Sequences sunburst

This example shows how it is possible to use a D3 sunburst visualization (partition layout) with data that describes sequences of events.

A good use case is to summarize navigation paths through a web site, as in the sample synthetic data file (visit_sequences.csv). The visualization makes it easy to understand visits that start directly on a product page (e.g. after landing there from a search engine), compared to visits where users arrive on the site's home page and navigate from there. Where a funnel lets you understand a single pre-selected path, this allows you to see all possible paths.

Features:

• works with data that is in a CSV format (you don't need to pre-generate a hierarchical JSON file, unless your data file is very large)
• interactive breadcrumb trail helps to emphasize the sequence, so that it is easy for a first-time user to understand what they are seeing
• percentages are shown explicitly, to help overcome the distortion of the data that occurs when using a radial presentation

If you want to simply reuse this with your own data, here are some tips for generating the CSV file:

• no header is required (but it's OK if one is present)
• use a hyphen to separate the steps in the sequence
• the step names should be one word only, and ideally should be kept short. Non-alphanumeric characters will probably cause problems (I haven't tested this).
• every sequence should have an "end" marker as the last element, unless it has been truncated because it is longer than the maximum sequence length (6, in the example). The purpose of the "end" marker is to distinguish a true end point (e.g. the user left the site) from an end point that has been forced by truncation.
• each line should be a complete path from root to leaf - don't include counts for intermediate steps. For example, include "home-search-end" and "home-search-product-end" but not "home-search" - the latter is computed by the partition layout, by adding up the counts of all the sequences with that prefix.
• to keep the number of permutations low, use a small number of unique step names, and a small maximum sequence length. Larger numbers of either of these will lead to a very large CSV that will be slow to process (and therefore require pre-processing into hierarchical JSON).

I created this example in my work at Google, but it is not part of any Google product. It is covered by the Apache license (see the LICENSE file).