AWK vs R

Overview

R Language

Stacks3.9K

Followers1.9K

Votes418

AWK

Stacks639

Followers49

Votes0

GitHub Stars2.1K

Forks181

AWK vs R: What are the differences?

Programming Paradigm: AWK is a procedural programming language specifically designed for text processing and data extraction. On the other hand, R is a statistical programming language used for data analysis, visualization, and statistical modeling. While AWK focuses on text manipulation, R is more geared towards statistical computing tasks.
Data Handling: In AWK, data is typically processed line by line from a file or input stream. It excels at working with structured text data such as log files or CSV files. In contrast, R operates on entire datasets loaded into memory, making it suitable for handling larger, more complex datasets efficiently.
Feature Set: AWK's feature set is limited to text processing capabilities such as pattern matching, data extraction, and transformation. R, being a comprehensive statistical programming language, offers a wide range of functions and libraries for statistical analysis, machine learning, and graphical data representation.
Community and Ecosystem: R has a large and active community of users and developers contributing to its ecosystem. As a result, there are numerous packages and resources available for various statistical and data analysis tasks. AWK, while powerful for text processing, has a smaller community and ecosystem focused mainly on its core functionality.
Learning Curve: Due to its more specialized focus on text processing and simpler syntax, AWK is generally considered easier to learn for beginners. R, with its vast array of functions and statistical capabilities, has a steeper learning curve, especially for those without a background in statistics or programming.
Use Cases: AWK is commonly used for tasks such as parsing log files, report generation, and text manipulation in shell scripts. On the other hand, R is preferred for statistical analysis, data visualization, machine learning, and academic research in fields like data science and bioinformatics.

In Summary, the key differences between AWK and R lie in their programming paradigms, data handling methods, feature sets, community support, learning curves, and use cases.

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CLI (Node.js)

Manual

Advice on R Language, AWK

Justin

Open Source Program Manager at Reblaze

Aug 15, 2019

Review

If you have a file (demo.txt) that has 3 columns:

Column-1    Column-2    Column-3
Row-1a      Row-2a      Row-3a         
Row-1b      Row-2b      Row-3b
Row-1c      Row-2c      Row-3c
Row-1d      Row-2d      Row-3d
Row-1e      Row-2e      Row-3e

and you want to only view the first column of the file in your CLI, run the following:

awk {'print $1'} demo.txt

Column-1
Row-1a
Row-1b
Row-1c
Row-1d
Row-1e

If you want to print the second column of demo.txt, just replace $1 with $2

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Comments

Detailed Comparison

R Language	AWK
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.	A data-driven scripting language consisting of a set of actions to be taken against streams of textual data – either run directly on files or used as part of a pipeline – for purposes of extracting or transforming text, such as producing formatted reports.
Statistics
GitHub Stars -	GitHub Stars 2.1K
GitHub Forks -	GitHub Forks 181
Stacks 3.9K	Stacks 639
Followers 1.9K	Followers 49
Votes 418	Votes 0
Pros & Cons
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 Messy syntax for string concatenation 2 No push command for vectors/lists	No community feedback yet
Integrations
No integrations available	GNU Bash Linux macOS Zsh (Z shell)

What are some alternatives to R Language, AWK?

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

AWK vs R: What are the differences?

Programming Paradigm: AWK is a procedural programming language specifically designed for text processing and data extraction. On the other hand, R is a statistical programming language used for data analysis, visualization, and statistical modeling. While AWK focuses on text manipulation, R is more geared towards statistical computing tasks.
Data Handling: In AWK, data is typically processed line by line from a file or input stream. It excels at working with structured text data such as log files or CSV files. In contrast, R operates on entire datasets loaded into memory, making it suitable for handling larger, more complex datasets efficiently.
Feature Set: AWK's feature set is limited to text processing capabilities such as pattern matching, data extraction, and transformation. R, being a comprehensive statistical programming language, offers a wide range of functions and libraries for statistical analysis, machine learning, and graphical data representation.
Community and Ecosystem: R has a large and active community of users and developers contributing to its ecosystem. As a result, there are numerous packages and resources available for various statistical and data analysis tasks. AWK, while powerful for text processing, has a smaller community and ecosystem focused mainly on its core functionality.
Learning Curve: Due to its more specialized focus on text processing and simpler syntax, AWK is generally considered easier to learn for beginners. R, with its vast array of functions and statistical capabilities, has a steeper learning curve, especially for those without a background in statistics or programming.
Use Cases: AWK is commonly used for tasks such as parsing log files, report generation, and text manipulation in shell scripts. On the other hand, R is preferred for statistical analysis, data visualization, machine learning, and academic research in fields like data science and bioinformatics.

In Summary, the key differences between AWK and R lie in their programming paradigms, data handling methods, feature sets, community support, learning curves, and use cases.

AWK vs R

Overview