@datamaps @smach @MichaelIngrisch @rstats
I am afraid the problem is deeper: why would you load any package when a one-liner equivalent is available in Rbase? The list of dependencies some people use is frightening. I am not sure to understand how this bad programming culture appeared in the R community, but this has to be fixed. R could be a solid base for reproducible science only if users stop to load dozens of packages for trivial tasks.
@datamaps @smach @MichaelIngrisch @rstats
Things could start to change if journals had strict policies about reproducible science for scripts associated with scientific results. CRAN is also responsible, by allowing monster dependency chains in their repositories.
@arnaudlerouzic @datamaps @smach @MichaelIngrisch @rstats
I think we're conflating two problems here.
Using base R does not ensure reproducibility, just like using tidyverse won't make a script less reproducible. For reproducibility, use renv, or similar solutions.
Loading tidyverse is a convenient way to have access to lots of functions without having to remember which package they come from. I like to be lazy like that of I'm just testing something... but will probably include single packages for a more important script (eg for a publication or a package)
Absolutely, what @Patrick Anker says.
Also, I think most problems of reproducibility (at least, those I encountered) are not dependent on package versions. The script might be incomplete, or the data to be fed to the script needs to be processed somehow and there are no clear instructions of how to do it etc.
These are much more common issues, at least in my field.