How do I avoid using for-loops - r

Question

I am currently working on listening data of a music platform in R.
I have a subset (listening.subset) of the total data set. It contains 6 columns (USER, artist, Week, COUNT, user_type, binary).
Each user can either be a focal user, a friend, or a neighbour. There are separate data sets that link focal users to their friends (friend.data) and neighbours (neighbour.data), but I added a column to indicate the type of user.
Now, I have the following for-loop to indicate whether a friend has listened to an artist in the 10 weeks before the focal user has listened to that same artist. If that is the case, the binary column must show a 0, else a 1.
listening.subset$binary <- NA
for (i in 1:count(listening.subset)$n) {
test_user <- listening.subset[i,]
test_week <- test_user$Week
test_artist <- test_user$artist
if (test_user$user_type == "friend") {
foc <- vlookup(test_user$USER, friend.data, result_column = 1, lookup_column = 2)
prior_listen <- listening.subset %>% filter(USER == foc) %>% group_by(artist) %>% filter(test_week >= (Week -10) & test_week <= Week) %>% filter(artist == test_artist)
if (nrow(prior_listen) > 0) {
listening.subset[i,]$binary <- 0
}
else(
listening.subset[i,]$binary <- 1)
}
}
The problem with this for-loop is that it takes too long to apply to the full data set. Therefore, I want to apply vectorization. However, This concept is vague to me and after reading up on it online, I still do not have a clue as to how I should adjust my code.
I hope someone knows how to use vectorization and could help me.
EDIT1: the total data set contains around 50 million entries. However, I could split it up in 10 data sets of 5 million each.
EDIT2: listening.subset:
"clubanddeform", "HyprMusic", "Peter-182", "komosionmel", "SHHitsKaty",
"Sonik_Villa", "Haalf"), artist = c("Justin Timberlake", "Ediya",
"Lady Gaga", "El Guincho", "Lighthouse Family", "Pidżama Porno",
"The Men", "Modest Mouse", "Com Truise", "April Smith and The Great Picture Show"
), Week = c(197L, 213L, 411L, 427L, 443L, 232L, 431L, 312L, 487L,
416L), COUNT = c(1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 6L, 11L), user_type = c("friend",
"friend", "friend", "friend", "neighbour", "friend", "neighbour",
"friend", "focal", "friend"), binary = c(1, 1, 1, 1, NA, 1, NA,
1, NA, 1)), row.names = c(NA, 10L), class = "data.frame")
Where Week is an indicator for which week the user listened to the particular band (ranging between 1 and 527), and COUNT equals the amount of times the user has listened to that artist in that particular week.
Recap: The binary variable should indicate whether the "friend user" has listened to the same band as the "focal user", in the 10 weeks before the focal user played the band. The social connections can be found in the friend.data, which is depicted below.
structure(list(USER = c("TheMariner", "TheMariner", "TheMariner",
"TheMariner", "TheMariner", "TheMariner", "TheMariner", "TheMariner",
"TheMariner", "TheMariner"), FRIEND = c("npetrovay", "marno25",
"lennonstarr116", "sachmonkey", "andrewripp", "daledrops", "Skittlebite",
"Ego_Trippin", "pistolgal5", "jjollett")), row.names = c(NA,
10L), class = "data.frame")
For each 190 focal users (first column), the friends are listed next to it, in the second column.