Project organization and reproducible code

By tim, April 14, 2016

An easy step towards making your research reproducible is containment: everything is a box, with one clear way in, and one clear way out. This is why programming with functions is great. The arguments are the way in, and the output is the way out. For example, in the following function, there is a way in through the stuff argument, and one way out as a boolean value indicating whether or not it will blend.

will_it_blend <- function(stuff="iphone") {
    return stuff %in% c("iphone", "ice", "rake")

This function does not need to know about anything external, nor does it needs to know about its surrounding. One you have put in the argument, it will give you the same output regardless of the state of e.g. other variables.

Of course a better way to write this function would be not to hard-code the names of things that will blend in the body of the function itself, and instead to be able to use a list as another argument. Which we can do as:

will_it_blend <- function(stuff="iphone", stuff_that_usually_blends){
    return stuff %in% stuff_that_usually_blends

Now, calling this function will depends on what you give as stuff_that_usually_blends. But again, what happens within the function is entirely independent from what happens outside of it. This is usually regarded as a Good Thing (the caps means it’s not simply a good thing, which is good, but not, y’know, Good).

So anyways, projects.

I tend to (and therefore fellow lab members usually humor me) design projects in a similar way. Nothing exists outside of the project. By the project, I mean a folder located somewhere on my computer in which there are files related to a future paper.

It usually go something like this:


The code reads data from their folder, produces figures that go in their folder, and the manuscript calls everything it needs. This is all automated thanks to the magic of makefiles, which these days tend to be self-documented.

A very important point is that a piece of code called, for example code/make_figure_1.r will go look for data in data/observations.csv, and write in figure/figure_1.pdf. This is very different from having the absolute path (for example /home/tpoisot/projects/explorations/qc_rangemaps/) in front every time.

We often work on several machines (our laptops/desktops in the lab, our trustworthy ada for medium-scale jobs, and Compute Québec machines when we need a lot of results). As a result, a project that lives at /home/tpoisot/projects/wherever/ on my laptop can live at /home/tpoisot/wherever/ on ada, and at /home/some_long_unique_id/scratch/running/wherever/ on Compute Québec. Relying on absolute paths means that I would have to maintain three different versions of the code just to deal with this. Using relative paths instead means that as long as I navigate to where the project is, it will all work on any machine.

And as I am wont to do, I recently explained this point in a calm, cogent, and didactic way on twitter:

Y’all should follow me, I’m charming.