metaRmat R package
Software development project in R to convert project specific meta-analysis code to an R package to generalize analyses to be used across projects and data.
Open-Source R Package for Meta-Analysis
As a consultant, I was brought on to address a significant limitation in an existing research project. The project’s team had developed a successful method for meta-analyzing correlation matrices and performing a path analysis, but the R code was hard-coded for a single dataset. This meant the valuable methodology couldn’t be easily applied by other researchers, limiting its potential impact.
My Role and Contributions
My primary objective was to transform the specialized code into a generalized, reusable, and robust tool. This involved a complete refactoring of the existing script into a user-friendly R package. I consulted with the project team to understand their core methodology and then developed a new code base that could handle diverse datasets.
Key contributions included:
- Refactoring and Generalizing Code: I re-engineered the original script, replacing dataset-specific variables and functions with a flexible framework that accepts various data inputs.
- Creating an R Package: I organized the generalized code into a formal R package, adhering to best practices for package development. This made the tool easily installable and accessible to a wider audience.
- Documentation and User Guidance: I created comprehensive documentation, including detailed function descriptions and practical examples, to ensure other researchers could easily learn and use the tool.
- Version Control and Collaboration: I managed the package’s development on GitHub, facilitating collaborative contributions and providing a platform for version control.
Project Impact
The final product is an open-source R package, now available to the public. By generalizing the original script, we’ve opened up a powerful meta-analytic method to a broader scientific community. Researchers can now easily synthesize correlation matrices and conduct complex path analyses on their own data, significantly expanding the reach and utility of this innovative research technique.
Resources
The package can be installed with the following R command, assuming that the remotes packages is already installed.
remotes::install_github("lebebr01/metaRmat")The full package website is available for you to view.