How to Learn R for Data Science, Fast!

Gain a deep understanding of R quickly, and for free. Learning R can take a lot of time. But while it’s impossible to become an expert overnight, there are plenty of things you can do to speed up the learning process. I’ve put together some recommendations for streamlining your path to R proficiency, based on my own experience.I should note right away that everyone’s experiences are different. Some people learn R in different ways and still achieve success. That said, I’ve got a lot of value from the following tips, as have many other people I know.

Learning R can take a lot of time. But while it’s impossible to become an expert overnight, there are plenty of things you can do to speed up the learning process. I’ve put together some recommendations for streamlining your path to R proficiency, based on my own experience.

I should note right away that everyone’s experiences are different. Some people learn R in different ways and still achieve success. That said, I’ve got a lot of value from the following tips, as have many other people I know.

1. Learn the Fundamentals of Programming First

As a language built by statisticians rather than programmers, R can seem a little different from many other languages. That said, it still shares the basic components of most programming languages. Actions like assigning variables, evaluating conditions, and calling functions are all commonplace. Thus, it’s a good idea to at least go in with some knowledge of these fundamentals:

• Common data types (strings, integers, booleans, and so on)
• Conditional (if/else) statements
• ‘For’ and ‘while’ loops
• Functions

When I started learning R, I found that knowing about these things substantially sped up my progress. It’s probable that you’ll learn new programming concepts by using R too — I have. But going in with at least some familiarity with the basics will save you a lot of time later on.

programming learning r data-analysis data-science

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