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assigning and saving datasets in a loop, with names changing with "i"

6 messages · Marie Pierre Sylvestre, Benilton Carvalho, Moshe Olshansky +3 more

Marie Pierre Sylvestre
# 
Dear R users,

I am analysing a very large data set and I need to perform several data
manipulations. The dataset is so big that the only way I can play with it
without having memory problems (E.g. "cannot allocate vectors of size...")
is to write a batch script to:

1. cut the data into pieces 
2. save the pieces in seperate .RData files
3. Remove everything from the environment
4. load one of the piece
5. perform the manipulations on it
6. save it and remove it from the environment
7. Redo 4-6 for every piece
8. Merge everything together at the end

It works if coded line by line but since I'll have to perform these tasks
on other data sets, I am trying to automate this as much as I can. 

I am using a loop in which I used 'assign' and 'get' (pseudo code below).
My problem is when I use 'get', it prints the whole object on the screen.
I am wondering whether there is a more efficient way to do what I need to
do. Any help would be appreciated. Please keep in mind that the whole
process is quite computer-intensive, so I can't keep everything in the
environment while R performs calculations.

Say I have 1 big dataframe called data. I use 'split' to divide it into a
list of 12 dataframes (call this list my.list)

my.fun is a function that takes a dataframe, performs several
manipulations on it and returns a dataframe.


for (i in 1:12){
  assign( paste( "data", i, sep=""),  my.fun(my.list[i]))   # this works
  # now I need to save this new object as a RData. 

  # The following line does not work
  save(paste("data", i, sep = ""),  file = paste(  paste("data", i, sep =
""), "RData", sep="."))
}

  # This works but it is a bit convoluted!!!
  temp <- get(paste("data", i, sep = ""))
  save(temp,  file = "lala.RData")
}


I am *sure* there is something more clever to do but I can't find it. Any
help would be appreciated.

best regards,

MP
Benilton Carvalho
# 
you want to use:

save(list=paste("data", i, sep=""), file=paste("data", i, ".Rdata",  
sep=""))

b
On Dec 18, 2007, at 9:24 PM, Marie Pierre Sylvestre wrote:

            

      
      
      
    

        

      
      
    

  
  


  


      

    

    
1 day later

    

      
      

        


  

        

      Moshe Olshansky
    

    

      
      #
    

  


  

      
Won't it be simpler to do:

for (i in 1:12){
data <- my.fun(my.list[i]))   
save(data,file = paste("data",i,".RData", sep="")) }


--- Marie Pierre Sylvestre

        
<MP.Sylvestre at epimgh.mcgill.ca> wrote:

        

            

      
      
      
    

        

      
      
    

  
  


  


      

    

    

      
      

        


  

        

      Henrik Bengtsson
    

    

      
      #
    

  


  

      
library(R.utils);

for (ii in 1:12) {
  value <- my.fun(my.list[ii]);
  saveObject(value, file=sprintf("data%02d.RData", ii));
  rm(value); gc();
}

for (ii in 1:12) {
  value <- loadObject(sprintf("data%02d.RData", ii));
}

        
On 18/12/2007, Marie Pierre Sylvestre <MP.Sylvestre at epimgh.mcgill.ca> wrote:

            

      
      
      
    

        

      
      
    

  
  


  


      

    

    

      
      

        


  

        

      Greg Snow
    

    

      
      #
    

  


  

      
Have you looked at the SQLiteDF package?  It seems like it would do what
you want in a better way and much simpler.  Even if that does not work
then a database approach (look at the other db packages, probably RODBC
first) could be simpler, faster, and easier.  You may also want to look
at the g.data package for another approach.

Depending on what you are doing you may also want to look at the biglm
package (some similar functionality is in SQLiteDF).

Hope this helps,

  
    

      
      
    

  


  


      

    

    
1 day later

    

      
      

        


  

        

      Tony Plate
    

    

      
      #
    

  


  

      
Marie Pierre Sylvestre wrote:

            

      
      
      
    

        
The trackObjs package is designed to make it easy to work in approximately 
this manner -- it saves objects automatically to disk but they are still 
accessible as normal.

Here's how you could do the above - this example works with 10 8Mb objects 
in a R session with a limit of 40Mb.

# allow R only 40Mb of vector memory
mem.limits(vsize=40e6)
mem.limits()/1e6
library(trackObjs)
# start tracking to store data objects in the directory 'data'
# each object is 8Mb, and we store 10 of them
track.start("data")
n <- 10
m <- 1e6
constructObject <- function(i) i+rnorm(m)
# steps 1, 2 & 3:
for (i in 1:n) {
    xname <- paste("x", i, sep="")
    cat("", xname)
    assign(xname, constructObject(i))
    # store in a file, accessible by name:
    track(list=xname)
}
cat("\n")
gc(TRUE)
# accessing object by name
object.size(x1)/2^20 # In Mb
mean(x1)
mean(x2)
gc(TRUE)
# steps 4:6
# accessing object through a constructed name
result <- sapply(1:n, function(i) mean(get(paste("x", i, sep=""))))
result
# remove the data objects
track.remove(list=paste("x", 1:n, sep=""))
track.stop()

Here's the a full transcript of the above - note how whenever gc() is 
called there is hardly any vector memory in use.

 > # allow R only 40Mb of vector memory
 > mem.limits(vsize=40e6)
    nsize    vsize
       NA 40000000
 > mem.limits()/1e6
nsize vsize
    NA    40
 > library(trackObjs)
 > # start tracking to store data objects in the directory 'data'
 > # each object is 8Mb, and we store 10 of them
 > track.start("data")
 > n <- 10
 > m <- 1e6
 > constructObject <- function(i) i+rnorm(m)
 > # steps 1, 2 & 3:
 > for (i in 1:n) {
+    xname <- paste("x", i, sep="")
+    cat("", xname)
+    assign(xname, constructObject(i))
+    # store in a file, accessible by name:
+    track(list=xname)
+ }
  x1 x2 x3 x4 x5 x6 x7 x8 x9 x10> cat("\n")

 > gc(TRUE)
Garbage collection 19 = 6+0+13 (level 2) ...
4.0 Mbytes of cons cells used (42%)
0.7 Mbytes of vectors used (5%)
          used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 148362  4.0     350000  9.4         NA   350000  9.4
Vcells  89973  0.7    1950935 14.9       38.2  2112735 16.2
 > # accessing object by name
 > object.size(x1)/2^20 # In Mb
[1] 7.629417
 > mean(x1)
[1] 0.998635
 > mean(x2)
[1] 1.999656
 > gc(TRUE)
Garbage collection 22 = 7+1+14 (level 2) ...
4.0 Mbytes of cons cells used (43%)
0.7 Mbytes of vectors used (6%)
          used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 149264  4.0     350000  9.4         NA   350000  9.4
Vcells  90160  0.7    1560747 12.0       38.2  2112735 16.2
 > # steps 4:6
 > result <- sapply(1:n, function(i) mean(get(paste("x", i, sep=""))))
 > result
  [1]  0.998635  1.999656  2.997368  4.000197  5.000159  6.001216  6.999552
  [8]  7.999743  8.999982 10.001355
 > # remove the data objects
 > track.remove(list=paste("x", 1:n, sep=""))
  [1] "x1"  "x2"  "x3"  "x4"  "x5"  "x6"  "x7"  "x8"  "x9"  "x10"
 > track.stop()
 >