Nim vs R

Overview

R Language

Stacks3.9K

Followers1.9K

Votes418

Nim

Stacks210

Followers154

Votes61

GitHub Stars17.5K

Forks1.5K

Nim vs R: What are the differences?

Syntax: One key difference between Nim and R is the syntax they use. Nim employs a Python-like syntax with a strong emphasis on readability and efficiency, making it easier for developers to write clean and concise code. On the other hand, R uses a unique syntax specifically designed for statistical computing and graphics, with a focus on data analysis and visualization.
Typing: Another significant difference is in their typing systems. Nim is a statically typed language, which means variables are required to have their data types declared before compilation, leading to better error checks and improved performance. In contrast, R is dynamically typed, allowing for more flexibility but potentially increasing the likelihood of run-time errors.
Usage: Nim is a general-purpose programming language suitable for a wide range of applications, including systems programming, scripting, and web development. It is known for its high performance and low-level capabilities. On the other hand, R is primarily used for statistical analysis, data visualization, and machine learning tasks, making it a popular choice among data scientists and statisticians.
Community and Ecosystem: Nim has a smaller but growing community compared to R, which has a large and established user base. R has a vast ecosystem of packages and libraries dedicated to statistical computing and data analysis, providing users with a rich set of tools and resources. Nim, on the other hand, may require more effort to find specific libraries or support for certain tasks.
Learning Curve: Nim is considered to have a steeper learning curve compared to R, especially for beginners due to its low-level features and emphasis on performance optimization. R, with its user-friendly syntax and specialized focus on data analysis, is often more approachable for those new to programming or statistics.
Performance: Nim is known for its high performance and compilation speed, making it well-suited for performance-critical applications where speed is a priority. On the other hand, R may sacrifice some performance in exchange for its ease of use and specialized statistical computing features.

In Summary, Nim and R differ in syntax, typing, usage, community, learning curve, and performance, catering to different needs and preferences in the programming and statistical analysis domains.

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Manual

Detailed Comparison

R Language	Nim
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.	It is an efficient, expressive and elegant language which compiles to C/C++/JS and more. It combines successful concepts from mature languages like Python, Ada and Modula.
-	Intuitive and clean syntax; Many garbage collector options; JavaScript compilation; Decentralised package management; Helpful tracebacks
Statistics
GitHub Stars -	GitHub Stars 17.5K
GitHub Forks -	GitHub Forks 1.5K
Stacks 3.9K	Stacks 210
Followers 1.9K	Followers 154
Votes 418	Votes 61
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 No push command for vectors/lists 2 Messy syntax for string concatenation	Pros 15 Expressive like Python 15 Extremely fast 11 Very fast compilation 7 Macros 5 Cross platform Cons 4 Small Community 0 [object Object]
Integrations
No integrations available	JavaScript C++ C lang Python Sapper Tokamak Sonic Server

What are some alternatives to R Language, Nim?

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

Nim vs R: What are the differences?

Syntax: One key difference between Nim and R is the syntax they use. Nim employs a Python-like syntax with a strong emphasis on readability and efficiency, making it easier for developers to write clean and concise code. On the other hand, R uses a unique syntax specifically designed for statistical computing and graphics, with a focus on data analysis and visualization.
Typing: Another significant difference is in their typing systems. Nim is a statically typed language, which means variables are required to have their data types declared before compilation, leading to better error checks and improved performance. In contrast, R is dynamically typed, allowing for more flexibility but potentially increasing the likelihood of run-time errors.
Usage: Nim is a general-purpose programming language suitable for a wide range of applications, including systems programming, scripting, and web development. It is known for its high performance and low-level capabilities. On the other hand, R is primarily used for statistical analysis, data visualization, and machine learning tasks, making it a popular choice among data scientists and statisticians.
Community and Ecosystem: Nim has a smaller but growing community compared to R, which has a large and established user base. R has a vast ecosystem of packages and libraries dedicated to statistical computing and data analysis, providing users with a rich set of tools and resources. Nim, on the other hand, may require more effort to find specific libraries or support for certain tasks.
Learning Curve: Nim is considered to have a steeper learning curve compared to R, especially for beginners due to its low-level features and emphasis on performance optimization. R, with its user-friendly syntax and specialized focus on data analysis, is often more approachable for those new to programming or statistics.
Performance: Nim is known for its high performance and compilation speed, making it well-suited for performance-critical applications where speed is a priority. On the other hand, R may sacrifice some performance in exchange for its ease of use and specialized statistical computing features.

In Summary, Nim and R differ in syntax, typing, usage, community, learning curve, and performance, catering to different needs and preferences in the programming and statistical analysis domains.

Nim vs R

Overview