  Context: Technical Debt is a metaphor used to describe code that is "not
quite right." Although TD studies have gained momentum, TD has yet to be
studied as thoroughly in non-Object-Oriented (OO) or scientific software such
as R. R is a multi-paradigm programming language, whose popularity in data
science and statistical applications has amplified in recent years. Due to R's
inherent ability to expand through user-contributed packages, several
community-led organizations were created to organize and peer-review packages
in a concerted effort to increase their quality. Nonetheless, it is well-known
that most R users do not have a technical programming background, being from
multiple disciplines. Objective: The goal of this study is to investigate TD in
the peer-review documentation of R packages led by rOpenSci. Method: We
collected over 5000 comments from 157 packages that had been reviewed and
approved to be published at rOpenSci. We manually analyzed a sample dataset of
these comments posted by package authors, editors of rOpenSci, and reviewers
during the review process to investigate the TD types present in these reviews.
Results: The findings of our study include (i) a taxonomy of TD derived from
our analysis of the peer-reviews (ii) documentation debt as being the most
prevalent type of debt (iii) different user roles are concerned with different
types of TD. For instance, reviewers tend to report some TD types more than
other roles, and the TD types they report are different from those reported by
the authors of a package. Conclusion: TD analysis in scientific software or
peer-review is almost non-existent. Our study is a pioneer but within the
context of R packages. However, our findings can serve as a starting point for
replication studies, given our public datasets, to perform similar analyses in
other scientific software or to investigate the rationale behind our findings.
