mutate() for new columns in R Programming

Introduction

The mutate() function helps you add new columns or change existing ones in a table of data easily.

You want to calculate a new value based on existing columns, like total price from quantity and price per item.

You need to add a column that shows a category based on some condition.

You want to create a new column that transforms data, like converting temperatures from Celsius to Fahrenheit.

You want to keep your original data but add extra information in new columns for analysis.

Syntax

R Programming

mutate(data_frame, new_column = expression, ...)

data_frame is your table of data.

You can add one or more new columns by giving each a name and a formula.

Examples

Adds a new column total by multiplying price and quantity.

R Programming

mutate(df, total = price * quantity)

Adds two new columns: discount_price and tax calculated from price.

R Programming

mutate(df, discount_price = price * 0.9, tax = price * 0.1)

Adds a new column category that labels rows as "Pass" or "Fail" based on score.

R Programming

mutate(df, category = ifelse(score > 50, "Pass", "Fail"))

Sample Program

This program creates a small table with names, prices, and quantities. Then it adds a new column total by multiplying price and quantity. Finally, it prints the new table.

R Programming

library(dplyr)
df <- data.frame(name = c("Anna", "Ben", "Cara"), price = c(10, 20, 15), quantity = c(2, 1, 3))
df_new <- mutate(df, total = price * quantity)
print(df_new)

Important Notes

mutate() does not change the original data unless you save the result.

You can use many functions inside mutate() like ifelse(), arithmetic, or string functions.

Summary

mutate() adds or changes columns in your data.

Use it to create new information from existing data easily.

Remember to save the result if you want to keep the changes.

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