Factor creation in R Programming - Mini Project: Build & Apply

Factor creation

📖 Scenario: You work in a small company that collects survey data about customer satisfaction. The survey answers are stored as text, but you want to organize them better for analysis.

🎯 Goal: You will create a factor variable in R from a character vector of survey answers. This will help group and analyze the answers easily.

📋 What You'll Learn

Create a character vector with exact survey answers

Create a factor variable from the character vector

Specify the levels of the factor in a given order

Print the factor variable to see the result

💡 Why This Matters

🌍 Real World

Survey data often comes as text answers. Converting them to factors helps organize and analyze the data efficiently.

💼 Career

Data analysts and statisticians use factors to handle categorical data in R for reports and visualizations.

1

Create the survey answers vector

Create a character vector called answers with these exact values: "Yes", "No", "Maybe", "Yes", "No".

R Programming

# Create the character vector answers with the given values
# Your code here

Hint

Use the c() function to combine the answers into a vector.

2

Create the factor variable

Create a factor variable called answers_factor from the answers vector using the factor() function.

R Programming

answers <- c("Yes", "No", "Maybe", "Yes", "No")
# Create the factor variable answers_factor from answers
# Your code here

Hint

Use factor(answers) to convert the character vector to a factor.

3

Specify the factor levels order

Create the factor variable answers_factor again from answers, but this time specify the levels in this exact order: "No", "Maybe", "Yes".

R Programming

answers <- c("Yes", "No", "Maybe", "Yes", "No")
# Create answers_factor with levels ordered as "No", "Maybe", "Yes"
# Your code here

Hint

Use the levels argument inside factor() to set the order.

4

Print the factor variable

Print the answers_factor variable to display its values and levels.

R Programming

answers <- c("Yes", "No", "Maybe", "Yes", "No")
answers_factor <- factor(answers, levels = c("No", "Maybe", "Yes"))
# Print the answers_factor variable
# Your code here

Hint

Use print(answers_factor) to show the factor values and their levels.

Practice

1. What is the main purpose of creating a factor in R?

easy

A. To represent categorical data with specific levels

B. To perform mathematical calculations faster

C. To store large numeric datasets efficiently

D. To create complex graphical plots automatically

Solution

1. Step 1: Understand factor usage in R

   Factors are used to represent categorical data with fixed categories called levels.

2. Step 2: Compare options with factor purpose

   Only To represent categorical data with specific levels correctly describes factors as categorical data with levels.

3. Final Answer:

   To represent categorical data with specific levels -> Option A

4. Quick Check:

   Factor = categorical data with levels [OK]

Hint: Factors are for categories, not numbers or plots [OK]

Common Mistakes:

• Thinking factors speed up math calculations
• Confusing factors with numeric vectors
• Assuming factors create plots automatically

2. Which of the following is the correct way to create a factor from a character vector colors <- c('red', 'blue', 'red')?

easy

A. matrix(colors)

B. factor(colors)

C. list(colors)

D. as.numeric(colors)

Solution

1. Step 1: Identify function to create factor

   The function factor() converts a vector into a factor with levels.

2. Step 2: Check other options

   as.numeric() converts to numbers, list() and matrix() create different structures, not factors.

3. Final Answer:

   factor(colors) -> Option B

4. Quick Check:

   Use factor() to create factors [OK]

Hint: Use factor() to convert vectors to factors [OK]

Common Mistakes:

• Using as.numeric() instead of factor()
• Confusing list() or matrix() with factor creation
• Not wrapping vector inside factor()

3. What will be the output of the following R code?

colors <- c('red', 'blue', 'red')
f <- factor(colors, levels = c('blue', 'red'))
levels(f)

medium

A. [1] "red" "blue"

B. [1] "red"

C. [1] "blue" "red"

D. [1] "blue"

Solution

1. Step 1: Understand factor levels argument

   The levels argument sets the order and categories explicitly as c('blue', 'red').

2. Step 2: Check levels(f) output

   Calling levels(f) returns the levels in the order specified: "blue", then "red".

3. Final Answer:

   [1] "blue" "red" -> Option C

4. Quick Check:

   levels() shows levels in given order [OK]

Hint: levels() shows factor levels in specified order [OK]

Common Mistakes:

• Assuming levels are sorted alphabetically by default
• Confusing factor values with levels
• Ignoring the levels argument order

4. Identify the error in this R code snippet:

colors <- c('red', 'blue', 'green')
f <- factor(colors, levels = c('red', 'blue'))
summary(f)

medium

A. factor() cannot take levels argument

B. colors vector must be numeric for factor()

C. summary() cannot be used on factors

D. The levels argument is missing 'green', causing NA values

Solution

1. Step 1: Check levels vs data values

   The vector has 'green' but levels only include 'red' and 'blue'. This causes 'green' to become NA in the factor.

2. Step 2: Understand summary() behavior

   summary() counts factor levels including NAs, so missing 'green' in levels causes unexpected NA counts.

3. Final Answer:

   The levels argument is missing 'green', causing NA values -> Option D

4. Quick Check:

   Levels must include all categories to avoid NA [OK]

Hint: Include all categories in levels to avoid NA [OK]

Common Mistakes:

• Thinking factor() ignores missing levels
• Believing summary() can't handle factors
• Assuming colors must be numeric

5. You have a character vector responses <- c('yes', 'no', 'yes', 'maybe', 'no'). You want to create an ordered factor with levels no < maybe < yes. Which code correctly creates this ordered factor?

hard

A. factor(responses, levels = c('no', 'maybe', 'yes'), ordered = TRUE)

B. factor(responses, levels = c('yes', 'no', 'maybe'), ordered = TRUE)

C. factor(responses, ordered = TRUE)

D. factor(responses, levels = c('no', 'maybe', 'yes'))

Solution

1. Step 1: Set levels in desired order

   Levels must be set as c('no', 'maybe', 'yes') to reflect the order no < maybe < yes.

2. Step 2: Make factor ordered

   Use ordered = TRUE to create an ordered factor reflecting this order.

3. Final Answer:

   factor(responses, levels = c('no', 'maybe', 'yes'), ordered = TRUE) -> Option A

4. Quick Check:

   Ordered factor needs levels and ordered=TRUE [OK]

Hint: Set levels in order and use ordered=TRUE [OK]

Common Mistakes:

• Not setting ordered=TRUE for ordered factors
• Incorrect levels order
• Omitting levels argument