left_join two data frames and overwrite - r

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 values provides a data.table option, but I'd like to know if there is a way to do this with dplyr. I've tried all of the _join options but none seem to do this. Is there a way to do this with dplyr?
Here is an example:
df1 <- data.frame(y = c("A", "B", "C", "D"), x1 = c(1,2,NA, 4))
df2 <- data.frame(y = c("A", "B", "C"), x1 = c(5, 6, 7))
Desired output:
y x1
1 A 5
2 B 6
3 C 7
4 D 4

Answer 1

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.

Answer 2

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)