计算机科学
工作流程
控制(管理)
软件
集合(抽象数据类型)
阅读(过程)
质量(理念)
数据科学
软件工程
重新调整用途
借口
万维网
数据库
程序设计语言
人工智能
政治学
法学
生态学
哲学
认识论
生物
标识
DOI:10.1080/00031305.2017.1399928
摘要
Data analysis, statistical research, and teaching statistics have at least one thing in common: these activities all produce many files! There are data files, source code, figures, tables, prepared reports, and much more. Most of these files evolve over the course of a project and often need to be shared with others, for reading or edits, as a project unfolds. Without explicit and structured management, project organization can easily descend into chaos, taking time away from the primary work and reducing the quality of the final product. This unhappy result can be avoided by repurposing tools and workflows from the software development world, namely, distributed version control. This article describes the use of the version control system Git and the hosting site GitHub for statistical and data scientific workflows. Special attention is given to projects that use the statistical language R and, optionally, R Markdown documents. Supplementary materials include an annotated set of links to step-by-step tutorials, real world examples, and other useful learning resources. Supplementary materials for this article are available online.
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