R data frame create new column from first existing column or NA if no existing columns

The following function return first existing column in data frame, or NA if no existing columns

library(dplyr, warn.conflicts = FALSE)
#> Warning: package 'dplyr' was built under R version 4.2.3

# `dplyr::coalesce` return fist non-missing, but these columns must exist

return_exist_var <- function(.var1, .var2, .fill = NA, .data) {
  # get the . from magrittr::%>%
  .data <- get(".", parent.frame())
  stopifnot(is.data.frame(.data))
  .var1 <- rlang::enexpr(.var1)
  .var2 <- rlang::enexpr(.var2)
  if (rlang::as_string(.var1) %in% names(.data)) {
    return(rlang::parse_expr(rlang::as_string(.var1)))
  } else if (rlang::as_string(.var2) %in% names(.data)) {
    return(rlang::parse_expr(rlang::as_string(.var2)))
  } else {
    return(.fill)
  }
}

df <- data.frame(
  ID = c("ID01", "ID01", "ID02", "ID02"),
  v1 = c("a", "b", "c", NA),
  v2 = c("A", "B", "C", "C")
)

return first existing column

df %>% dplyr::mutate(NEW = !!return_exist_var(v1))
#>     ID   v1 v2  NEW
#> 1 ID01    a  A    a
#> 2 ID01    b  B    b
#> 3 ID02    c  C    c
#> 4 ID02 <NA>  C <NA>

return first existing column in two column

df %>% dplyr::mutate(NEW = !!return_exist_var(v2, v1))
#>     ID   v1 v2 NEW
#> 1 ID01    a  A   A
#> 2 ID01    b  B   B
#> 3 ID02    c  C   C
#> 4 ID02 <NA>  C   C

return second existing column in two column

df %>% dplyr::mutate(NEW = !!return_exist_var(v1_x, v2))
#>     ID   v1 v2 NEW
#> 1 ID01    a  A   A
#> 2 ID01    b  B   B
#> 3 ID02    c  C   C
#> 4 ID02 <NA>  C   C

return NA if two column not eixst

df %>% dplyr::mutate(NEW = !!return_exist_var(v1_X, v2_X))
#>     ID   v1 v2 NEW
#> 1 ID01    a  A  NA
#> 2 ID01    b  B  NA
#> 3 ID02    c  C  NA
#> 4 ID02 <NA>  C  NA

work well on grouped data frame

df %>%
  dplyr::group_by(ID) %>%
  dplyr::mutate(NEW = !!return_exist_var(v1))
#> # A tibble: 4 × 4
#> # Groups:   ID [2]
#>   ID    v1    v2    NEW  
#>   <chr> <chr> <chr> <chr>
#> 1 ID01  a     A     a    
#> 2 ID01  b     B     b    
#> 3 ID02  c     C     c    
#> 4 ID02  <NA>  C     <NA>

Problem

How to update the function, then call function like below without !!, or define a new same function

df %>% dplyr::mutate(NEW = return_exist_var(v1))

Here is a simplified version of your function that relies on using any_of() to handle missing vars. As any_of() requires a character vector, the arguments need to be deparsed before use.

library(dplyr)

return_exist_var <- function(...) {
  var_list <-  match.call(expand.dots = FALSE)$...
  c(pick(any_of(sapply(var_list, deparse))), NA)[[1]]
  }

df %>%
  dplyr::mutate(NEW = return_exist_var(v1))

    ID   v1 v2  NEW
1 ID01    a  A    a
2 ID01    b  B    b
3 ID02    c  C    c
4 ID02 <NA>  C <NA>

df %>%
  dplyr::mutate(NEW = return_exist_var(v2, v1))

    ID   v1 v2 NEW
1 ID01    a  A   A
2 ID01    b  B   B
3 ID02    c  C   C
4 ID02 <NA>  C   C

df %>%
  dplyr::mutate(NEW = return_exist_var(v1_x, v2))

    ID   v1 v2 NEW
1 ID01    a  A   A
2 ID01    b  B   B
3 ID02    c  C   C
4 ID02 <NA>  C   C

df %>% 
  dplyr::mutate(NEW = return_exist_var(v1_X, v2_X))

    ID   v1 v2 NEW
1 ID01    a  A  NA
2 ID01    b  B  NA
3 ID02    c  C  NA
4 ID02 <NA>  C  NA

# Works if data is grouped
df %>%
  mutate(NEW = return_exist_var(v1), .by = ID)

    ID   v1 v2  NEW
1 ID01    a  A    a
2 ID01    b  B    b
3 ID02    c  C    c
4 ID02 <NA>  C <NA>