I have a dataframe which looks like this:
ID Name m1 m2 m3 m4 m5 m6 m7 m8
37 Grötlingbo Roes II 0 0 0 0 1 0 0 0
37 Grötlingbo Roes II 0 1 0 0 0 0 0 0
37 Grötlingbo Roes II 0 0 1 0 0 0 0 0
123 Hablingbo Havor III 0 0 0 0 0 0 0 0
123 Hablingbo Havor III 0 0 0 0 0 0 1 0
38 Hablingbo Havor I 0 0 1 0 0 0 0 0
38 Hablingbo Havor I 0 0 0 0 0 0 0 0
38 Hablingbo Havor I 0 0 0 0 0 0 0 0
38 Hablingbo Havor I 0 0 0 1 0 0 0 0
38 Hablingbo Havor I 0 1 0 0 0 1 0 0
I need to combine rows with the same name and their observations in columns m1 to m8 into one row. Only the row names are identical but not the observations. I know that the observations will not collide because I was already tidying the dataframe before. Since I don't want to add new columns, I cannot use pivot_wider. And I believe the unite function is only for uniting columns, not rows. I feel like it could be done with group_by but don't know what I need to add. I was already reading many entries here but did not find an answer to my problem yet. Does anyone have a solution?
Try the summarize(across()) approach:
library(dplyr)
df %>%
group_by(ID, Name) %>%
summarize(across(starts_with("M"), sum))
`summarise()` regrouping output by 'ID' (override with `.groups` argument)
# A tibble: 3 x 10
# Groups: ID [3]
ID Name m1 m2 m3 m4 m5 m6 m7 m8
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 37 "Grötlingbo Roes II" 0 1 1 0 1 0 0 0
2 38 "Hablingbo Havor I" 0 1 1 1 0 1 0 0
3 123 "Hablingbo Havor III" 0 0 0 0 0 0 1 0