How Long Does it Take to Learn R? (Answered!)


This post may contain paid links to my personal recommendations that help to support the site!

You’re currently on the fence about deciding if learning the R programming language is for you. Or you might also be considering picking up R as another programming language for your work or school projects. You might be pretty strapped for time and would like to know exactly how long will it take to learn R for yourself. Here’s a quick answer:

It takes 4-6 weeks to learn R without programming knowledge. For those with programming experience, it takes only about 2 weeks. The learning duration for R will vary depending on previous programming experience, learning time commitment, having the right resources, digital literacy, and exposure to coding projects.

Now that you’ve gotten the quick answer, your next few questions might be something like this: Am I able to fit my learning timeline within this range here? Do understand that there’s really no real limit to how fast you can learn R, but having to truly understand the basics and concepts of R would be highly dependent on several key factors. Let’s find out what they are!

What Factors Affect the Duration for Learning R?

When considering a timeline when learning R, you would most likely have to consider the following key factors to begin estimating how long your learning journey would be. Here are the 5 factors I gathered:

  1. Previous Programming Experience
  2. Time Commitment to Learning Per Day
  3. Having the Right Resources
  4. Digital Literacy
  5. Exposure to Practical Coding Projects

With each factor affecting the learning speed and timeline of every beginner R programmer differently, let’s begin with the most obvious difference.

1. Previous Programming Experience

This might be the first key factor that you might guess and would be most concerned about, and I would have to agree with that. The R programming language is an open-source programming language software that is commonly used for statistical computing and data visualizations. Similar to most programming languages, learning R would require you to learn concepts that are consistent across other programming languages. In every programming language, basics like data types, operators, variables, loops, and functions work in similar ways, although varying slightly from each other.

Therefore, if someone with some degree of knowledge of programming concepts would be able to grasp the syntax and different features of R faster than the regular individual without prior programming knowledge. For example, a data analyst with a strong understanding of basic Python programming would have an easier time picking up R. This might shorten the timeline for learning.

However, as R is commonly used specifically for statistical computing purposes, not many languages have many similarities in terms of use cases except for Python. In terms of data analysis and statistics, only Python would be a suitable language with good transferability to R, other than just the basic programming functions.

I’ve looked at a quick search on this page of Quora that mostly gave a sentiment that the only closest equal to R would be Python. Therefore, Python users would be able to learn R at a faster rate.

If you’re one without previous programming language experience before learning R just like I did, then you should know that this shouldn’t be stopping you! In fact, the next 4 factors played a big factor in my learning journey of R too.

2. Learning Time Commitment Per Day

Up next, the learning timeline for R would be most logically be dependent on your time commitment to learning each day. By dedicating more time to learning each day, you would be able to speed up your R learning progress. For example, if you were to dedicate 2 hours a day to learning R concepts, you would be able to achieve a learning speed that’s twice as fast as someone who only studies R for an hour a day. Here’s where dedication and hard work would help out those of you who might not have a prior programming background. This is where you’ll make the difference!

I’ve personally seen some increase their learning speed or R through various methods such as online courses and boot camps. Although I’ve yet to try out an R programming boot camp, I would assume that having a few full days of R practice and guidance would really give you a headstart to learning R.

3. Having the Right Resources

Sometimes, picking up a programming language can be frustrating and that might just be the reason that you’re likely to slow down or give up learning. However, with the right starting resources, you’ll be able to point your learning in the right directions where learning can be much smoother than usual. Thankfully, we currently live in the digital age, where access to the best online resources is not a problem.

For example, here are the different types of resources you should look into:

  • Books
  • Online Coding Exercises
  • Blogs
  • Online Communities
  • YouTube Videos

By properly utilizing the right resources like those mentioned above, you’ll be having a much easier time grasping the concepts through various learning methods. For example, I’m a rather visual learner myself so I would prefer a video explanation of programming concepts in R as compared to books. I would be able to learn R much faster if I were given the right YouTube video resources! If I were to start over and learn R from scratch, I’d be so much better off having such video resources to guide me through basic programming concepts.

If you’re looking for some of my personal favorites for learning R, I’ll be including a list of them in the section below so do read on!

4. Digital Literacy

The next factor is a pretty crucial one, simply because R is a computing language. Having a good sense of digital literacy would be able to boost your learning of R because of all the technical troubleshooting, debugging and Googling you’ll have to do when you first start out. However, since R is relatively simple compared to other languages in terms of technical setup having a basic level of digital literacy would be sufficient for you to learn R quickly.

5. Exposure to Practical Coding Projects

This might also be arguably the second most important factor when picking up R. Having a capstone project or multiple tests while you learn might be a good source of stimulus to drive you to learn much quicker than you thought you would. I’m all for setting end goals and achieving them because of how much they can motivate you to keep learning within a timeline.

If you’re someone who procrastinates quite a bit like me when it comes to learning something difficult, having a project end goal in mind would be perfect as a driving factor in your learning of R.

Here’s a YouTube playlist of the Data Professor Channel below, where the Data Professor goes through some projects on R. You might want to consider some of the resources from the channel when you begin your learning journey of R. He’s a great YouTube instructor and some of the projects also seem pretty cool! Do give it a watch!

Now that you’re aware of the factors that can either slow or speed up your learning, let’s have a deeper look of how an average timeline will look across the stages and examine how long would each stage take.

What Will the Stages of Learning R Be and How Long Will Each Stage Take?

In every learning timeline, there would be a few stages where a beginner has to go through before achieving a good grasp of the main concept. This is no different from R too!

Here are the general stages and their respective durations. I’ve put them in a table for easy viewing so do look through which stage would entail.

StageContentEstimated Duration (With programming background)Estimated Duration (Without programming background)
1Understanding BasicsProgramming environment, Installation, Data types, operators, variables4 Hours2 Hours
2Understanding Basics 2Using If Else conditional statements, Loops, and Functions14 Hours2 Hours
3Data ManipulationCreate dataframes and lists, Subset and join dataframes4 Hours3 Hours
4Data VisualizationUsing data visualization packages, Creating customized visuals24 Hours10 Hours
5Statistical ModelingUsing statistical packages, Using machine learning models24 Hours11 Hours
Total5 Weeks or 70 Hours2 Weeks or 28 Hours

The beginning stages might take shorter hours for a beginner to complete due to how new such programming concepts can be to someone without prior computing knowledge. For further package usage for data visualization and statistical modeling, one would expect a longer period of learning due to the complexities of the functions in the packages. The use cases of these R packages might be difficult for a beginner to grasp immediately.

Where Can I Get the Right Resources for Learning R?

Now here comes more juicy stuff – the right resources to help you get that head-start in learning R. Just as I’ve mentioned above, having the right resources can really set you on the fast track to learning R much faster.

These are the recommended steps and their respective resources that I would consider when learning R from scratch:

  1. Choose a SINGLE book or online course in R, to begin with
  2. Follow through with all practice exercises for all basic content
    • For each coding exercise, try to follow through with each example and do not skip any steps
  3. Use online resources for additional information or to troubleshoot and learn from mistakes
    • Use Kaggle for sample datasets
    • Use Stack Overflow for solutions when you’re stuck
  4. Participate in an online community
    • Take advantage of Discord servers for data science or programming discussions for help
  5. Finish with a full capstone project to add to your portfolio
    • A good programming project would boost your confidence in your skills
    • A good addition to your portfolio
    • Use Github to upload and showcase your R scripts

Here are some of my articles that cover areas within data science that are also really useful for learning R!

My Personal R Learning Journey

My journey of learning R was probably quite similar to what you are experiencing now – feeling pretty overwhelmed with the idea of learning coding from a non-coding college degree. I was a biological sciences major with an interest to pick up R for biological data analysis in research. It was then where I decided to begin learning R through a research attachment to a biomedical data science laboratory.

Because of the nature of research work, I was pushed to have an end goal in mind – conduct an analysis of the genomic data I was told to gather. That’s why I recommended having a project of some sort to allow yourself to have some healthy pressure.

At the end of my research attachment, I continued developing my skills in R in data visualizations and markdown, which felt really amazing, considering how I was pretty clueless before starting programming.

I personally took about almost 2 months of learning to completely grasp some concepts in R because of all the new knowledge in computing that I had to take in.

I took a training course that helped solidify all the key programming concepts in R too!

If you’re looking for the end of my timeline in my R learning journey, I would say that my learning experience for R never ends! Each new statistical package requires some learning involved where the process of learning continues.

Therefore, I’d advise you to consider a continual learning concept instead of thinking too much about how you’d be able to learn R within a short timeframe.

Even as my current self is typing this, I would say that I’m still learning and improving my R programming!

Is It Hard to Learn R?

R is not hard to learn. R programming is a relatively simple scripting language and learning to use R to get statistical packages is not hard. Also commonly used in data science, R has a simple syntax that is easy to learn. However, the R programming language has some inconsistencies, which can make learning hard.

How Can You Start Learning R?

I would recommend starting out by selecting a really good book on R programming for reference. You’ll need to complete the exercises in the book diligently and use the internet to look up any bugs or errors. You can also use 1 structured online course to help you in learning R.

Books

If you’re lost about which resources to get, I would recommend getting this book (Learning R) as it provides a really solid foundation of the various aspects of learning R programming. It also provides you with good resources when checking on your learning!

Online Courses

I’d also recommend this course on Coursera (Data Science: Foundations using R Specialization) for learning R because of its comprehensiveness when covering the essential parts of R. This is something you’ll find in this online course. Because it’s offered by John Hopkins University, you can expect high-quality content too!

Final Thoughts

Everyone learns at different paces, based on the factors I’ve mentioned above. If you’d like to have some guidance on how to begin, do consider the learning resources I’ve added in the section above too! Learning R is a process and takes time and I’m glad that you’re taking this first step to planning out your learning. Now go out there and just start programming in R!

My Favorite Data Learning Resources:

Here are some of the learning resources I’ve personally found to be useful as a data analyst and I hope you find them useful too. These may contain affiliate links and I earn a commission from them if you use them. However, I’d honestly recommend them to my juniors, friends, or even my family!

Recommended Online Course Provider: I find Coursera online courses the most well-structured and comprehensive! You can get a Coursera Plus Membership to get started here.

Using my link, you’ll only pay $1 for your first month (Offer ends 4 December 2021). I’d recommend using this to just get started, with just a small cost, and if you find that it’s not for you, you can always cancel before the next month!

Learning Data Analytics: I really like the Google Data Analytics Professional Certificate program made by Google, because of its credibility and focus on the skills required as a data analyst. You’d get the first month off of the subscription using my link!

Learning Tableau: Tableau is my main data visualization tool for work. I recommend going for Data Visualization with Tableau for an online course and Practical Tableau by Ryan Sleeper.

Learning Python: I’d recommend Learning Python for Data Analysis and Visualization for an online course and Python for Data Analysis as a resource book.

Learning Power BI: Power BI is a great tool I use for my personal projects and analysis for its lower cost. Getting Started with Power BI Desktop is a great online course to start with and Beginning Microsoft Power BI is a good book to accompany your learning.

Learning R: The Data Science: Foundations using R Specialization online course is real solid one you should check out. For books, I’d recommend Learning R.

Learning SQL: A good started course is Introduction to SQL from Datacamp and for books, SQL: The Ultimate Beginners Guide: Learn SQL Today should be a useful resource while you learn.

Learning Data Visualization: I personally think that the Big Book of Dashboards is an excellent book for reference when designing your dashboards, especially on Tableau.

To see all of my most up-to-date recommendations, check out this resource I’ve put together for you here.

Austin

A budding data analyst with great interest in writing all things about data!

Recent Posts