Alternatives to R Language logo

Alternatives to R Language

MATLAB, Python, Golang, SAS, and Rust are the most popular alternatives and competitors to R Language.
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What is R Language and what are its top alternatives?

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.
R Language is a tool in the Languages category of a tech stack.

Top Alternatives to R Language

  • MATLAB
    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

    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
    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
    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

    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

    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

    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

    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 logo

MATLAB

718
592
31
A high-level language and interactive environment for numerical computation, visualization, and programming
718
592
+ 1
31
PROS OF MATLAB
  • 16
    Simulink
  • 5
    Functions, statements, plots, directory navigation easy
  • 3
    Model based software development
  • 3
    S-Functions
  • 2
    REPL
  • 1
    Simple variabel control
  • 1
    Solve invertible matrix
CONS OF MATLAB
  • 1
    Parameter-value pairs syntax to pass arguments clunky
  • 0
    Does not support named function arguments
  • 0
    Doesn't allow unpacking tuples/arguments lists with *

related MATLAB posts

Python logo

Python

173K
144.2K
6.6K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
173K
144.2K
+ 1
6.6K
PROS OF PYTHON
  • 1.1K
    Great libraries
  • 937
    Readable code
  • 830
    Beautiful code
  • 774
    Rapid development
  • 677
    Large community
  • 422
    Open source
  • 381
    Elegant
  • 273
    Great community
  • 266
    Object oriented
  • 211
    Dynamic typing
  • 73
    Great standard library
  • 54
    Very fast
  • 51
    Functional programming
  • 39
    Easy to learn
  • 39
    Scientific computing
  • 32
    Great documentation
  • 25
    Productivity
  • 25
    Matlab alternative
  • 24
    Easy to read
  • 20
    Simple is better than complex
  • 18
    It's the way I think
  • 17
    Imperative
  • 15
    Free
  • 15
    Very programmer and non-programmer friendly
  • 14
    Powerfull language
  • 14
    Powerful
  • 13
    Fast and simple
  • 12
    Scripting
  • 12
    Machine learning support
  • 9
    Explicit is better than implicit
  • 8
    Ease of development
  • 8
    Unlimited power
  • 8
    Clear and easy and powerfull
  • 7
    Import antigravity
  • 6
    It's lean and fun to code
  • 6
    Print "life is short, use python"
  • 5
    Great for tooling
  • 5
    There should be one-- and preferably only one --obvious
  • 5
    Python has great libraries for data processing
  • 5
    High Documented language
  • 5
    I love snakes
  • 5
    Although practicality beats purity
  • 5
    Flat is better than nested
  • 5
    Fast coding and good for competitions
  • 4
    Readability counts
  • 3
    Lists, tuples, dictionaries
  • 3
    CG industry needs
  • 3
    Now is better than never
  • 3
    Multiple Inheritence
  • 3
    Great for analytics
  • 3
    Complex is better than complicated
  • 3
    Plotting
  • 3
    Beautiful is better than ugly
  • 3
    Rapid Prototyping
  • 3
    Socially engaged community
  • 2
    List comprehensions
  • 2
    Web scraping
  • 2
    Many types of collections
  • 2
    Ys
  • 2
    Easy to setup and run smooth
  • 2
    Generators
  • 2
    Special cases aren't special enough to break the rules
  • 2
    If the implementation is hard to explain, it's a bad id
  • 2
    If the implementation is easy to explain, it may be a g
  • 2
    Simple and easy to learn
  • 2
    Import this
  • 2
    No cruft
  • 2
    Easy to learn and use
  • 1
    Flexible and easy
  • 1
    Batteries included
  • 1
    Powerful language for AI
  • 1
    Should START with this but not STICK with This
  • 1
    Good
  • 1
    It is Very easy , simple and will you be love programmi
  • 1
    Better outcome
  • 1
    إسلام هشام
  • 1
    Because of Netflix
  • 1
    A-to-Z
  • 1
    Only one way to do it
  • 1
    Pip install everything
  • 0
    Powerful
  • 0
    Pro
CONS OF PYTHON
  • 51
    Still divided between python 2 and python 3
  • 29
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 21
    GIL
  • 19
    Package management is a mess
  • 14
    Too imperative-oriented
  • 12
    Dynamic typing
  • 12
    Hard to understand
  • 10
    Very slow
  • 8
    Not everything is expression
  • 7
    Indentations matter a lot
  • 7
    Explicit self parameter in methods
  • 6
    No anonymous functions
  • 6
    Poor DSL capabilities
  • 6
    Incredibly slow
  • 6
    Requires C functions for dynamic modules
  • 5
    The "lisp style" whitespaces
  • 5
    Fake object-oriented programming
  • 5
    Hard to obfuscate
  • 5
    Threading
  • 4
    Circular import
  • 4
    The benevolent-dictator-for-life quit
  • 4
    Official documentation is unclear.
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    Not suitable for autocomplete
  • 2
    Meta classes
  • 1
    Training wheels (forced indentation)

related Python posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 40 upvotes · 4.8M views

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

See more
Nick Parsons
Director of Developer Marketing at Stream · | 35 upvotes · 1.6M views

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

See more
Golang logo

Golang

15.2K
12.3K
3.2K
An open source programming language that makes it easy to build simple, reliable, and efficient software
15.2K
12.3K
+ 1
3.2K
PROS OF GOLANG
  • 530
    High-performance
  • 387
    Simple, minimal syntax
  • 354
    Fun to write
  • 295
    Easy concurrency support via goroutines
  • 267
    Fast compilation times
  • 189
    Goroutines
  • 177
    Statically linked binaries that are simple to deploy
  • 148
    Simple compile build/run procedures
  • 134
    Backed by google
  • 131
    Great community
  • 50
    Garbage collection built-in
  • 42
    Built-in Testing
  • 41
    Excellent tools - gofmt, godoc etc
  • 38
    Elegant and concise like Python, fast like C
  • 34
    Awesome to Develop
  • 25
    Used for Docker
  • 24
    Flexible interface system
  • 22
    Great concurrency pattern
  • 22
    Deploy as executable
  • 19
    Open-source Integration
  • 16
    Fun to write and so many feature out of the box
  • 15
    Easy to read
  • 14
    Its Simple and Heavy duty
  • 14
    Go is God
  • 13
    Powerful and simple
  • 13
    Easy to deploy
  • 11
    Concurrency
  • 11
    Best language for concurrency
  • 10
    Safe GOTOs
  • 10
    Rich standard library
  • 9
    Clean code, high performance
  • 9
    Easy setup
  • 8
    Simplicity, Concurrency, Performance
  • 8
    High performance
  • 8
    Hassle free deployment
  • 7
    Used by Giants of the industry
  • 7
    Single binary avoids library dependency issues
  • 6
    Cross compiling
  • 6
    Simple, powerful, and great performance
  • 5
    Excellent tooling
  • 5
    Very sophisticated syntax
  • 5
    Gofmt
  • 5
    WYSIWYG
  • 5
    Garbage Collection
  • 4
    Widely used
  • 4
    Kubernetes written on Go
  • 3
    Keep it simple and stupid
  • 1
    No generics
  • 1
    Operator goto
CONS OF GOLANG
  • 41
    You waste time in plumbing code catching errors
  • 25
    Verbose
  • 22
    Packages and their path dependencies are braindead
  • 15
    Google's documentations aren't beginer friendly
  • 15
    Dependency management when working on multiple projects
  • 10
    Automatic garbage collection overheads
  • 8
    Uncommon syntax
  • 6
    Type system is lacking (no generics, etc)
  • 2
    Collection framework is lacking (list, set, map)

related Golang posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 40 upvotes · 4.8M views

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

See more
Nick Parsons
Director of Developer Marketing at Stream · | 35 upvotes · 1.6M views

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

See more
SAS logo

SAS

58
65
0
A command-driven software package used for statistical analysis and data visualization
58
65
+ 1
0
PROS OF SAS
    Be the first to leave a pro
    CONS OF SAS
      Be the first to leave a con

      related SAS posts

      Rust logo

      Rust

      3.3K
      3.6K
      1.2K
      A safe, concurrent, practical language
      3.3K
      3.6K
      + 1
      1.2K
      PROS OF RUST
      • 137
        Guaranteed memory safety
      • 125
        Fast
      • 82
        Open source
      • 75
        Minimal runtime
      • 69
        Pattern matching
      • 61
        Type inference
      • 55
        Concurrent
      • 54
        Algebraic data types
      • 45
        Efficient C bindings
      • 43
        Practical
      • 37
        Best advances in languages in 20 years
      • 29
        Safe, fast, easy + friendly community
      • 29
        Fix for C/C++
      • 23
        Stablity
      • 22
        Closures
      • 21
        Zero-cost abstractions
      • 19
        Extensive compiler checks
      • 18
        Great community
      • 15
        No NULL type
      • 14
        Async/await
      • 14
        Completely cross platform: Windows, Linux, Android
      • 13
        No Garbage Collection
      • 12
        Great documentations
      • 12
        High-performance
      • 11
        Super fast
      • 11
        High performance
      • 10
        Fearless concurrency
      • 10
        Generics
      • 10
        Safety no runtime crashes
      • 9
        Helpful compiler
      • 9
        Compiler can generate Webassembly
      • 9
        Guaranteed thread data race safety
      • 8
        Easy Deployment
      • 8
        Macros
      • 8
        Prevents data races
      • 7
        RLS provides great IDE support
      • 7
        Painless dependency management
      • 6
        Real multithreading
      • 4
        Support on Other Languages
      • 4
        Good package management
      CONS OF RUST
      • 25
        Hard to learn
      • 23
        Ownership learning curve
      • 10
        Unfriendly, verbose syntax
      • 4
        High size of builded executable
      • 4
        Variable shadowing
      • 4
        Many type operations make it difficult to follow
      • 3
        No jobs

      related Rust posts

      James Cunningham
      Operations Engineer at Sentry · | 18 upvotes · 120.7K views
      Shared insights
      on
      PythonPythonRustRust
      at

      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).

      See more
      Jakub Olan
      Node.js Software Engineer · | 17 upvotes · 308.2K views

      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.

      See more
      Ruby logo

      Ruby

      28.1K
      17.8K
      3.9K
      A dynamic, interpreted, open source programming language with a focus on simplicity and productivity
      28.1K
      17.8K
      + 1
      3.9K
      PROS OF RUBY
      • 601
        Programme friendly
      • 535
        Quick to develop
      • 487
        Great community
      • 467
        Productivity
      • 429
        Simplicity
      • 271
        Open source
      • 233
        Meta-programming
      • 203
        Powerful
      • 155
        Blocks
      • 138
        Powerful one-liners
      • 67
        Flexible
      • 57
        Easy to learn
      • 49
        Easy to start
      • 41
        Maintainability
      • 36
        Lambdas
      • 30
        Procs
      • 20
        Fun to write
      • 19
        Diverse web frameworks
      • 12
        Reads like English
      • 9
        Rails
      • 9
        Makes me smarter and happier
      • 8
        Elegant syntax
      • 7
        Very Dynamic
      • 6
        Matz
      • 5
        Programmer happiness
      • 4
        Generally fun but makes you wanna cry sometimes
      • 4
        Fun and useful
      • 4
        Object Oriented
      • 3
        Elegant code
      • 3
        Friendly
      • 3
        There are so many ways to make it do what you want
      • 3
        Easy packaging and modules
      • 2
        Primitive types can be tampered with
      CONS OF RUBY
      • 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

      related Ruby posts

      Kamil Kowalski
      Lead Architect at Fresha · | 28 upvotes · 1.4M views

      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.

      See more
      Jonathan Pugh
      Software Engineer / Project Manager / Technical Architect · | 25 upvotes · 1.7M views

      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?

      See more
      Julia logo

      Julia

      428
      540
      121
      A high-level, high-performance dynamic programming language for technical computing
      428
      540
      + 1
      121
      PROS OF JULIA
      • 18
        Fast Performance and Easy Experimentation
      • 18
        Designed for parallelism and distributed computation
      • 14
        Free and Open Source
      • 13
        Multiple Dispatch
      • 12
        Calling C functions directly
      • 12
        Dynamic Type System
      • 12
        Lisp-like Macros
      • 8
        Powerful Shell-like Capabilities
      • 5
        REPL
      • 4
        Jupyter notebook integration
      • 2
        String handling
      • 2
        Emojis as variable names
      • 1
        Interoperability
      CONS OF JULIA
      • 5
        Immature library management system
      • 3
        Slow program start
      • 3
        Poor backwards compatibility
      • 2
        JIT compiler is very slow
      • 2
        Bad tooling
      • 2
        No static compilation

      related Julia posts

      Java logo

      Java

      101.1K
      77.1K
      3.7K
      A concurrent, class-based, object-oriented, language specifically designed to have as few implementation dependencies as possible
      101.1K
      77.1K
      + 1
      3.7K
      PROS OF JAVA
      • 587
        Great libraries
      • 441
        Widely used
      • 400
        Excellent tooling
      • 387
        Huge amount of documentation available
      • 331
        Large pool of developers available
      • 203
        Open source
      • 200
        Excellent performance
      • 155
        Great development
      • 149
        Vast array of 3rd party libraries
      • 147
        Used for android
      • 60
        Compiled Language
      • 49
        Used for Web
      • 46
        Managed memory
      • 45
        High Performance
      • 44
        Native threads
      • 42
        Statically typed
      • 35
        Easy to read
      • 33
        Great Community
      • 29
        Reliable platform
      • 24
        JVM compatibility
      • 24
        Sturdy garbage collection
      • 21
        Cross Platform Enterprise Integration
      • 20
        Good amount of APIs
      • 20
        Universal platform
      • 18
        Great Support
      • 13
        Great ecosystem
      • 11
        Lots of boilerplate
      • 11
        Backward compatible
      • 10
        Everywhere
      • 9
        Excellent SDK - JDK
      • 7
        Static typing
      • 6
        Mature language thus stable systems
      • 6
        Better than Ruby
      • 6
        Long term language
      • 6
        Cross-platform
      • 6
        Portability
      • 6
        It's Java
      • 5
        Vast Collections Library
      • 5
        Clojure
      • 5
        Used for Android development
      • 4
        Most developers favorite
      • 4
        Old tech
      • 3
        Javadoc
      • 3
        Stable platform, which many new languages depend on
      • 3
        Best martial for design
      • 3
        Great Structure
      • 3
        History
      • 3
        Testable
      • 2
        Faster than python
      • 1
        Type Safe
      CONS OF JAVA
      • 32
        Verbosity
      • 27
        NullpointerException
      • 16
        Overcomplexity is praised in community culture
      • 14
        Nightmare to Write
      • 11
        Boiler plate code
      • 8
        Classpath hell prior to Java 9
      • 6
        No REPL
      • 4
        No property
      • 2
        Non-intuitive generic implementation
      • 2
        There is not optional parameter
      • 2
        Code are too long
      • 2
        Floating-point errors
      • 1
        Returning Wildcard Types
      • 1
        Java's too statically, stronglly, and strictly typed
      • 1
        Terrbible compared to Python/Batch Perormence

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