为R中可重复的数据分析创造最佳条件
Creating optimal conditions for reproducible data analysis in R with 'fertile'
摘要：
科学知识的进步越来越依赖于确保数据驱动的研究是可重复的：两个人用相同的数据获得相同的结果。然而，虽然重复性的必要性是明确的，但存在重大的行为和技术挑战，阻碍了其广泛实施，并且在已发表的研究中，对重复性的标准没有明确的共识。我们提出了一个R包，它主要通过RStudio集成开发环境，重点关注程序员在R中进行数据科学项目时所犯的一系列常见错误。肥沃的运作在两种模式：主动（首先防止发生可重复性错误）和追溯（分析已经针对潜在问题编写的代码）。此外，fertile旨在教育用户为什么他们的错误是有问题的，以及如何修复它们。
英文摘要：
The advancement of scientific knowledge increasingly depends on ensuring that data-driven research is reproducible: that two people with the same data obtain the same results. However, while the necessity of reproducibility is clear, there are significant behavioral and technical challenges that impede its widespread implementation, and no clear consensus on standards of what constitutes reproducibility in published research. We present fertile, an R package that focuses on a series of common mistakes programmers make while conducting data science projects in R, primarily through the RStudio integrated development environment. fertile operates in two modes: proactively (to prevent reproducibility mistakes from happening in the first place), and retroactively (analyzing code that is already written for potential problems). Furthermore, fertile is designed to educate users on why their mistakes are problematic and how to fix them.
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