Question

The objective is to create indicators for a factor/string variable in a data frame. That dataframe has > 2mm rows, and running R on windows, I don’t have the option of using plyr with .parallel=T. So I’m taking the “divide and conquer” route with plyr and reshape2.

Running melt and cast runs out of memory, and using

ddply( idata.frame(items) , c("ID") , function(x){
       (    colSums( model.matrix( ~ x$element - 1) ) > 0   )
} , .progress="text" )    

or

ddply( idata.frame(items) , c("ID") , function(x){
           (    elements %in% x$element   )
    } , .progress="text" )  

does take a while. The fastest approach is the call to tapply below. Do you see a way to speed this up? The %in% statement runs faster than the model.matrix call. Thanks.

set.seed(123)

dd <- data.frame(
  id  = sample( 1:5, size=10 , replace=T ) ,
  prd = letters[sample( 1:5, size=10 , replace=T )]
  )

prds <- unique(dd$prd)

tapply( dd$prd , dd$id , function(x) prds %in% x )

Answer 1

For this problem, the packages bigmemory and bigtabulate might be your friends. Here is a slightly more ambitious example:

library(bigmemory)
library(bigtabulate)

set.seed(123)

dd <- data.frame(
  id = sample( 1:15, size=2e6 , replace=T ), 
  prd = letters[sample( 1:15, size=2e6 , replace=T )]
  )

prds <- unique(dd$prd)

benchmark(
bigtable(dd,c(1,2))>0,
table(dd[,1],dd[,2])>0,
xtabs(~id+prd,data=dd)>0,
tapply( dd$prd , dd$id , function(x) prds %in% x )
)

And the results of benchmarking (I’m learning new things all the time):

                                            test replications elapsed relative user.self sys.self user.child sys.child
1                      bigtable(dd, c(1, 2)) > 0          100  54.401 1.000000    51.759    3.817          0         0
2                    table(dd[, 1], dd[, 2]) > 0          100 112.361 2.065422   107.526    6.614          0         0
4 tapply(dd$prd, dd$id, function(x) prds %in% x)          100 178.308 3.277660   166.544   13.275          0         0
3                xtabs(~id + prd, data = dd) > 0          100 229.435 4.217478   217.014   16.660          0         0

And that shows bigtable winning out by a considerable amount. The results are pretty much that all prds are in all IDs, but see ?bigtable for details on the format of the results.