rw工作流:为R社区自动化可重复的实践
Brian M Schilder1,2, Alan E Murphy3,4, Nathan G Skene5,6
1Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, W12 0BZ, UK. brian_schilder@alumni.brown.edu.
Nature communications
|January 3, 2024
概括
可复制的研究具有挑战性. 新的rwworkflows工具自动化了R包质量检查和在GitHub上部署,为R社区增强了可用性和一致性.
科学领域:
- 计算生物学是一种计算生物学.
- 软件工程 软件工程 软件工程
- 数据科学是数据科学.
背景情况:
- 计算研究中的可复制性仍然是一个重大挑战.
- 超过50%的R包通过GitHub分发,GitHub缺乏内置的质量检查.
- 确保跨时代和跨平台的一致的R包可用性是很困难的.
研究的目的:
- 开发一种自动化解决方案,以提高R包在GitHub上的可复制性和可用性.
- 为所有技能水平的R包开发人员提供一个用户友好的工具.
- 促进R社区广泛采用最佳实践.
主要方法:
- 开发工作流程,一套用于持续集成和部署的工具.
- 通过单个R函数调用实现,具有可定制的选项.
- 在 GitHub 存储库更新时自动触发工作流.
主要成果:
- 工作流自动安装依赖关系,运行代码检查,并生成文档.
- 部署了公开可访问的集装箱环境.
- rworkflows套件是免费的,自动化的,并且很容易实现.
结论:
- rwworkflows显著提高了R包的质量和可重复性.
- 该工具降低了实施强大的软件开发实践的障碍.
- 广泛采用R工作流可以提高R生态系统的可靠性.
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