library(dplyr)
your_data |>
  mutate(country_total = sum(Sales), .by = c(ID, Country)) |>
  mutate(fraction = country_total / sum(Sales), .by = ID)
#   ID Country Sales country_total  fraction
# 1  1 Austria     6            12 0.6666667
# 2  1 Austria     6            12 0.6666667
# 3  1 Belgium     6             6 0.3333333
# 4  2 Belgium    10            10 0.5000000
# 5  2   Czech    10            10 0.5000000
# 6  3 Denmark     3             3 0.5000000
# 7  3 Germany     3             3 0.5000000

your_data = read.table(text = 'ID 	Country 	Sales
1 	Austria 	6
1 	Austria 	6
1 	Belgium 	6
2 	Belgium 	10
2 	Czech 	10
3 	Denmark 	3
3 	Germany 	3', header = T)

library(dplyr)
df %>% add_count(ID,name = 'n') %>% add_count(ID,Country, name = 'gn') %>% 
mutate(new=gn/n) %>% select(-c(n,gn))
# 输出
# A tibble: 7 × 4
     ID Country Sales   new
  <dbl> <chr>   <dbl> <dbl>
1     1 Austria     6 0.667
2     1 Austria     6 0.667
3     1 Belgium     6 0.333
4     2 Belgium    10 0.5  
5     2 Czech      10 0.5  
6     3 Denmark     3 0.5  
7     3 Germany     3 0.5  
```"
<details>
<summary>英文:</summary>
Alternatively we could use `add_count` to get the same result
````r
library(dplyr)
df %&gt;% add_count(ID,name = &#39;n&#39;) %&gt;% add_count(ID,Country, name = &#39;gn&#39;) %&gt;% 
mutate(new=gn/n) %&gt;% select(-c(n,gn))
# output
# A tibble: 7 &#215; 4
     ID Country Sales   new
  &lt;dbl&gt; &lt;chr&gt;   &lt;dbl&gt; &lt;dbl&gt;
1     1 Austria     6 0.667
2     1 Austria     6 0.667
3     1 Belgium     6 0.333
4     2 Belgium    10 0.5  
5     2 Czech      10 0.5  
6     3 Denmark     3 0.5  
7     3 Germany     3 0.5  

ID Country Sales  fraction
1  1 Austria     6 0.6666667
2  1 Austria     6 0.6666667
3  1 Belgium     6 0.3333333
4  2 Belgium    10 0.5000000
5  2   Czech    10 0.5000000
6  3 Denmark     3 0.5000000
7  3 Germany     3 0.5000000