## What is Factor in R?

**Factor in R** is a variable used to categorize and store the data, having a limited number of different values. It stores the data as a vector of integer values. Factor in R is also known as a categorical variable that stores both string and integer data values as levels. Factor is mostly used in Statistical Modeling and exploratory data analysis with R.

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

- In descriptive statistics for categorical variables in R, the value is limited and usually based on a particular finite group. For example, a categorical variable in R 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

Categorical variables in R are stored into a factor. Let’s check the code below to convert a character variable into a factor variable in R. 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 categorical data in R. 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 categorical data in R. For example, 1 can take the label `male` while 0, the label `female`.**ordered**: Determine if the levels should be ordered in categorical data in R.

**Example:**

Let’s create a factor data frame.

# Create gender vector gender_vector <- c("Male", "Female", "Female", "Male", "Male") class(gender_vector) # Convert gender_vector to a factor factor_gender_vector <-factor(gender_vector) class(factor_gender_vector)

**Output:**

## [1] "character" ## [1] "factor"

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

A categorical variable in R can be divided into **nominal categorical variable** and **ordinal categorical variable**.

### Nominal Categorical Variable

A categorical variable has several values but the order does not matter. For instance, male or female. Categorical variables in R does 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

**Output:**

## [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

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:**

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

# 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

**Output:**

## [1] evening morning afternoon midday midnight evening

**Example:**

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

**Output:**

## 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

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)

**Output**

## [1] "numeric"