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Haskell vs R: What are the differences?
Introduction
Haskell and R are two popular programming languages, each with its own unique features and functionalities. While both languages are used for data analysis and manipulation, there are key differences between the two.
Syntax: One major difference between Haskell and R lies in their syntax. Haskell follows a statically-typed, functional programming paradigm, with an emphasis on strong type checking and immutability. On the other hand, R is a dynamically-typed language that supports both functional and object-oriented programming styles. R's syntax is more flexible and forgiving compared to Haskell's.
Data Manipulation: Haskell and R have different approaches when it comes to data manipulation. Haskell focuses on the concept of pure functions and immutable data structures, which ensures that functions do not have side effects and can be composed easily. In contrast, R provides built-in functions and packages specifically designed for data manipulation and analysis, making it more convenient for tasks such as data cleaning, transformation, and exploration.
Performance: Performance is another aspect where Haskell and R differ. Haskell is known for its highly optimized, compiled code, which can offer significant performance advantages in terms of execution speed and memory usage. R, on the other hand, is interpreted and often relies on external libraries for performance-critical tasks. While R provides convenient high-level functions, it may be slower compared to Haskell for computationally intensive operations.
Type System: The type systems of Haskell and R also exhibit differences. Haskell has a strong static type system that enforces type safety at compile-time, reducing the chances of runtime errors and improving program correctness. R, being dynamically typed, allows for more flexibility but may exhibit unexpected behavior if types are not carefully handled. This difference can have implications for the maintainability and reliability of code written in these languages.
Community and Libraries: The communities surrounding Haskell and R differ in terms of size and focus. Haskell has a smaller but highly active community that emphasizes creating elegant and optimized code. R, on the other hand, has a larger community with a strong focus on data analysis and statistical computing. As a result, R has a wide range of libraries and packages specifically tailored for data analysis, making it a popular choice in the field.
Domain-specific Focus: Another key difference lies in the domain-specific focus of Haskell and R. Haskell is a general-purpose language that can be used for various applications, including web development, systems programming, and formal verification. R, on the other hand, is primarily designed for statistical computing and data analysis. It provides a rich set of statistical functions and packages, making it a preferred choice for statisticians and data scientists.
In Summary, Haskell and R differ in terms of syntax, approach to data manipulation, performance, type system, community and libraries, and domain-specific focus.
MACHINE LEARNING
Python is the default go-to for machine learning. It has a wide variety of useful packages such as pandas and numpy to aid with ML, as well as deep-learning frameworks. Furthermore, it is more production-friendly compared to other ML languages such as R.
Pytorch is a deep-learning framework that is both flexible and fast compared to Tensorflow + Keras. It is also well documented and has a large community to answer lingering questions.
We have a lot of experience in JavaScript, writing our services in NodeJS allows developers to transition to the back end without any friction, without having to learn a new language. There is also the option to write services in TypeScript, which adds an expressive type layer. The semi-shared ecosystem between front and back end is nice as well, though specifically NodeJS libraries sometimes suffer in quality, compared to other major languages.
As for why we didn't pick the other languages, most of it comes down to "personal preference" and historically grown code bases, but let's do some post-hoc deduction:
Go is a practical choice, reasonably easy to learn, but until we find performance issues with our NodeJS stack, there is simply no reason to switch. The benefits of using NodeJS so far outweigh those of picking Go. This might change in the future.
PHP is a language we're still using in big parts of our system, and are still sometimes writing new code in. Modern PHP has fixed some of its issues, and probably has the fastest development cycle time, but it suffers around modelling complex asynchronous tasks, and (on a personal note) lack of support for writing in a functional style.
We don't use Python, Elixir or Ruby, mostly because of personal preference and for historic reasons.
Rust, though I personally love and use it in my projects, would require us to specifically hire for that, as the learning curve is quite steep. Its web ecosystem is OK by now (see https://www.arewewebyet.org/), but in my opinion, it is still no where near that of the other web languages. In other words, we are not willing to pay the price for playing this innovation card.
Haskell, as with Rust, I personally adore, but is simply too esoteric for us. There are problem domains where it shines, ours is not one of them.
Pros of Haskell
- Purely-functional programming90
- Statically typed66
- Type-safe59
- Open source39
- Great community38
- Built-in concurrency31
- Built-in parallelism30
- Composable30
- Referentially transparent24
- Generics20
- Type inference15
- Intellectual satisfaction15
- If it compiles, it's correct12
- Flexible8
- Monads8
- Great type system5
- Proposition testing with QuickCheck4
- One of the most powerful languages *(see blub paradox)*4
- Purely-functional Programming4
- Highly expressive, type-safe, fast development time3
- Pattern matching and completeness checking3
- Great maintainability of the code3
- Fun3
- Reliable3
- Best in class thinking tool2
- Kind system2
- Better type-safe than sorry2
- Type classes2
- Predictable1
- Orthogonality1
Pros of R Language
- Data analysis86
- Graphics and data visualization64
- Free55
- Great community45
- Flexible statistical analysis toolkit38
- Easy packages setup27
- Access to powerful, cutting-edge analytics27
- Interactive18
- R Studio IDE13
- Hacky9
- Shiny apps7
- Shiny interactive plots6
- Preferred Medium6
- Automated data reports5
- Cutting-edge machine learning straight from researchers4
- Machine Learning3
- Graphical visualization2
- Flexible Syntax1
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Cons of Haskell
- Too much distraction in language extensions9
- Error messages can be very confusing8
- Libraries have poor documentation5
- No good ABI3
- No best practices3
- Poor packaging for apps written in it for Linux distros2
- Sometimes performance is unpredictable2
- Slow compilation1
- Monads are hard to understand1
Cons of R Language
- Very messy syntax6
- Tables must fit in RAM4
- Arrays indices start with 13
- Messy syntax for string concatenation2
- No push command for vectors/lists2
- Messy character encoding1
- Poor syntax for classes0
- Messy syntax for array/vector combination0
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What is Haskell?
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