Mastering R: A Comprehensive Guide to Learning the Statistical Programming Language

If you are new to programming and trying to decide where to start, the choices can seem endless. There are so many programming languages to choose from. The R programming language may be one of the languages you ran into in your search. And you might be wondering if R is the language for you. This article delves into the intricacies of learning R, a powerful language widely used for statistical computing, data analysis, and visualization. It provides a structured roadmap for aspiring R programmers, covering essential aspects such as the time commitment required, effective learning strategies, and the diverse applications of R across various industries.

Introduction to R Programming

R is the programming language for data. It was designed for statisticians and is specialized for statistical programming. There are a multitude of libraries that give the language capabilities in data visualization and machine-learning. You might think this specialization limits the usage of R. Modern business runs on data. Every company has potential insights that would remain buried in the terabytes of data generated every year if it wasn’t for data analysts and data scientists. In fact, businesses of all types and sizes choose to unlock these insights in their data with the R programming language.

Why Learn R?

R is one of the most in-demand programming languages in many industries. R’s versatility and specialized capabilities make it an invaluable asset for professionals seeking to extract meaningful insights from data.

Diverse Applications Across Industries

R is used in drug discovery to analyze the data in pre-clinical drug trials and to predict how a pandemic will spread in epidemiology. Fintech businesses use technology to handle money, and R is widely used in this industry. For example, quantitative analysts use R to devise trading models that automatically invest clients’ money in the stock market. Academics and researchers use the R programming language extensively. Just about every course at Cornell involving statistical computing teaches R. Retailers and e-commerce businesses use R to assess risk and create marketing strategies. R’s machine learning technology capabilities are used to increase profits and sales through cross-selling and suggesting related products at checkout. Retailers also use R to model sales and target advertisements. Many companies use the R programming language to analyze customer feedback and improve their products. To improve the design and appeal of Ford’s vehicles, Ford analyzes consumer sentiment using R. Data abounds in the social media industry. Every time we use the internet, we are tracked. Each and every action is recorded in some database, waiting for an analyst to examine it. In many cases, a social media site’s only source of revenue is the data it has on its users and targeted advertising.

R vs. Python: A Comparative Perspective

Both Python and R are popular programming languages for data science and machine learning. R creates powerful visualizations and has robust options for statistical analysis. We also recommend that, as a data specialist, you learn Python, and we built a guide for that as well. However, don't give up on R just yet! Though R and Python are both popular tools for data specialists, they differ in key ways that preserve R's value. Python is a general programming langauge that has fantastic data applications. However, R is a high-level statistical language, and it was built explicitly to deal with statistics and visualization. If you're serious about statistics, you should definitely add R to your skill set. Furthermore, it is a rarer skill. While over 58% of those learning to program in 2022 were studying Python, fewer than 6% of new learners focused on R. The short answer is that you should learn both in order to maximize your career opportunities and earning potential.

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Is R Difficult to Learn?

R is very popular among statisticians and people who work with data, and it continues to be one of the most highly compensated skills for data scientists across the board. R’s syntax is unique, and some people find that basic operations are less intuitive in R compared to other languages. In fact, a strong community of supportive programmers is one reason that R has maintained its popularity. Unlike other languages such as Python and Java, R is known for having a steep learning curve. But don’t let that put you off learning how to code in R. There are still plenty of reasons why learning R is a great investment. R is known for being hard to learn. This is in large part because R is so different from many programming languages. The syntax of R, unlike languages like Python, is very difficult to read. If you are already familiar with basic data science principles and have some coding experience, you’ll probably encounter a little bit of friction as you start learning R. R is hard to learn, but over time you’ll become more familiar with the rules of the language. This is the case for all programming skills. At first, you’ve got a long journey ahead.

Time Investment and Proficiency

For learners with programming experience, you can become proficient in R within a couple weeks or less. Brand new programmers may take six weeks to a few months to become comfortable with the R language. Three months is generally enough time for any new programmer to use the language and start applying it in their professional life. The time it takes to learn R depends on the time you devote to learning and what you want to do with the language. A beginner-friendly course like Learn R takes about 20 hours to complete. So if you have an hour a day to devote to learning R, then you can complete the course in less than a month. Of course, if you have more time, you can complete the course even quicker. It takes around one month to learn the basics of R, assuming you spend an hour a day devoted to your studies. Like any programming language, it’s hard to estimate how long it will take you to learn it. R is very different from many programming languages. How long it takes to learn R also depends on whether you already understand the statistical analyses you are trying to do. What’s more, there are thousands of packages you can use in R. These packages are written to apply R in various specific contexts.

How to Learn R Effectively

The question you may have now is: How do you learn R fast? To answer this, we’re going to break our question down into two parts.

Defining Your Motivation and Goals

Before you even start learning R programming, you should ask yourself: What is your motivation? It can be useful to have a clear idea in your mind of why you want to learn R. Do you want to analyze some personal data? Or do you want to become a data scientist? Or do you just enjoy working with datasets and want a challenge? If you want to become a data scientist, ask yourself what you want to analyze. Do you want to analyze stock market data, healthcare data, or sports data?

Mastering the Fundamentals

You’ve decided to learn R. Your main focus should be on mastering the basics. This will give you a solid foundation upon which you can build your skills. There’s no escaping learning the syntax for R, the rules that govern the language. It is notoriously complex to learn R’s basic syntax. After learning the basic syntax of R, you can go on to explore variables and data types. Data frames are objects that store data in a table structure with rows and columns. Think about a data frame like a spreadsheet, but one that can be more highly customized. Data cleaning refers to the process of preparing a dataset for analysis. To clean a dataset, you need to remove any data that is not useful for your target use case. Visualization is a technique used to turn the raw data with which you are working into charts. These charts showcase your data visually. R offers a wide range of features to perform statistical analysis on a data set. So, whether you want to calculate the median of a range of data, or perform a standard deviation calculation, R has you covered. Data analysts write a hypothesis and use data to figure out whether their hypothesis does or does not exist. An essential part of working as a data analyst is being able to test hypotheses.

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Hands-on Projects and Practical Experience

Taking online courses and following tutorials will only take you so far. Once you’ve mastered the syntax, you’ll be ready to start working on your own R projects. R projects let you build your skills while working on a project that is meaningful to you. Are you stuck for ideas? If that’s the case, ask yourself: What data do I generate every day? Then, try to think of an R project you could build using that data. Maybe you want to analyze data from your fitness tracker. Or maybe you want to analyze your finances. No matter what you end up working on, make sure it is relevant to one of your own interests. The first project you work on should be simple, to ensure that you don’t take on too much too soon. This is a common mistake made even by seasoned programmers who are learning a new skill. If you can’t find a data set to work on, you should check out Kaggle. Kaggle is a site for data analysts and scientists to work together and share project ideas. On Kaggle, there are thousands of open-source data sets that you can use in your projects free of charge. Once you’ve built a few projects, you can then go on to explore more difficult projects. This is especially important when you’re learning R because the steeper learning curve can make it easy to stop learning past a certain point. Here are a few questions you should ask yourself to encourage you to think about how you can improve. These questions will help you get thinking about how you can improve even once you’ve learned the basics. Because, after all, when you’re learning to code there is no real end. Can your project handle a larger data set? Can you add predictive features to your project? What biases may exist in your analysis?

Engaging with the R Community

Learning how to code is hard. And learning the syntax for R is its own challenge. As a result, you may encounter situations where you feel tempted to give up and do something else. One way to help address these situations is to join a developer community for R. Developer communities bring together large numbers of developers, from beginners to experts, into one room to discuss coding. Joining a developer community is not enough, though. Once you join an R community, you should start contributing as soon as possible. Volunteer to help others if you can, or ask other people useful questions to help keep discussions going.

RStudio Community: The RStudio community is a group of R developers and users of the RStudio package who come together to discuss R programming.
Dev.to: Dev.to is a community of developers helping each other out. There are a number of great data science threads that can be found on Dev.to, even though their R community is new.
StackOverflow: StackOverflow is a questions-and-answers site for programmers. Over the years, thousands of people have posted questions related to R.

Consistent Practice and Skill Development

Practicing is the best way to become an expert R developer. Learning how to code is a skill, and like any skill, practicing is the best way to get ahead. With R, practice is especially important because many of the syntax features you will encounter can be difficult to understand at first. We cannot emphasize the importance of practicing enough. The more you practice, the better you’ll get: it’s that simple. Are you looking for ways to practice your R programming skills? Build a project that interests you. Working on a project requires a big commitment. It is worth it because once you start you’ll not want to give up until you are done. Participate in coding challenges. Sites like Coderbyte and Codewars feature coding challenges specific to R that you can use to build your skills. Analyze datasets for fun.

Free Resources for Learning R

R is free, open-source software, so it only makes sense that it is easy to learn R for free. There are a lot of different options available to you, from self-guided reading to instructor-led classes. Programming is a practical skill, and you need to step through real-world examples to fully learn r programming.

Free online R courses. If you like a highly structured learning environment, an online course is a great way to learn R. In most free versions of online R courses, you aren’t required to do any assignments. However, you should always take the time to go work through any optional assignments because it is a great way to learn.
Free R books. R books are comprehensive resources that cover a lot of topics in R. It’s great to use these as references to help you piece together unrelated courses and tutorials. You can download a free eBook or borrow a physical copy from your local library.
Free R tutorials. If you like to learn by doing, tutorials are the best choice for you. These guided programs take you through practice problems in the R environment.
Free R discussion boards. One of the universal truths of coding is that you will run into error messages that you don’t understand. When this happens to you, search a free R discussion board or post a new question with the error message and your code. Either the answer already exists or someone should be able to point out a mistake that you missed.
Free R YouTube videos. Short YouTube videos usually focus on a very specific R topic, like how to import a CSV data set.

Choosing the Right Learning Resources

So far, we have discussed why you should learn R and what topics you should cover. That’s a great question! There is no right answer in terms of the “best resource” for learning R. The best resource for you will depend on your learning style. Ask yourself: Do you like reading guides, or do you prefer step-by-step tutorials? The best way to learn R is to experiment with a few different learning formats to see what sticks.

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Recommended Courses and Books

HarvardX Data Science Professional Certificate (via edX): Coming to us from Harvard via edX, this r programming course will teach you how to wrangle, analyze, and visualize data in R in just a few hours a week.
IBM Data Science Professional Certificate (via edX): IBM’s edX offering will have you doing basic data science in five weeks. You’ll learn how to use RStudio, practice how to write a program, and complete a final data analysis project.
The R Programming Course (via Coursera): The R programming course is taught by Roger Peng, author of one of the best R books on our list. This program is hosted by Coursera and takes 57 hours to complete.
R Programming A-Z: R For Data Science With Real Exercises! (via Udemy): If you have no experience in programming at all, consider this Udemy course targeted at absolute beginners. This R programming course is a ten-part series that also teaches you about the field of data science. The first part of the series covers 8 hours of recorded lectures.
R for Data Science by Hadley Wickham & Garrett Grolemund: It’s hard to go wrong with a nice overview written by an O’Reilly author. In fact, this is one of the best books to learn R. At over 500 pages, this volume will introduce you to the basics of importing data, tidying it up, analyzing it, and visualizing it.
Advanced R by Hadley Wickham: If you already have some coding language skills, Advanced R will show you the coding syntax needed to translate your experience to R.
Cookbook for R by Winston Chang: Cookbook for R is a repository of solutions for common R problems. This online resource covers topics like formulas, the basics, data input and output, and creating graphs.
R Bloggers: R Bloggers is a community of tutorials about R.
swirl: swirl is a downloadable package that teaches you how to program in R from the R console. It has a large library of tutorials that take 10 to 20 minutes each.
StackOverflow: StackOverflow has a page devoted to questions about R topics. It’s a searchable page, and you will probably find that someone else has already asked the same question you have.
Impatient R: Impatient R is an online tutorial aimed at those who are just starting to learn R.

Career Opportunities with R Programming Skills

Being able to code in R not only can help you unlock career opportunities. Knowing R will also give you a whole new range of skills that you can use to analyze data. Given the high demand for R skills in the data science field, many coding bootcamps have adopted R as part of their curricula. Online R programming courses can introduce you to the language and help you learn new skills and master key concepts.

Job Roles Utilizing R

Computer Programmer: Computer programmers use R, C++, and other programming languages to create new software, add functionality, and test programs for bugs. Some of their responsibilities overlap with software programmers. Successful computer programmer job seekers offer employers advanced analytical, communication, and problem-solving skills.
Data Scientist: Data scientists use their R programming skills to visualize and analyze data, present findings to stakeholders, and develop algorithms and models. They work in different industries, such as computer systems design and insurance. Some senior-level positions require a master's in data science or a doctoral degree.
Network and Computer Systems Administrator: Network and computer systems administrators ensure network security, develop and configure networking software, and troubleshoot problems. Knowing R helps these professionals create visualizations and draw conclusions from network activity.

Overcoming Challenges in Learning R

There’s no way around it, learning R is a challenge. Its syntax can be confusing even to professional programmers. R is constantly changing, so you may find that over time you need to come back to basic concepts and master them again.

Key Skills to Develop

In addition to technological knowledge, online R programming courses can help you develop many transferable soft skills, such as problem-solving, perseverance, and memorization.

Creating a Learning Roadmap

Before taking any R programming courses online, consider why you want to learn R over other programming languages. Research your options and make a list of pros and cons. You can also ask coworkers working in computer programming for their opinions. These are important first steps for many reasons, mainly because you want to make an informed decision. Creating a learning roadmap keeps you accountable as you take introductory, intermediate, and advanced R courses. You can also adjust your roadmap before paying for a costly course, certificate program, or degree.

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