Writing testable code
There are dozens of factors to consider when creating visualizations. Every design will have its own set of unique requirements and configuration capabilities to consider. If you build on the reusable pattern outlined by Mike Bostock, you will have a great framework to start with.
When working with data visualizations, we will have some form of data manipulation or logic that must be applied to incoming data. There are two notable best practices we can leverage to test and validate these operations. They are explained in the following sections.
Keeping methods/functions small
Small functions mean low cyclomatic complexity. This means there are fewer logic branches in each function and, therefore, fewer things to test. If we test each simple function thoroughly and independently, then there will be fewer chances of things going wrong when we compose them together into larger complex computations. A good guideline is to try and keep methods around 10 lines of code.
