The R Journal: article published in 2018, volume 10:1

A System for an Accountable Data Analysis Process in R
Jonathan Gelfond, Martin Goros, Brian Hernandez and Alex Bokov , The R Journal (2018) 10:1, pages 6-21.

Abstract Efficiently producing transparent analyses may be difficult for beginners or tedious for the experienced. This implies a need for computing systems and environments that can efficiently satisfy reproducibility and accountability standards. To this end, we have developed a system, R package, and R Shiny application called adapr (Accountable Data Analysis Process in R) that is built on the principle of accountable units. An accountable unit is a data file (statistic, table or graphic) that can be associated with a provenance, meaning how it was created, when it was created and who created it, and this is similar to the ’verifiable computational results’ (VCR) concept proposed by Gavish and Donoho. Both accountable units and VCRs are version controlled, sharable, and can be incorporated into a collaborative project. However, accountable units use file hashes and do not involve watermarking or public repositories like VCRs. Reproducing collaborative work may be highly complex, requiring repeating computations on multiple systems from multiple authors; however, determining the provenance of each unit is simpler, requiring only a search using file hashes and version control systems.

Received: 2016-09-30; online 2018-05-15
CRAN packages: knitr, rmarkdown, cacher, archivist, adapr, packrat
CRAN Task Views implied by cited CRAN packages: ReproducibleResearch

This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-001,
  author = {Jonathan Gelfond and Martin Goros and Brian Hernandez and
          Alex Bokov},
  title = {{A System for an Accountable Data Analysis Process in R}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-001},
  url = {https://doi.org/10.32614/RJ-2018-001},
  pages = {6--21},
  volume = {10},
  number = {1}
}