Usually, if I want to subset a dataframe conditioning of some values a variable I'm using subset and %in%:
x <- data.frame(u=1:10,v=LETTERS[1:10]) x subset(x, v %in% c("A","D")) Now, I found out that also == gives the same result:
subset(x, v == c("A","D")) I'm just wondering if they are identically or if there is a reason to prefere one over the other. Thanks for help.
Edit (@MrFlick): This question asks not the same as this here which asks how to not include several values: (!x %in% c('a','b')). I asked why I got the same if I use ==or %in%.
1 Answer
You should use the first one %in% because you got the result only because in the example dataset, it was in the order of recycling of A, D. Here, it is comparing
rep(c("A", "D"), length.out= nrow(x)) # 1] "A" "D" "A" "D" "A" "D" "A" "D" "A" "D" x$v==rep(c("A", "D"), length.out= nrow(x))# only because of coincidence #[1] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE subset(x, v == c("D","A")) #[1] u v #<0 rows> (or 0-length row.names) while in the above
x$v==rep(c("D", "A"), length.out= nrow(x)) #[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE whereas %in% works
subset(x, v %in% c("D","A")) # u v #1 1 A #4 4 D 
