> R is also thriving in bioinformatics with Python trailing behind as an afterthought.
Maybe this is sub-field dependent, I'm a bioinformatician who hasn't touched R in about 5 years, and everything is now in python.
Maybe this is sub-field dependent, I'm a bioinformatician who hasn't touched R in about 5 years, and everything is now in python.
It took a long time for Perl to go away from daily use in bioinformatics, solely dependent upon how common it was twenty years ago.
In our lab, there is me, a (mid career) polyglot who mixes Python, Go, Java, and R daily. I use tab delimited text files to transfer data. I also grew up coding in C++ and like learning new languages.
We also have a (mid career) staff scientist who grew up in Perl, but switched 100% to R and an (early career) postdoc who has always used 100% R. For both of these people, if work can be done in R, it is. If it can’t be done in R, they figure out how to do it in R anyway (even if that is shelling out to another program).
We also have a (young) grad student that is 95% Python. They try to keep to the Python tooling, even though they are quite aware of the R ecosystem.
There is a generational shift in the field, and it is more apparent each year. I find it interesting that Python took over from Perl first and now it’s trying to take over from R.
Both are under active development and are used in several transcriptomics atlas projects, as far as I can tell.
Look at the packages now for integration, pseudo time, pseudobulk - R (and therefore Seurat) dominates heavily