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Common Lisp vs R: What are the differences?
Introduction
In this Markdown code, we will discuss the key differences between Common Lisp and R. Both languages have their own unique features and are commonly used in different domains. Below are the main differences explained in detail.
Syntax: The syntax of Common Lisp and R differ significantly. Common Lisp follows a prefix notation called S-expressions, which uses parentheses to separate functions and arguments. On the other hand, R has a more traditional infix notation, where functions and arguments are separated by commas or parentheses are optional. The syntax of Common Lisp can be more complex and explicit, while R's syntax is simpler and more concise.
Functional Programming: Common Lisp is a versatile language that supports both procedural and functional programming paradigms, while R is primarily designed for statistical computing and data analysis, with a strong focus on functional programming. R provides powerful tools and libraries specifically geared towards statistical modeling, while Common Lisp offers a broader set of features for general-purpose programming.
Packages and Libraries: R has a vast collection of packages and libraries available through the Comprehensive R Archive Network (CRAN) and other sources. These packages provide specialized functions and tools for various statistical and data analysis tasks. Common Lisp also has libraries available, but the ecosystem is not as extensive as that of R for statistical computing. Common Lisp's libraries are more focused on general-purpose programming and application development.
Community and Adoption: R has gained significant popularity in the field of data analysis and is widely adopted in academia, research, and industry. There is a large community of R users and developers who actively contribute to the language and its ecosystem. Common Lisp, while being a powerful and flexible language, has a smaller community and is less commonly used in mainstream applications. However, Common Lisp has a dedicated following and is often favored by enthusiasts who appreciate its expressiveness and extensibility.
Metaprogramming: Common Lisp provides powerful metaprogramming capabilities through the use of macros. Macros allow for code generation and transformation, enabling the programmer to extend the language and create domain-specific languages. R, on the other hand, does not have native support for macros and metaprogramming. While R's functional capabilities provide some flexibility, it does not offer the same level of metaprogramming and language extension as Common Lisp.
Development Environment and Tooling: R has a rich ecosystem of development tools and integrated development environments (IDEs) specifically tailored for statistical computing and data analysis, such as RStudio. These tools provide features like code completion, debugging, and data visualization. Common Lisp also has some development environments available but may not offer the same level of specialized tooling for statistical computing as R. Common Lisp's tools are more focused on general-purpose programming and application development.
In summary, Common Lisp and R differ in terms of syntax, programming paradigms, available libraries, community adoption, metaprogramming capabilities, and development tooling. While R is designed for statistical computing and has a strong focus on functional programming, Common Lisp is a more versatile language with a broader set of features for general-purpose programming.
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.
Pros of Common Lisp
- Flexibility24
- High-performance22
- Comfortable: garbage collection, closures, macros, REPL17
- Stable13
- Lisp12
- Code is data8
- Can integrate with C (via CFFI)6
- Multi paradigm6
- Lisp is fun5
- Macros4
- Easy Setup4
- Parentheses3
- Open source3
- Purelly functional3
- Elegant3
- DSLs1
- Multiple values1
- CLOS/MOP1
- Clean semantics1
- Will still be relevant 100 years from now1
- Still decades ahead of almost all programming languages1
- Best programming language1
- Simple syntax1
- Powerful1
- Generic functions1
- Can implement almost any feature as a library1
- Formal specification, multiple implementations1
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 Common Lisp
- Too many Parentheses4
- Standard did not evolve since 19943
- Small library ecosystem2
- No hygienic macros2
- Inadequate community infrastructure1
- Ultra-conservative community1
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