left join + anti join in R

Is there a way to perform a left_join that excludes matches?

A<-sample(1:100, 10)
B<-sample(1:100, 10)
C<-sample(1:100, 10)
year<-sample(1000:2000,10)

x<-tibble(year,A,B)%>%
  mutate(B=B*12)
y<-tibble(year,B,C)

I want x + the cols from y that are not in x

EDIT: Year is the same across all the datasets, but I have transformed the B column in x so that it no longer matches the one in set y.

I want to finish with a frame: x(year,A,B,C) where the B is the one that was in x originally.

I tried with coalesce(x,y) but to no avail, anyone have any clues about this?
Am I trying to do something really stupid or missing some detail?

I know that I could just drop the columns I don''t want from y, or that I could just join by = "year" and then drop the ones with the .y suffix, but hoping that there a nicer way to accomplish this!

If I've understood correctly, one potential solution is to use the powerjoin package to do a power_left_join() and 'select' the value from x if there are any conflicts, e.g.

library(tidyverse)
# install.packages("powerjoin")
library(powerjoin)

set.seed(42)

A <- sample(1:100, 10)
B <- sample(1:100, 10)
C <- sample(1:100, 10)
year <- sample(1000:2000, 10)

y <- tibble(year, B, C)
x <- tibble(year, A, B)

# change the first "B" value in "x"
x$B[1] <- 1000

x %>%
  power_left_join(y, by = "year", conflict = ~.x)
#> # A tibble: 10 × 4
#>     year     A     C     B
#>    <int> <int> <int> <dbl>
#>  1  1313    49    34  1000
#>  2  1647    65    92    20
#>  3  1291    25     3    26
#>  4  1835    74    58     3
#>  5  1981    18    42    41
#>  6  1145   100    24    89
#>  7  1620    47    30    27
#>  8  1347    24    43    36
#>  9  1196    71    15     5
#> 10  1515    89    22    84

However, the alternative approach of dropping the ".y" columns and renaming the ".x" columns isn't that much 'harder' (this is what I personally use).

x %>%
  left_join(y, by = "year") %>%
  select(-ends_with(".y")) %>%
  rename_with(~gsub(".x", "", .x))
#> # A tibble: 10 × 4
#>     year     A     B     C
#>    <int> <int> <dbl> <int>
#>  1  1313    49  1000    34
#>  2  1647    65    20    92
#>  3  1291    25    26     3
#>  4  1835    74     3    58
#>  5  1981    18    41    42
#>  6  1145   100    89    24
#>  7  1620    47    27    30
#>  8  1347    24    36    43
#>  9  1196    71     5    15
#> 10  1515    89    84    22

^{Created on 2023-07-06 with reprex v2.0.2}

We could identify the new column names with setdiff (I'm using base::setdiff to avoid the potential conflict if someone has loaded lubridate.), and then join using year + those columns from y.

new_cols <- base::setdiff(colnames(y), colnames(x))

left_join(x, y[,c("year", new_cols)])


Joining with `by = join_by(year)`
# A tibble: 10 × 4
    year     A     B     C
   <int> <int> <int> <int>
 1  1336     8   100    66
 2  1643     1    57    76
 3  1896    58    24    15
 4  1965    65    66    69
 5  1361    74    18    43
 6  1526    92    38    78
 7  1870    36     8    68
 8  1151    15    86    92
 9  1748    35    16    19
10  1624    82    30    42