A System for an Accountable Data Analysis Process in R

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
R Journal Pub Date : 2018-05-15 DOI:10.32614/RJ-2018-001
J. Gelfond, M. Goros, B. Hernandez, A. Bokov
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引用次数: 14

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.
R中负责数据分析过程的系统
高效地进行透明分析对初学者来说可能很困难,对有经验的人来说可能很乏味。这意味着需要能够有效地满足再现性和责任标准的计算系统和环境。为此,我们开发了一个基于责任单位原则的系统、R包和R Shiny应用程序,称为adapr(R中的责任数据分析过程)。责任单位是一种数据文件(统计数据、表格或图形),可以与出处相关联,也就是说它是如何创建的,何时创建以及由谁创建的,这类似于Gavish和Donoho提出的“可验证计算结果”(VCR)概念。责任单位和风险控制报告都是版本控制的、可共享的,并且可以合并到一个协作项目中。然而,责任单位使用文件哈希,不涉及水印或VCR等公共存储库。复制协作工作可能非常复杂,需要多个作者在多个系统上重复计算;然而,确定每个单元的来源更简单,只需要使用文件哈希和版本控制系统进行搜索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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