Unnest or unchop dataframe containing lists of different lengths - r

Question

I have a dataframe with several columns containing list columns that I want to unnest (or unchop). BUT, they are different lengths, so the resulting error is Error: No common size for...
Here is a reprex to show what works and doesn't work.
library(tidyr)
library(vctrs)
# This works as expected
df_A <- tibble(
ID = 1:3,
A = as_list_of(list(c(9, 8, 5), c(7,6), c(6, 9)))
)
unchop(df_A, cols = c(A))
# A tibble: 7 x 2
ID A
<int> <dbl>
1 1 9
2 1 8
3 1 5
4 2 7
5 2 6
6 3 6
7 3 9
# This works as expected as the lists are the same lengths
df_AB_1 <- tibble(
ID = 1:3,
A = as_list_of(list(c(9, 8, 5), c(7,6), c(6, 9))),
B = as_list_of(list(c(1, 2, 3), c(4, 5), c(7, 8)))
)
unchop(df_AB_1, cols = c(A, B))
# A tibble: 7 x 3
ID A B
<int> <dbl> <dbl>
1 1 9 1
2 1 8 2
3 1 5 3
4 2 7 4
5 2 6 5
6 3 6 7
7 3 9 8
# This does NOT work as the lists are different lengths
df_AB_2 <- tibble(
ID = 1:3,
A = as_list_of(list(c(9, 8, 5), c(7,6), c(6, 9))),
B = as_list_of(list(c(1, 2), c(4, 5, 6), c(7, 8, 9, 0)))
)
unchop(df_AB_2, cols = c(A, B))
# Error: No common size for `A`, size 3, and `B`, size 2.
The output that I would like to achieve for df_AB_2 above is as follows where each list is unchopped and missing values are filled with NA:
# A tibble: 10 x 3
ID A B
<dbl> <dbl> <dbl>
1 1 9 1
2 1 8 2
3 1 5 NA
4 2 7 4
5 2 6 5
6 2 NA 6
7 3 6 7
8 3 9 8
9 3 NA 9
10 3 NA 0
I have referenced this issue on Github and StackOverflow here.
Any ideas how to achieve the result above?
Versions
> packageVersion("tidyr")
[1] ‘1.0.0’
> packageVersion("vctrs")
[1] ‘0.2.0.9001’

Answer 1

Here is an idea via dplyr that you can generalise to as many columns as you want,
library(tidyverse)
df_AB_2 %>%
pivot_longer(c(A, B)) %>%
mutate(value = lapply(value, `length<-`, max(lengths(value)))) %>%
pivot_wider(names_from = name, values_from = value) %>%
unnest() %>%
filter(rowSums(is.na(.[-1])) != 2)
which gives,
# A tibble: 10 x 3
ID A B
<int> <dbl> <dbl>
1 1 9 1
2 1 8 2
3 1 5 NA
4 2 7 4
5 2 6 5
6 2 NA 6
7 3 6 7
8 3 9 8
9 3 NA 9
10 3 NA 0

Answer 2

Defining a helper function to update the lengths of the element and proceeding with dplyr:
foo <- function(x, len_vec) {
lapply(
seq_len(length(x)),
function(i) {
length(x[[i]]) <- len_vec[i]
x[[i]]
}
)
}
df_AB_2 %>%
mutate(maxl = pmax(lengths(A), lengths(B))) %>%
mutate(A = foo(A, maxl), B = foo(B, maxl)) %>%
unchop(cols = c(A, B)) %>%
select(-maxl)
# A tibble: 10 x 3
ID A B
<int> <dbl> <dbl>
1 1 9 1
2 1 8 2
3 1 5 NA
4 2 7 4
5 2 6 5
6 2 NA 6
7 3 6 7
8 3 9 8
9 3 NA 9
10 3 NA 0
Using data.table:
library(data.table)
setDT(df_AB_2)
df_AB_2[, maxl := pmax(lengths(A), lengths(B))]
df_AB_2[, .(unlist(A)[seq_len(maxl)], unlist(B)[seq_len(maxl)]), by = ID]