r - What is the difference between `%in%` and `==`?

Question

df <- structure(list(x = 1:10, time = c(0.5, 0.5, 1, 2, 3, 0.5, 0.5, 
1, 2, 3)), .Names = c("x", "time"), row.names = c(NA, -10L), class = "data.frame")


df[df$time %in% c(0.5, 3), ]
##     x time
## 1   1  0.5
## 2   2  0.5
## 5   5  3.0
## 6   6  0.5
## 7   7  0.5
## 10 10  3.0

df[df$time == c(0.5, 3), ]
##     x time
## 1   1  0.5
## 7   7  0.5
## 10 10  3.0

What is the difference between %in% and == here?

Answer 1

The problem is vector recycling.

Your first line does exactly what you'd expect. It checks what elements of df$time are in c(0.5, 3) and returns the values which are.

Your second line is trickier. It's actually equivalent to

df[df$time == rep(c(0.5,3), length.out=nrow(df)),]

To see this, let's see what happens if use a vector rep(0.5, 10):

rep(0.5, 10) == c(0.5, 3)
[1]  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE

See how it returns every odd value. Essentially it's matching 0.5 to the vector c(0.5, 3, 0.5, 3, 0.5...)

You can manipulate a vector to produce no matches this way. Take the vector: rep(c(3, 0.5), 5):

rep(c(3, 0.5), 5) == c(0.5, 3)
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

They're all FALSE; you are matching every 0.5 with 3 and vice versa.