How to Collapse Factor Levels in R - Step by Step Examples

How to Collapse Factor Levels in R ?

Answer

To collapse factor levels in R, you can use the `fct_collapse` function from the `forcats` package. This function allows you to group existing levels of a factor into new combined levels, making it easier to analyze or visualize data.


✐ Examples

1 Collapsing Levels of a Factor Representing Age Groups

In this example,

  1. We start by creating a character vector named age_groups which contains the values 'Young', 'Middle', 'Old', and 'Elderly'. This vector represents different age groups.
  2. Next, we use the factor() function to convert the age_groups vector into a factor. We assign the result to a variable named age_factor. The factor() function automatically identifies the unique levels of the vector.
  3. We then use the fct_collapse() function from the `forcats` package to collapse the levels of the age_factor into fewer groups. In this example, we group 'Young' and 'Middle' into a new level 'Young-Middle' and 'Old' and 'Elderly' into a new level 'Old-Elderly'. We assign the result to a variable named collapsed_age_factor.
  4. We print the collapsed_age_factor to the console to verify the new levels.

R Program

library(forcats)
age_groups <- c('Young', 'Middle', 'Old', 'Elderly', 'Young', 'Old')
age_factor <- factor(age_groups)
collapsed_age_factor <- fct_collapse(age_factor,
                                    'Young-Middle' = c('Young', 'Middle'),
                                    'Old-Elderly' = c('Old', 'Elderly'))
print(collapsed_age_factor)

Output

[1] Young-Middle Young-Middle Old-Elderly  Old-Elderly  Young-Middle Old-Elderly 
Levels: Young-Middle Old-Elderly

2 Collapsing Levels of a Factor Representing Product Categories

In this example,

  1. We start by creating a character vector named product_categories which contains the values 'Electronics', 'Furniture', 'Clothing', 'Food', and 'Electronics'. This vector represents different product categories.
  2. Next, we use the factor() function to convert the product_categories vector into a factor. We assign the result to a variable named product_factor. The factor() function automatically identifies the unique levels of the vector.
  3. We then use the fct_collapse() function from the `forcats` package to collapse the levels of the product_factor into fewer groups. In this example, we group 'Electronics' and 'Furniture' into a new level 'Non-Consumable' and 'Clothing' and 'Food' into a new level 'Consumable'. We assign the result to a variable named collapsed_product_factor.
  4. We print the collapsed_product_factor to the console to verify the new levels.

R Program

library(forcats)
product_categories <- c('Electronics', 'Furniture', 'Clothing', 'Food', 'Electronics')
product_factor <- factor(product_categories)
collapsed_product_factor <- fct_collapse(product_factor,
                                         'Non-Consumable' = c('Electronics', 'Furniture'),
                                         'Consumable' = c('Clothing', 'Food'))
print(collapsed_product_factor)

Output

[1] Non-Consumable Non-Consumable Consumable      Consumable      Non-Consumable
Levels: Non-Consumable Consumable

3 Collapsing Levels of a Factor Representing Survey Responses

In this example,

  1. We start by creating a character vector named survey_responses which contains the values 'Strongly Agree', 'Agree', 'Neutral', 'Disagree', and 'Strongly Disagree'. This vector represents different levels of agreement in a survey.
  2. Next, we use the factor() function to convert the survey_responses vector into a factor. We assign the result to a variable named response_factor. The factor() function automatically identifies the unique levels of the vector.
  3. We then use the fct_collapse() function from the `forcats` package to collapse the levels of the response_factor into fewer groups. In this example, we group 'Strongly Agree' and 'Agree' into a new level 'Positive', 'Neutral' remains as it is, and 'Disagree' and 'Strongly Disagree' into a new level 'Negative'. We assign the result to a variable named collapsed_response_factor.
  4. We print the collapsed_response_factor to the console to verify the new levels.

R Program

library(forcats)
survey_responses <- c('Strongly Agree', 'Agree', 'Neutral', 'Disagree', 'Strongly Disagree')
response_factor <- factor(survey_responses)
collapsed_response_factor <- fct_collapse(response_factor,
                                          'Positive' = c('Strongly Agree', 'Agree'),
                                          'Negative' = c('Disagree', 'Strongly Disagree'))
print(collapsed_response_factor)

Output

[1] Positive Positive Neutral  Negative Negative
Levels: Positive Neutral Negative

Summary

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


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