Use list names inside purrr:::map_dfr function - r

Question

I was trying something relatively simple, but having some struggles. Let's say I have two dataframes df1 and df2:
df1:
id expenditure
1 10
2 20
1 30
2 50
df2:
id expenditure
1 30
2 50
1 60
2 10
I also added them to a named list:
table_list = list()
table_list[['a']] = df1
table_list[['b']] = df2
And now I want to perform some summary operation through a function and then bind those rows:
get_summary = function(table){
final_table = table %>% group_by(id) %>% summarise(total_expenditure= sum(expenditure))
}
And then apply this through map_dfr:
summary = table_list %>% map_dfr(get_summary, id='origin_table')
So, this will create a almost what I'm looking for:
origin_table id total_expenditure
a 1 40
a 2 70
b 1 90
b 2 60
But, what if I would like to do something specific depending on the element of the list that is being passed, something like this:
get_summary = function(table, name){
dummy_list = c(TRUE, FALSE)
names(dummy_list) = c('a', 'b')
final_table = table %>% group_by(id) %>% summarise(total_expenditure= sum(expenditure))
is_true = dummy_list[[name]] # Want to use the original name to call another list
if(is_true) final_table = final_table %>% mutate(total_expenditure = total_expenditure + 1)
return(final_table)
}
This would bring something like this:
origin_table id total_expenditure
a 1 41
a 2 71
b 1 90
b 2 60
So is there any way to use list names inside my function? Or any way to identify which element of my list I'm working with? Maybe map_dfr is too restricted and I have to use something else?
Edit: changed example so it is more grounded in reality

Answer 1

Instead of using map, use imap, which can return the names of the list in .y
library(purrr)
library(dplyr)
get_summary = function(dat, name){
dat %>%
group_by(id) %>%
summarise(total_expenditure= sum(expenditure, na.rm = TRUE),
.groups = "drop") %>%
mutate(total_expenditure = if(name=='a')
total_expenditure + 1 else total_expenditure)
}
-testing
> table_list %>%
imap_dfr(~ get_summary(.x, name = .y), .id = 'origin_table')
# A tibble: 4 × 3
origin_table id total_expenditure
<chr> <int> <dbl>
1 a 1 41
2 a 2 71
3 b 1 90
4 b 2 60
data
table_list <- list(a = structure(list(id = c(1L, 2L, 1L, 2L),
expenditure = c(10L,
20L, 30L, 50L)), class = "data.frame", row.names = c(NA, -4L)),
b = structure(list(id = c(1L, 2L, 1L, 2L), expenditure = c(30L,
50L, 60L, 10L)), class = "data.frame", row.names = c(NA,
-4L)))

Answer 2

Managed to do it, by adding origin_table as a pre-existing column on the dataframes:
df1 = df1 %>% mutate(origin_table = 'a')
df2 = df2 %>% mutate(origin_table = 'b')
Then I can extract the origin by doing the following:
get_summary = function(table){
dummy_list = c(TRUE, FALSE)
names(dummy_list) = c('a', 'b')
origin = table %>% distinct(origin_table) %>% pull
final_table = table %>% group_by(id) %>% summarise(total_expenditure= sum(expenditure))
is_true = dummy_list[[origin ]] # Want to use the original name to call another list
if(is_true) final_table = final_table %>% mutate(total_expenditure = total_expenditure + 1)
return(final_table)
}