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
- Simulink18
- Functions, statements, plots, directory navigation easy5
- Model based software development4
- 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 code947
- Beautiful code835
- Rapid development780
- Large community682
- Open source426
- Elegant385
- Great community278
- Object oriented268
- Dynamic typing214
- Great standard library75
- Very fast56
- Functional programming51
- Easy to learn43
- Scientific computing43
- Great documentation33
- Matlab alternative26
- Easy to read25
- Productivity25
- Simple is better than complex21
- It's the way I think18
- Imperative17
- Free15
- Very programmer and non-programmer friendly15
- Machine learning support14
- Powerfull language14
- Powerful14
- Fast and simple13
- Scripting12
- Explicit is better than implicit9
- Ease of development8
- Clear and easy and powerfull8
- Unlimited power8
- Import antigravity7
- It's lean and fun to code6
- Print "life is short, use python"6
- Python has great libraries for data processing5
- High Documented language5
- Fast coding and good for competitions5
- I love snakes5
- Great for tooling5
- Flat is better than nested5
- There should be one-- and preferably only one --obvious5
- Although practicality beats purity5
- Readability counts4
- Rapid Prototyping4
- Plotting3
- Web scraping3
- Now is better than never3
- Great for analytics3
- Lists, tuples, dictionaries3
- Socially engaged community3
- Complex is better than complicated3
- Multiple Inheritence3
- Beautiful is better than ugly3
- CG industry needs3
- No cruft2
- Easy to learn and use2
- 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
- Many types of collections2
- List comprehensions2
- Simple and easy to learn2
- Generators2
- Easy to setup and run smooth2
- Import this2
- Better outcome1
- Good for hacking1
- Powerful language for AI1
- Should START with this but not STICK with This1
- Because of Netflix1
- A-to-Z1
- Only one way to do it1
- Flexible and easy1
- Batteries included1
- It is Very easy , simple and will you be love programmi1
- Can understand easily who are new to programming1
- Pip install everything1
- Powerful0
- Still divided between python 2 and python 351
- Performance impact28
- Poor syntax for anonymous functions26
- GIL21
- Package management is a mess19
- Too imperative-oriented14
- Hard to understand12
- Dynamic typing12
- Very slow10
- Not everything is expression8
- Explicit self parameter in methods7
- Indentations matter a lot7
- Poor DSL capabilities6
- Incredibly slow6
- No anonymous functions6
- Requires C functions for dynamic modules6
- Hard to obfuscate5
- Threading5
- Fake object-oriented programming5
- The "lisp style" whitespaces5
- Official documentation is unclear.4
- Circular import4
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- Not suitable for autocomplete4
- The benevolent-dictator-for-life quit4
- 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-performance536
- Simple, minimal syntax390
- Fun to write357
- Easy concurrency support via goroutines298
- Fast compilation times270
- Goroutines192
- Statically linked binaries that are simple to deploy179
- Simple compile build/run procedures150
- Backed by google135
- Great community133
- Garbage collection built-in52
- Built-in Testing44
- Excellent tools - gofmt, godoc etc43
- Elegant and concise like Python, fast like C39
- Awesome to Develop36
- Used for Docker26
- Flexible interface system25
- Great concurrency pattern23
- Deploy as executable23
- Open-source Integration20
- Fun to write and so many feature out of the box17
- Easy to read16
- Go is God16
- Its Simple and Heavy duty14
- Powerful and simple14
- Easy to deploy14
- Best language for concurrency13
- Concurrency12
- Rich standard library11
- Safe GOTOs11
- Clean code, high performance10
- Easy setup10
- Simplicity, Concurrency, Performance9
- High performance9
- Single binary avoids library dependency issues8
- Hassle free deployment8
- Simple, powerful, and great performance7
- Cross compiling7
- Used by Giants of the industry7
- Gofmt6
- Garbage Collection6
- Very sophisticated syntax5
- WYSIWYG5
- Excellent tooling5
- Widely used4
- Keep it simple and stupid4
- Kubernetes written on Go4
- No generics2
- Operator goto1
- You waste time in plumbing code catching errors41
- Verbose25
- Packages and their path dependencies are braindead23
- Dependency management when working on multiple projects15
- Google's documentations aren't beginer friendly15
- Automatic garbage collection overheads10
- Uncommon syntax8
- Type system is lacking (no generics, etc)6
- Collection framework is lacking (list, set, map)3
- Best programming language2
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 safety138
- Fast125
- Open source83
- Minimal runtime75
- Pattern matching69
- Type inference61
- Concurrent55
- Algebraic data types55
- Efficient C bindings45
- Practical43
- Best advances in languages in 20 years36
- Fix for C/C++29
- Safe, fast, easy + friendly community29
- Stablity23
- Zero-cost abstractions22
- Closures22
- Extensive compiler checks19
- Great community18
- No NULL type16
- Completely cross platform: Windows, Linux, Android14
- Async/await14
- No Garbage Collection13
- Great documentations12
- High-performance12
- High performance11
- Super fast11
- Safety no runtime crashes10
- Fearless concurrency10
- Generics10
- Guaranteed thread data race safety10
- Compiler can generate Webassembly9
- Helpful compiler9
- Macros8
- Prevents data races8
- Easy Deployment8
- Painless dependency management7
- RLS provides great IDE support7
- Real multithreading6
- Good package management4
- Support on Other Languages4
- Hard to learn26
- Ownership learning curve23
- Unfriendly, verbose syntax11
- Variable shadowing4
- High size of builded executable4
- Many type operations make it difficult to follow4
- No jobs3
related Rust posts
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.
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).
Ruby
- Programme friendly604
- Quick to develop536
- Great community488
- Productivity467
- Simplicity430
- Open source272
- Meta-programming233
- Powerful204
- Blocks155
- Powerful one-liners138
- Flexible67
- Easy to learn57
- Easy to start50
- Maintainability41
- Lambdas36
- Procs30
- Fun to write21
- Diverse web frameworks19
- Reads like English13
- Makes me smarter and happier10
- Rails9
- Elegant syntax8
- Very Dynamic7
- Matz6
- Object Oriented5
- Programmer happiness5
- Fun and useful4
- Generally fun but makes you wanna cry sometimes4
- Friendly4
- Elegant code3
- 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 Experimentation21
- Designed for parallelism and distributed computation21
- Free and Open Source17
- Lisp-like Macros16
- Calling C functions directly16
- Dynamic Type System16
- Multiple Dispatch15
- Powerful Shell-like Capabilities9
- REPL7
- Jupyter notebook integration7
- String handling4
- Emojis as variable names4
- Interoperability3
- Immature library management system5
- Slow program start4
- JIT compiler is very slow3
- Poor backwards compatibility3
- Bad tooling2
- No static compilation2
related Julia posts
Java
- Great libraries593
- Widely used444
- Excellent tooling400
- Huge amount of documentation available390
- Large pool of developers available333
- Open source205
- Excellent performance201
- Great development155
- Vast array of 3rd party libraries149
- Used for android148
- Compiled Language60
- Used for Web51
- Managed memory46
- High Performance45
- Native threads44
- Statically typed43
- Easy to read35
- Great Community33
- Reliable platform29
- Sturdy garbage collection24
- JVM compatibility24
- Cross Platform Enterprise Integration22
- Universal platform20
- Good amount of APIs20
- Great Support18
- Great ecosystem14
- Backward compatible11
- Lots of boilerplate11
- Everywhere10
- Excellent SDK - JDK9
- It's Java7
- Static typing7
- Mature language thus stable systems6
- Better than Ruby6
- Long term language6
- Cross-platform6
- Portability6
- Clojure5
- Vast Collections Library5
- Used for Android development5
- Most developers favorite4
- Old tech4
- Javadoc3
- Stable platform, which many new languages depend on3
- History3
- Testable3
- Best martial for design3
- Great Structure3
- Faster than python2
- Type Safe2
- Verbosity33
- NullpointerException27
- Nightmare to Write16
- Overcomplexity is praised in community culture16
- Boiler plate code12
- Classpath hell prior to Java 98
- No REPL6
- No property4
- Code are too long3
- Non-intuitive generic implementation2
- There is not optional parameter2
- Floating-point errors2
- Java's too statically, stronglly, and strictly typed1
- Returning Wildcard Types1
- 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.