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You’re currently on the fence about deciding if learning the R programming language is for you. You’re curious 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:
- Previous Programming Experience
- Time Commitment to Learning Per Day
- Having the Right Resources
- Digital Literacy
- 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 to perform data analysis, perform statistical computing, and create 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 a 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.
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 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 direction 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:
- Online Coding Exercises
- 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 at 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 that 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 on a table for easy viewing so do look through which stage would entail.
|Stage||Content||Estimated Duration (With programming background)||Estimated Duration (Without programming background)|
|1||Understanding Basics||Programming environment, Installation, Data types, operators, variables||4 Hours||2 Hours|
|2||Understanding Basics 2||Using If Else conditional statements, Loops, and Functions||14 Hours||2 Hours|
|3||Data Manipulation||Create dataframes and lists, Subset and join dataframes||4 Hours||3 Hours|
|4||Data Visualization||Using data visualization packages, Creating customized visuals||24 Hours||10 Hours|
|5||Statistical Modeling||Using statistical packages, Using machine learning models||24 Hours||11 Hours|
|Total||5 Weeks or 70 Hours||2 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:
- Choose a SINGLE book or online course in R, to begin with
- 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
- 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
- Participate in an online community
- Take advantage of Discord servers for data science or programming discussions for help
- Finish with a full capstone project to add to your portfolio
- A good programming project would boost your confidence in your R 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!
- Why is Python Used More than R in Data Science?
- Top 10 Data Science Discord Servers
- Top 11 Subreddits for Data Science
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 that I decided to begin learning R through a research attachment to a biomedical data science laboratory.
Because of the nature of my research work, I was pushed to have an end goal in mind – to 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.
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!
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!
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 Learning Resources:
My Recommended Learning Platforms!
|Learning Platform||What’s Good About the Platform?|
|1||Coursera||Certificates are offered by popular learning institutes and companies like Google & IBM|
|2||DataCamp||Comes with an integrated coding platform, great for beginners!|
|3||Pluralsight||Strong focus on data skills, taught by industry experts|
|4||Stratascratch||Learn faster by doing real interview coding practices for data science|
|5||Udacity||High-quality, comprehensive courses|
My Recommended Online Courses + Books!
|1||Data Analytics||Google Data Analytics Professional Certificate||–|
|2||Data Science||IBM Data Science Professional Certificate||–|
|3||Excel||Excel Skills for Business Specialization||–|
|4||Python||Python for Everybody Specialization||Python for Data Analysis|
|5||SQL||Introduction to SQL||SQL: The Ultimate Beginners Guide: Learn SQL Today|
|6||Tableau||Data Visualization with Tableau||Practical Tableau|
|7||Power BI||Getting Started with Power BI Desktop||Beginning Microsoft Power BI|
|8||R Programming||Data Science: Foundations using R Specialization||Learning R|
|9||Data Visualization||–||Big Book of Dashboards|