It makes things a bit awkward when mixing with memoizable data. * Quickly changing data sets can't really benefit from memoization or mo, since the layouts themselves will constantly change. * React has to re-render entire hierarchies or massive lists depending on the traversal method that you choose to use for hierarchical datasets (topological or depth first) and React chokes on these pretty easily since they tend to be wide and flat * Zooming is non-trivial, you have to recreate it in React proper For example, the classic sunburst chart (example here: can be constructed from the section of code that contructs and partitions the hierarchy: However, a lot of D3 functions already give back most of the properties that you need to construct the DOM anyways. You basically have to take on a lot of the creation of the SVG element tags yourself, since having both D3 and React manage the DOM is a bit awkward. You could create an endpoint to return different views when you click on, eg, a different date, although then the user would have to wait for the data to load. It hits two endpoints, which return basically the maximum amount of data you can expect a browser to handle (the massive dataset is processed elsewhere). This election dashboard is a good example: have an API that returns a sliced portion of the data and create an interface that will request different sliced datasets when interacting with filters, controls, etc. write a python script to process my data and use d3 for the final visualization, or K is right that d3 has some helper functions for drawing on canvas, so most of the methods here will work:Ĭanvas is way more performant than SVG for a lot of shapes, since they don't have the overhead of a DOM node.īut in general, you want to work outside of the browser if you have that much data.
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