About The Author
Nick Zhu is a professional programmer and data engineer with more than a decade experience in software development, big data, and machine learning. Currently, he is one of the founders and CTO of Yroocom - meta search engine for online shopping. He is also the creator of dc.js—a popular multidimensional charting library built on D3.
The first thing you will need when you start a D3-powered data visualization project is a working development environment. Let’s see how a simple D3 development environment can be set up.
Selector support is standardized by W3C, so all modern web browsers have built-in support for the selector API. In this video, we will cover how D3's selection API works and some of its powerful features.
It is very common that at times you will need to select a single element on a page to perform some visual manipulation. Let’s see how to perform a targeted single element selection in D3 using a CSS selector.
Often selecting a single element is not good enough, but rather you want to apply a certain change to a set of elements on the page simultaneously. Let’s play with the D3 multi-element selector and its selection API.
Sometimes it is handy to be able to iterate through each element within a selection and modify each element differently according to their position. This video shows us how this can be achieved using D3 selection iteration API.
It is quite common that you will need to perform scoped selection when working on visualization. For example, selecting all div elements within a particular section element is one such use case of scoped selection. In this video, we will demonstrate how this can be achieved.
As we have seen so far, the D3 API is completely designed around the idea of function chaining. Therefore, it forms a DSL for building HTML/SVG elements dynamically. Let’s explore how the entire body structure can be constructed using D3 alone.
The task of matching each datum with its visual representation seems to be a complicated and tedious task. Let’s see how the enter-update-exit pattern helps us solve this.
Most of our data is stored in arrays, and we spend a lot of our effort working with arrays to format and restructure data. This is why D3 provides a rich set of array-oriented utilities functions, making this task a lot easier.
Imagine you need to filter D3 selection based on the associated data elements so that you can hide or show different sub-datasets based on the user's input. D3 selection provides a filter function to perform this kind of data-driven filtering.
In many cases, it is desirable to sort your visual elements according to the data they represent so that you can highlight the significance of different elements visually. In this video, we will explore how this can be achieved in D3.
It is probably very rare that you will only be visualizing static local data. The power of data visualization usually lies in the ability to visualize dynamic data typically generated by a server-side program.
In this video, we will demonstrate another very useful technique commonly used to process or generate data in large data visualization projects. It is usually necessary in complex visualization projects to load and merge multiple datasets from different sources before proceeding to visualizing.
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