Rsquared Academy · Open Courses

Open Courses

Free, self-paced R courses — no sign-up, no platform, just the contents.

Welcome to Rsquared Academy open courses. Here you’ll find the complete content of all our self-paced courses: videos, guides, e-book chapters, slides and code. We built this for everyone who would rather not sign up on a platform to access course material. Feedback is always welcome — reach out at support@rsquaredacademy.com.

15 courses
5 tracks
Beginner → Advanced
Free & open
No sign-up

Start with Intro to R Browse courses

Hex stickers of Rsquared Academy R packages

Learning path

Follow the tracks in order, or jump in wherever your current project needs. Each card below links to every format of that course.

Foundations

The R ecosystem and IDE, then importing flat files and Excel workbooks.

Wrangling

dplyr in three parts, tibbles, pipes and date & time handling.

Text & Web

Regular expressions first, then put them to work scraping the web.

Applied Analytics

RFM segmentation, market basket analysis and database workflows.

Comfortable with R already? Jump straight to 02 Wrangling. Command Line Basics is a companion that pairs well with any track.

Courses

FoundationsBeginner

Introduction to R

In this introductory course, you will learn about R and its ecosystem. You will learn about the evolution of R, its current capabilities and the organizations working for the development of R. You will be introduced to RStudio, a very popular IDE, the R community, and tidyverse, a collection of R packages designed for data science.

What’s inside

R ecosystem and its evolution
RStudio, the popular IDE for R
The R community
tidyverse overview

Online course YouTube playlist Blog: Variables in R Blog: Data Types in R Blog: Getting Help in R Blog: R Package Ecosystem eBook Slides: Introduction Slides: Getting Help Slides: Packages Slides: Variables GitHub

FoundationsBeginner

Import Data into R

Importing or reading data is one of the most basic and crucial stages of data analysis. In this introductory course, you will learn to read data into R from different sources and in multiple formats. You will examine the challenges in reading data and use appropriate methods to address them.

What’s inside

Reading data from different sources
Multiple data formats
Common import challenges and fixes

Online course YouTube playlist Blog: Import Data into R eBook chapter Slides: Import Data Part 1 GitHub

FoundationsBeginner

Import Excel Data into R

Importing or reading data is one of the most basic and crucial stages of data analysis. In this introductory course, you will learn to read Excel data into R. You will examine the challenges in reading Excel data and use appropriate methods to address them.

What’s inside

Reading Excel workbooks
Sheet and range handling
Common Excel import challenges and fixes

Online course YouTube video Blog: Import Excel Data into R eBook chapter Slides: Import Data Part 2 GitHub

WranglingBeginner

Data Wrangling with dplyr - Part I

In this course, you will master techniques for data manipulation using the dplyr package. You will learn to filter, select, sort, mutate and summarize data. You will learn to join tables, make your code readable using pipes and use tibbles instead of data frames.

What’s inside

filter and select
arrange and mutate
summarise and group_by

Online course YouTube video Blog: Data Wrangling Part 1 eBook chapter Slides: dplyr Part 1 GitHub

WranglingIntermediate

Data Wrangling with dplyr - Part II

In this course, you will learn to join tables using dplyr's two table verbs. Data does not come in the structure or shape in which we need it for analysis and a lot of time is spent in getting the data into shape. You will learn the best ways to combine split data sets to create single tables for your data analysis pipeline.

What’s inside

dplyr's two-table verbs
Join types in practice
Combining split data sets

Online course YouTube video Blog: Data Wrangling Part 2 eBook chapter Slides: dplyr Part 2 GitHub

WranglingIntermediate

Data Wrangling with dplyr - Part III

Data does not come in the structure or shape in which we need it for analysis and a lot of time is spent in getting the data into shape. As such reshaping data is an invaluable skill set for a data scientist. In this course, you will master sophisticated techniques for data manipulation using the dplyr package.

What’s inside

Reshaping data
Advanced manipulation techniques
Sophisticated dplyr workflows

Online course YouTube video Blog: Data Wrangling Part 3 eBook chapter Slides: dplyr Part 3 GitHub

WranglingBeginner

Introduction to tibbles

Data does not come in the structure or shape in which we need it for analysis and a lot of time is spent in getting the data into shape. In this course, you will learn about tibbles, an alternative for data frames.

What’s inside

tibbles vs data frames
Creating tibbles
Subsetting tibbles

Online course YouTube video Blog: Introduction to tibbles eBook chapter Slides: tibbles GitHub

WranglingBeginner

Readable Code with Pipes

R code contains a lot of parentheses in case of a sequence of multiple operations. When you are dealing with complex code, it results in nested function calls which are hard to read and maintain. The magrittr package by Stefan Milton Bache provides pipes enabling us to write R code that is readable.

What’s inside

The magrittr pipe operator
Rewriting nested calls
Readable code sequences

Online course YouTube video Blog: Readable Code with Pipes eBook chapter Slides: pipes GitHub

WranglingIntermediate

Handling Date & Time in R

This course is designed to give you a comprehensive introduction to handling date and time data in R. If you are someone who deals with data, you would know that date/time come in all possible weird formats. As such, it is important to know how to parse, manipulate and compute with date and time.

What’s inside

Parsing date and time values
Components and arithmetic
Time zones with lubridate

Online course YouTube playlist Blog: Handling Date & Time in R eBook chapter Slides: Date & Time GitHub Posit Cloud lab

Text & WebIntermediate

Regular Expressions in R

In this course, we will learn about using regular expressions in R. While it is aimed at absolute beginners, we hope experienced users will find it useful as well. The post is broadly divided into three sections. In the first section, we will introduce the pattern matching functions such as grep, grepl etc. as we will be using them in the rest of the post.

What’s inside

Pattern matching syntax
grep and grepl family of functions
Substitution and extraction

Blog: Regular Expressions in R Slides: Regular Expressions GitHub Posit Cloud lab

Text & WebAdvanced

Practical Introduction to Webscraping in R

Are you trying to compare prices of products across websites? Are you trying to monitor price changes every hour? Or planning to do some text mining or sentiment analysis on reviews of products or services? If yes, how would you do that? In this course, we will learn about web scraping using R.

What’s inside

How web scraping works
Extracting data from web pages
Text mining and sentiment analysis uses

YouTube video Blog: Web Scraping Slides: Web Scraping GitHub

Applied AnalyticsAdvanced

Association Rule Mining in R

Ever wondered why items are displayed in a particular way in retail/online stores. Why certain items are suggested to you based on what you have added to the cart? Blame it on market basket analysis or association rule mining.

What’s inside

Market basket analysis
Support, confidence and lift
Association rules in practice

Blog: Market Basket Analysis Slides: Market Basket Analysis GitHub Posit Cloud lab

Applied AnalyticsIntermediate

Customer Segmentation using RFM Analysis

In this course, students will learn to segment customers using recency, frequency and monetary value analysis. Students will look at a case study to understand how RFM can be used to segment customers and tailor offers to them. They will understand the difference between transaction level data and customer level data.

What’s inside

Recency, frequency, monetary value
Transaction vs customer level data
Segmenting customers

Online course YouTube video Blog: RFM Analysis Slides: RFM Analysis GitHub Posit Cloud lab

Applied AnalyticsIntermediate

Working with Databases using R

In this course, you will learn to interact with databases from R. From connecting to different databases from R, using SQL script in RStudio and the SQL engine in knitr while working with RMarkdown documents to querying, visualizing and modeling data while securely handling database credentials.

What’s inside

Connecting to databases from R
SQL in RStudio and knitr
Querying, visualizing and modeling
Handling credentials securely

Online course YouTube video Blog: Working with Databases eBook Slides: Databases & SQL GitHub Posit Cloud lab

ToolingBeginner

Command Line Basics

In this course, you will be introduced to shell commands. Our goal was to ensure that after completing this tutorial, readers should be able to use the shell for version control, managing cloud services (like deploying your own shiny server etc.), execute commands in R and RMarkdown and execute R scripts in the shell.

What’s inside

Shell fundamentals
Version control and cloud services
Running R and R scripts from the shell

Online course YouTube video Blog: Command Line Basics eBook Slides: Shell (PDF) GitHub Posit Cloud lab

Books behind the courses

Every course draws from one of six open-access textbooks, free to read online and downloadable as PDF or ePub.

CORE 01 Introduction to R 13 chapters CORE 02 Data Wrangling with R 13 chapters CORE 03 Data Visualization with ggplot2 22 chapters CORE 04 R, Databases and SQL 5 chapters COMPANION A Command Line Basics 12 chapters COMPANION B Data Visualization with Base R 12 chapters

Feedback

Spotted a mistake, a broken link, or a topic you would like covered next? Everything here is maintained by hand, so a note from you genuinely helps.

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