how to merge data across data.frame rows based on

Question

How would you merge values between rows that have identical values in id_3? I'm sure there's a better name for the question title but I'm struggling to find the appropriate operation/function name(s) for this procedure.

library(tidyverse)
id_1 <- c("x12", NA, "a_bc", NA)
id_2 <- c(NA, "gye", NA, "ab_c")
id_3 <- c("qwe", "ert", "abc", "abc")
param_1 <- c(0.21, 1.5, 0.23, NA)
param_12 <- c(0.05, 4.4, NA, 6.3)

df <- data.frame(id_1, id_2, id_3, param_1, param_12)
as_tibble(df)

#    id_1  id_2  id_3  param_1 param_12
#   <fct> <fct> <fct>   <dbl>    <dbl>
#  1 x12   NA    qwe      0.21     0.05
#  2 NA    gye   ert      1.5      4.4 
#  3 a_bc  NA    abc      0.23     NA   
#  4 NA    ab_c  abc      NA       6.3 

desired df:

#    id_1  id_2  id_3  param_1 param_12
#   <fct> <fct> <fct>   <dbl>    <dbl>
#  1 x12   NA    qwe     0.21     0.05
#  2 NA    gye   ert     1.5      4.4 
#  3 a_bc  ab_c  abc     0.23     6.3 

update - with additional column formats such as character:

id_1 <- c("x12", NA, "a_bc", NA)
id_2 <- c(NA, "gye", NA, "ab_c")
id_3 <- c("qwe", "ert", "abc", "abc")
param_1 <- c(0.21, 1.5, 0.23, NA)
param_12 <- c(0.05, 4.4, NA, 6.3)
desc_1 <- c("st", NA, "ko", NA)
desc_2 <- c(NA, "lo", NA, "vf")

df <- data.frame(id_1, id_2, id_3, param_1, param_12, desc_1, desc_2)
df <- df %>% mutate(desc_1 = as.character(desc_1), 
                    desc_2 = as.character(desc_2))

 # A tibble: 4 x 7
 #  id_1  id_2  id_3  param_1 param_12 desc_1 desc_2
 #  <fct> <fct> <fct>   <dbl>    <dbl> <chr>  <chr> 
 #1 x12   NA    qwe      0.21     0.05 st     NA    
 #2 NA    gye   ert      1.5      4.4  NA     lo    
 #3 a_bc  NA    abc      0.23    NA    ko     NA    
 #4 NA    ab_c  abc     NA        6.3  NA     vf 

df <- df %>% group_by(id_3) %>% 
      summarise_all(list(~ if(all(is.na(.))) NA else .[!is.na(.)]

`Error: Column `desc_1` can't promote group 1 to character`

Answer 1

We can group by 'id_3' and summarise all the columns to return NA if all the values in that particular column is NA for a group or else remove the NA and return the first non-NA element

library(tidyverse)
df %>% 
   group_by(id_3) %>% 
   summarise_all(list(~ if(all(is.na(.))) NA else .[!is.na(.)][1]))
# A tibble: 3 x 5
#  id_3  id_1  id_2  param_1 param_12
#  <fct> <fct> <fct>   <dbl>    <dbl>
#1 abc   a_bc  ab_c     0.23     6.3 
#2 ert   <NA>  gye      1.5      4.4 
#3 qwe   x12   <NA>     0.21     0.05

-Rstudio

Update

For the second dataset, after the columns were converted to character class, OP encountered error with if(all(is.na(.))) NAl. If the columns are of same type, we can specify NA_character, NA_real_, NA_integer_ to dispatch the correct NA for each type. Here, we cannot do that, but there is one hacky option to return the 1st NA element in that column which will also have the correct type

df %>% 
   group_by(id_3) %>% 
   summarise_all(list(~ if(all(is.na(.))) .[!is.na(.)][1] 
           else .[!is.na(.)]))
# A tibble: 3 x 7
#  id_3  id_1  id_2  param_1 param_12 desc_1 desc_2
#  <fct> <fct> <fct>   <dbl>    <dbl> <chr>  <chr> 
#1 abc   a_bc  ab_c     0.23     6.3  ko     vf    
#2 ert   <NA>  gye      1.5      4.4  <NA>   lo    
#3 qwe   x12   <NA>     0.21     0.05 st     <NA>