# How to Use ColMeans Function in R?

Last Updated : 24 Dec, 2021

In this article, we will discuss how to use the ColMeans function in R Programming Language.

## Using colmeans() function

The colmean() function call be simply called by passing the parameter as the data frame to get the mean of every column present in the data frame separately in the R language.

Syntax:

`colMeans(dataframe)`

where dataframe is the input dataframe.

Example:

Under this example, we will be using the colmeans() function with the data frame containing three different columns to get the mean of each column present in the R language.

## R

 `# create  dataframe with three columns` `data=``data.frame``(col1=``c``(1,34,56,32,23),` `                ``col2=``c``(21,34,56,32,34),` `                ``col3=``c``(1:5))`   `# get mean of all columns` `print``(``colMeans``(data))`

Output:

```col1 col2 col3
29.2 35.4  3.0 ```

## Calculate mean of specific columns

In this method, the user has an option to get the mean of the specific column of the given data frame either to get the mean of the complete data frame using the colmean() function with the name of the specific column within it for which mean is to be calculated in the R language.

Syntax:

`colMeans(dataframe)`

where,

• dataframe is the input dataframe
• columns are the columns to get mean

Example:

In this example, we will be using the colmean() function with the name of the column as its argument to get the mean of that particular column of the data frame in the R language.

## R

 `# create  dataframe with three columns` `data=``data.frame``(col1=``c``(1,34,56,32,23),` `                ``col2=``c``(21,34,56,32,34),` `                ``col3=``c``(1:5))`   `# get mean of col2 and col3` `print``(``colMeans``(data[``c``(``'col2'``, ``'col3'``)]))`

Output:

```col2 col3
35.4  3.0 ```

Here, we can also use column numbers to get the mean value using colMeans().

Syntax

`colMeans(dataframe)`

where

• col_value_start is the first column index
• col_value_end is the last column index

Example:

## R

 `# create  dataframe with three columns` `data=``data.frame``(col1=``c``(1,34,56,32,23),` `                ``col2=``c``(21,34,56,32,34),` `                ``col3=``c``(1:5))`   `# get mean from column1 to column3` `print``(``colMeans``(data[``c``(1,3)]))`

Output:

```col1 col3
29.2  3.0 ```

## Calculate the mean of every column & exclude NAâ€™s

In this example, the user has to use the colmean() function with the  na.rm argument to calculate the mean of a column by excluding NA. NA stands for Not  a number, we can do this by using na.rm() method, we will set it to True to remove NA values in the dataframe column.

Syntax:

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

Example:

In this example, we will create three columns that include three NA values and get the mean of all columns using the na.rm argument under the colmeans() function.

## R

 `# create dataframe with three columns` `data=``data.frame``(col1=``c``(1,34,56,32,23,``NA``,``NA``,``NA``),` `                ``col2=``c``(21,``NA``,``NA``,``NA``,34,56,32,34),` `                ``col3=``c``(1:5,``NA``,``NA``,``NA``))`   `# get mean of all columns excluding NA` `print``(``colMeans``(data,na.rm=``TRUE``))`

Output:

```col1 col2 col3
29.2 35.4  3.0 ```

## Calculate the mean of columns of the array in R

In this approach, the user needs to call the colmean() function with the name of the array with its dimensions as the parameter to get the mean of the columns of the given array in the R language.

Syntax:

`colMeans(data, dims )`

where,

• data is the input array
• dims stands for dimensions

Example:

in this example, we will create an array with 3 dimensions with 1 to 12 elements and calculate column means using the colmeans() function in the R programming language.

## R

 `# Initializing a 3D array` `data= ``array``(1:12, ``c``(2, 3, 3))`   `# colmeans for one dimension` `print``(``colMeans``(data, dims = 1))`   `# colmeans for two dimension` `print``(``colMeans``(data, dims = 2))`

Output:

```     [,1] [,2] [,3]
[1,]  1.5  7.5  1.5
[2,]  3.5  9.5  3.5
[3,]  5.5 11.5  5.5

[1] 3.5 9.5 3.5```