Why factors represent categorical data in R Programming - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this factor levels code?

Consider this R code that creates a factor variable. What will be the output of levels(fruit)?

R Programming

fruit <- factor(c("apple", "banana", "apple", "orange"))
levels(fruit)

A [1] "apple" "banana" "orange"

B [1] "apple" "orange"

C [1] "banana" "orange"

D [1] "apple" "banana"

💡 Hint

Think about what unique categories the factor stores.

Factors store unique categories as levels. Here, the unique fruits are apple, banana, and orange, so levels() returns all three.

🧠 Conceptual

intermediate

Why are factors used for categorical data in R?

Which of the following best explains why factors represent categorical data in R?

A Factors automatically sort data in alphabetical order without labels.

B Factors store only numeric data and cannot hold text values.

C Factors convert all data to character strings for display.

D Factors store data as integers with labels, making categories easy to manage and analyze.

💡 Hint

Think about how factors internally represent categories.

Factors store categorical data as integer codes with labels for each category. This helps R handle categories efficiently.

❓ Predict Output

advanced

What happens when you compare two factors with different levels?

What will be the output of this R code?

R Programming

f1 <- factor(c("low", "medium", "high"), levels = c("low", "medium", "high"))
f2 <- factor(c("low", "medium", "high"), levels = c("high", "low", "medium"))
f1 == f2

A [1] FALSE FALSE FALSE

B [1] TRUE TRUE TRUE

C Error: factors with different levels cannot be compared

D [1] TRUE FALSE TRUE

💡 Hint

Check how factor levels affect comparison.

Even though the labels look the same, the underlying integer codes differ because the levels are ordered differently, so all comparisons return FALSE.

🔧 Debug

advanced

Why does this factor conversion produce unexpected results?

What is the output of this code and why?

R Programming

x <- c("10", "2", "30")
f <- factor(x)
as.numeric(f)

A [1] 10 2 30

B [1] 1 2 3

C Error: cannot convert factor to numeric directly

D [1] NA NA NA

💡 Hint

Remember how factors store data internally.

as.numeric on a factor returns the internal integer codes, not the original numbers. Here, the factor levels are sorted alphabetically, so "10" is level 1, "2" is level 2, "30" is level 3.

🚀 Application

expert

How many unique categories does this factor have after modification?

Given this R code, what is the number of unique categories in f after the assignment?

R Programming

f <- factor(c("red", "blue", "green"))
levels(f) <- c("red", "blue", "yellow")
f <- c(f, "yellow")
length(levels(f))

A 5

B 4

C Error: invalid factor level

D 3

💡 Hint

Think about how levels are changed and what happens when adding a new value.

levels(f) <- c("red", "blue", "yellow") replaces the levels ("green" becomes NA). Adding "yellow" which is not a factor level causes an error.