mutate() for new columns in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

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

mutate() adds new columns or changes existing ones in a data frame without removing existing data.

beginner

How do you create a new column total as the sum of columns price and tax using mutate()?

df %>% mutate(total = price + tax)

beginner

Can mutate() use existing columns to create new ones?

Yes, mutate() can use existing columns to calculate values for new columns.

intermediate

What happens if you use mutate() to create a column with the same name as an existing one?

The existing column is replaced by the new values you provide.

beginner

Write a mutate() example that creates a new column discounted_price which is 10% less than price.

df %>% mutate(discounted_price = price * 0.9)

What is the main purpose of mutate() in dplyr?

A Filter rows based on conditions

B Summarize data by groups

C Sort rows by column values

D Add or modify columns in a data frame

mutate() is used to add new columns or change existing ones.

Which of the following creates a new column area as length * width using mutate()?

A df %>% mutate(area = length + width)

B df %>% filter(area = length * width)

C df %>% mutate(area = length * width)

D df %>% select(area = length * width)

Multiplying length and width inside mutate() creates the new column area.

If you want to replace an existing column score with its square, which is correct?

A df %>% mutate(new_score = score^2)

B df %>% mutate(score = score^2)

C df %>% filter(score = score^2)

D df %>% select(score = score^2)

Using the same column name in mutate() overwrites it.

Can mutate() create multiple new columns at once?

A Yes, by separating each new column with a comma

B No, only one column at a time

C Only if you use <code>summarize()</code> instead

D Only if you use <code>filter()</code> first

You can add many new columns in one mutate() call by separating them with commas.

What will this code do? df %>% mutate(new_col = NA)

A Create a new column <code>new_col</code> filled with missing values

B Delete the column <code>new_col</code>

C Filter rows where <code>new_col</code> is NA

D Rename <code>new_col</code> to NA

This creates a new column with all values set to missing (NA).

Explain how to use mutate() to add a new column based on existing columns in a data frame.

Think about how you create a new column that depends on others.

Describe what happens if you use mutate() to create a column with the same name as an existing one.

Consider how mutate handles column names.

Practice

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

easy

A. It sorts the data frame by a column.

B. It deletes rows from a data frame.

C. It merges two data frames together.

D. It adds new columns or modifies existing ones in a data frame.

Solution

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

   The mutate() function is designed to add new columns or change existing columns in a data frame.

2. Step 2: Compare with other functions

   Deleting rows is done by filter(), sorting by arrange(), and merging by join functions, not mutate().

3. Final Answer:

   It adds new columns or modifies existing ones in a data frame. -> Option D

4. Quick Check:

   mutate() = add/change columns [OK]

Hint: mutate() changes or adds columns, not rows or merges [OK]

Common Mistakes:

• Confusing mutate() with filter() for rows
• Thinking mutate() merges data frames
• Assuming mutate() sorts data

2. Which of the following is the correct syntax to add a new column total as the sum of columns a and b using mutate()?

easy

A. mutate(df, total = a + b)

B. mutate(df, total <- a + b)

C. mutate(df, total = sum(a, b))

D. mutate(df, total = a & b)

Solution

1. Step 1: Check assignment inside mutate()

   Inside mutate(), new columns are created with =, not with assignment operator <-.

2. Step 2: Understand how to sum columns element-wise

   To add columns element-wise, use a + b. Using sum(a, b) sums all values together, not element-wise. a & b is a logical AND, not addition.

3. Final Answer:

   mutate(df, total = a + b) -> Option A

4. Quick Check:

   Use = and + for element-wise sum [OK]

Hint: Use = and + inside mutate() for new column sums [OK]

Common Mistakes:

• Using <- instead of = inside mutate()
• Using sum() instead of + for element-wise addition
• Using & instead of + for addition

3. Given the data frame df <- data.frame(x = 1:3, y = 4:6), what will be the result of mutate(df, z = x * y)?

medium

A. A data frame with columns x, y, and z where z is the product of x and y.

B. A data frame with columns x and y only, no new column added.

C. An error because mutate() cannot multiply columns.

D. A data frame with column z only containing the product values.

Solution

1. Step 1: Understand mutate() behavior

   mutate() adds new columns without removing existing ones unless explicitly overwritten.

2. Step 2: Calculate new column values

   For each row, z = x * y means multiplying corresponding elements: 1*4=4, 2*5=10, 3*6=18.

3. Final Answer:

   A data frame with columns x, y, and z where z is the product of x and y. -> Option A

4. Quick Check:

   mutate() adds z = x * y element-wise [OK]

Hint: mutate() keeps old columns and adds new ones [OK]

Common Mistakes:

• Expecting mutate() to remove old columns
• Thinking mutate() cannot do arithmetic
• Assuming new column replaces all columns

4. What is wrong with this code snippet?

df <- data.frame(a = 1:3, b = 4:6)
df %>% mutate(c = a + d)

medium

A. The syntax of mutate() is incorrect; it needs a comma after c.

B. Column d does not exist in df, causing an error.

C. You cannot use the pipe operator %>% with mutate().

D. The code will run but c will be filled with NA values.

Solution

1. Step 1: Check column names in data frame

   The data frame df has columns a and b, but no column named d.

2. Step 2: Understand mutate() behavior with missing columns

   Trying to use a non-existent column d inside mutate() causes an error because R cannot find it.

3. Final Answer:

   Column d does not exist in df, causing an error. -> Option B

4. Quick Check:

   Using unknown columns in mutate() causes error [OK]

Hint: Check all columns exist before using in mutate() [OK]

Common Mistakes:

• Assuming mutate() creates missing columns automatically
• Thinking pipe operator %>% is invalid here
• Believing syntax error due to missing comma

5. You have a data frame df with columns score1 and score2. You want to add a new column average that is the average of these two scores, but only if both scores are greater than 0; otherwise, average should be NA. Which mutate() code correctly does this?

hard

A. mutate(df, average = (score1 + score2) / 2)

B. mutate(df, average = ifelse(score1 > 0 | score2 > 0, (score1 + score2) / 2, NA))

C. mutate(df, average = ifelse(score1 > 0 & score2 > 0, (score1 + score2) / 2, NA))

D. mutate(df, average = ifelse(score1 & score2, (score1 + score2) / 2, NA))

Solution

1. Step 1: Use ifelse() with correct condition

   The condition requires both score1 and score2 to be greater than 0, so use vectorized & inside ifelse().

2. Step 2: Calculate average or assign NA

   If condition is true, calculate average as (score1 + score2) / 2, else assign NA.

3. Final Answer:

   mutate(df, average = ifelse(score1 > 0 & score2 > 0, (score1 + score2) / 2, NA)) -> Option C

4. Quick Check:

   Use ifelse() with & for both conditions [OK]

Hint: Use ifelse() with & to check both conditions [OK]

Common Mistakes:

• Using | instead of & to require both conditions
• Omitting ifelse() and always calculating average
• Using score1 & score2 without comparison