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

Need a 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

Need a 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

Need a 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

Need a hint?

Use print(sales_data) to show the updated table.