Packages are installed into your system. May bio packages (bioconductor, etc) are difficult to run in any other way outside of a system install. So outside of running everything in a container, it is difficult to maintain a project level dependency versions.
No regard is given (by default) to install specific versions of a package. They just install the latest. So your build from 2 years ago will almost certainly be different, sometimes in important ways, from your build today, even if you used the same packages.
This says nothing as to R's suitability to help maintain bio-scientist's accuracy through the process. Often times they will just dump data to R data files, which are opaque to version control and difficult to read outside of the R environment, because the data files often contain references to types defined in packages, thus to decode the data you have to have the correct R packages installed. This makes reading it in an external environment infeasible.
R has many useful packages that just exist and work. But the verification and versioning, and reproducible system, is to me, makes it something to acutely avoid.
No regard is given (by default) to install specific versions of a package. They just install the latest. So your build from 2 years ago will almost certainly be different, sometimes in important ways, from your build today, even if you used the same packages.
This says nothing as to R's suitability to help maintain bio-scientist's accuracy through the process. Often times they will just dump data to R data files, which are opaque to version control and difficult to read outside of the R environment, because the data files often contain references to types defined in packages, thus to decode the data you have to have the correct R packages installed. This makes reading it in an external environment infeasible.
R has many useful packages that just exist and work. But the verification and versioning, and reproducible system, is to me, makes it something to acutely avoid.