Easier way to add rows with totals for groups in dplyr - r

Question

How could I add rows with the sum of VL-FOB_real for each CO_ANO-niv100-subsector group in an easier way? I couldn't figure how to use add_rows and the like to do so, only by creating a new dataframe and then appending it.
Here is what I have done:
df <- structure(list(CO_ANO = c("1996", "1990", "1993", "1993", "1994",
"1992", "1995", "1995", "1996", "1995",
"1994", "1990", "1989", "1992", "1995"),
CO_UF = c("32", "45", "45", "36", "55", "99", "36",
"34", "14", "25", "53", "41", "41", "41", "16"),
niv100 = c("2210","1530", "210", "3210", "1530", "2610", "2210",
"2630", "1030","1020", "3020", "3020", "410", "2510",
"1520"),
subsector = c("11","8", "1", "7", "8", "13", "11", "13", "4", "5",
"13", "13", "2","13", "8"),
VL_FOB_real = c(1, 2, 3,
1, 4, 5,
5, 6, 7,
6, 8, 9,
10, 11, 11)),
class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,-15L))
df1 <- df %>%
group_by(CO_ANO, subsector, niv100) %>%
summarise(VL_FOB_real = sum(VL_FOB_real)) %>%
mutate(CO_UF = 'Total')
df <- bind_rows(df1,df)

Answer 1

This groups the rows and then modify each group using adorn_totals.
library(dplyr)
library(janitor)
df %>%
group_by(CO_ANO, CO_UF, niv100) %>%
group_modify(~ adorn_totals(.x, where = "row"))
giving:
# A tibble: 30 x 5
# Groups: CO_ANO, CO_UF, niv100 [15]
CO_ANO CO_UF niv100 subsector VL_FOB_real
<chr> <chr> <chr> <chr> <dbl>
1 1989 41 410 2 10
2 1989 41 410 Total 10
3 1990 41 3020 13 9
4 1990 41 3020 Total 9
5 1990 45 1530 8 2
6 1990 45 1530 Total 2
7 1992 41 2510 13 11
8 1992 41 2510 Total 11
9 1992 99 2610 13 5
10 1992 99 2610 Total 5
# ... with 20 more rows
Another thing to try is the following which gives somewhat different output. It splits the input into groups and applies adorn_totals separately to each group giving a c("tabyl", "tbl_df", "tbl", "data.frame") object.
library(dplyr)
library(janitor)
library(purrr)
df %>%
group_split(CO_ANO, subsector, niv100, CO_UF) %>%
map_df(adorn_totals)

Answer 2

Honestly, I would do what you have done to add rows for each group but for the purpose of demonstrating way to use add_row here's an answer :
library(dplyr)
library(purrr)
df %>%
group_split(CO_ANO, subsector, niv100) %>%
map_df(~add_row(.x, CO_ANO = first(.x$CO_ANO), subsector = first(.x$subsector),
niv100 = first(.x$niv100),VL_FOB_real = sum(.x$VL_FOB_real), CO_UF = 'Total'))
# CO_ANO CO_UF niv100 subsector VL_FOB_real
# <chr> <chr> <chr> <chr> <dbl>
# 1 1989 41 410 2 10
# 2 1989 Total 410 2 10
# 3 1990 41 3020 13 9
# 4 1990 Total 3020 13 9
# 5 1990 45 1530 8 2
# 6 1990 Total 1530 8 2
# 7 1992 41 2510 13 11
# 8 1992 Total 2510 13 11
# 9 1992 99 2610 13 5
#10 1992 Total 2610 13 5
# … with 20 more rows
The only benefit I see of this approach is you get "Total" row for each group immediately after the group unlike in bind_rows where you get all "Total" rows together.