Le contenu du cours est disponible sur le site web https://gitlab.com/linogaliana/collaboratif. Le code source est disponible sur
Cours pour découvrir la manière d’utiliser R dans un projet collaboratif. Pour cela, nous faisons d’abord découvrir Git et sa pratique avec RStudio avant de se focaliser sur le développement de packages
I recently gave my opinion concerning the never-ending debate between {dplyr} and {data.table} fans (here). I listed three arguments in favor of {data.table} approach :
{data.table} is very stable while {dplyr} changes a lot. This makes processes depending on {dplyr} more likely to break. {data.table} is really fast and is not very demanding in terms of RAM. This is, of course, the main arguments in favor of {data.table}. {data.table} grammar is often considered harder to learn than {dplyr} equivalent verbs.
Genesis I started to use continuous integration with gitlab a few weeks ago and up to a few days was really happy with rocker image (basically docker + R).
I became ambitious and started to write a markdown that was comparing R and Python speed on simple operations. It was working fine on my laptop (anaconda is installed). However, because anaconda is not available in rocker image, markdown compilation naturally failed.