Programming languages come in many shapes and forms and serve many use cases. When we discuss the topic of programming, we usually attribute the skill to developers. The truth is, however, that there are many professions that benefit from coding, including data scientists, engineers, system administrators, mathematicians, and scientists – to name but a few. In this tutorial, we look at two powerful programming languages that can be used for much more than creating software: namely, Java and R. In the sections below, we will examine their features, syntax, use cases, and how they differ from one another in an effort to help you choose the right one for your next project or career path.
Read: What are the Benefits of Java?
What is the Difference Between Java and R?
In the paragraphs, we will examine the differences between Java and R in terms of:
- Syntax
- Use cases
- Libraries
- Performance
- Community
Syntax
Java is a known for being a statically-typed programming language with Object-oriented features. Statically typed refers to the fact that Java’s variables are explicitly declared, meaning you have to define their type prior to assigning them a value. Further, while many consider Java to be a true Object-oriented programming (OOP) language, in truth, it is not, as it allows for primitive and non-primitive data types. That being said, Java does have many OOP features and can mimic those it does not have, including classes, objects, polymorphism, encapsulation, and inheritance – to name but a few.
By relying on a class-based programming structure, Java developers can ensure their code is more portable, readable, maintainable, reusable, and less prone to errors. Not to mention lending itself to more efficient coding. Java code is organized into classes, which define objects (think of classes as a blueprint and objects as the item they create) and methods, which specify their behavior.
Finally, Java code relies on curly braces {} to denote and delineate blocks of code and semicolons ; to signify the end of a statement. Below is an example of Java’s syntax, whereby we create the classic “Hello, World!” application and demonstrate Java’s class-based nature:
public class HelloWorld {
public static void main(String[] args) {
System.out.println("Hello, World!");
}
}
R, for its part, is dynamically-typed language, meaning that the data a variable holds implies its type. Its primary role is for data analysis statistical modeling, and, as such, it has a rather concise syntax (compared to Java’s more verbose syntax), and is specifically designed with data manipulation in mind.
R uses functions and vectors primarily, making it possible to perform operations on entire datasets all at once. R, unlike Java, does not require explicit declaration for variable types, but, instead, uses implicit declaration.
Below is an example of how you would create the “Hello, World!” program in R – notice its simpler, more straightforward syntax:
hello <- function() {
print("Hello, World!")
}
hello()
Both of these code examples produce the same result – printing the text “Hello, World!” to the user’s screen. However, they use vastly different approaches to achieve this.
Read: Top Online Courses to Learn Java
Use Cases
Java has been around since 1995 and has had several decades to grow and evolve as a language. It has also benefited from the support of such tech giants as Sun Microsystems and its current benefactor, Oracle. This has allowed the programming language to expand to cover a wide range of applications, such as:
- Web applications
- Mobile apps for Android devices
- Desktop applications like word processors and productivity suites
- Database programming
- Video game creation
- Embedded systems and the IoT
- Enterprise-level software
Java was created under the concept of WORA, which stands for “Write Once, Run Anywhere”. This means the applications you create with Java can run on virtually any platform or system. This ability comes from the Java Virtual Machine (JVM), which compiles Java code into bytecode for machines to read.
R, as stated, was very specifically design to serve the purpose of statistical computing and data analysis. It is the perfect tool for performing tasks in the realm of data modeling, data visualization, statistical modeling, artificial intelligence, deep learning, and machine learning.
R is known for its vast libraries and packages devoted to data science, making it a prime choice for data scientists, data analysts, mathematicians, physicists, those in the financial industry, and researchers.
Libraries
Libraries are pre-built modules of code that can be used to perform common tasks in code, enhancing developer productivity and reducing errors. Java, hosts a vast amount of built-in libraries and third-party libraries for virtually every task imaginable. These libraries can also extend Java’s functionality.
Java also has a large developer ecosystem that includes many web frameworks for web development (such as Spring and Hibernate), tools for graphical user interface (GUI) design (including JavaFX), and unit testing tools like JUnit.
Finally, Java also has a wide range of other types of developer tools built specifically for the Java language, including integrated development environments (IDEs) and code editors, build and deploy software, and more.
R has its own set of libraries and extensions for data analysis and visualization, including the oddly named “tidyverse” collection, which contains packages such as ggplot2 and dplyr, boosting its abilities for data manipulation and data science, respectively. Other libraries, like caret (machine learning) and lubridate (date and time analysis) further enhance its functionality.
R, being a younger language and being more limited in its use scenarios, does not have as many developer tools or frameworks as Java, giving Java a big advantage in this arena.
Performance
Java syntax is compiled into bytecode, which, in turn, gets executed by the JVM. This initial abstraction can lead to a bit more overhead in terms of resources, but modern systems and additional optimizations performed by both the JVM and JIT compiler more than make up for this additional cost. As such, Java is highly-performant and capable of handling applications that require speed, efficiency, and are scalable. This is especially true for software that makes use of multithreading, concurrency, and parallelism – things which Java excels at.
R, being an interpreted programming language, has some performance bottlenecks when it comes to handling larger datasets and complicated computations. However, this can be mitigated through the use of R packages that make use of compiled code, including data table (for data manipulation) and Rcpp, which lets developers incorporate C++ code into their R codebases. Utilizing C++, programmers can gain more control over memory and processing resources at the hardware-level, allowing for performance optimizations.
Community
Java has a very large and thriving community that has built-up over the past few decades. This community provides tons of learning resources, such as video how-tos, tutorials, articles, and forum discussions on best practices, problem solving, and programming logic.
In addition, Oracle provides official documentation and the community as a whole contributes to development of Java’s core, providing additional features and security with each update.
As you might imagine, R has a small community, though they are known to be highly engaged and contribute much to the data science and statistics field. There are plenty of sites offering tutorials and forums for R and the community also contributes to the CRAN repository, which hosts R packages.
Final Thoughts on Java versus R
In this programming tutorial we compared the programming languages Java and R – both of which are very distinct and have different syntaxes. Java is class-based and focuses on code organization, code reusability, readability, and maintainability. It is a mature language that can be used for virtually every type of application and it is an excellent choice for large scale and enterprise-level software.
R, meanwhile, is a more concise language that focuses more on data analysis, manipulation, and statistics. It is ideal for data scientists and researchers.
Both languages are great additions to your developer toolkit and can enhance not only your skills, but your career, likelihood of being hired, and potential salary.
