Perl vs R

Overview

Perl

Stacks4.3K

Followers935

Votes575

GitHub Stars2.2K

Forks602

R Language

Stacks3.9K

Followers1.9K

Votes418

Perl vs R: What are the differences?

Introduction:

Perl and R are both powerful programming languages used for data analysis and manipulation. However, they have several key differences that set them apart in terms of syntax, functionality, and use cases.

Syntax: One of the main differences between Perl and R is their syntax. Perl has a more general-purpose syntax, similar to traditional programming languages, which allows for greater flexibility in coding. On the other hand, R has a specialized syntax designed specifically for statistical computing and graphics, making it more intuitive for data analysis tasks.
Data Manipulation: Another significant difference between Perl and R is their approach to data manipulation. Perl excels in text processing and pattern matching, as it provides powerful regular expression functionality. It allows for efficient file parsing, string manipulation, and complex data transformations. R, on the other hand, provides extensive built-in functions and libraries specifically tailored for data manipulation, making it easier to preprocess and analyze data sets.
Statistical Analysis: R is widely recognized as the go-to language for statistical analysis and data visualization. It offers a vast number of statistical functions and packages, making it easier to perform complex statistical computations, regression analysis, hypothesis testing, and data visualization. While Perl does have some statistical modules available, it lacks the extensive statistical functionality and visualization capabilities that R provides.
Community and Documentation: R is a popular language in the field of data science and has a large and active community. This translates into abundant resources, comprehensive documentation, and regular updates and improvements to the language and its packages. Perl also has a devoted community with a vast collection of libraries and modules, but it may not be as specialized or extensively documented for statistical analysis as R.
Integration with Other Tools: Perl is often chosen for its ability to integrate with other tools and systems seamlessly. It can be used for system administration, web development, and automation tasks. R, on the other hand, primarily focuses on statistical computing and may not have the same level of integration capabilities as Perl for non-statistical tasks.
Learning Curve: Perl and R have different learning curves. Perl's syntax and flexibility can make it more challenging for beginners to grasp, especially without prior programming experience. R, on the other hand, has a more specialized syntax and is specifically designed for statistical computing, making it more accessible and easier to learn for those interested in data analysis and manipulation.

**In Summary, Perl and R have distinct differences in terms of syntax, data manipulation capabilities, statistical analysis functions, community support, integration with other tools, and learning curve. Understanding these differences is crucial in choosing the appropriate language for specific data-related tasks and projects.

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Advice on Perl, R Language

Samuel

Oct 11, 2021

Decided

MACHINE LEARNING

Python is the default go-to for machine learning. It has a wide variety of useful packages such as pandas and numpy to aid with ML, as well as deep-learning frameworks. Furthermore, it is more production-friendly compared to other ML languages such as R.

Pytorch is a deep-learning framework that is both flexible and fast compared to Tensorflow + Keras. It is also well documented and has a large community to answer lingering questions.

158k views158k

Comments

Rubin

Software Cloud Developer at RUBIN THOMAS

Oct 8, 2020

Review

As a developer myself, I would recommend you not to restrict yourself to JAVA, PHP or any other language. New Tools/languages keep coming every day. If you do plan to move to freelancing. PHP has a lot of options in the freelance space and a lot of competition too.

Learning PHP is as simple as learning any other language. It depends merely on your interest.

Personally if you can code, you should not restrict yourself. I have had to code in many languages, PHP, Perl, shell script, Python, Java, Javascript, Ruby etc... I would keep your developing skills and logic, algorithms etc.. and increase your knowledge and experience in the different languages.

I agree with you JAVA is a lot more time consuming. But it also has its enterprise level scope.

At the same time learning a new language should not be a barrier for you to stop exploring what's out there and keeping your skills up to date. Learning new technologies should be your primary focus and getting project out of your stack helps you build a good reputation.

There are many options for you to pursue. Having an open mindset will help you move forward. If you look to learn now, you are setting yourself up for a brighter future.

684k views684k

Comments

Mohiuddin

Mar 7, 2022

Needs advice

Extract the daily COVID-19 confirmed cases for City1, City2, and City3 from all the cities. Normalize the daily COVID-19 confirmed cases for the three cities using their respective populations. The 2019 mid-year estimated population figures for City1, City2, and City3 are 100,000, 200,000, and 300,000 respectively.

df <- read.csv ("coronavirus.csv", header = TRUE ) library(dplyr) df %>% group_by(City.name) %>% summarise(Sum = sum(Daily.cases))

Cant select multiple variables from dplyr::Groupby. Can anyone help me with the right code along with the second part of the question as I am not able to find solution as well.

3.15k views3.15k

Comments

Detailed Comparison

Perl	R Language
Perl is a general-purpose programming language originally developed for text manipulation and now used for a wide range of tasks including system administration, web development, network programming, GUI development, and more.	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
GitHub Stars 2.2K	GitHub Stars -
GitHub Forks 602	GitHub Forks -
Stacks 4.3K	Stacks 3.9K
Followers 935	Followers 1.9K
Votes 575	Votes 418
Pros & Cons
Pros 72 Lots of libraries 66 Open source 61 Text processing 54 Powerful 49 Unix-style Cons 4 Messy $/@/% syntax 3 No exception handling 2 "1;" 2 No OS threads 2 Bad OO support	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 Perl, 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

Perl vs R: What are the differences?

Introduction:

Syntax: One of the main differences between Perl and R is their syntax. Perl has a more general-purpose syntax, similar to traditional programming languages, which allows for greater flexibility in coding. On the other hand, R has a specialized syntax designed specifically for statistical computing and graphics, making it more intuitive for data analysis tasks.
Data Manipulation: Another significant difference between Perl and R is their approach to data manipulation. Perl excels in text processing and pattern matching, as it provides powerful regular expression functionality. It allows for efficient file parsing, string manipulation, and complex data transformations. R, on the other hand, provides extensive built-in functions and libraries specifically tailored for data manipulation, making it easier to preprocess and analyze data sets.
Statistical Analysis: R is widely recognized as the go-to language for statistical analysis and data visualization. It offers a vast number of statistical functions and packages, making it easier to perform complex statistical computations, regression analysis, hypothesis testing, and data visualization. While Perl does have some statistical modules available, it lacks the extensive statistical functionality and visualization capabilities that R provides.
Community and Documentation: R is a popular language in the field of data science and has a large and active community. This translates into abundant resources, comprehensive documentation, and regular updates and improvements to the language and its packages. Perl also has a devoted community with a vast collection of libraries and modules, but it may not be as specialized or extensively documented for statistical analysis as R.
Integration with Other Tools: Perl is often chosen for its ability to integrate with other tools and systems seamlessly. It can be used for system administration, web development, and automation tasks. R, on the other hand, primarily focuses on statistical computing and may not have the same level of integration capabilities as Perl for non-statistical tasks.
Learning Curve: Perl and R have different learning curves. Perl's syntax and flexibility can make it more challenging for beginners to grasp, especially without prior programming experience. R, on the other hand, has a more specialized syntax and is specifically designed for statistical computing, making it more accessible and easier to learn for those interested in data analysis and manipulation.

Perl vs R

Overview