Top 10 Best Open Source Data Visualization Tools for 2022

With the vast amount of data sources available today, modern companies are able to leverage them to boost sales, enter new markets, and survive in highly demanding, competitive industries. While the amount of data available today is of great significance by itself, making that data clearer and more understandable helps companies make better decisions for success, and is even more valuable.

Data visualization is the representation of data converted into simple and straightforward graphic and visual forms that enable viewers to view data in a comprehensible and digestible manner. Data Visualization makes it quick and easy to identify invisible patterns and reveal precious insights. These ‘representations’ may include graphs, pie charts, bar charts, histograms, infographics, scatter plots, maps, etc.

Advantages of Visualizing Data

1. Reveals Hidden Patterns, Trends, and Insights

1. Organizing data into more understandable formats reveal hidden patterns and insights This is credited to using the most suitable visualization form for the given task.

2. Boosts Sales and Revenue

1. helps business analysts make better business decisions in shorter time spans, a boost in a company’s revenue is well expected.

3. Save Time and Money

1. The visualization process is a cost-effective method to understand the given data compared to other more sophisticated and complex methods. While those methods are very well necessary, visualization adds a different value for being able to draw simple conclusions.

4. Added Engagement

1. Easy-to-interpret graph that catches attention and increases engagement. Only experts in the field can draw conclusions from cold hard numbers but anyone can draw conclusions from visual graphs. Either way, experts will always make multiple data visualization graphs to make that much easier to extract valuable information.

Data Visualization Types

Since it’s not feasible to create an exhaustive list of the available visualization graphs out there, we will just state a few of the most common ones, and give a basic explanation of what they are. You may be familiar with many of these already!

1. Pie Charts

1. Pie charts are circular statistical graphics (shaped like a pie), which are divided into slices to illustrate numerical proportions useful for visualizing variables and their share in a category.

2. Bar Charts

1. A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars of various heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally to show visual quantitative comparisons between multiple variables.

3. Histograms

1. A histogram is a graphical representation that organizes a group of data points into user-specified ranges. Similar in appearance to a bar graph, the histogram condenses a data series into an easily interpreted visual by taking many data points and grouping them into logical ranges or bins useful for identifying percentiles, averages, and range outliers.

4. Scatter Plots

1. A scatter plot is a type of plot or mathematical diagram using an XY plane to display values for two variables for a set of data useful for visualizing strong or weak variance and displaying positive and negative correlations.

5. Tree Map

1. In information visualization and computing, treemapping is a method for displaying hierarchical data using nested figures, usually rectangles. A leaf node’s rectangle has an area proportional to a specified dimension on the data. Tree Maps are a more sophisticated way of a Pie Chart for comparing more variables.

6. Heat Maps

1. By using a 2D table and correlation percentages between the X and Y axis variables, heat maps show the magnitude of a phenomenon as color in two dimensions useful for visualizing variance across multiple variables to display patterns in correlations.

Best Data Visualization Tools

RawGraphs

As one of the best open-source data visualization tools out there, RawGraphs is an easy-to-use and efficient tool that users can use to visualize stored data.

Some of the benefits of using Raw Graphs include:

• Over 30 visualization models.
• Great data security from outside intruders.
• Save and export data as a vector or image.
• Ease of use.

Chart.js

Considered a very easy visualization tool to use, Chart.js is a javascript charting tool that allows for great ease and high visualization flexibility. Some benefits of using the Chart.js tool include:

• 8 chart types, each customizable and animated.
• Open-source tool, with frequent updates and improvements.
• Redraws charts with different window sizes.
• Compatibility across all modern browsers.

Charted

Extremely simple to use, Charted is an open-source tool created originally by the well-known article site Medium. To use Charted, users must provide a link to the data necessary.

As stated in the Charted GitHub repo, Charted provides the following benefits (in addition to being easy and efficient to use):

• Renders well on all screen sizes, including monitors
• Re-fetches data to update the chart every 30 minutes
• Move data series into separate charts
• Adjust the chart type, labels/titles, and background

The Charted tool supports .csv files, tsv files, Google Spreadsheets (set to shareable), and Dropbox share links to supported files.

Plotly

With 1 million+ downloads per month and a total of over 365 million total downloads, Plotly is a rising star in the visualization world. Also an open-source tool, Ploty provides its users with a well-designed interactive system that boasts high performance. Users can utilize open-source libraries for R, Python, Matlab, and javascript while using the Plotly tool. It also includes a paid version that offers more options than the basic one.

Leaflet

Unlike all the other open-source tools mentioned on this list which provide charts, graphs, histograms, etc, Leaflet provides its users with interactive mapping.

Leaflet is designed with simplicity, performance, and usability in mind. It works efficiently across all major desktop and mobile platforms, can be extended with lots of plugins, has a beautiful, easy to use and well-documented API and a simple, readable source code

D3.js

D3.js, which stands for Data-Driven Documents, is a javascript open-source visualization tool created with the purpose of utilizing modern browsers to the fullest. The D3.js tool converts arbitrary data into a term more common to web developers, known as Document Object Model or (DOM), and applies data-driven transformations to the document. Although it might not be the easiest tool to deal with on this list, it is definitely worth a try.

Data Wrapper

Quite interactive and easy to use, Datawrapper provides its users with 19 different chart types, 3 map types, and responsive data tables with different styling options.

Similar to Plotly, Datawrapper includes a paid version in addition to the normal version which can be used for free.

Polymaps

Similar to the Leaflet tool, Polymaps focuses on building beautifully designed and interactive maps. It supports a variety of different map designs and visualizations, allowing its users to create and experiment with many different options. The Polymaps tool works with SVG or Scalable Vector Graphics.

Google Charts

Created by Google, Google Charts is unsurprisingly one of the best visualization tools available today. With a whole pack of pie charts, bar graphs, tables, maps, and more, users can count on Google Charts to fit just about any task required.

ParaView

ParaView is an open-source visualization tool that focuses on visualizing extremely large data sets. It is successfully deployed on Windows, Mac OS X, Linux, SGI, IBM Blue Gene, Cray, and various Unix workstations, clusters, and supercomputers. Unlike most visualization tools, ParaView does provide 3D visualization graphs though.

Some benefits of using the ParaView tool include:

• Develop an open-source, multi-platform visualization application.
• Supports distributed computation models to process large data sets.
• Create an open, flexible, and intuitive user interface.
• Develop an extensible architecture based on open standards.

Data Visualization Tips & Best Practices

Choose an Appropriate Visualization Tool

Depending on the specific data used, a corresponding visualization tool should be selected. For example, if the data needs to be represented in a map-like, then Polymaps or Leaflet should be used. Moreover, even in this case where the potential tools have similar capabilities, each one will have its own unique impact on the final product.

Choose an Appropriate Graphical Representation

In a project where it’s best to represent given ratios that fill up to a whole 100 percent value, then it wouldn’t make sense to use a bar chart when the most fitting visualization would be a pie chart. Note that in some cases more than one visualization graph may be necessary to be able to reveal important details about the data that can’t be easily seen or understood using only one type of graphical representation.

Create an Engaging Visual

Even though the concept of having multiple representations (graphs, tables, etc) will help in grabbing the viewer’s attention with greater ease, paying attention to extra details (such as if a given graph has a perfect size, eye-catching colors, and for the graph to not be over packed, etc.) can give an extra edge in the visualization world!

The Importance of Visualizing Data With the Right Tools

In this article, we have explained what data visualization is, its advantages, and some well-known and commonly used visualization tools currently available. When it comes to which tool you should pick first, go with the tool that makes the best fit for the specific project. If a couple of tools seem like they would make sense to use, then utilize the one that is easiest to use and is the most efficient from your personal point of view. Practicing with them will help you get better at determining the best tool to use under various circumstances.

Training and optimizing AI models can benefit greatly when the data regarding the AI is easily digestible. Many of today’s models deploy visualization tools to help understand AI based on the provided dataset. By doing so it can monitor biases, potential errors, and where the AI is broken to better enhance the model to its peak potential.

With lots of rows packed with numbers, tough to understand data, and short deadlines, the ability to transform our data into an easy-to-understand simple model or form will benefit every single industry out there. Visualizing your data does not just make your life less stressful, it saves time & money, and ultimately leads to better, more informed final decisions.