Best Practices for Reproducible Research part 2

The aim of this tutorial is to sensibilize the audience to theexperiment and analysis reproducibility issue in particular incomputer science. I will present tools that help answering theanalysis problem and may also reveal useful for managing theexperimental process through notebooks.

More precisely, I will introduce the audience to the following tools:

• R and ggplot2 that provide a standard, efficient and flexibledata management and graph generation mechanism. Although R isquite cumbersome at first for computer scientists, it quicklyreveals an incredible asset compared to spreadsheets, gnuplot orgraphical libraries like matplotlib or tikz.
• knitR is a tool that enables to integrate R commands within aLaTeX or a Markdown document. It allows to fully automatize datapost-processing/analysis and figure generation down to theirintegration to a report. Beyond the gain in term of ease ofgeneration, page layout, uniformity insurance, such integrationallows anyone to easily check what has been done during theanalysis and possibly to improve graphs or analysis.
• I will explain how to use these tools with Rstudio, which is amulti-platform and easy-to-use IDE for R. For example, usingR+Markdown (Rmd files) in Rstudio, it is extremely easy toexport the output result to Rpubs and hence make the result ofyour research available to others in no more than two clicks.
• I will also mention other alternatives such as org-mode andbabel or the ipython notebook that allow a day-to-day practiceof reproducible research in a somehow more fluent way thanknitR but is mainly a matter of taste.

Depending on the question of the audience, I can also help theattendees analyzing some of their data and introduce them to thebasics of data analysis.