Cumulative count of observations per day with tally/dplyr, filling missing values

I have a list of observations per user; each user may have several observations of foo per day. For each distinct day, I'd like a cumulative count of the foo values. This is what I got so far:

library(tidyverse)
library(lubridate)

df = tribble(
  ~user_id, ~foo, ~bar, ~created_at,
  1, "a", "b", "2018-07-30",
  1, "a", "c", "2018-07-31",
  1, "a", "c", "2018-07-31",
  1, "b", "a", "2018-08-01",
  1, "b", "c", "2018-08-02",
  1, "b", "a", "2018-08-03",
  1, "a", "a", "2018-08-03",
  2, "b", "b", "2018-07-30",
  2, "b", "c", "2018-07-31",
  2, "a", "a", "2018-08-01",
  2, "a", "a", "2018-08-01",
  2, "a", "c", "2018-08-02",
  2, "a", "c", "2018-08-02",
  2, "a", "a", "2018-08-03"
) %>% mutate_at("created_at", as_datetime)

df %>%
  mutate(cutoff_date = created_at %>% date) %>% 
  group_by(user_id, foo, cutoff_date) %>% 
  tally %>%
  mutate(foo_cnt = cumsum(n)) %>%
  select(-n) %>% 
  arrange(user_id, cutoff_date, foo)

This gives me:

   user_id foo   cutoff_date foo_cnt
     <dbl> <chr> <date>        <int>
 1      1. a     2018-07-30        1
 2      1. a     2018-07-31        3
 3      1. b     2018-08-01        1
 4      1. b     2018-08-02        2
 5      1. a     2018-08-03        4
 6      1. b     2018-08-03        3
 7      2. b     2018-07-30        1
 8      2. b     2018-07-31        2
 9      2. a     2018-08-01        2
10      2. a     2018-08-02        4
11      2. a     2018-08-03        5

Great, so I know that until August 3, user 1 has seen a four times, and b three times. I would now like to know, for each date that occurs in my data (I do not care about missing dates):

• The total count of the specific foo observation until the date
• The relative amount of the observation compared to the others

That is, the output should be:

  user_id cutoff_date foo foo_cnt foo_cnt_total foo_pct
1      1. 2018-07-30  a         1             1     100
2      1. 2018-07-30  b         0             0       0
3      1. 2018-07-31  a         3             4     100
4      1. 2018-07-31  b         0             0       0
5      1. 2018-08-01  a         3             7    87.5
6      1. 2018-08-01  b         1             1    12.5
...

In row 5, it's 87.5% because the user has seen a seven times and b once until that point.

I have an idea of how to get there, but I am struggling with the including other values of foo for a date that is present in the data, but where there is no observation of foo. I have looked into complete() but I cannot figure out how to use it to fill the remaining values.

For example, when I add either of those, I get no additional columns:

complete(nesting(user_id, foo), cutoff_date)
complete(user_id, cutoff_date, foo)

What am I missing?

Update: I have added ungroup as suggested, and now I get the total count per day as well. I have used fill to fill previous values for the same value of foo:

df %>%
  mutate(cutoff_date = created_at %>% date) %>% 
  group_by(user_id, foo, cutoff_date) %>%
  tally %>%
  mutate(foo_cnt = cumsum(n)) %>%
  select(-n) %>% 
  ungroup() %>% 
  complete(nesting(user_id, foo), cutoff_date) %>% 
  arrange(user_id, cutoff_date, foo) %>% 
  group_by(user_id, foo) %>% 
  fill(foo_cnt) %>% 
  ungroup() %>% 
  group_by(user_id, cutoff_date) %>% 
  mutate(foo_cnt_total = sum(foo_cnt, na.rm = TRUE))

   user_id foo   cutoff_date foo_cnt foo_cnt_total
     <dbl> <chr> <date>        <int>         <int>
 1      1. a     2018-07-30        1             1
 2      1. a     2018-07-31        3             3
 3      1. a     2018-08-01        3             4
 4      1. a     2018-08-02        3             5
 5      1. a     2018-08-03        4             7
 6      1. b     2018-07-30       NA             1
 7      1. b     2018-07-31       NA             3
 8      1. b     2018-08-01        1             4
 9      1. b     2018-08-02        2             5
10      1. b     2018-08-03        3             7

However, the value of b should not start with NA. What would be needed here?