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.

Linear Least Squares
Consider the linear regression model, [ y_i=f_i(mathbf{x}|boldsymbol{beta})+varepsilon_i,quadmathbf{x}_i=left[ begin{array}{cccc} 1&x_{11}&cdots&x_{1p} end{array}right],quadboldsymbol{beta}=left[begin{array}{c}beta_0\beta_1\vdots\beta_pend{array}right], ] where $y_i$ is the response or the dependent variable at the $i$th case, $i=1,cdots, N$. The $f_i(mathbf{x}|boldsymbol{beta})$ is the deterministic part of the model that depends on both the parameters $boldsymbol{beta}inmathbb{R}^{p+1}$ and the predictor variable $mathbf{x}_i$, which in matrix form, say $mathbf{X}$, is represented as follows [ mathbf{X}=left[ begin{array}{cccccc} 1&x_{11}&cdots&x_{1p}\ 1&x_{21}&cdots&x_{2p}\ vdots&vdots&ddots&vdots\ 1&x_{N1}&cdots&x_{Np}\ end{array} right]. ] $varepsilon_i$ is the error term at the $i$th case which we assumed to be Gaussian distributed with mean 0 and variance $sigma^2$. So that [ mathbb{E}y_i=f_i(mathbf{x}|boldsymbol{beta}), ] i.e. $f_i(mathbf{x}|boldsymbol{beta})$ is the expectation function. The uncertainty around the response variable is also modelled by Gaussian distribution. Specifically, if $Y=f(mathbf{x}|boldsymbol{beta})+varepsilon$ and $yin Y$ such that $y>0$, then begin{align*} mathbb{P}[Yleq y]&=mathbb{P}[f(x|beta)+varepsilonleq y]\ &=mathbb{P}[varepsilonleq y-f(mathbf{x}|boldsymbol{beta})]=mathbb{P}left[frac{varepsilon}{sigma}leq frac{y-f(mathbf{x}|boldsymbol{beta})}{sigma}right]\ &=Phileft[frac{y-f(mathbf{x}|boldsymbol{beta})}{sigma}right], end{align*} where $Phi$ denotes the Gaussian distribution with density denoted by $phi$ below. Hence $Ysimmathcal{N}(f(mathbf{x}|boldsymbol{beta}),sigma^2)$. That is, begin{align*} frac{operatorname{d}}{operatorname{d}y}Phileft[frac{y-f(mathbf{x}|boldsymbol{beta})}{sigma}right]&=phileft[frac{y-f(mathbf{x}|boldsymbol{beta})}{sigma}right]frac{1}{sigma}=mathbb{P}[y|f(mathbf{x}|boldsymbol{beta}),sigma^2]\ &=frac{1}{sqrt{2pi}sigma}expleft{-frac{1}{2}left[frac{y-f(mathbf{x}|boldsymbol{beta})}{sigma}right]^2right}. end{align*} If the data are independent and identically distributed, then the log-likelihood function of $y$ is, begin{align*} mathcal{L}[boldsymbol{beta}|mathbf{y},mathbf{X},sigma]&=mathbb{P}[mathbf{y}|mathbf{X},boldsymbol{beta},sigma]=prod_{i=1}^Nfrac{1}{sqrt{2pi}sigma}expleft{-frac{1}{2}left[frac{y_i-f_i(mathbf{x}|boldsymbol{beta})}{sigma}right]^2right}\ &=frac{1}{(2pi)^{frac{n}{2}}sigma^n}expleft{-frac{1}{2}sum_{i=1}^Nleft[frac{y_i-f_i(mathbf{x}|boldsymbol{beta})}{sigma}right]^2right}\ logmathcal{L}[boldsymbol{beta}|mathbf{y},mathbf{X},sigma]&=-frac{n}{2}log2pi-nlogsigma-frac{1}{2sigma^2}sum_{i=1}^Nleft[y_i-f_i(mathbf{x}|boldsymbol{beta})right]^2. end{align*} And because the likelihood function tells us about the plausibility of the parameter $boldsymbol{beta}$ in explaining the sample data. We therefore want to find the best estimate of $boldsymbol{beta}$ that likely generated the sample. Thus our goal is to maximize the likelihood function which is equivalent to maximizing the log-likelihood with respect to $boldsymbol{beta}$. And that’s simply done by taking the partial derivative with respect to the parameter $boldsymbol{beta}$. Therefore, the first two terms in the right hand side of the equation above can be disregarded since it does not depend on $boldsymbol{beta}$. Also, the location of the maximum log-likelihood with respect to $boldsymbol{beta}$ is not affected by arbitrary positive scalar multiplication, so the factor $frac{1}{2sigma^2}$ can be omitted. And we are left with the following equation, begin{equation}label{eq:1} -sum_{i=1}^Nleft[y_i-f_i(mathbf{x}|boldsymbol{beta})right]^2. end{equation} One last thing is that, instead of maximizing the log-likelihood function we can do minimization on the negative log-likelihood. Hence we are interested on minimizing the negative of Equation (ref{eq:1}) which is begin{equation}label{eq:2} sum_{i=1}^Nleft[y_i-f_i(mathbf{x}|boldsymbol{beta})right]^2, end{equation} popularly known as the residual sum of squares (RSS). So RSS is a consequence of maximum log-likelihood under the Gaussian assumption of the uncertainty around the response variable $y$. For models with two parameters, say $beta_0$ and $beta_1$ the RSS can be visualized like the one in my previous article, that is

The code behind the app is written in R, and leverages the NCBI Eutils API via the rentrez package interface.
The methodology is fairly simple:
Build the search query in Pubmed syntax based on user input parameters.
Extract total number of articles from results.
Output a visualization of the total counts for both selected institutions.
Extract unique article identifiers from results.
Output the number of article identifiers that match (i.e. “collaborations”) between the two selected institutions.

Introduction
The aim of this project is to help students and colleagues who for some reason want to blog on R-related topics. If you have a particular Git Hub project that deals with R and you want to blog about your work as it develops, or if you simply want to blog about R in general, then you can use the material from my knitr-hyde repository to set up, with minimal fuss, a Jekyll-powered site with good styling borrowed from Mark Otto’s Hyde project. With help from Yihui Xie’s servr package and knitr-jekyll code you ‘ll be able to write your posts in R Markdown, build and preview the site locally, and push to your Git Hub Pages site when you are ready.

I have tried to minimize what you need to know about Jekyll (and web development generally) in order to get going. You can learn more about Jekyll when it suits you and eventually make thorough-going alterations to my blog-template, but for now I want you to be able to concentrate on getting your content out there to a waiting public.

Preliminaries
Platform
Jekyll is not officially supported on Windows, so you had best try this with Mac OSX, or with a Linux distribution. (Either way works well, in my experience.) But if you are determined to give it a try on Windows, consult the documentation here.

Get My Files
Consult the Github Pages guide. Decide whether you want a general user site or a site associated with a partcular project repository.

Getting Files for a Project Site

If you don’t already have an existing project but want a project-associated site, then fork my knitr-hyde repository from Git Hub, rename it as you wish and then clone it on your own machine. You can do your project work on the master branch and switch to the gh-pages branch for blogging.

If you already have a project repository on Git Hub and want a site associated with it, then simply create a gh-pages branch, delete all of the files, download a zip file of my gh-pages branch and extract it into your repo while you have your gh-pages branch checked out.

Getting Files for a User Site

Having created your user respository (yourgithubusername.github.io as per the GitHub Pages guide), clone your user repo onto your own machine. Stay on your master branch: you don’t create a gh-pages branch for a user site. Download a zip file of my gh-pages branch and extract it into your repo.

◎影片名称：《绝命海拔》
◎别　　名：圣母峰(台)/珠峰浩劫(港)/珠穆朗玛/远征珠峰/珠穆朗玛峰
◎片　　名：Everest
◎导　　演：巴塔萨·科马库
◎主　　演：杰克·吉伦哈尔,凯拉·奈特莉,罗宾·怀特,杰森·克拉科,乔什·布洛林,萨姆·沃辛顿,克莱夫·斯坦登,艾米丽·沃森,约翰·浩克斯,迈克尔·凯利,伊丽莎白·德比茨基,米娅·高斯,马丁·亨德森,凡妮莎·柯比,汤姆·古德曼-希尔
◎豆瓣评分：7.5
◎豆　　瓣：http://movie.**.com/subject/22265299
◎IMDb　　：http://www.imdb.com/title/tt2719848
◎BT/迅雷/百度云：http://www.92np.com/v/18979.html
◎语　　言：英语
◎地　　区：美国
◎年　　代：2015
◎时　　长：121分钟
◎上映时间：2015年09月02日

Configuring my Files for Your Use
In the root directory, locate the _config.yaml file. Make some choices:

Change the title and description.
Change the value of baseurl as per the commented directions. Make sure there is a trailing ‘/’ at the end of baseurl. For a site associated with a repository named myProject the base url will be set to “/myProject/”. For a user site, it’s just “/”. Either way. it begins and ends with a “/”!
Change url. Since you are pushing to Git Hub, it can be https://yourgithubusername.github.io.
Decide if you would like people to be able to comment on your posts. If you want this, leave disqus at true and register at the Disqus.com. You will have the opportunity to add Disqus to your site. Do this. As part of this process you will be asked to create a shortname for your site. Set shortname accordingly. If you don’t want commenting, simply set disqus to false.
Change twitter and facebook to false if you don’t want the Tweet and Facebook buttons for your posts.

Get the Packages
Ruby and Gems
You will need to install Ruby, and then install the Jekyll gem. It’s best if you install the same version of Jekyll that Git Hub will use to build your page. You can find the current version here. At the time of writing this is version 2.4.0, so once you have installed Ruby, open a terminal and run the command:

sudo gem install jekyll -v 2.4.0

You’ll also want a gem that keeps all dependencies of Jekyll at the same version level as used by Git Hub:

sudo gem install gh-pages

In order to stay current with Git Hub, update this gem frequently:

sudo gem update gh-pages

The servr Package
You’ll need Yihui Xie’s servr package. In R, run:

install.packages("servr")

水贴的魅力无人能挡

Boolean models are a drastic simplification of biological reality, but they have produced valuable results in the past and are especially suited for developmental gene regulatory networks (e.g., Macía et al., 2009).

How to Learn R

There are tons of resources to help you learn the different aspects of R, and as a beginner this can be overwhelming. It’s also a dynamic language and rapidly changing, so it’s important to keep up with the latest tools and technologies.

That’s why R-bloggers and DataCamp have worked together to bring you a learning path for R. Each section points you to relevant resources and tools to get you started and keep you engaged to continue learning. It’s a mix of materials ranging from documentation, online courses, books, and more.

Just like R, this learning path is a dynamic resource. We want to continually evolve and improve the resources to provide the best possible learning experience. So if you have suggestions for improvement please email tal.galili@gmail.com with your feedback.

Learning Path

Getting started:  The basics of R

Setting up your machine

R packages

Importing your data into R

Data Manipulation

Data Visualization

Data Science & Machine Learning with R

Reporting Results in R

Next steps

Getting started:  The basics of R

The best way to learn R is by doing. In case you are just getting started with R, this free introduction to R tutorial by DataCamp is a great resource as well the successorIntermediate R programming (subscription required). Both courses teach you R programming and data science interactively, at your own pace, in the comfort of your browser. You get immediate feedback during exercises with helpful hints along the way so you don’t get stuck.

Another free online interactive learning tutorial for R is available by O’reilly’s code school website called try R. An offline interactive learning resource is swirl, an R package that makes if fun and easy to become an R programmer. You can take a swirl course by (i)installing the package in R, and (ii) selecting a course from the course library. If you want to start right away without needing to install anything you can also choose for the online version of Swirl.

There are also some very good MOOC’s available on edX and Coursera that teach you the basics of R programming. On edX you can find Introduction to R Programming by Microsoft, an 8 hour course that focuses on the fundamentals and basic syntax of R. At Coursera there is the very popular R Programming course by Johns Hopkins. Both are highly recommended!

If you instead prefer to learn R via a written tutorial or book there is plenty of choice. There is the introduction to R manual by CRAN, as well as some very accessible books like Jared Lander’s R for Everyone or R in Action by Robert Kabacoff.

Setting up your machine

You can download a copy of R from the Comprehensive R Archive Network (CRAN). There are binaries available for Linux, Mac and Windows.

Once R is installed you can choose to either work with the basic R console, or with an integrated development environment (IDE).RStudio is by far the most popular IDE for R and supports debugging, workspace management, plotting and much more (make sure to check out the RStudio shortcuts).

Next to RStudio you also have Architect, and Eclipse-based IDE for R. If you prefer to work with a graphical user interface you can have a look at R-commander  (aka as Rcmdr), or Deducer.

R packages

R packages are the fuel that drive the growth and popularity of R. R packages are bundles of code, data, documentation, and tests that are easy to share with others. Before you can use a package, you will first have to install it. Some packages, like the base package, are automatically installed when you install R. Other packages, like for example the ggplot2 package, won’t come with the bundled R installation but need to be installed.

Many (but not all) R packages are organized and available fromCRAN, a network of servers around the world that store identical, up-to-date, versions of code and documentation for R. You can easily install these package from inside R, using the install.packages function. CRAN also maintains a set ofTask Views that identify all the packages associated with a particular task such as for exampleTimeSeries.

Next to CRAN you also have bioconductor which has packages for the analysis of high-throughput genomic data, as well as for example the github and bitbucket repositories of R package developers. You can easily install packages from these repositories using the devtools package.

Finding a package can be hard, but luckily you can easily search packages from CRAN, github and bioconductor usingRdocumentation, inside-R, or you can have a look at this quick list of useful R packages.

To end, once you start working with R, you’ll quickly find out that R package dependencies can cause a lot of headaches. Once you get confronted with that issue, make sure to check out packrat (seevideo tutorial) or checkpoint. When you’d need to update R, if you are using Windows, you can use the updateR() function from theinstallr package.

Importing your data into R

The data you want to import into R can come in all sorts for formats: flat files, statistical software files, databases and web data.

Getting different types of data into R often requires a different approach to use. To learn more in general on how to get different data types into R you can check out this online Importing Data into R tutorial (subscription required), this post on data importing, orthis webinarby RStudio.

• Flat files are typically simple text files that contain table data. The standard distribution of R provides functionality to import these flat files into R as a data frame with functions such as read.table() and read.csv() from the utils package. Specific R packages to import flat files data are readr, a fast and very easy to use package that is less verbose as utils and multiple times faster (more information), and data.table’sfread() function for importing and munging data into R (using the fread function).
  

• In case you want to get your excel files into R, it’s a good idea to have a look at thereadxl package. Alternatively, there is thegdata package which has function that supports the import of Excel data, and the XLConnectpackage. The latter acts as a real bridge between Excel and R meaning you can do any action you could do within Excel but you do it from inside R. Read more on importing your excel files into R.
  

• Software packages such as SAS, STATA and SPSS use and produce their own file types. The haven package by Hadley Wickham can deal with importing SAS, STATA and SPSS data files into R and is very easy to use. Alternatively there is theforeign package, which is able to import not only SAS, STATA and SPSS files but also more exotic formats like Systat and Weka for example. It’s also able to export data again to various formats. (Tip: if you’re switching from SAS,SPSS or STATA to R, check out Bob Muenchen’s tutorial (subscription required))
  

• The packages used to connect to and import from a relational database depend on the type of database you want to connect to. Suppose you want to connect to a MySQL database, you will need the RMySQL package. Others are for example theRpostgreSQL and ROracle package.The R functions you can then use to access and manipulate the database, is specified in another R package called DBI.
  

• If you want to harvest web data using R you need to connect R to resources online using API’s or through scraping with packages like rvest. To get started with all of this, there is this great resource freely available on the blog of Rolf Fredheim.
  

谢谢楼主！

分享的东西不错

顶起来！

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384084/

每日一球