How can I create a function to generate new variables based on values in different dataframe in R - r

Question

I would like to create a function like this (obviously not proper code):
forEach ID in DATAFRAME1 look at each row with ID in DATAFRAME2 {
if DATAFRAME2$VARIABLE1 = something {
DATAFRAME1$VARIABLE1 = TRUE;
DATAFRAME1$VARIABLE2 = DATAFRAME2$VARIABLE2
}
}
In plain text, I've got a list of individuals and a database with mixed information on these
individuals. Let's say DATAFRAME2 contains informations on books read c(id, title, author, date). I want to create a new variable in DATAFRAME1 with a boolean of if the individual has read a specific book (VARIABLE1 above) and the date they first read it (VARIABLE2above). Also adding a third variable with number of times read would be interesting but not neccesary.
I haven't really done this in R before, mostly doing basic statistics and basic wrangling with dplyr. I guess I could use dplyr and join but this feels like a better approach. Any help to get me started would be much appreciated.

Answer 1

The following function does what the question asks for. Its arguments are
DF1 and DF2 have an obvious meaning;
var1 and var2 are VARIABLE1 and VARIABLE2 in the question;
value is the value of something.
The test data is at the end.
fun <- function(DF1, DF2, ID = 'ID', var1, var2, value){
DF1[[var1]] <- NA
DF1[[var2]] <- NA
k <- DF2[[var1]] == value
for(id in df1[[ID]]){
i <- DF1[[ID]] == id
j <- DF2[[ID]] == id
if(any(j & k)){
DF1[[var1]][i] <- TRUE
DF1[[var2]][i] <- DF2[[var2]][j & k]
}
}
DF1
}
fun(df1, df2, value = 4, var1 = 'X', var2 = 'Y')
# ID X Y
#1 a NA NA
#2 d TRUE 19
Test data.
set.seed(1234)
df1 <- data.frame(ID = c("a", "d"))
df2 <- data.frame(ID = rep(letters[1:5], 4),
X = sample(20, 20, TRUE),
Y = sample(20))