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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 . . .

The post Casting a Wide (and Sparse) Matrix in R appeared first on Exegetic Analytics.

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