Overwrite left_join dplyr to update data - r

Question

My question is similar to this one however I have additional columns in the LHS that should be kept https://stackoverflow.com/a/35642948/9285732
y is a subset of x with updated values for val1. In x I want to overwrite the relevant values but keep the rest.
Sample data:
library(tidyverse)
x <- tibble(name = c("hans", "dieter", "bohlen", "hans", "dieter", "alf"),
location = c(1,1,1,2,2,3),
val1 = 1:6, val2 = 1:6, val3 = 1:6)
y <- tibble(name = c("hans", "dieter", "hans"),
location = c(2,2,1),
val1 = 10)
> x
# A tibble: 6 x 5
name location val1 val2 val3
<chr> <dbl> <int> <int> <int>
1 hans 1 1 1 1
2 dieter 1 2 2 2
3 bohlen 1 3 3 3
4 hans 2 4 4 4
5 dieter 2 5 5 5
6 alf 3 6 6 6
> y
# A tibble: 3 x 3
name location val1
<chr> <dbl> <dbl>
1 hans 2 10
2 dieter 2 10
3 hans 1 10
> # desired output
> out
# A tibble: 6 x 5
name location val1 val2 val3
<chr> <dbl> <dbl> <int> <int>
1 hans 1 10 1 1
2 dieter 1 2 2 2
3 bohlen 1 3 3 3
4 hans 2 10 4 4
5 dieter 2 10 5 5
6 alf 3 6 6 6
I wrote a function that is doing what I want, however it's quite cumbersome. I wonder if there's a more elegant way or even a dplyr function that I'm unaware of.
overwrite_join <- function(x, y, by = NULL){
bycols <- which(colnames(x) %in% by)
commoncols <- which(colnames(x) %in% colnames(y))
extracols <- which(!(colnames(x) %in% colnames(y)))
x1 <- anti_join(x, y, by = by) %>%
bind_rows(y) %>%
select(commoncols) %>%
left_join(x %>% select(bycols, extracols), by = by)
out <- x %>% select(by) %>%
left_join(x1, by = by)
return(out)
}
overwrite_join(t1, t2, by = c("name", "location"))

Answer 1

You could do something along the lines of
> x %>%
left_join(y = y, by = c("name", "location")) %>%
within(., val1.x <- ifelse(!is.na(val1.y), val1.y, val1.x)) %>%
select(-val1.y)
# # A tibble: 6 x 5
# name location val1.x val2 val3
# <chr> <dbl> <dbl> <int> <int>
# 1 hans 1 10 1 1
# 2 dieter 1 2 2 2
# 3 bohlen 1 3 3 3
# 4 hans 2 10 4 4
# 5 dieter 2 10 5 5
# 6 alf 3 6 6 6
and then rename val1.x.

Answer 2

My package safejoin might help. Only available on github so far but has a feature designed just for that.
The conflict argument below must be fed a function or lambda to deal with conflicting columns when joining, here we want in priority a value from the y data frame so we can use dplyr::coalesce() there. Note that we must first coerce y$val1 as in your example it's double while x$val1 is integer. Your real case might not need this step.
# remotes::install_github("moodymudskipper/safejoin")
library(safejoin)
library(dplyr)
y$val1 <- as.integer(y$val1)
safe_left_join(x, y, by = c("name", "location"), conflict = ~coalesce(.y, .x))
#> # A tibble: 6 x 5
#> name location val1 val2 val3
#> <chr> <dbl> <int> <int> <int>
#> 1 hans 1 10 1 1
#> 2 dieter 1 2 2 2
#> 3 bohlen 1 3 3 3
#> 4 hans 2 10 4 4
#> 5 dieter 2 10 5 5
#> 6 alf 3 6 6 6
Edit : inspired by your own solution here's a 100% dplyr option that you might like better, just like your option though it's not a proper join!
bind_rows(y, x) %>%
group_by(name, location) %>%
summarize_all(~na.omit(.x)[[1]]) %>%
ungroup()
#> # A tibble: 6 x 5
#> name location val1 val2 val3
#> <chr> <dbl> <dbl> <int> <int>
#> 1 alf 3 6 6 6
#> 2 bohlen 1 3 3 3
#> 3 dieter 1 2 2 2
#> 4 dieter 2 10 5 5
#> 5 hans 1 10 1 1
#> 6 hans 2 10 4 4

Answer 3

Try dplyr::coalesce
x %>%
left_join(y, by = c("name", "location")) %>%
mutate(val1 = coalesce(val1.y, val1.x)) %>%
select(-val1.x, -val1.y)
# A tibble: 6 x 5
name location val2 val3 val1
<chr> <dbl> <int> <int> <int>
1 hans 1 1 1 10
2 dieter 1 2 2 2
3 bohlen 1 3 3 3
4 hans 2 4 4 10
5 dieter 2 5 5 10
6 alf 3 6 6 6

Answer 4

This is the idiom I now use. It does not preserve the row or column order in x, if that is important.
I like it because I can evaluate the values to just before the bind_rows(), do a visual inspection, and if I like it, put the fixed rows back onto the base dataframe.
library(dplyr)
x <- tibble(name = c("hans", "dieter", "bohlen", "hans", "dieter", "alf"),
location = c(1,1,1,2,2,3),
val1 = 1:6, val2 = 1:6, val3 = 1:6)
y <- tibble(name = c("hans", "dieter", "hans"),
location = c(2,2,1),
val1 = 10)
keys <- c("name", "location")
out <- x %>%
semi_join(y, keys) %>%
select(-matches(setdiff(names(y), keys))) %>%
left_join(y, keys) %>%
bind_rows(x %>% anti_join(y, keys))
out %>%
print()
#> # A tibble: 6 x 5
#> name location val2 val3 val1
#> <chr> <dbl> <int> <int> <dbl>
#> 1 hans 1 1 1 10
#> 2 hans 2 4 4 10
#> 3 dieter 2 5 5 10
#> 4 dieter 1 2 2 2
#> 5 bohlen 1 3 3 3
#> 6 alf 3 6 6 6
Created on 2019-12-12 by the reprex package (v0.3.0)