使用R和JavaScript的专业数据可视化

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"Pro Data Visualization using R and JavaScript--2013" 这本书《Pro Data Visualization using R and JavaScript》是2013年出版的一本专业数据可视化指南，它旨在使R语言变得易于理解，并推广数据收集和分析的概念。作者通过讲解如何使用R语言处理和分析数据，然后利用D3 JavaScript库来格式化并以优雅、信息丰富且互动的方式展示数据。书中不仅教授如何高效地获取数据，还深入探讨了每种图表类型的哲学理念和实现方法，以便读者能够直观地呈现分析结果。书中的标签包括"Visualizatio"、"JavaScript"和"Data"，表明内容将涵盖数据可视化、JavaScript编程以及数据分析的基础知识。部分内容提到了该书的目录，包含多个章节，从基础到高级逐步深入。例如： - 第1章：背景，介绍了Web开发中数据可视化作为沟通工具的新概念。 - 第2章：R语言入门，为初学者提供了R语言的基本知识。 - 第3章：深入R语言，进一步探讨R的高级特性。 - 第4章：使用D3进行数据可视化，详细阐述如何使用D3库创建视觉效果。 - 后续章节则涉及空间数据可视化、时间序列数据可视化、柱状图、散点图相关联分析以及平行坐标图等具体的数据可视化技术。这本书对于希望结合R语言和JavaScript进行数据可视化的读者来说是一份宝贵的资源。通过学习，读者不仅可以掌握数据处理和分析的技能，还能学会如何使用D3创建动态、交互式的可视化作品，从而在Web上有效地传达复杂的数据信息。此外，书中还可能涉及如何从日志文件中提取空间数据，以及如何通过各种图表类型来揭示数据中的模式、趋势和关联性，这些都是数据科学领域的重要实践技能。

CHAPTER 1 ■ BACKGROUND

Another great tool for analysis is Wolfram|Alpha (http://wolframalpha.com). See Figure 1-14 for a screenshot of

the Wolfram|Alpha homepage.

Figure 1-14. Home page for Wolfram|Alpha

Wolfram|Alpha is not a search engine. Search engines spider and index content. Wolfram|Alpha is instead a

Question Answering (QA) engine that parses human readable sentences with natural language processing and

responds with computed results. Say, for example, you want to search for the speed of light. You might go to the

Wolfram|Alpha site and type in “What is the speed of light?” Remember that it uses natural language processing to

parse your search query, not the keyword lookup.

The results of this query can be seen in Figure 1-15. Wolfram|Alpha essentially looks up all the data it has

around the speed of light and presents it in a structured, categorized fashion. You can also export the raw data for

each result.

CHAPTER 1 ■ BACKGROUND

Creating data visualizations involves four core steps:

1. Identify a problem.

2. Gather the data.

3. Analyze the data.

4. Visualize the data.

Let’s walk through each step in the process and re-create one of the previous charts to demonstrate the process.

Identify a Problem

The very first step is to identify a problem we want to solve. This can be almost anything—from something as

profound and wide-reaching as figuring out why your bug backlog doesn’t seem to go down and stay down, to seeing

what feature releases over a given period in time caused the most production incidents, and why.

For our example, let’s re-create Figure 1-5 and try to quantify the interest in data visualization over time as

represented by the number of New York Times articles on the subject.

Gather Data

We have an idea of what we want to investigate, so let’s dig in. If you are trying to solve a problem or tell a story around

your own product, you would of course start with your own data—maybe your Apache logs, maybe your bug backlog,

maybe exports from your project tracking software.

Note ■ If you are focusing on gathering metrics around your product and you don’t already have data handy, you need to

invest in instrumentation. There are many ways to do this, usually by putting logging in your code. At the very least, you want to

log error states and monitor those, but you may want to expand the scope of what you track to include for debugging purposes

while still respecting both your user’s privacy and your company’s privacy policy. In my book, Pro JavaScript Performance:

Monitoring and Visualization, I explore ways to track and visualize web and runtime performance.

One important aspect of data gathering is deciding which format your data should be in (if you're lucky) or discovering

which format your data is available in. We’ll next be looking at some of the common data formats in use today.

JSON is an acronym that stands for JavaScript Object Notation. As you probably know, it is essentially a way to

send data as serialized JavaScript objects. We format JSON as follows:

[object]{

[attribute]: [value],

[method] : function(){},

[array]: [item, item]

}

Another way to transfer data is in XML format. XML has an expected syntax, in which elements can have attributes,

which have values, values are always in quotes, and every element must have a closing element. XML looks like this:

</parent>

Generally we can expect APIs to return XML or JSON to us, and our preference is usually JSON because as we can

see it is a much more lightweight option just in sheer amount of characters used.

CHAPTER 1 ■ BACKGROUND

"byline": "By AUTHOR",

"date": "20121011",

"title": "TITLE",

"url": "http:\/\/www.nytimes.com\/foo.html"

}, {

"body": "BODY COPY",

"byline": "By AUTHOR",

"date": "20121021",

"title": "TITLE",

"url": "http:\/\/www.nytimes.com\/bar.html"

}

"tokens": [

"JavaScript"

"total": 2

}

Looking at the high-level JSON structure, we see an attribute named offset, an array named results, an array

named tokens, and another attribute named total. The offset variable is for pagination (what page full of results

we are starting with). The total variable is just what it sounds like: the number of results that are returned for our

query. It’s the results array that we really care about; it is an array of objects, each of which corresponds to an article.

The article objects have attributes named body, byline, date, title, and url.

We now have data that we can begin to look at. That takes us to our next step in the process, analyzing our data.

Data SCrUBBING

There is often a hidden step here, one that anyone who’s dealt with data knows about: scrubbing the data. Often

the data is either not formatted exactly as we need it or, in even worse cases, it is dirty or incomplete.

In the best-case scenario in which your data just needs to be reformatted or even concatenated, go ahead and do

that, but be sure to not lose the integrity of the data.

Dirty data has fields out of order, fields with obviously bad information in them—think strings in ZIP codes—or

gaps in the data. If your data is dirty, you have several choices:

You could drop the rows in question, but that can harm the integrity of the data—a good example •

is if you are creating a histogram removing rows could change the distribution and change what

your results will be.

The better alternative is to reach out to whoever administers the source of your data and try and •

get a better version if it exists.

Whatever the case, if data is dirty or it just needs to be reformatted to be able to be imported into R, expect to

have to scrub your data at some point before you begin your analysis.

Analyze Data

Having data is great, but what does it mean? We determine it through analysis.

Analysis is the most crucial piece of creating data visualizations. It’s only through analysis that we can understand

our data, and it is only through understanding it that we can craft our story to share with others.

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