By default, base R includes the following functions:

**colMeans**: Calculates the mean of each column in a data frame**colSums**: Calculates the sum of each column in a data frame

However, there does not exist a **colMax** function to calculate the max of each column in a data frame.

Fortunately, you can use the following syntax to create a **colMax** function in R:

colMax <- function(data) sapply(data, max, na.rm=TRUE)

Once you’ve created this function, you can then use it to calculate the max value of each column in a data frame in R.

The following examples show how to use this function in practice with the following data frame in R:

#create data frame df <- data.frame(points=c(99, 91, 86, 88, 95), assists=c(33, 28, 31, 39, 34), rebounds=c(30, 28, 24, 24, 28), blocks=c(1, 4, 11, 0, 2)) #view data frame df points assists rebounds blocks 1 99 33 30 1 2 91 28 28 4 3 86 31 24 11 4 88 39 24 0 5 95 34 28 2

**Example 1: Use colMax to Calculate Max of All Columns**

We can use the following code to calculate the max value of each column in the data frame:

#calculate max value of each column in data frame colMax(df) points assists rebounds blocks 99 39 30 11

The output shows the max value in each column of the data frame.

For example:

- The max value in the
**points**column is**99**. - The max value in the
**assists**column is**39**.

And so on.

**Example 2: Use colMax to Calculate Max of Specific Columns**

We can use the following code to calculate the max value for only the **points** and **blocks** columns in the data frame:

#calculate max value of 'points' and 'blocks' columns in data frame colMax(df[, c('points', 'blocks')]) points blocks 99 11

The output shows the max value in the **points** and **blocks** columns only.

**Additional Resources**

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

How to Calculate Standard Deviation of Columns in R

How to Calculate the Mean by Group in R

How to Calculate the Sum by Group in R