Open Source Data Visualisation Tools

  • Post last modified:19 May 2023
  • Reading time:13 mins read
  • Post category:Business Analytics
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It is unsurprising that data visualisation is significant in the realm of data science. Without a comprehensive knowledge of the data, it is difficult to monitor and change it to make it work as it should. This is where data visualisation comes in; it takes the obtained data and displays it in a visual context. It also makes it easier to identify patterns, track trends, and so on.

All of this is only possible if you have the correct data visualisation tool. When it comes to data visualisation technologies, open-source has begun to acquire substantial popularity. Furthermore, people frequently conflate the terms “free” with “open-source.” The term “opensource” refers to having access to the source code and has nothing to do with free tools.

Open Source Data Visualisation Tools


Candela is an open-source web visualisation component suite for Kitware’s Resonant platform. Candela focuses on providing scalable, sophisticated visualisations via a standardised API for usage in realworld data science applications. Among the integrated components are:

  • LineUp dynamic ranking developed by the Harvard University Visual Computing Group and the Caleydo project.

  • Visualisation of the UpSet set by the Harvard University Visual Computing Group and the Caleydo project.

  • The Georgia Institute of Technology Information Interfaces Group created the OnSet set visualisation.

  • University of Washington Interactive Data Lab Vega visualisations ScatterPlot is an example component.

  • Kitware’s Resonant platform’s GeoJS geographic visualisations. GeoDots are an example component.

Candela is unquestionably one of the greatest solutions for data visualisation in terms of both open source and JavaScript. The package includes a normalised API for usage in real-world data science applications and is available via the Resonant platform.

When it comes to installation, Candela may be installed via regular package repositories or from the source. Furthermore, while the initial installation procedure is straightforward, there aren’t many public release versions available. However, if installed from the source, which is slightly more difficult, the user can run cutting-edge development versions.


Charted is a free and open-source data visualisation tool licensed under the MIT license. It was created by the blogging site Medium. com at first.

The automated display of data is, in my opinion, Charted’s mainstay. All you have to do is give a link to a data file, and the system will extract a complete, well-choreographed, and easily accessible collection of data from it.

Here are some further reasons why I believe the plot is one of the top open-source data visualisation tools on the market:

  • The platform was designed with usability in mind. The interface, for example, assembles just important features. As a result, it is much unfettered. I believe this is also due to the fact that the majority of the functions are already automated.

  • It displays its results equally effectively on different screen sizes.

  • Its charts are updated on a regular basis (at 30-minute intervals).

  • It excels in data sorting by separating data series and graphics.

  • With Charted, you can also sort accessible data by type, backdrop, titles or labels, and other criteria.

  • It also has a good amount of file support. It supports comma-separated values (CSV), tab-separated values (TSV), Dropbox share links, and Google Sheets files.


Chart.js is an open-source library (available on GitHub) that allows you to quickly visualise data using JavaScript. It is comparable to Chartist and Google Charts. It offers eight distinct chart kinds (including bars, lines, and pies), all of which are responsive. In other words, you create your chart once, and Chart.js does the heavy labour for you, ensuring that it is always legible (for example by removing some uncritical details if the chart gets smaller).

In a nutshell, here’s all you need to do to create a chart with Chart.js:

  1. Determine where you want the graph to appear on your page.
  2. Determine the type of graph you wish to create.
  3. Provide data, labels, and other settings to Chart.js.


D3.js may be used to visualise Big Data in virtually any way. D3.js is an abbreviation for Data-Driven Document, a JS library enabling interactive Big Data presentation in basically ANY real-time fashion. Given that this is not a tool, a user should have a basic grasp of JavaScript in order to interact with the data and display it in a human-readable format.

To elaborate, because this library presents the data in SVG and HTML5 formats, earlier browsers such as IE7 and 8 are unable to take advantage of D3.js features. Data from various sources, such as largescale data sets, is coupled in real-time with DOM to make interactive animations (2D and 3D alike) in an exceptionally fast manner. The D3 design enables users to heavily reuse code across a wide range of addons and plug-ins.

Data Wrapper

Data wrapper, like Google Charts, is a tool for creating charts, maps, and other visualisations for use online. The tool’s original target audience was reporters working on news items, but it may be useful to any professional in charge of administering a website.

While Data wrapper is simple to use, it is fairly limiting when compared to the other tools on our list. One of the main drawbacks is that it does not interface with data sources. Instead, you must manually copy and paste data into the tool, which may be time-consuming and error-prone if not done correctly.

Depending on how you wish to use the tool, you can choose between free and premium options.


Dygraphs is a free and open-source JavaScript-based charting framework that allows users to explore and comprehend large amounts of data. However, getting started with a chart necessitates some amount of web programming knowledge.

When it comes to the charts it generates, they are interactive. Mouse over specific numbers to highlight them, click and drag to zoom, double-click to zoom out, shift-drag to the pan, and so on.

Furthermore, one of the most appealing aspects of Dygraphs is their ability to manage large data volumes. Dygraphs is an open-source JavaScript graphing toolkit that is quick and configurable. It enables people to study and comprehend large amounts of data.

The features of the Dygraphs are as follows:

  • Handles massive data sets. Dygraphs can plot millions of points without becoming clogged.

  • Interactive right out of the box: zooming, panning, and mouseover are all enabled by default.

  • There is a lot of support for error bars and confidence intervals.

  • You can make dygraphs perform practically anything by utilising parameters and custom call-backs.

  • Dygraphs is compatible with all modern browsers. On mobile/tablet devices, you can even pinch to zoom.

  • There is a vibrant community of people working on and supporting dygraphs.


The leaflet is a free and open JavaScript library for creating mobilefriendly interactive maps. One of the nicest features of this tool is that it is incredibly lightweight, with only 38 KB of JS. The tool is created in such a manner that it has nearly all of the mapping functionality that most developers would ever require.

Leaflet not only comes with a plethora of plugins for adding functionality, but it also runs well on all major desktop and mobile platforms.

The booklet is intended to be simple, effective, and easy to use. It works well right out of the box on all major desktop and mobile platforms, making use of HTML5 and CSS3 on newer browsers while being accessible on older ones. It includes a large number of plugins that can be added, a beautiful, easy-to-use, and well-documented API, and a straightforward interface.

Businesses utilise leaflets to sell their products and services. Customers are constantly informed about new businesses, special deals, and events through them.

Raw Graphs

RAW Graphs, which is built on D3.js, makes data gathering and visualisation a breeze. Other features and functions of this tool that position it among the top open-source data visualisation tools available today include:

  • It includes several methods for automatically sourcing and displaying data. You may, for example, enter comma-separated-values, tab-separated values, or just copy and paste from a spreadsheet, and RAW Graphs will turn it into beautiful output.

  • It uses a broad range of graphic models to present data. These include both traditional (such as pie/bar charts and line graphs) and unorthodox (such as bar graphs).

  • RAW Graphs allows you to gain a better understanding of your data collection by analysing its patterns and trends. You may accomplish this by using visual variables to map the various aspects of your data collection.

  • You can export and edit your output as vector or raster graphics anywhere.

RAW Graphs is distributed as an open-source web application under the Apache 2.0 license. RAW Graphs is an open-source data visualisation platform designed to make visualising complicated data simple for everyone. RAW Graphs is primarily developed as a tool for designers and viz nerds, with the goal of filling a gap between spreadsheet tools (e.g., Microsoft Excel, Apple Numbers, Open Refine) and vector graphics editors (e.g. Adobe Illustrator, Inkscape, Sketch).

The project, which is managed and maintained by the Density Design Research Lab (Politecnico di Milano), was made public in 2013 and is widely recognised as one of the most important tools in the field of data visualisation.

Density Design, Calibro, and Inmagik, who joined the team in 2019, are responsible for the project’s design, development, and maintenance.

Article Reference
  • Dougherty, J., & Ilyankou, I. (2021). Hands-On Data Visualization: Interactive Storytelling From Spreadsheets to Code. O’Reilly.

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