mutate() for new columns in R Programming - Mini Project: Build & Apply

Using mutate() to Add New Columns in R

📖 Scenario: You work in a small bakery and keep track of daily sales data in a table. You want to add new columns to calculate the total revenue and a label for high or low sales days.

🎯 Goal: Learn how to use mutate() from the dplyr package in R to add new columns to a data frame.

📋 What You'll Learn

Create a data frame called sales_data with exact columns and values

Create a variable called high_sales_threshold with a specific numeric value

Use mutate() to add two new columns: total_revenue and sales_label

Print the updated sales_data data frame

💡 Why This Matters

🌍 Real World

Adding new columns to data tables is common in business to calculate totals, labels, or categories based on existing data.

💼 Career

Data analysts and scientists often use mutate() in R to prepare and enrich data for reports and decision-making.

1

Create the initial sales data frame

Create a data frame called sales_data with columns day, items_sold, and price_per_item. Use these exact values: day as c("Mon", "Tue", "Wed", "Thu", "Fri"), items_sold as c(10, 15, 7, 20, 13), and price_per_item as c(2.5, 2.5, 2.5, 2.5, 2.5).

R Programming

# Create the sales_data data frame with day, items_sold, and price_per_item columns
# Your code here

Hint

Use data.frame() to create the table with the exact column names and values.

2

Set the high sales threshold

Create a numeric variable called high_sales_threshold and set it to 30.

R Programming

sales_data <- data.frame(
  day = c("Mon", "Tue", "Wed", "Thu", "Fri"),
  items_sold = c(10, 15, 7, 20, 13),
  price_per_item = c(2.5, 2.5, 2.5, 2.5, 2.5)
)
# Create high_sales_threshold variable and set it to 30
# Your code here

Hint

Just assign the number 30 to the variable high_sales_threshold.

3

Add new columns with mutate()

Use mutate() from the dplyr package on sales_data to add two new columns: total_revenue which is items_sold * price_per_item, and sales_label which is "High" if total_revenue is greater than high_sales_threshold, otherwise "Low". Assign the result back to sales_data. Remember to load dplyr with library(dplyr).

R Programming

sales_data <- data.frame(
  day = c("Mon", "Tue", "Wed", "Thu", "Fri"),
  items_sold = c(10, 15, 7, 20, 13),
  price_per_item = c(2.5, 2.5, 2.5, 2.5, 2.5)
)
high_sales_threshold <- 30

# Load dplyr and use mutate() to add total_revenue and sales_label columns
# Your code here

Hint

Use mutate() to add columns. Use ifelse() to create the sales_label based on the threshold.

4

Print the updated sales_data

Write a print() statement to display the updated sales_data data frame.

R Programming

library(dplyr)
sales_data % mutate(
  total_revenue = items_sold * price_per_item,
  sales_label = ifelse(total_revenue > high_sales_threshold, "High", "Low")
)

# Print the sales_data data frame
# Your code here

Hint

Use print(sales_data) to show the updated table.

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