Gain insights into R for data science, from basics to machine learning. Delve into data manipulation, visualization, and version control, and discover how to optimize R workflows for real-world challenges.

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Tidyverse

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

As data scientists, we often need to look at many plots at once and share those plots with others. Combinations of related plots are essential for understanding and communicating insights from data. The `ggplot2` package provides two convenient features to help support this need. The first is faceting, which allows us to create a set of plots displayed as a matrix, each showing a subset of the data. The second is `ggsave`, which enables us to export our plots to several different file formats for use outside of R. In this lesson, we'll explore facets, how to use them to create a matrix of plots, and how to save plots in different formats for various purposes.

Learn to create matrices of related plots using facets and export plots using ggsave.

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

Faceting and Saving Plots