== vs. %in%

equals_vs_in.r

# Broadly speaking, it's safer to %in% instead of == when using a logical
# vector in R because R's indexing will return an NA - which is
# probably not the intuitive behavior. Here's what I mean:
x <- c("a", "b", NA, "c")

# Indexing with ==
x[x == "a"]

# Compared with %in%
x[x %in% "a"]

# If you compare the logical vectors created by the two
# comparisons, you can see that %in% *doesn't* return
# an NA - which makes it a lot safer for indexing.
x == "a"

x %in% a

# I almost exclusively use %in% for matching - and when
# I need to use ==, I make sure that I explicitly handle
# the NA cases (usually something like x == "a" & !is.na(x)