Releveling factors in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output after releveling a factor

What is the output of this R code after releveling the factor?

R Programming

f <- factor(c('low', 'medium', 'high', 'medium', 'low'))
f <- relevel(f, ref = 'medium')
levels(f)

A [1] "medium" "low" "high"

B [1] "low" "medium" "high"

C [1] "high" "medium" "low"

D [1] "medium" "high" "low"

💡 Hint

The relevel function changes the reference level to the one you specify, moving it to the first position.

The relevel function sets the specified level as the first level in the factor levels. Here, 'medium' becomes the first level, followed by the others in their original order.

❓ Predict Output

intermediate

Factor levels after releveling with a non-existent level

What happens when you try to relevel a factor with a reference level that does not exist?

R Programming

f <- factor(c('apple', 'banana', 'cherry'))
f <- relevel(f, ref = 'orange')
levels(f)

A [1] "apple" "banana" "cherry"

B [1] "banana" "apple" "cherry"

C Error in relevel(f, ref = "orange"): ref level 'orange' not found in factor levels

D [1] "orange" "apple" "banana" "cherry"

💡 Hint

Check if the reference level exists in the factor levels before releveling.

If the reference level is not found in the factor levels, relevel throws an error indicating the missing level.

🔧 Debug

advanced

Why does this relevel code not change the factor levels?

Consider this code snippet. Why does the factor levels remain unchanged after releveling?

R Programming

f <- factor(c('red', 'green', 'blue'))
relevel(f, ref = 'green')
levels(f)

A Because relevel returns a new factor and does not modify 'f' in place.

B Because 'green' is already the first level, so no change occurs.

C Because the factor 'f' is not a valid factor object.

D Because relevel only works on numeric factors.

💡 Hint

Check if the result of relevel is assigned back to a variable.

The relevel function returns a new factor with reordered levels but does not change the original factor unless you assign it back.

❓ Predict Output

advanced

Levels order after multiple relevel calls

What is the final order of levels after these relevel calls?

R Programming

f <- factor(c('small', 'medium', 'large'))
f <- relevel(f, ref = 'large')
f <- relevel(f, ref = 'small')
levels(f)

A [1] "medium" "small" "large"

B [1] "large" "small" "medium"

C [1] "small" "medium" "large"

D [1] "small" "large" "medium"

💡 Hint

Each relevel call resets the first level to the specified reference.

The first relevel sets 'large' as first, then the second relevel sets 'small' as first, so final order starts with 'small'.

🧠 Conceptual

expert

Effect of relevel on model contrasts

In R, why is releveling a factor important before fitting a linear model?

A Because it increases the number of factor levels automatically.

B Because it changes the baseline level, affecting the interpretation of model coefficients.

C Because it converts factors to numeric variables for the model.

D Because it removes unused factor levels from the dataset.

💡 Hint

Think about how the first level of a factor is used in regression models.

The first level of a factor is the baseline in model contrasts. Releveling changes which category is the baseline, thus changing coefficient interpretation.