Consider this example:

x <- rep(1:5, times = 2)

x == 1:3
# TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# With warnings

x %in% 1:3
# TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE

Question: Why does the results differ from each other?

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People need to compare values across objects every now and then. In this case, let’s say we want to find out which ones of x is 1, 2, or 3. Intuitively, we will do x == 1:3, but it is WRONG. Only the first three elements return TRUE even though we know that there is another set of 1:3. (Of course you can do x == 1 | x == 2 | x == 3 but imagine when you have many values to be compared with…)

The reason for that unexpected output is the vectorization characteristic of R. When we do x == 1:3, R does the following things:

• Are x[1:3] 1:3 respectively? (TRUE, TRUE, TRUE)
• Are x[4:6] 1:3 respectively? (FALSE, FALSE, FALSE)
• Are x[7:9] 1:3 respectively? (FALSE, FALSE, FALSE)
• Are x[10:12] 1:3? (All FALSE but there’s no x[11] and x[12] => warnings)

Alternatively we can do x %in% 1:3 because it checks every single element of x to find whether the element is within 1:3.