This post presents the video of a talk that I presented in July 2012 at Melbourne R Users on using knitr, R Markdown, and R Studio to perform reproducible analysis. I also provide links to a github repository where the R markdown examples can be examined and the slides can be downloaded.
Reproducible analysis represents a process for transforming text, code, and data to produce reproducible artefacts including reports, journal articles, slideshows, theses, and books. Reproducible analysis is important in both industry and academic settings for ensuring a high quality product. R has always provided a powerful platform for reproducible analysis. However, in the first half of 2012, several new tools have emerged that have substantially increased the ease with which reproducible analysis can be performed. In particular, knitr, R Markdown, and RStudio combine to create a user-friendly and powerful set of open source tools for reproducible analysis.
Specifically, in the talk I discuss caching slow analyses, producing attractive plots and tables, and using RStudio as an IDE. I present three live examples of using R Markdown. I also show how the markdown package on CRAN can be used to work with other R development environments and workflows for report production.
There is a github repository called rmarkdown-rmeetup-2012that contains:
- the slides and source code for the slides (I used a combination of beamer, markdown, and pandoc)
- the source code for the R Markdown examples presented in the talk
- and assorted brainstorming that recorded some of my thinking as I developed the slides (see the issue tracker)
Follow this link to download the slides directly.
Video of Talk
The talk is split over two parts.
More Videos from Melbourne R Users
We are gradually building up a fairly large back catalogue of videos about R all presented at Melbourne R Users.
The following links were either presented in the talk or are otherwise relevant to reproducible analysis.
- My post on getting started with R Markdown
- My thoughts on definitions of reproducible data analysis
- My thoughts on degrees of reproducible data analysis
- Reproducible Research Task View on CRAN
- Software used in talk: R, R Studio, pandocTeX distributions,
- Overview of markdown
- Getting started with writing LaTeX equations
- Slide show on benefits of knitr and Rstudio by Yihui Xie and JJ Allaire
- knitr options home page and knitr home page
- Documentation on using R Markdown with R Studio
- My existing posts on reproducible analysis
- Places to ask questions: R on StackOverflow, LaTeX on TeX.SE, and knitr on github.
- Extensive set of YouTube videos on reproducible analysis largely drawn from a workshop on "Reproducible Research: Tools and Strategies for Scientific Computing".
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