python - Virtual environment in R?

Question

I've found several posts about best practice, reproducibility and workflow in R, for example:

• How to increase longer term reproducibility of research (particularly using R and Sweave)
• Complete substantive examples of reproducible research using R

One of the major preoccupations is ensuring portability of code, in the sense that moving it to a new machine (possibly running a different OS) is relatively straightforward and gives the same results.

Coming from a Python background, I'm used to the concept of a virtual environment. When coupled with a simple list of required packages, this goes some way to ensuring that the installed packages and libraries are available on any machine without too much fuss. Sure, it's no guarantee - different OSes have their own foibles and peculiarities - but it gets you 95% of the way there.

Does such a thing exist within R? Even if it's not as sophisticated. For example simply maintaining a plain text list of required packages and a script that will install any that are missing?

I'm about to start using R in earnest for the first time, probably in conjunction with Sweave, and would ideally like to start in the best way possible! Thanks for your thoughts.

Answer 1

I'm going to use the comment posted by @cboettig in order to resolve this question.

Packrat

Packrat is a dependency management system for R. Gives you three important advantages (all of them focused in your portability needs)

• Isolated : Installing a new or updated package for one project won’t break your other projects, and vice versa. That’s because packrat gives each project its own private package library.

• Portable: Easily transport your projects from one computer to another, even across different platforms. Packrat makes it easy to install the packages your project depends on.

• Reproducible: Packrat records the exact package versions you depend on, and ensures those exact versions are the ones that get installed wherever you go.

What's next?

1. Walkthrough guide: http://rstudio.github.io/packrat/walkthrough.html

2. Most common commands: http://rstudio.github.io/packrat/commands.html

3. Using Packrat with RStudio: http://rstudio.github.io/packrat/rstudio.html

4. Limitations and caveats: http://rstudio.github.io/packrat/limitations.html

Update: Packrat has been soft-deprecated and is now superseded by renv, so you might want to check this package instead.