R
When it comes to data science, there’s no one best programming language. There are a few standouts, however, each with its own specialties, as well as packages, libraries, and extensions that further enhance their capabilities.
In this article, we’re going to take a closer look at three of the most popular languages used by data scientists: Java, Python, and R. You’ll learn the basics of each, as well as how to tell which one is right for your data needs.
R: beloved by data scientists
Originally developed by statisticians as an open-source alternative to expensive suites of statistical software like SAS and MATLAB, R is one of the most popular languages for data analysis. It’s been likened to Excel on steroids, able to sift through reams of data, execute sophisticated analyses, and produce publication-quality graphs and tables. What makes R special? In short, it’s a tool built with data analysis in mind.
As data science has become critical to many businesses, R’s popularity has skyrocketed. Organizations as large and diverse as Google, Facebook, Microsoft, Bank of America, and the National Weather Service have all turned to R for reporting, analysis, and visualization.
A key component of R is that, unlike object-oriented programming languages like Java or Python, R is a procedural language, meaning it relies on a series of step-by-step subroutines to execute a programming task. The key difference here is that R uses procedures to operate on data, where object-oriented programming bundles procedures and data together as parts of objects. The advantage of procedural programming is that it gives clear visibility into complex operations with lots of dependencies, which can be important for many data analysis tasks. The tradeoff is that this often requires more lines of code than object-oriented languages.
Another benefit of R? It’s supported by a vibrant community of developers, especially academic statisticians and data scientists.
Java: speed at scale
Java is powerful, portable, and scalable, which makes the platform perfect for building enterprise-scale applications and supporting rapid growth. Java also includes many tools, collectively known as the Java Platform. This robust, open-source development environment includes libraries, frameworks, APIs, the Java Runtime Environment, Java plug-ins, and the Java Virtual Machine (JVM). Taken together, these tools simplify coding with Java and support development at every level, giving developers everything they need to build Java web systems and applications.
Java’s speed allows it to outperform other languages and frameworks, which is a big part of why it’s so well suited to large-scale applications. These performance gains are what prompted Twitter to shift its search engine to Java from Ruby on Rails and move more of its back-end stack to the Java Virtual Machine.
Another key component of Java is that it comes as close to being 100% object-oriented as you can get. With that comes all the benefits of object-oriented programming, from ease of development to modular software to flexibility and extensibility. As one of the most widely known programming languages, it’s easy to find and hire talented developers. What’s more, Java’s massive community of developers means that there’s lots of excellent documentation around.
Python: built for flexibility
Like Java, Python is built to handle high-traffic sites. It’s fast and efficient, with an emphasis on code readability. Python’s motto is “there should be one—and preferably only one—obvious way to do it.” That can mean there’s a bit of a learning curve as developers learn the ins and outs of Python syntax, but the upside is an ability to express concepts with fewer lines of code than would be possible in languages like C++ or Java.
Python’s other great strength is an extensive set of libraries that allow it to perform a wide array of tasks. In particular, the libraries NumPy and matplotlib enable Python to perform many of the analysis and plotting functionalities of MATLAB. These libraries have since been built upon by a number of other libraries that extend Python’s functionality even further.
In short, Python represents a compromise between R and Java, combining the sophistication of the former with the speed and scalability of the latter.
Which language is right for your data needs?
The short answer is that it depends on the kind of work you’re trying to do. A good rule of thumb might be if your work is closer to mathematics and statistics, R is probably your best bet. If your work is closer to programming, go with Python, and if you’re building enterprise-size products, take a look at Java. That said, many data scientists are increasingly turning to combinations of languages that allow them to take advantage of the individual strengths of each.
R
Great For:
- In-Depth Statistical Analysis. Given that R was developed by and for statisticians, it’s no surprise that R is ideally suited to in-depth statistical analysis, whether you’re working with sensor data from an IOT device or elaborate financial models. What’s more, it’s very well supported by the statistics community through the CRAN repository, which contains literally thousands of packages that enable you to perform more elaborate analysis and visualization tasks.
- High-Quality Reporting. Well-produced images convey more than numbers alone, and R places a great emphasis on easily producing high-quality graphs and charts. On top of that, its basic capabilities can be extended with a number of packages, including ggplot2, ggvis, googleVis, and rCharts. The Shiny framework also allows you to turn those visuals into interactive web applications.
Not Great For:
- Performance. R was designed with data scientists in mind, not computers. As such, R is considerably slower than Python or Java.
- Creating large-scale data products. In these instances, data scientists will often prototype in R and then switch to a more flexible language like Java or Python for actual product development.
- Ease of Learning. If your background is in math or statistics, R’s array-oriented syntax can make implementation relatively straightforward. If you have programming experience, however, this approach is likely to seem counterintuitive.
Java
Great For:
- Excellent Performance on Large-Scale Systems. Java’s speed makes it best for building large-scale systems. While Python is significantly faster than R, Java provides even greater performance than Python. Speed and scalability are why Twitter, LinkedIn, and Facebook rely on Java as the backbone of their data engineering efforts.
- Faster Development Time. The Java Virtual Machine (JVM) is a great environment for developing custom tools quickly. The programming language Scala runs on JVM and is popular with data scientists for its combination of object-oriented and functional programming.
Not Great For:
Statistical modeling and visualization. Between these three languages, Java is definitely the least suited to hardcore analysis. Though packages do exist to add some of these functions, they’re neither as advanced nor as well supported as the ones you’ll find for Python and R.
Python
Great For:
- Workflow Integration. Python’s flexibility makes it a popular choice for developers who need to apply statistical techniques or data analysis in their work, or for data scientists whose tasks need to be integrated with web apps or production environments. If you’re looking for a single tool to manage your entire data-related workflow, Python is a great option.
- Machine Learning. The combination of specialized machine learning libraries (like scikit-learn, PyBrain, and TensorFlow) and general purpose flexibility makes Python uniquely suited to developing sophisticated models and prediction engines that plug directly into the production system.
Not Great For:
- Highly specialized data tasks. Though the Python community is catching up, there are still hundreds of R packages that have no Python equivalents. If you’re looking for very specific capabilities, you might be better off with R.
Hiring a data scientist?
Now that you understand the differences between some of the major languages in data science, who do you need to set up and maintain your data infrastructure? Data scientists come from a variety of backgrounds. Some specialize more in performing statistical analysis, while some are more focused on building products that interface directly with production systems. Explore data scientists on Upwork.