Reproducible Research in R: A Tutorial on how to Do the Same Thing More Than Once

Abstract

Reproducibility has long been considered integral to the scientific method. Something is called reproducible when an independent person obtains the same results from the same data. Until recently, detailed descriptions of methods and analyses were the primary instrument for ensuring scientific reproducibility. Technological advancements now enable scientists to achieve a more comprehensive standard; one in which any individual can be granted access to a digital research repository, and reproduce the analyses from the raw data to the final report including all relevant statistical analyses with a single command. This method has far-reaching implications for scientific archiving, reproducibility and replication, scientific productivity, and the credibility and reliability of scientific findings. One obstacle preventing the widespread adoption of this method is that the underlying technological advancements are complicated to use. This paper introduces `repro`, an R-package, which guides researchers in the installation and use of the tools required for making a research project reproducible. Finally, we suggest the use of the proposed tools for the preregistration of study plans as reproducible computer code (preregistration as code; PAC). Since computer code represents the planned analyses exactly as they will be executed, it is more precise than natural language descriptions of those analyses, which merely complement the PAC as a more readable summary. PAC circumvents the shortcomings of ambiguous preregistrations that may give researchers undesired degrees of freedom. Hence, reproducibility made convenient with automation has a wide range of applications to accelerate scientific progress.

Estimating Statistical Power for Structural Equation Models in Developmental Cognitive Science: A Tutorial in R

“…via containerization or at least in written form). We thus urge researchers to seriously consider the issue of reproducibility for their simulation analyses, to make outcomes of such a-priori power estimates easily accessible to other researchers and reviewers (for a more detailed guideline on a reproducible workflow in R see Peikert et al, 2021).…”