Add baseline/grand total with group_by() in dplyr - r

Question

When I've grouped my data by certain attributes, I want to add a "grand total" line that gives a baseline of comparison. Let's group mtcars by cylinders and carburetors, for example:
by_cyl_carb <- mtcars %>%
group_by(cyl, carb) %>%
summarize(median_mpg = median(mpg),
avg_mpg = mean(mpg),
count = n())
...yields these results:
> by_cyl_carb
# A tibble: 9 x 5
# Groups: cyl [?]
cyl carb median_mpg avg_mpg count
<dbl> <dbl> <dbl> <dbl> <int>
1 4 1 27.3 27.6 5
2 4 2 25.2 25.9 6
3 6 1 19.8 19.8 2
4 6 4 20.1 19.8 4
5 6 6 19.7 19.7 1
6 8 2 17.1 17.2 4
7 8 3 16.4 16.3 3
8 8 4 13.8 13.2 6
9 8 8 15 15 1
What is the code I need to make it provide a baseline or grand total that would sum (or mean or median) over all of the data? The desired data would be something like this:
cyl carb median_mpg avg_mpg count
<chr> <chr> <dbl> <dbl> <int>
1 4 1 27.3 27.6 5
2 4 2 25.2 25.9 6
3 6 1 19.8 19.8 2
4 6 4 20.1 19.8 4
5 6 6 19.7 19.7 1
6 8 2 17.1 17.2 4
7 8 3 16.4 16.3 3
8 8 4 13.8 13.2 6
9 8 8 15 15 1
10 ttl ttl 19.2 20.1 32
A twist on this would be able to manipulate the output so that the sub-grouped data would be rolled up. For example:
11 ttl 1 13.8 13.2 6
12 ttl 2 15 15 1
13 ttl 3 19.3 20.4 32
14 ... etc ...
The real-life example I am using this for is median sale price of homes by geography by year. Hence I want to report out the median sale price for each geography-year I'm interested in, but I want a baseline comparison for each year regardless of geography.
Edit: Solved with two solutions
#camille referenced this link, which solved the problem, as well as #MKR offering a solution. Here is one code that might work:
by_cyl_carb <- mtcars %>%
mutate_at(vars(c(cyl,carb)), funs(as.character(.))) %>%
bind_rows(mutate(., cyl = "All cylinders")) %>%
bind_rows(mutate(., carb = "All carburetors")) %>%
group_by(cyl, carb) %>%
summarize(median_mpg = median(mpg),
avg_mpg = mean(mpg),
count = n())
> by_cyl_carb
# A tibble: 19 x 5
# Groups: cyl [?]
cyl carb median_mpg avg_mpg count
<chr> <chr> <dbl> <dbl> <int>
1 4 1 27.3 27.6 5
2 4 2 25.2 25.9 6
3 4 All carburetors 26 26.7 11
4 6 1 19.8 19.8 2
5 6 4 20.1 19.8 4
6 6 6 19.7 19.7 1
7 6 All carburetors 19.7 19.7 7
8 8 2 17.1 17.2 4
9 8 3 16.4 16.3 3
10 8 4 13.8 13.2 6
11 8 8 15 15 1
12 8 All carburetors 15.2 15.1 14
13 All cylinders 1 22.8 25.3 7
14 All cylinders 2 22.1 22.4 10
15 All cylinders 3 16.4 16.3 3
16 All cylinders 4 15.2 15.8 10
17 All cylinders 6 19.7 19.7 1
18 All cylinders 8 15 15 1
19 All cylinders All carburetors 19.2 20.1 32

Answer 1

A solution using dplyr::bind_rows and mutate_at can be achieved as:
library(tidyverse)
mtcars %>%
group_by(cyl, carb) %>%
summarize(median_mpg = median(mpg),
avg_mpg = mean(mpg),
count = n()) %>%
ungroup() %>%
mutate_at(vars(cyl:carb), funs(as.character(.))) %>%
bind_rows(summarise(cyl = "ttl", carb = "ttl", mtcars, median_mpg = median(mpg),
avg_mpg = mean(mpg),
count = n()))
# # A tibble: 10 x 5
# cyl carb median_mpg avg_mpg count
# <chr> <chr> <dbl> <dbl> <int>
# 1 4 1 27.3 27.6 5
# 2 4 2 25.2 25.9 6
# 3 6 1 19.8 19.8 2
# 4 6 4 20.1 19.8 4
# 5 6 6 19.7 19.7 1
# 6 8 2 17.1 17.2 4
# 7 8 3 16.4 16.3 3
# 8 8 4 13.8 13.2 6
# 9 8 8 15.0 15.0 1
#10 ttl ttl 19.2 20.1 32