left_join two data frames and overwrite

Question

I\'d like to merge two data frames where df2 overwrites any values that are NA or present in df1. Merge data frames and overwrite val

Answer 1

This is the idiom I now use, as, in addition, it handles keeping columns that are not part of the update table. I use some different names than from the OP, but the flavor is similar.

The one thing I do is create a variable for the keys used in the join, as I use that in a few spots. But otherwise, it does what is desired.

In itself it doesn't handle the action of, for example, "update this row if a value is NA", but you should exercise that condition when creating the join table.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

.keys <- c("key1", "key2")

.base_table <- tribble(
    ~key1, ~key2, ~val1, ~val2,
    "A", "a", 0, 0,
    "A", "b", 0, 1,
    "B", "a", 1, 0,
    "B", "b", 1, 1)

.join_table <- tribble(
    ~key1, ~key2, ~val2,
    "A", "b", 100,
    "B", "a", 111)

# This works
df_result <- .base_table %>%
    # Pull off rows from base table that match the join table
    semi_join(.join_table, .keys) %>%
    # Drop cols from base table that are in join table, except for the key columns
    select(-matches(setdiff(names(.join_table), .keys))) %>%
    # Left join on the join table columns
    left_join(.join_table, .keys) %>%
    # Remove the matching rows from the base table, and bind on the newly joined result from above.
    bind_rows(.base_table %>% anti_join(.join_table, .keys))

df_result %>%
    print()
#> # A tibble: 4 x 4
#>   key1  key2   val1  val2
#>   <chr> <chr> <dbl> <dbl>
#> 1 A     b         0   100
#> 2 B     a         1   111
#> 3 A     a         0     0
#> 4 B     b         1     1

^{Created on 2019-12-12 by the reprex package (v0.3.0)}

Answer 2

I think what you want is to keep the values of df2 and only add the ones in df1 that are not present in df2 which is what anti_join does:

"anti_join return all rows from x where there are not matching values in y, keeping just columns from x."

My solution:

df3 <- anti_join(df1, df2, by = "y") %>% bind_rows(df2)

Warning messages:
1: In anti_join_impl(x, y, by$x, by$y) :
  joining factors with different levels, coercing to character vector
2: In rbind_all(x, .id) : Unequal factor levels: coercing to character

> df3
Source: local data frame [4 x 2]

      y    x1
  (chr) (dbl)
1     D     4
2     A     5
3     B     6
4     C     7

this line gives the desired output (in a different order) but, you should pay attention to the warning message, when working with your dataset be sure to read y as a character variable.