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R vs Sass: What are the differences?

Introduction

In this comparison, we will highlight the key differences between R and Sass.

  1. Syntax: R uses a syntax that is more similar to traditional programming languages like C, whereas Sass uses a syntax that is more akin to CSS with additional features like variables and nesting.

  2. Primary Use: R is primarily used for statistical computing and graphics, while Sass is a style sheet language initially designed to simplify and streamline the creation of cascading style sheets (CSS).

  3. Data Processing: R is particularly strong in data processing, analysis, and visualization with its vast array of libraries and tools, while Sass excels in providing more maintainable and organized stylesheets for web development.

  4. Variables Handling: In R, variables are assigned with "<-" or "=" symbols, while in Sass, variables are created using the "$" symbol followed by the variable name.

  5. Control Structures: R offers a wide range of control structures such as if-else statements, loops, and function calls, whereas Sass focuses more on mixins, functions, and loops to help generate CSS styles efficiently.

In Summary, R and Sass differ in syntax, primary use, data processing capabilities, variable handling, and control structures.

Advice on R Language and Sass
awesomebanana2018
Needs advice
on
PostCSSPostCSSSassSass
and
StylusStylus

Originally, I was going to start using Sass with Parcel, but then I learned about Stylus, which looked interesting because it can get the property values of something directly instead of through variables, and PostCSS, which looked interesting because you can customize your Pre/Post-processing. Which tool would you recommend?

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Replies (2)
Recommends
on
PostCSSPostCSS

You're not correct with saying "vs Postcss". You're using Less/Sass/Stylus/... to produce "CSS" (maybe extended means it has some future features) and then in any case PostCSS will play (it is shipped with Parcel/NextJS/CRA/...)

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Pros of R Language
Pros of Sass
  • 84
    Data analysis
  • 63
    Graphics and data visualization
  • 54
    Free
  • 45
    Great community
  • 38
    Flexible statistical analysis toolkit
  • 27
    Easy packages setup
  • 27
    Access to powerful, cutting-edge analytics
  • 18
    Interactive
  • 13
    R Studio IDE
  • 9
    Hacky
  • 7
    Shiny apps
  • 6
    Preferred Medium
  • 6
    Shiny interactive plots
  • 5
    Automated data reports
  • 4
    Cutting-edge machine learning straight from researchers
  • 3
    Machine Learning
  • 2
    Graphical visualization
  • 1
    Flexible Syntax
  • 613
    Variables
  • 594
    Mixins
  • 466
    Nested rules
  • 410
    Maintainable
  • 300
    Functions
  • 149
    Modular flexible code
  • 143
    Open source
  • 112
    Selector inheritance
  • 107
    Dynamic
  • 96
    Better than cs
  • 5
    Used by Bootstrap
  • 3
    If and for function
  • 2
    Better than less
  • 1
    Inheritance (@extend)
  • 1
    Custom functions

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Cons of R Language
Cons of Sass
  • 6
    Very messy syntax
  • 4
    Tables must fit in RAM
  • 3
    Arrays indices start with 1
  • 2
    Messy syntax for string concatenation
  • 2
    No push command for vectors/lists
  • 1
    Messy character encoding
  • 0
    Poor syntax for classes
  • 0
    Messy syntax for array/vector combination
  • 6
    Needs to be compiled

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What is R Language?

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.

What is Sass?

Sass is an extension of CSS3, adding nested rules, variables, mixins, selector inheritance, and more. It's translated to well-formatted, standard CSS using the command line tool or a web-framework plugin.

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What companies use Sass?
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Blog Posts

Aug 28 2019 at 3:10AM

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What are some alternatives to R Language and Sass?
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.
See all alternatives