【latex版】水贴

An Easy Start with Jekyll, for R-Bloggers

I would like my upper-level students to get more practice in writing about their data analysis work (well, to get get more practice in writing, generally). Blogging is one device by which students can be motivated to write carefully, with a particular audience in mind. So why not have students blog about aspects of their R-work?

我想我的上一级学生获得更多的实践，写他们的数据分析工作（当然，要获得获得更多的实践以书面形式，通常情况下）。博客是一个设备这样学生可以积极地认真书写，与一个特定的观众心中。那么，为什么不要求学生写博客对他们的R-方面工作？

Students need cheap hosting: as long as they know their way around git and Git Hub, then Git Hub Pages are a great (free) solution for that. But then of course they have to use Jekyll, think about web design issues, etc., and on top of that if they plan to blog seriously about R they are probably going to want to write from an R Markdown source document rather than from Markdown.

学生需要廉价的主机：只要他们知道自己周围的Git和Git中心的方式，那么Git的枢纽网页是一个伟大的（免费）的解决方案。但是，那他们当然就必须使用化身，想想网页设计的问题等，最重要的是，如果他们打算认真博客关于R他们可能会想从的R降价源文件，而不是从降价写。

All of this requires a lot of thinking about technical tools, at a time when students should focus as much as possible on fundamentals. Learning R is already enough of a technical challenge! Hence I decided to cobble together a framework that flattens the learning curve for students as much as possible.

所有这一切都需要大量的思考的技术工具，在当学生应该注重尽可能地对基本面的时间。学习R是已经足够了的技术挑战！因此，我决定凑齐了一个框架，变平，让学生尽可能多的学习曲线。

Yihui Xie’s servr package and knitr-jekyll code are a great way to address the R Markdown issue and to keep Jekyll in the background.

The remaining concern is site layout and styling. For this I chose to work from Mark Otto’s excellent Hyde project.

I added a few bells and whistles in the form of options for Disqus commenting and a couple of social media share buttons. It ain’t much but it will get the students going, I hope.

艺辉谢servr包和knitr-吉柯的代码是一个伟大的方式来解决第rMarkdown的的问题，并保持吉柯的背景。

剩下的问题是网站的布局和样式。为此，我选择了从马克·奥托的优秀海德项目工作。

我的DISQUS评论的选项和一些社交媒体共享按钮的形式添加了一些花俏。这是不是很多，但它让学生去，我希望。

In my previous article, we talked about implementations of linear regression models in R, Python and SAS. On the theoretical sides, however, I briefly mentioned the estimation procedure for the parameter $\boldsymbol{beta}$. So to help us understand how software does the estimation procedure, we’ll look at the mathematics behind it. We will also perform the estimation manually in R and in Python, that means we’re not gonna use any special packages, this will help us appreciate the theory.