Data Visualization Libraries in Javascript

1. D3.js

D3 or Data-driven documents is a JavaScript library that can be used to manipulate the data using HTML, CSS, and SVG to obtain custom data visualizations. D3 can combine documents with a Document object model and then transform the document based on the requirements. D3 also has different chart types for data analysis like box plots, and histograms, hierarchies like treemaps, networks like chart graphs, as well as common charts like scatter plots, line charts, bar charts, pie charts, etc. D3 also provides animation options like an animated treemap, zoomable bar charts, and icicles, bar chart races, etc.

2. Chart.js

Chart.js is an open-source javascript charting library that provides 8 broad chart types that include all the common charts such as bar charts, pie charts, histograms, scatterplots, error charts, etc. All these charts can be combined to produce mixed charts that are customizable and able to be animated as well. Chart.js can also render easily across all web browsers and adjust the chart according to the window size on the web browser. All the charts in this library can also be combined with the moment.js library if a time axis is needed.

3. Plotly

Plotly is a free open-source graphing library that can be used to form data visualizations. Plotly is a JavaScript library (plotly.js) and can be used to create web-based data visualizations that can be displayed in Jupyter notebooks or web applications using Dash or saved as individual HTML files. Plotly provides more than 40 unique chart types like scatter plots, histograms, line charts, bar charts, pie charts, error bars, box plots, multiple axes, sparklines, dendrograms, 3-D charts, etc. Plotly also provides contour plots, which are not that common in other data visualization libraries. In addition to all this, Plotly can be used offline with no internet connection.

Must Read

• Data Visualization with Python
• What is Data Visualization and Why is It Important?
• Data Visualization in R

Data is becoming the backbone of our current society. Companies can use data to predict their customer reactions, the success of their products and services, and the areas they need to work on. Data can also be used to understand many social and natural phenomena in the world such as social media trends, mass migration, global warming, etc. However, while data scientists can understand all these using various analytical procedures and statistical modeling of the data, it’s a very different thing to convey these findings to other people. That’s where data visualization is extremely important!

Data visualization allows data scientists to understand the layout and patterns in the data in visual form. It also allows them to demonstrate the findings from their data in simple terms to other people who are not experienced in data science. Therefore, data visualization is the language that is used to convey the data to regular people. Many data visualization libraries across programming languages can be used for this task. This article deals with the most popular data visualization libraries in Python, R, and Javascript. You can use these libraries to create basic charts like bar charts, pie charts, histograms, scatterplots, sparklines, dendrograms, contour plots, choropleth maps, etc. If you want to be more creative, you can even add animations to your charts and create data stories. So what are you waiting for? Check out these libraries and begin your data visualization journey by creating your first chart!