R packages are a superb opportunity to use new methods, and if anything, we
should only rejoice that there are so many of them in ecology. Yet, there is
a point that occurred to me over recent experiences (reviewing code for a few
papers recently, and reading the help page about one of my own metrics
bipartite package), and that I think is rarely discussed.
When a R package presenting a series of measures becomes available, it will
become the gold standard of future analyzes in this field. This is an
extremely strong effect. French students in ecology use the (French-developed)
ade4 package, while people in Québec use the
vegan package (most likely
because of its ties with the Numerical Ecology book, as Gavin
Simpson mentioned in the comments). There is a priority effect when
a package is released, and metrics that are not implemented in this package,
because they are more difficult to apply, will be less widely used. This is
a central point for methodological research papers. Describing the method is
a small fraction of the work. People are more likely to apply what is
proposed if there is a tool to do it, and as a consequence, (I assume
that) methods are used more if there is an easy and well-known way to
This is especially true of large-scale packages, that aim to become your single
interface with the metrics and analyzes in a domain. Ecologists are familiar
ape, for example. These “mega-packages”
implement a lot of measures, and for this reason, some will look through the
manual to see what kind of analyzes they can do. I most likely still do it
myself, especially so for side-projects where I’m not entirely familiar with
the methodological literature yet. So based on my experience, this is
particularly problematic for newcomers to a field, because when you don’t know,
you assume that the default values were chosen as defaults by
someone knowing what he was doing. Then I remember the Zen of
Python: “In the face of ambiguity, refuse the temptation
And as we all have different sensibilities, it shows in the way we think about code, and in the way we write it. The default options of a function, for example, can be a matter of choice, and especially so when this function becomes as broad as “measure diversity”. This partiality is not an issue when developing tools that you will use for your own research. But when developing tools intended for a broad audience, then this code becomes a service, and it is our duty as service providers that the defaults reflect our own biases the least, and the current consensus the most.
This is why extremely big packages can be a problem under some circumstances: they break Doug McIlroy’s Unix philosophy:
This is the Unix philosophy: Write programs that do one thing and do it well. Write programs to work together. Write programs to handle text streams, because that is a universal interface.
When we, as users, rely on a big package to do our work, we assume that every component of this package is (i) well implemented, (ii) conform to the original paper, and (iii) up-to-date. Given the frequency of commits in the biggest ecological packages, I’m sure this is the case, but it’s worth keeping in mind. My personal preference goes to using a lot of packages doing a small number of things. For my two papers relying on heavily on the development of comparison of new methods, I started by writing the R package (well I started at the drawing board, but that part is not relevant), then used the package to perform the analyzes described in the paper. The idea is that whenever someone will (hopefully) read the paper, there will be a package doing only what is described in the paper.
And I understand that some people would rather have one big package doing everything they need. There are arguments for that of course, namely the fact that with as many users, errors are most likely to be uncovered, and the quality of the package will increase (and the state of the R packages in ecology tends to confirm that). But on the other hand, when you increase the number of pieces, you increase the probability that one of them will behave in a way you would not have anticipated. With this regard, packages that require you to do a lot of small steps by hand have a great advantage, you know what is happening in real time (then again, I like C and doing every thing by hand, so perhaps I just enjoy hurting myself).
In any case, the “default” behavior of programs is something that should be held under intense scrutiny by the community, and improved code review will definitely help. This is also where big packages can somehow escape the system: even if they are reviewed formally (as opposed to being reviewed informally, which they are in real time as people use them), this will not happen for each release, and some new releases may introduce things that can be argued against. In short, my point is: R packages (or packages in other languages, but you get the idea) tend to rapidly represent the “canon” of the analyzes to perform, and for this reason, we should always keep a critical eye on them. Also, keep on writing open-source code, that will enrich the code base available to the community.
With that in mind, what would my ideal super-big package be? Ideally, it would only be a wrapper around other packages, each doing a single task, Unix philosophy-style. Proudly following the ecological tradition of sticking meta to every non-meta thing, it would obviously be called a meta-package. It would automate the process of taking data from one format to the other, and propose pipelines to do automated analysis, but with users checking each step of the process. As such a package would glue together other programs, there will be little development required. The role of the maintainers, however, would be to vet the new releases of the packages, find suitable additions, and so forth. This would be a good example of the community coming together to merge disparate tools. Social coding platforms allow to do this almost effortlessly, and it would have the benefit of showing how the sausage is made, since each individual package would be usable on its own. This would also shift the balance towards a system in which there is a strict one paper / one package relationship, which I think would considerably clarify the type of analyzes included in a package, in addition to matching these analyzes to the primary literature.