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'dstidyverse': An Implementation of TidyverseWithin the DataSHIELD Ecosystem
Tim Cadman1,2, Mariska Slofstra1, Demetris Avraam3
1Department of Genetics, Genomics Coordination Center, University Medical Centre, Groningen, The Netherlands.
F1000Research
|November 10, 2025
Summary
DataSHIELD users can now easily manipulate data without sharing it, thanks to the new dsTidyverse package. This R-based tool enhances data analysis efficiency in federated learning environments.
Area of Science:
- Bioinformatics
- Data Science
- Computational Biology
Background:
- DataSHIELD is an established R-based federated learning platform for multi-site analysis without data sharing.
- Existing DataSHIELD packages lack user-friendly data manipulation capabilities.
Purpose of the Study:
- To introduce dsTidyverse, a package that integrates Tidyverse functionalities into DataSHIELD.
- To enhance data manipulation within the DataSHIELD client-server architecture.
Main Methods:
- Implemented selected Tidyverse functions within DataSHIELD's architecture.
- Integrated disclosure checks to prevent individual-level data leakage.
Main Results:
- dsTidyverse offers tools for column selection, renaming, creation, conditional recoding, data frame combination, row filtering/arranging, and grouping.
- Demonstrated simplified data manipulation tasks within DataSHIELD using dsTidyverse examples.
Conclusions:
- dsTidyverse enhances user experience and analytical efficiency in DataSHIELD.
- The open-source package is available on CRAN and GitHub, encouraging further development.
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