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Matplotlib vs R: What are the differences?
# Matplotlib vs R
Matplotlib and R are two popular tools used for data visualization. While both serve the same purpose, they have key differences that distinguish them in terms of functionality and usage.
1. **Programming Language**: Matplotlib is primarily used with Python, a general-purpose programming language, while R is a statistical programming language that has built-in visualization capabilities.
2. **Syntax**: Matplotlib syntax is more verbose and complex compared to the ggplot2 library in R, which offers a more intuitive and concise way to create complex visualizations.
3. **Library Ecosystem**: Python and Matplotlib have a vast ecosystem of libraries that can be used in conjunction with Matplotlib for data manipulation and analysis, while R has a comprehensive set of packages specifically designed for statistical modeling and visualization.
4. **Backends**: Matplotlib provides a range of backends that allow users to customize and optimize their visualizations for various output formats, while R offers flexible graphics devices that support exporting plots to different file types.
5. **Interactivity**: R's Shiny framework enables users to create interactive web-based visualizations easily, while Matplotlib has limited support for interactive plots out of the box, requiring additional libraries like Plotly for such features.
6. **Community Support**: Python and Matplotlib have a larger and more diverse community, resulting in extensive documentation, tutorials, and resources, making it more accessible to users of varying skill levels compared to R.
In Summary, Matplotlib and R differ in terms of programming language, syntax, library ecosystem, backends, interactivity, and community support, catering to different user preferences and requirements.```
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Learn MorePros of Matplotlib
Pros of R Language
Pros of Matplotlib
- The standard Swiss Army Knife of plotting10
Pros of R Language
- Data analysis84
- Graphics and data visualization63
- Free54
- Great community45
- Flexible statistical analysis toolkit38
- Easy packages setup27
- Access to powerful, cutting-edge analytics27
- Interactive18
- R Studio IDE13
- Hacky9
- Shiny apps7
- Shiny interactive plots6
- Preferred Medium6
- Automated data reports5
- Cutting-edge machine learning straight from researchers4
- Machine Learning3
- Graphical visualization2
- Flexible Syntax1
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Cons of Matplotlib
Cons of R Language
Cons of Matplotlib
- Lots of code5
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
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What is Matplotlib?
It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. It can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.
What is R Language?
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.
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What companies use Matplotlib?
What companies use R Language?
What companies use Matplotlib?
What companies use R Language?
See which teams inside your own company are using Matplotlib or R Language.
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What tools integrate with Matplotlib?
What tools integrate with R Language?
What tools integrate with Matplotlib?
What tools integrate with R Language?
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What are some alternatives to Matplotlib and R Language?
Tableau
Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
MATLAB
Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
Bokeh
Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.
Plotly.js
It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R).
It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.
ggplot2
It is a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers.