How to use this tool?
This free online converter lets you convert code from Csharp to R in a click of a button. To use this converter, take the following steps -
- Type or paste your Csharp code in the input box.
- Click the convert button.
- The resulting R code from the conversion will be displayed in the output box.
Examples
The following are examples of code conversion from Csharp to R using this converter. Note that you may not always get the same code since it is generated by an AI language model which is not 100% deterministic and gets updated from time to time.
Example 1 - Is String Palindrome
Program that checks if a string is a palindrome or not.
Csharp
R
Example 2 - Even or Odd
A well commented function to check if a number if odd or even.
Csharp
R
Key differences between Csharp and R
Characteristic | Csharp | R |
---|---|---|
Syntax | C# syntax is similar to C++ and Java, with curly braces and semicolons to denote code blocks and statements. It also supports LINQ (Language Integrated Query) for querying data. | R syntax is designed for data analysis and statistical computing. It uses a functional programming style with a focus on vectors and data frames. R also has a wide range of built-in functions for data manipulation and analysis. |
Paradigm | C# supports object-oriented programming, as well as functional programming with the use of lambda expressions and LINQ. | R is primarily a functional programming language, but it also supports object-oriented programming with the use of S3 and S4 classes. |
Typing | C# is a statically typed language, meaning that variable types are determined at compile time. | R is a dynamically typed language, meaning that variable types are determined at runtime. |
Performance | C# is a compiled language, which generally results in faster performance than interpreted languages like R. It also has built-in support for multithreading and parallel processing. | R is an interpreted language, which can result in slower performance than compiled languages like C#. However, R has many optimized libraries for data analysis and statistical computing that can improve performance. |
Libraries and frameworks | C# has a wide range of libraries and frameworks for various purposes, including .NET Framework, ASP.NET, Entity Framework, and Xamarin. | R has a large collection of libraries for data analysis and statistical computing, including ggplot2, dplyr, and tidyr. |
Community and support | C# has a large and active community, with many resources and forums available for support and learning. | R also has a large and active community, with many resources and forums available for support and learning. However, it may be more specialized towards data analysis and statistical computing. |
Learning curve | C# has a moderate learning curve, especially for those with experience in C++ or Java. It also has many resources and tutorials available for beginners. | R has a steeper learning curve, especially for those without a background in programming or statistics. However, it has many resources and tutorials available for beginners. |