R: Introduction to Data Science

Re:R: Introduction to Data Science by AndyMcConachie on Saturday March 02, 2024 @10:40AM (#39573249)

I dipped my feet into R a few years back, but eventually stopped it because of the way it handles integers. At the time it treated all integers internally as signed 32-bit and if the number is too large for that it converted it to a float.

I don't know what R does now, but this was a deal breaker for me at the time because I was dealing with really large integers that regularly broke this limit.

[ Parent | on HN ]

Re:R: Introduction to Data Science by armchairhacker on Saturday March 02, 2024 @02:26PM (#39575091)

Integers are still only 32 bits. There’s a class which effectively represents 64-bit integers (https://www.rdocumentation.org/packages/csvread/versions/1.2...) as well as arbitrary-sized (https://cran.r-project.org/web/packages/gmp/index.html, https://www.rdocumentation.org/packages/gmp/versions/0.7-4/t...). I will say there are a few pitfalls where the integer bits are unexpectedly converted to something else, but it’s workable.

[ Parent | on HN ]

Re:R: Introduction to Data Science by nutshell42 on Saturday March 02, 2024 @04:56PM (#39576120)

Finally a real reason.

A lot of the stuff above was complaining about issues where Python is a lot worse than R, about non-issues or with a fundamental misunderstanding of the language. I'd given up hope of seeing a real weakness named as such :)

There is bit64 and doubles being used as 53bit pseudo-integers - but if I needed 64bit integers, R wouldn't be my first choice, definitely.

[ Parent | on HN ]