Introduction to R

via DataCamp

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Provider

DataCamp
Pricing

Free Trial Available
Languages

English
Certificate

Certificate Available
Duration & workload

4 hours
Sessions

On-Demand
Level

Beginner

Found in

Overview

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

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.

Syllabus

Intro to basics

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.

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.

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!

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.

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.

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.

Reviews

4.0 rating, based on 8 Class Central reviews

4.7 rating at DataCamp based on 612 ratings

Start your review of Introduction to R

Gregory J Hamel ( Life Is Study) @greg

Introduction to R is one of several free introductory level courses offered by DataCamp--an education platform for learning data science skills with a heavy focus on interactive coding exercises. The intro to R course is the first for 23 courses in…

Introduction to R is one of several free introductory level courses offered by DataCamp--an education platform for learning data science skills with a heavy focus on interactive coding exercises. The intro to R course is the first for 23 courses in DataCamp's "Data Scientist with" career track and covers the very basics of the R programming language. No prior programming experience is necessary to take the course, so it is a good place to start for anyone interested in learning R or about programming for data science in general. DataCamp tracks your course progress automatically as you complete exercises and although successfully completing exercises will award experience points, you can get hints or view full solutions at the cost of experience to progress and doing do does not seem to have any impact whether you can complete a course and earn a certificate of completion. The expected course completion time is listed as 4 hours.

Introduction to R is split up into 6 sections: R Basics, Vectors, Matrices, Factors, DataFrames and Lists, which cover most of the basic data structures and arithmetic operations used in R. Unlike most DataCamp courses, intro to R does have any lecture videos and instead consists solely of written tutorials with accompanying programming exercises, which lets you get your hands dirty with R right away. I usually prefer courses with periodic lecture videos, but for a course on basic nuts and bolts, hands on experience is particularly important and watching someone go over ground level basics in a lecture can get slow and tedious. The instructions and exercises are well made and I didn't encounter any with solution submissions.

Introduction to R is a nice primer on the basics of the R language that provides great value for your time spent. It is a good place to start if you are interested in learning R or even if you just want to test out the DataCamp platform. A lecture video or two at the start and end of the course might be nice, if nothing else to serve as an orientation for students new to R and the DataCamp platform, but their absence doesn't really detract from the course.

I give Introduction to R 4.5 out of 5 stars: Great.
L P

Only the first chapter is accessible for free.
It seems to be good quality, and in-browser programming is really convenient.
But past a few basic exercises... you need to pay.
John Charles

Hands down the best way to learn R. Extremely informative and clearly written lessons that provide the right balance of instruction and challenge. I would recommend DataCamp (and have) to anyone looking to learn the functional skills for a career in data science.
Claudia Scwz

This course is too easy! It could condense information a lot more and provide more practical examples. I guess it works if you're an absolute beginner and not at all familiar with any programming languages.
Anonymous

Datacamp classes give you a really nice walk through of the concepts with coding examples in the language with talks in between sections that provide a clear overview of the topic.

You're not going to be an expert after taking a section but will know where to look and also build on familiarity with the language. Much better than just reading about code in a textbook or taking on a huge project with no idea where to start.
Anonymous

Great course to start with R in data science. I'm 5 courses in at this point and love the DataCamp model of using video and coding exercises together. For me, it is the best tool out there.
Anonymous

You only get 1 module for free, and it took me ~20 minutes to complete. It had a nice layout though, and I did like its approach to teaching.
Monal Jain