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R vs SciPy: What are the differences?
What is R? A language and environment for statistical computing and graphics. 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.
What is SciPy? Scientific Computing Tools for Python. Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
R and SciPy are primarily classified as "Languages" and "Data Science" tools respectively.
SciPy is an open source tool with 6.01K GitHub stars and 2.85K GitHub forks. Here's a link to SciPy's open source repository on GitHub.
Instacart, Zalando, and Thumbtack are some of the popular companies that use R, whereas SciPy is used by Suggestic, Botimize, and Zetaops. R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to SciPy, which is listed in 12 company stacks and 4 developer stacks.
Pros of R Language
- Data analysis83
- Graphics and data visualization62
- Free53
- Great community45
- Flexible statistical analysis toolkit38
- Easy packages setup27
- Access to powerful, cutting-edge analytics27
- Interactive18
- R Studio IDE13
- Hacky9
- Shiny apps7
- Preferred Medium6
- Shiny interactive plots6
- Automated data reports5
- Cutting-edge machine learning straight from researchers4
- Machine Learning3
- Graphical visualization2
- Flexible Syntax1
Pros of SciPy
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Cons of R Language
- Very messy syntax6
- Tables must fit in RAM4
- Arrays indices start with 13
- Messy syntax for string concatenation2
- No push command for vectors/lists2
- Messy character encoding1
- Poor syntax for classes0
- Messy syntax for array/vector combination0