CODEWITHSUNDEEP
rdplyr
silver arrow
silver arrow

Reputation: 117

Merging complex data.frame rows by a common identifier

example <- structure(list(id = c(1, 1, 2, 2, 3, 3, 4), var1 = c(5, NA, 8, 
9, 10, NA, 3), var2 = c(23, NA, 8, 9, NA, NA, 6), var3 = c(NA, 
NA, NA, NA, NA, NA, NA), var4 = c(5, 5, 6, 6, 7, 7, 8)), row.names = c(NA, 
-7L), class = "data.frame")

I have a large and complex data frame where the same measure may be present more than once, and the different entries for the same measure may contain different data. I have created a simplified example above. What I would like to do is merge this down as much as possible. I think it's easiest for me to explain if I consult the example:

print(example)

  id var1 var2 var3 var4
1  1    5   23   NA    5
2  1   NA   NA   NA    5
3  2    8    8   NA    6
4  2    9    9   NA    6
5  3   10   NA   NA    7
6  3   NA   NA   NA    7
7  4    3    6   NA    8

Thus, the desired output looks like:

  id var1 var2 var3 var4
1  1    5   23   NA    5
2  2    8    8   NA    6
3  2    9    9   NA    6
4  3   10   NA   NA    7
5  4    3    6   NA    8

There are similar questions on StackOverflow, but none of them deal with all of these features simultaneously. The closest I've got to this is:

example %>% 
   group_by(id) %>% 
   summarise_all(funs(list(na.omit(.))))

     id var1      var2      var3      var4     
  <dbl> <list>    <list>    <list>    <list>   
1     1 <dbl [1]> <dbl [1]> <lgl [0]> <dbl [2]>
2     2 <dbl [2]> <dbl [2]> <lgl [0]> <dbl [2]>
3     3 <dbl [1]> <dbl [0]> <lgl [0]> <dbl [2]>
4     4 <dbl [1]> <dbl [1]> <lgl [0]> <dbl [1]>

Which essentially does what I want, but puts everything into lists, which hinders any downstream work with the dataframe. Instead of getting two rows for ID 2 I get a single row, which contains a list with 8 and 9 for var1, for example. This also occurs for matching variables: for ID1, var4 you get a list containing 5, 5.

Upvotes: 2

Views: 56

Answers (1)

Ronak Shah
Ronak Shah

Reputation: 388982

You can do this across. For each column and id, you can keep only unique non-NA values,

library(dplyr)

example %>% 
  group_by(id) %>% 
  summarise(across(.fns = ~{x <- unique(na.omit(.));x[1:max(1, length(x))]}))
  #With summarise_all you can do
  #summarise_all(~{x <- unique(na.omit(.));x[1:max(1, length(x))]})


#    id  var1  var2 var3   var4
#  <dbl> <dbl> <dbl> <lgl> <dbl>
#1     1     5    23 NA        5
#2     2     8     8 NA        6
#3     2     9     9 NA        6
#4     3    10    NA NA        7
#5     4     3     6 NA        8

Upvotes: 2

Related Questions