R equivalent to SAS "merge" "by" - r

Question

If you only use "merge" and "by" in SAS to merge datasets that contain several variables with equal names (beside the ID(s) that you merge by), SAS will combine these variables in to one using the value read last - it is described here https://communities.sas.com/t5/SAS-Programming/Merge-step-overwriting-shared-vars/m-p/281542#M57117
Text from above link:
"There is a rule: whichever value was read last. But that rule is simple only when the merge is one-to-one. In that case, the value you get depends on the order in the MERGE statement:
merge a b;
by id;
The value of common variables (for a one-to-one merge) comes from data set B. SAS reads a value from data set A, then reads a value from data set B. The value from B is read last, and overwrites the value read from data set A.
If there is a mismatch, and an ID appears only in data set A but not in data set B, the value will be the one found in data set A."
How do I make R behave the same way without having to combine the rows afterwards after certain conditions? (in SAS, values are not overwritten by NAs)
library(tidyverse)
#create tibbles
df1 <- tibble(id = c(1:3), y = c("tt", "ff", "kk"))
df2 <- tibble(id = c(1,2,4), y = c(4,3,8))
df3 <- tibble(id = c(1:3), y = c(5,7,NA))
#combine the tibbles
combined_df <- list(df1, df2, df3) %>%
reduce(full_join, by = "id")
# desired output
combined_df_desired <- tibble(id = 1:4, y = c(5,7,"kk",8))

Answer 1

I don't know exactly what you mean with "certain conditions". There isn't a way to change the inner workings of full_join() but you can do:
list(df1, df2, df3) %>%
reduce(full_join, by = "id") %>%
mutate_all(as.character) %>%
mutate(y = coalesce(y, y.y , y.x,)) %>%
select(id, y)
A tibble: 4 x 2
id y
<chr> <chr>
1 1 5
2 2 7
3 3 kk
4 4 8
coalesce() takes a set of columns and returns the first non-NA value for each row. You can order the columns inside the function according to your priorities.