fast or space-efficient lookup?

hi patrick. ?thanks. ?I think you are right..

combined <- merge( main, aggregate.data, by="day", all.x=TRUE, all.y=FALSE )
lm( stockreturn ~ marketreturn, data=combined )

becomes something like

main <- as.data.table(main)
setkey(main, "yyyymmdd")
aggregate.data <- as.data.table(aggregate.data)
setkey(aggregate.data, "yyyymmdd")
main <- main[ aggregate.data ]

this is fast and memory efficient.

for me, on the plus side, data.table is a data.frame, so it can be
used easily elsewhere. ?on the plus side, data.table is
super-efficient. ?on the minus side, data.table often has very strange
syntax. ?for example, main[aggregate.data] is counterintuitive.
passing of functions in a slot that should be a tensor index is also
strange. ?I would much prefer it if all non-tensor functionality was
in functions, and not in arguments following [ ].

I have written this before: ?Given that applied end users of
statistics typically use data.frame as their main container for data
sets, data.frame should be as efficient and tuned as possible. ?cell
assignments should be fast. ?indexing and copying should be fast. ?it
would give R a whole lot more appeal. ?the functionality in data.table
should be core functionality, not requiring an add-on with strange
syntax. ?just my 5 cents...of course, the R developers are saints,
putting in a lot of effort with no compense, so complaining is unfair.
?and thanks to Matthew Doyle for writing data.table, without which I
couldn't do this AT ALL.

regards,

/iaw

----
Ivo Welch (ivo.welch at gmail.com)





On Sun, Oct 9, 2011 at 10:42 AM, Patrick Burns <pburns at pburns.seanet.com> wrote:

I think you are looking for the 'data.table'
package.

On 09/10/2011 17:31, ivo welch wrote:

Dear R experts---I am struggling with memory and speed issues. ?Advice
would be appreciated.

I have a long data set (of financial stock returns, with stock name
and trading day). ?All three variables, stock return, id and day, are
irregular. ?About 1.3GB in object.size (200MB on disk). ?now, I need
to merge the main data set with some aggregate data (e.g., the S&P500
market rate of return, with a day index) from the same day. ?this
"market data set" is not a big data set (object.size=300K, 5 columns,
12000 rows).

let's say my (dumb statistical) plan is to run one grand regression,
where the individual rate of return is y and the market rate of return
is x. ?the following should work without a problem:

combined<- merge( main, aggregate.data, by="day", all.x=TRUE, all.y=FALSE
)
lm( stockreturn ~ marketreturn, data=combined )

alas, the merge is neither space-efficient nor fast. ?in fact, I run
out of memory on my 16GB linux machine. ?my guess is that by whittling
it down, I could work it (perhaps doing it in chunks, and then
rbinding it), but this is painful.

in perl, I would define a hash with the day as key and the market
return as value, and then loop over the main data set to supplement
it.

is there a recommended way of doing such tasks in R, either super-fast
(so that I merge many many times) or space efficient (so that I merge
once and store the results)?

sincerely,

/iaw

----
Ivo Welch (ivo.welch at gmail.com)

--
Patrick Burns
pburns at pburns.seanet.com
twitter: @portfolioprobe
http://www.portfolioprobe.com/blog
http://www.burns-stat.com
(home of 'Some hints for the R beginner'
and 'The R Inferno')