Skip to main content

Dynamic Content with RStudio, Markdown, and Marked.

As Markus Gesmann recently pointed out, the new version of RStudio (0.96) has some really nice features for creating dynamic reports with Yihui Xie’s knitr. You can integrate not just R and LaTeX, but also R and Markdown (as well as some other formats).

If you haven’t used Markdown before, it’s basically a really simplified syntax for writing web content, though it can easily be converted not just to HTML but also LaTeX and other formats with Pandoc.

See this post by Yihui Xie for a discussion of how to make HTML presentations with knitr and Pandoc. These programs make it much easier to create HTML presentations that display interactive R output from packages like googleVis (like I did in an earlier post).

I’ve been using RStudio’s new features in the preview version for a few weeks and it has been really great. It has made creating web content much easier. I’ve even decided to pretty much move my entire introductory data analysis course to the web because I can create lecture notes and assignments with nice syntax highlighting and R output integration (especially interactive output).

I remember a few years ago saying to my PhD supervisor that I thought it would someday be standard for theses to be written in HTML. Maybe I need to revise that slightly: theses may be displayed in HTML, but written in Markdown or (more specifically) MultiMarkdown (which has footnote and BibTeX integration).

Recommendation

A small program I really recommend purchasing if you are using RStudio with Markdown is Marked. RStudio has a Markdown previewer, but its capabilities are a bit limited. Marked gives you nicer previews with multiple styles to choose from, word counts, hyperlink validation, and some other stuff that definitely justifies its $3.99 price.

To use Marked with RStudio just drag the .Rnw or .md file you're working on in RStudio on top of the Marked icon. It will update any time you save or compile the files.

(Oh, note I think Marked is Mac only. Also, I have no affiliation with RStudio or Marked, I just really like them.)

Comments

Brett said…
Thanks for the Marked mention, Christopher! (I'm the developer)
Unknown said…
Sure, as I mentioned I really like your program.

Oh, one feature I would really appreciate being added is an ability to Save As LaTeX.
Pvde said…
Marked is not working well with Rstudio : graphics are missing....
Pvde said…
Marked is not working well with Rstudio : graphics are missing....
Unknown said…
Can you give a reproducible example.

Popular posts from this blog

Dropbox & R Data

I'm always looking for ways to download data from the internet into R. Though I prefer to host and access plain-text data sets (CSV is my personal favourite) from GitHub (see my short paper on the topic) sometimes it's convenient to get data stored on Dropbox . There has been a change in the way Dropbox URLs work and I just added some functionality to the repmis R package. So I though that I'ld write a quick post on how to directly download data from Dropbox into R. The download method is different depending on whether or not your plain-text data is in a Dropbox Public folder or not. Dropbox Public Folder Dropbox is trying to do away with its public folders. New users need to actively create a Public folder. Regardless, sometimes you may want to download data from one. It used to be that files in Public folders were accessible through non-secure (http) URLs. It's easy to download these into R, just use the read.table command, where the URL is the file name

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame. I've found the various R methods for doing this hard to remember and usually need to look at old blog posts . Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command– slide –to the DataCombine R package (v0.1.5). Building on the shift function TszKin Julian posted on his blog , slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data. Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996

A Link Between topicmodels LDA and LDAvis

Carson Sievert and Kenny Shirley have put together the really nice LDAvis R package. It provides a Shiny-based interactive interface for exploring the output from Latent Dirichlet Allocation topic models. If you've never used it, I highly recommend checking out their XKCD example (this paper also has some nice background). LDAvis doesn't fit topic models, it just visualises the output. As such it is agnostic about what package you use to fit your LDA topic model. They have a useful example of how to use output from the lda package. I wanted to use LDAvis with output from the topicmodels package. It works really nicely with texts preprocessed using the tm package. The trick is extracting the information LDAvis requires from the model and placing it into a specifically structured JSON formatted object. To make the conversion from topicmodels output to LDAvis JSON input easier, I created a linking function called topicmodels_json_ldavis . The full function is below. To