You can use the following basic syntax to apply a function to each row in a data frame in R using functions from dplyr:

df %>% rowwise() %>% mutate(mean_value = mean(c(col1, col2, col3), na.rm=TRUE))

This particular example calculates the mean value of **col1**, **col2,** and **col3** for each row in the data frame, but you can replace the **mean()** function with any function you’d like to calculate a different metric.

The following examples show how to use this syntax in practice with the following data frame that contains information about points scored by various basketball players during different games:

#create data frame df <- data.frame(game1=c(22, 25, 29, 13, 22, 30), game2=c(12, 10, 6, 6, 8, 11), game3=c(NA, 15, 15, 18, 22, 13)) #view data frame df game1 game2 game3 1 22 12 NA 2 25 10 15 3 29 6 15 4 13 6 18 5 22 8 22 6 30 11 13

**Example 1: Mean of Specific Columns in Each Row**

The following code shows how to calculate the mean value of the **game1 **and **game3** columns for each row in the data frame:

**library(dplyr)
#calculate mean of game1 and game3
df %>%
rowwise() %>%
mutate(mean_points = mean(c(game1, game3), na.rm=TRUE))
# A tibble: 6 x 4
# Rowwise:
game1 game2 game3 mean_points
1 22 12 NA 22
2 25 10 15 20
3 29 6 15 22
4 13 6 18 15.5
5 22 8 22 22
6 30 11 13 21.5**

From the output we can see:

- The mean value of game1 and game3 in the first row is
**22**. - The mean value of game1 and game3 in the second row is
**20**. - The mean value of game1 and game3 in the third row is
**22**.

And so on.

**Example 2: Max of Specific Columns in Each Row**

The following code shows how to calculate the max value of the **game2 **and **game3** columns for each row in the data frame:

**library(dplyr)
#calculate max of game2 and game3
df %>%
rowwise() %>%
mutate(max_points = max(c(game2, game3), na.rm=TRUE))
# A tibble: 6 x 4
# Rowwise:
game1 game2 game3 max_points
1 22 12 NA 12
2 25 10 15 15
3 29 6 15 15
4 13 6 18 18
5 22 8 22 22
6 30 11 13 13**

From the output we can see:

- The max value of game2 and game3 in the first row is
**12**. - The max value of game2 and game3 in the second row is
**15**. - The max value of game2 and game3 in the third row is
**15**.

And so on.

**Example 3: Standard Deviation of Specific Columns in Each Row**

The following code shows how to calculate the standard deviation of the values in the **game2 **and **game3** columns for each row in the data frame:

**library(dplyr)
#calculate standard deviation of game2 and game3
df %>%
rowwise() %>%
mutate(sd_points = sd(c(game2, game3), na.rm=TRUE))
# A tibble: 6 x 4
# Rowwise:
game1 game2 game3 sd_points
1 22 12 NA NA
2 25 10 15 3.54
3 29 6 15 6.36
4 13 6 18 8.49
5 22 8 22 9.90
6 30 11 13 1.41**

From the output we can see:

- The standard deviation of game2 and game3 in the first row is NA (since standard deviation can’t be calculated from only one value).
- The standard deviation of game2 and game3 in the second row is
**3.54**. - The standard deviation of game2 and game3 in the first row
**6.36**.

And so on.

**Note**: You can find the complete documentation for the **rowwise()** function in dplyr here.

**Additional Resources**

The following tutorials explain how to perform other common tasks using dplyr:

How to Count Distinct Values Using dplyr

How to Sum Across Multiple Columns Using dplyr

How to Replace Multiple Values in Data Frame Using dplyr