In this introduction to R course, you'll master the basics of this widely used open source language&mdash;including vectors, factors, lists, and data frames. With the coding skills you'll gain in this course, you'll be ready to undertake your own data analysis in R. There are millions of R users worldwide, cementing it as a leading programming language in statistics and data science. More and more organizations are using R, so begin your coding journey in one of DataCamp's most popular courses today!

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

DataCamp's Into to R training course teaches you how to use R programming for data science at your own pace with video tutorials & interactive challenges.

Introduction to R Online

Introduction to R

Intermediate R is the next stop on your journey in mastering the R programming language. In this R training, you will learn about conditional statements, loops, and functions to power your own R scripts. Next, make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in R, data structure manipulations, and times and dates. This course will allow you to take the next step in advancing your overall knowledge and capabilities while programming in R.

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

Discover conditional statements, loops, and functions to power your own R scripts, and learn to make your R code more efficient using the apply functions.

Intermediate R

The ability to produce meaningful and beautiful data visualizations is an essential part of your skill set as a data scientist. This course, the first R data visualization tutorial in the series, introduces you to the principles of good visualizations and the grammar of graphics plotting concepts implemented in the ggplot2 package. ggplot2 has become the go-to tool for flexible and professional plots in R. Here, we’ll examine the first three essential layers for making a plot - Data, Aesthetics and Geometries. By the end of the course you will be able to make complex exploratory plots.

Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

Introduction to Data Visualization with ggplot2

Say you've found a great dataset and would like to learn more about it. How can you start to answer the questions you have about the data? You can use dplyr to answer those questions—it can also help with basic transformations of your data. You'll also learn to aggregate your data and add, remove, or change the variables. Along the way, you'll explore a dataset containing information about counties in the United States. You'll finish the course by applying these tools to the babynames dataset to explore trends of baby names in the United States.

Learn to transform and manipulate your data using dplyr. 

Learn how to use dplyr to transform and aggregate data, then add, remove, or change variables. You'll then apply your skills to a real-world case study.

Data Manipulation with dplyr

Importing data into R should be the easiest step in your analysis. Unfortunately, that is almost never the case. Data can come in many formats, ranging from .csv and text files, to statistical software files, to databases and HTML data. Knowing which approach to use is key to getting started with the actual analysis.
In this course, you’ll start by learning how to read .csv and text files in R. You will then cover the readr and data.table packages to easily and efficiently import flat file data. After that, you will learn how to read .xls files in R using readxl and gdata.

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

Learn to read .xls, .csv, and text files in R using readxl and gdata, before learning how to use readr and data.table packages to import flat file data.

Introduction to Importing Data in R

Often in data science, you'll encounter fascinating data that is spread across multiple tables. This course will teach you the skills you'll need to join multiple tables together to analyze them in combination. You'll practice your skills using a fun dataset about LEGOs from the Rebrickable website. The dataset contains information about the sets, parts, themes, and colors of LEGOs, but is spread across many tables. You'll work with the data throughout the course as you learn a total of six different joins! You'll learn four mutating joins: inner join, left join, right join, and full join, and two filtering joins: semi join and anti join. In the final chapter, you'll apply your new skills to Stack Overflow data, containing each of the almost 300,000 Stack Oveflow questions that are tagged with R, including information about their answers, the date they were asked, and their score. Get ready to take your dplyr skills to the next level!

Learn to combine data across multiple tables to answer more complex questions with dplyr.

Learn to combine data across multiple tables to answer complex questions with dplyr. You'll learn 6 different joins including inner, full, anti, and more.

Joining Data with dplyr

This ggplot2 course builds on your knowledge from the introductory course to produce meaningful explanatory plots. Statistics will be calculated on the fly and you’ll see how Coordinates and Facets aid in communication. You’ll also explore details of data visualization best practices with ggplot2 to help make sure you have a sound understanding of what works and why. By the end of the course, you’ll have all the tools needed to make a custom plotting function to explore a large data set, combining statistics and excellent visuals.

Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.

Intermediate Data Visualization with ggplot2

It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time analyzing it. The time spent cleaning is vital since analyzing dirty data can lead you to draw inaccurate conclusions.
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In this course, you'll learn how to clean dirty data. Using R, you'll learn how to identify values that don't look right and fix dirty data by converting data types, filling in missing values, and using fuzzy string matching. As you learn, you’ll brush up on your skills by working with real-world datasets, including bike-share trips, customer asset portfolios, and restaurant reviews—developing the skills you need to go from raw data to awesome insights as quickly and accurately as possible!

Develop the skills you need to go from raw data to awesome insights as quickly and accurately as possible.

Cleaning Data in R

The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis, so you can reduce computational time and get insights as quickly as possible.

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

Writing Efficient R Code

In this course, you will take a deeper dive into the wide range of data formats out there. More specifically, you'll learn how to import data from relational databases and how to import and work with data coming from the web. Finally, you'll get hands-on experience with importing data from statistical software packages such as SAS, STATA, and SPSS.

Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

Learn how you can use R to parse data in any format, whether that data is from flat files, statistical software, databases, or data right from the web.

Intermediate Importing Data in R

Once you've started learning tools for data manipulation and visualization like dplyr and ggplot2, this course gives you a chance to use them in action on a real dataset. You'll explore the historical voting of the United Nations General Assembly, including analyzing differences in voting between countries, across time, and among international issues. In the process you'll gain more practice with the dplyr and ggplot2 packages, learn about the broom package for tidying model output, and experience the kind of start-to-finish exploratory analysis common in data science.

Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

Use your data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

Case Study: Exploratory Data Analysis in R

Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective, so that you can get from data to insights as quickly as possible.


This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective to get insights quickly.

Unsupervised Learning in R

In this course, you'll learn to work with data using tools from the tidyverse in R. By data, we mean your own data, other people's data, messy data, big data, small data - any data with rows and columns that comes your way! By work, we mean doing most of the things that sound hard to do with R, and that need to happen before you can analyze or visualize your data. But work doesn't mean that it is not fun - you will see why so many people love working in the tidyverse as you learn how to explore, tame, tidy, and transform your data. Throughout this course, you'll work with data from a popular television baking competition called "The Great British Bake Off."

Learn to work with data using tools from the tidyverse, and master the important skills of taming and tidying your data.

Working with Data in the Tidyverse

This course will help you take your data visualization skills beyond the basics and hone them into a powerful member of your data science toolkit. Over the lessons we will use two interesting open datasets to cover different types of data (proportions, point-data, single distributions, and multiple distributions) and discuss the pros and cons of the most common visualizations. In addition, we will cover some less common alternatives visualizations for the data types and how to tweak default ggplot settings to most efficiently and effectively get your message across.

Learn to  effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.

Visualization Best Practices in R

With an increasing amount of data and more complex algorithms available to scientists and practitioners today, parallel processing is almost always a must, and in fact, is expected in packages implementing time-consuming methods. This course introduces you to concepts and tools available in R for parallel computing and provides solutions to a few important non-trivial issues in parallel processing like reproducibility, generating random numbers and load balancing.

This course covers in detail the tools available in R for parallel computing.

Parallel Programming in R

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. You’ll then discover how to optimize your code using code profiling and benchmarking. Finally, you'll get to grips with the fundamentals of parallel programming, including reproducibility, generating random numbers, and load balancing. By the end of this track, you’ll be ready to tackle more complex tasks including advanced data visualization and machine learning.

Learn about fundamental data types, logic, and how to create your own functions using the R language.

Level-up your R programming skills! Learn how to work with common data structures, optimize code, and use efficient parallel programming techniques.

R Programming

Understanding how to prep your data is an essential skill when working in R. It’s what you have to do before you can reveal the insights that matter. In this track, you’ll learn how to import your data from a variety of sources, including .csv, .xls, text files, and more. You'll then gain the skills you'll need to prepare your data for analysis, including converting data types, filling in missing values, and using fuzzy string matching. Throughout the track, you'll have the chance to apply your skills to real-world data such as customer asset portfolios and restaurant reviews. Start this track and gain the data prepping skills you need to clean your dirty data.

Gain the real-world skills you need to import and clean your data when working in R—making it possible for you to reveal the insights that matter.

with R

Importing & Cleaning Data 

Bring your data into focus. Develop the invaluable skills you need to analyze and display data using R—essential when communicating insights and discoveries to non-technical stakeholders. In this track, you'll learn how to create and modify plots using ggplot2, optimizing the aesthetics and geoms to help you build beautiful and accurate visualizations. From here you’ll progress your ggplot2 skills and learn how to calculate statistics and use facets and coordinates. You'll also learn some best practices for creating visualizations, including three plot types you should avoid. Start this track and learn how to tell better data stories using data visualization.

Bring your data into focus. Gain the graphical and plot building skills you need to tell better data stories using ggplot2.

Data Visualization 

This beginner-level introduction to machine learning covers four of the most common classification algorithms. You will come away with a basic understanding of how each algorithm approaches a learning task, as well as learn the R functions needed to apply these tools to your own work.

In this course you will learn the basics of machine learning for classification.

Supervised Learning in R: Classification

The data.table package provides a high-performance version of base R's data.frame with syntax and feature enhancements for ease of use, convenience and programming speed. This course shows you how to create, subset, and manipulate data.tables. You'll also learn about the database-inspired features of data.tables, including built-in groupwise operations. The course concludes with fast methods of importing and exporting tabular text data such as CSV files. Upon completion of the course, you will be able to use data.table in R for a more efficient manipulation and analysis process. Throughout the course you'll explore the San Francisco Bay Area bike share trip dataset from 2014.

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table. 

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