D3：Web交互数据可视化权威指南

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《D3：互动数据可视化为网络》是一本经典的IT专业书籍，由Scott Murray撰写，于2013年首次出版。该书专为web开发人员和数据可视化爱好者设计，深入探讨了如何利用D3.js库进行交互式数据可视化，这在大数据时代尤其重要，因为数据驱动的可视化有助于用户更好地理解和探索复杂的数据集。 D3（Data-Driven Documents）是一个强大的JavaScript库，它允许开发者通过编程方式将数据转换成美观且交互式的图表、图形和视觉元素。《Interactive Data Visualization for the Web》详尽地介绍了如何通过D3构建动态的网络图表、地图、网络图以及各种定制化的可视化效果。作者以浅显易懂的方式，引导读者掌握从数据绑定、选择、操作DOM到制作高级动画和交互设计的关键概念和技术。本书适合初学者和有经验的开发者，不仅包括理论知识，还提供了大量的示例代码和实战项目，让读者能够快速上手并深入理解D3的核心理念。此外，书中还会讨论如何优化性能，以及如何将数据可视化融入现代Web应用和响应式设计中。值得注意的是，版权方面，这本书保留所有权利，但O'Reilly Media提供了在线版本供教育、商业或销售推广使用。如果你对图书的修订历史、电子版购买、错误报告等内容感兴趣，可以访问O'Reilly官网的相关链接获取详细信息。《D3：互动数据可视化为网络》不仅仅是一本技术指南，更是推动数据可视化创新的重要资源，它强调了数据驱动决策的力量，对于那些希望提升数据可视化的技能，或者寻求在Web开发中引入创新可视化体验的读者来说，具有很高的实用价值。通过学习这本书，读者不仅能掌握D3的核心技术，还能培养出将数据转化为引人入胜视觉故事的能力。

More complex visualizations are generated from datasets more complex than the se‐

quence of numbers shown in Figure 1-1 and more complex sets of mapping rules.

Why Write Code?

Mapping data by hand can be satisfying, yet is slow and tedious. So we usually employ

the power of computation to speed things up. The increased speed enables us to work

with much larger datasets of thousands or millions of values; what would have taken

years of effort by hand can be mapped in a moment. Just as important, we can rapidly

experiment with alternate mappings, tweaking our rules and seeing their output re-

rendered immediately. This loop of write/render/evaluate is critical to the iterative pro‐

cess of refining a design.

Sets of mapping rules function as design systems. The human hand no longer executes

the visual output; the computer does. Our human role is to conceptualize, craft, and

write out the rules of the system, which is then finally executed by software.

Unfortunately, software (and computation generally) is extremely bad at understanding

what, exactly, people want. (To be fair, many humans are also not good at this challenging

task.) Because computers are binary systems, everything is either on or off, yes or no,

this or that, there or not there. Humans are mushier, softer creatures, and the computers

are not willing to meet us halfway—we must go to them. Hence the inevitable struggle

of learning to write software, in which we train ourselves to communicate in the very

limited and precise syntax that the computer can understand.

Yet we continue to write code because seeing our visual creations come to life is so

rewarding. We practice data visualization because it is exciting to see what has never

before been seen. It is like summoning a magical, visual genie out of an inscrutable data

bottle.

Why Interactive?

Static visualizations can offer only precomposed “views” of data, so multiple static views

are often needed to present a variety of perspectives on the same information. The

number of dimensions of data are limited, too, when all visual elements must be present

on the same surface at the same time. Representing multidimensional datasets fairly in

static images is notoriously difficult. A fixed image is ideal when alternate views are

neither needed nor desired, and required when publishing to a static medium, such as

print.

Dynamic, interactive visualizations can empower people to explore the data for them‐

selves. The basic functions of most interactive visualization tools have changed little

since 1996, when Ben Shneiderman of the University of Maryland first proposed a

2 | Chapter 1: Introduction

“Visual Information-Seeking Mantra”: overview first, zoom and filter, then details-on-

demand.

This design pattern is found in most interactive visualizations today. The combination

of functions is successful, because it makes the data accessible to different audiences,

from those who are merely browsing or exploring the dataset to those who approach

the visualization with a specific question in search of an answer. An interactive visual‐

ization that offers an overview of the data alongside tools for “drilling down” into the

details may successfully fulfill many roles at once, addressing the different concerns of

different audiences, from those new to the subject matter to those already deeply familiar

with the data.

Of course, interactivity can also encourage engagement with the data in ways that static

images cannot. With animated transitions and well-crafted interfaces, some visualiza‐

tions can make exploring data feel more like playing a game. Interactive visualization

can be a great medium for engaging an audience who might not otherwise care about

the topic or data at hand.

Why on the Web?

Visualizations aren’t truly visual unless they are seen. Getting your work out there for

others to see is critical, and publishing on the Web is the quickest way to reach a global

audience. Working with web-standard technologies means that your work can be seen

and experienced by anyone using a recent web browser, regardless of the operating

system (Windows, Mac, Linux) and device type (laptop, desktop, smartphone, tablet).

Best of all, everything covered in this book can be done with freely accessible tools, so

the only investment required is your time. And everything we’ll talk about uses open

source, web-standard technologies.

By avoiding proprietary software and plug-ins, you can ensure that your projects are

accessible on the widest possible range of devices, from typical desktop computers to

tablets and even phones. The more accessible your visualization, the greater your au‐

dience and your impact.

What This Book Is

This book is a practical introduction to merging three practices—data visualization,

interactive design, and web development—using D3, a powerful tool for custom, web-

based visualization.

These chapters grew out of my own process of learning how to use D3. Many people,

including myself, come to D3 with backgrounds in design, mapping, and data visuali‐

zation, but not programming and computer science.

Why on the Web? | 3

D3 has a bit of an unfair reputation for being hard to learn. D3 itself is not so complicated,

but it operates in the domain of the Web, and the Web is complicated. Using D3 com‐

fortably requires some prior knowledge of the web technologies with which it interacts,

such as HTML, CSS, JavaScript, and SVG. Many people (myself included) are self-taught

when it comes to web skills. This is great, because the barrier to entry is so low, but

problematic because it means we probably didn’t learn each of these technologies from

the ground up—more often, we just hack something together until it seems to work,

and call it a day. Yet successful use of D3 requires understanding some of these tech‐

nologies in a fundamental way.

Because D3 is written in JavaScript, learning to use D3 often means learning a lot about

JavaScript. For many datavis folks, D3 is their introduction to JavaScript (or even web

development generally). It’s hard enough to learn a new programming language, let

alone a new tool built on that language. D3 will enable you to do great things with

JavaScript that you never would have even attempted. The time you spend learning both

the language and the tool will provide an incredible payoff.

My goal is to reduce that learning time, so you can start creating awesome stuff sooner.

We’ll take a ground-up approach, starting with the fundamental concepts and gradually

adding complexity. I don’t intend to show you how to make specific kinds of visualiza‐

tions so much as to help you understand the workings of D3 well enough to take those

building blocks and generate designs of your own creation.

Who You Are

You may be an absolute beginner, someone new to datavis, web development, or both.

(Welcome!) Perhaps you are a journalist interested in new ways to communicate the

data you collect during reporting. Or maybe you’re a designer, comfortable drawing

static infographics but ready to make the leap to interactive projects on the Web. You

could be an artist, interested in generative, data-based art. Or a programmer, already

familiar with JavaScript and the Web, but excited to learn a new tool and pick up some

visual design experience along the way.

Whoever you are, I hope that you:

• Have heard of this new thing called the “World Wide Web”

•

Are a bit familiar with HTML, the DOM, and CSS

• Might even have a little programming experience already

• Have heard of jQuery or written some JavaScript before

• Aren’t scared by unknown initialisms like CSV, SVG, or JSON

• Want to make useful, interactive visualizations

4 | Chapter 1: Introduction

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D3：Web交互数据可视化权威指南

Interactive Data Visualization for the Web(O'Reilly,2ed,2017)

Interactive Data Visualization for the Web(英文)

D3技巧和窍门v3.x：Web浏览器中的交互式数据可视化D3 Tips and Tricks v3.x: Interactive Data Visualization in a Web Browser

无法解析的外部符号：QtDataVisualization::QAbstract3DGraph

pcl::visualization::pclvisualizer 重写鼠标

std::vector<pcl::visualization::Camera>

pcl::visualization::PCLVisualizer::setShapeRenderingProperties

QtDataVisualization库使用cmake怎么构建

boost::shared_ptr<pcl::visualization::PCLVisualizer>

pcl::visualization::PCLVisualizer

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