What is R?
Introduction
R is a free, open-source programming language designed specifically for data mining, statistical analysis, data visualization, and machine learning. R’s capacity for visualizing data makes it popular with data scientists and data analysts. Making sense of data can be tricky, and it helps to have graphs, charts, and images when you’re sharing your findings with non-technical teams.
The R language is a cross-platform language, meaning you can use it with Windows, Mac OSX, or Linux. Read on to learn more about R’s defining features, how it compares to Python, and the different careers that use it!
R language features
R is more than just a programming language. It’s actually a complete development environment with all the tools you need to start data analysis, machine learning, or data visualization. The R programming environment comes with these features:
- A well-defined but simple programming language with loops, conditionals, input and output functions, and the ability to write recursive functions
- Many operators doing not only calculations of arrays but also matrix math
- Useful and powerful data handling and storage facilities
- Extensive collection of third-party tools for data analysis
- Full-featured and flexible graphical toolsets that can generate charts and graphs for printing or web-based reports
To learn more about R’s features and put them to practice, try our Learn R course. But, now that you understand R’s features, let’s look at how it stacks up to Python, another popular programming language with similar applications in data science.
R vs. Python
Both R and Python are free, open-source languages that have support from a large community of developers. Python is used for much of the same purposes as R, but not vice versa. Here are some of the key differences and similarities between R and Python:
- Python is a general-purpose language that can handle general data science projects, but R is designed specifically for statistical analysis, visualization, and other data-intensive projects.
- The IDE of choice for R is RStudio. Python has many available IDEs, including tools like Jupyter that can execute Python code in the browser.
- Both R and Python have an enormous selection of open-source libraries that do similar things in dealing with data. Still, Python will have more general-purpose libraries for creating desktop applications, web applications, and more.
- R is designed to run locally or as a service from the command line. Python can be used this way too, but it can also be integrated into other types of applications.
- Both R and Python have libraries that allow data scientists to retrieve and store data in databases for convenience.
Which programming language you choose depends on what you plan to do with it. If your focus is data analytics, machine learning, and visualization, R is a superb choice. If you’re more interested in Python’s versatility, learn more about its defining features and applications.
Who uses R?
So, who uses R? The R programming language is an essential tool for data analysis used by companies like Google, Facebook, Microsoft, and Twitter. Using R, data analysts can provide the business intelligence needed to guide companies to a better future. Programmers who know how to program with R have quite a few potential career paths.
Data scientists extract data from multiple sources, clean it, analyze it, and provide actionable insights. They also build machine learning models that can predict trends in business, financial markets, and more. Data scientists are always in demand, and because R is designed for data, learning how to use it is a great first step toward entering the job market.
Data analysts also work with data. They deal with complex data sets using their analytical skills to provide insights that help companies make business decisions. Using R and its powerful statistical libraries and tools allows them to find these answers quickly.
Quantitative analysts work for financial, banking, and other industries. They calculate the risk and return of investment portfolios. R is perfect for quantitative analysts because it allows them to collect and analyze data and visualize it with stunning graphs that are easy to understand.
Along with the careers listed above, R is heavily used by data architects, data visualization experts, geo statisticians, and database administrators, and more. For more information about careers in data science, check out our data science career guide.
Learn more about R
The R programming language definitely has its niche, but it’s a big one. Data runs the world today, and R was built specifically to deal with it. If you’re interested in a career in data science, then learning R is a great first step. In this course, you’ll learn the fundamentals of R programming and use it to collect, manipulate, and visualize data. If you’re more interested in R’s advanced applications like statistics or machine learning, check out the courses below:
