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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. ...
- Python
Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...
- Golang
Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language. ...
- SAS
It is a command-driven software package used for statistical analysis and data visualization. It is available only for Windows operating systems. It is arguably one of the most widely used statistical software packages in both industry and academia. ...
- Rust
Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory. ...
- Ruby
Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming. ...
- Julia
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. ...
- Java
Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere! ...
R Language alternatives & related posts
MATLAB
- Simulink20
- Model based software development5
- Functions, statements, plots, directory navigation easy5
- S-Functions3
- REPL2
- Simple variabel control1
- Solve invertible matrix1
- Parameter-value pairs syntax to pass arguments clunky2
- Doesn't allow unpacking tuples/arguments lists with *2
- Does not support named function arguments2
related MATLAB posts
Python
- Great libraries1.2K
- Readable code963
- Beautiful code847
- Rapid development788
- Large community691
- Open source438
- Elegant393
- Great community282
- Object oriented273
- Dynamic typing221
- Great standard library77
- Very fast60
- Functional programming55
- Easy to learn50
- Scientific computing46
- Great documentation35
- Productivity29
- Matlab alternative28
- Easy to read28
- Simple is better than complex24
- It's the way I think20
- Imperative19
- Very programmer and non-programmer friendly18
- Free18
- Machine learning support17
- Powerfull language17
- Fast and simple16
- Scripting14
- Explicit is better than implicit12
- Ease of development11
- Clear and easy and powerfull10
- Unlimited power9
- Import antigravity8
- It's lean and fun to code8
- Print "life is short, use python"7
- Python has great libraries for data processing7
- High Documented language6
- I love snakes6
- Readability counts6
- Rapid Prototyping6
- Now is better than never6
- Although practicality beats purity6
- Flat is better than nested6
- Great for tooling6
- There should be one-- and preferably only one --obvious6
- Fast coding and good for competitions6
- Web scraping5
- Lists, tuples, dictionaries5
- Great for analytics5
- Beautiful is better than ugly4
- Easy to learn and use4
- Easy to setup and run smooth4
- Multiple Inheritence4
- CG industry needs4
- Socially engaged community4
- Complex is better than complicated4
- Plotting4
- Simple and easy to learn4
- List comprehensions3
- Powerful language for AI3
- Flexible and easy3
- It is Very easy , simple and will you be love programmi3
- Many types of collections3
- If the implementation is easy to explain, it may be a g3
- If the implementation is hard to explain, it's a bad id3
- Special cases aren't special enough to break the rules3
- Pip install everything3
- No cruft3
- Generators3
- Import this3
- Batteries included2
- Securit2
- Can understand easily who are new to programming2
- Should START with this but not STICK with This2
- A-to-Z2
- Because of Netflix2
- Only one way to do it2
- Better outcome2
- Good for hacking2
- Best friend for NLP1
- Sexy af1
- Procedural programming1
- Automation friendly1
- Slow1
- Keep it simple0
- Powerful0
- Ni0
- Still divided between python 2 and python 353
- Performance impact28
- Poor syntax for anonymous functions26
- GIL22
- Package management is a mess19
- Too imperative-oriented14
- Hard to understand12
- Dynamic typing12
- Very slow12
- Indentations matter a lot8
- Not everything is expression8
- Incredibly slow7
- Explicit self parameter in methods7
- Requires C functions for dynamic modules6
- Poor DSL capabilities6
- No anonymous functions6
- Fake object-oriented programming5
- Threading5
- The "lisp style" whitespaces5
- Official documentation is unclear.5
- Hard to obfuscate5
- Circular import5
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- The benevolent-dictator-for-life quit4
- Not suitable for autocomplete4
- Meta classes2
- Training wheels (forced indentation)1
related Python posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages
Golang
- High-performance553
- Simple, minimal syntax397
- Fun to write364
- Easy concurrency support via goroutines303
- Fast compilation times273
- Goroutines195
- Statically linked binaries that are simple to deploy181
- Simple compile build/run procedures151
- Backed by google137
- Great community137
- Garbage collection built-in53
- Built-in Testing47
- Excellent tools - gofmt, godoc etc44
- Elegant and concise like Python, fast like C40
- Awesome to Develop37
- Used for Docker26
- Flexible interface system26
- Great concurrency pattern25
- Deploy as executable24
- Open-source Integration21
- Easy to read19
- Fun to write and so many feature out of the box17
- Go is God17
- Powerful and simple14
- Easy to deploy14
- Its Simple and Heavy duty14
- Concurrency14
- Best language for concurrency13
- Safe GOTOs11
- Rich standard library11
- Clean code, high performance10
- Easy setup10
- High performance10
- Simplicity, Concurrency, Performance9
- Cross compiling8
- Single binary avoids library dependency issues8
- Hassle free deployment8
- Used by Giants of the industry7
- Simple, powerful, and great performance7
- Gofmt7
- Garbage Collection6
- WYSIWYG5
- Very sophisticated syntax5
- Excellent tooling5
- Keep it simple and stupid4
- Widely used4
- Kubernetes written on Go4
- No generics2
- Looks not fancy, but promoting pragmatic idioms1
- Operator goto1
- You waste time in plumbing code catching errors42
- Verbose25
- Packages and their path dependencies are braindead23
- Google's documentations aren't beginer friendly16
- Dependency management when working on multiple projects15
- Automatic garbage collection overheads10
- Uncommon syntax8
- Type system is lacking (no generics, etc)7
- Collection framework is lacking (list, set, map)5
- Best programming language3
- A failed experiment to combine c and python1
related Golang posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages
related SAS posts
- Guaranteed memory safety145
- Fast132
- Open source88
- Minimal runtime75
- Pattern matching72
- Type inference63
- Algebraic data types57
- Concurrent57
- Efficient C bindings47
- Practical43
- Best advances in languages in 20 years37
- Safe, fast, easy + friendly community32
- Fix for C/C++30
- Stablity25
- Zero-cost abstractions24
- Closures23
- Extensive compiler checks20
- Great community20
- Async/await18
- No NULL type18
- Completely cross platform: Windows, Linux, Android15
- No Garbage Collection15
- Great documentations14
- High-performance14
- Generics12
- Super fast12
- High performance12
- Safety no runtime crashes11
- Fearless concurrency11
- Compiler can generate Webassembly11
- Macros11
- Guaranteed thread data race safety11
- Helpful compiler10
- RLS provides great IDE support9
- Prevents data races9
- Easy Deployment9
- Real multithreading8
- Painless dependency management8
- Good package management7
- Support on Other Languages5
- Type System1
- Hard to learn28
- Ownership learning curve24
- Unfriendly, verbose syntax12
- High size of builded executable4
- Many type operations make it difficult to follow4
- No jobs4
- Variable shadowing4
- Use it only for timeoass not in production1
related Rust posts
Hello!
I'm a developer for over 9 years, and most of this time I've been working with C# and it is paying my bills until nowadays. But I'm seeking to learn other languages and expand the possibilities for the next years.
Now the question... I know Ruby is far from dead but is it still worth investing time in learning it? Or would be better to take Python, Golang, or even Rust? Or maybe another language.
Thanks in advance.
Sentry's event processing pipeline, which is responsible for handling all of the ingested event data that makes it through to our offline task processing, is written primarily in Python.
For particularly intense code paths, like our source map processing pipeline, we have begun re-writing those bits in Rust. Rust’s lack of garbage collection makes it a particularly convenient language for embedding in Python. It allows us to easily build a Python extension where all memory is managed from the Python side (if the Python wrapper gets collected by the Python GC we clean up the Rust object as well).
Ruby
- Programme friendly607
- Quick to develop538
- Great community492
- Productivity469
- Simplicity432
- Open source274
- Meta-programming235
- Powerful208
- Blocks157
- Powerful one-liners140
- Flexible70
- Easy to learn59
- Easy to start52
- Maintainability42
- Lambdas38
- Procs31
- Fun to write21
- Diverse web frameworks19
- Reads like English14
- Makes me smarter and happier10
- Rails9
- Elegant syntax9
- Very Dynamic8
- Matz7
- Programmer happiness6
- Object Oriented5
- Elegant code4
- Friendly4
- Generally fun but makes you wanna cry sometimes4
- Fun and useful4
- There are so many ways to make it do what you want3
- Easy packaging and modules3
- Primitive types can be tampered with2
- Memory hog7
- Really slow if you're not really careful7
- Nested Blocks can make code unreadable3
- Encouraging imperative programming2
- No type safety, so it requires copious testing1
- Ambiguous Syntax, such as function parentheses1
related Ruby posts
When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.
I needed to choose a full stack of tools for cross platform mobile application design & development. After much research and trying different tools, these are what I came up with that work for me today:
For the client coding I chose Framework7 because of its performance, easy learning curve, and very well designed, beautiful UI widgets. I think it's perfect for solo development or small teams. I didn't like React Native. It felt heavy to me and rigid. Framework7 allows the use of #CSS3, which I think is the best technology to come out of the #WWW movement. No other tech has been able to allow designers and developers to develop such flexible, high performance, customisable user interface elements that are highly responsive and hardware accelerated before. Now #CSS3 includes variables and flexboxes it is truly a powerful language and there is no longer a need for preprocessors such as #SCSS / #Sass / #less. React Native contains a very limited interpretation of #CSS3 which I found very frustrating after using #CSS3 for some years already and knowing its powerful features. The other very nice feature of Framework7 is that you can even build for the browser if you want your app to be available for desktop web browsers. The latest release also includes the ability to build for #Electron so you can have MacOS, Windows and Linux desktop apps. This is not possible with React Native yet.
Framework7 runs on top of Apache Cordova. Cordova and webviews have been slated as being slow in the past. Having a game developer background I found the tweeks to make it run as smooth as silk. One of those tweeks is to use WKWebView. Another important one was using srcset on images.
I use #Template7 for the for the templating system which is a no-nonsense mobile-centric #HandleBars style extensible templating system. It's easy to write custom helpers for, is fast and has a small footprint. I'm not forced into a new paradigm or learning some new syntax. It operates with standard JavaScript, HTML5 and CSS 3. It's written by the developer of Framework7 and so dovetails with it as expected.
I configured TypeScript to work with the latest version of Framework7. I consider TypeScript to be one of the best creations to come out of Microsoft in some time. They must have an amazing team working on it. It's very powerful and flexible. It helps you catch a lot of bugs and also provides code completion in supporting IDEs. So for my IDE I use Visual Studio Code which is a blazingly fast and silky smooth editor that integrates seamlessly with TypeScript for the ultimate type checking setup (both products are produced by Microsoft).
I use Webpack and Babel to compile the JavaScript. TypeScript can compile to JavaScript directly but Babel offers a few more options and polyfills so you can use the latest (and even prerelease) JavaScript features today and compile to be backwards compatible with virtually any browser. My favorite recent addition is "optional chaining" which greatly simplifies and increases readability of a number of sections of my code dealing with getting and setting data in nested objects.
I use some Ruby scripts to process images with ImageMagick and pngquant to optimise for size and even auto insert responsive image code into the HTML5. Ruby is the ultimate cross platform scripting language. Even as your scripts become large, Ruby allows you to refactor your code easily and make it Object Oriented if necessary. I find it the quickest and easiest way to maintain certain aspects of my build process.
For the user interface design and prototyping I use Figma. Figma has an almost identical user interface to #Sketch but has the added advantage of being cross platform (MacOS and Windows). Its real-time collaboration features are outstanding and I use them a often as I work mostly on remote projects. Clients can collaborate in real-time and see changes I make as I make them. The clickable prototyping features in Figma are also very well designed and mean I can send clickable prototypes to clients to try user interface updates as they are made and get immediate feedback. I'm currently also evaluating the latest version of #AdobeXD as an alternative to Figma as it has the very cool auto-animate feature. It doesn't have real-time collaboration yet, but I heard it is proposed for 2019.
For the UI icons I use Font Awesome Pro. They have the largest selection and best looking icons you can find on the internet with several variations in styles so you can find most of the icons you want for standard projects.
For the backend I was using the #GraphCool Framework. As I later found out, #GraphQL still has some way to go in order to provide the full power of a mature graph query language so later in my project I ripped out #GraphCool and replaced it with CouchDB and Pouchdb. Primarily so I could provide good offline app support. CouchDB with Pouchdb is very flexible and efficient combination and overcomes some of the restrictions I found in #GraphQL and hence #GraphCool also. The most impressive and important feature of CouchDB is its replication. You can configure it in various ways for backups, fault tolerance, caching or conditional merging of databases. CouchDB and Pouchdb even supports storing, retrieving and serving binary or image data or other mime types. This removes a level of complexity usually present in database implementations where binary or image data is usually referenced through an #HTML5 link. With CouchDB and Pouchdb apps can operate offline and sync later, very efficiently, when the network connection is good.
I use PhoneGap when testing the app. It auto-reloads your app when its code is changed and you can also install it on Android phones to preview your app instantly. iOS is a bit more tricky cause of Apple's policies so it's not available on the App Store, but you can build it and install it yourself to your device.
So that's my latest mobile stack. What tools do you use? Have you tried these ones?
Julia
- Fast Performance and Easy Experimentation25
- Designed for parallelism and distributed computation22
- Free and Open Source19
- Dynamic Type System17
- Calling C functions directly17
- Multiple Dispatch16
- Lisp-like Macros16
- Powerful Shell-like Capabilities10
- Jupyter notebook integration10
- REPL8
- String handling4
- Emojis as variable names4
- Interoperability3
- Immature library management system5
- Slow program start4
- JIT compiler is very slow3
- Poor backwards compatibility3
- Bad tooling2
- No static compilation2
related Julia posts
Java
- Great libraries603
- Widely used446
- Excellent tooling401
- Huge amount of documentation available396
- Large pool of developers available334
- Open source208
- Excellent performance203
- Great development158
- Used for android150
- Vast array of 3rd party libraries148
- Compiled Language60
- Used for Web52
- Managed memory46
- High Performance46
- Native threads45
- Statically typed43
- Easy to read35
- Great Community33
- Reliable platform29
- Sturdy garbage collection24
- JVM compatibility24
- Cross Platform Enterprise Integration22
- Good amount of APIs20
- Universal platform20
- Great Support18
- Great ecosystem14
- Backward compatible11
- Lots of boilerplate11
- Everywhere10
- Excellent SDK - JDK9
- Cross-platform7
- It's Java7
- Static typing7
- Portability6
- Mature language thus stable systems6
- Better than Ruby6
- Long term language6
- Used for Android development5
- Clojure5
- Vast Collections Library5
- Best martial for design4
- Most developers favorite4
- Old tech4
- Testable3
- History3
- Javadoc3
- Stable platform, which many new languages depend on3
- Great Structure3
- Faster than python2
- Type Safe2
- Job0
- Verbosity33
- NullpointerException27
- Nightmare to Write17
- Overcomplexity is praised in community culture16
- Boiler plate code12
- Classpath hell prior to Java 98
- No REPL6
- No property4
- Code are too long3
- Non-intuitive generic implementation2
- There is not optional parameter2
- Floating-point errors2
- Java's too statically, stronglly, and strictly typed1
- Returning Wildcard Types1
- Terrbible compared to Python/Batch Perormence1
related Java posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.