What is R Language and what are its top alternatives?
Top Alternatives to 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. ...
- 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. ...
- Julia
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. ...
- 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! ...
R Language alternatives & related posts
MATLAB
- Simulink16
- Functions, statements, plots, directory navigation easy5
- Model based software development3
- S-Functions3
- REPL2
- Simple variabel control1
- Solve invertible matrix1
- Parameter-value pairs syntax to pass arguments clunky1
- Does not support named function arguments0
- Doesn't allow unpacking tuples/arguments lists with *0
related MATLAB posts
Python
- Great libraries1.1K
- Readable code937
- Beautiful code830
- Rapid development774
- Large community677
- Open source422
- Elegant381
- Great community273
- Object oriented266
- Dynamic typing211
- Great standard library73
- Very fast54
- Functional programming51
- Easy to learn39
- Scientific computing39
- Great documentation32
- Productivity25
- Matlab alternative25
- Easy to read24
- Simple is better than complex20
- It's the way I think18
- Imperative17
- Free15
- Very programmer and non-programmer friendly15
- Powerfull language14
- Powerful14
- Fast and simple13
- Scripting12
- Machine learning support12
- Explicit is better than implicit9
- Ease of development8
- Unlimited power8
- Clear and easy and powerfull8
- Import antigravity7
- It's lean and fun to code6
- Print "life is short, use python"6
- Great for tooling5
- There should be one-- and preferably only one --obvious5
- Python has great libraries for data processing5
- High Documented language5
- I love snakes5
- Although practicality beats purity5
- Flat is better than nested5
- Fast coding and good for competitions5
- Readability counts4
- Lists, tuples, dictionaries3
- CG industry needs3
- Now is better than never3
- Multiple Inheritence3
- Great for analytics3
- Complex is better than complicated3
- Plotting3
- Beautiful is better than ugly3
- Rapid Prototyping3
- Socially engaged community3
- List comprehensions2
- Web scraping2
- Many types of collections2
- Ys2
- Easy to setup and run smooth2
- Generators2
- Special cases aren't special enough to break the rules2
- If the implementation is hard to explain, it's a bad id2
- If the implementation is easy to explain, it may be a g2
- Simple and easy to learn2
- Import this2
- No cruft2
- Easy to learn and use2
- Flexible and easy1
- Batteries included1
- Powerful language for AI1
- Should START with this but not STICK with This1
- Good1
- It is Very easy , simple and will you be love programmi1
- Better outcome1
- إسلام هشام1
- Because of Netflix1
- A-to-Z1
- Only one way to do it1
- Pip install everything1
- Powerful0
- Pro0
- Still divided between python 2 and python 351
- Performance impact29
- Poor syntax for anonymous functions26
- GIL21
- Package management is a mess19
- Too imperative-oriented14
- Dynamic typing12
- Hard to understand12
- Very slow10
- Not everything is expression8
- Indentations matter a lot7
- Explicit self parameter in methods7
- No anonymous functions6
- Poor DSL capabilities6
- Incredibly slow6
- Requires C functions for dynamic modules6
- The "lisp style" whitespaces5
- Fake object-oriented programming5
- Hard to obfuscate5
- Threading5
- Circular import4
- The benevolent-dictator-for-life quit4
- Official documentation is unclear.4
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- Not suitable for autocomplete4
- Meta classes2
- Training wheels (forced indentation)1
related Python posts











How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages
Golang
- High-performance530
- Simple, minimal syntax387
- Fun to write354
- Easy concurrency support via goroutines295
- Fast compilation times267
- Goroutines189
- Statically linked binaries that are simple to deploy177
- Simple compile build/run procedures148
- Backed by google134
- Great community131
- Garbage collection built-in50
- Built-in Testing42
- Excellent tools - gofmt, godoc etc41
- Elegant and concise like Python, fast like C38
- Awesome to Develop34
- Used for Docker25
- Flexible interface system24
- Great concurrency pattern22
- Deploy as executable22
- Open-source Integration19
- Fun to write and so many feature out of the box16
- Easy to read15
- Its Simple and Heavy duty14
- Go is God14
- Powerful and simple13
- Easy to deploy13
- Concurrency11
- Best language for concurrency11
- Safe GOTOs10
- Rich standard library10
- Clean code, high performance9
- Easy setup9
- Simplicity, Concurrency, Performance8
- High performance8
- Hassle free deployment8
- Used by Giants of the industry7
- Single binary avoids library dependency issues7
- Cross compiling6
- Simple, powerful, and great performance6
- Excellent tooling5
- Very sophisticated syntax5
- Gofmt5
- WYSIWYG5
- Garbage Collection5
- Widely used4
- Kubernetes written on Go4
- Keep it simple and stupid3
- No generics1
- Operator goto1
- You waste time in plumbing code catching errors41
- Verbose25
- Packages and their path dependencies are braindead22
- Google's documentations aren't beginer friendly15
- Dependency management when working on multiple projects15
- Automatic garbage collection overheads10
- Uncommon syntax8
- Type system is lacking (no generics, etc)6
- Collection framework is lacking (list, set, map)2
related Golang posts











How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages
SAS
related SAS posts
- Guaranteed memory safety137
- Fast125
- Open source82
- Minimal runtime75
- Pattern matching69
- Type inference61
- Concurrent55
- Algebraic data types54
- Efficient C bindings45
- Practical43
- Best advances in languages in 20 years37
- Safe, fast, easy + friendly community29
- Fix for C/C++29
- Stablity23
- Closures22
- Zero-cost abstractions21
- Extensive compiler checks19
- Great community18
- No NULL type15
- Async/await14
- Completely cross platform: Windows, Linux, Android14
- No Garbage Collection13
- Great documentations12
- High-performance12
- Super fast11
- High performance11
- Fearless concurrency10
- Generics10
- Safety no runtime crashes10
- Helpful compiler9
- Compiler can generate Webassembly9
- Guaranteed thread data race safety9
- Easy Deployment8
- Macros8
- Prevents data races8
- RLS provides great IDE support7
- Painless dependency management7
- Real multithreading6
- Support on Other Languages4
- Good package management4
- Hard to learn25
- Ownership learning curve23
- Unfriendly, verbose syntax10
- High size of builded executable4
- Variable shadowing4
- Many type operations make it difficult to follow4
- No jobs3
related Rust posts
Sentry's event processing pipeline, which is responsible for handling all of the ingested event data that makes it through to our offline task processing, is written primarily in Python.
For particularly intense code paths, like our source map processing pipeline, we have begun re-writing those bits in Rust. Rust’s lack of garbage collection makes it a particularly convenient language for embedding in Python. It allows us to easily build a Python extension where all memory is managed from the Python side (if the Python wrapper gets collected by the Python GC we clean up the Rust object as well).
In our company we have think a lot about languages that we're willing to use, there we have considering Java, Python and C++ . All of there languages are old and well developed at fact but that's not ideology of araclx. We've choose a edge technologies such as Node.js , Rust , Kotlin and Go as our programming languages which is some kind of fun. Node.js is one of biggest trends of 2019, same for Go. We want to grow in our company with growth of languages we have choose, and probably when we would choose Java that would be almost impossible because larger languages move on today's market slower, and cannot have big changes.
Ruby
- Programme friendly601
- Quick to develop535
- Great community487
- Productivity467
- Simplicity429
- Open source271
- Meta-programming233
- Powerful203
- Blocks155
- Powerful one-liners138
- Flexible67
- Easy to learn57
- Easy to start49
- Maintainability41
- Lambdas36
- Procs30
- Fun to write20
- Diverse web frameworks19
- Reads like English12
- Rails9
- Makes me smarter and happier9
- Elegant syntax8
- Very Dynamic7
- Matz6
- Programmer happiness5
- Generally fun but makes you wanna cry sometimes4
- Fun and useful4
- Object Oriented4
- Elegant code3
- Friendly3
- There are so many ways to make it do what you want3
- Easy packaging and modules3
- Primitive types can be tampered with2
- Memory hog7
- Really slow if you're not really careful7
- Nested Blocks can make code unreadable3
- Encouraging imperative programming2
- Ambiguous Syntax, such as function parentheses1
related Ruby posts
When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.














I needed to choose a full stack of tools for cross platform mobile application design & development. After much research and trying different tools, these are what I came up with that work for me today:
For the client coding I chose Framework7 because of its performance, easy learning curve, and very well designed, beautiful UI widgets. I think it's perfect for solo development or small teams. I didn't like React Native. It felt heavy to me and rigid. Framework7 allows the use of #CSS3, which I think is the best technology to come out of the #WWW movement. No other tech has been able to allow designers and developers to develop such flexible, high performance, customisable user interface elements that are highly responsive and hardware accelerated before. Now #CSS3 includes variables and flexboxes it is truly a powerful language and there is no longer a need for preprocessors such as #SCSS / #Sass / #less. React Native contains a very limited interpretation of #CSS3 which I found very frustrating after using #CSS3 for some years already and knowing its powerful features. The other very nice feature of Framework7 is that you can even build for the browser if you want your app to be available for desktop web browsers. The latest release also includes the ability to build for #Electron so you can have MacOS, Windows and Linux desktop apps. This is not possible with React Native yet.
Framework7 runs on top of Apache Cordova. Cordova and webviews have been slated as being slow in the past. Having a game developer background I found the tweeks to make it run as smooth as silk. One of those tweeks is to use WKWebView. Another important one was using srcset on images.
I use #Template7 for the for the templating system which is a no-nonsense mobile-centric #HandleBars style extensible templating system. It's easy to write custom helpers for, is fast and has a small footprint. I'm not forced into a new paradigm or learning some new syntax. It operates with standard JavaScript, HTML5 and CSS 3. It's written by the developer of Framework7 and so dovetails with it as expected.
I configured TypeScript to work with the latest version of Framework7. I consider TypeScript to be one of the best creations to come out of Microsoft in some time. They must have an amazing team working on it. It's very powerful and flexible. It helps you catch a lot of bugs and also provides code completion in supporting IDEs. So for my IDE I use Visual Studio Code which is a blazingly fast and silky smooth editor that integrates seamlessly with TypeScript for the ultimate type checking setup (both products are produced by Microsoft).
I use Webpack and Babel to compile the JavaScript. TypeScript can compile to JavaScript directly but Babel offers a few more options and polyfills so you can use the latest (and even prerelease) JavaScript features today and compile to be backwards compatible with virtually any browser. My favorite recent addition is "optional chaining" which greatly simplifies and increases readability of a number of sections of my code dealing with getting and setting data in nested objects.
I use some Ruby scripts to process images with ImageMagick and pngquant to optimise for size and even auto insert responsive image code into the HTML5. Ruby is the ultimate cross platform scripting language. Even as your scripts become large, Ruby allows you to refactor your code easily and make it Object Oriented if necessary. I find it the quickest and easiest way to maintain certain aspects of my build process.
For the user interface design and prototyping I use Figma. Figma has an almost identical user interface to #Sketch but has the added advantage of being cross platform (MacOS and Windows). Its real-time collaboration features are outstanding and I use them a often as I work mostly on remote projects. Clients can collaborate in real-time and see changes I make as I make them. The clickable prototyping features in Figma are also very well designed and mean I can send clickable prototypes to clients to try user interface updates as they are made and get immediate feedback. I'm currently also evaluating the latest version of #AdobeXD as an alternative to Figma as it has the very cool auto-animate feature. It doesn't have real-time collaboration yet, but I heard it is proposed for 2019.
For the UI icons I use Font Awesome Pro. They have the largest selection and best looking icons you can find on the internet with several variations in styles so you can find most of the icons you want for standard projects.
For the backend I was using the #GraphCool Framework. As I later found out, #GraphQL still has some way to go in order to provide the full power of a mature graph query language so later in my project I ripped out #GraphCool and replaced it with CouchDB and Pouchdb. Primarily so I could provide good offline app support. CouchDB with Pouchdb is very flexible and efficient combination and overcomes some of the restrictions I found in #GraphQL and hence #GraphCool also. The most impressive and important feature of CouchDB is its replication. You can configure it in various ways for backups, fault tolerance, caching or conditional merging of databases. CouchDB and Pouchdb even supports storing, retrieving and serving binary or image data or other mime types. This removes a level of complexity usually present in database implementations where binary or image data is usually referenced through an #HTML5 link. With CouchDB and Pouchdb apps can operate offline and sync later, very efficiently, when the network connection is good.
I use PhoneGap when testing the app. It auto-reloads your app when its code is changed and you can also install it on Android phones to preview your app instantly. iOS is a bit more tricky cause of Apple's policies so it's not available on the App Store, but you can build it and install it yourself to your device.
So that's my latest mobile stack. What tools do you use? Have you tried these ones?
Julia
- Fast Performance and Easy Experimentation18
- Designed for parallelism and distributed computation18
- Free and Open Source14
- Multiple Dispatch13
- Calling C functions directly12
- Dynamic Type System12
- Lisp-like Macros12
- Powerful Shell-like Capabilities8
- REPL5
- Jupyter notebook integration4
- String handling2
- Emojis as variable names2
- Interoperability1
- Immature library management system5
- Slow program start3
- Poor backwards compatibility3
- JIT compiler is very slow2
- Bad tooling2
- No static compilation2
related Julia posts
Java
- Great libraries587
- Widely used441
- Excellent tooling400
- Huge amount of documentation available387
- Large pool of developers available331
- Open source203
- Excellent performance200
- Great development155
- Vast array of 3rd party libraries149
- Used for android147
- Compiled Language60
- Used for Web49
- Managed memory46
- High Performance45
- Native threads44
- Statically typed42
- Easy to read35
- Great Community33
- Reliable platform29
- JVM compatibility24
- Sturdy garbage collection24
- Cross Platform Enterprise Integration21
- Good amount of APIs20
- Universal platform20
- Great Support18
- Great ecosystem13
- Lots of boilerplate11
- Backward compatible11
- Everywhere10
- Excellent SDK - JDK9
- Static typing7
- Mature language thus stable systems6
- Better than Ruby6
- Long term language6
- Cross-platform6
- Portability6
- It's Java6
- Vast Collections Library5
- Clojure5
- Used for Android development5
- Most developers favorite4
- Old tech4
- Javadoc3
- Stable platform, which many new languages depend on3
- Best martial for design3
- Great Structure3
- History3
- Testable3
- Faster than python2
- Type Safe1
- Verbosity32
- NullpointerException27
- Overcomplexity is praised in community culture16
- Nightmare to Write14
- Boiler plate code11
- Classpath hell prior to Java 98
- No REPL6
- No property4
- Non-intuitive generic implementation2
- There is not optional parameter2
- Code are too long2
- Floating-point errors2
- Returning Wildcard Types1
- Java's too statically, stronglly, and strictly typed1
- Terrbible compared to Python/Batch Perormence1
related Java posts











How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.