R help - Jun 2010 - Matrix to "database" -- best practices/efficiency?

I have a matrix of, say, M and N dimensions:

my_matrix=matrix(c(1:60),nrow=6,ncol=10)

I have two "id" vectors corresponding to the rows and columns, e.g.:

id_m=seq(10,60,by=10)
id_n=seq(100,1000,by=100)

I would like to create a "proper" database (let's say a data.frame
for
this example -- i'm going to be loading these into an SQLite database,
but we'll leave that complication out of this discussion for now) of m
x n rows, and 3 columns, where the 3 columns relate to the values from
m, n, and my_matrix respectively, e.g. a single row follows the form:

c(id_m[a],id_n[b],my_matrix[a,b])

I can, of course, for-loop this thing with an if-then, e.g.:

***

for (a in 1:length(id_m))
{
	for (b in 1:length(id_n))
	{
		if ((a==1) && (b==1))
		{
			my_database=c(id_m[a],id_n[b],my_matrix[a,b])
		} else
		{		
			my_database=rbind(my_database,c(id_m[a],id_n[b],my_matrix[a,b]))
		}
	}
}

***

But my gut is telling me this is an incredibly inefficient way of
doing this -- is there a faster approach to doing this same process?
Thanks!

--j

Try this:
> mm <- matrix(1:6, 3, dimnames = list(LETTERS[1:3], letters[1:2]))
> mm
  a b
A 1 4
B 2 5
C 3 6
> library(reshape)
> melt(mm)
  X1 X2 value
1  A  a     1
2  B  a     2
3  C  a     3
4  A  b     4
5  B  b     5
6  C  b     6


On Mon, Jun 7, 2010 at 10:33 PM, Jonathan Greenberg
<greenberg at ucdavis.edu> wrote:
> I have a matrix of, say, M and N dimensions:
>
> my_matrix=matrix(c(1:60),nrow=6,ncol=10)
>
> I have two "id" vectors corresponding to the rows and columns,
e.g.:
>
> id_m=seq(10,60,by=10)
> id_n=seq(100,1000,by=100)
>
> I would like to create a "proper" database (let's say a
data.frame for
> this example -- i'm going to be loading these into an SQLite database,
> but we'll leave that complication out of this discussion for now) of m
> x n rows, and 3 columns, where the 3 columns relate to the values from
> m, n, and my_matrix respectively, e.g. a single row follows the form:
>
> c(id_m[a],id_n[b],my_matrix[a,b])
>
> I can, of course, for-loop this thing with an if-then, e.g.:
>
> ***
>
> for (a in 1:length(id_m))
> {
> ? ? ? ?for (b in 1:length(id_n))
> ? ? ? ?{
> ? ? ? ? ? ? ? ?if ((a==1) && (b==1))
> ? ? ? ? ? ? ? ?{
> ? ? ? ? ? ? ? ? ? ? ? ?my_database=c(id_m[a],id_n[b],my_matrix[a,b])
> ? ? ? ? ? ? ? ?} else
> ? ? ? ? ? ? ? ?{
> ? ? ? ? ? ? ? ? ? ? ?
?my_database=rbind(my_database,c(id_m[a],id_n[b],my_matrix[a,b]))
> ? ? ? ? ? ? ? ?}
> ? ? ? ?}
> }
>
> ***
>
> But my gut is telling me this is an incredibly inefficient way of
> doing this -- is there a faster approach to doing this same process?
> Thanks!
>
> --j
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

Hi Jonathan,

Following Gabor Grothendieck's advice, try also:

mm <- matrix(1:6, 3, dimnames = list(LETTERS[1:3], letters[1:2]))
as.data.frame.table(mm)

HTH,
Jorge


On Mon, Jun 7, 2010 at 10:33 PM, Jonathan Greenberg <> wrote:
> I have a matrix of, say, M and N dimensions:
>
> my_matrix=matrix(c(1:60),nrow=6,ncol=10)
>
> I have two "id" vectors corresponding to the rows and columns,
e.g.:
>
> id_m=seq(10,60,by=10)
> id_n=seq(100,1000,by=100)
>
> I would like to create a "proper" database (let's say a
data.frame for
> this example -- i'm going to be loading these into an SQLite database,
> but we'll leave that complication out of this discussion for now) of m
> x n rows, and 3 columns, where the 3 columns relate to the values from
> m, n, and my_matrix respectively, e.g. a single row follows the form:
>
> c(id_m[a],id_n[b],my_matrix[a,b])
>
> I can, of course, for-loop this thing with an if-then, e.g.:
>
> ***
>
> for (a in 1:length(id_m))
> {
>        for (b in 1:length(id_n))
>        {
>                if ((a==1) && (b==1))
>                {
>                        my_database=c(id_m[a],id_n[b],my_matrix[a,b])
>                } else
>                {
>
>  my_database=rbind(my_database,c(id_m[a],id_n[b],my_matrix[a,b]))
>                }
>        }
> }
>
> ***
>
> But my gut is telling me this is an incredibly inefficient way of
> doing this -- is there a faster approach to doing this same process?
> Thanks!
>
> --j
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
	[[alternative HTML version deleted]]

I think what you are groping for is something like this

my_matrix <- matrix(1:60, nrow = 6)
id_a <- seq(10,60,by=10)
id_b <- seq(100,1000,by=100)
my_database <- cbind(
  expand.grid(id_a = id_a, id_b = id_b),
  mat = as.vector(my_matrix)
)


-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On Behalf Of Jonathan Greenberg
Sent: Tuesday, 8 June 2010 12:34 PM
To: r-help
Subject: [R] Matrix to "database" -- best practices/efficiency?

I have a matrix of, say, M and N dimensions:

my_matrix=matrix(c(1:60),nrow=6,ncol=10)

I have two "id" vectors corresponding to the rows and columns, e.g.:

id_m=seq(10,60,by=10)
id_n=seq(100,1000,by=100)

I would like to create a "proper" database (let's say a data.frame
for
this example -- i'm going to be loading these into an SQLite database,
but we'll leave that complication out of this discussion for now) of m
x n rows, and 3 columns, where the 3 columns relate to the values from
m, n, and my_matrix respectively, e.g. a single row follows the form:

c(id_m[a],id_n[b],my_matrix[a,b])

I can, of course, for-loop this thing with an if-then, e.g.:

***

for (a in 1:length(id_m))
{
	for (b in 1:length(id_n))
	{
		if ((a==1) && (b==1))
		{
			my_database=c(id_m[a],id_n[b],my_matrix[a,b])
		} else
		{		
			my_database=rbind(my_database,c(id_m[a],id_n[b],my_matrix[a,b]))
		}
	}
}

***

But my gut is telling me this is an incredibly inefficient way of
doing this -- is there a faster approach to doing this same process?
Thanks!

--j

______________________________________________
R-help at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.