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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.
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.
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.
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.
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.
Pros of R Language
- Data analysis86
- Graphics and data visualization64
- Free55
- Great community45
- Flexible statistical analysis toolkit38
- Easy packages setup27
- Access to powerful, cutting-edge analytics27
- Interactive18
- R Studio IDE13
- Hacky9
- Shiny apps7
- Shiny interactive plots6
- Preferred Medium6
- Automated data reports5
- Cutting-edge machine learning straight from researchers4
- Machine Learning3
- Graphical visualization2
- Flexible Syntax1
Pros of Ruby
- Programme friendly607
- Quick to develop538
- Great community492
- Productivity469
- Simplicity432
- Open source274
- Meta-programming235
- Powerful208
- Blocks157
- Powerful one-liners140
- Flexible70
- Easy to learn59
- Easy to start52
- Maintainability42
- Lambdas38
- Procs31
- Fun to write21
- Diverse web frameworks19
- Reads like English14
- Makes me smarter and happier10
- Rails9
- Elegant syntax9
- Very Dynamic8
- Matz7
- Programmer happiness6
- Object Oriented5
- Elegant code4
- Friendly4
- Generally fun but makes you wanna cry sometimes4
- Fun and useful4
- There are so many ways to make it do what you want3
- Easy packaging and modules3
- Primitive types can be tampered with2
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Cons of R Language
- Very messy syntax6
- Tables must fit in RAM4
- Arrays indices start with 13
- Messy syntax for string concatenation2
- No push command for vectors/lists2
- Messy character encoding1
- Poor syntax for classes0
- Messy syntax for array/vector combination0
Cons of Ruby
- Memory hog7
- Really slow if you're not really careful7
- Nested Blocks can make code unreadable3
- Encouraging imperative programming2
- No type safety, so it requires copious testing1
- Ambiguous Syntax, such as function parentheses1
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