使用 R 进行项目组织 [英] project organization with R

问题描述

可能的重复:
用于统计分析和报告编写的工作流程

我用 R 编程不久，但遇到了一个项目组织问题，我希望有人能给我一些提示.我发现我所做的很多分析都是临时的:也就是说，我运行了一些东西，考虑了结果，周而复始，然后再运行一些.这在概念上不同于像 C++ 这样的语言，在 C++ 中，您在编码之前考虑要运行的整个事物.这是解释型语言的巨大好处.但是，出现的问题是我最终保存了很多 .RData 文件，因此我不必每次都source 我的脚本.有没有人对如何组织我的项目有任何好的想法，以便我可以在一个月后返回它并清楚地了解每个文件与什么相关联?

I have been programming with R for not too long but am running into a project organization question that I was hoping somebody could give me some tips on. I am finding that a lot of the analysis I do is ad hoc: that is, I run something, think about the results, tweek it and run some more. This is conceptually different than in a language like C++ where you think about the entire thing you want to run before coding. It is a huge benefit of interpreted languages. However, the issue that comes up is I end up having a lot of .RData files that I save so I don't have to source my script every time. Does anyone have any good ideas about how to organize my project so I can return to it a month later and have a good idea of what each file is associated with?

我猜这是一个文档问题.我是否应该在每条腿上记录我的整个项目，并积极清理不再需要但作为研究副产品的文件?这是我目前的系统，但它有点麻烦.还有其他人有什么建议吗?

This is sort of a documentation question I guess. Should I document my entire project at each leg and be vigorous about cleaning up files that will no longer be necessary but were a byproduct of the research? This is my current system but it is a bit cumbersome. Does anyone else have any other suggestions?

根据下面的评论:我试图避免的关键事情之一是 .R 分析文件和随之而来的 .RData 集的激增.

Per the comment below: One of the key things that I am trying to avoid is the proliferation of .R analysis files and .RData sets that go along with them.

推荐答案

关于研究项目组织的一些想法:

Some thoughts on research project organisation here:

http://software-carpentry.org/4_0/data/mgmt/

带回家的信息是:

• 为您的程序使用版本控制
• 使用合理的目录名称
• 对元数据使用版本控制
• 真的，版本控制是一件好事.

这篇关于使用 R 进行项目组织的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！