Introduction to Baidu data visualization practices for csdn open-source summer camp

First of all, I would like to thank Miss Lin Feng for choosing me. @ Kener-Lin Feng

(1) Next I will introduce Baidu's data visualization components echarts and zrender.

As we all know, the arrival of the big data era not only brings challenges but also opportunities, but it is only a start. The big data era will have a major impact on our lives. As Victor may-schenberger, author of The Big Data age, said, "Today, data has become a commercial capital and an important economic investment, new economic benefits can be created. In fact, once the thinking changes, data can be cleverly used to stimulate new products and services. The mysteries of data are only known to those who are humble, willing to listen, and have mastered the means of listening ." So what is your listening means?
Although we already have excellent data visualization tools such as Excel, SPSS, Matlab, and tableau desktop, they have some shortcomings.

(2) What else do we need?

A simple and quick way is required to visually present data that can reflect the current status or predict future trends.

It should have the following features: Quick, intuitive, vivid, interactive, and highly personalized visual data charts.

(3) Next we will discuss the tools to be used:
Echarts (Enterprise charts commercial product chart Library), based on [HTML5] canvas (zrender), pure JavaScript chart library.

(4) functions of echarts

[Drag-and-drop re-computing] integrates the data you are concerned with to help you integrate things you are interested in.

[Drag and drop to recalculate] Remove malformed data. As we all know, malformed data or noisy data is always used in some cases, what should we do?

 

[Data View] data can be modified in batches to meet your data requirements.

[Dynamic type Switching] This function allows you to switch chart types and stacked tile statuses as you like by attempting to display different types of charts.

[Value range roaming] focuses on the values you care about.

[Data area scaling] focuses on the data you care about and provides the data area scaling function, you can easily browse data areas by adding three drag-and-drop handles to the scale bar with a global value shadow.

[Multi-graph Association] more friendly association data analysis

[Large-scale scatter plot] shows the charm of big data. A pixel-based large-scale scatter plot can present 0.36 million sets of data without repeating the following scatter plot, with modern browsers, you can easily display millions of Scatter Data.

[Force-directed layout] The most beautiful presentation of complex relationship networks

[Mix and match] use the best combination to present your unique data
The underlying layer of echarts is based on zrender (a brand new lightweight canvas class library). It creates basic components such as the coordinate system, legends, prompts, and toolbox, then, a line chart, column chart, scatter chart, K-line chart, pie chart, radar chart, MAP, Chord chart, force-directed layout chart, dashboard, and funnel chart are constructed, supports stacking and multi-chart hybrid display in any dimension.



Basic Structure of echarts:

 


There are a lot of other functions that I will not explain carefully. For me, its core thing is code. My task is to get familiar with function interfaces and obtain useful data, make a topic on data visualization that is close to the people's livelihood. Here is a preface. The specific progress will be mentioned in the next blog. Thank you again !!

Links:

For specific functions, see:
Official website of echarts: http://echarts.baidu.com/
Zrender: http://ecomfe.github.io/zrender/

Miss Lin Feng's topic:
Air quality in major cities in China
Ten-year time-space roaming of China's Economy

China's population flow tide