Temporal Dependency Archaeology: Reconstructing Historical R Environments for Reproducible Text Analysis Pipelines

• Eeshwar Pasula

  University of Texas at Arlington

  Author

Reproducibility in computational text analysis is often undermined not by missing data or code, but by the gradual decay of software environments. This paper introduces a framework for the retrospective reconstruction of R-based computational environments used in natural language processing research, enabling the re-execution of legacy text analysis pipelines that would otherwise fail due to package obsolescence, version conflicts, or system-level dependencies. We present a method that queries archival snapshots of CRAN, Bioconductor, and GitHub to resolve complete dependency graphs at specific historical timestamps, capturing not only package versions but also R interpreter versions and system requirements. These specifications are then containerized via Docker, producing isolated environments that faithfully replicate the original computational context. We demonstrate the utility of this approach on several case studies drawn from computational social science and bioinformatics, including the recovery of a defunct text classification package (maxent) and the successful re-execution of code accompanying a quanteda software paper. The method supports both retrospective archaeology and prospective compendium construction, offering a pathway toward long-term executability of NLP workflows independent of commercial snapshot services. All tools are open-source and designed to integrate with existing reproducibility ecosystems.

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