Get the row name used to annotation - r

Question

First of all, my question is related to these other ones:
Lazy evaluation to annotations expanding function
R nested map through columns
So, I got this example data:
t <- tibble(a = c("a", "b", "c", "d", "e", "f", "g", "h"),
b = c( 1, 1, 1, 1, 2, 2, 2, 2),
c = c( 1, 1, 2, 2, 3, 3, 4, 4),
d = c( NA, NA, NA, "D", "E", NA, NA, NA),
e = c("A", NA, "C", NA, NA, NA, "G", "H")
)
And this functions
f1 <- function(data, group_col, expand_col){ #, return_group_col = TRUE, name_group_col = "group_col"){
data %>%
dplyr::group_by({{group_col}}) %>%
dplyr::mutate(
{{expand_col}} := dplyr::case_when(
!is.na({{expand_col}}) ~ {{expand_col}} ,
any( !is.na({{expand_col}}) ) & is.na({{expand_col}}) ~
paste(unique(unlist(str_split(na.omit({{expand_col}}), " ")) ),
collapse = " "),
TRUE ~ NA_character_
)
) %>%
dplyr::ungroup()
}
f2 <- function(data, group_col, expand_col, fun=f1){
v1 <- rlang::syms( colnames(data)[group_col] )
v2 <- rlang::syms( colnames(data)[expand_col] )
V <- tidyr::crossing( v1, v2 )
purrr::reduce2( V$v1, V$v2, fun, .init=data )
}
The function f1 use two columns, the first one {{group_col}} is a group identifier the second one {{expand_col}} may contain an annotation or NA. After a group_by by the {{group_col}} the {{expand_col}} is filled with the data from the other rows from the same group if it is NA. Example: f1(t, c, d).
The function f2 just propagates the function f1 using two sets of columns, the first set refers to grouping columns and the second set refers to annotation columns.
Then, I want to modify the function f1 to create (if needed) another column which will contain the information about the which {{group_col}} the {{expand_col}} was felled.
That means, imagine you run: t %>% f2(3:2, 4:5) you will get this:
structure(list(a = c("a", "b", "c", "d", "e", "f", "g", "h"),
b = c(1, 1, 1, 1, 2, 2, 2, 2), c = c(1, 1, 2, 2, 3, 3, 4,
4), d = c("D", "D", "D", "D", "E", "E", "E", "E"), e = c("A",
"A", "C", "C", "G H", "G H", "G", "H")), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -8L))
Which is the same to run:
t %>%
f1(c, d)# %>%
f1(b, d) %>%
f1(c, e) %>%
f1(b, e)
You may notice that some rows were annotated previously. These rows should be filled with 'self' or something equivalent.
Here the example of the output I want:
structure(list(a = c("a", "b", "c", "d", "e", "f", "g", "h"),
b = c(1, 1, 1, 1, 2, 2, 2, 2),
c = c(1, 1, 2, 2, 3, 3, 4, 4),
d = c("D", "D", "D", "D", "E", "E", "E", "E"),
e = c("A", "A", "C", "C", "G H", "G H", "G", "H"),
d_fill = c("b", "b", "c", "self", "self", "c", "b", "b"),
e_fill = c("self", "c", "self", "c", "b", "b", "self", "self")
),
class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -8L))
Then I tried this unsuccessful modification:
f1 <- function(data, group_col, expand_col){ #, return_group_col = TRUE, name_group_col = "group_col"){
fill_column <- str_c(deparse(substitute(group_col)), "fill", sep = "_")
data %>%
dplyr::group_by({{group_col}}) %>%
dplyr::mutate(
{{fill_column}} := dplyr::if_else(
!is.na({{expand_col}}) & is.na({{fill_column}}) ~ "self",
is.na({{expand_col}}) & is.na({{fill_column}}) ~ deparse(substitute(group_col)),
TRUE ~ NA_character_
),
{{expand_col}} := dplyr::case_when(
!is.na({{expand_col}}) ~ {{expand_col}} ,
any( !is.na({{expand_col}}) ) & is.na({{expand_col}}) ~
paste(unique(unlist(str_split(na.omit({{expand_col}}), " ")) ),
collapse = " "),
TRUE ~ NA_character_
)
) %>%
dplyr::ungroup()
}
But when I run t %>% f1(c, d) to test it, I got this:
Error: `condition` must be a logical vector, not a `formula` object
Run `rlang::last_error()` to see where the error occurred.
25.
stop(fallback)
24.
signal_abort(cnd)
23.
.abort(text)
22.
glubort(fmt_args(args), ..., .envir = .envir)
21.
bad_args("condition", "must be a logical vector, not {friendly_type_of(condition)}")
20.
dplyr::if_else(!is.na(~d) & is.na(~"c_fill") ~ "self", is.na(~d) &
is.na(~"c_fill") ~ deparse(substitute(group_col)), TRUE ~
NA_character_)
19.
mutate_impl(.data, dots, caller_env())
18.
mutate.tbl_df(., `:=`({
{
fill_column
} ...
17.
dplyr::mutate(., `:=`({
{
fill_column
} ...
16.
function_list[[i]](value)
15.
freduce(value, `_function_list`)
14.
`_fseq`(`_lhs`)
13.
eval(quote(`_fseq`(`_lhs`)), env, env)
12.
eval(quote(`_fseq`(`_lhs`)), env, env)
11.
withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
10.
data %>% dplyr::group_by({
{
group_col
} ...
9.
f1(., c, d)
8.
function_list[[k]](value)
7.
withVisible(function_list[[k]](value))
6.
freduce(value, `_function_list`)
5.
`_fseq`(`_lhs`)
4.
eval(quote(`_fseq`(`_lhs`)), env, env)
3.
eval(quote(`_fseq`(`_lhs`)), env, env)
2.
withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
1.
t %>% f1(c, d)
I didn't figure out what is wrong.
Thanks in advance.