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R vs Ruby: What are the differences?

Introduction

In this Markdown code, I will provide the key differences between R and Ruby programming languages. These differences will be specific and concise, with each difference explained in a single paragraph. The purpose is to highlight the distinctions between these two languages in terms of their features and functionalities.

1. Data Analysis vs General Programming: R is designed specifically for statistical analysis and data visualization purposes. It provides a wide range of libraries and packages that are tailored for statistical modeling, data manipulation, and graphical representation. On the other hand, Ruby is a general-purpose programming language that can be used for various purposes like web development, scripting, and automation. While Ruby does offer libraries for statistical analysis, it is not as specialized as R in this domain.

2. Syntax and Readability: R has a syntax that is heavily influenced by the S programming language, making it more focused on statistical programming and data exploration. Its syntax is relatively simple and concise, with a greater emphasis on vectorized operations. On the contrary, Ruby has a more general-purpose syntax that focuses on simplicity and readability. Ruby code is often considered more elegant and easier to comprehend, with a syntax that emphasizes clarity and expressiveness.

3. Object-Oriented Programming (OOP) vs Functional Programming: Ruby is a fully object-oriented programming language, meaning that everything in Ruby is an object, including numbers, strings, and even classes. It follows the principles of OOP, such as encapsulation, inheritance, and polymorphism. In contrast, while R also supports OOP principles, it primarily follows a functional programming paradigm, with a focus on data transformation and manipulation through functions and pipelines.

4. Package Ecosystem and Community Support: R has a vast and mature package ecosystem that is specifically developed for statistical analysis, data manipulation, and visualization tasks. The R community is highly active and supportive, with numerous resources, tutorials, and forums available for learning and problem-solving. Ruby, on the other hand, has a broader range of packages and libraries, catering to different domains like web development, networking, and automation. It also has a strong community, but its focus is generally more distributed across various use cases.

5. Performance and Scalability: In terms of performance, Ruby is an interpreted language. While it offers great productivity and ease of use, it might not be as efficient or fast as compiled languages like C or Java. R, however, relies on optimized packages and libraries for numerical computations, making it faster for statistical operations compared to general-purpose programming languages. Additionally, R has better scalability for handling large datasets and complex statistical calculations.

6. Job Opportunities and Market Demand: The job market for R is primarily focused on data analysis, data science, and statistics. R is heavily used in academia, research, and industry settings where statistical modeling and data exploration are critical. On the other hand, Ruby's job market is more diversified, with opportunities in web development, software engineering, and automation. Ruby is widely used in web frameworks like Ruby on Rails, which makes it a popular choice for web-based applications.

In Summary, R is specialized for statistical analysis and has a strong package ecosystem for data manipulation and visualization, while Ruby is a general-purpose language focusing on simplicity, readability, and broader application domains such as web development and automation.

Advice on and R Language
Caue Carvalho
Needs advice
on
GolangGolangPythonPython
and
RubyRuby

Hello!

I'm a developer for over 9 years, and most of this time I've been working with C# and it is paying my bills until nowadays. But I'm seeking to learn other languages and expand the possibilities for the next years.

Now the question... I know Ruby is far from dead but is it still worth investing time in learning it? Or would be better to take Python, Golang, or even Rust? Or maybe another language.

Thanks in advance.

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Replies (8)
Angel Ramirez
Recommends
on
GolangGolangPythonPython
at

Hi Caue, I don't think any language is dead in 2022, and we still see a lot of Cobol and Fortran out there, so Ruby is not going to die for sure. However, based on the market, you'll be better off learning Goland and Python. For example, for data science, machine learning, and similar areas, Python is the default language while backend API, services, and other general purpose Goland is becoming the preferred.

I hope this helps.

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Recommends
on
GolangGolangPythonPython

I feel most productive using go. It has all the features I need and doesn't throw road blocks in your way as you learn. Rust is the most difficult to learn as borrow checking and other features can puzzle a newcomer for days. Python is a logical next step as it has a huge following, many great libraries, and one can find a gig using python in a heartbeat. Ruby isn't awful, it's just not that popular as the others.

Another reason to use python is that it is not compiled. You can muck around in the interpreter until you figure things out. OTOH, that makes it less performant. You really need to think about your use cases, your interest in lower-lever versus high-level coding, and so on.

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Roman Glushko
Machine Learning, Software Engineering and Life · | 4 upvotes · 73.8K views
Recommends
on
GolangGolangPythonPythonRustRust

I enjoy coding in Python. I think it's minimalistic and readable syntax and lang features are just unparalleled. They are perfect for prototyping and for the software engineering in general. If I'm not wrong Gitlab marked Python as #2 popular language after JavaScript. Beyond that, Python ecosystem and areas of usage are enormous. In areas like ML/DL, it's important to know Python to leverage variety of existing tools and frameworks.

Then, I have learned and worked with Golang. I use it where I think I would need a slightly better performance than in Python. Plus, relatively small and self-contained executable is a great thing to have. If you plan to write distributed systems, extend Kubernetes or do similar things I think Golang is a great choice. It's also simple and straightforward, especially when you want to do effective multithreading. Although I don't like that Golang is more low-level than Python. Sometimes I feel like I need to implement myself too much things.

Now, about Rust. It's my second try to learn Rust. First time I decided to learn Golang as I understood it in 30mins or so while I was struggling to compile/do anything meaningful there for quite a bit. So I personally don't think Rust is super easy. I have got back to learning Rust as it's going to fill one of gaps in my problem solving toolkit - let me write low-level system programs (e.g. linux kernel modules). I don't want to learn "obsolete" C/C++ (my reasons are similar to why Google has recently introduced Carbon - a replacement for C/C++ codebases). If you are not going to tight your life with system-like programming, Rust may be an overkill for you.

Finally, I have never coded in Ruby, so are not going to comment it.

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Recommends
on
GolangGolangPythonPythonRubyRuby

it is highly recommended to take a look at that survey

https://survey.stackoverflow.co/2022/

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Recommends
on
PythonPython

Because it opens endless possibilities you can do anything and everything you want to. from ai to app development to web development.

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Recommends

I'm almost same position as you. 8 years same company with c#. I tried both Python and Golang. I like working with Golang. Check this litte go doc. After reading this document and following its examples, I decided to work with "go" https://www.openmymind.net/assets/go/go.pdf

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Recommends
at

Since you are very experienced, picking up a language will not take you more than a week. Rust is a very new language. Many startups are still experimenting with it. Golang is very popular nowadays. You can see a lot of golang jobs in the market. The best part is, compiled code is single binary and has a minimal footprint. Rails is a compelling framework; believe me, many websites like Shopify, GitHub, GitLab, etc., are powered by the rails framework. You can also leverage the power of metaprogramming in Ruby. Python is memory and CPU intensive. It is not as performant as the other three. If you want to go into Data Science, Python is the language. Good luck, buddy. Feel free to connect with me: https://twitter.com/avirajkhare00

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A Nielsen
Fullstack Dev at ADTELA · | 1 upvotes · 70.1K views
Recommends
on
GolangGolangPythonPython

Either Python or Golang, for all the enlightened reasons already mentionned in all advices/comments :) Enjoy!

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Decisions about and R Language

A developer and project manager from our team X says the following about our use of Rails at i22:

"We use Rails to build stable and flexible backend systems. Rails is extremely good for managing data structures and quickly setting up new systems. It is the perfect base for most use cases."

I asked the same Team X member why the team prefers to work with Ruby on Rails, rather than Python and Django:

"Because Python is a scripting language and from my point of view not suitable for building stable web services. Python is for me rather good for scripts and fast small tools. Not for stable business applications. And if I want it fast I prefer Go."

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MACHINE LEARNING

Python is the default go-to for machine learning. It has a wide variety of useful packages such as pandas and numpy to aid with ML, as well as deep-learning frameworks. Furthermore, it is more production-friendly compared to other ML languages such as R.

Pytorch is a deep-learning framework that is both flexible and fast compared to Tensorflow + Keras. It is also well documented and has a large community to answer lingering questions.

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Ing. Alvaro Rodríguez Scelza
Software Systems Engineer at Ripio · | 12 upvotes · 381.1K views

I was considering focusing on learning RoR and looking for a work that uses those techs.

After some investigation, I decided to stay with C# .NET:

  • It is more requested on job positions (7 to 1 in my personal searches average).

  • It's been around for longer.

  • it has better documentation and community.

  • One of Ruby advantages (its amazing community gems, that allows to quickly build parts of your systems by merely putting together third party components) gets quite complicated to use and maintain in huge applications, where building and reusing your own components may become a better approach.

  • Rail's front end support is starting to waver.

  • C# .NET code is far easier to understand, debug and maintain. Although certainly not easier to learn from scratch.

  • Though Rails has an excellent programming speed, C# tends to get the upper hand in long term projects.

I would avise to stick to rails when building small projects, and switching to C# for more long term ones.

Opinions are welcome!

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Timm Stelzer
VP Of Engineering at Flexperto GmbH · | 18 upvotes · 647.9K views

We have a lot of experience in JavaScript, writing our services in NodeJS allows developers to transition to the back end without any friction, without having to learn a new language. There is also the option to write services in TypeScript, which adds an expressive type layer. The semi-shared ecosystem between front and back end is nice as well, though specifically NodeJS libraries sometimes suffer in quality, compared to other major languages.

As for why we didn't pick the other languages, most of it comes down to "personal preference" and historically grown code bases, but let's do some post-hoc deduction:

Go is a practical choice, reasonably easy to learn, but until we find performance issues with our NodeJS stack, there is simply no reason to switch. The benefits of using NodeJS so far outweigh those of picking Go. This might change in the future.

PHP is a language we're still using in big parts of our system, and are still sometimes writing new code in. Modern PHP has fixed some of its issues, and probably has the fastest development cycle time, but it suffers around modelling complex asynchronous tasks, and (on a personal note) lack of support for writing in a functional style.

We don't use Python, Elixir or Ruby, mostly because of personal preference and for historic reasons.

Rust, though I personally love and use it in my projects, would require us to specifically hire for that, as the learning curve is quite steep. Its web ecosystem is OK by now (see https://www.arewewebyet.org/), but in my opinion, it is still no where near that of the other web languages. In other words, we are not willing to pay the price for playing this innovation card.

Haskell, as with Rust, I personally adore, but is simply too esoteric for us. There are problem domains where it shines, ours is not one of them.

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Andrew Carpenter
Chief Software Architect at Xelex Digital, LLC · | 16 upvotes · 427K 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 · 247.6K 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 · 241.4K 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
  • 86
    Data analysis
  • 64
    Graphics and data visualization
  • 55
    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
    Shiny interactive plots
  • 6
    Preferred Medium
  • 5
    Automated data reports
  • 4
    Cutting-edge machine learning straight from researchers
  • 3
    Machine Learning
  • 2
    Graphical visualization
  • 1
    Flexible Syntax
Cons of R Language
  • 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
What companies use R Language?
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What tools integrate with R Language?
What are some alternatives to and R Language?
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