The best open source data visualization tools to explore in 2023

Open source data visualization tools have grown in popularity due to their accessibility, flexibility, and ability to adapt to the specific needs of each business. Compared to proprietary solutions, open source tools offer several advantages, such as:

• Reduced cost : Open source tools are generally free or inexpensive, allowing businesses of all sizes to access quality data visualization solutions without increasing their budget.
• Customization: these tools offer the ability to customize and adapt functionalities according to the specific needs of each project, making them more versatile than their proprietary counterparts.
• Regular updates: they benefit from an active developer community that is constantly working to improve and update features, ensuring that users have access to the latest advancements in data visualization.
• Collaboration and support : Open source tools are often accompanied by a community of users and developers who are ready to share their knowledge and help if problems arise.

In this article, we'll show you the best open source data visualization tools available in 2023, which will allow you to: to improve your analyses, to increase productivity and to get the most out of your data.

Open source tools for data visualization beginners

If you're new to data visualization, it's important to choose tools that are simple and easy to use to get familiar with basic concepts. Here are some of the best open source tools that are suitable for beginners:

Datawrapper

Datawrapper is a user-friendly online tool that allows you to quickly create interactive charts, maps, and tables without requiring programming skills. Simply upload your data, select the type of visualization you want, and customize the design. Datawrapper also offers integration options for sharing your visualizations on websites or blogs.

RawGraphs

RawGraphs is another online tool that allows you to easily create data visualizations from your CSV, Excel, or Google Sheets files. With a simple and intuitive interface, you can choose from a variety of visualization templates, such as bar charts, treemaps, and bubble charts. Once your visualization is created, you can export it in a variety of formats, such as SVG, PNG, or JSON.

Google Charts

Google Charts is a free and easy to use online tool that allows you to create a variety of interactive visualizations for the web. With a user-friendly interface and clear documentation, Google Charts is accessible to beginners who want to get started with charting. You can choose from a wide range of chart types, such as histograms, maps, and pie charts. Additionally, Google Charts integrates easily with other Google services, such as Google Sheets, making it easy to streamline workflow and share visualizations easily.

In summary, for those new to data visualization, here are the three open source options to consider:

a. Datawrapper : a user-friendly online tool for quickly creating interactive charts, maps, and tables without programming skills.
b. RawGraphs : an intuitive online tool that allows you to create data visualizations from CSV, Excel, or Google Sheets files, with a variety of visualization models.
c. Google Charts: a free and accessible online tool for creating interactive visualizations for the web, which integrates easily with other Google services.

Remember to consider ease of use, compatibility with your data sources, and export and sharing options when choosing the best tool for your needs and skills.

Open source libraries for visualizing data with programming languages

If you already have some experience with data visualization and want to explore more advanced tools, here are some open source options specific to each programming language that will give you more flexibility and options:

Python

Matplotlib

A versatile 2D data visualization library for creating static, animated, and interactive charts. Matplotlib is widely used in a variety of fields, such as finance, scientific research, and data analysis.

Seaborn

A Matplotlib-based library that makes it easy to create engaging and informative statistical data visualizations. Seaborn offers more aesthetic graphical models and allows you to create complex visualizations with less code.

Bokeh

Bokeh is a Python library designed to create interactive and elegant data visualizations for the web. It offers simple syntax and powerful features to customize the appearance and behavior of your charts. Bokeh is ideal for users familiar with Python who want to create interactive visualizations without having to master JavaScript. Additionally, Bokeh pairs well with other Python libraries, such as Pandas, to make data processing and visualization easier.

R

ggplot2

A popular and powerful library based on graph grammar, for creating complex and aesthetically appealing data visualizations. ggplot2 is particularly useful for data mining and reporting results in research and statistical analysis.

Shiny

An R package for creating interactive and responsive web applications for data visualization. Shiny allows R users to create dashboards, reports, and applications without the need for extensive web development skills.

JavaScript

eCharts

eCharts is an open source data visualization library developed by Apache. This JavaScript library offers a variety of interactive and engaging charts for the web, including bar charts, line charts, pie charts, maps, and tree charts. eCharts is particularly appreciated for its ease of use, customization, and performance. With clear and concise syntax, users can quickly create impressive and informative data visualizations, while enjoying the flexibility to adapt charts to their specific needs.

Three.js

A JavaScript library for creating 3D data visualizations, using WebGL for graphics rendering. Three.js offers a wide range of customization options to create engaging and immersive 3D visualizations.

D3.js

A powerful and flexible JavaScript library for manipulating data-based documents. D3.js makes it possible to create interactive and dynamic data visualizations in web browsers, using web standards such as SVG, HTML, and CSS.

Cross-language: Plotly

Plotly is an open source data visualization library that supports multiple programming languages, such as Python, R, and Julia. It allows you to create high-quality, interactive graphics for the web or for desktop applications. Plotly offers a wide variety of chart types, such as 3D charts, choropleth maps, and network diagrams. While Plotly offers a graphical interface for creating visualizations, its power lies in its ability to be used with programming languages to create fully customized visualizations.

Open source data visualization tools for specific application areas

Some data visualization projects require specialized tools to meet specific needs. Here are some examples of open source tools adapted to different areas of application:

Geospatial mapping and visualization

Leaflet

A lightweight and powerful JavaScript library for creating interactive maps on the web.

QGIS

An open source geographic information system (GIS) that allows geospatial data to be created, edited, visualized, and analyzed.

Visualization of networks and graphs

Gephi

An open source network visualization and analysis software that allows you to explore and understand complex network structures.

Cytoscape

An open source software for visualizing and analyzing biomolecular networks, social networks, and other types of complex networks.

Visualization of temporal data

Time.Graphics

A free online tool for creating interactive timelines and sharing historical events, projects, or processes.

TimelineJS

An open source JavaScript library for creating interactive and visually appealing timelines.

By identifying the open source tools that are best suited to your specific application areas, you can create more relevant and effective data visualizations for your projects.

Resources to learn and master data visualization

Once you've chosen the data visualization tools that best fit your needs and skill level, it's important to know what resources are available to learn and improve your data visualization skills. Here are some resources that can help you become a subject matter expert:

Online courses and tutorials

Coursera

This platform offers several online courses on data visualization, covering various tools, techniques, and programming languages.

EdX

Another online course platform that offers training in data visualization, led by renowned universities and institutions.

DataCamp

A site dedicated to learning data science skills, including data visualization, with interactive courses and practical exercises.

Books and guides

“The Visual Display of Quantitative Information” by Edward Tufte

A classic book that explores the fundamentals of data visualization and provides examples and case studies to illustrate best practices.

“Data Visualization: A Practical Introduction” by Kieran Healy

A practical guide to learning how to create data visualizations using ggplot2 and other R tools.

“Interactive Data Visualization for the Web” by Scott Murray

A book dedicated to learning D3.js and creating interactive data visualizations for the web.

Blogs and online communities

FlowingData

A popular blog that explores data visualization, infographics, and data science through articles, tutorials, and examples.

Reddit r/dataisbeautiful

An online community where users share and discuss data visualizations, techniques, and tools.

Stack Overflow

A Q&A platform for developers, where you can ask questions and find solutions to solve data visualization problems.

By exploring these resources and actively engaging in learning and practicing, you will be able to develop your data visualization skills and create more effective and informative visualizations for your projects.

In conclusion, data visualization is a key element in understanding and effectively communicating information from large data sets. The choice of data visualization tools tailored to your skill level, preferred programming language, and specific areas of application is essential to create compelling and informative visualizations.

In this article, we reviewed several open source tool options for beginners, intermediate, and advanced users, as well as tools that are specific to various application areas. Take into account the ease of use, compatibility with your data sources, and customization options to choose the tool that best fits your needs.

Finally, don't forget to invest time in learning and mastering skills in data visualization by taking advantage of online resources, books, blogs, and online communities. By increasing your knowledge and practicing regularly, you will be able to create effective data visualizations that will allow your audience to better understand and appreciate the information you are presenting.

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