the code should run without them, you’ll just get different fonts. Note that dependencies used only for formatting-related niceties ( extrafont, hrbrthemes are listed as “Suggests” and not installed by default. Layer 2 is the R package: you can install the compendium locally using install_github(), this will grab dependencies automatically. In principle, most readers familiar with R can probably just copy-paste code out of the Rmd appendix successfully with no concern about versions. To grab a concrete example, I’ve tried to do this here. I see the additional layers as “fall back” options, which can be useful down the road as software changes. I really like Noam’s notion of layered reproducibility. Of course something could happen to MRAN or Rocker or another upstream dependency that would prevent building this environment, so as a third layer, one can store the binary of the build in your repository, as well.Įach successive layer is slightly more involved to use, and less easy to incorporate into other work, but more likely to be reproducible in the future. With Rocker docker images with fixed R versions, MRAN is used as the CRAN mirror with a fixed date, which should ensure that the same versions of packages always install. This has the advantage of being easy to deploy on a lot of services, too. So I prefer to also have Dockerfile a creates the environment, based on a Rocker image. This has its limits, as you mention above. Making an R package is a very good way of wrapping up the pieces of the package for re-use, with dependencies specified in the DESCRIPTION. That said, I try to aim for layered reproducibility, such that you maximized the reproducibility of the project within successive tools. I’d say using Docker or a similar environment is the best approach, especially if you have external system dependencies. Is there a way to create a package to always reproduce a paper exactly, or is the answer to use Docker? Since R can’t have multiple versions of the same package loaded at the same time, specifying an exact dependency dramatically increases the chance of conflicting versions.įollowing this advice ( MASS (>= 7.3.0)), it seems possible that a future user could use a different version and potentially encounter broken code or obtain different results. You almost always want to specify a minimum version rather than an exact version ( MASS (= 7.3.0)). In his book “R Packages”, Hadley has the following advice: Specifically, I’m looking for advice on external dependencies. I’m wondering how to create a package for maximum reproducibility. In many ways, the easiest option I’ve found is to create a Docker image file that will fully re-create the environment for reproducing the paper.īut I’m also intrigued by the idea of creating a package for the paper. I’ve tried using packrat in the past, but I just could not seem to get it to work right. Share and manage access to R- and Python-based interactive applications, dashboards, and APIs, all in a single place.My objective is to share a fully reproducible analysis that will allow anyone to generate our pre-print. Leverage a single infrastructure to launch and manage Jupyter Notebooks, JupyterLab, VSCode and the RStudio IDE, while giving your team easy access to Kubernetes and other resources. In this webinar, you will learn how RStudio helps organizations tackle these challenges, with a focus on some of the recent additions to our products that have helped deepen the happy relationship between R and Python:Įasily combine R and Python in a single Data Science project using a single IDE. Even experienced data scientists familiar with both languages often struggle to combine them without painful context switching and manual translations. Data Science leaders and their business partners find it difficult to make key data science content easily discoverable and available for decision-making, while IT Admins and DevOps engineers grapple with how to efficiently support these teams without duplicating infrastructure. Many Data Science teams today leverage both R and Python in their work, but struggle to use them together.
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