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

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

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

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

Hint

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

Practice

1. What is the main reason tidy data makes analysis easier in R?

easy

A. Data is stored in a single file format

B. Data is always numeric

C. Each variable is a column and each observation is a row

D. Data is sorted alphabetically

Solution

1. Step 1: Understand tidy data structure

   Tidy data means each variable is stored in its own column and each observation in its own row.

2. Step 2: Connect tidy data to analysis ease

   This structure makes it easier for R functions and packages to process and analyze data efficiently.

3. Final Answer:

   Each variable is a column and each observation is a row -> Option C

4. Quick Check:

   Tidy data = variable as column, observation as row [OK]

Hint: Remember: tidy means variables in columns, observations in rows [OK]

Common Mistakes:

• Thinking tidy data means data is sorted
• Assuming tidy data must be numeric only
• Confusing file format with data structure

2. Which of the following is the correct way to represent tidy data in R?

easy

A. Each column is a variable, each row is an observation

B. Data stored as a list of vectors without names

C. Each row is a variable, each column is an observation

D. Data stored as a single vector with mixed types

Solution

1. Step 1: Recall tidy data format

   Tidy data requires each column to represent a variable and each row to represent an observation.

2. Step 2: Match options to tidy data

   Each column is a variable, each row is an observation correctly states this format, while others do not follow tidy data principles.

3. Final Answer:

   Each column is a variable, each row is an observation -> Option A

4. Quick Check:

   Columns = variables, rows = observations [OK]

Hint: Variables go in columns, observations go in rows [OK]

Common Mistakes:

• Swapping rows and columns roles
• Using unnamed lists instead of data frames
• Mixing data types in a single vector

3. Given this R data frame:

df <- data.frame(
  id = c(1, 2),
  age = c(30, 25),
  height = c(170, 160)
)

Which statement about this data is true?

medium

A. It is tidy because each variable is a column and each observation is a row

B. It is not tidy because variables are mixed in rows

C. It is not tidy because data types differ in columns

D. It is tidy because all data is numeric

Solution

1. Step 1: Examine data frame structure

   The data frame has columns named id, age, and height, each representing a variable.

2. Step 2: Check observations in rows

   Each row represents one observation with values for all variables.

3. Final Answer:

   It is tidy because each variable is a column and each observation is a row -> Option A

4. Quick Check:

   Variables in columns and observations in rows = tidy [OK]

Hint: Check if columns are variables and rows are observations [OK]

Common Mistakes:

• Confusing data type differences as untidiness
• Thinking numeric-only data is required
• Assuming mixing variables in rows is okay

4. Consider this R code snippet:

df <- data.frame(
  name = c("Alice", "Bob"),
  score = c(85, 90),
  score = c(88, 92)
)

What is the main problem with this data frame?

medium

A. Rows are not observations

B. Data frame has missing rows

C. Data types are inconsistent in columns

D. Duplicate column names cause data loss

Solution

1. Step 1: Identify duplicate column names

   The data frame defines two columns both named 'score', which causes the first to be overwritten.

2. Step 2: Understand impact on tidy data

   Duplicate names cause loss of data and break tidy data principles where each variable must have a unique column.

3. Final Answer:

   Duplicate column names cause data loss -> Option D

4. Quick Check:

   Duplicate columns overwrite data causing loss [OK]

Hint: Check for unique column names to avoid data loss [OK]

Common Mistakes:

• Ignoring duplicate column names
• Assuming missing rows cause the issue
• Confusing data type inconsistency with column duplication

5. You have a messy data frame where multiple variables are combined in one column like this:

df <- data.frame(
  id = c(1, 2),
  info = c("age:30;height:170", "age:25;height:160")
)

Which approach best tidies this data for analysis in R?

hard

A. Keep 'info' as one column and analyze text directly

B. Split the 'info' column into separate 'age' and 'height' columns

C. Convert 'info' into a list column without splitting

D. Remove the 'info' column to simplify data

Solution

1. Step 1: Identify problem with combined variables

   The 'info' column mixes two variables (age and height) in one column, which is untidy.

2. Step 2: Apply tidying principle

   Splitting 'info' into separate columns for age and height makes each variable its own column, following tidy data rules.

3. Final Answer:

   Split the 'info' column into separate 'age' and 'height' columns -> Option B

4. Quick Check:

   Separate variables into columns for tidy data [OK]

Hint: Split combined columns into separate variable columns [OK]

Common Mistakes:

• Analyzing combined text without splitting
• Removing useful data instead of tidying
• Using list columns that complicate analysis