I routinely use `melt()`

and `cast()`

from the reshape2 package as part of my data munging workflow. Recently I’ve noticed that the data frames I’ve been casting are often extremely sparse. Stashing these in a dense data structure just feels wasteful. And the dismal drone of page thrashing is unpleasant.

So I had a look around for an alternative. As it turns out, it’s remarkably easy to cast a sparse matrix using `sparseMatrix()`

from the Matrix package. Here’s an example.

First we’ll put together some test data.

```
> set.seed(11)
>
> N = 10
>
> data = data.frame(
+ row = sample(1:3, N, replace = TRUE),
+ col = sample(LETTERS, N, replace = TRUE),
+ value = sample(1:3, N, replace = TRUE))
>
> data = transform(data,
+ row = factor(row),
+ col = factor(col))
```

It’s just a data.frame with two fields which will be transformed into the rows and columns of the matrix and a third field which gives the values to be stored in the matrix.

```
> data
row col value
1 1 E 1
2 1 L 3
3 2 X 2
4 1 W 2
5 1 T 1
6 3 O 2
7 1 M 2
8 1 I 1
9 3 E 1
10 1 M 2
```

Doing the cast is pretty easy using `sparseMatrix()`

because you specify the row and column for every entry inserted into the matrix. Multiple entries for a single cell (like the highlighted records above) are simply summed, which is generally the behaviour that I am after anyway.

```
> library(Matrix)
>
> data.sparse = sparseMatrix(as.integer(data$row), as.integer(data$col), x = data$value)
>
> colnames(data.sparse) = levels(data$col)
> rownames(data.sparse) = levels(data$row)
```

And here’s the result:

```
> data.sparse
3 x 8 sparse Matrix of class "dgCMatrix"
E I L M O T W X
1 1 1 3 4 . 1 2 .
2 . . . . . . . 2
3 1 . . . 2 . . .
```