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.
In this example,
departments
which contains the values 'HR'
, 'Finance'
, 'IT'
, and 'HR'
. This factor represents different departments in a company.'Admin'
to the factor using the levels()
function. This creates an unused level in the factor.droplevels()
function to remove any unused levels from the factor. This function drops levels that do not have any corresponding values in the factor.clean_departments
and print it to the console to see the factor with unused levels removed.departments <- factor(c('HR', 'Finance', 'IT', 'HR'))
levels(departments) <- c(levels(departments), 'Admin')
clean_departments <- droplevels(departments)
print(clean_departments)
print(levels(clean_departments))
[1] HR Finance IT HR Levels: Finance HR IT [1] "Finance" "HR" "IT"
In this example,
responses
which contains the values 'Agree'
, 'Neutral'
, 'Disagree'
, and 'Agree'
. This factor represents survey responses.'Strongly Agree'
to the factor using the levels()
function. This creates an unused level in the factor.droplevels()
function to remove any unused levels from the factor. This function drops levels that do not have any corresponding values in the factor.clean_responses
and print it to the console to see the factor with unused levels removed.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))
[1] Agree Neutral Disagree Agree Levels: Agree Disagree Neutral [1] "Agree" "Disagree" "Neutral"
In this example,
cities
which contains the values 'New York'
, 'Los Angeles'
, 'Chicago'
, and 'New York'
. This factor represents different cities.'Houston'
to the factor using the levels()
function. This creates an unused level in the factor.droplevels()
function to remove any unused levels from the factor. This function drops levels that do not have any corresponding values in the factor.clean_cities
and print it to the console to see the factor with unused levels removed.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))
[1] New York Los Angeles Chicago New York Levels: Chicago Los Angeles New York [1] "Chicago" "Los Angeles" "New York"
In this tutorial, we learned How to Deal with Unused Factor Levels in R language with well detailed examples.