### Introduction to R

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

4 hours

Go to courseSkip to main content# Learn R Programming

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### Introduction to R

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### Intermediate R

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### Writing Efficient R Code

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### Introduction to Writing Functions in R

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### Object-Oriented Programming with S3 and R6 in R

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R Programming### Introduction to the Tidyverse

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### Data Manipulation with dplyr

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### Joining Data with dplyr

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### Introduction to Data Visualization with ggplot2

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### Intermediate Data Visualization with ggplot2

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### Reporting with R Markdown

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### Introduction to Importing Data in R

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### Intermediate Importing Data in R

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### Cleaning Data in R

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### Working with Dates and Times in R

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### Introduction to Writing Functions in R

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### Exploratory Data Analysis in R

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### Case Study: Exploratory Data Analysis in R

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### Introduction to Statistics in R

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### Introduction to Regression in R

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### Intermediate Regression in R

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### Supervised Learning in R: Classification

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### Supervised Learning in R: Regression

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### Unsupervised Learning in R

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### Cluster Analysis in R

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Visit DataCamp's Data Scientist with R Track### Supervised Learning in R: Classification

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### Supervised Learning in R: Regression

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### Unsupervised Learning in R

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### Intermediate Regression in R

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### Cluster Analysis in R

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### Machine Learning with caret in R

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### Modeling with tidymodels in R

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### Machine Learning with Tree-Based Models in R

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### Support Vector Machines in R

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### Fundamentals of Bayesian Data Analysis in R

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### Topic Modeling in R

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### Hyperparameter Tuning in R

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### Bayesian Regression Modeling with rstanarm

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### Introduction to Spark with sparklyr in R

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Machine Learning Scientist with R Track## Get Started Learning R

Start Learning for FreeGet Started Discover R CoursesExplore R Tracks R Tutorials### Learn with

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### How long does it take to learn R?

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### Why should I learn R?

### How do I learn R?

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### Where should I learn R?

### What is the best way to learn R?

### Why is R so hard to learn?

### What do I do after learning R?

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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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.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

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

4 hours

Go to course2

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

4 hours

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

4 hours

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

4 hours

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

4 hours

Go to courseBecome 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

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.

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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Learn to create dynamic reports with R Markdown.

4 hours

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

3 hours

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

3 hours

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Learn to quickly and accurately clean data using R.

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

Go to courseLearn 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

Get started learning the basics of machine learning for classification.

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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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.

4 hours

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

4 hours

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

4 hours

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

4 hours

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

4 hours

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

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.

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.

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.

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.

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.

Charlotte Wickham

R

6,905,728 learners

Filip Schouwenaars

R

6,905,728 learners

David Robinson

Python

6,905,728 learners