How to Deal with Unused Factor Levels in R - Step by Step Examples

How to Deal with Unused Factor Levels in R ?

Answer

To deal with unused factor levels in R, you can use the `droplevels` function to remove any levels that are not present in the data. This helps in cleaning up the factor levels and ensures that only relevant levels are kept.


✐ Examples

1 Removing Unused Levels from a Factor Representing Departments

In this example,

  1. We start by creating a factor named departments which contains the values 'HR', 'Finance', 'IT', and 'HR'. This factor represents different departments in a company.
  2. Next, we add an extra level 'Admin' to the factor using the levels() function. This creates an unused level in the factor.
  3. We use the droplevels() function to remove any unused levels from the factor. This function drops levels that do not have any corresponding values in the factor.
  4. We assign the result to a variable named clean_departments and print it to the console to see the factor with unused levels removed.

R Program

departments <- factor(c('HR', 'Finance', 'IT', 'HR'))
levels(departments) <- c(levels(departments), 'Admin')
clean_departments <- droplevels(departments)
print(clean_departments)
print(levels(clean_departments))

Output

[1] HR      Finance IT      HR     
Levels: Finance HR IT
[1] "Finance" "HR" "IT"

2 Cleaning Unused Levels from a Factor Representing Survey Responses

In this example,

  1. We start by creating a factor named responses which contains the values 'Agree', 'Neutral', 'Disagree', and 'Agree'. This factor represents survey responses.
  2. Next, we add an extra level 'Strongly Agree' to the factor using the levels() function. This creates an unused level in the factor.
  3. We use the droplevels() function to remove any unused levels from the factor. This function drops levels that do not have any corresponding values in the factor.
  4. We assign the result to a variable named clean_responses and print it to the console to see the factor with unused levels removed.

R Program

responses <- factor(c('Agree', 'Neutral', 'Disagree', 'Agree'))
levels(responses) <- c(levels(responses), 'Strongly Agree')
clean_responses <- droplevels(responses)
print(clean_responses)
print(levels(clean_responses))

Output

[1] Agree    Neutral  Disagree Agree   
Levels: Agree Disagree Neutral
[1] "Agree" "Disagree" "Neutral"

3 Dropping Unused Levels from a Factor Representing Cities

In this example,

  1. We start by creating a factor named cities which contains the values 'New York', 'Los Angeles', 'Chicago', and 'New York'. This factor represents different cities.
  2. Next, we add an extra level 'Houston' to the factor using the levels() function. This creates an unused level in the factor.
  3. We use the droplevels() function to remove any unused levels from the factor. This function drops levels that do not have any corresponding values in the factor.
  4. We assign the result to a variable named clean_cities and print it to the console to see the factor with unused levels removed.

R Program

cities <- factor(c('New York', 'Los Angeles', 'Chicago', 'New York'))
levels(cities) <- c(levels(cities), 'Houston')
clean_cities <- droplevels(cities)
print(clean_cities)
print(levels(clean_cities))

Output

[1] New York    Los Angeles Chicago    New York   
Levels: Chicago Los Angeles New York
[1] "Chicago" "Los Angeles" "New York"

Summary

In this tutorial, we learned How to Deal with Unused Factor Levels in R language with well detailed examples.


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