Factor creation in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a factor in R?

A factor in R is a data structure used to represent categorical data. It stores the data as integer codes with corresponding labels called levels.

beginner

How do you create a factor from a character vector in R?

Use the function factor() and pass the character vector as an argument. For example: factor(c("apple", "banana", "apple")).

intermediate

What does the levels argument do in factor()?

The levels argument sets the possible categories (levels) of the factor. It controls the order and which categories are included, even if not present in the data.

beginner

How can you check the levels of a factor in R?

Use the levels() function on the factor variable. It returns a character vector of the factor's levels.

intermediate

What happens if you create a factor with numeric values?

R treats the numeric values as categories, not numbers. The factor stores them as levels, so arithmetic operations won't work directly on factors.

Which function is used to create a factor in R?

A factor()

B as.factor()

C levels()

D categorize()

The factor() function creates a factor from a vector.

What does the levels argument in factor() control?

A The order and categories of the factor

B The numeric values of the factor

C The length of the vector

D The data type of the vector

The levels argument sets which categories exist and their order.

If you create a factor from the vector c("red", "blue", "red"), how many levels will it have?

A 1

B 3

C 2

D 0

There are two unique categories: "red" and "blue".

What will levels(factor(c(1, 2, 1))) return?

A An error

B [1, 2]

C ["1", "2", "3"]

D ["1", "2"]

Levels are stored as character strings representing the unique values.

Can you perform arithmetic directly on factors?

A Yes, factors behave like numbers

B No, factors represent categories, not numbers

C Only if levels are numeric

D Only with the <code>levels</code> argument

Factors represent categories, so arithmetic operations do not work directly.

Explain how to create a factor in R and why factors are useful.

Think about how R stores categories and why that helps with data analysis.

Describe what happens when you create a factor from numeric values and how it differs from numeric vectors.

Consider how factors treat numbers as labels, not quantities.

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