Alternatives to RStudio logo

Alternatives to RStudio

Python, Jupyter, Atom, Anaconda, and MATLAB are the most popular alternatives and competitors to RStudio.
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What is RStudio and what are its top alternatives?

An integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution. Publish and distribute data products across your organization. One button deployment of Shiny applications, R Markdown reports, Jupyter Notebooks, and more. Collections of R functions, data, and compiled code in a well-defined format. You can expand the types of analyses you do by adding packages.
RStudio is a tool in the Text Editor category of a tech stack.
RStudio is an open source tool with 4.2K GitHub stars and 1K GitHub forks. Here’s a link to RStudio's open source repository on GitHub

Top Alternatives to RStudio

  • 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. ...

  • Jupyter
    Jupyter

    The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media. ...

  • Atom
    Atom

    At GitHub, we're building the text editor we've always wanted. A tool you can customize to do anything, but also use productively on the first day without ever touching a config file. Atom is modern, approachable, and hackable to the core. We can't wait to see what you build with it. ...

  • Anaconda
    Anaconda

    A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda. ...

  • 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. ...

  • Architect
    Architect

    Create, deploy, and maintain next-generation AWS cloud function-based serverless infrastructure with full local, offline workflows, and more. ...

  • Tableau
    Tableau

    Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click. ...

  • Power BI
    Power BI

    It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. ...

RStudio alternatives & related posts

Python logo

Python

196.4K
169.9K
6.7K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
196.4K
169.9K
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6.7K
PROS OF PYTHON
  • 1.2K
    Great libraries
  • 947
    Readable code
  • 835
    Beautiful code
  • 780
    Rapid development
  • 682
    Large community
  • 426
    Open source
  • 385
    Elegant
  • 278
    Great community
  • 268
    Object oriented
  • 214
    Dynamic typing
  • 75
    Great standard library
  • 56
    Very fast
  • 51
    Functional programming
  • 43
    Scientific computing
  • 43
    Easy to learn
  • 33
    Great documentation
  • 26
    Matlab alternative
  • 25
    Productivity
  • 25
    Easy to read
  • 21
    Simple is better than complex
  • 18
    It's the way I think
  • 17
    Imperative
  • 15
    Free
  • 15
    Very programmer and non-programmer friendly
  • 14
    Machine learning support
  • 14
    Powerfull language
  • 14
    Powerful
  • 13
    Fast and simple
  • 12
    Scripting
  • 9
    Explicit is better than implicit
  • 8
    Ease of development
  • 8
    Clear and easy and powerfull
  • 8
    Unlimited power
  • 7
    Import antigravity
  • 6
    It's lean and fun to code
  • 6
    Print "life is short, use python"
  • 5
    Python has great libraries for data processing
  • 5
    High Documented language
  • 5
    Fast coding and good for competitions
  • 5
    I love snakes
  • 5
    Great for tooling
  • 5
    Flat is better than nested
  • 5
    There should be one-- and preferably only one --obvious
  • 5
    Although practicality beats purity
  • 4
    Readability counts
  • 4
    Rapid Prototyping
  • 3
    Plotting
  • 3
    Web scraping
  • 3
    Now is better than never
  • 3
    Great for analytics
  • 3
    Lists, tuples, dictionaries
  • 3
    Socially engaged community
  • 3
    Complex is better than complicated
  • 3
    Multiple Inheritence
  • 3
    Beautiful is better than ugly
  • 3
    CG industry needs
  • 2
    No cruft
  • 2
    Easy to learn and use
  • 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
    Many types of collections
  • 2
    List comprehensions
  • 2
    Simple and easy to learn
  • 2
    Generators
  • 2
    Easy to setup and run smooth
  • 2
    Import this
  • 1
    Better outcome
  • 1
    Can understand easily who are new to programming
  • 1
    Powerful language for AI
  • 1
    Should START with this but not STICK with This
  • 1
    Because of Netflix
  • 1
    A-to-Z
  • 1
    Only one way to do it
  • 1
    Flexible and easy
  • 1
    Batteries included
  • 1
    It is Very easy , simple and will you be love programmi
  • 1
    Good for hacking
  • 1
    Pip install everything
  • 0
    Powerful
CONS OF PYTHON
  • 51
    Still divided between python 2 and python 3
  • 28
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 21
    GIL
  • 19
    Package management is a mess
  • 14
    Too imperative-oriented
  • 12
    Hard to understand
  • 12
    Dynamic typing
  • 10
    Very slow
  • 8
    Not everything is expression
  • 7
    Explicit self parameter in methods
  • 7
    Indentations matter a lot
  • 6
    Poor DSL capabilities
  • 6
    Incredibly slow
  • 6
    No anonymous functions
  • 6
    Requires C functions for dynamic modules
  • 5
    Hard to obfuscate
  • 5
    Threading
  • 5
    Fake object-oriented programming
  • 5
    The "lisp style" whitespaces
  • 4
    Official documentation is unclear.
  • 4
    Circular import
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    Not suitable for autocomplete
  • 4
    The benevolent-dictator-for-life quit
  • 2
    Meta classes
  • 1
    Training wheels (forced indentation)

related Python posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 41 upvotes · 5.7M 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

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Nick Parsons
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 1.8M 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

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Jupyter logo

Jupyter

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Multi-language interactive computing environments.
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PROS OF JUPYTER
  • 18
    In-line code execution using blocks
  • 10
    In-line graphing support
  • 7
    Can be themed
  • 6
    Multiple kernel support
  • 3
    Best web-browser IDE for Python
  • 3
    Export to python code
  • 2
    LaTex Support
  • 1
    HTML export capability
  • 1
    Multi-user with Kubernetes
CONS OF JUPYTER
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    related Jupyter posts

    Jan Vlnas
    Developer Advocate at Superface · | 5 upvotes · 30.9K views

    From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.

    I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.

    Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.

    See more
    Guillaume Simler

    Jupyter Anaconda Pandas IPython

    A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

    The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

    See more
    Atom logo

    Atom

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    13.6K
    2.7K
    A hackable text editor for the 21st Century
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    PROS OF ATOM
    • 528
      Free
    • 447
      Open source
    • 342
      Modular design
    • 320
      Hackable
    • 316
      Beautiful UI
    • 170
      Github integration
    • 147
      Backed by github
    • 119
      Built with node.js
    • 113
      Web native
    • 107
      Community
    • 35
      Packages
    • 18
      Cross platform
    • 5
      TypeScript editor
    • 5
      Nice UI
    • 5
      Multicursor support
    • 3
      cli start
    • 3
      Chrome Inspector works IN EDITOR
    • 3
      Simple but powerful
    • 3
      Open source, lots of packages, and so configurable
    • 3
      Snippets
    • 2
      It's powerful
    • 2
      Code readability
    • 2
      Awesome
    • 2
      Smart TypeScript code completion
    • 2
      Well documented
    • 1
      "Free", "Hackable", "Open Source", The Awesomness
    • 1
      Apm publish minor
    • 1
      works with GitLab
    • 1
      full support
    • 1
      vim support
    • 1
      Split-Tab Layout
    • 1
      Consistent UI on all platforms
    • 1
      User friendly
    • 1
      Hackable and Open Source
    • 0
      Publish
    CONS OF ATOM
    • 19
      Slow with large files
    • 7
      Slow startup
    • 2
      Most of the time packages are hard to find.
    • 1
      No longer maintained
    • 1
      Cannot Run code with F5
    • 1
      Can be easily Modified

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    Jerome Dalbert
    Principal Backend Software Engineer at StackShare · | 13 upvotes · 699.5K views

    I liked Sublime Text for its speed, simplicity and keyboard shortcuts which synergize well when working on scripting languages like Ruby and JavaScript. I extended the editor with custom Python scripts that improved keyboard navigability such as autofocusing the sidebar when no files are open, or changing tab closing behavior.

    But customization can only get you so far, and there were little things that I still had to use the mouse for, such as scrolling, repositioning lines on the screen, selecting the line number of a failing test stack trace from a separate plugin pane, etc. After 3 years of wearily moving my arm and hand to perform the same repetitive tasks, I decided to switch to Vim for 3 reasons:

    • your fingers literally don’t ever need to leave the keyboard home row (I had to remap the escape key though)
    • it is a reliable tool that has been around for more than 30 years and will still be around for the next 30 years
    • I wanted to "look like a hacker" by doing everything inside my terminal and by becoming a better Unix citizen

    The learning curve is very steep and it took me a year to master it, but investing time to be truly comfortable with my #TextEditor was more than worth it. To me, Vim comes close to being the perfect editor and I probably won’t need to switch ever again. It feels good to ignore new editors that come out every few years, like Atom and Visual Studio Code.

    See more
    Julian Sanchez
    Lead Developer at Chore Champion · | 9 upvotes · 558.8K views

    We use Visual Studio Code because it allows us to easily and quickly integrate with Git, much like Sublime Merge ,but it is integrated into the IDE. Another cool part about VS Code is the ability collaborate with each other with Visual Studio Live Share which allows our whole team to get more done together. It brings the convenience of the Google Suite to programming, offering something that works more smoothly than anything found on Atom or Sublime Text

    See more
    Anaconda logo

    Anaconda

    396
    447
    0
    The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders
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    PROS OF ANACONDA
      Be the first to leave a pro
      CONS OF ANACONDA
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        related Anaconda posts

        Shared insights
        on
        JavaJavaAnacondaAnacondaPythonPython

        I am going to learn machine learning and self host an online IDE, the tool that i may use is Python, Anaconda, various python library and etc. which tools should i go for? this may include Java development, web development. Now i have 1 more candidate which are visual studio code online (code server). i will host on google cloud

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        Which one of these should I install? I am a beginner and starting to learn to code. I have Anaconda, Visual Studio Code ( vscode recommended me to install Git) and I am learning Python, JavaScript, and MySQL for educational purposes. Also if you have any other pro-tips or advice for me please share.

        Yours thankfully, Darkhiem

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        MATLAB logo

        MATLAB

        758
        655
        34
        A high-level language and interactive environment for numerical computation, visualization, and programming
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        655
        + 1
        34
        PROS OF MATLAB
        • 18
          Simulink
        • 5
          Functions, statements, plots, directory navigation easy
        • 4
          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

        Architect logo

        Architect

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        83
        0
        The simplest, most powerful way to build serverless applications
        33
        83
        + 1
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        PROS OF ARCHITECT
          Be the first to leave a pro
          CONS OF ARCHITECT
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            related Architect posts

            Tableau logo

            Tableau

            1.1K
            1.2K
            9
            Tableau helps people see and understand data.
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            PROS OF TABLEAU
            • 6
              Capable of visualising billions of rows
            • 1
              3
            • 1
              Intuitive and easy to learn
            • 1
              Responsive
            CONS OF TABLEAU
            • 1
              Very expensive for small companies

            related Tableau posts

            Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

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            Power BI logo

            Power BI

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            781
            25
            Empower team members to discover insights hidden in your data
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            25
            PROS OF POWER BI
            • 16
              Cross-filtering
            • 2
              Powerful Calculation Engine
            • 2
              Access from anywhere
            • 2
              Intuitive and complete internal ETL
            • 2
              Database visualisation
            • 1
              Azure Based Service
            CONS OF POWER BI
              Be the first to leave a con

              related Power BI posts

              Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

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              Which among the two, Kyvos and Azure Analysis Services, should be used to build a Semantic Layer?

              I have to build a Semantic Layer for the data warehouse platform and use Power BI for visualisation and the data lies in the Azure Managed Instance. I need to analyse the two platforms and find which suits best for the same.

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