[clusterhacks]

I am a CS person who works with bioinformaticians every day as part of my job.

I really like that Seq seems to have built-in some parallelization ability. I spend no small amount of time in my day job doing that manually in R with RcppParallel for loops that are totally independent across each iteration.

Bioinformaticians are often educated to use a specific programming language and environment. They aren't usually looking to try other languages. For example, I support our bioinformatics group and they are basically 100% R and RStudio users. We have a single user of Python and that user is doing "typical" tensorflow stuff with images.

I've noticed this same bias towards a single language for some other academic niches. Like SAS or Stata camps in public health or psychology - I think of these languages as basically the same, but for non-CS folks the perception seems to be more like English vs Russian.

Even more complicated, researchers may be extremely committed to a specific library in a language and suspicious of languages that don't have their favorite library available.

Any shift to new tooling for these highly-committed users will almost certainly require large and obvious benefits to gain traction.

[psychometry]

Scientists like using R instead of because the language lets them get set up and coding quickly with RStudio. More importantly, the language, tooling, and ecosystem is very forgiving when it comes to code quality and style. There is good R code out there, but the R community generally lacks the wide acceptance of good coding practices you see with Python users: unit tests, sane dependency management, type hints, documentation, safe namespacing, etc.

It's really saying something when scientists think writing Python code is a pain, because Python's a pretty forgiving language, too.

[travisgriggs]

So basically, the same thing that kept(keeps?) Visual Basic in use for so long.

My son works in polysci analytics and I see the same thing you describe. A group will pick a tool and flog all problems with it. Change rarely occurs. He was in the Stata camp at one university, the TidyVerse at MIT.

It’s very weird for me, I develop and maintain a piece of software that that has 3 OSes, and 5 languages to wrestle with as well as multiple “tool” technologies like Ansible/MQTT, etc. so I’m very much in a polyglot-best-tool-for-the-job environment. Observationally from a casual POV, I see pros/cons both ways.

[ativzzz]

I assume you are a software engineer? If so, part of our job is to use a variety of software tools, since that's our specialty. The researchers are not software developers. They learn how to use one particular tool to do their jobs, but they are not software specialists, nor do they desire to be.

[dr_kiszonka]

Very interesting! I noticed a similar phenomenon in the GIS space. All of my colleagues with formal training in GIS use ArcGIS and its Python API, but those without such background gravitate towards FLOSS solutions.

I am aware of only one case where a community migrated to other software. Many economists I know switched from Stata to R. Some of them later moved on to Python.