While reading a paper on mutualistic networks in press at *Ecology*, by Jimena Dorado *et al.*, I found the following sentence in the Materials & methods section:

Correlation analyses were done using the cor.test function in the basepackage of R statistical software (https://www.r-project.org/)

Shortly followed by

It is implemented in the nestednof function of the bipartite package (Dorman et al. 2008) of R statistical software (https://www.r-project.org/)

I already mentioned my opinion on developing and sharing R packages to facilitate the reproducibility of analyses. It seems to me that these authors are taking the next step forward, in that they clearly indicate, not only the kind of analysis they conducted (i.e. correlation and nestedness with the NODF metric), but allow the reader to see by himself with what tool these analyses were made.

Explicitly writing in a paper that « I used R to do so and so » goes with two important consequences. First, it allows anyone to reproduce your analysis, given that most trophic networks are deposited to the NCEAS InteractionWeb database (and that facilitates meta-analyses). Second, it establishes the fact that R is a *lingua franca* in ecology.

One can only rejoice to notice that the reproducibility of research in increased, and that analyses are done with a common tool to which anyone can contribute. These are all very good news.

*Related*

To

**leave a comment** for the author, please follow the link and comment on their blog:

** Timothée Poisot » R**.

R-bloggers.com offers

**daily e-mail updates** about

R news and

tutorials on topics such as:

Data science,

Big Data, R jobs, visualization (

ggplot2,

Boxplots,

maps,

animation), programming (

RStudio,

Sweave,

LaTeX,

SQL,

Eclipse,

git,

hadoop,

Web Scraping) statistics (

regression,

PCA,

time series,

trading) and more...

If you got this far, why not

__subscribe for updates__ from the site? Choose your flavor:

e-mail,

twitter,

RSS, or

facebook...

**Tags:** Ecology, publication, R, Research, trophic networks