| This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown) | |
| using [knitr](http://yihui.name/knitr/) in Rstudio 0.96. | |
| This combination of tools provides an exciting improvement in usability for | |
| [reproducible analysis](http://stats.stackexchange.com/a/15006/183). | |
| Specifically, this post | |
| (1) discusses getting started with R Markdown and `knitr` in Rstudio 0.96; | |
| (2) provides a basic example of producing console output and plots using R Markdown; | |
| (3) highlights several code chunk options such as caching and controlling how input and output is displayed; | |
| (4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and | |
| (5) discusses the implications of R Markdown. |
| ## Corey Chivers, 2012 ## | |
| sim_bayes<-function(p=0.5,N=100,y_lim=20,a_a=2,a_b=10,b_a=8,b_b=3) | |
| { | |
| ## Simulate outcomes in advance | |
| outcomes<-sample(1:0,N,prob=c(p,1-p),replace=TRUE) | |
| success<-cumsum(outcomes) | |
| for(frame in 1:N) | |
| { | |
| png(paste("plots/",1000+frame,".png",sep="")) |
See http://animation.r-forge.r-project.org/knitr/ for a preview of all knitr themes in HTML, and http://rclickhandbuch.files.wordpress.com/2014/09/knitrthemesoverview.pdf for a preview in LaTeX (credit to Berry Boessenkool).
The knitr themes manual can be found at (Rnw source).
| git rebase --interactive HEAD~2 | |
| # we are going to squash c into b | |
| pick b76d157 b | |
| pick a931ac7 c | |
| # squash c into b | |
| pick b76d157 b | |
| s a931ac7 c |
| --- | |
| title: "twee demo" | |
| author: "Jenny Bryan" | |
| date: "17 August, 2014" | |
| output: | |
| html_document: | |
| toc: TRUE | |
| keep_md: TRUE | |
| --- |
2017-08-03: Since I wrote this in 2014, the universe, specifically Kirill Müller (https://github.com/krlmlr), has provided better solutions to this problem. I now recommend that you use one of these two packages:
I love these packages so much I wrote an ode to here.
I use these packages now instead of what I describe below. I'll leave this gist up for historical interest. 😆
| .tufteize(@gutter, @gap) { | |
| > p { margin-right: @gutter + @gap; } | |
| .newthought { font-variant: small-caps; } | |
| p>strong, | |
| p>a:not([href^='#'])::before, | |
| p>a[href^='#'] + * { | |
| width: @gutter; | |
| margin-right: -(@gutter + @gap); |
| # Twitter Topic Modeling Using R | |
| # Author: Bryan Goodrich | |
| # Date Created: February 13, 2015 | |
| # Last Modified: April 3, 2015 | |
| # | |
| # Use twitteR API to query Twitter, parse the search result, and | |
| # perform a series of topic models for identifying potentially | |
| # useful topics from your query content. This has applications for | |
| # social media, research, or general curiosity | |
| # |
This gist shows how to convert a nested JSON file to an R data.frame. To do this, it uses jsonlite and data.tree.
The gist contains two examples: one is a bit simpler, the second one a bit more advanced.
In the first example, we download all the repos from Hadley Wickham's Github account from https://api.github.com/users/hadley/repos . This JSON contains a nested owner object. The code shows how to convert that in a flat data.frame in three statements:
Updated the queryAPI and created a new function, stattle that wraps it and makes walking easier. jsonlite is great, but I started to bump
into some issues with dplyr::bind_rows. Right now these two functions play nice together, but needs to be further tested.
## source the functions
devtools::source_url('https://gist.githubusercontent.com/tcash21/44667f7b8578cc8e061e/raw/fdac84a0a445e448427cc3557e8c9393ce1fa470/queryAPI.r')