NumPy vs R

Overview

R Language

Stacks3.9K

Followers1.9K

Votes418

NumPy

Stacks4.3K

Followers799

Votes15

GitHub Stars30.7K

Forks11.7K

NumPy vs R: What are the differences?

Introduction: In this article, we will explore the key differences between NumPy and R, two popular programming languages used for data analysis and scientific computing.

Integration with other languages: One major difference between NumPy and R is their integration with other programming languages. NumPy is primarily used with Python, which allows for seamless integration with other powerful libraries like Pandas, Matplotlib, and Scikit-learn. On the other hand, R is designed to be a standalone language and does not have the same level of integration with other languages.
Syntax and coding style: NumPy and R have different syntax and coding styles. NumPy follows the Python syntax, which is known for its simplicity and readability. R, on the other hand, has its own unique syntax, which some users may find more intuitive for statistical analysis and data manipulation.
Data structures: Another key difference is the way data structures are handled in NumPy and R. NumPy primarily uses multi-dimensional arrays, known as ndarrays, for storing and manipulating data. R, on the other hand, uses a variety of different data structures, including vectors, matrices, lists, and data frames, each with its own specific use cases.
Package ecosystem: The package ecosystem in NumPy and R is another important difference. NumPy has a vast and rapidly growing ecosystem of packages, making it easy to find and use libraries for specific tasks such as linear algebra, statistical analysis, and machine learning. R also has a rich package ecosystem, with numerous libraries available for statistical modeling, data visualization, and data manipulation.
Statistical capabilities: While both NumPy and R have statistical capabilities, R is often considered the go-to language for statistical analysis and modeling. R provides a wide range of built-in statistical functions and packages, making it particularly well-suited for data analysis and hypothesis testing. NumPy, on the other hand, focuses more on numerical computing and provides efficient tools for array manipulation and linear algebra operations.
Community and support: The community and support for NumPy and R are also different. NumPy benefits from the vast Python community, which provides extensive documentation, tutorials, and Stack Overflow support. R has its own dedicated community, with many active contributors, mailing lists, and forums specifically focused on statistical analysis and modeling.

In Summary, NumPy and R differ in their integration with other languages, syntax and coding style, data structures, package ecosystem, statistical capabilities, and community and support.

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Detailed Comparison

R Language	NumPy
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.	Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
-	Powerful n-dimensional arrays; Numerical computing tools; Interoperable; Performant; Easy to use
Statistics
GitHub Stars -	GitHub Stars 30.7K
GitHub Forks -	GitHub Forks 11.7K
Stacks 3.9K	Stacks 4.3K
Followers 1.9K	Followers 799
Votes 418	Votes 15
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	Pros 10 Great for data analysis 4 Faster than list
Integrations
No integrations available	Python

What are some alternatives to R Language, NumPy?

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.

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NumPy vs R: What are the differences?

Introduction: In this article, we will explore the key differences between NumPy and R, two popular programming languages used for data analysis and scientific computing.

Integration with other languages: One major difference between NumPy and R is their integration with other programming languages. NumPy is primarily used with Python, which allows for seamless integration with other powerful libraries like Pandas, Matplotlib, and Scikit-learn. On the other hand, R is designed to be a standalone language and does not have the same level of integration with other languages.
Syntax and coding style: NumPy and R have different syntax and coding styles. NumPy follows the Python syntax, which is known for its simplicity and readability. R, on the other hand, has its own unique syntax, which some users may find more intuitive for statistical analysis and data manipulation.
Data structures: Another key difference is the way data structures are handled in NumPy and R. NumPy primarily uses multi-dimensional arrays, known as ndarrays, for storing and manipulating data. R, on the other hand, uses a variety of different data structures, including vectors, matrices, lists, and data frames, each with its own specific use cases.
Package ecosystem: The package ecosystem in NumPy and R is another important difference. NumPy has a vast and rapidly growing ecosystem of packages, making it easy to find and use libraries for specific tasks such as linear algebra, statistical analysis, and machine learning. R also has a rich package ecosystem, with numerous libraries available for statistical modeling, data visualization, and data manipulation.
Statistical capabilities: While both NumPy and R have statistical capabilities, R is often considered the go-to language for statistical analysis and modeling. R provides a wide range of built-in statistical functions and packages, making it particularly well-suited for data analysis and hypothesis testing. NumPy, on the other hand, focuses more on numerical computing and provides efficient tools for array manipulation and linear algebra operations.
Community and support: The community and support for NumPy and R are also different. NumPy benefits from the vast Python community, which provides extensive documentation, tutorials, and Stack Overflow support. R has its own dedicated community, with many active contributors, mailing lists, and forums specifically focused on statistical analysis and modeling.

In Summary, NumPy and R differ in their integration with other languages, syntax and coding style, data structures, package ecosystem, statistical capabilities, and community and support.