calculate weights from a column in a dataframe with conditions R

I need to calculate weights from a column in a dataframe based on some conditions.

I have Total Assets from several banks, with different countries, years and specializations.

For each bank I want to calculate a weight (w) where w(i) = Tot_Asset (bank) / sum (Tot_Ass of all banks within same year, country and Specialization)

Example dataframe:

banks <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
Country <- c("NL", "ES", "IT", "IT", "ES", "NL", "FR", "NL", "ES", "NL", "IT", "IT", "NL", "ES", "NL")
year <- c(2020, 2019, 2018, 2019, 2020, 2020, 2018, 2019, 2019, 2019, 2018, 2019, 2020, 2018, 2020)
Specialization <- c("cooperative", "saving", "cooperative", "cooperative", "saving", "cooperative", "saving", "cooperative", "cooperative", "saving", "cooperative", "saving", "cooperative", "cooperative", "cooperative")
Tot_Assets <- c(100, 200, 145, 300, 200, 345, 543, 190, 150, 120, 310, 210, 110, 210, 220)
data <- data.frame(banks, Country, year, Specialization, Tot_Assets)

As an example of what I would like to obtain:

The bank 1 is in NL, is a cooperative and the Total Asset is from 2020; bank 6, 13 and 15 has the same caracteristics. So the program has to do :

w(1) = 100 / (100 + 345 + 110 + 220) = 0.13
w(6) = 345 / (100 + 345 + 110 + 220) = 0.45
and so on

I hope that I explained myself and that you can help me, thanks in advance!

Solution

We may group by 'Country', 'year', 'Specialization', and create the 'weights' column by dividing the 'Tot_Assets' with the sum of 'Tot_Assets'

library(dplyr)
data <- data %>% 
  group_by(Country, year, Specialization) %>%
  mutate(weights = Tot_Assets/sum(Tot_Assets)) %>%
  ungroup

-output

data
# A tibble: 15 × 6
   banks Country  year Specialization Tot_Assets weights
   <dbl> <chr>   <dbl> <chr>               <dbl>   <dbl>
 1     1 NL       2020 cooperative           100   0.129
 2     2 ES       2019 saving                200   1    
 3     3 IT       2018 cooperative           145   0.319
 4     4 IT       2019 cooperative           300   1    
 5     5 ES       2020 saving                200   1    
 6     6 NL       2020 cooperative           345   0.445
 7     7 FR       2018 saving                543   1    
 8     8 NL       2019 cooperative           190   1    
 9     9 ES       2019 cooperative           150   1    
10    10 NL       2019 saving                120   1    
11    11 IT       2018 cooperative           310   0.681
12    12 IT       2019 saving                210   1    
13    13 NL       2020 cooperative           110   0.142
14    14 ES       2018 cooperative           210   1    
15    15 NL       2020 cooperative           220   0.284