R for Data Science


Learn to tackle a wide variety of data science challenges, using the best parts of R.

Before you can turn raw data into a story, you need to have a solid understanding of data science tools. The free e-book, R for Data Science, is one resource that can help budding data journalists build a strong foundation in the science, using R.

As its name suggests, the book will show readers how to get their data into R, get it into the most useful structure, transform it, visualise it and model it.

Image: R for Data Science.

Unlike most how-to books, the authors, Garrett Grolemund and Hadley Wickham, steer clear of teaching tools in the order in which they are used in an analysis, in recognition that that isn't always the best way to maximise learning.

"Starting with data ingest and tidying is sub-optimal because 80% of the time it’s routine and boring, and the other 20% of the time it’s weird and frustrating. That’s a bad place to start learning a new subject," they explain

"Instead, we’ll start with visualisation and transformation of data that’s already been imported and tidied. That way, when you ingest and tidy your own data, your motivation will stay high because you know the pain is worth it."

Each chapter generally sticks to a similar pattern: starting with some motivating examples so you can see the bigger picture, and then diving into the details. There are also exercises throughout each chapter to help you practice what you’ve learned. 

Explore R for Data Science here.