Factor creation in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a factor from the vector 'colors'.

R Programming

colors <- c("red", "blue", "green", "blue")
factor_colors <- [1](colors)

Drag options to blanks, or click blank then click option'

Amatrix

Bas.character

Clist

Dfactor

Attempts:

3 left

💡 Hint

Common Mistakes

Using as.character() instead of factor()

Using list() which creates a list, not a factor

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

2fill in blank

medium

Complete the code to create a factor with specified levels.

R Programming

colors <- c("red", "blue", "green", "blue")
factor_colors <- factor(colors, levels = [1])

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Ac("red", "blue", "green")

Blist("red", "blue", "green")

Cc(1, 2, 3)

Dc("yellow", "purple")

Attempts:

3 left

💡 Hint

Common Mistakes

Using list() instead of c() for levels

Using numeric values instead of character strings for levels

The levels argument expects a character vector of the factor levels.

3fill in blank

hard

Fix the error in the code to create a factor from 'sizes'.

R Programming

sizes <- c("small", "medium", "large", "medium")
factor_sizes <- factor([1])

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Ac(sizes)

Bsize

Csizes

Dfactor(sizes)

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong variable name 'size'

Wrapping factor() inside factor() causing nested factors

The variable name is sizes. Using it directly creates the factor.

4fill in blank

hard

Fill both blanks to create an ordered factor with levels from small to large.

R Programming

sizes <- c("small", "medium", "large", "medium")
factor_sizes <- factor(sizes, levels = [1], ordered = [2])

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Ac("small", "medium", "large")

BTRUE

CFALSE

Dc("large", "medium", "small")

Attempts:

3 left

💡 Hint

Common Mistakes

Using unordered levels or reversed order

Setting ordered to FALSE or omitting it

Levels must be in order from smallest to largest, and ordered = TRUE makes the factor ordered.

5fill in blank

hard

Fill all three blanks to create a factor from 'fruits' with levels and labels.

R Programming

fruits <- c("a", "b", "c", "a")
factor_fruits <- factor(fruits, levels = [1], labels = [2], ordered = [3])

Drag options to blanks, or click blank then click option'

Ac("a", "b", "c")

Bc("apple", "banana", "cherry")

CFALSE

DTRUE

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up levels and labels

Setting ordered to TRUE when not needed

Levels are the original codes, labels are the descriptive names, and ordered is FALSE for unordered factors.

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