st: RE: AW: AW: Categorize variable

["Nick Cox" <n.j.cox@durham.ac.uk>]

gen mygroups = 10 * ceil(weight/10) 

would get you most of the way. This has two small but notable
advantages: 

1. The values are transparent as bin limits, 170 meaning (160, 170] etc.


2. Very simple code, including no need for value labels. 

I realise that this will produce more classes than intended if there are
values below 140 or above 190, but there are solutions for that if it's
a real nuisance (e.g. -recode-). 

The larger question is why you want to do this, as it is throwing away
detail that might be useful. 

-irecode()- is another way to do it. 

Nick 
n.j.cox@durham.ac.uk 

Martin Weiss

BTW, my solution is a bit more complicated than initially imagined
(http://www.stata.com/statalist/archive/2002-08/msg00178.html):

*************
clear*
set seed 123456
set obs 1000
gen weight=rnormal(165,6)
su weight, mean
egen float mygroups = cut(weight), at(`=r(min)-0.01' 150(10)180
`r(max)')
icodes
la def mylab 0 "<150" 1 "150-160" 2 "160-170" 3 "170-180" 4 ">180"
la val mygroups mylab
table mygroups, contents(freq min weight max weight) mis
*************

Martin Weiss

Look at -help egen, cut()-

Saretta

I have a continuous variabile called weight. How can I categorize this 
variable into groups: <=150lb (150-160], (150-160], (170-180] and >180?

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