В Thu, 18 Apr 2024 16:31:46 +0000
"Deramus, Thomas Patrick" <tdera...@mgb.org> пишет:
> Basically, each list contains a set of doubles, with the first
> indicating a specific index (based on the 0 beginning python index),
> and a certain value (e.g. 0.5).
>
> What I would like to do is generate set of columns based on the rang
> of unique indexes of each nested list. e.g.: col_1, col_2, col_3,
> col_4, col_5
It's possible to golf it down to something like the following:
newcol <- t(sapply(tibble$nestedvals, \(x) {
x <- simplify2array(x)
ret <- numeric(5)
ret[x[1,]] <- x[2,]
ret
}))
...which you can then rename and cbind() to your tibble.
But the problem remains that the desired data structure has to be
generated row by row and then transformed back into a column-oriented
data structure. Do you need a sparse matrix?
spec <- do.call(cbind, Map(
\(row, cols) rbind(row, simplify2array(cols)),
seq_along(tibble$nestedvals), tibble$nestedvals
))
sparse <- Matrix::sparseMatrix(spec[1,], spec[2,], x = spec[3,])
--
Best regards,
Ivan
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