R for Data Analysis in Academic Research: An Open-Source Perspective

The academic use of open-source statistical software has witnessed explosive growth in the last decade. One of the leading contenders is R, which is applicable to data analysis across a number of fields. The adoption of R is rapidly growing. Still, no systematic study exists regarding R’s involvement in research. This review synthesized evidence from fourteen peer-reviewed studies published between 2010 and 2025, examining R adoption, its benefits, and its impact on research quality. This review looks at three issues. First, how and why researchers use R. Second, how it performs against proprietary alternatives in the academic context. Third, regarding how it enhances methodological precision and reproducibility, the results indicate that the main drivers of R adoption are zero cost, transparency, and a full-package ecosystem. The application of R in ecological studies rose from 11.4% in 2008 to 58.0% in 2017. It has been found to be more reproducible and more customizable than proprietary tools. The biggest disadvantage of R is that there is a learning curve compared to commercial software. Using R-based workflows makes research easier to replicate and more transparent. However, there are barriers to adopting it, including programming limitations and a lack of institutional support. The results of the review discussed in this article provide broad evidence-based advice to researchers, educators, and institutions that face choices of statistical software. The review also identifies the gaps in the literature that require further investigation.