coursera data visualization

", layout); Display Display=NewDisplay (VIS); Display.setsize (700, 600); Display.addcontrollistener (NewDragcontrol ()); Display.addcontrollistener (NewPancontrol ()); Display.addcontrollistener (NewZoomcontrol ()); Display.addcontrollistener (NewWheelzoomcontrol ()); Display.addcontrollistener (NewFocuscontrol (1)); Display.addcontrollistener (NewZoomtofitcontrol ()); JFrame JF=NewJFrame (); Jf.setdefaultcloseoperation (Jframe.exit_on_close); Jf.add (display); Jf.pack ();

This two-part article series will demonstrate a visualization technique that helps extract information from the data that has business value, and this article is the first part of the series. You'll see how to use Scalable Vector Graphics (SVG) and open source D3 JavaScript libraries to create visual representations that can be viewed through the browser, conveying information through shapes and colors. I'l

Learn about graphical calculations for the components used to draw various permutations The 1th part of this two-part series outlines the combined use of SVG and D3, providing some basic examples of creating browsing data visualization representations of social media. Part 2nd will describe the steps for arranging or laying out different graphic components in SVG graphics. You will learn how to use D3 powe

Charts are an essential feature point for enterprise-wide Web development. is also "a concrete rendering of data visualization". Today I see Nanyi translation of the "Data Visualization: Basic Chart" article, while combing the company's current project use of the EChart2.0 class library. Nanyi's articles are also suita

OverviewMany scenarios in the business work need to visualize the data, in order to meet the needs of users, improve the user experience, we have developed more data visualization control. is introduced to everyone, forming a series.Today is the Chronicle of memorabilia control Verticaltimeline, which splits a series of events on a yearly basis, clicking on the y

segmentation situation. The red part is the process of running the program.11, continue to write code, the frequency of the statistical summary, the code implementation as shown.12, the program run, get a TXT and Excel file, inside is about the word frequency statistics information, as shown. The red part is the result of the program running, and there is no error.13. Import these keywords into WordArt for visualization, as shown in.14, set a case, f

In many practical problems, the data given is often visualized for easy observation.Today, the data visualization module in Python is--matplotlib this content system to make it easy to find and use. This article comes from a summary of "data analysis using Python" and some online blogs.1 Matplotlib Introduction Matplot

for this project, they are all for the final visualization service. I strive to balance visual attractiveness with user interaction, allowing users to explore and understand the subject's trends without guidance. The diagram I started with was stacked blocks, and I realized that simple line drawings were sufficient and clear. I use D3.js for visualization, which is appropriate for the

, where you can design the main body of the new analysis.650) this.width=650; "src=" http://pic.pc6.com/up/2015-1/14224140158935530.jpg "style=" border:0px; "alt=" 14224140158935530.jpg "/>Here, the data source and the new data analysis are done, and the next step is to do a detailed analysis of the data, such as documentation examples.Gender dimension analysis b

= Rev (levels (Uspopage$agegroup)))Operation Result:If you need to draw a percentage stacked chart, simply modify the underlying data based on the work above.The R language implementation code is as follows:# Convert data to percent format Uspopage_prop = ddply (Uspopage, "year", transform, Percent = Thousands/sum (Thousands) * 100) # base function Ggplot (USP Opage_prop, AES (x = year, y = Percent, fill =

ActiveReports provides a wide range of data visualization solutions. Users can display data in an image to make the report data more visual and easy to understand. ActiveReports reports provide most common 2D and 3D chart types, including XY tables and financial charts. By using the custom functions of the chart contro

Before processing, let's talk about data visualization. Data Visualization-as the name suggests, it is a research on the visual representation of data. It presents data in other ways to make it more intuitive, It is clearer and ea

method is simple to add the marginal carpet function on the basis of the original scatter plot function. The R language implementation code is as follows:# base function Ggplot (Faithful, AES (x = eruptions, y = waiting)) + # Scatter graph function geom_point () + # Marginal carpet function Geom_rug ()　　Operation Result:add a label to a scatter plotThis example uses the following test data set:The method of adding labels to scatter plots is als