Factor in R: Categorical & Continuous Variables

What is Factor in R?

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Factors are variables in R which take on a limited number of different values; such variables are often referred to as categorical variables.

In a dataset, we can distinguish two types of variables: categorical and continuous.

In a categorical variable, the value is limited and usually based on a particular finite group. For example, a categorical variable can be countries, year, gender, occupation. A continuous variable, however, can take any values, from integer to decimal. For example, we can have the revenue, price of a share, etc..

Categorical Variables

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R stores categorical variables into a factor. Let’s check the code below to convert a character variable into a factor variable. Characters are not supported in machine learning algorithm, and the only way is to convert a string to an integer.

Syntax

factor(x = character(), levels, labels = levels, ordered = is.ordered(x))

Arguments: + x: A vector of data. Need to be a string or integer, not decimal. + Levels: A vector of possible values taken by x. This argument is optional. The default value is the unique list of items of the vector x. + Labels: Add a label to the x data. For example, 1 can take the label male while 0, the label female. + ordered: Determine if the levels should be ordered.

Example: Let’s create a factor data frame.

# Create gender vector
gender_vector <- c("Male", "Female", "Female", "Male", "Male")
class(gender_vector)

## [1] "character"

# Convert gender_vector to a factor
factor_gender_vector <- factor(gender_vector)
class(factor_gender_vector)

## [1] "factor"

It is important to transform a string into factor when we perform Machine Learning task.

A categorical variable can be divided into nominal categorical variable and ordinal categorical variable.

Nominal Categorical Variable

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A categorical variable has several values but the order does not matter. For instance, male or female categorical variable do not have ordering.

# Create a color vector
color_vector <- c('blue', 'red', 'green', 'white', 'black', 'yellow')
# Convert the vector to factor
factor_color <- factor(color_vector)
factor_color

## [1] blue   red    green  white  black  yellow
## Levels: black blue green red white yellow

From the factor_color, we can’t tell any order.

Ordinal Categorical Variable

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Ordinal categorical variables do have a natural ordering. We can specify the order, from the lowest to the highest with order = TRUE and highest to lowest with order = FALSE.

Example 1:

We can use summary to count the values for each factor.

# Create Ordinal categorical vector 
day_vector <- c('evening', 'morning', 'afternoon', 'midday', 'midnight', 'evening')
# Convert `day_vector` to a factor with ordered level
factor_day <- factor(day_vector, order = TRUE, levels = c('morning', 'midday', 'afternoon', 'evening', 'midnight'))
# Print the new variable
factor_day

## [1] evening   morning   afternoon midday    midnight  evening  
## Levels: morning < midday < afternoon < evening < midnight

Example 2:

## Levels: morning < midday < afternoon < evening < midnight
# Append the line to above code
# Count the number of occurence of each level
summary(factor_day)

##   morning    midday afternoon   evening  midnight 
##         1         1         1         2         1

R ordered the level from ‘morning’ to ‘midnight’ as specified in the levels parenthesis.

Continuous Variables

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Continuous class variables are the default value in R. They are stored as numeric or integer. We can see it from the dataset below. mtcars is a built-in dataset. It gathers information on different types of car. We can import it by using mtcars and check the class of the variable mpg, mile per gallon. It returns a numeric value, indicating a continuous variable.

dataset <- mtcars
class(dataset$mpg)

## [1] "numeric"

 

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A work by Gianluca Sottile

gianluca.sottile@unipa.it