R has never been the most popular language in the world. The reason is that it’s designed for a very specific purpose: statistical analysis and data visualization. This very niche – as per today’s business lingo — programming language gained popularity in the data science circles. Not surprisingly, as statistical analysis is one of the foundational disciplines of data science.
Of course, some newer languages are gaining traction, such as Julia, Scala, and Go. However, R, along with Python, is a data science industry standard, especially for statistics and visualizations.
The motivation to learn it is there, but where are the learning resources? In this article, I have selected five free tutorials for learning R. I also threw in several additional resources as a bonus to make your R knowledge more comprehensive and applicable to the real world.
Let’s have a look at each of these free tutorials.
1. IBM: R Programming Basics for Data Science on edX [Beginner]
IBM: R Programming Basics for Data Science is a beginner course focused on practical learning. During the course, you will write a program in RStudio, manipulate data, and do a data analysis project using Watson Studio and Jupyter.
You will learn the following R fundamentals:
- Data manipulation
- Defining and manipulating data structures (e.g., vectors, factors, lists, and data frames)
- Control program flow
- Defining functions
- Perform operations on string and date data
- Regular expressions
- Handling errors
- Reading, writing, and saving data files
- Web scraping
2. R Programming For Beginners by Simplilearn on YouTube [Beginner]
R Programming For Beginners is yet another beginner’s tutorial; this one’s Simplilearn’s full R course from 2023. If you’re looking for video-based learning, this one’s for you.
The topics covered in this tutorial are, more or less, the same as in the previous one:
- Variables and data types
- Logical operators
- Lists
- Matrices
- Data frame
- Flow control
- Functions
- Built-in functions
- Regular expressions
- Data manipulation
- Time series analysis
3. Learn R by R CODER [Beginner]
Learn R is a beginner tutorial that also delves into some intermediate territory. The topics are very elegantly and clearly explained, with every explanation supported by example code, which you can run in your code editor. Topics covered are, again, similar to those covered earlier.
Here’s the list of the topics you’ll learn:
- Data structures
- Data wrangling
- Programming
- Import and export data
- Basic plots and graphics
4. HarvardX: Statistics and R on edX [Intermediate]
HarvardX: Statistics and R is an intermediate course that bridges the gap between R programming and statistics. It’s designed for those with basic programming and math knowledge.
The statistical concepts you will learn in the R context are:
- Random variables
- Distributions
- Inference: p-values and confidence intervals
- Exploratory data analysis (EDA)
- Non-parametric statistics
5. R Screencasts by David Robinson [AAdvanced]
R Screencasts is a slightly different approach to learning R. David Robinson is an experienced data scientist who recorded around 80 screencasts showing you real data analyses in R. It’s not for watching; you can do those analyses on your own, as both code and datasets are available on GitHub.
Bonus Learning Materials
Along with those tutorials, there are also other resources for learning R. Let’s have a look at 4 more.
- R for Data Science: A popular book on R, originally published in 2017, with the second edition published in 2023. Both first and second editions are available for free. If you’re looking for comprehensive learning materials, you’ve found one now.
- Hands-On Programming with R: Another great book (by one of the authors of the book above), with this one focusing on practical examples of data science problems.
- StrataScratch: With more than 600 actual coding interview questions, StrataScratch is perfect for practicing all the R concepts you learned and applying them to solving actual business problems.
- Publicly Available Datasets: Use publicly available datasets (e.g., from Kaggle or Data.gov, or even built-in R datasets) to perform statistical analysis and visualize data in R.
Conclusion
R is still a highly valued tool in data science, especially for statistical analysis and data visualization. Your idea of learning is commendable and, in truth, a smart career decision.
These 5 free tutorials — along with the bonus 4 — will get you started and equip you with solid R foundations.
