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Best chart types

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Allison Horst

Trevor Eddy

Observable

A practical guide to alternatives to standard chart types using Observable Plot to build advanced visualizations that can help uncover trends, changes, flow, distribution, and relationships. 

Beyond bar charts: A guide to advanced visualizations

By thinking more like data journalists, data teams can make their visualizations and dashboards clearer, more targeted, and more useful. In this guide, we unpack practical strategies from data journalism that analysts can adapt for their own day-to-day workflows. 

What data teams can learn from journalists about data visualization

Learn about the expert-level chart types and features you get for free in Observable Canvases including automatic responsiveness, extreme value handling, built-in visual filtering, and performant charts no matter the size of your data.

5 ways we’re democratizing advanced data visualization in Observable Canvases

There are many factors that go into data visualization decisions, but one of the most important is selecting the right chart. This post outlines which charts work best for different types of data.  

Choosing the right chart for your data

Advanced, interactive charts can be hard to build from scratch. But in Observable Canvases, you can make and share them in minutes. We highlight six new charts now available in canvases: Sankey diagrams, beeswarm charts, streamgraphs, bump charts, arc maps, and circle packing charts.

Expanding our suite of built-in charts in Observable Canvases

Grid cartograms are thematic maps that represent spatial regions with simplified geometric shapes like squares or hexagons. In this overview, we introduce grid cartograms, highlight pros and cons of this chart type, describe common alternatives, and share resources that can help you start building them with Observable.

What is a grid cartogram?

There was so much hype around the promise of self-serve analytics: data-driven insights, efficiency gains and competitive advantage. In this post, we outline why self-service analytics has failed, and if its promise can ever be realized.

Why has the promise of self-serve analytics failed?

The Observable team and community have created thousands of openly shared data visualizations in Observable Plot and D3. To easily reuse them, your data needs to be in the expected shape. Learn about the data shapes that many Plot and D3 charts consume, and how to transform your data so you can drop it into existing examples.

Reshaping data for visualizations with D3 and Observable Plot

In Observable Canvases, charts representing millions of records are generated almost instantaneously. Learn how we keep data visualizations fast by limiting data returned by SQL queries and giving charts just the information they need.

Big data, fast data visualizations

Given how common histograms are in BI tools, you might think they’re easy to design. Think again. We share challenges we encountered, and how we handled them, while designing better out-of-the-box histograms in Observable Canvases.

Data visualization insights