The **all()** and **any()** functions in R can be used to check if all or any values in a vector evaluate to TRUE for some expression.

These functions use the following syntax:

**#check if ***all* values in x are less than 10
all(x < 10)
#check if *any* values in x are less than 10
any(x < 10)

The following examples show how to use each function in practice.

**Example 1: Use all() and any() with Vector**

We can use the following **all()** and **any()** functions to check if all or any values in a vector are less than 10:

**#define vector of data values
data <- c(3, 4, 4, 8, 12, 15)
#check if all values are less than 10
all(data < 10)
[1] FALSE
#check if any values are less than 10
any(data < 10)
[1] TRUE
**

The **all()** function evaluates to **FALSE** because not all values in the vector are less than 10.

The **any()** function evaluates to **TRUE** because at least one value in the vector is less than 10.

**Example 2: Use all() with NA Values**

If we use the **all()** function with a vector that has NA values, we may receive **NA** as a result:

**#define vector of data values with some NA values
data <- c(3, 4, 4, 8, NA, NA)
#check if all values are less than 10
all(data < 10)
[1] NA
**

To avoid this, we must specify **na.rm=TRUE** to first remove the NA values from the vector before checking if all values meet some condition:

**#define vector of data values with some NA values
data <- c(3, 4, 4, 8, NA, NA)
#check if all values are less than 10 (and ignore NA values)
all(data < 10, na.rm=TRUE)
[1] TRUE
**

The **all()** function now evaluates to **TRUE** because every value in the vector is less than 10, assuming we ignore NA values.

**Example 3: Use all() and any() with Data Frame Columns**

We can also use the **all()** and **any()** functions to evaluate expressions for data frame columns.

For example, suppose we have the following data frame in R:

**#define data frame
df <- data.frame(points=c(30, 22, 19, 20, 14, NA),
assists=c(7, 8, 13, 13, 10, 6),
rebounds=c(8, 12, NA, NA, 5, 8))
#view data frame
df
points assists rebounds
1 30 7 8
2 22 8 12
3 19 13 NA
4 20 13 NA
5 14 10 5
6 NA 6 8**

We can use the **all()** and **any()** functions to evaluate different expressions for the values in the “rebounds” column:

**#check if all values are less than 10 in rebounds column
all(df$rebounds < 10, na.rm=TRUE)
[1] FALSE
#check if any values are less than 10 in rebounds column
any(df$rebounds < 10, na.rm=TRUE)
[1] TRUE
#check if there are any NA values in rebounds column
any(is.na(df$rebounds))
[1] TRUE
**

From the output we can see:

- Not all values are less than 10 in the rebounds column.
- At least one value is less than 10 in the rebounds column.
- There is at least one NA value in the rebounds column.

**Related:** How to Use is.na in R (With Examples)

**Additional Resources**

The following tutorials explain how to perform other common tasks in R:

How to Add a Column to a Data Frame in R

How to Add an Empty Column to a Data Frame in R

How to Sort a Data Frame by Column in R