You can use the following methods to summarise multiple columns in a data frame using dplyr:

Method 1: Summarise All Columns

#summarise mean of all columns
df %>%
  group_by(group_var) %>%
  summarise(across(everything(), mean, na.rm=TRUE))

Method 2: Summarise Specific Columns

#summarise mean of col1 and col2 only
df %>%
  group_by(group_var) %>%
  summarise(across(c(col1, col2), mean, na.rm=TRUE))

Method 3: Summarise All Numeric Columns

#summarise mean and standard deviation of all numeric columns
df %>%
  group_by(group_var) %>%
  summarise(across(where(is.numeric), list(mean=mean, sd=sd), na.rm=TRUE))

The following examples show how to each method with the following data frame:

#create data frame
df <- data.frame(team=c('A', 'A', 'A', 'B', 'B', 'B'),
                 points=c(99, 90, 86, 88, 95, 90),
                 assists=c(33, 28, 31, 39, 34, 25),
                 rebounds=c(NA, 28, 24, 24, 28, 19))

#view data frame
df

  team points assists rebounds
1    A     99      33       NA
2    A     90      28       28
3    A     86      31       24
4    B     88      39       24
5    B     95      34       28
6    B     90      25       19

Example 1: Summarise All Columns

The following code shows how to summarise the mean of all columns:

library(dplyr)

#summarise mean of all columns, grouped by team
df %>%
  group_by(team) %>%
  summarise(across(everything(), mean, na.rm=TRUE))

# A tibble: 2 x 4
  team  points assists rebounds
           
1 A       91.7    30.7     26  
2 B       91      32.7     23.7

Example 2: Summarise Specific Columns

The following code shows how to summarise the mean of only the points and rebounds columns:

library(dplyr)

#summarise mean of points and rebounds, grouped by team
df %>%
  group_by(team) %>%
  summarise(across(c(points, rebounds), mean, na.rm=TRUE))

# A tibble: 2 x 3
  team  points rebounds
        
1 A       91.7     26  
2 B       91       23.7

Example 3: Summarise All Numeric Columns

The following code shows how to summarise the mean and standard deviation for all numeric columns in the data frame:

library(dplyr)

#summarise mean and standard deviation of all numeric columns
df %>%
  group_by(team) %>%
  summarise(across(where(is.numeric), list(mean=mean, sd=sd), na.rm=TRUE))

# A tibble: 2 x 7
  team  points_mean points_sd assists_mean assists_sd rebounds_mean rebounds_sd
                                            
1 A            91.7      6.66         30.7       2.52          26          2.83
2 B            91        3.61         32.7       7.09          23.7        4.51

The output displays the mean and standard deviation for all numeric variables in the data frame.

Note that in this example we used the list() function to list out several summary statistics that we wanted to calculate.

Note: In each example, we utilized the dplyr across() function. You can find the complete documentation for this function .

Additional Resources

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