Skip to main content
HomeAbout RLearn R

Factors in R Tutorial

Learn about the factor function in R, along with an example, and it's structure, order levels, renaming of the levels, and finally, with the ordering of categorical values.
Jun 2020  · 5 min read

The factors are the variable in R, which takes the categorical variable and stores data in levels. The primary use of this function can be seen in data analysis and specifically in statistical analysis. Also, it helps to reduce data redundancy and to save a lot of space in the memory.

Note: A categorical variable is those variables that take values based on the labels or names. For example, the blood type of a human can be A, B, AB, or O.

factor function

Usage: Categorize the data which have less number of values.
Parameters:factor(v):v can be vector of values.

Let's see the example of a factor in action.

You can see below code where there are two categorical variables, namely "Male" and "Female", also called factor values.

gender <- c("Male","Female","Female","Male","Female")

Let's create a factor for the gender where 'factor(gender)' is used and saved to a variable called 'gender.factor'.

gender.factor <- factor(gender)
  1. Male
  2. Female
  3. Female
  4. Male
  5. Female
  1. 'Female'
  2. 'Male'

The above code gives the output as below:

Male Female Female Male Female
'Female' 'Male'

You can see above where values are printed the same as the input vector. Additionally, 'Levels', which are 'Female' and 'Male' are sorted alphabetically.

Structure of factor function

Let's examine the structure for factor function by using 'str(gender.factor)' in the code below.

 Factor w/ 2 levels "Female","Male": 2 1 1 2 1 
Factor w/ 2 levels "Female","Male": 2 1 1 2 1

The above output shows that there is a factor of 2 levels. factor converts the character vector as gender into a vector of integer values. "Female" is the first level encoded as 1 whereas the "Male" is the second level, encoded as 2.

Also, the primary purpose of encoding from character to numeric is that the categories can belong, repeating is redundant, which can take a lot of space in the memory. But, using factor reduces all the burden to save up space in the memory.

Changing Order Levels

Let's change the order levels, so the levels of "Female" will become 2 and "Male" as 1.

Let's make a new factor for the gender by changing the levels of "Male" and "Female' by passing it as a vector input to the "levels". Finally, the resultant output is saved to the variable named "gender.factor2'.

gender.factor2 <- factor(gender,levels=c("Male","Female"))
  1. Male
  2. Female
  3. Female
  4. Male
  5. Female
  1. 'Male'
  2. 'Female'
 Factor w/ 2 levels "Male","Female": 1 2 2 1 2

The 'gender.factor2' is printed along with it's structure printed using 'str(gender.factor2)' where the following changes can be seen.

Male Female Female Male Female
'Male' 'Female'
 Factor w/ 2 levels "Male","Female": 1 2 2 1 2

The above code gives the output where the encoding of "Male" is 1, and "Female" is 2. It's different from 'gender.factor', which was opposite in the above code.

Renaming a Factor levels

Let's change the name of the vector values in the input by specifying the regular use of 'levels' as the first argument with values "Male" and "Female" and the expected changed vector values using 'labels' as the second argument with "Gen_Male" and "Gen_Female" respectively.

factor(gender,levels = c("Male","Female"),labels = c("Gen_Male","Gen_Female"))
  1. Gen_Male
  2. Gen_Female
  3. Gen_Female
  4. Gen_Male
  5. Gen_Female
  1. 'Gen_Male'
  2. 'Gen_Female'
Gen_Male Gen_Female Gen_Female Gen_Male Gen_Female
'Gen_Male' 'Gen_Female'

The above code gives the output where the name is changed for "Male" to "Gen_Male" and "Female" to "Gen_Female".

Ordering a Categorical Variable

Let's look at a different example when dealing with ordinal categorical values where ordered matters. For instance, for the size of a pant, there might be a size which is considered as Large as "L", Extra Large as "XL" and Extra extra Large as "XXL" is arranged in ascending order. The code below contains the collection of vector input of characters "L", "XL" and "XXL" and stored to 'pant'. 'pant.factor' is the variable which has parameter containing levels arranged in ascending order as 'levels = c("L", "XL", "XXL")' and finally 'ordered = TRUE', which makes the sorting possible according to your need.

pant <- c("XL","L","XL","XXL","L","XL")
pant.factor <- factor(pant,ordered = TRUE,levels = c("L","XL","XXL"))
pant.factor[1] > pant.factor[2]
  1. XL
  2. L
  3. XL
  4. XXL
  5. L
  6. XL
  1. 'L'
  2. 'XL'
  3. 'XXL'


 Levels:'L'< 'XL' < 'XXL'

The above output shows the normal output at first where all the vector values (XL, L, XL, XXL, L, XL) are printed out by using 'pant.factor'. The levels of vector values 'L' < 'XL' < 'XXL' are arranged in the ascending order, which is printed at the console. Also, 'pant.factor[1] > pant.factor[2]' compares whether "XL" is greater than "L", which results in TRUE being printed.


Congratulations, you have made it to the end of this tutorial!

In this tutorial, you have covered the factor function in R, along with an example, and its structure, order levels, renaming of the levels, and finally, with the ordering of categorical values.

If you would like to learn more about R, take DataCamp's Introduction to R course.

Check out out tutorial on Using Functions in R.

R Courses

Certification available

Introduction to R

BeginnerSkill Level
4 hr
Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.
See DetailsRight Arrow
Start Course
See MoreRight Arrow

Google Cloud for Data Scientists: Harnessing Cloud Resources for Data Analysis

How can using Google Cloud make data analysis easier? We explore examples of companies that have already experienced all the benefits.

Oleh Maksymovych

9 min

40 R Programming Interview Questions & Answers For All Levels

Learn the 40 fundamental R programming interview questions and answers to them for all levels of seniority: entry-level, intermediate, and advanced questions.
Elena Kosourova's photo

Elena Kosourova

20 min

A Guide to Docker Certification: Exploring The Docker Certified Associate (DCA) Exam

Unlock your potential in Docker and data science with our comprehensive guide. Explore Docker certifications, learning paths, and practical tips.
Matt Crabtree's photo

Matt Crabtree

8 min

Bash & zsh Shell Terminal Basics Cheat Sheet

Improve your Bash & zsh Shell skills with the handy shortcuts featured in this convenient cheat sheet!
Richie Cotton's photo

Richie Cotton

6 min

Functional Programming vs Object-Oriented Programming in Data Analysis

Explore two of the most commonly used programming paradigms in data science: object-oriented programming and functional programming.
Amberle McKee's photo

Amberle McKee

15 min

A Comprehensive Introduction to Anomaly Detection

A tutorial on mastering the fundamentals of anomaly detection - the concepts, terminology, and code.
Bex Tuychiev's photo

Bex Tuychiev

14 min

See MoreSee More