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Re:R: Introduction to Data Science by nomilk on Saturday March 02, 2024 @07:52AM (#39572210)
About half our team can wrangle and plot as fast as we can think of ideas. It creates an incredibly tight cycle time between us having ideas and getting answers; sometimes many (e.g. 10-20+) of those cycles in a single meeting. Before we used R, it would require someone jotting down things to investigate and reporting back in the next meeting. But we can do ~80% of whatever people can think of on the spot (more involved research questions can take more time).

The unique qualities of R that allow this are that it's so easy to use, extremely reliable for package installation (problems occur approximately never), and the tidyverse makes it incredible easy to translate ideas into code, not only in its broad, easy to understand and powerful vocabulary, but in there being little 'nesting' required; instead working left to right and top to bottom (via the magrittr pipe) - i.e. your code, for the most part, is like reading a page in a book.

Re:R: Introduction to Data Science by j7ake on Saturday March 02, 2024 @07:47PM (#39577299)
For exploring data, plotting data, and fitting complex models like GLMs and GAMs, using R is essentially as fast as thought at this point.

So if you have tabular data, it’s a no brainer to use R.

Getting the data into table form is often better suited for python. Fitting models that leverage autograd are also better with python.

Re:R: Introduction to Data Science by jgalt212 on Saturday March 02, 2024 @10:39PM (#39578225)
> Getting the data into table form is often better suited for python. Fitting models that leverage autograd are also better with python.

Totally agree. If it's in a dataframe, I prefer R. If I get involved pre-dataframe, I prefer Python.