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How to Learn R Programming

R is well known in the data science community. This language is perfect for clearing and analysing data, visualising data, and other tasks that relate to working with data. Using this language allows you to go from having a raw dataset to deriving insights hidden within the data you have.

In this guide, we talk about what you need to know about the R programming language and discuss what resources are useful for R beginners. Toward the end of the article, we will talk about communities you can use as you learn R.

What You Need to Know About R Programming

You could spend a lifetime learning the R programming language. But there are a few concepts that you will need to know irrespective of what you do with R, whether you clean data, analyse data, or use R in a professional setting. 

Let’s discuss some of the fundamental skills you need to learn at the start of your journey using R:

  • Syntax. You need to learn the basics of how R code is written and processed. This will give you a solid foundation upon which to learn other concepts.
  • Data Types and Operations. After you have learned the basic R syntax, you should learn how to execute mathematical sums in R. You should also learn what types of data you can store in the language. Both of these topics will come up throughout your career using R.
  • Cleaning Data. While some datasets come ready to use out of the box, most datasets you will use will require some cleaning. The clearing process helps prepare a dataset for analysis. You should learn how to clean data using R.
  • Averages and statistical analysis. Averages, quartiles, standard deviation, and other statistical concepts are all present in the R language. Once you have learned the basics of this language, you should start to learn how common statistical equations can be implemented in R.
  • Loops and conditional statements. You should learn how to use loops and if…else statements to control the logic in your programs.
  • Data visualization. You can use R to create charts and other graphics that display your data in a visual way. Data visualization should be on your list of topics to learn toward the start of your learning journey.
  • Debugging. Every developer makes mistakes. What matters is that you can effectively respond to those mistakes. Learning debugging from an early stage will help you diagnose issues in your code.

You will spend months learning these topics and perhaps even longer if you decide to pursue a career that uses R. But these building blocks give you the knowledge you need to learn more advanced topics, like conducting a data science study using R from start to finish.

Skills Needed to Learn R Programming

To learn the R programming language, some knowledge of mathematics and statistics is required. This is because the R programming language implements a range of mathematical and statistical concepts. Knowing a thing or two about data analysis, such as processes used, will help you on your journey to learning R.

R is not the most beginner-friendly language, so you may find that learning a language like Python first is preferable. Learning data science using the Python language will give you a solid understanding of data science in the context of writing code. But, there is no reason that with the right dedication of time and attention you can learn R without knowing another language.

Why You Should Learn R Programming

The R programming language is widely used in the data science community. This means there is a big community of R developers you can rely on if you have any questions while you learn R. In fact, R is in the top 20 of the “most popular technologies” according to the 2020 Stack Overflow Developer Survey, making it easy to find others learning the language.

R is a powerful language when it comes to data science. If you know how to code in R, you should be able to handle most of your data science tasks, from cleaning data to visualizing insights you have discovered in your dataset.

R opens up good career prospects for those that know the language. According to Glassdoor, the average data analyst, a job title which is common for those who know how to code in R, earns $52,453 per year. That is an impressive sum. We will talk more about the career prospects for R developers later in the article.

How Long Does It Take to Learn R Programming?

Because R is not as friendly as other programming languages, you may find that it takes you more time to learn R than it would another technology. Expect to spend two to three months learning the basics of R and another two to three months to learn more advanced topics. But you could find yourself spending a year of learning R part-time before you feel ready to use it in the workplace.

Learning R Programming: A Study Guide

Where can I go to learn R? That is a great question. Below we have curated some top-rated R resources to help you get started on — or continue — your learning journey. These resources range from tutorials to courses to books.

Learn R by Codecademy

  • Resource Type: Course
  • Price: Free
  • Audience: Beginner and intermediate

The Codecademy Learn R course covers the topics you need to know to build a good understanding of R. In this course, you will learn how to clean data and conduct basic statistical analysis on a data set. Toward the end of the course, you will learn how to run a hypothesis test to prove or disprove a theory you have.

Data Science: R Basics by Harvard on edX

  • Resource Type: Course
  • Price: Free
  • Audience: Beginners

The R Basics course, taught by Harvard University on edX, is a good place to start learning R. The course, which is spread out over eight weeks, talks you through some basic R concepts. You will then be given a data set about crime to analyse so you can practice your skills on the sort of data science challenge you may face in the workforce.

R Programming by Johns Hopkins University on Coursera

  • Resource Type: Course
  • Price: Free
  • Audience: Intermediate

If you are already familiar with the basics of R and regression, you may find this course useful. Taught by instructors from Johns Hopkins University, you will learn how to code in R, import R packages, configure your software, and use the R profiler.

This course takes approximately 57 hours to complete. But you can study at your own pace and deadlines are flexible. So, you do not need to worry about falling behind, which is crucial because R can take a while to get your head around.

R Tutorial by W3Schools

  • Resource Type: Tutorial
  • Price: Free
  • Audience: Beginner

The R Tutorial by W3Schools covers the basic syntax for the R programming language, how to use data structures, and the basics of graphics. This tutorial is very high-level but features extensive code snippets and code outputs to help you master the basics. This resource is great if you have little to no knowledge about R.

‘The Book of R’ by Tilman M. Davies

  • Resource Type: (Book, Course)
  • Price: $34.10
  • Audience: Beginner

Written for beginners, The Book of R covers the basic R concepts you need to know. You will learn the basics of R including syntax and writing your first program. Then, you will move onto statistics, data visualizations, modelling, and other concepts.

The Book of R covers a wide range of material, spanning a total of 832 pages. You can learn at your own pace with this book and come back to concepts as you need to. In fact, this book may also be useful as a reference once you have learned the basics of R.

Communities for People Studying R Programming

R has an extensive community of developers which you can access if you want to learn R. This is important because the community gives you a place to go with questions and concerns as you learn. Below we have listed three of the top communities for R developers.

RStudio Community

The RStudio Community, run by the people who develop the RStudio IDE, is a good place to go for any questions about R and RStudio. This forum contains a range of active threads in which you can participate. You should have no trouble finding answers to questions you have or learning more about topics with which you are less familiar.

R Weekly

Who does not like a good newsletter? The R Weekly newsletter summarizes some of the key insights and articles the authors of the newsletter have found. You will also find lists of podcasts and videos, and packages which have undergone updates. This newsletter is great if you want to stay up to date in the community.

R-bloggers

The content on the R-bloggers site was contributed by members of the community who are developing projects with R. This site spans a range of topics, from packages you can use to cool features you can build with R. For instance, one article tells you how to create animated graphs and GIFs in R.

How Hard is It to Learn R Programming?

The R programming language is considered quite difficult to learn by those who have acquired the skill. This is in part due to the syntax but mainly because of all of the complex topics you may end up using in R. For instance, to do statistical analysis you will need a good understanding of statistics before you learn how to use statistics in R.

Will Learning R Programming Help Me Find a Job?

The R programming language is widely valued in the data science community. You will have no trouble finding a job that suits you if you build a good, working understanding of the R language. To help you understand how learning R could impact your career, we have compiled some employment statistics which are listed below.

Conclusion: Should You Learn R Programming?

While a range of technologies are used for data science, R is among the most popular. Once you learn R, you can clean, analyse, and visualize the data you have at hand.

But whether you decide to learn R is up to you. The R syntax is not as friendly as that of other programming languages. Also, you will need to have a good understanding of statistics and mathematics to learn R. The challenges are not insurmountable, however; plenty of people have successfully learned R.

If you want to pursue any career in data, learning R is something you should consider.

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