Skip to main content

Slidify: Things are coming together fast

Tools for using R/RStudio as a one-stop shop for research and presentation have been coming out quickly. I think this one has a good shot of being included in future releases of RStudio:

The other day I ran across a new R package called slidify by Ramnath Vaidyanathan. In previous posts I had been messing around with Pandoc and deck.rb to turn knitr Markdown files into HTML presentations.

Slidify has two key advantages over these approaches:

  • it can directly convert .Rnw files in R into slideshows, i.e. no toggling between R and the Terminal,

  • there are lots of slideshow options (deck.js, dzslides, html5slides, shower, and slidy).

It’s not on CRAN yet, but it worked pretty well for me.

The syntax is simple.

  • In the Markdown document demarcate new slides with --- (it has to be three dashes and there can’t be spaces after the dashes).

  • When you want to convert your .Rnw into a presentation just type:

    library(slidify)
slidify("presentation.Rnw")

The default style is html5slides. The package isn’t that well documented right now, but to change to a different style just use framework. For example:

    slidify("presentation.Rnw", framework = "deck.js")

I used slidify to put together a slideshow that advertises an intro applied stats course I’m teaching next semester. The slideshow is here. (You can see that I’m trying to attract social science students who are reluctant to take a stats class).

I sloppily removed the default Slidify logo by deleting the images folder in the html5slides folder slidify creates.

PS

Oh, also you might notice that I’m using github to host the course. I hope to blog about this in the near future.

Labels:

Comments

Forrest Stevens said…
Brilliant stuff, and a great presentation. I taught an introductory programming course using R to try to entice people who wanted to program and people who wanted to start climbing the learning curve of R and it was pretty successful. I'd do a few things differently the second time round but congratulations on a really nice presentation.
15:40
Unknown said…
awesome! very clear, I like it.

Thanks!
17:11
Ramnath said…
Thanks for using Slidify, and writing about it. The documentation is thin currently, since I have still not finalized the API. This was a pre-release to get feedback from the early adopters :-). Let me know if you have any suggestions/feedback by posting on the issues page of github.
17:37
Tal Galili said…
Two words:
1) Wow!
2) Thanks!

Cheers,
Tal
18:32
Unknown said…
Great to hear people found this useful.

It's the first time I've taught the intro stats course with such a focus on R, so it would be great to hear any suggests people might have.

Ramnath, your package basically answered my prayers. Thanks for putting it together. I'm really looking forward to seeing the final version.
01:27
Vijay said…
Great presentation! I did not find it in this page, but have you posted the source code for this presentation somewhere or is it just the completed presentation.

Vijay
13:00
Unknown said…
Sorry Vijay. I haven't posted the markup file. The slidify syntax has been under going a bit of change recently and my old markup doesn't work any more.

I'll update and post it when slidify cools down.

10:40
Post a Comment

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
52 comments
Read more

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
46 comments
Read more

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
22 comments
Read more