我不确定我是否正确理解了这个问题，因为你的数据示例过于通用。我希望我的回答与你想要的有关，并能对你有用！
首先，我回顾了示例数据集，考虑到其中一个字符串表示大学的名称，另一个是引用女性的定性变量。下面是dput：

df <- structure(list(W_or_not = c("chr", "W", "chr", "W", "chr", "W", 
"chr", "chr", "chr", "chr"), Univ = c("univ1", "univ1", "univ1", 
"univ1", "univ1", "univ2", "univ2", "univ2", "univ2", "univ2"
), year1 = c(1, 0, 0, NA, 0, 1, 1, NA, 0, 1), year2 = c(1, 1, 
NA, 1, 1, 0, NA, NA, 0, 1), year3 = c(1, 0, NA, 1, 0, 0, NA, 
0, 0, 1)), class = "data.frame", row.names = c(NA, -10L))

head(df)

   W_or_not  Univ year1 year2 year3
1       chr univ1     1     1     1
2         W univ1     0     1     0
3       chr univ1     0    NA    NA
4         W univ1    NA     1     1
5       chr univ1     0     1     0
6         W univ2     1     0     0
7       chr univ2     1    NA    NA
8       chr univ2    NA    NA     0
9       chr univ2     0     0     0
10      chr univ2     1     1     1

我建议你先计算每所大学的学生总数（第一个表），然后计算每所大学的女生总数（第二个表）。最后，您可以通过计算每所大学的女性百分比来更新第一个表，如下所示：

### First table
df_sum_univ <- df %>%
  group_by(Univ) %>%
  summarise_if(is.numeric, .funs = ~ sum(.x, na.rm = TRUE)) %>%
  rename_if(is.numeric, ~paste0('sum_', .))

### Second one
df_sum_univ_F <- df %>%
  filter(W_or_not == 'W') %>%
  group_by(Univ) %>%
  summarise_if(is.numeric, .funs = ~ sum(.x, na.rm = TRUE)) %>%
  rename_if(is.numeric, ~paste0('perc_w_', .))

### Updating the first one in a new table
df_final <- df_sum_univ %>%
  bind_cols(df_sum_univ_F[,2:4]/df_sum_univ[,2:4]*100)

head(df_final)
# A tibble: 2 × 7
  Univ  sum_year1 sum_year2 sum_year3 perc_w_year1 perc_w_year2 perc_w_year3
  <chr>     <dbl>     <dbl>     <dbl>        <dbl>        <dbl>        <dbl>
1 univ1         1         4         2          0             50           50
2 univ2         3         1         1         33.3            0            0