The paste-y argument is my usual trick in these situations. I forget
that tapply can take multiple ordering arguments :)
Abhijit
On 08/24/2010 02:17 PM, David Winsemius wrote:
On Aug 24, 2010, at 1:59 PM, Abhijit Dasgupta, PhD wrote:
The only problem with this is that Chris's unique individuals are a
combination of Type and ID, as I understand it. So Type=A, ID=1 is a
different individual from Type=B,ID=1. So we need to create a unique
identifier per person, simplistically by uniqueID=paste(Type, ID,
sep=''). Then, using this new identifier, everything follows.
I see your point. I agree that a tapply method should present both
factors in the indices argument.
> new.df <- txt.df[ -which( txt.df$nn <=1), ]
> new.df <- new.df[ with(new.df, order(Type, ID) ), ] # and possibly
needs to be ordered?
> new.df$diffdays <- unlist( tapply(new.df$dt2, list(new.df$ID,
new.df$Type), function(x) x[1] -x) )
> new.df
Type ID Date Value dt2 nn diffdays
1 A 1 16/09/2020 8 2020-09-16 3 0
2 A 1 23/09/2010 9 2010-09-23 3 3646
4 B 1 13/5/2010 6 2010-05-13 3 0
But do not agree that you need, in this case at least, to create a
paste()-y index. Agreed, however, such a construction can be useful in
other situations.
--
Abhijit Dasgupta, PhD
Director and Principal Statistician
ARAASTAT
Ph: 301.385.3067
E: adasgu...@araastat.com
W: http://www.araastat.com
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