dataviz.cafe
Find the right tool to visualize your data
This article was co-authored by Andrea B. and George S.
Dataviz.cafe is a public resource curated by IQT Labs for anyone interested in open-source software for data visualization. With over 700 software packages — summarized and tagged by data type, programming language, and other keywords — dataviz.cafe is designed to help people find free visualization tools for a wide variety of use-cases.
In recent years, the open-source community has developed hundreds of highly performant visualization authoring tools. As a result, the quality and variety of data visualization tools in circulation today is staggering. Even as the paid software segment enjoys continued growth and accelerated commercial consolidation, free and open-source software (FOSS) is tackling important visualization problems. These range from general-purpose charting, mapmaking, and dashboarding to more specialized use-cases like displaying brain structures, tracking satellite trajectories, and visualizing load patterns on electrical grids.
While visually captivating and technologically exciting, the abundance of tool options and the rapid pace of change in the field has a downside: information overload. As a consequence, it can be challenging to keep track of capabilities and to make thoughtful, informed decisions about what visualization software to use. Enter dataviz.cafe.
Built with data scientists, visualization designers, web developers, and creative coders in mind, dataviz.cafe is a curated catalog of over 700 open-source visualization tools and software packages that can support a multiplicity of individual needs and use-cases.
In assembling dataviz.cafe, we sought to reflect the diverse array of visualization programming languages in use today, from JavaScript to Python (the two most well-represented languages in the context of the dataviz.cafe tool catalog), to R-based visualization tools, to iOS, Ruby, PHP, and C++. To be as inclusive as possible, we also included low-code DIY visualization tools, like Chart Tool, which cater to less technically-oriented newcomers working in a web browser with non-sensitive data.
The dataviz.cafe website organizes tools and software packages into five categories, based on the type of data they visualize: geospatial, network, quantitative/numerical, text, and miscellaneous. Tools that span multiple categories are cross-listed under all appropriate data types. Each entry has a summary and tags by data type, programming language, and topical keywords. Users who visit dataviz.cafe can explore the entire collection, they can use the data type buttons to filter results, or they can use the search bar to find tools for a specific programming language (“JavaScript,” “Python,” “R-project,” etc.), chart type (e.g. “Sankey” or “treemap”), or keyword (e.g. “bio” or “machine learning”).
Given the popularity of D3.js visualizations, when users type “D3” into the keyword search box, D3.js will appear, along with D3-derived and D3-compatible libraries and modules. (The same is true for jQuery-, Angular-, and React-based visualizations in JavaScript, as well as for PyTorch, Keras, and TensorFlow, which underpin several Python-based visualization tools included in the collection.)
Each entry in the catalog includes the following information:
a) A representative image or screenshot to demonstrate the visualization tool’s basic output/functionality.
b) The name of the visualization software package, with a link to the official tool website or code repository.
c) A summary of the tool’s purpose, programming language, and primary capabilities.
d) The total number of GitHub stars that the software project has received, which dataviz.cafe uses as an approximate measure of popularity. By accessing the GitHub REST API, the site lists the most starred open-source visualization tools at the top of the page, with other tools appearing in descending order. Since GitHub is a dynamic community of programmers, the individual totals are continually changing as a function of more widespread web development trends and visualization software usage patterns.
e) The Software Package Data Exchange (SPDX) license type for the software. Many of the visualization tools on dataviz.cafe are either Apache 2.0 or MIT/X11 licensed, but the catalog also captures less common license types for select software libraries. This information is critical for anyone looking to engage in code reuse/adaptation.
NOTE: While IQT Labs has worked to include the most accurate licensing summaries in dataviz.cafe, we encourage users to consult the official license documents in each tool repository, as these details occasionally change depending on the software project.
Many of the tools and software packages included in dataviz.cafe assume some basic familiarity with client-side (i.e. in-browser) visualization as well as the increased popularity of visual notebook sharing in the context of machine learning. Awareness of adjacent fields like information design, web development, interaction programming, and visual data science is helpful, but not necessarily required.
Many packages offer useful “starter code” (templates for newcomers), with modular architectures and permissive licensing terms that allow users to modify the software to generate custom visualizations. Typically, the underlying source code is available on version control sites like GitHub and GitLab, where one can learn more about the software’s origins and evolution.
Additionally, many of the projects have individual maintainers who volunteer to patch reported software bugs. Aside from implementing critical hotfixes in some circumstances, project contributors may also perform basic upkeep to keep pace with changing technical standards. However, while a small segment of the open-source visualization ecosystem receives direct support from private sector backers, most projects do not. Consequently, obsolescence and cybersecurity risk are important issues to keep in mind.
IQT Labs plans to continue updating this public resource as new open-source visualization tools become available, but we need your help to keep the collection current!
To contribute new tool listings to the collection, please gather the essential information detailed on the dataviz.cafe GitHub repository (at a minimum, tool name, project URL, description, and licensing terms). Once you’re ready, please open a pull request so the team can review your submission. For more information, consult the dataviz.cafe contributor guide at https://github.com/dataviz-cafe/contribute.
Please note that the software listings on dataviz.cafe are for informational purposes only. This catalog of tools refers to third-party sites, software packages, and code modules that are not maintained by In-Q-Tel/IQT Labs. Listing here does not constitute an endorsement or recommendation, and your usage of the dataviz.cafe site is subject to both IQT’s Privacy Policy and Terms of Use.
