Markdown vs R Language

Overview

Markdown

Stacks22.2K

Followers16.5K

Votes960

R Language

Stacks3.9K

Followers1.9K

Votes418

Markdown vs R Language: What are the differences?

Introduction: In this comparison, we will explore the key differences between Markdown and R Language, focusing on their distinct characteristics and intended use cases.

Syntax Complexity: Markdown is a lightweight markup language with simple syntax for formatting text, while R Language is a programming language that requires more complex syntax for data analysis and statistical computing, including functions, loops, and conditional statements.
Purpose: Markdown is primarily used for creating formatted text documents with basic styling, whereas R Language is specifically designed for statistical computing, data visualization, and data analysis tasks, making it a more specialized tool for data science professionals.
Output: Markdown is commonly used for creating static documents such as README files, documentation, or simple web pages, while R Language is used for generating dynamic reports, interactive visualizations, and performing complex statistical analysis on data.
Extensions: Markdown has limited functionality compared to R Language, as it does not support advanced data manipulation, statistical modeling, or machine learning algorithms, which are core features of R Language through its extensive library ecosystem.
Interactive Console: R Language provides an interactive console environment where users can run code, perform data analysis tasks, and visualize results in real-time, while Markdown does not have an interactive programming environment and is focused on displaying static content.
Customization: R Language allows for extensive customization through the creation of reusable functions, packages, and extensions, enabling users to tailor their data analysis workflows to specific requirements, which is not possible in Markdown due to its basic functionality for text formatting.

In Summary, Markdown and R Language have distinct purposes, with Markdown being a simple markup language for text formatting and R Language being a powerful programming language for statistical computing and data analysis.

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Advice on Markdown, R Language

Rick

founder at Webcompose.ca

May 8, 2020

Needs adviceon

GitHub

Markdown

npm

I am a newbie to StackShare and the GitHub community. I want to understand how to use an include statement to get a collection of Markdown files to create a book. I have been told that there are a number of useful tools. My problem is that npm and Node.js are also very new to me. Any suggestions on how to get my md chapters into a printable document would be helpful.

80.3k views80.3k

Comments

Detailed Comparison

Markdown	R Language
Markdown is two things: (1) a plain text formatting syntax; and (2) a software tool, written in Perl, that converts the plain text formatting to HTML.	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.
Statistics
Stacks 22.2K	Stacks 3.9K
Followers 16.5K	Followers 1.9K
Votes 960	Votes 418
Pros & Cons
Pros 345 Easy formatting 246 Widely adopted 194 Intuitive 132 Github integration 41 Great for note taking Cons 2 Cannot centralise (HTML code needed) 1 Non-extensible 1 No right indentation 1 No underline 1 Unable to indent tables	Pros 86 Data analysis 64 Graphics and data visualization 55 Free 45 Great community 38 Flexible statistical analysis toolkit Cons 6 Very messy syntax 4 Tables must fit in RAM 3 Arrays indices start with 1 2 No push command for vectors/lists 2 Messy syntax for string concatenation

What are some alternatives to Markdown, R Language?

JavaScript

JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.

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.

PHP

Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

Ruby

Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.

Java

Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!

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.

HTML5

HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997.

C#

C# (pronounced "See Sharp") is a simple, modern, object-oriented, and type-safe programming language. C# has its roots in the C family of languages and will be immediately familiar to C, C++, Java, and JavaScript programmers.

Scala

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.

Elixir

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

Related Comparisons

Markdown vs R Language: What are the differences?

Introduction: In this comparison, we will explore the key differences between Markdown and R Language, focusing on their distinct characteristics and intended use cases.

Syntax Complexity: Markdown is a lightweight markup language with simple syntax for formatting text, while R Language is a programming language that requires more complex syntax for data analysis and statistical computing, including functions, loops, and conditional statements.
Purpose: Markdown is primarily used for creating formatted text documents with basic styling, whereas R Language is specifically designed for statistical computing, data visualization, and data analysis tasks, making it a more specialized tool for data science professionals.
Output: Markdown is commonly used for creating static documents such as README files, documentation, or simple web pages, while R Language is used for generating dynamic reports, interactive visualizations, and performing complex statistical analysis on data.
Extensions: Markdown has limited functionality compared to R Language, as it does not support advanced data manipulation, statistical modeling, or machine learning algorithms, which are core features of R Language through its extensive library ecosystem.
Interactive Console: R Language provides an interactive console environment where users can run code, perform data analysis tasks, and visualize results in real-time, while Markdown does not have an interactive programming environment and is focused on displaying static content.
Customization: R Language allows for extensive customization through the creation of reusable functions, packages, and extensions, enabling users to tailor their data analysis workflows to specific requirements, which is not possible in Markdown due to its basic functionality for text formatting.

Markdown vs R Language

Overview