure that you are not in your user directory when you run checkpoint. A mitigating factor is that this should happen only once, as long as the checkpoint directory remains intact.
For an even more stringent form of reproducibility, you can use the following:
library(checkpoint)
checkpoint("2020-01-01", r_version="3.6.2") # replace with desired date and R versionThis requires that anyone running the script must be using the specified version of R. The benefit of this is because changes in R over time can affect reproducibility just like changes in third-party packages, so by restricting the script to only one R version, we remove another possible source of variation. However, R itself is usually very stable, and requiring a specific version can be excessively demanding especially in locked-down IT environments. For this reason, specifying the R version is optional.
checkpoint will automatically add the rmarkdown package as a dependency if it finds any Rmarkdown-based files (those with extension .Rmd, .Rpres or .Rhtml) in your project. This allows you to continue working with such documents after checkpointing.
To reset your session to the way it was before checkpointing, call uncheckpoint(). Alternatively, you can simply restart R.
To update an existing checkpoint, for example if you need new packages installed, call create_checkpoint() again. Any existing packages will remain untouched.
The functions delete_checkpoint() and delete_all_checkpoints() allow you to remove checkpoint directories that are no longer required. They check that the checkpoint(s) in question are not actually in use before deleting.
Each time create_checkpoint() is run, it saves a series of json files in the main checkpoint directory. These are outputs from the pkgdepends package, which checkpoint uses to perform the actual package installation, and can help you debug any problems that may occur.
<date>_<time>_refs.json: Packages to be installed into the checkpoint<date>_<time>_config.json: Configuration parameters for the checkpoint<date>_<time>_resolution.json: Dependency resolution result<date>_<time>_solution.json: Solution to package dependencies<date>_<time>_downloads.json: Download result<date>_<time>_install_plan.json: Package install plan<date>_<time>_installs.json: Final installation resultFor more information, see the help for pkgdepends::pkg_installation_proposal.
checkpoint is on CRAN:
The development version of checkpoint is on GitHub:
https://github.com/RevolutionAnalytics/checkpoint
Post an issue on the Issue tracker at https://github.com/RevolutionAnalytics/checkpoint/issues
https://github.com/RevolutionAnalytics/checkpoint-server
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.