Merge R dataframes - r

Question

What I would like to do is a bit difficult to explain, but the code would look something like this:
df_merged <- merge(df1, df2,
by.x = c("City", "District"),
by.y = c("City", "District" | "Area"),
all.x = TRUE)
Here "|", in the code above, would mean "OR".
The basic point is that I would like to merge the two frames by two columns. "City" matches for both data frames. However, I also need there to be a match based on "District".
The problem is that, due to human error while the dataset was originally made, in df2 some values for "District" were put in the "Area" column. Hence, ideally, if we have "District" being "A" in df1, then the merge occurs if "A" is found in either the "District" or "Area" column from df2.
Here is an example:
df1 <- data.frame(City = c("A", "B"), District = c("cc", "dd"))
df2 <- data.frame(City = c("A", "A", "B", "B"), Code = c("1a","2a","3a","4a"), District = c("cc", "Apple", "Pear", "Orange"), Area = c("e", "a", "dd", "f"))
df3 <- data.frame(City = c("A", "B"), District = c("cc","dd"), Code = c("1a", "3a"))
Here df3 is what I am aiming for! As you can see in df2, there is something messed up and the values for district got into the wrong column. In my original dataset, it is difficult to clean up this error.
> df1
City District
1 A cc
2 B dd
> df2
City Code District Area
1 A 1a cc e
2 A 2a Apple a
3 B 3a Pear dd
4 B 4a Orange f
> df3
City District Code
1 A cc 1a
2 B dd 3a

Answer 1

Here are a couple of options. One idea is that district might be NA if it is actually in Area. In this case you could coalesce the NA and join on District. Alternatively, you could map out rows in df2 that match rows in df1 and then expand df1 to accommodate those rows.
library(tidyverse)
df1 <- tibble(City = c(rep("Chicago", 4), rep("Tucson", 3)),
District = c("A", "A", "A", "B", "A", "B", "C"))
df2 <- tibble(City = c("Chicago", "Chicago", "Tucson", "Tucson"),
District = c("A", "B", NA, "C"),
Area = c("10", "30", "A", "20"),
value = c(1:4))
#option 1
left_join(df1,
df2 |>
mutate(District = coalesce(District, Area)),
by = c("City", "District" ))
#> # A tibble: 7 x 4
#> City District Area value
#> <chr> <chr> <chr> <int>
#> 1 Chicago A 10 1
#> 2 Chicago A 10 1
#> 3 Chicago A 10 1
#> 4 Chicago B 30 2
#> 5 Tucson A A 3
#> 6 Tucson B <NA> NA
#> 7 Tucson C 20 4
#option 2
df1 |>
mutate(matches = map2(City, District,
~filter(df2, City == .x & (District == .y | Area == .y))|>
select(-City, -District))) |>
unnest_wider(matches)
#> # A tibble: 7 x 4
#> City District Area value
#> <chr> <chr> <chr> <int>
#> 1 Chicago A 10 1
#> 2 Chicago A 10 1
#> 3 Chicago A 10 1
#> 4 Chicago B 30 2
#> 5 Tucson A A 3
#> 6 Tucson B <NA> NA
#> 7 Tucson C 20 4