How to Learn R: Your Step-by-Step Guide to Data Science Mastery

Starting your journey to learn a new programming language, especially R, might seem like climbing a steep mountain. When I first encountered R, much like many beginners, coding was a completely new world to me, and I was juggling a full-time job alongside. The initial learning curve felt daunting, but as I persisted, I discovered the immense power of R in solving real-world problems.

My turning point came with a project focused on estimating bird fatalities at a solar farm. This hands-on experience was transformative. It allowed me to apply statistical methods to address a significant environmental issue. This project not only sharpened my analytical skills but also ignited a deep passion for data analysis. With the invaluable guidance from experienced colleagues, I successfully transitioned my career path. Today, I lead course development at Dataquest, where I help others navigate their own paths in learning R and data science.

For anyone aspiring to become a data scientist, mastering R is an invaluable asset. Its robust capabilities make it indispensable for various data science tasks, including:

Statistical analysis
Data manipulation
Data visualization

3D map visualization created with R rayshader library demonstrating how to learn R for data visualization.

Explore three-dimensional data visualizations in R with rayshader

Furthermore, R’s widespread adoption across both industry and academia significantly enhances career opportunities in diverse sectors.*

This guide delves into effective strategies on How To Learn R, emphasizing key aspects such as:

Identifying your motivation
Selecting the best learning resources
Applying your skills through real-world projects

Ready to transform from an R novice to a proficient data science professional? Let’s embark on this journey together!

Why Learn R for Data Science?

If you’re aiming to enter the field of data science or advance your current career, learning R could be the most strategic move you make. Let’s explore the compelling reasons why R is becoming an essential skill in today’s data-driven world.

The Increasing Demand for R Skills

The job market for professionals skilled in R programming is experiencing rapid growth. Data scientist, AI/ML engineer, and data analyst roles are consistently listed among the most sought-after tech positions in 2024.* This surge in demand is fueled by businesses increasingly relying on data-driven insights for strategic decision-making.

Learning R can significantly enhance your career prospects. For instance, a small survey highlighted that even a basic understanding of R could lead to a substantial salary increase for data professionals.

R’s Versatility Across Diverse Industries

One of R’s key strengths is its broad applicability across various industries. Its flexibility makes it a powerful tool in numerous fields:

Healthcare: Analyzing clinical trial data, genomic research, and epidemiological studies.
Finance: Assessing financial risks, detecting fraudulent activities, and developing algorithmic trading strategies.
Marketing: Understanding consumer behavior, optimizing marketing campaigns, and personalizing customer experiences.
Technology: Implementing machine learning algorithms, creating interactive data visualizations, and building data pipelines.

Career Advancement Opportunities with R

Mastering R opens up a plethora of opportunities for career advancement:

Expanded Job Opportunities: Access to a wider range of data-centric roles across different industries.
Higher Earning Potential: R skills are associated with higher salary brackets due to the high demand and specialized expertise.
Engaging Project Diversity: The chance to work on diverse and impactful projects that span multiple sectors.
Enhanced Analytical and Problem-Solving Skills: R empowers you to tackle complex data challenges, sharpening your analytical and critical thinking abilities.
Contribution to Cutting-Edge Research: Opportunity to contribute to innovative research and development in various scientific and technological domains.

R’s Pivotal Role in Data-Driven Decision Making

As organizations become increasingly reliant on data to inform their strategies, R skills have become highly valued. The International Data Corporation (IDC) forecasts that the global datasphere will reach 175 zettabytes by 2025.* This massive volume of data necessitates professionals who can effectively analyze and interpret complex datasets.

By learning R, you equip yourself with the necessary skills to navigate complex data landscapes and drive innovation in your chosen field. Whether you are a student starting your career or a seasoned professional looking to upskill, R can be your key to success in the evolving data science landscape.

Why Do Many New Learners Struggle with R?

Learning R, while rewarding, can be challenging, especially for those new to programming. Let’s explore common pitfalls that beginners face and strategies to overcome them.

R’s unique syntax and initially steep learning curve often surprise newcomers. Its complex data structures and sometimes cryptic error messages can feel overwhelming, particularly for individuals without prior programming experience. A primary issue is that many learners rush into advanced topics without establishing a strong foundation in the fundamental concepts of R. This approach often leads to frustration and discouragement, causing many to abandon their learning journey.

To set yourself up for success in learning R, consider these essential strategies:

Start with the Fundamentals: Focus on mastering the basic concepts of R before moving on to more advanced techniques.
Consistent Practice: Regularly engage with R by working on real datasets to apply what you learn practically.
Utilize Structured Learning Resources: Choose learning materials that provide clear, step-by-step guidance and incorporate practical exercises to reinforce learning.

Achieving proficiency in R requires dedication and time. Consistent practice and applying your skills to real-world problems are crucial for continuous improvement. With patience and the right learning approach, you can develop the skills needed to effectively use R for data analysis and beyond.

The Limitations of Common R Learning Resources

Choosing the right learning resources is crucial when starting to learn R. My own experience with popular learning tools revealed some common shortcomings that many beginners encounter.

When I began learning R, I was overwhelmed by the sheer volume of available resources. After some initial research, I opted for two widely recommended tools: the book “R for Data Science” by Hadley Wickham and an interactive online learning platform. I believed these popular resources would accelerate my learning process. However, they instead led to frustration and inefficient use of my time.

The Drawbacks of Oversimplified Learning Platforms

Initially, the interactive platform seemed ideal. It offered bite-sized lessons and instant feedback, which gave a sense of immediate progress. Yet, after several months, I realized I wasn’t retaining much of what I learned. The platform’s heavy reliance on fill-in-the-blank coding exercises didn’t adequately prepare me for writing code independently. When I attempted to apply my skills to personal projects, I felt unprepared and lost.

The Double-Edged Sword of Comprehensive Books

Turning to the “R for Data Science” book, I encountered a different set of challenges. While incredibly comprehensive, the sheer volume of information was often overwhelming for a beginner like myself. I frequently found myself bogged down in technical details or struggling with error messages specific to my situation, losing sight of how to apply the concepts to real-world problems with different datasets. Solving these issues often required extensive trial and error, research, and consulting online forums, which, while educational, was also very time-consuming.

It’s important to note that “R for Data Science” is an excellent book and highly recommended as a resource. However, it’s most effective when supplemented with other curated learning resources that can help bridge the gap between theory and practical application.

Common Issues with Standard R Tutorials

My experience is not unique. Many R programming tutorials and courses attempt to cover an extensive range of topics, from basic syntax to advanced methodologies. While thoroughness is intended, this approach can often:

Overwhelm Beginners: Too much information at once can be daunting and lead to cognitive overload.
Lack Practical Application Focus: Many tutorials do not sufficiently emphasize hands-on application, making it difficult for learners to translate theory into practice.
Distract from Core Concepts: Excessive time spent on setup, troubleshooting errors, and less essential topics can detract from focusing on core programming concepts.

Furthermore, traditional R tutorials often fall short in preparing learners for the full scope of data science roles. These roles increasingly require skills beyond just R programming, such as SQL, machine learning operations (MLOps), and a solid understanding of data ethics.* This gap between what is typically taught and what is actually needed in the professional field can leave new learners feeling unprepared for real-world data science work.

The Necessity of a Practical, Hands-On Approach

Reflecting on my initial learning struggles, I realized the critical need for a more practical and hands-on approach to learning R programming. An effective learning resource should ideally:

Tailor Content to Learner Goals: Focus on relevant skills and knowledge that align with specific career paths in data science.
Incorporate Hands-On Projects with Real Data: Provide opportunities to apply learned concepts through practical projects using realistic datasets.
Integrate In-Demand Additional Skills: Include training in complementary skills such as SQL, MLOps, and data ethics that are essential for data science professionals.

Adopting such an approach would have significantly better prepared me for the challenges of real-world data analysis and substantially accelerated my learning curve.

An Easier and More Effective Way to Learn R

Let’s explore a learning method that addresses the common pitfalls and offers a more streamlined path to R proficiency.

The Iterative Learning Process for R

The key to effectively learning R lies in adopting an iterative learning strategy. This process involves:

Focused Study: Begin by studying a small, manageable section from a comprehensive R learning resource.
Immediate Application: Directly apply the newly learned concepts to a practical project using a dataset that genuinely interests you.
Targeted Review: When you encounter challenges or difficulties, revisit the learning material to reinforce your understanding and troubleshoot effectively.
Iterative Progression: Repeat this cycle, advancing to the next learning section only after successfully completing your project and solidifying your understanding.

Utilizing this iterative, project-based learning method can effectively address many common challenges faced by new R learners. As you complete projects, you gain tangible evidence of your progress, significantly boosting your confidence in your R abilities. This approach also enhances motivation by directly linking your learning to practical applications, making the entire process more engaging and ultimately more rewarding.

Remember, becoming proficient in R is a journey that requires time and consistent practice. By emphasizing practical application and adopting an iterative learning approach, you will be better equipped to handle the complexities of R. Start with smaller projects, gradually build your skill set, and you’ll find yourself confidently tackling complex data analysis tasks in no time.

Now, let’s delve into a step-by-step process to guide you on your journey to becoming an R expert.

Step 1: Identifying Your Motivation for Learning R

What is driving you to learn R? Understanding your motivation is the crucial first step that will help ensure you stay committed and engaged throughout your learning journey. Let’s explore why R is valuable and how to pinpoint your personal reasons for learning it.

R is a powerful tool across numerous fields:

Data Science: R provides a comprehensive suite of tools for data analysis, statistical modeling, and creating insightful data visualizations.
Finance: Financial institutions utilize R for complex statistical analysis, risk management, and algorithmic trading.
Healthcare: R plays a vital role in genetics research, drug discovery, and disease tracking and prevention.
General Data Analysis: R is highly effective for working with diverse data sources to generate meaningful insights and reports across various sectors.

To ensure R aligns with your career aspirations, consider the specific field you are interested in. If you are passionate about data analysis and its applications, R is an excellent choice. However, if your primary interest lies in web development, you might consider exploring languages like JavaScript or Python before heavily investing in R.

Pinpointing Your Personal Motivation to Learn R

If you are not entirely certain whether R is the right path for you, take some time to ask yourself these key questions:

What specific tasks do I want to accomplish with R? (e.g., data visualization, statistical modeling, machine learning)
How will R skills benefit my career or current job? (e.g., improve data analysis efficiency, open new job opportunities, enhance problem-solving capabilities)
Which features of R align with my interests or industry needs? (e.g., extensive package ecosystem, strong community support, specific functionalities for your field)

Maintaining motivation is paramount to successfully learning R. It is what will propel you through challenges and help you achieve your learning goals. Whenever you encounter frustration with a project or struggle to grasp a new concept, revisiting your initial motivations can remind you why you started this journey and help you persevere through the difficulties.

Step 2: Learning the R Basics Efficiently

Ready to dive into learning R? My primary recommendation is to avoid spending excessive time on learning basic syntax in isolation. It’s akin to attempting to learn a new spoken language by merely memorizing vocabulary lists – you might accumulate words, but you’ll lack the ability to construct meaningful sentences or engage in actual conversations. In the long run, this approach is likely to lead to frustration, and you are more prone to forget what you’ve learned if you do not apply it practically.

With this in mind, let’s explore how to efficiently and effectively build your R skills.

Essential Skills for R Beginners

To establish a solid foundation in R, concentrate on mastering these key areas:

Basic Syntax and Data Structures: Understand fundamental syntax and data structures like vectors and data frames, which are the building blocks of R.
Data Manipulation with dplyr: Learn to use packages like dplyr for efficient data manipulation and wrangling, essential for data cleaning and preparation.
Data Visualization with ggplot2: Master ggplot2 for creating compelling and informative data visualizations, crucial for communicating insights effectively.
Writing Functions for Reusable Code: Develop the ability to write functions to automate tasks and create reusable code, enhancing efficiency and code organization.
Statistical Analysis Fundamentals: Gain a basic understanding of statistical analysis principles to effectively apply R in statistical contexts.

These skills are the foundational building blocks of R. They will set you on the path to success in data analysis and more advanced R applications.

Recommended Resources to Begin Your R Learning Journey

So, where should you start learning these essential R basics? Here are some excellent resources tailored for beginners:

Dataquest’s Introduction to R Course: Offers an interactive, hands-on learning experience that is perfect for beginners.
R for Data Science (Chapters on Basics): Focus on the initial chapters of this book to grasp fundamental R concepts.
Online Interactive Platforms (like Codecademy, Coursera, edX): Look for introductory R courses that offer interactive exercises and immediate feedback.

Remember, the goal is to quickly learn the key concepts and then transition to practical applications. This method will help you build skills efficiently and maintain motivation as you progress in learning R.

Step 3: Working on Guided R Projects

Once you have grasped the basic R syntax, guided projects are your next crucial step. They will help you apply what you’ve learned in a structured environment and build your confidence in using R to solve real-world problems.

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

Three Beginner-Friendly R Project Ideas

Data Analysis of Sales Data: Analyze a sales dataset to identify trends, top-selling products, and customer behavior patterns.
Exploratory Data Analysis of Housing Prices: Investigate a housing dataset to explore factors influencing housing prices and visualize key relationships.
Social Media Sentiment Analysis: Perform sentiment analysis on social media data to understand public opinion on a particular topic or brand.

These projects offer more than just practice with R concepts. They provide hands-on experience with realistic scenarios, significantly enhancing knowledge retention compared to passive tutorials. As you complete each project, you will grow more confident in your ability to solve data-related problems using R.

Resources for Finding More R Project Ideas

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

Dataquest Project Portfolio: Offers a variety of guided projects suitable for different skill levels.
Kaggle Datasets and Competitions: Explore Kaggle for diverse datasets and project ideas from the data science community.
TidyTuesday Projects: Participate in the weekly TidyTuesday challenge for data visualization and analysis projects using tidyverse tools.

Choosing the Right R Project for You

When selecting projects, aim to align them with your personal interests and career goals. This approach can significantly boost your motivation and lead to higher quality work that can impress potential employers. For example:

If you are interested in finance: Consider projects analyzing stock market data or financial trends.
If you are focused on healthcare: Look for projects involving medical datasets or public health statistics.

By working on practical, real-world R projects, you will directly experience the value of your newly acquired skills. These projects not only reinforce your learning but also help build a portfolio that effectively showcases your abilities to potential employers.

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

Step 4: Building Your Own Independent R Projects

You’ve mastered the basics and completed guided projects. What’s next? It’s time to elevate your R skills by working on your own independent projects. This crucial step will solidify your learning and better prepare you for the unpredictable challenges of real-world data science tasks.

Transitioning to Independent R Projects

Moving from structured, guided projects to independent projects might initially feel daunting. However, it’s a natural and essential part of the learning process. The key is to start with smaller, manageable projects and gradually increase complexity as you gain confidence. For example, you could begin by modifying a guided project you’ve already completed, adding new features or analyzing a different dataset. This approach will boost your confidence and sharpen your problem-solving skills.

Finding Engaging R Project Ideas

Selecting the right project is crucial for maintaining interest and motivation. Here are some tips to help you discover captivating R projects:

Expand on Guided Projects: Take a guided project you enjoyed and extend it by adding new functionalities or exploring different datasets.
Explore Datasets on Platforms like Kaggle or data.gov: These platforms offer a vast array of datasets that align with various interests, from environmental data to economic indicators.
Participate in TidyTuesday Challenges: Engage with the R community through TidyTuesday, which offers weekly datasets for practicing data visualization and analysis.
Contribute to Open-Source R Packages on GitHub: Contributing to open-source projects is an excellent way to learn advanced R techniques and collaborate with experienced developers.
Automate Personal Tasks with R: Identify repetitive tasks in your daily life or work that can be automated using R scripts.
Solve Problems in Your Current Job or Field: Apply R to analyze data or solve problems relevant to your current profession, demonstrating practical application of your skills.

The most effective projects are those that genuinely interest you and address real-world problems. For example, if you are passionate about sports, you could analyze performance data for your favorite team. This personal connection will keep you motivated, help you achieve your goals, and gain practical experience that is directly relevant to your interests.

Overcoming Obstacles in Independent R Projects

Working on your own projects will inevitably present challenges. It’s part of the learning curve, and overcoming these obstacles is how you truly grow. Here are strategies to help you navigate challenges and stay motivated:

Break Down Large Tasks: Decompose complex projects into smaller, more manageable steps. This makes the project less overwhelming and easier to tackle incrementally.
Set Achievable Goals: Define clear, achievable goals for each project phase. Celebrating small wins along the way helps maintain momentum and motivation.
Utilize Project Management Techniques: Employ project management methodologies like Scrum or Kanban to organize your work, track progress, and manage timelines effectively.

Remember, persistence is key. Each challenge you overcome not only strengthens your R skills but also prepares you for more complex projects in the future. For instance, you might initially struggle with data cleaning, but with practice, you will become more efficient and confident in handling messy datasets.

Essential Resources for Troubleshooting R Projects

When you encounter roadblocks or need assistance, these resources can be invaluable:

Stack Overflow: A community-driven question and answer website for programming issues. It’s an excellent place to find solutions to specific coding problems.
R Documentation (CRAN): The official documentation for R functions and packages. It provides comprehensive reference materials and guides.
R-Specific Forums and Communities: Online forums and communities dedicated to R programming, where you can seek advice from experienced R programmers and peers.

Don’t hesitate to utilize these resources. Learning to find solutions independently is a critical skill in data science. Think of these resources as your toolkit – the more tools and knowledge you acquire, the better equipped you will be to tackle any problem that arises.

Continuous Learning and Skill Enhancement in R

As you work on independent projects, you will undoubtedly encounter new concepts and techniques that you need to learn. Embrace these moments as opportunities to expand your knowledge and refine your skills. You might start with basic data visualization techniques and gradually progress to more sophisticated analyses.

Consider exploring advanced topics such as data management, SQL integration for database querying within R, and machine learning operations (MLOps) to further enhance your R toolkit.* These advanced skills can significantly set you apart in the competitive job market and open doors to new and exciting career opportunities.

Building your own R projects is about more than just writing code—it’s about developing a programmer’s mindset. Each project you successfully complete is a significant step towards becoming a proficient R programmer and a capable data professional. So, roll up your sleeves, start coding, and watch your skills grow!

Step 5: Tackling Advanced R Projects

Finally, it’s time to challenge yourself with more complex, advanced projects. These projects are designed to help you solve real-world data science problems and automate sophisticated analytical processes.

As you progress from basic to advanced R projects, you will gain a deeper, more nuanced understanding of R’s extensive capabilities. This progression is crucial because it helps you develop the advanced skills needed to:

Manage and Analyze Large Datasets: Learn techniques to efficiently handle and process large volumes of data, a common requirement in many data science roles.
Implement Machine Learning Algorithms: Apply machine learning algorithms in R to build predictive models and gain insights from complex datasets.
Develop Robust Data Analysis Pipelines: Create end-to-end data analysis pipelines that automate data processing, analysis, and reporting, enhancing efficiency and reliability.

Working on increasingly difficult projects can be challenging, but it is also incredibly rewarding. Here are three strategies to help you stay motivated and engaged when tackling advanced projects:

Set Clear Project Goals: Define specific, measurable, achievable, relevant, and time-bound (SMART) goals for each project to maintain focus and direction.
Break Down Complexity: Decompose complex projects into smaller, more manageable tasks. This approach makes the project less intimidating and allows for incremental progress.
Engage with Online Communities: Connect with online communities and forums for support, inspiration, and collaborative problem-solving. Communities like Reddit can be excellent resources for project ideas and peer support.

Remember, it’s perfectly acceptable to take small steps and celebrate each milestone. Breaking down your learning journey into smaller, achievable segments and acknowledging your progress can significantly help you maintain momentum and motivation. Online communities can also provide valuable insights and encouragement when you face tough challenges.

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

Build a Machine Learning Pipeline for Big Data Analysis: Utilize packages like sparklyr or h2o to develop a machine learning pipeline capable of handling and analyzing large datasets efficiently.
Create an Interactive Shiny Dashboard for Real-Time Data Analysis and Visualization: Develop a Shiny dashboard that provides real-time data analysis and interactive visualizations for dynamic datasets.
Implement a Recommendation System Using Collaborative Filtering: Design and implement a recommendation system using collaborative filtering techniques to predict user preferences or recommend products.
Develop a Natural Language Processing (NLP) Tool for Sentiment Analysis: Create an NLP tool in R for performing sentiment analysis on social media data or textual datasets to understand public opinion.
Design a Custom R Package for Advanced Statistical Analysis: Develop your own R package tailored for advanced statistical analysis in your specific field of interest, contributing to the R ecosystem and enhancing your expertise.

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

Conclusion

As you reflect on your learning plan to learn R, remember these key takeaways:

Find Your Motivation and Embrace Challenges: Let your passion drive you and see challenges as opportunities for growth.
Don’t Overdwell on Syntax: Focus on practical application rather than getting bogged down in syntax memorization.
Apply Knowledge to Meaningful Projects: Work on projects that genuinely interest you to stay engaged and motivated.
Take Small Steps and Celebrate Progress: Break down your learning into manageable steps and acknowledge your achievements along the way.
Seek Help and Learn from Mistakes: Don’t hesitate to ask for help when needed and view mistakes as valuable learning experiences.

These principles will continue to guide you as you advance your R skills and pursue your data science aspirations.

Next Steps in Your R Learning Journey

To further enhance your R skills and broaden your knowledge, consider exploring interactive courses and career paths specifically designed for data science. Dataquest’s Data Analyst in R Path offers a comprehensive curriculum to help you advance your skills systematically.

As you continue to develop your R proficiency, you are unlocking opportunities in a wide array of data-driven fields. Your ability to analyze data, create compelling visualizations, and uncover actionable insights will be invaluable across industries, from finance and technology to healthcare and beyond.

Stay curious, keep learning, and continuously take on new challenges. Your journey with R is just the beginning of an exciting and impactful career in shaping the future through data science.

FAQs About Learning R

Is it hard to learn R programming?

Learning R can be challenging, and moments of frustration are normal. Maintaining motivation is one of the biggest hurdles. However, by following a step-by-step approach focused on practical projects that genuinely interest you, you can effectively navigate these challenges and make the learning process more manageable and rewarding.

Can you learn R for free?

Yes, there are numerous free resources available to learn R. Dataquest, for instance, offers a variety of free R tutorials and a free sign-up option for their interactive data science learning platform, which includes many free R lessons. The internet is rich with free R learning materials. However, learning for free often requires piecing together various resources, which can be time-consuming and less structured compared to paid platforms. Paid platforms may offer better teaching methodologies and save you time by providing a curated curriculum.

Can you learn R from scratch with no prior coding experience?

Absolutely. Many learners at Dataquest start with no coding experience and successfully transition into roles as data analysts, data scientists, and data engineers. R is a beginner-friendly language, especially with the advent of the tidyverse collection of packages, which simplifies data manipulation and analysis. This Dataquest tutorial offers a great introduction to using the tidyverse for data handling.

How long does it take to learn R effectively?

Learning a programming language is an ongoing process, much like learning a spoken language. You are continuously learning as the language evolves and new techniques emerge. However, you can achieve the ability to write functional R code relatively quickly. The time it takes to become job-ready depends on your career goals, the specific job requirements, and the amount of time you dedicate to studying. Dataquest learners have reported achieving their learning goals in under a year, many in less than six months, with less than ten hours of study per week.

Do you need an R certification to get a job?

Generally, no. While certifications exist, they typically hold less weight in data science than demonstrable skills and experience. Employers in data science prioritize your ability to showcase your R skills through a portfolio of projects, such as a GitHub repository filled with well-written R code, over formal certifications.

Is R still a valuable language to learn in the current tech landscape?

Yes, R remains highly valuable. It is a popular and versatile language widely used in professional contexts across various sectors, including data analysis, machine learning, finance, academia, and business. Even if you don’t aspire to be a full-time data scientist, R skills are incredibly beneficial for many roles that involve data handling and analysis, offering significant advantages over traditional spreadsheet software for complex tasks.

How much can R programmers earn?

Salaries for professionals proficient in R vary widely based on their role, industry, and experience level. Many R professionals work in research or data science roles and possess complementary skills like SQL. Ziprecruiter reported the average salary for an R developer in the US to be around $130,000 as of April 2021. Similarly, Indeed.com listed the average salary for data scientists at approximately $121,000 in April 2021, indicating a strong earning potential for R-related roles.

Should I learn base R or tidyverse first as a beginner?

This is a common debate in the R community. At Dataquest, we advocate for learning a mix of both base R and tidyverse methods right from the start in our Introduction to Data Analysis in R course. We are strong proponents of the tidyverse due to its power, intuitiveness, and user-friendly approach. However, a foundational understanding of base R syntax and data types is crucial for fully grasping and utilizing tidyverse tools effectively. Therefore, we find that teaching a balanced mix of base R and tidyverse methods is the most effective approach for introductory R courses.

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