The colMeans() function in R is **“used to** **calculate the mean of several columns of a data frame or matrix”**.

**Syntax**

`colMeans(x, na.rm = FALSE)`

**Parameters**

**x:**It is an array of two or more dimensions containing numeric, complex, integer, or logical values or a numeric data frame.**na.rm:**It is a logical argument. If**TRUE**, NA values are ignored.

**Return value**

The colMeans() function returns the mean of the columns of a data frame or matrix.

**Example 1: Calculating the mean of every column in a data frame**

**Syntax**

`colMeans(df)`

You can calculate the mean of every data frame column using the **colMeans()** function in **R**.

```
# Create data frame
df <- data.frame(
player1 = c(11, 55, 99, 444, 888),
player2 = c(22, 66, 111, 555, 999),
player3 = c(33, 77, 222, 666, 11),
player4 = c(44, 88, 333, 777, 22)
)
# Calculate column means
colMeans(df)
```

**Output**

```
player1 player2 player3 player4
299.4 350.6 201.8 252.8
```

**Example 2****: Calculating the mean of every column and Exclude NA values**

**Syntax**

`colMeans(dataframe,na.rm=TRUE)`

You can calculate the mean of every column, excluding NA values in R, using the colMeans() function.

```
# Create data frame
df <- data.frame(
player1 = c(11, 55, 99, NA, 888),
player2 = c(22, 66, 111, 555, 999),
player3 = c(33, 77, NA, 666, 11),
player4 = c(44, 88, 333, 777, NA)
)
# Calculate column means
colMeans(df, na.rm = TRUE)
```

**Output**

```
player1 player2 player3 player4
263.25 350.60 196.75 310.50
```

**Example 3: Calculating the mean of specific columns**

**Syntax**

`colMeans(df[c("col1", "col2")])`

Let’s write code based on the above syntax.

```
# Create data frame
df <- data.frame(
player1 = c(11, 55, 99, 444, 888),
player2 = c(22, 66, 111, 555, 999),
player3 = c(33, 77, 222, 666, 11),
player4 = c(44, 88, 333, 777, 22)
)
# Calculate column means of "player2" and "player4"
colMeans(df[c("player2", "player4")])
```

**Output**

```
player2 player4
350.6 252.8
```

That’s it.

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Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.