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Introduction to R

4.7+
377 reviews
Beginner

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

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4 Hours62 Exercises2,468,713 Learners6200 XPData Analyst with R TrackData Scientist with R TrackR Programming Track

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Course Description

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.

    Play Chapter Now
    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.

    Play Chapter Now
    Create a vector
    100 xp
    Create a vector (2)
    100 xp
    Create a vector (3)
    100 xp
    Naming a vector
    100 xp
    Naming a vector (2)
    100 xp
    Calculating total winnings
    100 xp
    Calculating total winnings (2)
    100 xp
    Calculating total winnings (3)
    100 xp
    Comparing total winnings
    100 xp
    Vector selection: the good times
    100 xp
    Vector selection: the good times (2)
    100 xp
    Vector selection: the good times (3)
    100 xp
    Vector selection: the good times (4)
    100 xp
    Selection by comparison - Step 1
    100 xp
    Selection by comparison - Step 2
    100 xp
    Advanced selection
    100 xp
  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!

    Play Chapter Now
    What's a matrix?
    100 xp
    Analyze matrices, you shall
    100 xp
    Naming a matrix
    100 xp
    Calculating the worldwide box office
    100 xp
    Adding a column for the Worldwide box office
    100 xp
    Adding a row
    100 xp
    The total box office revenue for the entire saga
    100 xp
    Selection of matrix elements
    100 xp
    A little arithmetic with matrices
    100 xp
    A little arithmetic with matrices (2)
    100 xp
  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.

    Play Chapter Now
    What's a factor and why would you use it?
    100 xp
    What's a factor and why would you use it? (2)
    100 xp
    What's a factor and why would you use it? (3)
    100 xp
    Factor levels
    100 xp
    Summarizing a factor
    100 xp
    Battle of the sexes
    100 xp
    Ordered factors
    100 xp
    Ordered factors (2)
    100 xp
    Comparing ordered factors
    100 xp
  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.

    Play Chapter Now
    What's a data frame?
    100 xp
    Quick, have a look at your dataset
    100 xp
    Have a look at the structure
    100 xp
    Creating a data frame
    100 xp
    Creating a data frame (2)
    100 xp
    Selection of data frame elements
    100 xp
    Selection of data frame elements (2)
    100 xp
    Only planets with rings
    100 xp
    Only planets with rings (2)
    100 xp
    Only planets with rings but shorter
    100 xp
    Sorting
    100 xp
    Sorting your data frame
    100 xp
  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.

    Play Chapter Now
    Lists, why would you need them?
    100 xp
    Lists, why would you need them? (2)
    100 xp
    Creating a list
    100 xp
    Creating a named list
    100 xp
    Creating a named list (2)
    100 xp
    Selecting elements from a list
    100 xp
    Creating a new list for another movie
    100 xp

In the following tracks

Data Analyst with RData Scientist with RR Programming
Jonathan Cornelissen Headshot

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.

More information at: jonathancornelissen.com
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*4.7
from 377 reviews
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  • Asmaa A.
    3 days

    This course is very great

  • Sanga F.
    4 days

    Great 👍

  • Stephen I.
    5 days

    At first, the system was frustrating my learning because I could not answer some of the exercises. But when I paid more attention, I discovered that the class is interesting.

  • Oluwafunmibi A.
    6 days

    Great

  • Urich K.
    6 days

    Very good training

    • Loading ...

"This course is very great"

Asmaa A.

"Great 👍"

Sanga F.

"At first, the system was frustrating my learning because I could not answer some of the exercises. But when I paid more attention, I discovered that the class is interesting."

Stephen I.

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