D vs R

Overview

R Language

Stacks3.9K

Followers1.9K

Votes418

Stacks777

Followers136

Votes160

D vs R: What are the differences?

Key Differences between D and R

D and R are both programming languages that are commonly used for data analysis and statistical modeling. While they have some similarities, there are several key differences between the two.

Syntax: One major difference between D and R is the syntax they use. D has a syntax that is more similar to other programming languages like C++, with its C-style syntax and object-oriented features. On the other hand, R has a syntax that is specifically designed for statistical analysis and data manipulation, making it more concise and intuitive for those tasks.
Performance: D is known for its high performance and efficient execution, making it a popular choice for applications that require speed and computational power. R, on the other hand, is not as fast as D and is better suited for analyzing smaller datasets or performing statistical calculations that do not require real-time processing.
Package Ecosystem: R has a rich package ecosystem with thousands of community-contributed packages that provide specialized functionalities for data analysis, visualization, and statistical modeling. These packages make it easy to perform complex analyses and create high-quality graphics in R. In comparison, D has a smaller package ecosystem with fewer specialized packages for data analysis and statistical modeling.
Type System: D has a statically typed system, which means that variables are required to have explicit types assigned at compile-time. This allows for better optimization and error checking, but it also requires more upfront planning and can be less flexible. R, on the other hand, has a dynamic type system, which allows for more flexibility but can also lead to errors if not used carefully.
Community and Support: R has a larger and more active community compared to D. This means that there are more online resources, forums, and tutorials available for learning and troubleshooting R. Additionally, R is often used in academia and research, which means that there are many experts and statisticians proficient in R who can provide support and guidance. While D does have a growing community, it is not as extensive or established as the R community.
Application Domain: D is a general-purpose programming language that can be used for a wide range of applications beyond data analysis and statistical modeling. It can be utilized in areas like systems programming, game development, and web development. On the other hand, R is specifically designed for statistical analysis, data mining, and visualization. It provides built-in statistical functions and a user-friendly interface that makes it easier to perform data analysis tasks.

In Summary, D and R differ in terms of syntax, performance, package ecosystem, type system, community and support, and application domain. While D is a powerful general-purpose programming language with high performance, R is specifically designed for statistical analysis and has a rich package ecosystem and strong community support.

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Detailed Comparison

R Language	D
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.	D is a language with C-like syntax and static typing. It pragmatically combines efficiency, control, and modeling power, with safety and programmer productivity.
Statistics
Stacks 3.9K	Stacks 777
Followers 1.9K	Followers 136
Votes 418	Votes 160
Pros & Cons
Pros 86 Data analysis 64 Graphics and data visualization 55 Free 45 Great community 38 Flexible statistical analysis toolkit Cons 6 Very messy syntax 4 Tables must fit in RAM 3 Arrays indices start with 1 2 Messy syntax for string concatenation 2 No push command for vectors/lists	Pros 16 Compile-time function execution 12 Productive 12 Makes functional programming style easier 12 Much easier to do Concurrent/Parallel vs C/C++ 11 Simple but Powerful template-based generics

What are some alternatives to R Language, D?

JavaScript

JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.

Python

Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.

PHP

Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

Ruby

Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.

Java

Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!

Golang

Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.

HTML5

HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997.

C#

C# (pronounced "See Sharp") is a simple, modern, object-oriented, and type-safe programming language. C# has its roots in the C family of languages and will be immediately familiar to C, C++, Java, and JavaScript programmers.

Scala

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.

Elixir

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

Related Comparisons

D vs R: What are the differences?

Key Differences between D and R

D and R are both programming languages that are commonly used for data analysis and statistical modeling. While they have some similarities, there are several key differences between the two.

Syntax: One major difference between D and R is the syntax they use. D has a syntax that is more similar to other programming languages like C++, with its C-style syntax and object-oriented features. On the other hand, R has a syntax that is specifically designed for statistical analysis and data manipulation, making it more concise and intuitive for those tasks.
Performance: D is known for its high performance and efficient execution, making it a popular choice for applications that require speed and computational power. R, on the other hand, is not as fast as D and is better suited for analyzing smaller datasets or performing statistical calculations that do not require real-time processing.
Package Ecosystem: R has a rich package ecosystem with thousands of community-contributed packages that provide specialized functionalities for data analysis, visualization, and statistical modeling. These packages make it easy to perform complex analyses and create high-quality graphics in R. In comparison, D has a smaller package ecosystem with fewer specialized packages for data analysis and statistical modeling.
Type System: D has a statically typed system, which means that variables are required to have explicit types assigned at compile-time. This allows for better optimization and error checking, but it also requires more upfront planning and can be less flexible. R, on the other hand, has a dynamic type system, which allows for more flexibility but can also lead to errors if not used carefully.
Community and Support: R has a larger and more active community compared to D. This means that there are more online resources, forums, and tutorials available for learning and troubleshooting R. Additionally, R is often used in academia and research, which means that there are many experts and statisticians proficient in R who can provide support and guidance. While D does have a growing community, it is not as extensive or established as the R community.
Application Domain: D is a general-purpose programming language that can be used for a wide range of applications beyond data analysis and statistical modeling. It can be utilized in areas like systems programming, game development, and web development. On the other hand, R is specifically designed for statistical analysis, data mining, and visualization. It provides built-in statistical functions and a user-friendly interface that makes it easier to perform data analysis tasks.

D vs R

Overview

D vs R: What are the differences?