Concatenating strings / rows using dplyr, group_by with mutate() or summarize() & str

When concatenating strings using dplyr, group_by() and mutate() or summarize () with paste() and collapse, NA values are coerced to a string "NA".

When using str_c() instead of paste(), strings that are concatenated with NA are removed (?str_c: whenever a missing value is combined with another string the result will always be missing). When having such combinations of NA & non-NA values, how can I remove the NA instead of the non-NA in the concatenation?

See my example below:

library(dplyr)
library(stringr)
ID <- c(1,1,2,2,3,4)
string <- c(' asfdas ', 'sdf', NA,'sadf', 'NA', NA)
df <- data.frame(ID, string)
#   ID   string
# 1  1  asfdas 
# 2  1      sdf
# 3  2     <NA> # ID 2 has both NA and non-NA values
# 4  2     sadf #
# 5  3       NA
# 6  4     <NA>

Both,

df%>%
 group_by(ID)%>%
 summarize(string = paste(string, collapse = "; "))%>%
 distinct_all()

and

df_conca <-df%>%
 group_by(ID)%>%
 dplyr::mutate(string = paste(string, collapse = "; "))%>%
 distinct_all()

result in

     ID string               
1     1 " asfdas ; sdf"
2     2 "NA; sadf"           
3     3 "NA"
4     4 "NA" # NA coerced to "NA"

I.e. NA becomes "NA":

while

df %>%
  group_by(ID)%>%
  summarize(string = str_c(string, collapse = "; "))

results in:

     ID string               
1     1 " asfdas ; sdf"
2     2 NA     
3     3 "NA" 
4     4 NA

I.e. "sadf" is removed according to the str_c rule: NA combined with string, results in NA.

However, I would like to keep the true NA values (in e.g. 'ID' 4) and the strings only (in e.g. 'ID' 2), as such:

     ID string             
1     1 " asfdas ; sdf"
2     2 "sadf"           
3     3 "NA"
4     4 NA

Ideally, I would like to stay within the dplyr workflow.

This question is an extension of Concatenating strings / rows using dplyr, group_by & collapse or summarize, but maintain NA values

CodePudding user response：

Using pivot_wider and unite

library(dplyr)
library(tidyr)
library(data.table)
df %>% 
   mutate(rn = rowid(ID)) %>%
   pivot_wider(names_from = rn, values_from = string) %>% 
   unite(string, `1`, `2`, na.rm = TRUE, sep = " ; ")%>%
   mutate(string = na_if(string, ""))

-output

# A tibble: 4 x 2
     ID string          
  <dbl> <chr>           
1     1 " asfdas  ; sdf"
2     2 "sadf"          
3     3 "NA"            
4     4  <NA>

Or may also use coalesce

df %>%
    group_by(ID) %>%
    summarise(string = na_if(coalesce(str_c(string, collapse = " ; "),
     str_c(string[complete.cases(string)], collapse = " ; ")), ""))

-output

# A tibble: 4 x 2
     ID string          
  <dbl> <chr>           
1     1 " asfdas  ; sdf"
2     2 "sadf"          
3     3 "NA"            
4     4  <NA>

CodePudding user response：

Here's a solution in the dplyr framework. This removes all the 'NA' values using filter() - which initially loses ID 4 - then replaces the missing ID using a join.

df_IDs <- data.frame(ID = unique(df$ID))
df%>%
  group_by(ID)%>%
  filter(!is.na(string)) %>%
  summarize(string = paste(string, collapse = "; ")) %>%
  full_join(df_IDs, by = "ID")

results in

     ID string                  
1     1 " asfdas ; sdf"
2     2 "sadf"         
3     3 "NA"           
4     4  NA