When working with vectors, you'll often need to filter out values that meet a criteria. In R, this process is known as filtering, and the platform provides several functions to help you extract subsets of data.

`any()`

FunctionThe `any()`

function returns `TRUE`

or `FALSE`

, depending on whether all arguments match that criteria. The `TRUE`

and `FALSE`

are of type `logical`

.

```
> x <- 1:100
> any(x > 101)
FALSE
> any(x == 2)
TRUE
> any(x <= 50)
TRUE
```

`all()`

FunctionOn the flip side, we can use the `all()`

function to test if *all* values meet a certain criteria.

```
> x < 1:100
> all(x > 40)
FALSE
> all(x > 0)
TRUE
```

With vectors, we may run comparison operations to return vector containing logical values. For example:

```
> x <- c(1,2,3,4,5,6)
> x > 3
[1] FALSE FALSE FALSE TRUE TRUE TRUE
```

As you can see, we are returned a logical vector containing TRUE and FALSE values, depending on how that positional element was evaluated.

How is this useful? We can use these resulting logical vectors to pull out subvectors. Let's say we only want to pull out odd values - we can write:

```
> x <- c(12,423,52,21,324)
> x[x %% 2 == 1]
[1] 423 21
```

The `x %% 2 == 1`

returns a logical vectors. All positions where `TRUE`

is held are then printed.

We can further use this feature to replace values that meet a certain criteria:

```
> x <- c(1,2,3,4,5,6)
> x[x*x>20] = 1337
> x
[1] 1 2 3 4 1337 1337
```

`subset()`

In the methods mentioned above, `NA`

values are included in the subvector, no matter the condition.

```
> x <- c(1,2,3,NA,5,6)
> x[x>2]
[1] 3 NA 5 6
```

In the case when you need to exclude the `NA`

, you may use the `subset()`

function.

```
> subset(x, x>2)
[1] 3 5 6
```

`which()`

If you need to pull out not the actual values but just the indicies in which the values of a certain condition reside, then use the `which()`

function. This will return all the indicies that match a certain criteria.

```
> z <- c(1,2,3,4,5,6)
> which(z > 3)
[1] 4 5 6
```

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