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NumPy VS R Lang

Compare NumPy VS R Lang and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

R Lang logo R Lang

R is a free software environment for statistical computing and graphics.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • R Lang Landing page
    Landing page //
    2019-10-24

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

R Lang features and specs

  • Comprehensive Statistical Analysis
    R is specifically designed for statistical analysis and data visualization. It offers a wide array of statistical tests, models, and other quantitative techniques.
  • Extensive Package Ecosystem
    The Comprehensive R Archive Network (CRAN) hosts thousands of packages, making it easy to extend the language’s capabilities with specialized tools and libraries.
  • Data Visualization
    R excels at producing high-quality plots and charts through packages like ggplot2 and lattice, providing powerful tools for data visualization.
  • Strong Community Support
    R has a large and active user community that contributes to forums, documentation, and packages, facilitating easier troubleshooting and knowledge sharing.
  • Open Source
    R is open-source, meaning it is free to use and has a high level of transparency. Users can inspect, modify, and enhance the source code.

Possible disadvantages of R Lang

  • Memory Consumption
    R can consume a significant amount of memory, particularly with large datasets, which can lead to performance issues.
  • Learning Curve
    R has a steep learning curve for beginners, especially for those without a strong background in statistics or programming.
  • Speed
    R is interpreted and can be slower than compiled languages like C++ or Java, especially for computationally-intensive tasks.
  • Less Optimal for General-Purpose Programming
    Although R excels at statistical computing, it is less suited for general-purpose programming tasks compared to languages like Python or Java.
  • Inconsistent Function Names and Syntax
    Because R's packages are often developed independently, there can be inconsistencies in function names and syntax, making it harder for users to seamlessly work across different packages.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Analysis of R Lang

Overall verdict

  • Yes, R is a good choice, especially for those who need to perform complex statistical analyses and create high-quality visualizations. Its extensive ecosystem of packages and support for a variety of data formats make it a versatile tool in data science.

Why this product is good

  • R is highly regarded for its capabilities in statistical analysis and data visualization. It is an open-source programming language that offers a vast array of packages and libraries designed for data analysis, making it a powerful tool for statisticians and data scientists. Its community is active and continuously contributes to its development, ensuring that it stays updated with the latest methods in data analysis.

Recommended for

  • Statisticians who need robust tools for performing detailed data analysis.
  • Data scientists looking for comprehensive libraries for data manipulation and visualization.
  • Researchers who need to perform statistical tests and model implementation.
  • Academics and educators who teach statistics and data analysis.

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

R Lang videos

No R Lang videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to NumPy and R Lang)

NumPy

R Lang

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Technical Computing
0 0%
Technical Computing
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Numerical Computation
0 0%
Numerical Computation
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare NumPy and R Lang

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

R Lang Reviews

We have no reviews of R Lang yet.
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Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than R Lang. While we know about 121 links to NumPy, we've tracked only 5 mentions of R Lang. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

NumPy mentions (121)

  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A…. - Source: dev.to / 14 days ago
  • Your 2025 Roadmap to Becoming an AI Engineer for Free for Vue.js Developers
    AI starts with math and coding. You don’t need a PhD—just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Python’s syntax is straightforward. - Source: dev.to / about 2 months ago
  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 8 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / about 1 year ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / about 1 year ago
View more

R Lang mentions (5)

  • How to generate a great website and reference manual for your R package
    Generating a website for your R package is always a great idea. If the package is based on some paper, it will help it get noticed and eventually used. And once you have a website, it's just as well to include a reference manual for the package in it, that complements or is a bit more updated than the one published in CRAN. Or simply in another format. - Source: dev.to / over 1 year ago
  • R
    This package is definitely related to R language) (see package URL, it points to r-project.org subdomain). Source: about 3 years ago
  • Rr
    Common misconception. Actually it's a Fibonacci sequence, so the next one is https://rrrrr-project.org. This does also mean that there's https://-project.org, and that https://r-project.org secretly disambiguates into two different projects. - Source: Hacker News / over 3 years ago
  • Rr
    We already have https://r-project.org. Now we have https://rr-project.org. So, https://rrr-project.org is next? - Source: Hacker News / over 3 years ago
  • r-project.org is down?
    Thank you, but unfortunately, the archive I'm talking about is the archive of old package versions, which seems to only be available through r-project.org. Source: over 3 years ago

What are some alternatives?

When comparing NumPy and R Lang, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

C++ - Has imperative, object-oriented and generic programming features, while also providing the facilities for low level memory manipulation

OpenCV - OpenCV is the world's biggest computer vision library

D (Programming Language) - D is a language with C-like syntax and static typing.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Go Programming Language - Go, also called golang, is a programming language initially developed at Google in 2007 by Robert...

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