Alternatives to R Language logo

Alternatives to R Language

MATLAB, Python, Go, 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. ...

  • Go

    Go

    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

562
495
27
A high-level language and interactive environment for numerical computation, visualization, and programming
562
495
+ 1
27
PROS OF MATLAB
  • 14
    Simulink
  • 5
    Functions, statements, plots, directory navigation easy
  • 3
    S-Functions
  • 2
    Model based software development
  • 1
    Simple variabel control
  • 1
    REPL
  • 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

134.8K
109K
6.5K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
134.8K
109K
+ 1
6.5K
PROS OF PYTHON
  • 1.1K
    Great libraries
  • 929
    Readable code
  • 819
    Beautiful code
  • 769
    Rapid development
  • 672
    Large community
  • 418
    Open source
  • 379
    Elegant
  • 268
    Great community
  • 261
    Object oriented
  • 209
    Dynamic typing
  • 71
    Great standard library
  • 53
    Very fast
  • 49
    Functional programming
  • 35
    Scientific computing
  • 34
    Easy to learn
  • 30
    Great documentation
  • 25
    Matlab alternative
  • 23
    Productivity
  • 22
    Easy to read
  • 19
    Simple is better than complex
  • 17
    Imperative
  • 17
    It's the way I think
  • 15
    Very programmer and non-programmer friendly
  • 14
    Powerful
  • 14
    Free
  • 13
    Fast and simple
  • 13
    Powerfull language
  • 12
    Scripting
  • 10
    Machine learning support
  • 9
    Explicit is better than implicit
  • 8
    Unlimited power
  • 8
    Ease of development
  • 7
    Clear and easy and powerfull
  • 7
    Import antigravity
  • 6
    Print "life is short, use python"
  • 6
    It's lean and fun to code
  • 5
    Flat is better than nested
  • 5
    Fast coding and good for competitions
  • 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
    Great for tooling
  • 4
    Readability counts
  • 3
    CG industry needs
  • 3
    Beautiful is better than ugly
  • 3
    Multiple Inheritence
  • 3
    Complex is better than complicated
  • 3
    Great for analytics
  • 3
    Socially engaged community
  • 3
    Rapid Prototyping
  • 3
    Lists, tuples, dictionaries
  • 3
    Plotting
  • 2
    Generators
  • 2
    Simple and easy to learn
  • 2
    Import this
  • 2
    No cruft
  • 2
    Easy to learn and use
  • 2
    List comprehensions
  • 2
    Special cases aren't special enough to break the rules
  • 2
    Now is better than never
  • 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
  • 1
    Many types of collections
  • 1
    Better outcome
  • 1
    Batteries included
  • 1
    Ys
  • 1
    Good
  • 1
    Pip install everything
  • 1
    Easy to setup and run smooth
  • 1
    Because of Netflix
  • 1
    Flexible and easy
  • 1
    Web scraping
  • 1
    Should START with this but not STICK with This
  • 1
    Powerful language for AI
  • 1
    It is Very easy , simple and will you be love programmi
  • 1
    Only one way to do it
  • 1
    A-to-Z
  • 0
    Pro
  • 0
    Powerful
CONS OF PYTHON
  • 50
    Still divided between python 2 and python 3
  • 28
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 19
    Package management is a mess
  • 19
    GIL
  • 13
    Too imperative-oriented
  • 12
    Hard to understand
  • 11
    Dynamic typing
  • 9
    Very slow
  • 8
    Not everything is expression
  • 7
    Explicit self parameter in methods
  • 7
    Indentations matter a lot
  • 6
    Poor DSL capabilities
  • 6
    No anonymous functions
  • 6
    Requires C functions for dynamic modules
  • 5
    Threading
  • 5
    The "lisp style" whitespaces
  • 5
    Hard to obfuscate
  • 4
    Fake object-oriented programming
  • 4
    Incredibly slow
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    The benevolent-dictator-for-life quit
  • 3
    Official documentation is unclear.
  • 3
    Circular import
  • 3
    Not suitable for autocomplete
  • 1
    Training wheels (forced indentation)
  • 1
    Meta classes

related Python posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 38 upvotes · 3.8M 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 · 1.3M 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
Go logo

Go

12.3K
10.5K
3K
An open source programming language that makes it easy to build simple, reliable, and efficient software
12.3K
10.5K
+ 1
3K
PROS OF GO
  • 519
    High-performance
  • 379
    Simple, minimal syntax
  • 345
    Fun to write
  • 291
    Easy concurrency support via goroutines
  • 263
    Fast compilation times
  • 185
    Goroutines
  • 176
    Statically linked binaries that are simple to deploy
  • 145
    Simple compile build/run procedures
  • 128
    Backed by google
  • 128
    Great community
  • 47
    Garbage collection built-in
  • 40
    Built-in Testing
  • 38
    Excellent tools - gofmt, godoc etc
  • 34
    Elegant and concise like Python, fast like C
  • 29
    Awesome to Develop
  • 23
    Used for Docker
  • 22
    Flexible interface system
  • 21
    Great concurrency pattern
  • 20
    Deploy as executable
  • 17
    Open-source Integration
  • 14
    Fun to write and so many feature out of the box
  • 13
    Easy to read
  • 12
    Go is God
  • 12
    Its Simple and Heavy duty
  • 12
    Powerful and simple
  • 11
    Easy to deploy
  • 10
    Concurrency
  • 9
    Safe GOTOs
  • 9
    Rich standard library
  • 9
    Best language for concurrency
  • 8
    Clean code, high performance
  • 8
    Easy setup
  • 7
    High performance
  • 7
    Simplicity, Concurrency, Performance
  • 7
    Hassle free deployment
  • 6
    Single binary avoids library dependency issues
  • 6
    Used by Giants of the industry
  • 5
    Cross compiling
  • 5
    Simple, powerful, and great performance
  • 4
    Gofmt
  • 4
    WYSIWYG
  • 4
    Garbage Collection
  • 4
    Excellent tooling
  • 4
    Very sophisticated syntax
  • 3
    Kubernetes written on Go
  • 2
    Keep it simple and stupid
  • 2
    Widely used
  • 0
    Operator goto
  • 0
    No generics
CONS OF GO
  • 40
    You waste time in plumbing code catching errors
  • 24
    Verbose
  • 22
    Packages and their path dependencies are braindead
  • 15
    Dependency management when working on multiple projects
  • 14
    Google's documentations aren't beginer friendly
  • 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 Go posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 38 upvotes · 3.8M 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 · 1.3M 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

38
42
0
A command-driven software package used for statistical analysis and data visualization
38
42
+ 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

      2.4K
      2.9K
      950
      A safe, concurrent, practical language
      2.4K
      2.9K
      + 1
      950
      PROS OF RUST
      • 125
        Guaranteed memory safety
      • 113
        Fast
      • 74
        Open source
      • 69
        Minimal runtime
      • 60
        Pattern matching
      • 55
        Type inference
      • 52
        Algebraic data types
      • 47
        Concurrent
      • 43
        Efficient C bindings
      • 39
        Practical
      • 31
        Best advances in languages in 20 years
      • 22
        Safe, fast, easy + friendly community
      • 22
        Fix for C/C++
      • 18
        Closures
      • 17
        Stablity
      • 16
        Zero-cost abstractions
      • 14
        Extensive compiler checks
      • 12
        Great community
      • 9
        No NULL type
      • 9
        No Garbage Collection
      • 8
        Super fast
      • 8
        Completely cross platform: Windows, Linux, Android
      • 8
        Async/await
      • 7
        Great documentations
      • 7
        Safety no runtime crashes
      • 6
        Fearless concurrency
      • 6
        Generics
      • 6
        Guaranteed thread data race safety
      • 6
        High performance
      • 6
        High-performance
      • 5
        RLS provides great IDE support
      • 5
        Compiler can generate Webassembly
      • 5
        Easy Deployment
      • 5
        Prevents data races
      • 5
        Painless dependency management
      • 4
        Helpful compiler
      • 4
        Macros
      • 1
        Real multithreading
      • 1
        Support on Other Languages
      CONS OF RUST
      • 21
        Hard to learn
      • 20
        Ownership learning curve
      • 7
        Unfriendly, verbose syntax
      • 3
        Variable shadowing
      • 2
        Many type operations make it difficult to follow
      • 2
        No jobs
      • 2
        High size of builded executable

      related Rust posts

      James Cunningham
      Operations Engineer at Sentry · | 18 upvotes · 104.7K views
      Shared insights
      on
      Python
      Rust
      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 · 204.8K 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

      23.6K
      15.2K
      3.9K
      A dynamic, interpreted, open source programming language with a focus on simplicity and productivity
      23.6K
      15.2K
      + 1
      3.9K
      PROS OF RUBY
      • 600
        Programme friendly
      • 533
        Quick to develop
      • 488
        Great community
      • 467
        Productivity
      • 430
        Simplicity
      • 272
        Open source
      • 234
        Meta-programming
      • 203
        Powerful
      • 157
        Blocks
      • 138
        Powerful one-liners
      • 66
        Flexible
      • 56
        Easy to learn
      • 48
        Easy to start
      • 40
        Maintainability
      • 36
        Lambdas
      • 30
        Procs
      • 19
        Fun to write
      • 19
        Diverse web frameworks
      • 11
        Reads like English
      • 9
        Rails
      • 8
        Makes me smarter and happier
      • 7
        Elegant syntax
      • 6
        Very Dynamic
      • 5
        Programmer happiness
      • 5
        Matz
      • 4
        Generally fun but makes you wanna cry sometimes
      • 4
        Fun and useful
      • 3
        Friendly
      • 3
        Object Oriented
      • 3
        There are so many ways to make it do what you want
      • 2
        Easy packaging and modules
      • 2
        Primitive types can be tampered with
      • 2
        Elegant code
      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 · | 27 upvotes · 986.8K 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 · 1.5M 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

      340
      468
      111
      A high-level, high-performance dynamic programming language for technical computing
      340
      468
      + 1
      111
      PROS OF JULIA
      • 17
        Designed for parallelism and distributed computation
      • 16
        Fast Performance and Easy Experimentation
      • 13
        Free and Open Source
      • 12
        Multiple Dispatch
      • 11
        Dynamic Type System
      • 11
        Lisp-like Macros
      • 11
        Calling C functions directly
      • 8
        Powerful Shell-like Capabilities
      • 4
        Jupyter notebook integration
      • 4
        REPL
      • 2
        Emojis as variable names
      • 2
        String handling
      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

      82.6K
      61.1K
      3.5K
      A concurrent, class-based, object-oriented, language specifically designed to have as few implementation dependencies as possible
      82.6K
      61.1K
      + 1
      3.5K
      PROS OF JAVA
      • 576
        Great libraries
      • 436
        Widely used
      • 396
        Excellent tooling
      • 380
        Huge amount of documentation available
      • 329
        Large pool of developers available
      • 198
        Open source
      • 195
        Excellent performance
      • 151
        Great development
      • 144
        Used for android
      • 144
        Vast array of 3rd party libraries
      • 54
        Compiled Language
      • 46
        Used for Web
      • 43
        Managed memory
      • 42
        Native threads
      • 41
        High Performance
      • 36
        Statically typed
      • 32
        Easy to read
      • 30
        Great Community
      • 26
        Reliable platform
      • 23
        Sturdy garbage collection
      • 23
        JVM compatibility
      • 19
        Cross Platform Enterprise Integration
      • 18
        Universal platform
      • 16
        Good amount of APIs
      • 16
        Great Support
      • 11
        Lots of boilerplate
      • 10
        Backward compatible
      • 10
        Great ecosystem
      • 9
        Everywhere
      • 7
        Excellent SDK - JDK
      • 6
        Mature language thus stable systems
      • 5
        Portability
      • 5
        Better than Ruby
      • 5
        Static typing
      • 5
        It's Java
      • 5
        Cross-platform
      • 5
        Clojure
      • 4
        Vast Collections Library
      • 4
        Long term language
      • 4
        Old tech
      • 3
        Stable platform, which many new languages depend on
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        Most developers favorite
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        Great Structure
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        Used for Android development
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        Best martial for design
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        Testable
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        Javadoc
      CONS OF JAVA
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        Verbosity
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        NullpointerException
      • 15
        Overcomplexity is praised in community culture
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        Boiler plate code
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        Classpath hell prior to Java 9
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        Code are too long
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        There is not optional parameter
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        Floating-point errors
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        Terrbible compared to Python/Batch Perormence
      • 1
        Java's too statically, stronglly, and strictly typed
      • 1
        Non-intuitive generic implementation
      • 1
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