summarise() with group_by() in R Programming

Introduction

We use group_by() with summarise() to quickly find summary information for different groups in our data. It helps us see patterns or totals for each group separately.

You want to find the average score for each class in a school.

You need to count how many sales happened in each region.

You want to find the total hours worked by each employee.

You want to see the maximum temperature recorded each day.

Syntax

R Programming

data %>%
  group_by(group_column) %>%
  summarise(
    summary_name = summary_function(column)
  )

group_by() splits the data into groups based on one or more columns.

summarise() creates a new smaller table with summary values for each group.

Examples

This groups data by Category and sums the Value column for each group.

R Programming

library(dplyr)
data %>%
  group_by(Category) %>%
  summarise(Total = sum(Value))

This finds the average Score for each Team.

R Programming

data %>%
  group_by(Team) %>%
  summarise(AverageScore = mean(Score))

This finds the highest temperature recorded on each Date.

R Programming

data %>%
  group_by(Date) %>%
  summarise(MaxTemp = max(Temperature))

Sample Program

This program groups the sales data by Region and calculates the total sales for each region. Then it prints the summary table.

R Programming

library(dplyr)

# Sample data frame
sales <- data.frame(
  Region = c("North", "South", "North", "East", "South", "East"),
  Sales = c(100, 150, 200, 130, 170, 120)
)

# Group by Region and sum Sales
summary <- sales %>%
  group_by(Region) %>%
  summarise(TotalSales = sum(Sales))

print(summary)

Important Notes

Always load dplyr library before using group_by() and summarise().

You can group by multiple columns by adding them inside group_by(), like group_by(Col1, Col2).

If you want to keep the grouping after summarising, use summarise(.groups = 'keep').

Summary

group_by() splits data into groups based on columns.

summarise() calculates summary values for each group.

Use them together to get quick summaries for different parts of your data.