Does being able to analyze large data sets interest you? If that’s the case, then learning R is a useful skill to have in your toolbelt.
R is a popular programming language used for a wide range of purposes in data science and analysis. The R programming language provides a range of statistical and graphical tools that make it easy to analyze datasets and to do so at scale.
The R programming language can be used for everything—from analyzing small data sets about stock prices to performing large-scale analyses of data collected on social networks. This flexibility makes it a highly-favored skill among data scientists.
But how do you get started learning how to code in R? That’s the question this guide is going to answer. We’ll break down a few top tips to help you start your journey to learning R free, and on a good footing.
What Is R?
R is a programming language and environment for mathematical and statistical computing. The language was developed at Bell Laboratories by John Chambers and a team of colleagues and is often considered to be a newer implementation of another data science language, S.
The R programming language allows you to perform statistical analysis on data sets, such as through using linear modeling, time-series analysis, classification, and more. Furthermore, the language was written with design in mind, which means that the features you need to analyze a data set should be easy to access and use.
Why Should You Learn R?
Unlike other languages such as Python and Java, R is renowned 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.
Data is everywhere. When you order a coffee at Starbucks, data will be stored on your order; when you make a purchase on Amazon, data will be stored on what you have purchased. In fact, there is no way to escape data in our modern world, as it powers an increasingly large portion of the economy.
As a result, having skills in data science can take you a long way in both your career and personal life. Learning R gives you the skills you need to analyze data sets in great depth, even if you only want to analyze the healthcare data stored on your FitBit.
But, if you want to go further and use your skills to advance your career, there are plenty of companies that actively recruit data scientists, especially those who are skilled in R.
How to Learn R Fast
The R programming language may not be as exciting as Python or Java, but learning R is still a worthy investment of your time if you are interested in data.
The question you may have now is: How do you learn R fast? To answer this, we’re going to break it down into two parts—the core skills you need to master R and the resources you can use to build those skills.
Establish Your Motivation
Before you even start learning R, you should ask yourself: What is your motivation? Because R can be more difficult to learn than other languages, 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? Whatever your motivation, try to make it clear and specific. If you want to become a data scientist, ask yourself what you want to analyze. Do you want to analyze stock market data, or healthcare data, or sports data?
Once you’ve established your motivation—and made it clear in your mind—you will be ready to continue on your journey to learning how to code in R.
Learning the Basics
So, you’ve decided to learn R. Now your main focus should be on mastering the basics, which will give you a solid foundation upon which you can build your skills. To help you get started, we’ll break down a list of the top skills you should learn.
Syntax and Variables
There’s no escaping learning the syntax for R—the rules that govern the language—even though it is notoriously complex to learn. The good news, however, is that once you’ve learned the syntax for R, you will not have to learn it again!
After learning the basic syntax of R, you can go on to explore variables and data types, which are two concepts you need to know about early in your journey. Here are the main syntactical topics you should cover:
- How to perform mathematical calculations
- Data types
- Variables and vectors
- Comparison and logical operators
- Importing functions
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.
The main topics you should learn about related to data frames are:
- What is a data frame?
- How to work with CSVs in R
- How to save data to a CSV
- How to inspect a data frame
- How to select and exclude columns and rows
- How to arrange rows
Data Cleaning and Visualization
Data cleaning refers to the process of preparing a dataset for analysis and removing 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—numbers and letters—into charts that showcase your data visually. Here are the main topics you should cover in these two fields:
- How to tidy/clean data
- Reshaping data and removing duplicate values
- Splitting a dataset by index and character
- Parsing strings
- How to use ggplot2 to create visualizations
- Aesthetics for data visualization
- Adding labels to a data visualization
Statistics with R
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. Here are a few of the main statistical concepts you should learn about:
- Mean, median, mode, and range in R
- Variance and standard deviations
- Quartiles and interquartile ranges
Data analysts develop hypotheses—proposed explanations for a trend—then 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. Here are the main topics you should cover to master testing hypotheses:
- Formulating a hypothesis
- Designing an experiment to prove a hypothesis
- Significance levels using p-values
- Assumptions associated with hypothesis testing
There is a lot more you can do with R, but the topics we have discussed above cover the main concepts with which you will need to be familiar before you start building more complex projects using the R programming language.
Where to Learn R
So far, we have discussed why you should learn R and what topics you should cover. But where can you go to actually learn these topics?
That’s a great question! There is no right answer in terms of the “best resource” for learning R because 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? Do you like online courses, or prefer interactive walkthroughs?
You may want to experiment with a few different learning formats to see what sticks, then focus on one or two sources when you have found something you like.
To help you out, here are a few resources you can use to learn R:
Online courses that teach R are all over the internet. The advantage of learning from an online course is that you’ll be able to hear an instructor talk you through concepts, which is especially beneficial given the complexities of working with R.
Here are a few good online courses for learning R:
- Introduction to R by DataCamp
- Statistical Computing with R Programming by UCL
- Learn R Programming by Coursera and Johns Hopkins University
- Harvard Data Science: R Basics
Many coders prefer to learn from books, which provide comprehensive guides to programming languages and other technologies. If you enjoy working your way through a book, then good news—there are plenty of resources out there from which you can learn R.
Here are a few top books for new R developers:
Tutorials are another popular method of learning R. Working your way through a tutorial allows you to take a step-by-step approach to code, and if you encounter an issue, you can always go back to an earlier point in the tutorial to fill in the gaps in your knowledge.
Here are a few good online tutorials for R:
Attend a Coding Bootcamp
Given the high demand for R skills in the data science field, many coding bootcamps have adopted R as part of their curricula. If you are looking for a more immersive, project-focused experience, then looking into coding bootcamps for data science may be worth your time.
To help you get started, check out the Career Karma bootcamp directory, which features a list of hundreds of coding bootcamps, including many that specialize in R and data science.
Work on Projects
Taking online courses and following tutorials will only take you so far. Once you’ve mastered the syntax—which is a great feat in itself—you’ll be ready to start working on your own projects.
Projects are a great way to build your skills because not only can you apply what you’ve learned in the classroom, but you can do so while working on a project that is more meaningful to you. That’s right. When you work on a self-driven project, you get to choose what you work on and how it turns out.
Are you stuck for ideas? If that’s the case, ask yourself: What data do I generate every day? Then, try to think of a 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. Or maybe you are interested in weather trends from your local area in spring.
No matter what you end up working on, make sure it is relevant to one of your own interests. This will ensure that, even if you encounter a challenge, you’ll have no problem finding the motivation you need to keep going.
Here are a few ideas to help you out:
- Analyze the performance of the stock market
- Sentiment analysis of Amazon reviews
- Movie recommendation engine
- Analyze donations data for the U.S. 2016 Presidential election
- Analyze voting turnout data for the U.K. 2017 General Election
The first project you work on should be simple, to ensure that you don’t take on too much too soon (which is a common mistake made even by seasoned programmers who are learning a new skill). Then, when you feel more confident, you can start adding new complexities to your projects.
In addition, if you can’t find a data set with which to work, you should check out Kaggle, 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, which almost eliminates the problem of “I can’t find the data I want!” when you’re working on a new project.
Explore More Difficult Topics
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. You need to keep going and challenge yourself to reach new heights.
Need help figuring out how to refine your skills? Here are a few questions you should ask yourself to encourage you to think about how you can improve:
- Can your project handle a larger data set?
- Can you add predictive features to your project?
- What biases may exist in your analysis? How can you combat those biases?
- How can you improve the performance of your project?
- Could you generate more visualizations from your data?
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”— there’s always something new you can learn.
Join a Developer 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. Developer communities are exactly what you think they are—communities of programmers who come together to share ideas and collaborate on projects with each other.
The main advantage of joining a developer community is that they bring together large numbers of developers, from beginners to experts, into one room to discuss a shared interest—programming. As a result, these communities are often great places to meet new coders and find solutions to the problems everyone is facing.
Here are a few great communities for aspiring R developers:
- 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. While their R community is not as well developed as others, there are a number of great data science threads that can be found on the platform.
- StackOverflow: StackOverflow is a questions-and-answers site for programmers. Over the years, thousands of people have posted questions related to R, so if you need help with something, you should be able to find an answer—or someone who can assist you—on StackOverflow.
- R-Help Mailing List: Mailing lists may be an old idea, but the R-Help mailing list is a great place for R developers to stay up to date with the latest goings-on in the R community.
Joining a developer community is not enough, though. Once you join a 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.
Practice Your R Skills
Practicing is the best way to become an expert R developer. As the old saying goes, “practice makes perfect.”
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. But, as you get more practice working with R, you’ll adapt to the quirks of the language.
Are you looking for ways to practice your R skills? Here are a few suggestions for how you can do so:
- Build a project that interests you. Working on a project requires a big commitment, but 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. If you have a spare few hours, don’t hesitate to go onto Kaggle, read over a challenge, and give it a go.
Practice. Practice. Practice. We cannot emphasize the importance of this principle enough. The more you practice, the better you’ll get: it’s that simple.
R is a programming language that has become increasingly popular over the last few years among data scientists and analysts. Being able to code in R not only can help you unlock career opportunities, but it will also give you a whole new range of skills that you can use to analyze data, and approach problems in your life using data.
There’s no way around it—learning R is a challenge, and its syntax can be confusing even to existing programmers. And, R is constantly changing, so you may find that over time you need to come back to basic concepts and master them again. But that’s just part of being a programmer because all the best programming languages evolve over time.
Learning R is not only possible but within reach, if you follow the steps we outlined in this article. Here’s a reminder of what we recommend:
- Find learning resources
- Learn the basics
- Build a project
- Look for ways to improve your projects
- Join a developer community
- Practice your R skills
Following these steps will help make your journey to learning R more smooth. Then, once you’ve learned R, you can start working on a wide range of exciting projects—the sky’s the limit!