This post presents the video of a talk that I presented in July 2012 atMelbourne R Users on using knitr, R Markdown, and R Studio to performreproducible analysis. I also provide links to a github repository where theR markdown examples can be examined and the slides can be downloaded.
Talk Overview
Reproducible analysis represents a process for transforming text, code, and datato produce reproducible artefacts including reports, journal articles,slideshows, theses, and books. Reproducible analysis is important in bothindustry and academic settings for ensuring a high quality product. R hasalways provided a powerful platform for reproducible analysis. However, in thefirst half of 2012, several new tools have emerged that have substantiallyincreased the ease with which reproducible analysis can be performed. Inparticular, knitr, R Markdown, and RStudio combine to create a user-friendly andpowerful set of open source tools for reproducible analysis.
Specifically, in the talk I discuss caching slow analyses, producing attractive plots andtables, and using RStudio as an IDE. I present three live examples of usingR Markdown. I also show how the markdown package on CRAN can beused to work with other R development environments and workflows for reportproduction.
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 allpresented at Melbourne R Users.
The playlist of Melbourne R Users Videos can be viewed here.
Relevant links:
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 largelydrawn from a workshop on "Reproducible Research: Tools and Strategies for Scientific Computing".
If viewing through syndication, feel free to subscribe to my blog on psychology and statistics here.
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