  The advancement of scientific knowledge increasingly depends on ensuring that
data-driven research is reproducible: that two people with the same data obtain
the same results. However, while the necessity of reproducibility is clear,
there are significant behavioral and technical challenges that impede its
widespread implementation, and no clear consensus on standards of what
constitutes reproducibility in published research. We present fertile, an R
package that focuses on a series of common mistakes programmers make while
conducting data science projects in R, primarily through the RStudio integrated
development environment. fertile operates in two modes: proactively (to prevent
reproducibility mistakes from happening in the first place), and retroactively
(analyzing code that is already written for potential problems). Furthermore,
fertile is designed to educate users on why their mistakes are problematic and
how to fix them.
