Learn R Programming In A Few Easy Steps

The R Programming Online Training for a more advanced approach to becoming proficient in this language.


Manvi Narvey

a year ago | 3 min read


Learning R may be challenging for someone with no programming background or who is more familiar with dealing with point-and-click statistical applications instead of an actual programming language. This learning route is for new R users, but it will also cover some of the most recent developments in the language that may interest expert R users.

Developing this learning curve requires a constant trade-off between being practical and comprehensive. However, many free resources are available on the internet to learn R programming, but we can not address all of them here. As a result, we have compiled crucial steps to learn this advanced language. Alternatively, you can enroll in the R Programming Online Course for more advanced ways to become proficient as an R programming expert.

R Programming: Meaning

R is a programming language and free software created in 1993 by Ross Ihaka and Robert Gentleman. It has a large library of statistical and graphical techniques. However, it comprises machine learning techniques, linear regression, time series, and statistical inference. Although most libraries are in R language, for heavy computation, C, C++, and Fortran codes are necessary. Furthermore, R is trusted by academics and by many big corporations like Uber, Google, Airbnb, Facebook, and others.

Easy Steps to Learn R Programming Language

Below are the steps you must follow to learn R programming language.

Understand Why you should learn R

R is quickly becoming the global language of data science. From its inception, you may see R language in a growing number of corporate contexts, where it competes with commercial software like SAS, STATA, and SPSS. Moreover, R's popularity grows year after year, and in 2015, IEEE named R one of the top 10 languages of the year.

Setting Up of the Language

Before you can begin working with R, you must first download a copy to your local computer. However, R is evolving, and several versions are available since R's inception in 1993. Moreover, installing R is simple, and binaries for Linux, Mac, and Windows are available through the Comprehensive R Archive Network (CRAN). So, after downloading R, you should install one of R's integrated development environments.

Get familiar with R Syntax.

Learning the grammar of a computer language like R is comparable to learning an actual language like French or Spanish. All you have to do is practice and action. However, there are multiple resources available online for learning R. Remember that the more you indulge yourself in working on projects, the quicker you will learn R.

Work on Structured Projects

After mastering the syntax, you will go on to more organized projects of your own. Projects are an excellent method to study since they allow you to apply what you've previously learned while challenging you to learn new things and solve difficulties along the way. Furthermore, developing projects will assist you in compiling a portfolio that you can offer to potential employers later on. At this time, you probably don't want to start working on unique projects. You'll get stuck frequently, and the procedure may be frustrating at times. Instead, search for controlled tasks to help you gain experience and increase your comfort level.

Create a Project on your Own

Now, you are ready to move on to the next step of learning R. After you have completed several organized assignments, creating your own unique data science projects is quite essential. It's difficult to tell how much you have learned unless you try something of your own. Working on one-of-a-kind projects that interest you will give you a good notion of not just how far you've come but what you might like to study next.

Increase the Difficulty Level

Working on projects is excellent, but if you want to master R, you must keep studying. You may do a lot with simple data visualization, but it doesn't mean you should create 20 projects in a row that solely employ your data visualization skills. Each project should be more difficult and intricate than the other one. Each endeavor should push you to discover something new about yourself.

So, if you're not sure how to go about it, here are some questions you may ask yourself to add complexity and difficulty to any project:

  1. Can you educate a novice on how to create this project by developing a guide?
  2. Can you scale up your project to accommodate additional data?
  3. Can you make it perform better? Could it possibly be faster?
  4. Can you make the visualization better? Could you please clarify? Is it possible to make it interactive?
  5. Is it possible to make it predictive?


Hopefully, you may find this article informative. We have compiled a few easy steps to learn the R programming language. These steps help an individual to move ahead in their career and become an R programming expert. Alternatively, you can enroll in the R Programming Online Training for a more advanced approach to becoming proficient in this language.


Created by

Manvi Narvey







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