Why tidy data enables analysis in R Programming - See It in Action

Why tidy data enables analysis

📖 Scenario: Imagine you work in a small bakery. You keep track of daily sales for different types of bread. The data is messy and hard to understand. You want to tidy it so you can easily find out which bread sells best each day.

🎯 Goal: You will create a small messy data frame, then tidy it step-by-step using R. This will help you see why tidy data makes analysis easier and faster.

📋 What You'll Learn

Create a data frame called sales with messy sales data

Create a vector called days with day names

Use tidyr::pivot_longer() to tidy the data into a long format

Print the tidy data frame called tidy_sales

💡 Why This Matters

🌍 Real World

Tidy data helps businesses quickly analyze sales trends by day or product.

💼 Career

Data analysts and scientists use tidy data to prepare datasets for clear and efficient analysis.

1

Create messy sales data

Create a data frame called sales with columns bread, Monday, and Tuesday. Use these exact values: bread has "Sourdough", "Baguette", "Rye"; Monday has 10, 15, 7; Tuesday has 12, 18, 5.

R Programming

# Create the data frame sales with bread, Monday, and Tuesday columns
# Your code here

Need a hint?

Use data.frame() with named vectors for each column.

2

Create a vector of days

Create a character vector called days with the exact values "Monday" and "Tuesday".

R Programming

sales <- data.frame(
  bread = c("Sourdough", "Baguette", "Rye"),
  Monday = c(10, 15, 7),
  Tuesday = c(12, 18, 5)
)
# Create the vector days with "Monday" and "Tuesday"
# Your code here

Need a hint?

Use c() to create a vector with the two day names.

3

Tidy the sales data

Use tidyr::pivot_longer() on sales to create a new data frame called tidy_sales. Pivot the columns Monday and Tuesday into two columns: day and sales. Use names_to = "day" and values_to = "sales".

R Programming

sales <- data.frame(
  bread = c("Sourdough", "Baguette", "Rye"),
  Monday = c(10, 15, 7),
  Tuesday = c(12, 18, 5)
)
days <- c("Monday", "Tuesday")
# Use tidyr::pivot_longer() to create tidy_sales with day and sales columns
# Your code here

Need a hint?

Use pivot_longer() with cols = days to select the day columns.

4

Print the tidy data

Print the tidy_sales data frame to see the tidy data format.

R Programming

library(tidyr)
sales <- data.frame(
  bread = c("Sourdough", "Baguette", "Rye"),
  Monday = c(10, 15, 7),
  Tuesday = c(12, 18, 5)
)
days <- c("Monday", "Tuesday")
tidy_sales <- pivot_longer(sales, cols = days, names_to = "day", values_to = "sales")
# Print tidy_sales
# Your code here

Need a hint?

Use print(tidy_sales) to show the tidy data frame.