Finally, based on our results, we propose a set of recommendations for code dissemination aimed at researchers, journals, and repositories. We also analyze the replication datasets from journals’ collections and discuss the impact of the journal policy strictness on the code re-execution rate. We find that 74% of R files failed to complete without error in the initial execution, while 56% failed when code cleaning was applied, showing that many errors can be prevented with good coding practices. Common coding errors were identified, and some of them were solved with automatic code cleaning to aid code execution. Second, we execute the code in a clean runtime environment to assess its ease of reuse. First, we retrieve and analyze more than 2000 replication datasets with over 9000 unique R files published from 2010 to 2020. For this study, we define ten questions to address aspects impacting research reproducibility and reuse. Research code is typically created by a group of scientists and published together with academic papers to facilitate research transparency and reproducibility. This article presents a study on the quality and execution of research code from publicly-available replication datasets at the Harvard Dataverse repository.
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