R Language vs Ruby

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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Decisions about R Language and Ruby
Andrew Carpenter
Chief Software Architect at Xelex Digital, LLC · | 16 upvotes · 408.3K views

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.

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Thomas Miller
Talent Co-Ordinator at Tessian · | 16 upvotes · 233.3K views

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.

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Mike Fiedler
Enterprise Architect at Warby Parker · | 3 upvotes · 225.3K views

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.

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Pros of R Language
Pros of Ruby
  • 84
    Data analysis
  • 63
    Graphics and data visualization
  • 54
    Free
  • 45
    Great community
  • 38
    Flexible statistical analysis toolkit
  • 27
    Easy packages setup
  • 27
    Access to powerful, cutting-edge analytics
  • 18
    Interactive
  • 13
    R Studio IDE
  • 9
    Hacky
  • 7
    Shiny apps
  • 6
    Preferred Medium
  • 6
    Shiny interactive plots
  • 5
    Automated data reports
  • 4
    Cutting-edge machine learning straight from researchers
  • 3
    Machine Learning
  • 2
    Graphical visualization
  • 1
    Flexible Syntax
  • 605
    Programme friendly
  • 536
    Quick to develop
  • 490
    Great community
  • 468
    Productivity
  • 432
    Simplicity
  • 273
    Open source
  • 234
    Meta-programming
  • 207
    Powerful
  • 156
    Blocks
  • 139
    Powerful one-liners
  • 69
    Flexible
  • 58
    Easy to learn
  • 51
    Easy to start
  • 42
    Maintainability
  • 37
    Lambdas
  • 30
    Procs
  • 21
    Fun to write
  • 19
    Diverse web frameworks
  • 13
    Reads like English
  • 10
    Makes me smarter and happier
  • 9
    Rails
  • 8
    Very Dynamic
  • 8
    Elegant syntax
  • 6
    Matz
  • 5
    Object Oriented
  • 5
    Programmer happiness
  • 4
    Elegant code
  • 4
    Generally fun but makes you wanna cry sometimes
  • 4
    Friendly
  • 4
    Fun and useful
  • 3
    Easy packaging and modules
  • 3
    There are so many ways to make it do what you want
  • 2
    Primitive types can be tampered with

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Cons of R Language
Cons of Ruby
  • 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
  • 1
    Messy character encoding
  • 0
    Poor syntax for classes
  • 0
    Messy syntax for array/vector combination
  • 7
    Memory hog
  • 7
    Really slow if you're not really careful
  • 3
    Nested Blocks can make code unreadable
  • 2
    Encouraging imperative programming
  • 1
    Ambiguous Syntax, such as function parentheses

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What is R Language?

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.

What is 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.

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What are some alternatives to R Language and Ruby?
MATLAB
Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
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.
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.
SAS
It is a command-driven software package used for statistical analysis and data visualization. It is available only for Windows operating systems. It is arguably one of the most widely used statistical software packages in both industry and academia.
Rust
Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory.
See all alternatives