如何根据其他列的字符值（不包括NA和值）创建第三列？

Question

How can I create a new column called 'title' based on values of other columns attributes?

I have shown the example below, where 'title' needs to be created based on the columns Post, Tel, Surname, and Emp. 'title' just indicates which values are not NA. I have this

   ID1    ID2   Post     Tel          Surname   Emp        
   <chr> <chr> <chr>   <chr>          <chr>   <chr>        
 1 S04   S03   NA      369 990247      NA      NA      
 2 S14   S08   NA      069 990351      NA      NA      
 3 S18   S03   N165HT  NA             Jones    NA      
 4 S19   S13   NA      3069 90685      NA      NA      
 5 S20   S16   NA      3069 90954      NA      NA      
 6 S20   S17   CO19RF  NA              NA      Ocean   

And I want to create this:

   ID1    ID2   Post     Tel          Surname   Emp     title    
   <chr> <chr> <chr>   <chr>          <chr>   <chr>     <chr>    
 1 S04   S03   NA      369 990247      NA      NA       Tel
 2 S14   S08   NA      069 990351      NA      NA       Tel
 3 S18   S03   N165HT  NA             Jones    NA       Post,Surname
 4 S19   S13   NA      3069 90685      NA      NA       Tel
 5 S20   S16   NA      3069 90954      NA      NA       Tel
 6 S20   S17   CO19RF  NA              NA      Ocean    Post,Emp

Answer 1

An option here would be to gather into 'long' format (while removing the NA elements with na.rm = TRUE ) after creating a unique row identiier ('rn'), grouped by 'rn', paste the 'key' elements in summarise and bind with the original dataset

library(tidyverse)
df1 %>%
   rownames_to_column('rn') %>%
   gather(key, val, Post:Emp, na.rm = TRUE) %>%
   group_by(rn) %>%
   summarise(title = toString(key)) %>%
   ungroup %>%
   select(-rn) %>%
   bind_cols(df1, .)
#   ID1 ID2   Post        Tel Surname   Emp         title
#1 S04 S03   <NA> 369 990247    <NA>  <NA>           Tel
#2 S14 S08   <NA> 069 990351    <NA>  <NA>           Tel
#3 S18 S03 N165HT       <NA>   Jones  <NA> Post, Surname
#4 S19 S13   <NA> 3069 90685    <NA>  <NA>           Tel
#5 S20 S16   <NA> 3069 90954    <NA>  <NA>           Tel
#6 S20 S17 CO19RF       <NA>    <NA> Ocean     Post, Emp

data

df1 <- structure(list(ID1 = c("S04", "S14", "S18", "S19", "S20", "S20"
), ID2 = c("S03", "S08", "S03", "S13", "S16", "S17"), Post = c(NA, 
NA, "N165HT", NA, NA, "CO19RF"), Tel = c("369 990247", "069 990351", 
NA, "3069 90685", "3069 90954", NA), Surname = c(NA, NA, "Jones", 
NA, NA, NA), Emp = c(NA, NA, NA, NA, NA, "Ocean")), row.names = c("1", 
"2", "3", "4", "5", "6"), class = "data.frame")

Answer 2

In Base R:

cols <- c("Post", "Tel", "Surname", "Emp")

d$title <- apply(d[, cols], 1, function(x){
  paste(cols[which(!is.na(x))], collapse = ",")
})