mutate() for new columns in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add a new column 'total' as the sum of 'x' and 'y'.

R Programming

library(dplyr)
data <- tibble(x = 1:3, y = 4:6)
data <- data %>% mutate(total = x [1] y)

Drag options to blanks, or click blank then click option'

A/

B-

C*

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using subtraction or multiplication instead of addition.

Forgetting to use mutate() to add a new column.

The mutate() function adds a new column. Using + sums the columns x and y.

2fill in blank

medium

Complete the code to create a new column 'ratio' as 'x' divided by 'y'.

R Programming

library(dplyr)
data <- tibble(x = 10:12, y = 2:4)
data <- data %>% mutate(ratio = x [1] y)

Drag options to blanks, or click blank then click option'

A/

B+

C*

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication or addition instead of division.

Mixing up the order of operands.

The / operator divides x by y to create the 'ratio' column.

3fill in blank

hard

Fix the error in the code to correctly add a new column 'diff' as the difference between 'a' and 'b'.

R Programming

library(dplyr)
data <- tibble(a = 5:7, b = 3:5)
data <- data %>% mutate(diff = a [1] b)

Drag options to blanks, or click blank then click option'

A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition or multiplication instead of subtraction.

Swapping the order of operands.

The difference is calculated by subtracting b from a using the - operator.

4fill in blank

hard

Fill both blanks to create a new column 'score' as the product of 'm' and 'n' plus 10.

R Programming

library(dplyr)
data <- tibble(m = 2:4, n = 5:7)
data <- data %>% mutate(score = m [1] n [2] 10)

Drag options to blanks, or click blank then click option'

A*

B+

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition before multiplication without parentheses.

Using subtraction or division instead of addition.

First multiply m and n with *, then add 10 with +.

5fill in blank

hard

Fill all three blanks to create a new column 'result' as the uppercase of 'name', length of 'name', and check if length is greater than 4.

R Programming

library(dplyr)
data <- tibble(name = c('Anna', 'Brian', 'Clara'))
data <- data %>% mutate(result = list([1], [2], [3]))

Drag options to blanks, or click blank then click option'

Atoupper(name)

Bnchar(name)

Cnchar(name) > 4

Dtolower(name)

Attempts:

3 left

💡 Hint

Common Mistakes

Using tolower() instead of toupper().

Forgetting to compare length with a number.

Use toupper(name) for uppercase, nchar(name) for length, and nchar(name) > 4 to check if length is greater than 4.

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