Applying group_by and summarise(sum) but keep columns with non-relevant conflicting data?

Question

My question is very similar to Applying group_by and summarise on data while keeping all the columns' info but I would like to keep columns which get excluded because th

Answer 1

Here's the data.table solution, I'm assuming you want the mean() of Proportion, since these grouped proportions are likely not additive.

setDT(df)

df[, .(Type =paste(Type,collapse="_"), 
  Proportion=mean(Proportion),N= sum(N),C=sum(C)), by=.(Label,Code)]
  [order(Label)]

   Label Code                        Type Proportion  N  C
1:  203c    c                   wholefish   1.000000  1  1
2:  203c    a                       flesh   1.000000  2  2
3:  204a    a               flesh_formula   0.499995  8  8
4:  204a    b     fleshdelip_formuladelip   0.499995 10 10
5:  204a    c           formula_wholefish   0.499995 16 16
6:  204a    d formuladelip_wholefishdelip   0.499995 18 18

I'm not sure this is the cleanest dplyr solution, but it works:

df %>% group_by(Label, Code) %>% 
  mutate(Type = paste(Type,collapse="_")) %>% 
  group_by(Label,Type,Code) %>% 
  summarise(N=sum(N),C=sum(C),Proportion=mean(Proportion))

Note the key here is to re-group once you create the combined Type column.

   Label                        Type   Code     N     C Proportion
                                  
1   203c                       flesh      a     2     2   1.000000
2   203c                   wholefish      c     1     1   1.000000
3   204a               flesh_formula      a     8     8   0.499995
4   204a     fleshdelip_formuladelip      b    10    10   0.499995
5   204a           formula_wholefish      c    16    16   0.499995
6   204a formuladelip_wholefishdelip      d    18    18   0.499995