  An essential part of research and scientific communication is researchers'
ability to reproduce the results of others. While there have been increasing
standards for authors to make data and code available, many of these files are
hard to re-execute in practice, leading to a lack of research reproducibility.
This poses a major problem for students and researchers in the same field who
cannot leverage the previously published findings for study or further inquiry.
To address this, we propose an open-source platform named RE3 that helps
improve the reproducibility and readability of research projects involving R
code. Our platform incorporates assessing code readability with a machine
learning model trained on a code readability survey and an automatic
containerization service that executes code files and warns users of
reproducibility errors. This process helps ensure the reproducibility and
readability of projects and therefore fast-track their verification and reuse.
