Learn R the Right Way in 5 Steps

Starting to learn a new programming language can feel overwhelming. When I first encountered R, I was a complete newcomer to coding with a full-time job. The learning curve seemed steep, but as I persevered, I discovered how R could solve real-world problems.

My breakthrough came with a project estimating bird deaths at a solar farm. This hands-on experience allowed me to apply statistical methods to an important environmental issue. It not only strengthened my skills but also ignited my passion for data analysis. With guidance from experienced colleagues, I transformed my career. Now, I lead course development at Dataquest, helping others navigate their own R learning paths.

For aspiring data scientists, R is a great tool to master. Its powerful capabilities make it useful for data science tasks like:

• Statistical analysis
• Data manipulation
• Data visualization

Create three-dimensional data visualizations in R with rayshader

Moreover, R's widespread use in both industry and academia can significantly boost career prospects across various fields.*

This article explores effective ways to learn R, focusing on:

• Finding motivation
• Choosing the right resources
• Applying skills to real-world projects

Ready to start learning how to move from R novice to data science professional? Let's get started!

Why learn R?

If you're looking to break into data science or advance your career, learning R could be your next smart move. Let's explore why R is becoming a must-have skill:

The growing demand for R skills

The job market for R programmers is booming. In fact, data scientist, AI/ML engineer, and data analyst roles are among the most in-demand tech jobs for 2024.* Why? Because businesses are increasingly using data to make decisions.

Learning R can give your career a significant boost. For example, a small 5% improvement in forecasting accuracy could save major retailers millions each year. This is why professionals with advanced R skills are in high demand.*

R's versatility across industries

One of R's strengths is its flexibility. It's useful in many different fields:

• Healthcare: Analyzing clinical trials and genomic research
• Finance: Assessing risks and spotting fraud
• Marketing: Understanding customers and testing campaigns
• Technology: Applying machine learning techniques and creating data visualizations

How R can advance your career

Learning R can open doors for you:

• More job opportunities in data-focused roles
• Higher salary potential
• Chance to work on diverse projects across industries
• Better problem-solving and analytical thinking skills
• Opportunity to contribute to cutting-edge research

R's importance in decision making

As companies rely more on data to guide their strategies, R skills have become highly sought-after. The International Data Corporation (IDC) predicts that by 2025, we'll be dealing with 175 zettabytes of data globally.* This means there's a growing need for professionals who can make sense of all this information.

By learning R, you'll equip yourself with the skills to tackle complex data challenges and drive innovation in your field. Whether you're a student looking to launch your career or a professional aiming to upskill, R could be your key to success in the evolving world of data science.

Why most new learners fail

Learning R can be tough, especially for beginners. Let's explore why many struggle and how to overcome these challenges.

R's unique syntax and steep learning curve often surprise new learners. Its complex data structures and error messages can be overwhelming, particularly for those new to programming. The main issue? Many learners rush into advanced topics without building a solid foundation in core R concepts. This often leads to frustration and quitting.

How can beginners set themselves up for success? Here are some key strategies:

• Start with the basics: Focus on mastering core concepts before moving to more advanced topics
• Practice regularly: Work with real datasets to apply what you've learned
• Use structured resources: Find learning materials that offer clear guidance and practical exercises

Becoming proficient in R takes time and effort. Regular practice and applying your skills to real-world problems are key to improvement. With patience and the right approach, you can develop the skills needed to use R effectively for data analysis.

The problem with most learning resources

Learning R can be challenging, especially when choosing the right resources. My experience with popular learning tools revealed some common issues that many beginners face.

When I started learning R, I felt overwhelmed by the number of resources available. After some research, I chose two main learning tools: the "R for Data Science" book by Hadley Wickham and an interactive online platform. I thought these popular resources would speed up my learning. Instead, they led to frustration and wasted time.

The drawback of oversimplified platforms

At first, the interactive platform seemed great. It offered short lessons and immediate feedback, making me feel like I was making progress. But after a few months, I realized I wasn't retaining much. Why? The platform relied heavily on fill-in-the-blank coding exercises, which didn't prepare me for writing code from scratch. When I tried my own projects, I felt lost and unprepared.

The reward and challenge of content-heavy books

Turning to the R for Data Science book, I faced a different issue. While comprehensive, the amount of information was sometimes too much for a beginner like me. Every so often, I got stuck on technical details or an error message unique to my situation, losing sight of how to apply the concepts to real-world problems (e.g. on a different dataset). I solved these issues through trial and error, research, and occasional visits to online forums, which was both educational and rewarding, but this was time consuming!

Don’t get me wrong, I absolutely love this book and highly recommend that you check it out. But try to supplement learning from R for Data Science with another curated learning resource.

Common issues with R tutorials

My experience isn't unique. Most R programming tutorials and courses try to cover everything, teaching a wide range of topics from basic syntax to advanced methods. While thorough, this approach can:

• Overwhelm beginners
• Not emphasize practical applications enough
• Distract from core concepts with too much time spent on setup and error resolution

Moreover, traditional R tutorials often don't adequately prepare learners for data science roles. These roles require important additional skills such as SQL, machine learning operations (MLOps), and data ethics.* This gap between learning and real-world application can leave new learners feeling unprepared for actual data science work.

The need for a practical approach

Looking back, I regret the time I spent on ineffective resources. I realized that what I needed was a more hands-on, practical approach to learning R programming. An ideal learning resource should:

• Tailor content to learners' goals
• Include hands-on projects with real data
• Cover in-demand additional skills

This approach would have better prepared me for the challenges of real-world data analysis and sped up my learning journey.

An easier way to learn R

Let's explore a method that addresses common obstacles and provides a clearer path to proficiency.

The iterative learning process

The key to success lies in adopting an iterative learning strategy. Here's how it works:

1. Study a small portion from a comprehensive resource
2. Apply the concepts immediately to a new project using a dataset that interests you
3. When you encounter difficulties, revisit the material
4. Repeat this process, moving to the next section only after completing your project

Using this iterative, project-based learning method can help you tackle many common challenges faced by new R learners. As you complete projects, you'll see your progress and gain confidence in your abilities. This approach also helps maintain motivation by linking your learning to practical applications, making the process more engaging and fulfilling.

Remember, becoming proficient in R takes time and practice. By focusing on practical application and iterative learning, you'll be better equipped to handle the complexities of R. Start with small projects, build your skills gradually, and before you know it, you'll be tackling complex data analysis tasks with confidence.

Now, let's take a closer look at the step-by-step process that will help you become an R expert:

Step 1: identifying motivation

What drives you to learn R? Knowing your motivation is the first step to help ensure you don't lose steam. Let's explore why R matters and how to find your personal reason for learning it.

R is valuable across many fields:

• Data Science: R provides tools for analyzing data, handling statistics, and creating visuals.
• Finance: Banks use R for complex statistics and managing risk.
• Healthcare: R helps with genetics studies, finding new drugs, and tracking diseases.
• General Data Analysis: R works with many data sources to create useful insights.

To make sure R is the right choice for you, consider the field you're interested in. If you're passionate about data analysis, R could be a great fit. If you're more interested in web development, you might want to explore other languages like JavaScript or Python before getting too invested in R.

Finding your R motivation

If you aren't 100% certain what your motivation is, that's okay! Take some time to explore the possibilities. You might find that R can help you in ways you hadn't considered before. To help, ask yourself these questions:

1. What tasks do I want to do with R?
2. How will R skills help my career or current job?
3. Which R features match my interests or industry needs?

Staying motivated is key to learning R successfully - it's what helps you overcome challenges and reach your goals. When you get frustrated with a project or struggle to understand a new concept, you can always return to your motivation to remind you why you're putting in the hard work.

Step 2: learning the basics quickly

Ready to learn R? My recommendation is not to spend too much time on learning the basic syntax. It's sort of like trying to learn a new language by memorizing vocabulary - sure, you'll know new words, but you'll have no idea how to use them in an actual conversation. Long term, this is a recipe for frustration, and you're much more likely to forget what you've learned if you don't apply it.

With that in mind, let's explore how to build your skills efficiently and effectively.

Essential skills for R beginners

To create a solid foundation in R, focus on these key areas:

• Basic syntax and data structures (vectors, data frames)
• Data manipulation using packages like dplyr
• Data visualization with ggplot2
• Writing functions for reusable code
• Statistical analysis fundamentals

These skills are the building blocks of R. They'll set you up for success in data analysis and beyond.

Resources to start your R learning journey

So where should you learn these R basics? Here are some excellent resources:

• Dataquest's "Data Analyst in R" path - A comprehensive, project-based curriculum
• "R for Data Science" by Hadley Wickham - An in-depth guide to data science with R
• Swirl - An interactive R tutorial that runs in your R console

Remember, the goal is to learn key concepts and move to practical applications quickly. This method will help you build skills efficiently and stay motivated as you learn R.

Step 3: work on guided projects

Once you get the basic R syntax, guided projects are your next step. They'll help you apply what you've learned and build your confidence in using R for real-world problems.

Why are guided projects so valuable? They provide a structured way to practice R, helping you become more productive and confident in your skills. Let's explore some beginner-friendly projects to get you started:

Three projects to kickstart your R journey

• Install RStudio: Take the first steps with R by installing R and RStudio. You'll set up a professional programming environment and explore RStudio's features for efficient R coding and analysis. Through guided exercises, you'll write scripts, import data, and create visualizations.
• Winning Jeopardy: Assume the role of a Jeopardy contestant analyzing a dataset of past questions. Using chi-squared tests and text analysis techniques in R, you'll identify common categories and terms associated with high-value clues. By uncovering insights into question patterns, you'll develop data-driven strategies to improve your odds of winning. This project lets you apply probability, hypothesis testing, and string manipulation skills to a fun, real-world scenario - demonstrating how data science can give you a winning edge even in a game show setting
• Creating An Efficient Data Analysis Workflow: Step into the role of a data analyst hired by a company selling programming books. Your mission is to analyze their sales data to determine which titles are most profitable. You'll apply key R programming concepts like control flow, loops, and functions to develop an efficient data analysis workflow. This project provides valuable practice in data cleaning, transformation, and analysis, culminating in a structured report of your findings and recommendations.

These projects offer more than just practice with R concepts. They give you hands-on experience with real-world scenarios, helping you retain knowledge better than passive tutorials. As you complete each project, you'll grow more confident in your ability to solve data-related problems using R.

Where can you find more project ideas?

Looking for additional inspiration? Here are some great resources to explore:

• Dataquest's R Projects for Beginners: A variety of data science projects with source code.
• R-bloggers Project Ideas: Suggestions for building a data science portfolio.
• KDnuggets Project Guide: A template for structuring data science projects.

Choosing the right project for you

When selecting projects, try to align them with your interests and career goals. This approach can boost your motivation and lead to high-quality work that stands out to potential employers. For example:

• Interested in finance? Consider analyzing stock market data.
• Focused on healthcare? Look for projects involving medical datasets.

By working on practical, real-world R projects, you'll see the immediate value of your new skills. These projects not only reinforce your learning but also help build a portfolio that showcases your abilities to potential employers.

Remember, the goal is to apply your skills to solve actual problems. This bridges the gap between theory and practical application in data science, setting you up for success in your future career.

Step 4: build your own projects

You've learned the basics and completed some guided projects. What's next? It's time to take your R skills to the next level by working on your own projects. This step will help solidify your learning and prepare you for real-world data science challenges.

Transitioning to independent projects

Moving from structured to independent projects might seem daunting at first. Don't worry - it's a natural part of the learning process. The key is to start small and build up gradually. For instance, you might begin by tweaking a guided project you've already completed. This approach will boost your confidence and sharpen your problem-solving skills.

Finding captivating R project ideas

Picking the right project to work on is important. Here are some tips to help you find interesting R projects:

• Expand on previous guided projects by adding new features or analyzing different datasets
• Explore datasets on platforms like Kaggle or data.gov that align with your interests
• Complete a TidyTuesday challenge and engage in the R community.
• Contribute to open-source R packages on GitHub
• Use R to automate a personal task or analyze data related to your hobbies
• Solve a problem in your current job or field using R

The best projects are those that genuinely interest you and solve real problems. For example, if you're a sports fan, you could analyze your favorite team's performance data. This approach will help you stay motivated, achieve your goals, and gain practical experience.

Overcoming obstacles in independent projects

Working on your own projects will inevitably present challenges. But don't let that discourage you. Here are strategies to help you overcome obstacles and stay motivated:

• Break large tasks into smaller, manageable steps
• Set achievable goals and celebrate small wins
• Use project management techniques like Scrum to organize your work

Remember, persistence is key. Each challenge you overcome strengthens your skills and prepares you for more complex projects. For instance, you might struggle with data cleaning at first, but as you practice, you'll become more efficient and confident in handling messy datasets.

Resources for troubleshooting

When you hit a roadblock or need help, these resources can be lifesavers:

• Stack Overflow: A community-driven Q&A site for programming issues
• R Documentation: Official reference for R functions and packages
• R-specific forums and communities: Places to seek guidance from experienced R programmers

Don't hesitate to use these resources. Learning to find solutions independently is a valuable skill in data science. It's like having a toolbox - the more tools you know how to use, the better equipped you'll be to tackle any problem.

Continuous learning and improvement

As you work on independent projects, you'll likely encounter new concepts and techniques. Embrace these opportunities to expand your knowledge. You might start with simple data visualization and gradually move on to more complex analyses.

Consider exploring advanced topics like data management, SQL integration, and machine learning operations (MLOps) to enhance your R toolkit.* These skills can set you apart in the job market and open up new opportunities.

Building your own R projects is about more than just coding - it's about developing a programmer's mindset. Each project you complete is a step towards becoming a proficient R programmer and data professional. So, roll up your sleeves and start coding!

Step 5: Work on more advanced projects

Finally, it's time to tackle more complex projects. These advanced projects will help you solve real-world data science problems and automate sophisticated analyses.

As you move from basic to advanced R projects, you'll gain a deeper understanding of R's capabilities. This progression is important because it helps you develop the skills needed to:

• Handle large datasets
• Implement machine learning algorithms
• Create robust data analysis pipelines

Of course, working on increasingly difficult projects can be challenging. Here are three strategies to help you stay motivated:

• Set clear, achievable goals for each project
• Break down complex projects into smaller tasks
• Connect with online communities for support and inspiration

Remember, it's okay to take small steps. Breaking down your learning journey and celebrating each milestone can help you maintain momentum. Additionally, online communities like Reddit can be great sources of project ideas and support.

Looking for some project ideas? Here are five advanced R projects to challenge and expand your skills:

1. Build a machine learning pipeline for big data analysis using packages like sparklyr or h2o
2. Create an interactive Shiny dashboard for real-time data analysis and visualization
3. Implement a recommendation system using collaborative filtering techniques
4. Develop a natural language processing tool for sentiment analysis on social media data
5. Design a custom R package for advanced statistical analysis in your field of interest

By consistently challenging yourself with advanced R projects, you'll not only improve your programming skills but also build a strong portfolio. This hands-on experience with complex data tasks can set you apart in the competitive field of data science and prepare you for the challenges of professional work.

Conclusion

As you reflect on your learning plan, remember these important points:

• Find your motivation and welcome challenges
• Don't spend too much time on learning syntax
• Put your knowledge to work on projects you care about
• Take small steps and celebrate your progress
• Embrace asking for help and learning from mistakes

These lessons will continue to be valuable as you advance your R skills.

Next steps in your learning journey

To further enhance your R skills and broaden your knowledge, consider exploring Dataquest's interactive courses and career paths. The Data Analyst in R path provides a comprehensive curriculum to advance your skills.

As you continue to develop your R skills, you're creating opportunities in various data-driven fields. Your ability to analyze data, create visualizations, and uncover insights will be valuable across industries, from finance to healthcare and beyond.

Stay curious, keep learning, and take on new challenges. With your growing R skills, you're well-equipped to contribute meaningfully to the world of data science. Your journey with R is just the beginning of an exciting career in shaping the future through data.

FAQs

Is it hard to learn R?

Learning R can certainly be challenging, and you're likely to have frustrating moments. Staying motivated to keep learning is one of the biggest challenges.

However, if you take the step-by-step approach we've outlined here, you should find that it's easy to power through frustrating moments, because you'll be working on projects that genuinely interest you.

Can you learn R for free?

There are lots of free R learning resources out there — here at Dataquest, we have a bunch of free R tutorials and our interactive data science learning platform, which teaches R, is free to sign up for and includes many free lessons.

The internet is full of free R learning resources! The downside to learning for free is that to learn what you want, you'll probably need to patch together a bunch of different free resources. You'll spend extra time researching what you need to learn next, and then finding free resources that teach it. Platforms that cost money may offer better teaching methods (like the interactive, in-browser coding Dataquest offers), and they also save you the time of having to find and build your own curriculum.

Can you learn R from scratch (with no coding experience)?

Yes. At Dataquest, we've had many learners start with no coding experience and go on to get jobs as data analysts, data scientists, and data engineers. R is a great language for programming beginners to learn, and you don't need any prior experience with code to pick it up.

Nowadays, R is easier to learn than ever thanks to the tidyverse collection of packages. The tidyverse is a collection of powerful tools for accessing, cleaning, manipulating, analyzing, and visualizing data with R. This Dataquest tutorial provides a great introduction to the tidyverse.

How long does it take to learn R?

Learning a programming language is a bit like learning a spoken language — you're never really done, because programming languages evolve and there's always more to learn! However, you can get to a point of being able to write simple-but-functional R code pretty quickly.

How long it takes to get to job-ready depends on your goals, the job you're looking for, and how much time you can dedicate to study. But for some context, Dataquest learners reported reaching their learning goals in less than a year — many in less than six months — with less than ten hours of study per week.

Do you need an R certification to find work?

We've written about certificates in depth, but the short answer is: probably not. Different companies and industries have different standards, but in data science, certificates don't carry much weight. Employers care about the skills you have — being able to show them a GitHub full of great R code is much more important than being able to show them a certificate.

Is R still a good language to learn?

Yes. R is a popular and flexible language that's used professionally in a wide variety of contexts. We teach R for data analysis and machine learning, for example, but if you wanted to apply your R skills in another area, R is used in finance, academia, and business, just to name a few.

Moreover, R data skills can be really useful even if you have no aspiration to become a full-time data scientist or programmer. Having some data analysis skills with R can be useful for a wide variety of jobs — if you work with spreadsheets, chances are there are things you could be doing faster and better with a little R knowledge.

How much money do R programmers make?

This is difficult to answer, because most people with R skills work in research or data science, and they have other technical skills like SQL, too. Ziprecruiter lists the average R developer salary as $130,000 in the US (as of April 2021).

The average salary for a data scientist is pretty similar — $121,000 according to Indeed.com as of April 2021.

Should I learn base R or tidyverse first?

This is a popular debate topic in the R community. Here at Dataquest, we teach a mix of base R and tidyverse methods in our Introduction to Data Analysis in R course. We are big fans of the tidyverse because it is powerful, intuitive, and fun to use.

But to have a complete understanding of tidyverse tools, you'll need to understand some base R syntax and have an understanding of data types in R. For these reasons, we find it most effective to teach a mix of base R and tidyverse methods in our introductory R courses.

I needed a resource for beginners; something to walk me through the basics with clear, detailed instructions. That is exactly what I got in Dataquest’s Introduction to R course.