Combine data.frames of different dimensions creating duplicates where needed /r dplyr - r

Question

I am looking for a way to combine two tables of different dimensions by ID. But the final table should have some douplicated values depending on each table.
Here is a random example:
IDx = c("a", "b", "c", "d")
sex = c("M", "F", "M", "F")
IDy = c("a", "a", "b", "c", "d", "d")
status = c("single", "children", "single", "children", "single", "children")
salary = c(30, 80, 50, 40, 30, 80)
x = data.frame(IDx, sex)
y = data.frame(IDy, status, salary)
Here is x:
IDx sex
1 a M
2 b F
3 c M
4 d F
Here is y:
IDy status salary
1 a single 30
2 a children 80
3 b single 50
4 c children 40
5 d single 30
6 d children 80
I am looking for this:
IDy sex status salary
1 a M single 30
2 a M children 80
3 b F single 50
4 c M children 40
5 d F single 30
6 d F children 80
Basically, sex should be matched to fit the needs of table y. All values in both tables should be used, the actual table is a lot larger. Not all IDs will need to duplicate.
This should be fairly simple, but I cannot find a good answer anywhere online.
Note, I don't want NAs to be introduced.
I am new in R and since I have been focused in dplyr it would help if the example comes from there. It might be simple with base R, too.
UPDATE
The bolded sentences above might be confusing to the final answer. Sorry, it has been a confusing case which I realised should include one extra column tha complicates things, but more of that later.
First, I tried to see what is happening on my actuall table and to find which suggested answer fits my needs. I removed any problematic columns for the following result. So, I checked this:
dim(x)
> [1] 231 2
dim(y)
> [1] 199 8
# left_join joins matching rows from y to x
suchait <- left_join(x, y, by= c("IDx" = "IDy"))
# inner_join retains only rows in both sets
jdobres <- inner_join(y, anno2, by = c(IDx = "IDy"))
dim(suchait) # actuall table used
> [1] 225 9
dim(jdobres)
> [1] 219 9
But why/where do they look different?
This shows the 6 rows that are introduced in suchait's table but not on jdobres and it is because of the different approach.
setdiff(suchait, jdobres )

Answer 1

Using dplyr:
library(dplyr)
df <- left_join(x, y, by = c("IDx" = "IDy"))
Your result would be:
IDx sex status salary
1 a M single 30
2 a M children 80
3 b F single 50
4 c M children 40
5 d F single 30
6 d F children 80
Or you could do:
df <- left_join(y, x, by = c("IDy" = "IDx"))
It would give:
IDy status salary sex
1 a single 30 M
2 a children 80 M
3 b single 50 F
4 c children 40 M
5 d single 30 F
6 d children 80 F
You can also reorder your columns to get it exactly the way you wanted:
df <- df[, c("IDy", "sex", "status", "salary")]
result:
IDy sex status salary
1 a M single 30
2 a M children 80
3 b F single 50
4 c M children 40
5 d F single 30
6 d F children 80