  This article presents a study on the quality and execution of research code
from publicly-available replication datasets at the Harvard Dataverse
repository. Research code is typically created by a group of scientists and
published together with academic papers to facilitate research transparency and
reproducibility. For this study, we define ten questions to address aspects
impacting research reproducibility and reuse. First, we retrieve and analyze
more than 2000 replication datasets with over 9000 unique R files published
from 2010 to 2020. Second, we execute the code in a clean runtime environment
to assess its ease of reuse. Common coding errors were identified, and some of
them were solved with automatic code cleaning to aid code execution. We find
that 74\% of R files crashed in the initial execution, while 56\% crashed when
code cleaning was applied, showing that many errors can be prevented with good
coding practices. We also analyze the replication datasets from journals'
collections and discuss the impact of the journal policy strictness on the code
re-execution rate. Finally, based on our results, we propose a set of
recommendations for code dissemination aimed at researchers, journals, and
repositories.
