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How to Learn R Programming

Have you ever wondered how data analysts interpret data and draw conclusions? Are you curious about how people make intricate data charts? Behind a lot of data analysis and visualization is the R programming language, widely used for statistical computing and data science.

In this guide, we’re going to answer the question, “how do you learn R programming?” We will start with the basics, explaining exactly what R is. Then we will discuss what you should know about R and where you can go to learn R programming.

What Is R Programming?

R is a programming language used for statistical and mathematical computing. It is commonly used in academic settings to analyze and process data and calculations. R has also become popular for companies that need to analyze large datasets, from social networks to streaming websites.

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Designed by Ross Ihaka and Robert Gentleman, the language is based on the S programming language. Using R, a programmer can analyze a dataset, create visualizations, conduct time-series analysis, clean datasets, and more. There are also libraries that extend the R programming language for specific purposes.

Jobs that Require R Programming Skills

R programming is mainly used in jobs that involve working with data. After all, R is designed for mathematical computing and working with data. In any job, you will probably be using R to clean, process, and analyze data. Here are a few jobs that may require R skills:

  • Data analyst
  • Data scientist
  • Data engineer
  • Academic researcher
  • Machine learning engineer
  • Artificial Intelligence engineer

All of these jobs involve working with data. For instance, an academic researcher may have a dataset on economic statistics from which they need to draw a conclusion. Or a machine learning engineer may use R to help inform a model they are putting together for a project.

It is worth noting there are other programming languages, like Python and Julia, used for data analysis. But R has become a mainstay in many data science teams and projects around the world because it is very powerful. R skills go a long way and there will always be a company out there looking for a skilled R developer.

What Industries Use R Programming?

The R programming language is used in a variety of industries because data analysis is useful in many settings. Businesses can use data science to improve their products and make more informed decisions. In academia, data science is used for research. Governments can use data science to compile national statistics.

Below is a list of some of the industries that use R programming:

  • Finance
  • Healthcare
  • Social media
  • E-commerce
  • Research
  • Manufacturing

All of these industries can apply data analysis and visualization principles in their work. R is a great tool to use for these purposes. It is flexible, powerful, and supported by a global community of developers who are working to improve and expand its applications.

Salary for R Programmers

According to Glassdoor, the average R programmer earns $113,309 per year. You may see R programmers being employed as data scientists, data analysts, machine learning engineers, artificial intelligence engineers, or in other positions that involve data.

The salary you earn applying your R skills will depend on what position you are working in. For instance, on average, machine learning engineers earn more than the average data analyst ($114,121 versus $62,453). Of course, the more experience you have, the more you are likely to earn.

What Skills Do I Need to Master R Programming

To learn R programming, you will need a good understanding of data science theory. R has a steep learning curve, so any preexisting knowledge of data science will go a long way to helping you understand the basics of this programming language. You will encounter data science topics as soon as you start learning R. 

An understanding of mathematics and statistics is also useful. While you can pick up a lot about mathematics as you learn R, there are a lot of concepts you will need to sit down and learn independently. You will need to be familiar with statistical analysis, standard deviations, mean, median, mode, and range.

Where to Learn R Programming

There are many websites and courses online devoted to teaching R programming, from the basics all the way to more advanced principles. This is good news for you: there is plenty of options to learn R, so you are likely to find one that suits your learning style.

Below is a list of five learning resources you can use to study R programming.

R Programming

  • Author: Johns Hopkins University (on Coursera)
  • Price: Free
  • Type of Resource: Course

Taught by Johns Hopkins University staff, this online course will get you started with data analysis using the R programming language. You will learn how to configure your R software and how to write R programs to analyze a dataset. You will also learn to use R debugging tools to find and solve issues in your programs.

This course takes approximately 57 hours to complete and is recommended for those who already know something about regression, a topic that is essential in data analysis. By the end of this course, you should feel comfortable reading data into R, profiling your code, and analyzing data using various techniques.

Data Analysis with R

  • Author: Facebook (on Udacity)
  • Price: Free
  • Type of Resource: Course

This course, created by Facebook, discusses how to analyze data with the R programming language. With a self-paced learning approach and interactive quizzes, it normally takes about two months to complete.  

You will start by learning about exploratory data analysis and the basics of R. You will then learn to explore one, two, and more variables in your data analysis studies. At the end of the course, you will learn to investigate a dataset alongside a data scientist from Facebook. You will also work on a project to practice all of the skills you learned.

Introduction to R for Data Science

  • Author: Purdue University and The Center for Science of Information (on FutureLearn)
  • Price: To be announced
  • Type of Resource: Course

This introductory course explores what you need to know to start using the R programming language. No prior experience with R is necessary. You don’t even need to install any R tools to get started; the course will guide you through installing the R toolkit on your computer.

In this course, you will learn to import data into R, use vectors, manipulate data, and make data-driven decisions based on your analysis of a dataset.

Introduction to R

  • Author: DataCamp
  • Price: First module free, other modules require a subscription ($25.00/month)
  • Type of Resource: Course

This course is divided into six modules that cover various R topics. You will start by learning to use the R console and building an understanding of the data types in R. Over the course of the next five modules, you will learn about vectors, matrices, factors, data frames, and lists.

With this course, you will learn what you need to know to analyze a dataset using R. Likewise, you will understand how to work with some of the key features in the R language.

R for Data Science

  • Author: Hadley Wickham and Garrett Grolemund
  • Price: Free
  • Type of Resource: eBook

This eBook will teach you how to start analyzing datasets with R. You will start by learning the data analysis workflow and the basics of exploratory data analysis. As you progress through this book, you will learn to wrangle (clean) datasets, build models, and communicate information from your analysis.

This book starts with the very basics, so don’t worry about not having any prior knowledge of the language. The creators of the website also promise to keep the eBook free to use, which means you will be able to continue going back to this text throughout your learning journey.

R Programming Tutorials

A lot of learning resources on the Internet are presented in the form of tutorials rather than full courses. These tutorials can be helpful if you seek to learn a specific concept or if you just want to dip your toes.

Getting started with R and RStudio by Coding Club

This tutorial is a resource if you are not ready to commit to a course and want to explore the basics of R. Requiring little commitment to get started, this tutorial walks you through how to download R and RStudio, a tool used in R development. You will then learn a bit about how to analyze a dataset using various R techniques.

swirl: Learn R in R

swirl is an R package that will teach you how to use R in the actual R console. This tool is designed to be highly interactive, thus suitable for beginners who have just installed R and are still finding their way around it. swirl features courses on the basics of R, the R programming environment, cleaning data, and more.

Rachael’s R Tutorials on Kaggle

Rachael Tatman, a data scientist at Kaggle, has compiled a list of her tutorials in this post. These tutorials span a wide range of topics, from getting started in R to visualizing data. In this post, she explains exactly what you will learn in each of her tutorials so you can choose the guides that meet your needs.

A Guide to Learning R Programming

Where do you start learning R? The R language is quite complicated so it is wise that you take a moment to consider where you should begin your learning journey. To help you get started, we have researched which topics R beginners must study first:

  • Background and Syntax: What is R? What does a line of R code look like? Why was R created? Knowing the answers to these questions means you have a solid foundation upon which to build your knowledge of R.
  • Calculations and Data Types: You should learn how to run basic mathematical operations in the R language. You will also need to know what types of data you can store in R and how you can use those types of data in your programs.
  • Conditionals and Loops: Conditionals control which lines of code are run in a program and which ones are not. Loops reduce repetition by running the same lines of code a specified number of times. You will need to learn about both of these topics early on.
  • Cleaning Data: R is commonly used to prepare a dataset for analysis. You should learn about what data cleaning is and what techniques you can use to clean data using R.
  • Reading Data from Files: You should feel comfortable reading data from files and using the data from files in your analysis.
  • Analyzing Data: You should research the main ways in which data is analyzed. For instance, you should be familiar with concepts like mean, median, mode, and range calculations, as well as other statistical techniques used for analyzing data.
  • Preparing Hypotheses: Data studies begin with a question that needs to be answered or a theory that needs to be proven or disproven. You should learn how to write and test a hypothesis.
  • Data Visualization: Once you develop a solid understanding of data analysis, you should learn how to turn the data you have analyzed into graphs and charts.

The above list is by no means comprehensive. R is a powerful language and, as a result, there are a lot of features that you can learn. However, the above topics will give you a good footing in R and prepare you for some of the more advanced features further down the line.

How Long Does it Take to Learn R Programming?

R has a steep learning curve, which means you should expect to invest a considerable amount of time learning it. You may be able to learn the basics within about a month of study. You may need to spend an additional two or three months refining your skills before you feel ready to take on more advanced challenges.

Learning R to the point where you are ready to enter the workforce is a completely different story. You will probably need to study for about six months to a year to learn what you need to use R on a professional project. Even then, you may only be able to get an entry-level job with the knowledge you’ve acquired. Like any skill, the key to mastering R is practice.

Should I Learn R Programming?

Do you see yourself sitting behind a desk analyzing data at work? Do you find numbers and statistics fascinating? Do you like solving complex problems? These are the sorts of questions you need to entertain if you are considering studying R.

Compared to Python, another language used for data analysis, R is quite difficult to use. There is a lot you will need to learn before you can use R effectively. With that in mind, the time you spend learning R will pay off; there are so many companies out there that need talented data workers who know how to use R.

Learning R programming is a personal decision, and a big one too. Before you go deep into the weeds and get lost among lines of code, make sure you feel confident that R is the right technology for you.

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