Hi experts.
I have a tibble with a column containing a nested list (<list<list<double>>>
data type to be specific).
Looks something like the following (but in R/Arrow format):
ID
Nestedvals
001
[[1]](1,0.1)[[2]](2,0.2)[[3]](3,0.3)[[4]](4,0.4)[[5]](5,0.5)
002
[[1]](1,0.1)[[2]](2,0.2)[[3]](3,0.3)[[4]](4,0.4)
003
[[1]](1,0.1)[[2]](2,0.2)[[3]](3,0.3)
004
[[1]](1,0.1)[[2]](2,0.2)
005
[[1]](1,0.1)
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
Which I have done with the following:
tibble[paste0("col_", 1:5)] <- 0
And then replace each 0 with the value (second number in the nested list),
based on the index (first number in each nested list), for each row of the
tibble.
I wrote a function to split each nested list:
nestsplit <- function(x, y) {
`unlist(lapply(x, [[`, y))
}
And then generate unique columns with the column names (by index) and values of
interest to append to the tibble:
tibble <-
tibble |> rowwise() |> mutate(index_names = list(paste0(
"col_", as.character(nestsplit(nestedvals, 1))
)),
index_values = list(nestsplit(nestedvals, 2)))
But I would like to see if there is an efficient, tidyverse/dplyr-based
solution to individually assign these values rather than writing a loop to
assign each of them by row.
So that an output like this:
ID
Nestedvals
col_1
col_2
col_3
col_4
col_5
001
<Nested list of 5 pairs of values>
0
0
0
0
0
002
<Nested list of 4 pairs of values>
0
0
0
0
0
003
<Nested list of 3 pairs of values>
0
0
0
0
0
004
<Nested list of 2 pairs of values>
0
0
0
0
0
005
<Nested list of 1 pair of values>
0
0
0
0
0
Looks instead like the following:
ID
Nestedvals
col_1
col_2
col_3
col_4
col_5
001
<Nested list of 5 pairs of values>
0.1
0.2
0.3
0.4
0.5
002
<Nested list of 4 pairs of values>
0.1
0.2
0.3
0.4
0
003
<Nested list of 3 pairs of values>
0.1
0.2
0.3
0
0
004
<Nested list of 2 pairs of values>
0.1
0.2
0
0
0
005
<Nested list of 1 pair of values>
0.1
0
0
0
0
-------------------------------------------------------------------------------------------------------------------------
I would love to give an example to simulate the exact nature of the data, but
I'm unfortunately not sure how to recreate this class for an example:
> typeof(tibble$var)
[1] "list"
> class(tibble$var)
[1] "arrow_list" "vctrs_list_of" "vctrs_vctr" "list"
The closest I have ever been able to get is with:
tibble(ID = c("001", "002", "003", "004", "005"), nestedvals =
list(list(c(1,0.1),c(2,0.2),c(3,0.3),c(4,0.4),c(5,0.5)),list(c(1,0.1),c(2,0.2),c(3,0.3),c(4,0.4)),list(c(1,0.1),c(2,0.2),c(3,0.3)),list(c(1,0.1),c(2,0.2)),list(c(1,0.1))))
Which gives a list datatype instead of <list<list<double>>>
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