How to Start Learning R for Data Science

In the world of data science, R is an increasingly popular programming language for a reason. It was built with statistical manipulation in mind, and there’s an incredible ecosystem of packages for R that let you do amazing things – particularly in data visualization – that would be much more difficult in Python.

But R is also considered less “beginner friendly” than Python, and if you’ve considered R in the past, you may have been put off by online comments highlighting its comparatively steep learning curve. Maybe you even tried one of our R courses before, but weren’t impressed.

If that’s you, it’s time to take another look, because we’ve launched totally revamped Intro to R courses that make picking up R easier than ever.

New R Courses

Our own R programming expert, data scientist Dr. Rose Martin (Ph.D), has completely redone our most foundational R courses:

The aim of these new courses is to make learning R easier and more accessible and fun than ever before.

Dr. Martin is an R native who was crunching massive datasets in academia and at the EPA before coming to Dataquest. She knows how to work efficiently in R and her new courses take full advantage of popular user packages (hello, Tidyverse!) to get you up and running fast, wrangling data like an R expert.


Why you should learn R

Whether you’re just starting to learn data science or you’re an experienced data scientist with a pocket full of Python skills already, it’s a great idea to learn R. Here are a few reasons why:

It’s a popular language for data science at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. Twitter uses R for data visualization and semantic clustering. Microsoft, Uber, AirBnb, IBM, HP – they all hire data scientists who can program in R.

And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. Even the New York Times uses R!

Learning the data science basics is arguably easier in R. Python may be one of the most beginner-friendly programming languages, but once you get past the syntax, R has a big advantage: it was designed specifically with data manipulation and analysis in mind. Because of that, learning the core skills of data science – data manipulation, data visualization, and machine learning – can actually be easier in R once you’ve gotten through the basic fundamentals. Check out, for example, how straightforward it is to create these common data visualization styles in R.

Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. The dplyr package, for example, makes data manipulation a breeze, and ggplot2 is a fantastic tool for data visualization. But that’s just the tip of the iceberg.

Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R. And because there are so many enthusiastic R users, you can find R packages integrating almost any app you can think of!

Put another tool in your toolkit. Even if you’re already a Python expert, no one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.

If you’re interested in learning R, or even just curious about what the language is like, why wait? Hop into our new Intro to R course. Five minutes from now you’ll have written your first lines in R, and taken the first step in your journey toward being a better data scientist.