这里有一个替代方案，如果你感兴趣：

library(data.table)
setDT(df1)
setDT(df2)

dcast(
  merge(melt(df1,"v1"), melt(df2,"v1"), by=c("v1", "variable"),all = T)[!is.na(value.y), value.x:=value.y],
  v1~variable, value.var = "value.x"
)

输出：

v1     v2     v3     v4     v5
   <char> <char> <char> <char> <char>
1:    OJK     BR    ILL     PE   <NA>
2:   OPCS     BR    ILL   <NA>     P2
3:    RDA    COL    ILL   <NA>     P3

library(dplyr)

new <- setdiff(names(df2), names(df1))
merge_vars <- "v1"

rows_patch(df1, select(df2, -all_of(new)), by = merge_vars) |>
  left_join(select(df2, all_of(c(merge_vars, new))), by = merge_vars)

?rows_patch仅覆盖NA值。如果你想更新任何值，你可以使用rows_update。注意，要使用rows_patch，df2中的所有列都需要存在于df1中，这就是为什么我使用setdiff来暂时删除“v5”。

输出

v1  v2  v3   v4 v5
1  RDA COL ILL <NA> P3
2 OPCS  BR ILL <NA> P2

我写了一个自定义函数multiple_replacer()：

library(purrr)
library(dplyr)

multiple_replacer <- function(x, y){
bind_rows(list(A = df1, B = df2), .id = 'id') %>%
  reframe(cur_data()[seq(nrow(df1)), ], .by = id) %>%
  group_split(id) %>%
  map(~ select(., -id)) %>% 
  set_names(c("mat1", "mat2")) %>%
  imap(function(df, name) assign(name, as.matrix(df), envir = .GlobalEnv)) %>%
  { mat1 <- get("mat1", envir = .GlobalEnv)
  mat2 <- get("mat2", envir = .GlobalEnv)
  mat1[is.na(mat1)] <- mat2[is.na(mat1)]
  as.data.frame(mat1)
  }
}

multiple_replacer(df1, df2)

v1  v2  v3   v4   v5
1  RDA COL ILL <NA>   P3
2 OPCS  BR ILL <NA>   P2
3  OJK  BR ILL   PE <NA>

另一种选择是使用data.table。

setDT(df1)
setDT(df2)

df1[df2, on="v1", 
    `:=`(v2 = fcoalesce(v2, i.v2),
         v3 = fcoalesce(v3, i.v3),
         v5 = i.v5)]

输出

v1     v2     v3     v4     v5
1:    RDA    COL    ILL   <NA>     P3
2:   OPCS     BR    ILL   <NA>     P2
3:    OJK     BR    ILL     PE   <NA>