combine rows in data frame containing NA to make complete row

user_7958356 0
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I know this is a duplicate Q but I can't seem to find the post again

Using the following data

df <- data.frame(A=c(1,1,2,2),B=c(NA,2,NA,4),C=c(3,NA,NA,5),D=c(NA,2,3,NA),E=c(5,NA,NA,4))

  A  B  C  D  E
  1 NA  3 NA  5
  1  2 NA  2 NA
  2 NA NA  3 NA
  2  4  5 NA  4

Grouping by A, I'd like the following output using a tidyverse solution

  A  B  C  D  E
  1  2  3  2  5
  2  4  5  3  4

I have many groups in A. I think I saw an answer using coalesce but am unsure how to get it work. I'd like a solution that works with characters as well. Thanks!

tidyverse coalesce r
7 Answers

A simple way is to summarise by taking the max across all columns for each group with tidyverse:

library(tidyverse)

df %>% 
  group_by(A) %>% 
  summarise(across(everything(), ~ max(., na.rm = T)))

Output

      A     B     C     D     E
  <dbl> <dbl> <dbl> <dbl> <dbl>
1     1     2     3     2     5
2     2     4     5     3     4

Another option with using na.omit and unique:

df %>% 
  group_by(A) %>% 
  summarise(across(everything(), ~ na.omit(unique(.))))
user_152931910

A different tidyverse possibility could be:

df %>%
 gather(var, val, -A, na.rm = TRUE) %>%
 group_by(A, var) %>%
 distinct(val) %>%
 spread(var, val)

      A     B     C     D     E
  <dbl> <dbl> <dbl> <dbl> <dbl>
1     1     2     3     2     5
2     2     4     5     3     4

Here it, first, performs a wide-to-long data-transformation, excluding the "A" column and removing the missing values. Second, it groups by "A" column and the variable names. Third, it removes the duplicate values. Finally, it returns the data to its original wide format.

user_59645570

This is functionally identical to @Oriol Mirosa's answer without requiring a custom function:

EDIT: NAs must be omitted as per @thelatemail's comment. This answer was also given by @MrFlick in the duplicate thread linked above.

df %>% group_by(A) %>% summarise_all(~first(na.omit(.)))

I wanted to add to this as it seems to come up regularly for me and I've revisited this thread many times. @Oriol Mirosa's answer works, however I'm resistant to it because it's just complex enough to be difficult to remember (hence my return to this thread).

Personally, I also don't like writing small custom functions like if I don't need to. Attempting to substitute coalesce_by_column with the actual coalesce call results in type errors (which I find strange as the rows aren't interacting with each other but whatever). This can be resolved by first doing mutate_all(as.character), however my goal here is to minimize syntax so it's easily remembered on the fly.

Furthermore, this substitution changes the behavior such that non-identical values within a column throws an error (why things sometimes behave slightly differently within a function is beyond me). This behavior may be preferred in some situations, however in that case I would recommend @Jerry T's solution as there is no custom function and the ones used are familiar, readable, and the ordering of them (na.omit and unique) isn't relevant.

user_104519040
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