Unnesting related list-columns of different size - r

Question

After parsing xml files I have data looking like this:
example_df <-
tibble(id = "ABC",
wage_type = "salary",
name = c("Description","Code","Base",
"Description","Code","Base",
"Description","Code"),
value = c("wage_element_1","51B","600",
"wage_element_2","51C","740",
"wage_element_3","51D"))
example_df
# A tibble: 8 x 4
id wage_type name value
<chr> <chr> <chr> <chr>
1 ABC salary Description wage_element_1
2 ABC salary Code 51B
3 ABC salary Base 600
4 ABC salary Description wage_element_2
5 ABC salary Code 51C
6 ABC salary Base 740
7 ABC salary Description wage_element_3
8 ABC salary Code 51D
with roughly 1000 different id, and each having three possible values for wage_type.
I want to change the values in the name column into columns.
I have tried to use pivot but I am struggling to handle the resulting list-cols: since not all salary have a Base, the resulting list-cols are of different size as below:
example_df <- example_df %>%
pivot_wider(id_cols = c(id, wage_type),
names_from = name,
values_from = value)
example_df
# A tibble: 1 x 5
id wage_type Description Code Base
<chr> <chr> <list> <list> <list>
1 ABC salary <chr [3]> <chr [3]> <chr [2]>
So when I try to unnest the cols it throws an error:
example_df%>%
unnest(cols = c(Description,Code,Base))
Error: Can't recycle `Description` (size 3) to match `Base` (size 2).
I understand that is because tidyr functions do not recycle, but I could not find a way around this or a base rsolution to my problem. I have tried to make a df with
unlist(strsplit(as.character(x)) solution as per how to split one row into multiple rows in R but also ran into a resulting column length issue.
Desired output is as follows:
desired_df <-
tibble(
id=c("ABC","ABC","ABC"),
wage_type=c("salary","salary","salary"),
Description = c("wage_element_1","wage_element_2","wage_element_3"),
Code = c("51B","51C","51D"),
Base = c("600","740",NA))
desired_df
id wage_type Description Code Base
<chr> <chr> <chr> <chr> <chr>
1 ABC salary wage_element_1 51B 600
2 ABC salary wage_element_2 51C 740
3 ABC salary wage_element_3 51D NA
I would love a tidyr solution but any help would be appreciated. Thanks.

Answer 1

I would suggest this approach using tidyverse functions. The issue you had was due to how functions manage different rows. So, by creating an id variable like id2 you can avoid list outputs in your final reshaped data:
library(tidyverse)
#Code
example_df %>%
arrange(name) %>%
group_by(id,wage_type,name) %>%
mutate(id2=1:n()) %>% ungroup() %>%
pivot_wider(names_from = name,values_from=value) %>%
select(-id2)
Output:
# A tibble: 3 x 5
id wage_type Base Code Description
<chr> <chr> <chr> <chr> <chr>
1 ABC salary 600 51B wage_element_1
2 ABC salary 740 51C wage_element_2
3 ABC salary NA 51D wage_element_3