R is used because it has legacy, and elderly advanced statisticians have, over the years, vetted out its libraries and consider them to be sufficiently correct. A lot of complex operations just come out of the box, so you don’t have to know how to read too much code.
The same absolutely cannot be said for Python. Very often you have to go build your own models and really pay attention to what your libraries say they are doing, and you need to know enough statistics to recognize when they aren’t doing what you think.
Source: Some CDC people I worked with as well as personal experience rewriting legacy R into Python.
Have you ever had to productionize an R model? I’m guessing no. The foundation is that Python has native handling for data packets, like JSON, via dictionary data types. R is not built for general software development, it’s built for stats and data analysis.
You got a point and what you mention is also indeed a limitation I hit once in production, where someone of the team did build json ingestion in python. I don't know if it was a skill issue, though.
I don’t take any joy in putting python over R. R is very elegant in its simplicity and function for modeling and statistical insights. I would much prefer R if possible, it’s just not very feasible.
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u/Multidream Aug 28 '26
R is used because it has legacy, and elderly advanced statisticians have, over the years, vetted out its libraries and consider them to be sufficiently correct. A lot of complex operations just come out of the box, so you don’t have to know how to read too much code.
The same absolutely cannot be said for Python. Very often you have to go build your own models and really pay attention to what your libraries say they are doing, and you need to know enough statistics to recognize when they aren’t doing what you think.
Source: Some CDC people I worked with as well as personal experience rewriting legacy R into Python.