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Learn R Programming

R is one of the most commonly used programming languages in data mining and offers great packages and resources for data analysis, visualization, and data science. 

When you’ve built your R skills, you’ll be able to analyze complex data, build interactive web apps, and create machine learning models. You'll also use extensive tidyverse packages to organize, visualize, and manage your data workflows. 

Explore our courses, skill tracks, and career tracks in R, and align your skillset with data professionals at Amazon, Google, Deloitte, and Accenture. 

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Why Learn R?

R CAN OPEN THE DOOR TO A LUCRATIVE CAREER IN DATA SCIENCE

R skills are in high demand, and learning R can unlock a variety of lucrative career options across a huge range of industries. 

Companies including Facebook, Twitter, Google, Mckinsey, and even The New York Times all use R for data analysis, data visualizations, statistical modeling, and more.

If you want to land a well-paid job in the statistics, machine learning, or data analysis fields, R programming skills will put you ahead. Having R coding skills in your arsenal helps you in any area of data specialization where knowledge of statistical techniques is key.

R is especially useful for data science. This language was developed by statisticians for statistical analysis, so it’s the perfect tool for harnessing the power of data, performing predictive modeling, and getting the kind of insights that matter. 

Little wonder then that R skills are required by companies of all sizes and across all industries.

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Learning R with DataCamp

LEARN UP TO 10X FASTER THAN AT UNIVERSITY

DataCamp's innovative and immersive courses make learning R fun and engaging. Plus, you can learn at your own speed, from anywhere in the world.

Your journey from R beginning through to expert may be challenging at times, but R is one of the most valuable programming languages that you can add to your skillset. 

Once you have R under your belt, you have all of this statistical language's analytical power and flexibility at your fingertips.

Learning R doesn't have to mean hours spent poring over complicated syntax. You'll retain R better when you learn it to solve specific problems and start coding from the get-go. 

DataCamp's range of R courses and career tracks give you the skills you need to compete in today’s competitive talent marketplace. With programs for absolute beginners through to seasoned pros, it doesn’t matter if you’re brand new to code or have intermediate-level skills.

Each DataCamp R course is taught by in-house instructors - R experts who work in academia, industry, governments, and organizations around the world. When you learn R with DataCamp, you're learning from leading professionals.

Find out why over nine million learners worldwide and employees at Google, Uber, PayPal, and more all trust us.

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Don’t just take our word for it.

R Courses and Tracks

Learn how to code like a programmer in this beginner’s track. First, you’ll learn how to work with common data structures in R like vectors, matrices, and data frames before expanding your skills by mastering conditional statements, loops, and vectorized functions.

1

Introduction to R

About this course

Your journey of Data Anylsis starts here. Master the basics by learning common data structures like vectors, matrices, and data frames.

4 hours

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2

Intermediate R

About this course

Take the next step to mastering R here. Learn the loops, functions and conditional statements to power your own R scripts.

3

Writing Efficient R Code

About this course

Unlock the secrets of writing efficient R code here. Discover benchmarking and profiling and how they can be utilized in parallel programming.

4

Introduction to Writing Functions in R

About this course

Sharpen your R skills by learning to write reusable, efficient functions.

5

Object-Oriented Programming with S3 and R6 in R

About this course

Specify your relationships between functions by learning object-oriented programming.

R Programming

Become a master of the data by learning how to use R to import, clean, and manipulate data. Create data visualizations, learn about the most popular R packages and the tidyverse, and answer complex questions with the help of dplyr. You’ll learn to write your own R functions and you’ll perform analysis on real historical data from the United Nations.

1

Introduction to the Tidyverse

About this course

Learn about the powerful collection of R tools, Tidyverse, and explore how you can manipulate and visualize data using the tools dplyr and ggplot 2.

2

Data Manipulation with dplyr

About this course

Practice your knowledge of the tidyverse toolset and learn strategies to solve data errors via the rlang package.

3

Joining Data with dplyr

About this course

Deepen your understanding dplyr and complex data questions by learning to combine data across multiple tables.

4

Introduction to Data Visualization with ggplot2

About this course

Master the grammar of graphics and create meaningful and beautiful data visualizations with ggplot2.

5

Intermediate Data Visualization with ggplot2

About this course

Build on your knowledge of ggplot2 and learn how to create meaningful explanatory plots using facets, coordinate systems, and statistics.

6

Reporting with R Markdown

About this course

Learn to create dynamic reports with R Markdown.

7

Introduction to Importing Data in R

About this course

Learn to use tools like readxl and data.table. to read differently formatted data and import into R.

8

Intermediate Importing Data in R

About this course

Build your skills and learn to dissect and analyze data in any format.

9

Cleaning Data in R

About this course

Learn to quickly and accurately clean data using R.

10

Working with Dates and Times in R

About this course

Learn to manipulate and analyse Date and Time data using R.

11

Introduction to Writing Functions in R

About this course

Sharpen you R skills by learning to write reusable, efficient functions.

12

Exploratory Data Analysis in R

About this course

Peek inside and uncover the structure of your data using graphic and numerical techniques in R .

13

Case Study: Exploratory Data Analysis in R

About this course

Explore the voting of the united nations general assembly through the eyes of a data anaylsist using data manipulation and visualization skills in R.

14

Introduction to Statistics in R

About this course

Begin your statistical R journey and learn to collect, analyze, and determine accurate results from Data

15

Introduction to Regression in R

About this course

Unlock the secrets within data sets by learning to analyze and interpret data using regression analysis in R

16

Intermediate Regression in R

About this course

Expand your knowledge of regression and learn to perform linear and logistical regression with multiple explanatory variables.

17

Supervised Learning in R: Classification

About this course

Learn the basics of Machine Learning and the most common classification algorithms.

18

Supervised Learning in R: Regression

About this course

Learn about different regression models and how they can be used to predict future events.

19

Unsupervised Learning in R

About this course

Learn the basics of clustering and dimensionality reduction in R from a machine learning perspective

20

Cluster Analysis in R

About this course

Step up your data analysis game and learn how to use hierarchical and k- means clustering to extract information from your data.

Visit DataCamp's Data Scientist with R Track

Learn the basics of machine learning for classification before turning your hand to predicting events using linear regression, clustering, and dimensionality reduction with R. You’ll also learn how to use the R tidyverse to generate and evaluate machine learning models, perform cluster analysis and much more.

1

Supervised Learning in R: Classification

About this course

Get started learning the basics of machine learning for classification.

2

Supervised Learning in R: Regression

About this course

Learn future earn prediction using linear regression, generalized additive models, random forests, and xgboost.

3

Unsupervised Learning in R

About this course

This course provides an intro to Gain your first insights into clustering and dimensionality reduction in R from a machine learning viewpoint.

4

Intermediate Regression in R

About this course

Discover how to perform linear and logistic regression with multiple explanatory variables.

5

Cluster Analysis in R

About this course

Get a clear understanding of how hierarchical and k-means clustering work and how to apply this understanding to extract insights from your data.

6

Machine Learning with caret in R

About this course

In this course, you'll be knee-deep in learning the machine learning big idea such as how to build and evaluate predictive models.

7

Modeling with tidymodels in R

About this course

Learn how to ensure your machine learning workflows are streamlined with tidymodels.

8

Machine Learning with Tree-Based Models in R

About this course

In this course you will gain the knowledge required to use tree-based models and ensembles to make classification and regression predictions with tidymodels.

9

Support Vector Machines in R

About this course

Discover the working of the support vector machine (SVM) using an intuitive, visual approach.

10

Fundamentals of Bayesian Data Analysis in R

About this course

Discover and digest the fundamentals of Bayesian data analysis. How it works, and why it's one of the most versatile and useful tools to have in your data science toolkit.

11

Topic Modeling in R

About this course

Gain an understanding of the Latent Dirichlet Allocation algorithm and how to use it to to fit topic models.

12

Hyperparameter Tuning in R

About this course

Fine-tune your model's hyperparameters to ensure the best predictive results.

13

Bayesian Regression Modeling with rstanarm

About this course

Learn leveraging Bayesian estimation methods to make more useful inferences about linear regression models.

14

Introduction to Spark with sparklyr in R

About this course

After completing this course, analyzing huge datasets will be a breeze, with your new skills in Apache Spark, R, and the sparklyr package.

Machine Learning Scientist with R Track

Get Started Learning R

If you're new to R, make sure you start here with our most popular course for beginners.

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DataCamp Certification

Can I Get a Professional Certification with R?

Taking your first course in R is just the beginning of a journey - if you’d like to create an excellent data science resumé and portfolio, you can start a skill or career track and work towards gaining a professional certification in data science or data analysis. 

Our certifications are based on in-depth analysis of data science jobs and their requirements, so the assessments are designed to show that you’re ready for a demanding and lucrative job as a data scientist or analyst. 

You can start your assessments straight away if you already have strong R experience, or take a certification preparation track if you’d prefer to brush up on your skills first.

Get Started
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What Areas of R Should I Learn?

It depends on your end goal and why you want to learn R. 

Let’s say you already work in finance and you’d like to learn how to manipulate and analyze financial data. You should learn how to use R as a calculator alongside R vectors, matrices, and data frames—all of which are covered in DataCamp’s Introduction to R for Finance.

If you want to become a data scientist or analyst, you should learn how to import, clean, and visualize data with R. You’ll also want to learn how to navigate and use the tidyverse and popular R packages such as ggplot2.

Or perhaps you want to learn R for marketing analytics, in which case you’ll learn how to measure user engagement, analyze your business competitors, and glean intel from social media with the help of R.

Discover R Courses
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Practical Ways to Learn R from Scratch

When you learn R with DataCamp, you can take advantage of our skilled instructors and our tried and tested learning method.

We've fine-tuned this method over many years, and we know how to make learning R immersive, engaging, and most importantly, easy to retain. If you're starting from scatch and want to become an expert, we've designed Tracks to help you build a well-rounded skill set in R. 

Skill Tracks such as R Programming will teach you now to code like a programmer and prepare you for complex tasks like advanced data visualization. If you're focused on career goals rather than specific skills, Data Scientist with R, and R Programmer will get you career-ready and teach you how perform key tasks for your chosen role. 

Explore R Tracks
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All the R Resources You Need

DataCamp is home to all the R resources you need to support your learning. From R cheat sheets that make importing data easy all the way through to coding and data analysis competitions with cash prizes, we’ve got you covered.

And with more than nine million learners worldwide, there’s plenty of support from our bustling community. DataCamp’s R resources include:

  • In-depth and easy to understand R guides and cheat sheets
  • DataCamp Signal™ where you can test your R skills on a range of assessments
  • Practice projects to help solidify what you’ve learned
  • Guided projects where you’ll use R to interpret real-world data
  • Competitions with prizes where you’ll compete against other DataCamp learners
  • Webinars and live training sessions
  • And much more

It doesn’t matter whether you’re just getting started with R or grappling with object-oriented programming in R, DataCamp has the resources to support you.

R Tutorials

Test Your R Skills with DataCamp Signal™

Benchmark your skills against your R peers. Determine your R strengths and weaknesses, whilst receiving personalized R learning recommendations. Take a 10-minute skill assessment today.

RData Management with R
RCoding for Production with R
RStatistical Experimentation with R
Data Camp Signal  - R Assessments
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Instructors

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Learn with
Charlotte Wickham

Assistant Professor in the Department of Statistics at Oregon State University and an avid R programmer  See full Bio

RR
6,412,242 learners
Filip Schouwenaars Headshot

Learn with
Filip Schouwenaars

Filip is the passionate developer behind several DataCamp R courses head's up the DataCamp workspace team See full Bio

RR

Instructor of Intermediate R and 2 other courses

924,849 learners
6,412,242 learners
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Learn R FAQs

How long does it take to learn R?

If you have no prior coding knowledge, it will take 4-6 weeks to wrap your head around R’s foreign syntax. Unlike other coding languages like Python, R’s syntax is nothing like English and takes getting used to. With some prior knowledge, it can be learned faster - especially with the help of DataCamp’s R courses and tutorials. These interactive activities will ensure you’re getting practical, hands-on experience from the get-go.

What level should I be at before I start learning R ?

Any level, but prior coding knowledge is helpful. It is not often recommended to start with R as your first coding language. R has difficult syntax to read and interpret, and this can be challenging when you are learning the basic concepts of coding in general at the same time. It’s recommended to learn easier languages like SQL and Python first before continuing to R. This ensures you already have the building blocks in place to succeed with this challenging programming language.

Why should I learn R?

R is the gateway to a lucrative career in data science. Whilst Python is more popular due to its simple syntax and greater versatility (it is also popular as a web and software development language), R is extremely important for statistical and data analysis. Its fast data processing and interactive nature make it a staple amongst data scientists. 

How do I learn R?

Enroll in Datacamp's free "Introduction to R" course. R is considered one of the harder coding languages to learn, but Datacamp's R course track ensures there will be a course to take at every level - supporting you as you rise through the ranks of R proficiency. 

How hard is it to learn R?

It is harder to learn R than other programming languages like SQL and Python. Unlike Python and SQL, R’s syntax is nothing like the English language and can be quite difficult to read, understand, and learn in the beginning.

Where should I learn R?

Online, with DataCamp.  DataCamp has an abundance of useful resources to assist you in mastering R: courses, projects, competitions, assessments, tutorials, cheat sheets, and R skill tracks that are tailored to the career you wish to pursue.  

What is the best way to learn R?

Definitely online in a "go at your pace" environment. R is not the easiest of coding languages and people learn it all at different paces.  R also requires lots of hands-on experience to get you familiar with its concepts and language - which is why DataCamp's interactive tutorials are perfect for online learning. 

Why is R so hard to learn?

R is considered hard to learn because of its difficult syntax. It is completely different from English, and if you are learning programming in general at the same time you are learning R, things can become confusing. You are better off learning the basics of code alongside an easier language like SQL and Python. DataCamp has beginners courses on both.

What do I do after learning R?

Learn Python and SQL. If you want to become a data scientist and you haven’t learned Python yet, learn it now since R and Python are the most used languages in Data Science. SQL programming will be essential regardless of whether you choose to focus on Python or R.