With the onset of data science and analysis came the need for tools to make sense of it all. The programming language R is one such concoction. Developed by statisticians Ross Ihaka and Robert Gentleman in the 1990s, R is now a go-to for researchers, data scientists & analysts worldwide.
R is a great programming language to learn in 2024 and may become a valuable addition to your skill set. It has excellent support for statistical models, even better than Python, and is invaluable in data science and research.
Of course, we all know how much hate ‘R’ gets online. Some of it is fair; R’s great, but no Python regarding the learning curve and software development.
However, that’s just for software and web development. Turn towards statistical analysis or data science, and R becomes indispensable.
Table of Contents
Why should you learn R?
1. Well-suited for Analytical Tasks
As I mentioned earlier, R was developed by Ross & Robert, both of whom were great statisticians. Unsurprisingly, the language was purposefully designed to assist with data analysis.
R offers various techniques, including linear and nonlinear modeling, time-series analysis, classification, and clustering. This makes it perfectly suited for data analysis tasks.
2. Packages for Everything
This language’s greatest strength is its vast collection of user-contributed packages. The Comprehensive R Archive Network (CRAN) hosts over 18,000 such packages!
They cover close to everything, so you can hope to find one for anything from complex statistical analyses to stunning visualizations.
For example, packages like ggplot2 can help you create high-quality and publication-ready plots and graphs.
3. Reproducibility
We are in a time where transparency is increasingly important, even in research and analysis.
R can document your entire workflow– from data import to final results. This reproducibility is a game-changer for many in the field, especially in academic or scientific settings, where others might need to verify or build upon your work.
4. Active Community & Resources
Besides being open-source and free, R has a large, active community of users and developers. This reflects quite well in our second point about the user-contributed packages. So besides these packages, you can access learning resources like online courses, books, forums, etc.
5. Easy to Integrate
R is easily integrated with other programming languages– C++, Java, Python, etc. This means that you can conveniently access & use the existing code and libraries of those languages, too. All in all, R is a flexible addition to your tech stack.
Who should consider learning R?
R’s versatility can be seen across various fields. But for certain groups of professionals, as well as students or trainees, it’s rather well-suited:
1. Data Analysts (and Scientists!)
Data analysts and scientists are obvious candidates. We have gone over this already– R is essential for anyone looking forward to a Data Science & Analysis career.
It is common for professionals in this field to use R when working with large datasets, statistical modeling, and visualization.
2. Business & Financial Analysts
Even if not full-time, business professionals working with data can benefit from R. Its statistical potential makes it a valuable tool for business and financial analysts.
As a business analyst, you can extract insights from business data and perform and create market analysis reports. For financial analysts, R will help you perform financial modeling, portfolio analysis, and risk assessment.
3. Healthcare Professionals
R is incredibly useful for analyzing clinical trial data, epidemiological studies, and genomic research, among other significant studies.
Besides the above groups, students in STEM fields should definitely give R a look, especially those considering a career in data-related roles. Environmental & social scientists are at an advantage, too, when using R to see any patterns that form.
And if you enjoy solving puzzles and finding patterns in data, learning R could be a rewarding challenge, even if it’s not directly related to your current job.
Where R Falls Short…
Like any other tool, R isn’t perfect, and its limitations are worth reviewing before you start working with it.
1. Learning Curve: R can be a little quirky at first, specifically for those new to programming, compared to other languages. The syntax isn’t always as intuitive, and some concepts may take a while to click.
2. Speed: R is a powerful tool to work with data, but boy is it slow! The speed might be a bother when working with large datasets or when you need fast processing.
3. Popularity: Coming to general purpose programming, R doesn’t happen to be the go-to choice, typically. Of course, you can use R for tasks beyond data analysis, but other languages might be more suitable.
Even though R’s popularity has grown significantly in recent years, it still lags behind when it comes to adoption across certain industries. This translates to fewer opportunities in some job markets or specific companies than for other widely adopted languages like Python or SQL.
Top Career Opportunities for R Programmers
Investing your time should get you the right ROI, and as for learning R, you’ll be glad to know the variety of career paths it can open up for you.
1. Data Scientists
The most obvious one on the list is of course, any role in Data Science or analysis. Such roles often list R as a desired (or required) skill. R serves multiple purposes for professionals in this field– statistical analysis, visualization and machine learning, to name a few.
Salary Info:
- $1,58,218 on average ($2,38,845 for senior data scientists)
- €68,000 in Germany
- €48,000 in France
- £48,546 in the United Kingdom
2. Finance Analyst
R is increasingly popular in the financial sector for tasks ranging from risk analysis to trading strategies. So if you’re someone interested in Fintech or quantitative finance, having R on your skillset will surely give you a competitive edge.
Salary Info:
- $79,238 in the US
- €60,000 in Germany
- €45,000 in France
- £38,915 in the United Kingdom
3. Healthcare & Pharmaceutical Roles
A health data analyst plays a critical role in analyzing and interpreting health-related data to improve patient outcomes, optimize operations, and support decision-making in a healthcare setting.
Salary Info:
- $1,10,397 in the US
- £53,417 in the UK
- €72,323 in Germany
4. Business Analyst
A Business Analyst bridges the gap between business needs and IT solutions by gathering requirements. They ensure that implemented solutions meet stakeholder expectations and improve business processes
Salary Info:
- $91,687 in the US
- €58,000 in Germany
- €44,250 in France
To add to our non-exhaustive list, academia, and research roles deserve a special mention. Research institutions frequently use R, and many universities now incorporate R into their stats or data science-related curricula.
FAQs
1. Which programming languages are similar to R?
Those familiar with R would know it is similar to statistical languages like SAS. Else, Python, MATLAB and Julia are some common programming languages known to be similar to R. Python has a few libraries like Panda and numpy, which make its capabilities similar to R’s.
2. Is R better than Python?
It’s not a matter of being ‘better’– R and Python are powerful tools with their strengths. R is great at statistical analysis and graphics, while Python is more versatile. The latter is more general-purpose and easier to grasp for beginners.
The final choice will depend on your needs or the project’s background and requirements.
3. How long does it take to learn?
With focused efforts, you could grasp the basics of R in a few weeks. You might take 3-6 months of regular practice to reach’ proficiency’. Remember, learning remains an ongoing process.
Even those proficient in R constantly learn and adapt to new techniques and packages.
Author: Katrina Walker
CEO & Founder of CodeOp,
An International Tech School for Women, Trans and Nonbinary People
Originally from the San Francisco Bay Area, I relocated to South Europe in 2016 to explore the growing tech scene from a data science perspective. After working as a data scientist in both the public...
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