summarise() with group_by() in R Programming - Step-by-Step Execution

Concept Flow - summarise() with group_by()

Start with data frame

↓

Apply group_by()

↓

Data split into groups

↓

Apply summarise() to each group

↓

Combine results into summary data frame

↓

End

First, data is grouped by one or more columns, then summarise() calculates summary values for each group, producing a smaller summary table.

Execution Sample

R Programming

library(dplyr)
data <- tibble(name = c("A", "A", "B", "B"), score = c(10, 20, 30, 40))
data %>% group_by(name) %>% summarise(avg_score = mean(score))

This code groups data by 'name' and calculates the average 'score' for each group.

Execution Table

Step	Action	Group State	summarise Calculation	Output Row
1	Start with data frame	No groups	No calculation	Full data frame
2	Apply group_by(name)	Groups: A, B	No calculation yet	Data grouped by name
3	summarise(avg_score = mean(score)) for group A	Group A: rows with name 'A'	mean(10, 20) = 15	Row: name = A, avg_score = 15
4	summarise(avg_score = mean(score)) for group B	Group B: rows with name 'B'	mean(30, 40) = 35	Row: name = B, avg_score = 35
5	Combine summary rows	Groups combined	Summary complete	Output: 2 rows with avg_score per name

💡 All groups processed, summarise returns one row per group with calculated summaries.

Variable Tracker

Variable	Start	After group_by	After summarise	Final
data	[4 rows]	[Grouped by name: A, B]	[2 rows: avg_score per group]	[2 rows: summary output]
group	None	A, B groups created	Groups processed	Groups combined in output
avg_score	None	None	15 (A), 35 (B)	Summary column with averages

Key Moments - 2 Insights

Why does summarise() return fewer rows than the original data?

Because summarise() calculates one summary row per group created by group_by(), reducing multiple rows into one per group as shown in execution_table rows 3-5.

What happens if you use summarise() without group_by()?

summarise() then calculates summary over the entire data frame, returning a single row, unlike when grouped as shown in execution_table step 2.

Visual Quiz - 3 Questions

Look at the execution_table, what is the avg_score for group B at step 4?

A 35

B 40

C 30

D 70

💡 Hint

Check execution_table row 4 under 'summarise Calculation' for group B.

At which step does the data get split into groups?

A Step 1

B Step 3

C Step 2

D Step 5

💡 Hint

Look at execution_table row 2 where group_by() is applied.

If we remove group_by(), how many rows will summarise() return?

A Same as original data

B One row

C Zero rows

D Two rows

💡 Hint

Refer to key_moments explanation about summarise() without group_by().

Concept Snapshot

summarise() with group_by() in R:
- group_by() splits data into groups
- summarise() computes summary per group
- Output has one row per group
- Without group_by(), summarise() summarizes whole data
- Use with dplyr pipe %>% for chaining

Full Transcript

This visual trace shows how summarise() works with group_by() in R. First, the data frame is grouped by a column, splitting rows into groups. Then summarise() calculates summary statistics like mean for each group, returning one row per group. The execution table shows each step: starting with data, grouping by 'name', calculating average scores per group, and combining results. Variables like 'data', 'group', and 'avg_score' change as grouping and summarising happen. Key moments clarify why summarise() reduces rows and what happens without grouping. The quiz tests understanding of group values, steps, and summarise behavior. The snapshot summarizes usage and behavior for quick reference.

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