Seven Secrets of data visualization experts

Source: Internet
Author: User
Keywords Data visualization or as well as
Tags based browsers data data visualization developers development javascript visual

The path of data visualization is full of invisible traps and mazes, and the recent two-bit data visualization developers of ClearStory have shared 7 of their data visualization development, and ordinary developers understand that these methods can enhance their horizons and minimize detours.

The era of data visualization, especially web-based data visualization, has come. Visual libraries like JavaScript, such as D3.js, Raphaël, and Paper.js, as well as canvas and SVG supported by the latest browsers, are becoming simpler and more complex visualizations that used to be developed only by computer experts and professional designers.

Data visualization is now an essential feature of many Web site projects. Startups like Platfora, datameerclearstory data and Chartio can leverage a browser-based analytics platform to invest millions of of billions of dollars.

Data visualization is an important way of data exploration and data performance, however, there are still many challenges to be faced by the developers of data visualization. The way to meet these challenges is a secret that many professional data visualization developers are unwilling to let others know. ClearStory two-bit data visualization developer Nate Argrin and Nick Rabinowitz share their 7 Secrets of data visualization development and how to deal with them in practice. The IT Manager network compiles as follows:

Secret one: Real data is often ugly

Most of the data visualization tutorials will make it easy for you to start with a raw dataset. Whether you're learning a basic histogram or a force-oriented network diagram, your data is clean and collated. These perfect JSON or CSV files are as neat and tidy as the countertops on TV's cooking shows. And actually, when you're dealing with real data in reality, you're 80% of the time to search, get, load, clean, and transform your data.

Such a process can sometimes be done with automated tools. However, almost any work that requires cleaning for more than two datasets will require more or less manual work. There are many tools that can convert an XLS file into XML format or a timestamp to another date format. However, if you want to compare the types of sales that a company uses internally with competitors, or check for input errors, or check the text produced by different encoding or OCR, you can only handle it by hand.

Tools and Treatment methods:

1 in the data visualization project to allow sufficient time for data cleansing, especially in the need to deal with multiple data sources, the need to manually input or OCR data, different categories of matching, or need to deal with some non-standard format, need to set aside more time.

2 Google Refine (editor: Need to flip the wall) is a good data cleaning tool, although in some places, especially in the processing of tabular data is somewhat inadequate. In addition, there are a number of data-cleansing-specific tools such as Wranger and Mr. Data Accept. However, a lot of data cleansing work still requires familiarity with scripting languages such as Python or requiring you to do some manual work in Excel. Remember to file your scripts and you'll definitely use them later.

3 using a simple scatter plot or histogram to find some abnormal range of error data.

(Responsible editor: The good of the Legacy)

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