Suppose I have a factor A with 3 levels A1, A2, A3 and with NA's. Each appears in 10 cases, so there is a total of 40 cases. If I do

```
subset1 <- df[df$A=="A1",]
dim(subset1) # 20, i.e., 10 for A1 and 10 for NA's
summary(subset1$A) # both A1 and NA have non-zero counts
subset2 <- df[df$A %in% c("A1"),]
dim(subset2) # 10, as expected
summary(subset2$A) # only A1 has non-zero count
```

And it is the same whether the class of the variable used for subsetting is factor or integer. Is it just how equal (and >, <) works? So should I just stick to `%in%`

for factors and always include `!is.na`

when using equal? Thanks!

# Best How To :

Yes, the return types of `==`

and `%in%`

are different with respect to `NA`

because of how `"%in%"`

is defined...

```
# Data...
x <- c("A",NA,"A")
# When NA is encountered NA is returned
# Philosophically correct - who knows if the
# missing value at NA is equal to "A"?!
x=="A"
#[1] TRUE NA TRUE
x[x=="A"]
#[1] "A" NA "A"
# When NA is encountered by %in%, FALSE is returned, rather than NA
x %in% "A"
#[1] TRUE FALSE TRUE
x[ x %in% "A" ]
#[1] "A" "A"
```

This is because (from the docs)...

`%in%`

is an alias for `match`

, which is defined as

```
"%in%" <- function(x, table) match(x, table, nomatch = 0) > 0
```

If we redefine it to the standard definition of `match`

you will see that it behaves in the same way as `==`

```
"%in2%" <- function(x,table) match(x, table, nomatch = NA_integer_) > 0
x %in2% "A"
#[1] TRUE NA TRUE
```