Hello,
I do not understand the correct way to approach the following problem
in R.
I have observations of pairs of variables, v1, o1, v2, o2, etc,
observed every 30 seconds. What I would like to do is compute the
correlation matrix, but not for all my data, just for, say 5 minutes
or 1 hour chunks.
In sql, what I would say is
select id, date_trunc('hour'::text, ts) as tshour, corr(n1,o1) as corr1
from raw30s
where id = 1201087 and
(ts between 'Mar 1, 2007' and 'Apr 1, 2007')
group by id,tshour order by id,tshour;
I've pulled data from PostgreSQL into R, and have a dataframe
containing a timestamp column, v, and o (both numeric).
I created an grouping index for every 5 minutes along these lines:
obsfivemin <- trunc(obsts,units="hours")
+( floor( (obsts$min / 5 ) ) * 5 * 60 )
(where obsts is the sql timestamp converted into a DateTime object)
Then I tried aggregate(df,by=obsfivemin,cor), but that seemed to pass
just a single column at a time to cor, not the entire data frame. It
worked for mean and sum, but not cor.
In desperation, I tried looping over the different 5 minute levels and
computing cor, but I'm so R-clueless I couldn't even figure out how to
assign to a variable inside of that loop!
code such as
for (f in fivemin){
output[f] <- cor(df[grouper==f,]); }
failed, as I couldn't figure out how to initialize output so that
output[f] would accept the output of cor.
Any help or steering towards the proper R-way would be appreciated.
Regards,
James Marca
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