Java vs R language: what is the difference?

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Programming languages ​​come in many forms and serve many use cases. When we talk about programming, we usually attribute the skill to developers. The truth is, however, that many professions benefit from coding, including data scientists, engineers, system administrators, mathematicians, and scientists, to name just 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 look at their features, syntax, use cases and how they differ from each other. another with the aim of helping you choose the right one for your next project or career path.

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What is the difference between Java and R?

In the paragraphs, we will look at the differences between Java and R in terms of:

• Syntax
• Use case
• Libraries
• Performance
• Community

Syntax

Java is known to be a statically typed programming language with object-oriented features. Static typing refers to the fact that Java variables are explicitly declared, meaning that you must define their type before assigning a value to them. Additionally, although many consider Java to be a true object-oriented programming (OOP) language, this is actually not the case, as it allows both primitive and non-primitive data types. That being said, Java has many OOP features and can imitate those it doesn’t, including classes, objects, polymorphism, encapsulation, and inheritance, to name a few.

By leveraging a class-based programming structure, Java developers can ensure that their code is more portable, readable, maintainable, reusable, and less error-prone. Not to mention lend itself to more efficient coding. Java code is organized into classes that define objects (think of classes as a model and objects as the element they create) and methods that specify their behavior.

Finally, Java code relies on curly braces {} to designate and delimit blocks of code and semicolons ; to signify the end of a statement. Below is an example of Java syntax, thanks to which we create the classic “Hello, World!” » application and demonstrate the class-based nature of Java:

public class HelloWorld {
    public static void main(String() args) {
        System.out.println("Hello, World!");
    }
}

R, for its part, is a dynamically typed language, meaning that the data contained in a variable implies its type. Its primary role is statistical modeling of data analysis, and as such it has a rather concise syntax (compared to Java’s more verbose syntax) and is specifically designed for data manipulation.

R primarily uses functions and vectors, which allows operations to be performed on entire datasets at the same time. R, unlike Java, does not require an explicit declaration for variable types, but instead uses an implicit declaration.

Below is an example of how you would create the “Hello, World!” » program in R – notice its simpler and more direct syntax:

hello <- function() {
    print("Hello, World!")
}

hello()

These two code examples produce the same result: print the text “Hello, World!” » on the user’s screen. However, they use very different approaches to achieve this.

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Use case

Java has been around since 1995 and has had several decades to develop and evolve as a language. It has also benefited from the support of technology giants such 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
• Office applications such as word processors and productivity suites
• Database programming
• Creation of video games
• Embedded systems and IoT
• Enterprise-grade software

Java was created under the concept of WORA, which means “Write once, run anywhere”. This means that the applications you build with Java can run on virtually any platform or system. This capability comes from the Java Virtual Machine (JVM), which compiles Java code into bytecode for machines to read.

R, as noted, was designed very specifically to meet the goals of statistical computing and data analysis. It is the ideal tool for performing tasks in the areas of data modeling, data visualization, statistical modeling, artificial intelligence, deep learning and machine learning.

R is known for its extensive data science libraries and packages, making it a top choice for data scientists, data analysts, mathematicians, physicists, financial industry professionals, and researchers. .

Libraries

Libraries are predefined code modules that can be used to perform common tasks in code, improving developer productivity and reducing errors. Java hosts a large amount of built-in and third-party libraries for virtually any task imaginable. These libraries can also extend the functionality of Java.

Java also has a large developer ecosystem that includes many web frameworks for web development (such as Spring and Hibernate), graphical user interface (GUI) design tools (including JavaFX), and testing tools units such as JUnit.

Finally, Java also offers a wide range of other types of development tools designed specifically for the Java language, including integrated development environments (IDEs) and code editors, software creation and deployment, 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, strengthening its data manipulation and data science capabilities respectively. Other libraries, like caret (machine learning) and lubridate (date and time parsing), further enhance its functionality.

R, being a younger language and more limited in its usage scenarios, does not have as many development tools or frameworks as Java, which gives Java a big advantage in this area.

Performance

Java syntax is compiled into bytecode, which, in turn, is executed by the JVM. This initial abstraction may incur a bit more resource overhead, but modern systems and additional optimizations made by JVM and JIT compilers more than make up for this additional cost. As such, Java is very performant and capable of handling applications that require speed, efficiency and scalability. This is especially true for software that uses multithreading, concurrency, and parallelism – areas in which Java excels.

R, being an interpreted programming language, has some performance bottlenecks when it comes to handling larger datasets and complex calculations. However, this can be mitigated through the use of R packages that use compiled code, including data table (for data manipulation) and Rcppwhich allows developers to incorporate C++ code into their R codebases. With C++, programmers can better control memory and processing resources at the hardware level, which helps optimize performance.

Community

Java has a very large and thriving community that has grown over the past few decades. This community provides tons of learning resources, such as how-to videos, tutorials, articles, and discussion forums on best practices, problem solving, and programming logic.

Additionally, Oracle provides official documentation and the community as a whole contributes to the development of Java core, bringing additional features and security with each update.

As you can imagine, R has a small community, although it is known to be very engaged and contribute greatly to the field of data science and statistics. There are many sites offering tutorials and forums for R and the community also contributes to the CRAN repository, which hosts R packages.

Final Thoughts on Java vs. R

In this programming tutorial, we compared the Java and R programming languages, both of which are very distinct and have different syntaxes. Java is class-based and focuses on code organization, reusability, readability, and maintainability of code. It is a mature language that can be used for virtually any type of application and is an excellent choice for large-scale and enterprise-level software.

R, on the other hand, 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 development toolbox and can improve not only your skills, but also your career, your chances of getting hired, and your potential salary.

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