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.
3.2K
1.9K
+ 1
412

What is R Language and what are its top alternatives?

R Language is a high-level programming language widely used for statistical computing and data analysis. It offers a vast array of tools and libraries for data manipulation, visualization, and statistical modeling. R is known for its comprehensive data analysis capabilities, interactive graphics, and strong community support. However, some limitations of R include its steep learning curve, inefficiency for handling large datasets, and lack of built-in support for machine learning algorithms.

  1. Python: Python is a versatile programming language that has gained popularity in data science and machine learning applications. It offers a wide range of libraries such as Pandas, NumPy, and Scikit-learn for data manipulation, numerical computing, and machine learning tasks. Python is known for its readability, flexibility, and vast community support. However, compared to R, Python may have a steeper learning curve for beginners in data science.

  2. Julia: Julia is a high-performance programming language designed for scientific computing, machine learning, and data analysis. It boasts of fast execution speeds and a user-friendly syntax. Julia's key features include multiple dispatch, built-in package manager, and seamless integration with C and Fortran libraries. On the downside, Julia's ecosystem is still evolving, and it may lack some specialized libraries available in R.

  3. SQL: SQL (Structured Query Language) is a domain-specific language used for managing and querying relational databases. It is essential for data manipulation, retrieval, and aggregation tasks. SQL's key features include declarative syntax, scalability, and ACID compliance for transaction management. However, SQL is more focused on database operations and may lack advanced statistical analysis capabilities compared to R.

  4. MATLAB: MATLAB is a numerical computing environment popular in engineering, science, and finance fields. It offers built-in algorithms for mathematics, data analysis, and visualization. MATLAB's key features include simulation capabilities, interactive development environment, and extensive toolboxes for various domains. However, MATLAB is a proprietary software with licensing costs, limiting its accessibility compared to open-source tools like R.

  5. SAS: SAS is a software suite commonly used for advanced analytics, business intelligence, and data management. It provides a range of tools for data manipulation, statistical analysis, and predictive modeling. SAS's key features include a user-friendly interface, comprehensive analytics capabilities, and enterprise-grade security. However, SAS is a paid software with high licensing costs, making it less accessible for individual users or small businesses compared to open-source alternatives like R.

  6. Spark: Apache Spark is a distributed computing system designed for big data processing and analysis. It offers in-memory processing, fault tolerance, and support for various data sources. Spark's key features include scalability, speed, and support for multiple programming languages like Python, Scala, and Java. However, Spark is more suited for big data processing tasks and may require additional setup compared to R for smaller datasets.

  7. Scala: Scala is a general-purpose programming language that runs on the Java Virtual Machine (JVM) and is known for its functional programming capabilities. It offers concise syntax, type inference, and scalability for building concurrent and distributed applications. Scala's key features include interoperability with Java libraries, pattern matching, and immutability. However, Scala may have a steeper learning curve compared to R for beginners in data science.

  8. Excel: Microsoft Excel is a popular spreadsheet program used for data entry, manipulation, and visualization. It offers a wide range of functions and add-ins for data analysis tasks. Excel's key features include intuitive interface, pivot tables, and charting capabilities. However, Excel may have limitations in handling complex data analytics tasks compared to specialized tools like R.

  9. Tableau: Tableau is a data visualization tool known for its interactive dashboards and storytelling features. It allows users to connect to various data sources, create visualizations, and share insights with others. Tableau's key features include drag-and-drop interface, advanced analytics capabilities, and seamless integration with different data sources. However, Tableau is more focused on visualization rather than statistical analysis and may require additional tools like R for advanced modeling tasks.

  10. KNIME: KNIME is an open-source data analytics platform used for data blending, exploration, and modeling. It offers a visual workflow builder, extensive library of pre-built nodes, and integration with various data science tools. KNIME's key features include flexibility, scalability, and ease of use for data processing tasks. However, KNIME may lack some advanced statistical modeling capabilities compared to R's comprehensive library of packages and functions.

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

1K
689
37
A high-level language and interactive environment for numerical computation, visualization, and programming
1K
689
+ 1
37
PROS OF MATLAB
  • 20
    Simulink
  • 5
    Model based software development
  • 5
    Functions, statements, plots, directory navigation easy
  • 3
    S-Functions
  • 2
    REPL
  • 1
    Simple variabel control
  • 1
    Solve invertible matrix
CONS OF MATLAB
  • 2
    Parameter-value pairs syntax to pass arguments clunky
  • 2
    Doesn't allow unpacking tuples/arguments lists with *
  • 2
    Does not support named function arguments

related MATLAB posts

Python logo

Python

239.6K
195.5K
6.9K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
239.6K
195.5K
+ 1
6.9K
PROS OF PYTHON
  • 1.2K
    Great libraries
  • 961
    Readable code
  • 846
    Beautiful code
  • 787
    Rapid development
  • 689
    Large community
  • 435
    Open source
  • 393
    Elegant
  • 282
    Great community
  • 272
    Object oriented
  • 220
    Dynamic typing
  • 77
    Great standard library
  • 59
    Very fast
  • 55
    Functional programming
  • 49
    Easy to learn
  • 45
    Scientific computing
  • 35
    Great documentation
  • 29
    Productivity
  • 28
    Easy to read
  • 28
    Matlab alternative
  • 23
    Simple is better than complex
  • 20
    It's the way I think
  • 19
    Imperative
  • 18
    Free
  • 18
    Very programmer and non-programmer friendly
  • 17
    Powerfull language
  • 17
    Machine learning support
  • 16
    Fast and simple
  • 14
    Scripting
  • 12
    Explicit is better than implicit
  • 11
    Ease of development
  • 10
    Clear and easy and powerfull
  • 9
    Unlimited power
  • 8
    It's lean and fun to code
  • 8
    Import antigravity
  • 7
    Print "life is short, use python"
  • 7
    Python has great libraries for data processing
  • 6
    Although practicality beats purity
  • 6
    Flat is better than nested
  • 6
    Great for tooling
  • 6
    Rapid Prototyping
  • 6
    Readability counts
  • 6
    High Documented language
  • 6
    I love snakes
  • 6
    Fast coding and good for competitions
  • 6
    There should be one-- and preferably only one --obvious
  • 6
    Now is better than never
  • 5
    Great for analytics
  • 5
    Lists, tuples, dictionaries
  • 4
    Easy to learn and use
  • 4
    Simple and easy to learn
  • 4
    Easy to setup and run smooth
  • 4
    Web scraping
  • 4
    CG industry needs
  • 4
    Socially engaged community
  • 4
    Complex is better than complicated
  • 4
    Multiple Inheritence
  • 4
    Beautiful is better than ugly
  • 4
    Plotting
  • 3
    If the implementation is hard to explain, it's a bad id
  • 3
    Special cases aren't special enough to break the rules
  • 3
    Pip install everything
  • 3
    List comprehensions
  • 3
    No cruft
  • 3
    Generators
  • 3
    Import this
  • 3
    It is Very easy , simple and will you be love programmi
  • 3
    Many types of collections
  • 3
    If the implementation is easy to explain, it may be a g
  • 2
    Batteries included
  • 2
    Should START with this but not STICK with This
  • 2
    Powerful language for AI
  • 2
    Can understand easily who are new to programming
  • 2
    Flexible and easy
  • 2
    Good for hacking
  • 2
    A-to-Z
  • 2
    Because of Netflix
  • 2
    Only one way to do it
  • 2
    Better outcome
  • 1
    Sexy af
  • 1
    Slow
  • 1
    Securit
  • 0
    Ni
  • 0
    Powerful
CONS OF PYTHON
  • 53
    Still divided between python 2 and python 3
  • 28
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 22
    GIL
  • 19
    Package management is a mess
  • 14
    Too imperative-oriented
  • 12
    Hard to understand
  • 12
    Dynamic typing
  • 12
    Very slow
  • 8
    Indentations matter a lot
  • 8
    Not everything is expression
  • 7
    Incredibly slow
  • 7
    Explicit self parameter in methods
  • 6
    Requires C functions for dynamic modules
  • 6
    Poor DSL capabilities
  • 6
    No anonymous functions
  • 5
    Fake object-oriented programming
  • 5
    Threading
  • 5
    The "lisp style" whitespaces
  • 5
    Official documentation is unclear.
  • 5
    Hard to obfuscate
  • 5
    Circular import
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    The benevolent-dictator-for-life quit
  • 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 · | 44 upvotes · 10.1M 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
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.5M 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

22.1K
13.7K
3.3K
An open source programming language that makes it easy to build simple, reliable, and efficient software
22.1K
13.7K
+ 1
3.3K
PROS OF GOLANG
  • 548
    High-performance
  • 395
    Simple, minimal syntax
  • 363
    Fun to write
  • 301
    Easy concurrency support via goroutines
  • 273
    Fast compilation times
  • 193
    Goroutines
  • 180
    Statically linked binaries that are simple to deploy
  • 150
    Simple compile build/run procedures
  • 136
    Backed by google
  • 136
    Great community
  • 53
    Garbage collection built-in
  • 45
    Built-in Testing
  • 44
    Excellent tools - gofmt, godoc etc
  • 39
    Elegant and concise like Python, fast like C
  • 37
    Awesome to Develop
  • 26
    Used for Docker
  • 25
    Flexible interface system
  • 24
    Deploy as executable
  • 24
    Great concurrency pattern
  • 20
    Open-source Integration
  • 18
    Easy to read
  • 17
    Fun to write and so many feature out of the box
  • 16
    Go is God
  • 14
    Easy to deploy
  • 14
    Powerful and simple
  • 14
    Its Simple and Heavy duty
  • 13
    Best language for concurrency
  • 13
    Concurrency
  • 11
    Rich standard library
  • 11
    Safe GOTOs
  • 10
    Clean code, high performance
  • 10
    Easy setup
  • 9
    High performance
  • 9
    Simplicity, Concurrency, Performance
  • 8
    Hassle free deployment
  • 8
    Single binary avoids library dependency issues
  • 7
    Gofmt
  • 7
    Cross compiling
  • 7
    Simple, powerful, and great performance
  • 7
    Used by Giants of the industry
  • 6
    Garbage Collection
  • 5
    Very sophisticated syntax
  • 5
    Excellent tooling
  • 5
    WYSIWYG
  • 4
    Keep it simple and stupid
  • 4
    Widely used
  • 4
    Kubernetes written on Go
  • 2
    No generics
  • 1
    Operator goto
  • 1
    Looks not fancy, but promoting pragmatic idioms
CONS OF GOLANG
  • 42
    You waste time in plumbing code catching errors
  • 25
    Verbose
  • 23
    Packages and their path dependencies are braindead
  • 16
    Google's documentations aren't beginer friendly
  • 15
    Dependency management when working on multiple projects
  • 10
    Automatic garbage collection overheads
  • 8
    Uncommon syntax
  • 7
    Type system is lacking (no generics, etc)
  • 5
    Collection framework is lacking (list, set, map)
  • 3
    Best programming language
  • 1
    A failed experiment to combine c and python

related Golang posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.1M 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
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.5M 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

79
86
0
A command-driven software package used for statistical analysis and data visualization
79
86
+ 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

      5.7K
      4.8K
      1.2K
      A safe, concurrent, practical language
      5.7K
      4.8K
      + 1
      1.2K
      PROS OF RUST
      • 143
        Guaranteed memory safety
      • 130
        Fast
      • 87
        Open source
      • 75
        Minimal runtime
      • 70
        Pattern matching
      • 63
        Type inference
      • 56
        Concurrent
      • 56
        Algebraic data types
      • 46
        Efficient C bindings
      • 43
        Practical
      • 37
        Best advances in languages in 20 years
      • 32
        Safe, fast, easy + friendly community
      • 30
        Fix for C/C++
      • 25
        Stablity
      • 24
        Zero-cost abstractions
      • 23
        Closures
      • 20
        Extensive compiler checks
      • 20
        Great community
      • 18
        Async/await
      • 18
        No NULL type
      • 15
        Completely cross platform: Windows, Linux, Android
      • 15
        No Garbage Collection
      • 14
        Great documentations
      • 14
        High-performance
      • 12
        Generics
      • 12
        Super fast
      • 12
        High performance
      • 11
        Macros
      • 11
        Fearless concurrency
      • 11
        Guaranteed thread data race safety
      • 11
        Safety no runtime crashes
      • 10
        Helpful compiler
      • 10
        Compiler can generate Webassembly
      • 9
        Prevents data races
      • 9
        Easy Deployment
      • 9
        RLS provides great IDE support
      • 8
        Painless dependency management
      • 8
        Real multithreading
      • 7
        Good package management
      • 5
        Support on Other Languages
      CONS OF RUST
      • 26
        Hard to learn
      • 23
        Ownership learning curve
      • 11
        Unfriendly, verbose syntax
      • 4
        Variable shadowing
      • 4
        High size of builded executable
      • 4
        Many type operations make it difficult to follow
      • 3
        No jobs
      • 1
        Use it only for timeoass not in production

      related Rust posts

      Caue Carvalho
      Shared insights
      on
      RustRustGolangGolangPythonPythonRubyRubyC#C#

      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.

      See more
      James Cunningham
      Operations Engineer at Sentry · | 18 upvotes · 314.9K 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
      Ruby logo

      Ruby

      41.2K
      21.2K
      4K
      A dynamic, interpreted, open source programming language with a focus on simplicity and productivity
      41.2K
      21.2K
      + 1
      4K
      PROS OF RUBY
      • 605
        Programme friendly
      • 536
        Quick to develop
      • 490
        Great community
      • 468
        Productivity
      • 432
        Simplicity
      • 273
        Open source
      • 234
        Meta-programming
      • 207
        Powerful
      • 156
        Blocks
      • 139
        Powerful one-liners
      • 69
        Flexible
      • 58
        Easy to learn
      • 51
        Easy to start
      • 42
        Maintainability
      • 37
        Lambdas
      • 30
        Procs
      • 21
        Fun to write
      • 19
        Diverse web frameworks
      • 13
        Reads like English
      • 10
        Makes me smarter and happier
      • 9
        Rails
      • 8
        Very Dynamic
      • 8
        Elegant syntax
      • 6
        Matz
      • 5
        Object Oriented
      • 5
        Programmer happiness
      • 4
        Elegant code
      • 4
        Generally fun but makes you wanna cry sometimes
      • 4
        Friendly
      • 4
        Fun and useful
      • 3
        Easy packaging and modules
      • 3
        There are so many ways to make it do what you want
      • 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 · 3.9M 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 · 2.9M 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

      621
      666
      166
      A high-level, high-performance dynamic programming language for technical computing
      621
      666
      + 1
      166
      PROS OF JULIA
      • 24
        Fast Performance and Easy Experimentation
      • 21
        Designed for parallelism and distributed computation
      • 18
        Free and Open Source
      • 17
        Dynamic Type System
      • 16
        Multiple Dispatch
      • 16
        Calling C functions directly
      • 16
        Lisp-like Macros
      • 10
        Powerful Shell-like Capabilities
      • 9
        Jupyter notebook integration
      • 8
        REPL
      • 4
        String handling
      • 4
        Emojis as variable names
      • 3
        Interoperability
      CONS OF JULIA
      • 5
        Immature library management system
      • 4
        Slow program start
      • 3
        JIT compiler is very slow
      • 3
        Poor backwards compatibility
      • 2
        Bad tooling
      • 2
        No static compilation

      related Julia posts

      Java logo

      Java

      132.6K
      100.3K
      3.7K
      A concurrent, class-based, object-oriented, language specifically designed to have as few implementation dependencies as possible
      132.6K
      100.3K
      + 1
      3.7K
      PROS OF JAVA
      • 599
        Great libraries
      • 445
        Widely used
      • 400
        Excellent tooling
      • 395
        Huge amount of documentation available
      • 334
        Large pool of developers available
      • 208
        Open source
      • 202
        Excellent performance
      • 157
        Great development
      • 150
        Used for android
      • 148
        Vast array of 3rd party libraries
      • 60
        Compiled Language
      • 52
        Used for Web
      • 46
        High Performance
      • 46
        Managed memory
      • 44
        Native threads
      • 43
        Statically typed
      • 35
        Easy to read
      • 33
        Great Community
      • 29
        Reliable platform
      • 24
        Sturdy garbage collection
      • 24
        JVM compatibility
      • 22
        Cross Platform Enterprise Integration
      • 20
        Universal platform
      • 20
        Good amount of APIs
      • 18
        Great Support
      • 14
        Great ecosystem
      • 11
        Backward compatible
      • 11
        Lots of boilerplate
      • 10
        Everywhere
      • 9
        Excellent SDK - JDK
      • 7
        It's Java
      • 7
        Cross-platform
      • 7
        Static typing
      • 6
        Mature language thus stable systems
      • 6
        Better than Ruby
      • 6
        Long term language
      • 6
        Portability
      • 5
        Clojure
      • 5
        Vast Collections Library
      • 5
        Used for Android development
      • 4
        Most developers favorite
      • 4
        Old tech
      • 3
        History
      • 3
        Great Structure
      • 3
        Stable platform, which many new languages depend on
      • 3
        Javadoc
      • 3
        Testable
      • 3
        Best martial for design
      • 2
        Type Safe
      • 2
        Faster than python
      • 0
        Job
      CONS OF JAVA
      • 33
        Verbosity
      • 27
        NullpointerException
      • 17
        Nightmare to Write
      • 16
        Overcomplexity is praised in community culture
      • 12
        Boiler plate code
      • 8
        Classpath hell prior to Java 9
      • 6
        No REPL
      • 4
        No property
      • 3
        Code are too long
      • 2
        Non-intuitive generic implementation
      • 2
        There is not optional parameter
      • 2
        Floating-point errors
      • 1
        Java's too statically, stronglly, and strictly typed
      • 1
        Returning Wildcard Types
      • 1
        Terrbible compared to Python/Batch Perormence

      related Java posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.1M 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
      Kamil Kowalski
      Lead Architect at Fresha · | 28 upvotes · 3.9M 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