Like many aspiring Data Scientists, you might want to learn R programming but worry it will take too much time. R is considered one of the more difficult programming languages to learn due to how different its syntax is from other languages like Python and its extensive set of commands. It takes most learners without prior coding experience roughly four to six weeks to learn R. Of course, this depends on several factors. Keep reading to learn about how you can learn R programming and some resources to help speed the process along.

What is R Programming?

R is a programming language that statisticians created for statistical data analytics. This popular language has a range of applications for performing statistical computing and creating data visualizations. It is often used by Data Scientists, Business Analysts, Data Analysts, and those working in academia or science for tasks specifically involving statistical analysis. R is currently available for free and can run on Windows and Mac OS, as well as a variety of UNIX platforms and related systems.

R provides users with a range of graphical and statistical techniques, such as time-series analysis, clustering, classification, and linear and nonlinear modeling. One of the benefits of working with R is that it simplifies the process of creating publication-quality plots, especially those that incorporate formulas or mathematical symbols. This versatile language includes a fully integrated suite of software tools, such as a data storage and handling facility, operations for performing calculations on arrays, an extensive, integrated set of data analytics tools, and graphical tools designed to analyze and visualize data.

Read more about what R programming is and why you should learn it.

What Can You Do with R Programming?

R was created to primarily help with graphics and statistical computations. This language can accomplish various tasks, from data storage to data analysis to generating statistical models. Of all the available programming languages, R is considered to be the one with the most tools devoted exclusively to statistics. This language can aid with descriptive statistics tasks, like calculating standard deviations and designing models for mapping linear regression.

One of R’s most useful features is its ability to help users create customized data visualizations and dashboards. Some consider R’s ggplot2 package the best data visualization tool available. This package allows users to draw nearly any plot they can conceive. In addition, those who wish to take their data visualizations in R to the next level can combine ggplot2’s syntax with Plotly’s interactive features to make dashboards that are as engaging as they are interactive. 

R draws from various machine learning tools so that users can make accurate, data-driven predictions. Users can select from an array of machine learning models, which have applications for creating predictive models, such as movie recommendation systems and churn models. In addition, R users can automate reporting by using R Markdown documents. R Markdown offers a straightforward, accessible syntax to generate various reports, such as presentations, books, or other written documents. This helps R users easily communicate data analysis results with others. 

Average Time it Takes to Learn R Programming

R is a comprehensive programming language with various packages and concepts to learn. It requires time and persistence to become familiar with all of them. Although the time it takes to learn R depends on several factors, most individuals can begin working with basic R programming tasks in approximately four to six weeks. This range could extend to several months for individuals who are new to coding, have a less-developed mathematical background, and cannot commit to regular study.

It may only take a week or two to learn this language for those who already have coding knowledge. Some estimates say that individuals with a solid coding background can be ready to use R in as little as one week if they commit to studying this language daily and in-depth. Like any other skill, the more time you invest in becoming familiar with R’s rules, syntax, and applications, the easier it will ultimately be to work with this language.

Other Factors

Because R’s syntax differs from other languages, many consider R more challenging to read and learn than Python or other comparable languages. Some data scientists with prior programming knowledge consider R a complex language to learn because of its extensive GUIs, inconsistently named functions, and the many available commands. Additional factors, like experience with statistics and coding, access to real-world examples, and the time commitment you’re willing to make to studies all factor into the time it will take to master R fully.

Previous Coding Experience

Although R is commonly used for data visualization and statistical computing purposes, many of the concepts that are used in R are consistent across other coding languages. Basics programming features, such as operators, loops, variables, and data types, function similarly in R as in other languages. This means that if you have prior knowledge of these programming concepts, you likely will acquire R fluency in less time than someone new to computer programming.

Background in Statistics

Because R was created by Statisticians to be mainly used for statistical computing tasks, individuals with a solid foundation in statistics will most likely be able to learn R faster than those without. Statistics is the branch of applied mathematics that involves collecting quantitative data so that it can be described and analyzed to draw insightful conclusions. It draws from math concepts like probability theory, linear algebra, and differential and integral calculus. Those with prior experience working with common statistical concepts, such as what-if analysis, time-series analysis, predictive analysis, prescriptive analysis, forecasting, and regression models, should find it easier to work with R for statistical computing tasks.

Time Commitment to Learning R

The only way to truly learn a new skill is to study and practice it. Therefore, the more time you devote each day to regular R study, the faster the learning process will be. Spending several hours a day working with R through self-paced study or other learning methods like bootcamps can expedite the time it takes to learn this language. Rigorous study can also help you retain the information you learn. For learners who wish to master R as quickly as possible, it will take several hours a day of structured learning to become comfortable with this language in just a week or two.

Access to Real-World Examples and Data

Learning never takes place in a vacuum. The more opportunities you have to incorporate real-world coding examples into your R study, the easier it will be to learn new ideas and remember the content you’ve already studied. For this reason, it can be hugely beneficial to work with actual data when studying R. For example, you may consider testing your R skills by mining data from various social media outlets. You might also search for online tests or sample projects that provide a way to measure how well you’re retaining the information that you’ve studied.

Level of Difficulty, Prerequisites, & Cost 

Because R is offered as a free software environment for graphical and statistical computing tasks, downloading and using this language does not require any additional costs. R can run on various UNIX platforms, as well as macOS and Windows. If you want to download R, you can do so directly from The R Project for Statistical Computing’s website. You will need to select your preferred CRAN mirror before downloading. 

If you’re interested in learning R, there are a few prerequisites to consider studying first that can help you acquire this programming language easier and faster. Because R is often used for statistical analysis, it’s essential to have a strong background in mathematics and statistics. In addition, since R also has applications for data visualization, it’s helpful to be familiar with basic visualization options, such as working with plots and graphs. Some people who learn R also find it helpful to understand fundamental analytics skills and practices so that it will be easier to spot and use the patterns that emerge in data. In addition, you may consider learning basic programming concepts before studying R. 

R is known to be challenging to learn for most people. Because its syntax is so different from most other programming languages like Python, it can be hard to read R. In addition, core operations, such as naming, selecting, and renaming variables, tend to be more challenging for most R users than in other languages. For those who have a background in other programming languages or have worked previously in the data sciences, it may be easier to learn R than those who are novices to coding or this field. However, some Data Scientists struggle using R due to its numerous GUIs, extensive commands, and inconsistent function names. Like any skill, the more time you spend becoming familiar with R’s rules, the easier it will be to work with this language.

Read about how difficult it is to learn R programming

Watch a Free R Programming Online Course

Are you looking instead for an overview of R programming before diving into an entire course? If this sounds like you, you can begin learning about R programming and other core data science concepts for free online. In this free Intro to Data Science seminar offered by Noble Desktop, you’ll receive an 80-minute overview of how Python is used in data science. This webinar covers the advantages of working with Python in data science and an introduction to the field of data science. This seminar is a great way to learn about data science before enrolling in a class or certificate program.

Other top educational providers also provide free beginner-friendly R materials. Coursera’s R Programming is a fully online, intermediate-level R class that spans 57 hours. Participants learn how R is used for data analysis and different practical issues that can occur in statistical computing, such as using R loop functions and debugging tools. Udemy’s R Basics: R Programming Language Introduction is a free, beginner-friendly class in which participants study how to navigate the RStudio interface, create basic graphs, and execute common commands using R. Udemy also offers a business-specific R course: learn R for Business Analytics. Those enrolled receive instruction on how to leverage R for business analytics. Topics covered include leading packages into R, importing data into R, and running various macros with this programming language.

Read more about free R programming videos and online tutorials.

Learn R Programming with Hands-on Training at Noble Desktop

Noble Desktop has several excellent learning options for those new to R and courses for more advanced programmers interested in mastering complex R skills. Noble’s Data Analytics with R Bootcamp is an immersive class designed to take participants from the basics of coding to a portfolio showcasing your experience working with R. Those enrolled receive expert instruction and can retake the class for up to one year to brush up on course materials. 

Noble also offers a variety of in-person and live online coding classes that prepare participants to become Data Scientists or Web Developers. Courses are available in popular programming languages such as JavaScript and Python, as well as front end and full stack web development. Certificates are also offered in software engineering and data science. For students seeking instruction about using programming for finance technology, Noble currently has a FinTech bootcamp available as well, which includes one-on-one mentoring for all participants.

In addition to the variety of programming courses Noble teaches, this top educational provider also has several in-person and live online Data Analytics classes. Noble’s Data Analytics Technologies Bootcamp is a beginner-friendly course that prepares students to work with core data analytics tools like SQL, Excel, and Tableau. In addition, a certificate in data analytics is also available for those interested in becoming a Business Analyst or Data Analyst. This rigorous learning opportunity prepares students to perform data analysis, statistical analysis, and data visualization, as well as how to use relational databases. All students receive one-on-one mentoring to help with their learning process.

Key Takeaways