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1-based for statistics is a STRONG reason to use it. 0 based is ridiculous for a statistics program and the reason why I left Python. I understand for looping a

by baldfat 10y ago

1-based for statistics is a STRONG reason to use it. 0 based is ridiculous for a statistics program and the reason why I left Python. I understand for looping and the arras to be internally 0 but when doing statistics I should have to use two different index.

n00b101 10y ago
1-based for statistics is a STRONG reason to use it. 0 based is ridiculous for a statistics program Could you elaborate on that? What about statistics do you feel makes it necessary to have 1-based indexing?
- cschmidt 10y ago
  Mathematical notation is almost always 1 based. Let $v = 1 \dots n$, that sort of thing. So I find translating mathematics into code (which I seem to do quite frequently) much easier with 1 based indexing. (One exception being if you have a lot of modulo arithmetic.) I've used both 0 based (python, C++), and 1 based (R, matlab, mathematica), but I really belive 1 based is the right choice for Julia. It isn't necessary, but it feels better.
- Fomite 10y ago
  My flippant answer: "No one is ever assigned Patient ID 0" It's mostly about expectations. Users of statistical software, who may or may not be programmers by inclination, use 1-based indexing. The program they came from uses 1-based indexing (R, SAS, Stata, etc.). When you talk about data, you rarely talk about the 0th observation. It's just a recipe for errors to have to code switch between 0 and 1-based indexing.
hasch 10y ago
can you give me an example? I did all sorts of numerical indexing programming, from fortran, matlab, python/numpy and R, and I am definitely more comfortable with 0 based indexing.
- baldfat 10y ago
  My quick philosophy/logic background can you make a strong arguement for why Julia/R (statistical programming) should use 0 based? I am totally fine with zero base in Python and Lisp and such. BUT when I am doing statistics and the math is 1 based I think the ability to make a mistake is to big. Also for subset in R df[1, 1] would be the 2nd row and 2nd column would just throw most users of R.

by baldfat 10y ago

n00b101 10y ago

1-based for statistics is a STRONG reason to use it. 0 based is ridiculous for a statistics program Could you elaborate on that? What about statistics do you feel makes it necessary to have 1-based indexing?

cschmidt 10y ago
Mathematical notation is almost always 1 based. Let $v = 1 \dots n$, that sort of thing. So I find translating mathematics into code (which I seem to do quite frequently) much easier with 1 based indexing. (One exception being if you have a lot of modulo arithmetic.) I've used both 0 based (python, C++), and 1 based (R, matlab, mathematica), but I really belive 1 based is the right choice for Julia. It isn't necessary, but it feels better.
Fomite 10y ago
My flippant answer: "No one is ever assigned Patient ID 0" It's mostly about expectations. Users of statistical software, who may or may not be programmers by inclination, use 1-based indexing. The program they came from uses 1-based indexing (R, SAS, Stata, etc.). When you talk about data, you rarely talk about the 0th observation. It's just a recipe for errors to have to code switch between 0 and 1-based indexing.

hasch 10y ago

can you give me an example? I did all sorts of numerical indexing programming, from fortran, matlab, python/numpy and R, and I am definitely more comfortable with 0 based indexing.

baldfat 10y ago
My quick philosophy/logic background can you make a strong arguement for why Julia/R (statistical programming) should use 0 based? I am totally fine with zero base in Python and Lisp and such. BUT when I am doing statistics and the math is 1 based I think the ability to make a mistake is to big. Also for subset in R df[1, 1] would be the 2nd row and 2nd column would just throw most users of R.