Introduction to data visualization application development in Java language

The Java language has a wide range of applications in data visualization and can help developers quickly and efficiently develop a variety of beautiful and efficient data visualization applications. In this article, we will introduce the basic concepts, common tools and development processes of data visualization in the Java language to help readers understand the development process of data visualization applications in the Java language and be able to successfully develop their own data visualization applications.

1. Basic concepts of data visualization

Data visualization refers to presenting a set of data in graphics, tables or other visual forms so that users can understand the data more intuitively and clearly. meaning and rules. In data visualization, commonly used graphics include line charts, bar charts, scatter charts, pie charts, etc., while tables are another commonly used form of data visualization. Data visualization can be used both as a tool for data exploration and analysis and as a way to present data to users.

2. Data visualization tools in Java language

In Java language, there are many tools that support data visualization development, among which the following are the more popular ones:

1. JFreeChart

JFreeChart is a free chart library in Java language. It can be used to generate various types of charts, including line charts, bar charts, pie charts, etc. Using JFreeChart is very simple, it provides an easy-to-use API and only requires a few lines of code to generate a beautiful chart.

2. JUNG

JUNG is a free graphics library in Java language. It is mainly used to draw various types of graphics, such as trees, networks, graphics, etc. JUNG is not an ordinary graph library, it provides a series of algorithms for analyzing and processing complex graph data, such as shortest path, community discovery, etc.

3. JTable

JTable is an important component in the Java language and can be used to create tables. Through JTable, developers can display and edit data very conveniently. Tables can have multiple columns and rows, and can also support cell merging, row and column sorting, and so on.

3. Development process of data visualization in Java language

Developing data visualization applications in Java language generally requires the following steps:

1. Data preparation

First, you need to prepare the data source to be visualized. Data can be saved in files or databases. To read files, you can use classes such as FileReader or BufferedReader in Java, and to read databases, you can use tools such as JDBC.

2. Select visualization tools

When selecting visualization tools, you need to consider the needs of the application and select appropriate tools for development. For example: if you need to show the distribution of data, you can use JFreeChart to draw a histogram or scatter chart; if you need to show the relationship between existing data, you can use JUNG to draw a network diagram or tree diagram.

3. Develop code

Write code to visualize data based on the selected tool. For example: Use JFreeChart to create ChartData, XYDataset, etc., and set the format of the chart. Using JUNG you can create different types of graph nodes, edges, layouts, and more.

4. Test

After completing the development of the visual application, you need to test it to ensure that the display effect is correct and the performance is good.

5. Publish and deploy

Finally, publish the visual application to the server or locally for users to use.

4. Summary

Data visualization is an important tool and skill in the field of data science. It can be used to display the characteristics, trends and patterns of data and help people better understand the data. In the Java language, developing data visualization applications requires selecting appropriate tools and following the development process for development, testing, and deployment. This article introduces the development of data visualization applications in Java language from three parts: basic concepts, common tools and development processes, hoping to help readers better understand and develop data visualization applications.

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