Reading large files in R

van.call <- call('sqlQuery',con,query='select * from vandrivers;')

van.call <- sqlQuery(con,'select * from vandrivers;')

You can also use the RODBC package to hold the data in a database, say 
MySQL
and only import it when you do the modelling, e.g.

library(RODBC)
library(sspir)
con <- odbcConnect("MySQL Test")
data(vandrivers)
sqlSave(con,dat=vandrivers,append=FALSE)
rm(vandrivers)
gc()
van.call <- sqlQuery(con,'select * from vandrivers;')
vd <- ssm( y ~ tvar(1) + seatbelt + sumseason(time,12),
          time=time, family=poisson(link="log"),
          data=eval(van.call))
vd$ss$phi["(Intercept)"] <- exp(- 2*3.703307 )
vd$ss$C0 <- diag(13)*1000
vd.res <- kfs(vd)
gc()

In this case I have first saved the vandriver data in 'MySQL Test', but 
one
can obviously write the data directly to the database. Since the data is 
not
held in memory I find that I can do much larger computations than is
otherwise possible. The downside is of course that computations take a bit
longer.
Best wishes,

Andreas

=====================
Andreas D Hary
Email:    u08adh at hotmail.com
Mobile:   07906860987
Phone:   02076554940




----- Original Message ----- 
From: "Berton Gunter" <gunter.berton at gene.com>
To: <ramasamy at cancer.org.uk>; "'Jean-Pierre Gattuso'" 
<gattuso at obs-vlfr.fr>
Cc: <r-help at stat.math.ethz.ch>
Sent: Monday, August 08, 2005 8:35 PM
Subject: Re: [R] Reading large files in R

... and it is likely that even if you did have enough memory (several
times
the size of the data are generally needed) it would take a very long 
time.

If you do have enough memory and the data are all of one type -- numeric
here -- you're better off treating it as a matrix rather than converting
it
to a data frame.

-- Bert Gunter
Genentech Non-Clinical Statistics
South San Francisco, CA

"The business of the statistician is to catalyze the scientific learning
process."  - George E. P. Box

-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of
Adaikalavan Ramasamy
Sent: Monday, August 08, 2005 12:02 PM
To: Jean-Pierre Gattuso
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] Reading large files in R

From Note section of help("read.delim") :

     'read.table' is not the right tool for reading large matrices,
     especially those with many columns: it is designed to read _data
     frames_ which may have columns of very different classes. Use
     'scan' instead.

So I am not sure why you used 'scan', then converted it to a
data frame.

1) Can provide an sample of the data that you are trying to read in.
2) How much memory does your machine has ?
3) Try reading in the first few lines using the nmax argument in scan.

Regards, Adai



On Mon, 2005-08-08 at 12:50 -0600, Jean-Pierre Gattuso wrote:

Dear R-listers:

I am trying to work with a big (262 Mb) file but apparently

reach a

memory limit using R on a MacOSX as well as on a unix machine.

This is the script:

 > type=list(a=0,b=0,c=0)
 > tmp <- scan(file="coastal_gebco_sandS_blend.txt", what=type,

sep="\t", quote="\"", dec=".", skip=1, na.strings="-99",

nmax=13669628)

Read 13669627 records

 > gebco <- data.frame(tmp)

Error: cannot allocate vector of size 106793 Kb


Even tmp does not seem right:

 > summary(tmp)

Error: recursive default argument reference


Do you have any suggestion?

Thanks,
Jean-Pierre Gattuso