summarise() with group_by() in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

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

group_by() splits the data into groups based on one or more variables. It prepares the data so that you can perform operations on each group separately.

beginner

What is the purpose of summarise() in combination with group_by()?

summarise() creates a summary statistic for each group created by group_by(). For example, it can calculate the average or total for each group.

beginner

How do you calculate the average of a column score for each group of team in a dataframe df?

df %>%
  group_by(team) %>%
  summarise(avg_score = mean(score))

This groups the data by team and calculates the average score for each team.

beginner

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

summarise() will calculate the summary statistic for the entire dataset, not by groups.

intermediate

Can you use multiple summary functions inside summarise() after grouping?

Yes, you can calculate many summaries at once. For example:

df %>%
  group_by(team) %>%
  summarise(avg_score = mean(score), max_score = max(score))

What does group_by() do before using summarise()?

A Splits data into groups based on variables

B Deletes rows from the data

C Sorts the data alphabetically

D Changes data types of columns

group_by() organizes data into groups so that summarise() can calculate summaries for each group separately.

What will summarise() do if used without group_by()?

A Throw an error

B Calculate summary for each row

C Calculate summary for the whole dataset

D Create new groups automatically

Without grouping, summarise() returns a single summary for the entire data.

Which of these is a valid way to calculate the total sales per region using dplyr?

A df %>% filter(region) %>% summarise(total_sales = sum(sales))

B df %>% summarise(total_sales = sum(sales)) %>% group_by(region)

C df %>% group_by(sales) %>% summarise(total_region = sum(region))

D df %>% group_by(region) %>% summarise(total_sales = sum(sales))

First group by region, then summarise sales per group.

Can you use multiple summary calculations inside one summarise() call?

A Yes, you can calculate many summaries at once

B No, only one summary is allowed

C Only if you use <code>group_by()</code> twice

D Only with special packages

summarise() can include many summary columns separated by commas.

What does this code do?

df %>% group_by(category) %>% summarise(count = n())

A Calculates mean of category

B Counts rows in each category group

C Filters rows with category

D Creates new categories

n() counts the number of rows in each group.

Explain how group_by() and summarise() work together to summarize data.

Think about how you might count or average scores for each team separately.

Describe a real-life example where you would use group_by() with summarise().

Imagine you have sales data and want to know total sales per store.

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