The **mean()** is a built-in **R** function that takes an input vector as an argument and returns the mean of the vector elements. For example, mean(c(1, 2, 3)) function returns 2.

**Syntax**

```
mean(x, trim = 0, na.rm = FALSE, ...)
```

**Parameters**

The **x** is an input vector.

The **trim** drops some observations from both ends of the sorted vector.

The **na.rm** is used to remove the missing values from the input vector.

**Example 1: How to find a mean of a vector in R**

The mean is calculated by taking a sum of the values and dividing it by the number of values in the data. If the data series is a vector, the mean is calculated by taking a sum of vector elements and dividing it by the number of elements of the series.

```
# Create a vector.
rv <- c(11, 18, 19, 21, 29, 46)
# Find Mean of rv vector.
meanResult <- mean(rv)
print(meanResult)
```

**Output**

```
[1] 24
```

That means the sum of all the vector elements is 144, and 144 / 6 is 24.

**Example 2: Passing the trim option to the mean() function**

The **mean()** function optionally takes the **trim **parameter. When you pass the trim parameters, the values in the vector get sorted, and then the required observations are dropped from calculating the mean.

If you pass the **0.3, **3 values from each end will be dropped from the calculations to find the mean.

```
# Create a vector.
rv <- c(11, 18, 19, 21, 29, 46)
# Find Mean of rv vector.
meanResult <- mean(rv, trim = 0.3)
print(meanResult)
```

**Output**

`[1] 21.75`

**Example 3: Passing NA Option**

If there are missing values, then the mean() function returns **NA**.

```
# Create a vector.
rv <- c(11, 18, 19, NA, 29, 46)
# Find Mean of rv vector.
meanResult <- mean(rv)
print(meanResult)
```

**Output**

`[1] NA`

To drop the missing values from the calculation, use **na.rm = TRUE**. Which means removing the **NA** values.

```
# Create a vector.
rv <- c(11, 18, 19, NA, 29, 46)
# Find Mean of rv vector.
meanResult <- mean(rv, na.rm = TRUE)
print(meanResult)
```

**Output**

`[1] 24.6`

**Example 4: How to apply the mean() function on a data frame**

To **apply** the **mean()** **function** to a **dataframe** in **R**, you can use the **apply()** function. The **apply()** function takes a **dataframe** or a **matrix** as an input and applies a function to each row or column. For example, if you have a dataframe called **df** with four columns, **A**, **B**, **C**, and **D**, you can calculate the mean of each column by using **apply(df, 2, mean)**. This will return a vector of four means.

```
# Create a sample data frame
df <- data.frame(x = c(1, 2, 3), y = c(4, 5, 6), z = c(7, 8, 9))
# Apply mean() function to each column
means <- apply(df, 2, mean)
# Print the means
print(means)
```

**Output**

```
x y z
2 5 8
```

**Conclusion**

In **R**, you can **calculate** the **mean** of a **vector** using the mean() function. The function accepts a vector as input and returns the average as a numeric.

**See also**

Krunal Lathiya is a Software Engineer with over eight years of experience. He has developed a strong foundation in computer science principles and a passion for problem-solving. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language.