How to Find the Max Value Across Multiple Columns in R

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R provides a versatile environment for manipulating data, especially when dealing with statistical and data analysis. Often, users are intrigued to calculate the maximum value across multiple columns in a dataframe to perform comparative analysis, data cleaning, or data transformations. This article provides various methods to find the maximum value across multiple columns, offering insights into R functions, packages, and programming constructs to achieve this task efficiently.

Sample Dataframe

Let’s create a sample dataframe to illustrate the methods for finding the maximum value across multiple columns.

# Creating a sample dataframe
data <- data.frame(
  Column1 = c(10, 20, 30, 40),
  Column2 = c(5, 25, 35, 15),
  Column3 = c(8, 28, 18, 48)

Method 1: Using apply( ) Function

The apply() function is a versatile R function that allows applying a function to the rows or columns of a matrix or, in some cases, a dataframe.

max_value <- apply(data, 1, max) # ‘1’ implies applying the function across rows

In this case, max_value will hold the maximum value from each row across all columns.

Method 2: Using pmax( ) Function

The pmax() function is another robust method to find the maximum value element-wise across columns.

max_value <-, data)

Method 3: Using dplyr Package

The dplyr package, part of the tidyverse package collection, provides several helpful functions for data manipulation.


data %>%
  rowwise() %>%
  mutate(Max_Value = max(c_across(all_of(everything()))))

Here, c_across() combined with max() will calculate the maximum value in each row across all columns.

Method 4: Using tidyverse and purrr Package

Another way to use the tidyverse approach is by combining it with the purrr package.


data %>%

Method 5: Custom Function Approach

Creating a custom function can provide more flexibility to handle complex scenarios that might not be addressed directly by built-in functions or packages.

max_across_columns <- function(row) {
  max_value <- max(as.numeric(row), na.rm = TRUE)

max_value <- apply(data, 1, max_across_columns)

Selecting the Appropriate Method

Choosing the right method depends on the specific requirements, the complexity of the data, and personal preference. For instance:

  • If simplicity and speed are prioritized, using base R functions like apply() or pmax() can be advantageous.
  • For more complex data manipulation tasks, leveraging the dplyr or tidyverse packages can be more suitable.
  • When handling specific edge cases or unique scenarios, creating a custom function can offer the greatest flexibility.

Handling Missing Values

When dealing with real-world data, managing missing values is crucial as they can skew the results. For handling missing values while finding the maximum value across columns, the na.rm = TRUE parameter can be passed to the max() function within any of the methods mentioned above, ensuring that NA values are removed before computation.

Computing Max Value Over Specific Columns

In scenarios where the maximum value needs to be computed only over specific columns, the column indices or names can be selectively provided to the applied method. For instance, using apply() on specific columns would look like this:

max_value <- apply(data[c("Column1", "Column3")], 1, max)

Extending to Min, Sum, and Other Aggregations

The methods described for finding the maximum value can be easily extended to find the minimum value, sum, average, or any other aggregation across multiple columns by replacing the max() function with the corresponding aggregation function like min(), sum(), mean(), etc.


Finding the maximum value across multiple columns is a frequent necessity in data analysis and can be approached using various methods in R. The built-in apply() and pmax() functions offer a quick and efficient way to perform this task, while packages like dplyr and tidyverse provide more sophisticated data manipulation capabilities. Creating custom functions offers flexibility to accommodate unique requirements and edge cases, and considering missing values and specific column selections are important aspects in real-world data analysis.

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