Best Way to Learn R Programming
R is a popular programming language, especially in the world of data science and data analysis. You might even have heard people saying that learning R is simple! But relatively easy. If you’re not sure how to deal with it, learning R can be a frustrating challenge. And learning R can be good for your career, of course. Data science is a rapidly growing sector with high average salaries.
Suppose you deal with data, statistics, or any form of analysis and create graphs for business visualization or analysis. In that case, R is probably the right choice. It differs from other languages, particularly by its vectorization characteristics. In addition, advanced features and rich packages make it easy and exciting for analysis and visualizing. So many students think of this question: How to start learning R programming? Or the best way to learn R basics.
Best methods to learn R programming
1. Watch informational videos:
Perhaps most people will go for online free instructive videos. First of all, these videos are free; second, there’s plenty, and you can learn at your own pace. Sometimes people prefer to learn from Youtube or similar videos. It is better to watch the video and code along in R so that you can learn R. Also, refer to the advanced R instructor videos to know beyond the basics.
2. Online courses:
There are several online classes to learn specific technical skills. These courses are, in many cases, free or very affordable and offer discounts to college students. Why do you need to spend thousands of dollars on a university course? Instead, you can make an excellent online understanding.
3. Books:
Books are often given a bad rap because most programming concepts are found free online. Of course, you’d be better off saving that money and searching Google if you’ve been using the book as a referral. However, if you are a fresher who would like to learn the basics, working on a whole book at the fundamental level provides a high level of understanding.
Depending on your preferences and situations, decide on the best method to learn R.
Let’s examine five effective tactics to learn this essential data science language. You should use these tactics to complement each other by mastering the most powerful statistical language in the world!
Top smart tips to learn R programming
● Step 1. Identify your motivation to learn R
Find something in this process that motivates you. It helps you define your ultimate objective, and without being boring, it will let you achieve that goal. Take a step more profound than “to be a data scientist.” There are many types of data scientists working on a wide range of issues and projects. Choose one or two things you want and get prepared to stick with. Learn about them and develop projects aligning with your interests.
● Step 2. Understand the basic syntax
Syntax is an even more important programming language than human language syntax. Learning syntax is nevertheless boring, so you should spend as little time as possible learning syntax. Instead, gain knowledge of as much syntax as you can while working on the real-time problems that excite you to get motivated even if the syntax itself isn’t interesting.
● Step 3. Practice on structured projects
Projects are an excellent way of learning since they allow you to apply what you have already learned while generally challenging you to learn new things and solve problems. Furthermore, building projects will help you create a portfolio that you can later show to future employers.
● Step 4. Create your projects
Once some structured projects have been finished, you are likely ready to proceed to the next stage of learning R: to do your unique data science projects. It isn’t easy to know how much you have learned until you step outside and try to be doing something yourself. You’ll have a great idea about how far you have come from working on unique projects which interest you, but also what you might want to learn.
And while you are going to build your project, you’re not going to work alone. You still are referring to aid resources and new techniques and approaches while you are working. In particular, if you are using R, you can find a package dedicated to helping with the precise type of project you are working on.
● Step 5. Climb the hard career ladder
Doing more projects is great, but you must ensure you keep learning if you want to learn R. For example, you can do a lot with just data visualization, but that does not mean that you must build 20 projects in a row using those skills only. Each task should be a little more challenging and a little more complex than the previous one. Each project ought to challenge you to learn something you did not previously know.
Endnote: Don’t give up learning R
Learning R can be excellent for your professional life. The field of data science is rapidly growing with high average pay. Tons of firms and organizations are using R for data science work. There is great demand for R from academia and government and thousands and thousands of companies all across the globe. Even in this pandemic situation, it is clear that they wanted someone with R skills who could work in almost any industry.
Big tech, video games, fashion, finance, pharmaceuticals, insurance—all industries require people who can work with data, which means all industries need R programming skills to use.
Learning R is like learning a second spoken language — you’ll be comfortable and fluent in it, but never will you be done learning. Even experienced data scientists who work with R have been learning new stuff for years because the language itself is emerging, with new packages all the time. You must be curious and learn, but don’t forget to look back and see how far from time to time you have come too.