Identifying matching observations in dyadic data in R - r

Question

Hell everyone,
I am struggling with the following issue. Currently, I have a dataset looking like this:
living_in from Year stock
Austria Australia 2014 2513
Austria Australia 2013 2000
Germany Austria 2010 6000
Australia Austria 2014 3000
Austria Australia 1993 NA
Now I would like to identify all observations that fulfill the following criteria:
Should be from same year
Should contain the same country pairs in that year
Should not contain NA
For instance, I want to find all observations for combinations of two countries like Austria-Australia and Australia-Austria within the same year that contain values. This is due to the fact that some combinations in a given year in the dataset have only one value for stock not two. I want to remove those.
What is the best way to proceed here? Many thanks in advance!
P.S. I have about 14 country pairs in my dataset that need this kind of identification
A helpful output might be something like this.
living_in from Year stock dummy
Austria Australia 2014 2513 1
Austria Australia 2013 2000 0
Germany Austria 2010 6000 0
Australia Austria 2014 3000 1
Austria Australia 1993 NA 0

Answer 1

For each combination of country irrespective of their order (A-B is same as B-A) assign 1 to dummy column if for the same Year it has more than 1 row and all the stock values are non-NA or else assign 0.
library(dplyr)
df %>%
group_by(col1 = pmin(living_in, from), col2 = pmax(living_in, from), Year) %>%
mutate(dummy = as.integer(n() > 1 && all(!is.na(stock)))) %>%
ungroup %>%
select(-col1, -col2)
# living_in from Year stock dummy
# <chr> <chr> <int> <int> <int>
#1 Austria Australia 2014 2513 1
#2 Austria Australia 2013 2000 0
#3 Germany Austria 2010 6000 0
#4 Australia Austria 2014 3000 1
#5 Austria Australia 1993 NA 0
data
df <- structure(list(living_in = c("Austria", "Austria", "Germany",
"Australia", "Austria"), from = c("Australia", "Australia", "Austria",
"Austria", "Australia"), Year = c(2014L, 2013L, 2010L, 2014L,
1993L), stock = c(2513L, 2000L, 6000L, 3000L, NA)),
class = "data.frame", row.names = c(NA, -5L))