# Introduction to R

4.7+
382 reviews
Beginner

Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.

4 Hours62 Exercises2,472,130 Learners6200 XPData Analyst with R TrackData Scientist with R TrackData Scientist Professional with R TrackR Programming Track

or

## Learn R Programming

R programming language is a useful tool for data scientists, analysts, and statisticians, especially those working in academic settings. R's ability to handle complex analyses such as machine learning, financial modeling, and more makes it a valuable asset for a wide range of data-related tasks.

This introduction to R course covers the basics of this open source language, including vectors, factors, lists, and data frames. You’ll gain useful coding skills and be ready to start your own data analysis in R.

## Gain an Introduction to R

You’ll get started with basic operations, like using the console as a calculator and understanding basic data types in R. Once you’ve had a chance to practice, you’ll move on to creating vectors and try out your new R skills on a data set based on betting in Las Vegas.

Next, you’ll learn how to work with matrices in R, learning how to create them, and perform calculations with them. You’ll also examine how R uses factors to store categorical data. Finally, you’ll explore how to work with R data frames and lists.

## Master the R Basics for Data Analysis

By the time you’ve completed our Introduction to R course, you’ll be able to use R for your own data analysis. These sought-after skills can help you progress in your career and set you up for further learning. This course is part of several tracks, including Data Analyst with R, Data Scientist with R, and R Programming, all of which can help you develop your knowledge.
1. 1

### Intro to basics

Free

Take your first steps with R. In this chapter, you will learn how to use the console as a calculator and how to assign variables. You will also get to know the basic data types in R. Let's get started.

How it works
100 xp
Arithmetic with R
100 xp
Variable assignment
100 xp
Variable assignment (2)
100 xp
Variable assignment (3)
100 xp
Apples and oranges
100 xp
Basic data types in R
100 xp
What's that data type?
100 xp
2. 2

### Vectors

We take you on a trip to Vegas, where you will learn how to analyze your gambling results using vectors in R. After completing this chapter, you will be able to create vectors in R, name them, select elements from them, and compare different vectors.

3. 3

### Matrices

In this chapter, you will learn how to work with matrices in R. By the end of the chapter, you will be able to create matrices and understand how to do basic computations with them. You will analyze the box office numbers of the Star Wars movies and learn how to use matrices in R. May the force be with you!

4. 4

### Factors

Data often falls into a limited number of categories. For example, human hair color can be categorized as black, brown, blond, red, grey, or white—and perhaps a few more options for people who color their hair. In R, categorical data is stored in factors. Factors are very important in data analysis, so start learning how to create, subset, and compare them now.

5. 5

### Data frames

Most datasets you will be working with will be stored as data frames. By the end of this chapter, you will be able to create a data frame, select interesting parts of a data frame, and order a data frame according to certain variables.

6. 6

### Lists

As opposed to vectors, lists can hold components of different types, just as your to-do lists can contain different categories of tasks. This chapter will teach you how to create, name, and subset these lists.

In the following tracks

Data Analyst with RData Scientist with RData Scientist Professional with RR Programming

#### Jonathan Cornelissen

Co-founder of DataCamp

Jonathan Cornelissen is one of the co-founders of DataCamp and the initial DataCamp CEO, and is interested in everything related to data science, education and entrepreneurship. He holds a PhD in financial econometrics, and was the original author of an R package for quantitative finance.

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## Don’t just take our word for it

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• Rokesh P.
2 days

Introduction to R was pretty neat. I already know R basics but not an expert. It served as a refresher and the content was well-made. But as I progressed through other courses (completed introduction to tidyverse, data manipulation with dplyr, joining data with dplyr), I felt that the content could have been much better, especially with the joins. Some introduction to one to many, many to many, etc relationships should have been made available with the intro to sets. data manipulation with dplyr was also awesome but the detail on aggregation function n() and the arguments that the functions could take might also have been made available.

• Mushiirah M.
4 days

Learning R coding from scratch and Data Camp is a great enabler.

• Xeni L.
5 days

I particularly liked that there was no video on this training, I found it easier to do at my own pace.

• Asmaa A.
9 days

This course is very great

• Sanga F.
10 days

Great 👍

"Learning R coding from scratch and Data Camp is a great enabler."

Mushiirah M.

"I particularly liked that there was no video on this training, I found it easier to do at my own pace."

Xeni L.

"This course is very great"

Asmaa A.