Oddly in biology I’m seeing an uptick in R use as it’s easier to install and run tools in R for non programmers. The python 2/3 confusion (why doesn’t this google result work?), and various install packages (conda/pip...) seem to have made R
more popular. This is despite biopython which is quite good. The newest single sell rna seq analysis tools are in R.
R is a strang thing to me, though it has its moments (graphing). I still think pandas which gives python R like data frames is one of the great tools out there.
pandas gives base R a run for its money, but I find the multi-indexing really confusing, and R tidyverse is just way way nicer than pandas.
The sentiment that R is easier than python to install/manage is common among some R users but I disagree. With R I'm constantly facing dependency hell problems -- one package wants an old version of R, while another needs the newest. Conda/venv solves this problem very nicely in python.
Recently lI've been using Julia more and really like it. One nonstandard case where I've found it really shines is parsing large bioinformatics data, such as pileup files. Python is just so slowww here, but neither do I want to write a C program to do the text parsing. Julia is perfect in this case.
R is a strang thing to me, though it has its moments (graphing). I still think pandas which gives python R like data frames is one of the great tools out there.