One of the most common frustrations among newbies to data science is: “I don’t know which data science programming language I should learn!”
It makes sense why so many people express this concern. There are many technologies that you can use for data science, from R to Python to SQL, that figuring out which one is worth your time can be difficult.
Because your time is limited, you may be wondering which one technology is worth learning. While learning any data science technology will add value to your career and help you better understand the field, one common suggestion made by data scientists is to learn how to code in R.
You may be asking yourself: “Why are so many data scientists devoted to R? What makes the language so special? Those are two questions we’re going to discuss in this article.”
Through this guide, we’re going to discuss the top five reasons why you should consider learning R, to help you better understand the hype around R.
What is R?
R is a programming language commonly used for statistical computing and graphics.
The R language was developed by John Chambers to improve upon the S programming language, which Chambers saw to be lacking in features.
R provides access to a wide range of statistical and graphical tools, from the ability to perform time-series analysis to the ability to cluster data. One of the main reasons why R is used is because it allows you to easily generate professional data plots and visualizations using mathematical formulas.
Now that we have covered the basics, let’s discuss the top five reasons why you should learn to code in R.
#1: R is built for data science
The R programming language, unlike Python, was built specifically to support mathematical calculations and data analysis.
R is widely used by data scientists around the world and with good reason: almost all the features data scientists need are available with R. According to a survey by O’Reilly Media conducted in 2014, R is the most popular programming language used by data science professionals.
The R programming language allows you to run data science calculations without any compiler, which makes the development of code more efficient. Also, R is a statistical language, so it’s easy to convert statistical models into code using R.
R was originally designed by statisticians for statistical analysis, so mathematical computing is at the core of the programming language. Besides, because so many statisticians are involved with R, there’s plenty of resources out there that document the language from data science and mathematical perspectives.
#2: R is a great language for learning the basics
While R may have a steeper learning curve than other languages commonly used in data science such as Python, it is a great language to use if you’re looking to master the basics of data science.
This is because R was designed with statistics in mind, all the core data science concepts that you’ll need to master are already native. From data visualization to data cleaning, the tools you need for data science are already there. You don’t need to import any external packages as you would in Python; everything is out-of-the-box with R.
#3: R works with a wide range of packages
The R programming language is powerful, but if you have an additional use case you need to meet that is not covered by the language, you don’t need to worry. There is a massive amount of packages available that you can use with R for data science.
For instance, ggplot2 allows you to effectively create data visualizations. You can also use dplyr with R, which makes data ‘wrangling’ and manipulation seamless.
#4: R has a supportive developer community
R has a vastly supportive growing developer community.
As data has become an increasingly important part of the tech industry, and as more non-tech businesses are realizing the power of data, the data science field has grown commensurately. According to StackOverflow, R is one of the fastest-growing programming languages in the world, which shows how widespread this technology is.
Because R is so popular, it’s easy to find other developers to help with the issues you are facing. The R language is also well documented and there are already thousands of questions on sites like StackOverflow about R, which means that you should have no trouble finding a solution to a problem.
#5: R is used in industry and academia
R is not just a language used by hobbyists for data analysis, it is the tool the big players are using in industry. For instance, Facebook uses R for behavior analysis related to status updates. Google uses R to measure the effectiveness of its advertising campaigns. Microsoft acquired the Revolution R company and has also used R throughout its organization. Bing, Merck, TechCrunch, and Mozilla are just a few more of many names that use R.
According to Indeed.com, as of May 4, there are 4,072 jobs in the United States for the query “R Data Scientist”. This number will likely only represent a small number of jobs requiring R because having skills in the language is crucial in data science roles.
R is very popular in academic environments, too. This is an important factor to consider because the tools that are taught in universities are often those used by the labor market, too.
Given the utility of the R language, as the supply of talented R developers increases, so too will the demand for R skills; companies will always want to hire people with the most robust skillset.
The Bottom Line
There is no right answer when it comes to “which programming language is best for data science”. It depends on where you are in your learning journey and what types of data analysis you are performing.
Learning how to code in R is a valuable investment. R comes with many tools that you can use to perform comprehensive statistical analysis on a data set. That’s not all. R was developed by statisticians for statisticians, which highlights that the language was written with math and data science applications in mind.
Of course, knowing other tools like Python, D3, and Tableau can be incredibly useful as the more tools you know, the more effective of a data scientist you will be. However, if you are looking to choose one technology to learn, R is definitely worth your time.