Travail collaboratif avec R

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

Variable name in functions, it's easy with datatable

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.

pocker: A docker container to integrate R and Python in CI/CD frameworks

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.