How to Use group_by in dplyr: Simple Guide with Examples

Use group_by() in dplyr to group rows of a data frame by one or more variables. This lets you perform operations like summarizing or mutating within each group separately.

Syntax

The basic syntax of group_by() is:

• group_by(data, column1, column2, ...): Groups the data by one or more columns.
• Inside group_by(), list the columns you want to group by.
• It returns a grouped data frame that you can use with other dplyr verbs like summarize() or mutate().

r

library(dplyr)

grouped_data <- group_by(data, column1, column2)

Example

This example shows how to group the built-in mtcars dataset by the number of cylinders (cyl) and then calculate the average miles per gallon (mpg) for each group.

r

library(dplyr)

# Group mtcars by 'cyl' and calculate average mpg
result <- mtcars %>% 
  group_by(cyl) %>% 
  summarize(avg_mpg = mean(mpg))

print(result)

Output

  cyl avg_mpg
1   4  26.66364
2   6  19.74286
3   8  15.10000

Common Pitfalls

Common mistakes when using group_by() include:

• Forgetting to use %>% pipe to chain group_by() with other dplyr functions.
• Not calling summarize() or another summarizing function after grouping, which means no aggregation happens.
• Grouping by columns that do not exist in the data frame, causing errors.

Here is an example of a wrong and right way:

r

# Wrong: grouping without summarizing
library(dplyr)

wrong <- mtcars %>% 
  group_by(cyl)

print(wrong)  # Just groups but no summary

# Right: grouping with summarizing
right <- mtcars %>% 
  group_by(cyl) %>% 
  summarize(avg_mpg = mean(mpg))

print(right)

Output

# A tibble: 3 × 2
    cyl avg_mpg
  <dbl>   <dbl>
1     4    26.7
2     6    19.7
3     8    15  

Quick Reference

Function	Purpose	Example
group_by()	Group data by one or more columns	group_by(data, col1, col2)
summarize()	Create summary statistics per group	summarize(avg = mean(value))
ungroup()	Remove grouping from data	ungroup(data)
mutate()	Add or change columns within groups	mutate(new_col = mean(value))

Key Takeaways

Use group_by() to split data into groups based on column values.

Always follow group_by() with summarize() or mutate() to perform calculations per group.

Use the pipe operator %>% to chain group_by() with other dplyr functions.

Check that the grouping columns exist in your data to avoid errors.

Use ungroup() to remove grouping when done.