Explore R for data science, from basics to machine learning. Learn data manipulation, visualization, version control, and workflow optimization for real-world challenges.

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Embark on an exciting journey with R as your trusted ally for data science. This comprehensive course will equip you with essential skills to leverage R's power for data manipulation and analysis. The course is suitable even if you have limited R experience, empowering you to perform data science tasks effectively.

The journey starts with R fundamentals to lay a strong foundation. You’ll master the tidyverse to create powerful and readable code. Then, you will explore the import of various data sources, visualization, and best practices before gaining hands-on machine learning experience. Also, you’ll learn version control with Git and GitHub and to optimize your R code for efficient data science workflows.

At course completion, you'll emerge as a confident R data scientist, ready to tackle real-world challenges. You'll be well-equipped to advance your career in data science with R's extensive capabilities in your toolbelt.

Data Science in R: From Basics to Machine Learning

When working with R, we often work on a project over several days, weeks, or even months. That means we’ll be opening and closing the files occasionally and will need to reload from where we last left off.



An R project is a convenient solution to that need. It bundles all the files and directories so that everything is contained in one coherent package. It keeps one set of work together while keeping it separate from other projects’ tasks. Leveraging projects is also the first step in setting up our work in R to work with Git and GitHub. This lesson will explore R projects and how to create and manage them.

An R project is a directory on our computer where we store all the files related to a specific task. This directory contains our R scripts, data files, figures, and any other files associated with the project. The project is the root directory of our working directory structure. It'll be located above the main folders such as scripts, inputs, outputs, etc. When we create an R project, we're assigning our root directory for a task. We can add our usual subfolder structure within that root directory to keep things well-organized.

There are two primary methods for creating a project in R. Both of these, however, require that we're in an active RStudio session. One option is to create the project using the user interface in RStudio; this is left as an exercise for the reader. Otherwise, we can directly manage our projects from the R terminal, inside of RStudio, using the `rstudioapi` package:

Learn to create R projects using rstudioapi.

Why R?

R Fundamentals

R Fundamentals Exercises

Readable Coding with tidyverse

Tidyverse Exercises

Importing More Data Sources

Data Visualization with ggplot2

Best Practices for Data Scientists

Statistical Analysis and Machine Learning with tidymodels

Exploring tidymodels through Exercises

Useful Libraries for Data Science

Git Integration

Getting The Most Out of R

Appendix

Credit Card Fraud Detection using the R Language

R Projects

What is an R project?

Working with R projects