omit empty cells in crosstab?

Perhaps this is a common question but I haven't been able to find the answer.

I have data with many factors, each taking many values. However, only
relatively few combinations appear in the data, ie have nonzero counts, in
other words the resulting table is sparse. Say we have 10 factors each with
10 levels. The result of table() would exceed the memory space (on a 32bit
machine). Is there any way to produce a table with empty cells omitted?
(without first producing the whole table and then removing rows.)

Thanks,
Steve

--
View this message in context: http://www.nabble.com/omit-empty-cells-in-crosstab--tp23222263p23222263.html
Sent from the R help mailing list archive at Nabble.com.

I have data with many factors, each taking many values. However, only
relatively few combinations appear in the data, ie have nonzero counts, in
other words the resulting table is sparse. Say we have 10 factors each with
10 levels. The result of table() would exceed the memory space (on a 32bit
machine). Is there any way to produce a table with empty cells omitted?
(without first producing the whole table and then removing rows.)

sjaffe <sjaffe <at> riskspan.com> writes:

I have data with many factors, each taking many values. However, only
relatively few combinations appear in the data, ie have nonzero counts, in
other words the resulting table is sparse. Say we have 10 factors each with
10 levels. The result of table() would exceed the memory space (on a 32bit
machine). Is there any way to produce a table with empty cells omitted?
(without first producing the whole table and then removing rows.)

It would be easier if you had a reproducible base example, but I 
suggest to create ONE new factor of the pasted levels using unique(), 
and  creating a table of these.

sjaffe <sjaffe <at> riskspan.com> writes:

I have data with many factors, each taking many values. However, only
relatively few combinations appear in the data, ie have nonzero counts,
in
other words the resulting table is sparse. Say we have 10 factors each
with
10 levels. The result of table() would exceed the memory space (on a
32bit
machine). Is there any way to produce a table with empty cells omitted?
(without first producing the whole table and then removing rows.)

It would be easier if you had a reproducible base example, but I 
suggest to create ONE new factor of the pasted levels using unique(), 
and  creating a table of these.

Dieter

set.seed(25)
x = as.data.frame(replicate(10,sample(1:10,12,replace=TRUE)))
res = apply(x,1,paste,collapse=':')
tt1 = as.data.frame(table(res))
vals = strsplit(as.character(tt1$res),":")
answer = data.frame(do.call(rbind,vals),Freq=tt1$Freq)
answer

small example:

a<-c(1.1, 2.1, 9.1)
b<-cut(a,0:10)
c<-data.frame(b,b)
d<-table(c)
dim(d)
##result: c(10, 10)

But only 9 of the 100 cells are non-zero.
If there were 10 columns, the table have 10 dimensions each of length 10, so
have 10^10 elements, too much even to fit in memory


Dieter Menne wrote:

sjaffe <sjaffe <at> riskspan.com> writes:

I have data with many factors, each taking many values. However, only
relatively few combinations appear in the data, ie have nonzero counts,
in
other words the resulting table is sparse. Say we have 10 factors each
with
10 levels. The result of table() would exceed the memory space (on a
32bit
machine). Is there any way to produce a table with empty cells omitted?
(without first producing the whole table and then removing rows.)

It would be easier if you had a reproducible base example, but I
suggest to create ONE new factor of the pasted levels using unique(),
and  creating a table of these.

Dieter

--
View this message in context: http://www.nabble.com/omit-empty-cells-in-crosstab--tp23222263p23224071.html
Sent from the R help mailing list archive at Nabble.com.

small example:

a<-c(1.1, 2.1, 9.1)
b<-cut(a,0:10)
c<-data.frame(b,b)
d<-table(c)
dim(d)
##result: c(10, 10)

But only 9 of the 100 cells are non-zero.
If there were 10 columns, the table have 10 dimensions each of length 10, so
have 10^10 elements, too much even to fit in memory

small example:

a<-c(1.1, 2.1, 9.1)
b<-cut(a,0:10)
c<-data.frame(b,b)
d<-table(c)
dim(d) 
##result: c(10, 10)

But only 9 of the 100 cells are non-zero.
If there were 10 columns, the table have 10 dimensions each of length 10, so
have 10^10 elements, too much even to fit in memory

d