Packages vs ProjectTemplate

(This article was first published on Deciphering life: One bit at a time :: R, and kindly contributed to R-bloggers)

Packages vs ProjectTemplate

tl;dr

Imposing a different structure than R packages for distributing R code is a bad idea, especially now that R package tools have gotten to the point where managing a package has become much easier.

ProjectTemplate ??

My last two posts (1, 2) provided an argument and an example of why one should use R packages to contain analyses. They were partly motivated by trends I had seen in other areas, including the appearance of the package ProjectTemplate. I was reminded about it thanks to Hadley Wickham:

John Myles White has an R package, ProjectTemplate for handling analyses. In contrast to the package philosophy that I espoused previously, ProjectTemplate has a large, logical folder layout (you can read all about each folder and why it exists here). There is a folder for raw data, a folder for R scripts, a folder for reports, etc.

Hadley probably wanted me to do the same analysis using ProjectTemplate, but I don't have the time for that. Instead I'm going to write below about the features ProjectTemplate has, and why I think packages are a better general solution. So, just to be clear, I have not done any analyses using ProjectTemplate, I have only the website descriptions to go on.

Now, I think I understand the philosophy of ProjectTemplate, in that when writing a manuscript or report in an external editor such as LaTex, Word, LibreOffice (or even something else), you want a directory of outputs (graphs, tables, etc) resulting from applying a series of transformations on some data (R scripts applied to .txt or .csv, etc). In addition, a package is for storing general functions that will work across multiple projects.

Differences

Keeping Directories Straight

I can understand this POV, and in the past would have largely agreed. However, with the development of devtools and other tools that make managing R packages rather easy, I think it makes more sense to use the R package mechanism instead of a custom format. In the ProjectTemplate you may write functions or code in the /lib, /munge or /src directory depending on their purpose (general, data munging, actual analysis, respectively). Keeping all of this straight seems to me a waste of time from doing the actual analysis.

In a package, functions are in the /R directory, and that is where they live. They may be organized into different files depending on their purpose, names, or methods and classes (not fun S4, not fun), but at least I know where they are.

Documentation

This for me is the kicker. With packages (and roxygen2 and devtools), I can document everything in such a way that the documentation is available without looking at the source file; (i.e. ?function) be reminded of the various arguments, or look up the properties of my documented data set.

Dependencies

R packages naturally tell you what other packages they depend on, and will try to resolve those dependencies when you install them. Now, granted, this isn't a perfect process (anyone trying to upgrade their full R package library knows this), and with the proliferation of packages on github is likely to get more difficult (hmmm, would be nice to have a central registry of github available packages), but in general it does work (for a different solution, check out packrat. This means that an analysis that is a package can naturally get it's dependencies at install time (really try it, go ahead and install my example package without having stringr installed).

In contrast, ProjectTemplate assumes all of the required packages are already installed, and if you shared your analysis directory with someone else, they have to look at the list and install everything before they are able to run it (although this is probably not that bad).

Package Reproducibility

In this day and age of seeking to provide reproducible analyses, having all of the entities (code, data, final report) in a container that knows how to install it's dependencies, and that works within the language ecosystem to provide documentation on the functions used, seems really useful. I believe that R packages provide that better than most other solutions I have seen lately.

Outputs

“But I don't want to write my final report using Markdown or Latex” you cry! “How will I get my graphs or tables otherwise?” if using a package? I didn't mention this in the previous posts, but it is possible to write the code in such a way to save graphics or write text files into a specific location (I would recommend /inst/extdata/output if it was me).

Final Thoughts

As I said, I think I understand John's thought process into designing ProjectTemplate, but given how easy it is to work with packages in the current R ecosystem, and that R packages are the built-in way to share things, I think it is more natural to use packages directly with prose of the analysis as a package vignette. But everyone works differently. If you are writing analyses in R, please use a sane directory setup (either packages, or ProjectTemplate or something else), and use version control (really, please learn how to use mercurial or git, your future self will thank you for it).

Remember, when the reviewer (or your boss, or you) asks for something to be added, or to change something slightly in a figure, or add a new dataset, you want to be able to do it easily, and regenerate all of your results without manual intervention.

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