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.

Practice

1. What does the group_by() function do in R's dplyr package?

easy

A. It splits data into groups based on specified columns.

B. It sorts the data in ascending order.

C. It removes duplicate rows from the data.

D. It changes the data type of columns.

Solution

1. Step 1: Understand the purpose of group_by()

   The group_by() function is used to split data into groups based on one or more columns.

2. Step 2: Compare with other options

   Sorting, removing duplicates, or changing data types are done by other functions, not group_by().

3. Final Answer:

   It splits data into groups based on specified columns. -> Option A

4. Quick Check:

   group_by() = splits data into groups [OK]

Hint: Remember: group_by() groups data by columns [OK]

Common Mistakes:

• Confusing group_by() with arrange() for sorting
• Thinking group_by() removes duplicates
• Assuming group_by() changes data types

2. Which of the following is the correct syntax to group a data frame df by the column category and then summarise the mean of value?

easy

A. df %>% group_by(category) %>% summarise(mean_value = mean(value))

B. df %>% summarise(mean_value = mean(value)) %>% group_by(category)

C. group_by(df, category) %>% summarise(mean_value = mean(value))

D. df %>% group_by(category) %>% mean(value)

Solution

1. Step 1: Identify correct pipe sequence

   The correct order is to first group the data, then summarise it. So df %>% group_by(category) %>% summarise(...) is correct.

2. Step 2: Check function usage

   mean(value) inside summarise is correct. df %>% summarise(mean_value = mean(value)) %>% group_by(category) reverses the order, C uses wrong syntax, and D misses summarise.

3. Final Answer:

   df %>% group_by(category) %>% summarise(mean_value = mean(value)) -> Option A

4. Quick Check:

   group_by() then summarise() = df %>% group_by(category) %>% summarise(mean_value = mean(value)) [OK]

Hint: Always group before summarising with %>% [OK]

Common Mistakes:

• Putting summarise() before group_by()
• Using mean() outside summarise()
• Incorrect function chaining syntax

3. Given the data frame df below:

df <- data.frame(group = c('A', 'A', 'B', 'B', 'B'), value = c(10, 20, 30, 40, 50))

What is the output of:

df %>% group_by(group) %>% summarise(total = sum(value))

medium

A. group | total
-----|------
A | 10
B | 30

B. group | total
-----|------
A | 30
B | 90

C. group | total
-----|------
A | 20
B | 50

D. group | total
-----|------
A | 30
B | 120

Solution

1. Step 1: Group data by 'group'

   There are two groups: 'A' with values 10 and 20, and 'B' with values 30, 40, 50.

2. Step 2: Calculate sum of 'value' for each group

   Sum for 'A' = 10 + 20 = 30; Sum for 'B' = 30 + 40 + 50 = 120.

3. Final Answer:

   group | total -----|------ A | 30 B | 120 -> Option D

4. Quick Check:

   sum values by group = group | total -----|------ A | 30 B | 120 [OK]

Hint: Add values per group to find sum quickly [OK]

Common Mistakes:

• Adding only first two values of group B
• Mixing group labels and values
• Confusing sum with mean

4. Consider this code snippet:

df <- data.frame(team = c('X', 'X', 'Y'), score = c(5, 7, 8))
df %>% summarise(avg_score = mean(score)) %>% group_by(team)

What is the main issue here?

medium

A. The code will produce an error because group_by() must come first.

B. The summarise() is used before group_by(), so grouping is lost.

C. The mean() function is used incorrectly inside summarise().

D. The data frame df is missing required columns.

Solution

1. Step 1: Analyze function order

   The code summarises the whole data first, losing group info, then tries to group after summarising.

2. Step 2: Understand grouping effect

   Grouping after summarise has no effect because summarise returns a smaller data frame without original groups.

3. Final Answer:

   The summarise() is used before group_by(), so grouping is lost. -> Option B

4. Quick Check:

   group_by() must come before summarise() [OK]

Hint: Always group before summarising to keep groups [OK]

Common Mistakes:

• Assuming group_by() after summarise() works
• Thinking summarise() keeps original groups
• Believing mean() usage is wrong here

5. You have a data frame sales with columns region, product, and revenue. You want to find the average revenue per product only for regions where total revenue exceeds 1000. Which code correctly achieves this?

hard

A. sales %>% group_by(region) %>% summarise(total_rev = sum(revenue)) %>% filter(total_rev > 1000) %>% group_by(product) %>% summarise(avg_rev = mean(revenue))

B. sales %>% group_by(region, product) %>% summarise(total_rev = sum(revenue)) %>% filter(total_rev > 1000) %>% summarise(avg_rev = mean(total_rev))

C. sales %>% group_by(region) %>% filter(sum(revenue) > 1000) %>% group_by(product) %>% summarise(avg_rev = mean(revenue))

D. sales %>% group_by(product) %>% summarise(avg_rev = mean(revenue)) %>% filter(region %in% (sales %>% group_by(region) %>% summarise(total_rev = sum(revenue)) %>% filter(total_rev > 1000) %>% pull(region)))

Solution

1. Step 1: Filter regions with total revenue > 1000

   Using filter(sum(revenue) > 1000) inside group_by(region) keeps only those regions.

2. Step 2: Group by product and summarise average revenue

   After filtering, regroup by product and calculate mean revenue per product.

3. Step 3: Check other options

   sales %>% group_by(region) %>% summarise(total_rev = sum(revenue)) %>% filter(total_rev > 1000) %>% group_by(product) %>% summarise(avg_rev = mean(revenue)) tries to summarise twice but loses revenue values for product grouping. sales %>% group_by(region, product) %>% summarise(total_rev = sum(revenue)) %>% filter(total_rev > 1000) %>% summarise(avg_rev = mean(total_rev)) mixes grouping and summarising incorrectly. sales %>% group_by(product) %>% summarise(avg_rev = mean(revenue)) %>% filter(region %in% (sales %>% group_by(region) %>% summarise(total_rev = sum(revenue)) %>% filter(total_rev > 1000) %>% pull(region))) filters after summarising by product, which is wrong.

4. Final Answer:

   sales %>% group_by(region) %>% filter(sum(revenue) > 1000) %>% group_by(product) %>% summarise(avg_rev = mean(revenue)) -> Option C

5. Quick Check:

   Filter groups before summarise for correct subset [OK]

Hint: Filter groups with filter(sum(...) > val) before summarise [OK]

Common Mistakes:

• Summarising before filtering groups
• Losing original revenue values after summarise
• Incorrect chaining of group_by and summarise