Collapse rows in R-CodePudding

I have a dataframe

df <- data.frame(id1 = c("a" , "b", "b", "c"),
                 id2 = c(NA,"a","a",NA),
                 id3 = c("a", "a", "a", "e"),
                 n1 = c(2,2,2,3),
                 n2 = c(2,1,1,1),
                 n3 = c(0,1,1,3),
                 n4 = c(0,1,1,2))

I want to collapse the 2nd and 3rd rows into one. Afterwards, I will do aggregate by column id3 sharing same character (i.e. a).

My real dataframe is long contaning many different latin names, filter by name i.e. a doesn´t make sense this case. I am thinking to collapse rows with the condition id3 == id2, but I could not do it. Any sugesstions for me?

My desired out put like this

id1 id2 id3 n1 n2 n3 n4
a   NA  a   2  2  0  0
b   a   a   2  1  1  1
c   NA  e   3  1  3  2

#Afterthat, it should be
id1 id3 n1 n2 n3 n4
a    a   4  3  1  1
c    e   3  1  3  2

(I just updated the dataframe, sorry for my mistake)

CodePudding user response：

We get the distinct rows to generate the first expected

library(dplyr)
df %>%
  distinct
  id1  id2 id3 n1 n2 n3 n4
1   a <NA>   a  2  2  0  0
2   b    a   a  2  1  1  1
3   c <NA>   e  3  1  3  2

The final output we can get from the above, i.e. after the distinct step, do a group by coalesced 'id2', 'id1' along with 'id3' and then get the sum of numeric columns

df %>%
    distinct %>%
    group_by(id1 = coalesce(id2, id1), id3) %>% 
    summarise(across(where(is.numeric), sum), .groups = 'drop')

-output

# A tibble: 2 × 6
  id1   id3      n1    n2    n3    n4
  <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 a     a         4     3     1     1
2 c     e         3     1     3     2

CodePudding user response：

Here is a slightly different way using slice after group_by instead of distinct:

df %>% 
  group_by(id1, id3) %>% 
  dplyr::slice(1L) %>% 
  mutate(id1 = coalesce(id2,id1)) %>% 
  summarise(across(where(is.numeric), sum))

output:

  id1   id3      n1    n2    n3    n4
  <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 a     a         4     3     1     1
2 c     e         3     1     3     2