Video: knitr, R Markdown, and R Studio: Introduction to Reproducible Analysis

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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.

Talk Overview

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:

  1. the slides and source code for the slides (I used a combination of beamer, markdown, and pandoc)
  2. the source code for the R Markdown examples presented in the talk
  3. 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 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.

If viewing through syndication, feel free to subscribe to my blog on psychology and statistics here.

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