In the demo application for this article we have provided example code for most basic chart types common in web applications. Each of these charts is fully reactive to data changes in documents, adding or removing documents, and sorting.
The approach in this document seems to only work with documents that are bound directly to DOM elements. It doesn't work when data is post-processed with `d3.nest` followed by a `d3.layout`, for example - which are often required for more advanced aggregate visualizations. However, doing the aggregate operations continuously may be doable if the underlying data array is updated efficiently; see comment below.
We will use the bar chart example to talk about the basic pattern used to integrate D3 into a Meteor application. This same basic pattern was used for all common chart examples and should be extendable to any D3 visualization within a Meteor application.
We start by defining an empty SVG element in template markup
Not much here at all, this is really just a placeholder for D3 to do its work.
All D3 code goes in the rendered callback for the template. Some code has been omitted using ... for brevity and to more clearly illustrate the pattern.
//perform a reactive query on the collection to get an array
var dataset = Bars.find(...).fetch();
//update scale domains and axises
//select elements that correspond to documents
var bars = svg.selectAll("rect")
.data(dataset, key); //bind dataset to objects using key function
//handle new documents via enter()
//handle updates to documents via transition()
//handle removed documents via exit()
Following this basic pattern allows for relatively straightforward refactoring of most D3 examples to be driven via Meteor collections. The key part is separating the D3 code into what gets run once to initiate the visual (before the Deps.autorun block) and what gets run on each collection query result change (inside the Deps.autorun block).
This seems like it will only work with very small datasets. `cursor.fetch()` deep copies all documents when it is called, so this will be extremely slow. It seems like the best approach is to create a single reactive array that is updated using `cursor.observe`, specifically using the `addedAt` and `removedAt` callbacks, which can then be repeatedly joined with `selection.data` in D3. This basically accomplishes the same effect as repeatedly calling `fetch()` without the huge CPU and memory usage of a large collection, and could be easily implemented as part of a d3 helpers package.
We want our visualization to update reactively whenever there is a change in the results of the collection query, contained in the variable dataset.
var dataset = Bars.find(...).fetch();
Since we encased all of the D3 code that updates the visualization within the Deps.autorun block, we already have reactivity when the results of the query change in any way. All we have to do is make sure that the D3 visualization update code handles added, changed, and removed documents. We use D3 to accomplish this by selecting a group of elements from the SVG and binding them to a data array.
var bars = svg.selectAll("rect").data(dataset, key);
D3 compares the supplied data to the existing graph and computing the needed changes. Any elements that are to be newly created are returned by enter() while any elements that should be removed are returned by exit(). By using these two D3 functions coupled with an update to any documents that may have been altered, via transition(), we have all possible data changes accounted for.
Once we have the reactivity supplied by Meteor and the handling of data changes by D3, we can make simple changes to the collection documents and the graph will automatically update itself. Since the visualization code is reactively driven by the collection documents, the source of the data change is not relevant. The changes can be made via event handlers, as shown below, or by any other mechanism.