一个负责任的数据分析过程系统。

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS R Journal Pub Date : 2018-07-01 Epub Date: 2018-05-15
Jonathan Gelfond, Martin Goros, Brian Hernandez, Alex Bokov
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引用次数: 0

摘要

有效地生成透明的分析对于初学者来说可能是困难的,对于有经验的人来说则是乏味的。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,我们开发了一个系统,R包和R Shiny应用程序,称为adapr (R中的可问责数据分析过程),它建立在可问责单元的原则之上。可问责单位是一个数据文件(统计数据、表格或图形),可以与出处相关联,这意味着它是如何创建的,何时创建的以及谁创建的,这类似于Gavish和Donoho提出的“可验证计算结果”(VCR)概念。责任制单元和vcr都是版本控制的、可共享的,并且可以合并到协作项目中。然而,责任单元使用文件哈希,不涉及水印或vcr等公共存储库。再现协作工作可能非常复杂,需要在多个作者的多个系统上重复计算;然而,确定每个单元的来源更简单,只需要使用文件散列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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A System for an Accountable Data Analysis Process in R.

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.

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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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