R Language vs Ruby

Overview

Ruby

Stacks46.0K

Followers21.8K

Votes4.0K

GitHub Stars23.0K

Forks5.5K

R Language

Stacks3.9K

Followers1.9K

Votes418

R Language vs Ruby: What are the differences?

Introduction: R Language and Ruby are two popular programming languages that have their own unique features and characteristics. While both languages are used for data analysis and general-purpose programming, there are key differences between them that set them apart.

Syntax: One of the main differences between R Language and Ruby is their syntax. R Language has a syntax that is specifically designed for statistical computing and data analysis. It uses a combination of S expressions and functions, which can be a bit complex for beginners. On the other hand, Ruby has a more straightforward and readable syntax, which makes it easier to learn and understand.
Data Analysis Capabilities: R Language is widely used for statistical computing and data analysis. It provides a wide range of built-in libraries and packages specifically designed for data manipulation, statistical modeling, and visualization. Ruby, on the other hand, is a general-purpose programming language and does not have the same level of built-in data analysis capabilities as R Language. However, Ruby does have some libraries and frameworks that can be used for data analysis, but they are not as extensive as the ones available in R Language.
Object-Oriented Programming: Both R Language and Ruby are object-oriented programming (OOP) languages. However, the approach to OOP is different in each language. In R Language, objects are mainly used for data manipulation and analysis, while in Ruby, objects are used for general programming purposes. Ruby has a more robust and extensive support for OOP concepts such as classes, inheritance, and object methods.
Community and Libraries: R Language has a strong community of statisticians, data scientists, and researchers, which has led to the development of a vast number of libraries and packages specifically tailored for statistical computing and data analysis. These libraries allow users to perform complex statistical operations and data manipulations with ease. Ruby, on the other hand, has a larger community of web developers and general-purpose programmers. The libraries and frameworks available in Ruby are more focused on web development and general programming tasks.
Performance: Another key difference between R Language and Ruby is their performance. R Language is known for its efficiency in handling large datasets and performing complex statistical computations. It is optimized for data analysis tasks and can handle large computations efficiently. Ruby, on the other hand, is a more general-purpose language and may not perform as well as R Language when it comes to intensive data analysis tasks.
Domain-specific Use Cases: Due to their differences in syntax and capabilities, R Language and Ruby have different domain-specific use cases. R Language is commonly used in fields such as data science, statistics, and academic research. It is widely used in industries that require complex statistical computations and data analysis, such as healthcare, finance, and social sciences. Ruby, on the other hand, is widely used in web development, scripting, automation, and general programming tasks. It is commonly used to build web applications, websites, and software tools.

In Summary, R Language and Ruby have distinct syntax, data analysis capabilities, object-oriented programming approaches, communities and libraries, performance characteristics, and domain-specific use cases that set them apart from each other.

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Advice on Ruby, R Language

Thomas

Talent Co-Ordinator at Tessian

Mar 11, 2020

Decided

In December we successfully flipped around half a billion monthly API requests from our Ruby on Rails application to some new Python 3 applications. Our Head of Engineering has written a great article as to why we decided to transition from Ruby on Rails to Python 3! Read more about it in the link below.

263k views263k

Comments

Andrew

Chief Software Architect at Xelex Digital, LLC

Jun 27, 2020

Decided

In 2015 as Xelex Digital was paving a new technology path, moving from ASP.NET web services and web applications, we knew that we wanted to move to a more modular decoupled base of applications centered around REST APIs.

To that end we spent several months studying API design patterns and decided to use our own adaptation of CRUD, specifically a SCRUD pattern that elevates query params to a more central role via the Search action.

Once we nailed down the API design pattern it was time to decide what language(s) our new APIs would be built upon. Our team has always been driven by the right tool for the job rather than what we know best. That said, in balancing practicality we chose to focus on 3 options that our team had deep experience with and knew the pros and cons of.

For us it came down to C#, JavaScript, and Ruby. At the time we owned our infrastructure, racks in cages, that were all loaded with Windows. We were also at a point that we were using that infrastructure to it's fullest and could not afford additional servers running Linux. That's a long way of saying we decided against Ruby as it doesn't play nice on Windows.

That left us with two options. We went a very unconventional route for deciding between the two. We built MVP APIs on both. The interfaces were identical and interchangeable. What we found was easily quantifiable differences.

We were able to iterate on our Node based APIs much more rapidly than we were our C# APIs. For us this was owed to the community coupled with the extremely dynamic nature of JS. There were tradeoffs we considered, latency was (acceptably) higher on requests to our Node APIs. No strong types to protect us from ourselves, but we've rarely found that to be an issue.

As such we decided to commit resources to our Node APIs and push it out as the core brain of our new system. We haven't looked back since. It has consistently met our needs, scaling with us, getting better with time as continually pour into and expand our capabilities.

446k views446k

Comments

Mike

Enterprise Architect at Warby Parker

Dec 22, 2019

Decided

When I was evaluating languages to write this app in, I considered either Python or JavaScript at the time. I find Ruby very pleasant to read and write, and the Ruby community has built out a wide variety of test tools and approaches, helping e deliver better software faster. Along with Rails, and the Ruby-first Heroku support, this was an easy decision.

258k views258k

Comments

Detailed Comparison

Ruby	R Language
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.	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.
Statistics
GitHub Stars 23.0K	GitHub Stars -
GitHub Forks 5.5K	GitHub Forks -
Stacks 46.0K	Stacks 3.9K
Followers 21.8K	Followers 1.9K
Votes 4.0K	Votes 418
Pros & Cons
Pros 608 Programme friendly 538 Quick to develop 492 Great community 469 Productivity 432 Simplicity Cons 7 Really slow if you're not really careful 7 Memory hog 3 Nested Blocks can make code unreadable 2 Encouraging imperative programming 1 No type safety, so it requires copious testing	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
Integrations
Rails	No integrations available

What are some alternatives to Ruby, R Language?

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.

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.

Swift

Writing code is interactive and fun, the syntax is concise yet expressive, and apps run lightning-fast. Swift is ready for your next iOS and OS X project — or for addition into your current app — because Swift code works side-by-side with Objective-C.

Related Comparisons

R Language vs Ruby: What are the differences?

Syntax: One of the main differences between R Language and Ruby is their syntax. R Language has a syntax that is specifically designed for statistical computing and data analysis. It uses a combination of S expressions and functions, which can be a bit complex for beginners. On the other hand, Ruby has a more straightforward and readable syntax, which makes it easier to learn and understand.
Data Analysis Capabilities: R Language is widely used for statistical computing and data analysis. It provides a wide range of built-in libraries and packages specifically designed for data manipulation, statistical modeling, and visualization. Ruby, on the other hand, is a general-purpose programming language and does not have the same level of built-in data analysis capabilities as R Language. However, Ruby does have some libraries and frameworks that can be used for data analysis, but they are not as extensive as the ones available in R Language.
Object-Oriented Programming: Both R Language and Ruby are object-oriented programming (OOP) languages. However, the approach to OOP is different in each language. In R Language, objects are mainly used for data manipulation and analysis, while in Ruby, objects are used for general programming purposes. Ruby has a more robust and extensive support for OOP concepts such as classes, inheritance, and object methods.
Community and Libraries: R Language has a strong community of statisticians, data scientists, and researchers, which has led to the development of a vast number of libraries and packages specifically tailored for statistical computing and data analysis. These libraries allow users to perform complex statistical operations and data manipulations with ease. Ruby, on the other hand, has a larger community of web developers and general-purpose programmers. The libraries and frameworks available in Ruby are more focused on web development and general programming tasks.
Performance: Another key difference between R Language and Ruby is their performance. R Language is known for its efficiency in handling large datasets and performing complex statistical computations. It is optimized for data analysis tasks and can handle large computations efficiently. Ruby, on the other hand, is a more general-purpose language and may not perform as well as R Language when it comes to intensive data analysis tasks.
Domain-specific Use Cases: Due to their differences in syntax and capabilities, R Language and Ruby have different domain-specific use cases. R Language is commonly used in fields such as data science, statistics, and academic research. It is widely used in industries that require complex statistical computations and data analysis, such as healthcare, finance, and social sciences. Ruby, on the other hand, is widely used in web development, scripting, automation, and general programming tasks. It is commonly used to build web applications, websites, and software tools.

R Language vs Ruby

Overview