R: select top products in groups - r

Question

I need to select 3 top selling products in each category, but if category dose not have 3 products I should add more products from best available category ("a" being the best category, "c" worst).
Every day the products change so I would like to this automatically. Previously I did choose top 3 products and if there was not available I did not bothered, but unfortunately the conditions changed. For that I used code as follows:
Selected <- items %>% group_by(Cat) %>% dplyr:: filter(row_number() < 3) %>% ungroup
Sample data:
items <- data.frame(Cat = c("a", "a", "a", "b", "b", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c"),
ranking = 1:15)
Desired results:
"a", "a", "a", "b", "b", "c", "c", "c", "c"
Sample data - 2:
items <- data.frame(Cat = c("a", "a", "a", "a", "b", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c"),
ranking = 1:15)
Desired results - 2:
"a", "a", "a", "a", "b", "c", "c", "c", "c"

Answer 1

Here is a possible answer. I'm not entirely sure if I'm getting what you are after - if not let me know.
items <- data.frame(Cat = c("a", "a", "a",
"b", "b",
"c", "c", "c", "c", "c", "c", "c", "c", "c", "c"),
ranking = 1:15)
First we order the data according from best to worst category and add the count number within category.
Selected <- items %>% group_by(Cat) %>%
mutate(id = row_number()) %>%
ungroup() %>% arrange(Cat)
Then we can make the filter and fill up with remaining rows from best to worst
Selected %>% filter(id<=3) %>% # Select top 3 in each group
bind_rows(Selected %>% filter(id>3)) %>% # Merge with the ones that weren't selected
mutate(id=row_number()) %>%
filter(id <= 3*length(unique(Cat))) # Extract the right number
This produces
# A tibble: 9 x 3
Cat ranking id
<fctr> <int> <int>
1 a 1 1
2 a 2 2
3 a 3 3
4 b 4 4
5 b 5 5
6 c 6 6
7 c 7 7
8 c 8 8
9 c 9 9
The second data example yields
# A tibble: 9 x 3
Cat ranking id
<fctr> <int> <int>
1 a 1 1
2 a 2 2
3 a 3 3
4 b 5 4
5 c 6 5
6 c 7 6
7 c 8 7
8 a 4 8
9 c 9 9
which seems to be what you were after.