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D vs R: What are the differences?
Key Differences between D and R
D and R are both programming languages that are commonly used for data analysis and statistical modeling. While they have some similarities, there are several key differences between the two.
Syntax: One major difference between D and R is the syntax they use. D has a syntax that is more similar to other programming languages like C++, with its C-style syntax and object-oriented features. On the other hand, R has a syntax that is specifically designed for statistical analysis and data manipulation, making it more concise and intuitive for those tasks.
Performance: D is known for its high performance and efficient execution, making it a popular choice for applications that require speed and computational power. R, on the other hand, is not as fast as D and is better suited for analyzing smaller datasets or performing statistical calculations that do not require real-time processing.
Package Ecosystem: R has a rich package ecosystem with thousands of community-contributed packages that provide specialized functionalities for data analysis, visualization, and statistical modeling. These packages make it easy to perform complex analyses and create high-quality graphics in R. In comparison, D has a smaller package ecosystem with fewer specialized packages for data analysis and statistical modeling.
Type System: D has a statically typed system, which means that variables are required to have explicit types assigned at compile-time. This allows for better optimization and error checking, but it also requires more upfront planning and can be less flexible. R, on the other hand, has a dynamic type system, which allows for more flexibility but can also lead to errors if not used carefully.
Community and Support: R has a larger and more active community compared to D. This means that there are more online resources, forums, and tutorials available for learning and troubleshooting R. Additionally, R is often used in academia and research, which means that there are many experts and statisticians proficient in R who can provide support and guidance. While D does have a growing community, it is not as extensive or established as the R community.
Application Domain: D is a general-purpose programming language that can be used for a wide range of applications beyond data analysis and statistical modeling. It can be utilized in areas like systems programming, game development, and web development. On the other hand, R is specifically designed for statistical analysis, data mining, and visualization. It provides built-in statistical functions and a user-friendly interface that makes it easier to perform data analysis tasks.
In Summary, D and R differ in terms of syntax, performance, package ecosystem, type system, community and support, and application domain. While D is a powerful general-purpose programming language with high performance, R is specifically designed for statistical analysis and has a rich package ecosystem and strong community support.
Pros of D
- Compile-time function execution16
- Makes functional programming style easier12
- Productive12
- Much easier to do Concurrent/Parallel vs C/C++12
- Simple but Powerful template-based generics11
- Powerful static function to avoid macro11
- Meta program is much easier to read/write vs. C++10
- It support unittest etc9
- Assembler is support directly in the language9
- System program language like C++ and C9
- Supports code covarge directly in the compiler9
- Metaprogramming7
- Supports both manuel memory and garbage collection7
- Plugs directly into C6
- Easy to translate from Java and C# to D6
- Feels and looks like C, so it's easy to learn5
- Amazing developer productivity4
- Fast2
- Performance2
- Syntax uniformity across pre-compile/compile/runtime1
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 D
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